HomeMy WebLinkAboutAPA3428VOLUME 4
EXHIBIT B
APPLICATION FOR LICENSE FOR MAJOR PROJECT
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Alaska Resources
Library &InfOrmation Services
Anchorage.AlaskaNovember1985
SUSITNA HYDROELECTRIC PROJECT
DRAFT LICENSE APPLICATION
BEFORE THE
FEDERAL ENERGY REGULATORY COMMISSION
APPENDIX B2
RAILBELT ELECTRICITY DEMAND (RED)MODEL
TECHNICAL DOCUMENTATION REPORT
(1983 VERSION)
APPENDIX B3
RAIL BELT ELECTRICITY DEMAND (RED)MODEL
CHANGES MADE JULY 1983 TO AUGUST 1985
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VOLUME COMPARISON
VOLUME NUMBER COMPARISON
LICENSE APPLICATION AMENDMENT VS.JULY 29,1983 LICENSE APPLICATION
JULY 29,1983
AMENDMENT APPLICATION
VOLUME NO.VOLUME NO.EXHIBIT
A
B
C
D
E
CHAPTER
Entire
Entire
App.B1
App.B2
App.B3
Entire
Entire
App.D1
1
2
Tables
Figures
Figures
3
DESCRIPTION
Project Description
Pr9j~ct 9peration and Resource
Utlllzatlon
MAP Model Documentation Report
RED Model Documentation Report
RED Model Update
Proposed Construction
Schedule
Project Costs and Financing
Fue Is Pricing
General Description of Locale
Water Use and Quality
Fish ,Wildlife and Botanical
Resources (Sect.1 and 2)
1
2
3
4
4
5
5
5
6
6
7
8
9
1
2 &2A
2B
2C
1
1
1
SA
SA
SA
5B
5B
6A
6B
Socioeconomic Impacts
Geological and Soil Resources
4
5
6
Fish,Wildlife and Botanical 10
.Resources (Sect.3)
Fish,Wildlife and Botanical 11
Resources (Sect.4,5,6,&7)
Historic &Archaeological Resources 12
12
12
6A
6B
6A
6B
7
7
7
7
8
9
10
11
Recreational Resources
Aesthetic Resources
Land Use
Alternative Locations,
and Energy Sources
Agency Consultation
Designs
13
13
13
14
14
8
8
8
9
lOA
lOB
J
F
F
G
Entire
Entire
Entire
Project Design Plates
Supporting Design Report
Project Limits and Land Ownership
Plates
15
16
17
3
4
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SUMMARY TABLE OF CONTENTS
i
1.1 -General Arrangement (**)·A-1-2
1".2 -Dam Embankment (**)···A-1-4
1.3 -Diversion (**)·· ·
· ··· · ····A-1-6
1.4 -Emergency Release Facilities (**)A-1-9
1.5 -Outlet Facilities (**)· ······A-I-I0
1.6 -Spillway (**)· · ··A-l-13
1.7 -This section deleted ···· ··· ·
A-1-15
1.8-Power Intake (**)····A-I-IS
1.9-Power Tunnels and Penstocks (**)····A-I-IS
1.10 -Powerhouse (**)· ··A-1-19
1.11 -Tailrace (**)· ··· · ·····A-1-22
1.12 -Main Access Plan (**)··· ·
·A-1-23
1.13 -Site Facilities (**)•· ···A-1-25
1.14 -Relict Channel (***)··· ·
A-1-29
2 -RESERVOIR DATA -WATANA STAGE I (**)•· ·
•··•··•A-2-1
3 -TURBINES AND GENERATORS -WATANA STAGE I (**)• •·• •
A-3-1
4.1 -Miscellaneous Mechanical Equipment (**)•••••
4.2 -Accessory Electrical Equipment (**)
4.3 -SF6 Gas-Insulated 345 kV Substation (GIS)(***)
4 -APPURTENANT MECHANICAL AND ELECTRICAL EQUIPMENT -
WATANA STAGE I (**)••••••••••••••••
5 -TRANSMISSION FACILITIES FOR WATANA STAGE I (0)
A-4-1
A-1-2
A-5-1
A-3-1
A-3-1
A-3-1
A-3-3
A-4-1
A-4-5
A-4-12
A-5-1
A-5-1
A-5-11
Page No.
·.·..
· ... ..
· .
1
·.. . ...
EXHIBIT A
PROJECT DESCRIPTION
SUMMARY TABLE OF CONTENTS
SUSITNA HYDROELECTRIC PROJECT
LICENSE APPLICATION
3.1 -Unit Capacity (**)•
3.2 -Turbines (***)•••.
3.3 -Generators (**)
3.4 -Governor System (0)
5.1 -Transmission Requirements (0)
5.2 -Description of Facilities (0)
5.3 -Construction Staging (0)•••
Title
1 -PROJECT STRUCTURES -WATANA STAGE I (**)
851014
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SUMMARY TABLE OF CONTENTS (cont'd)
EXHIBIT A
PROJECT DESCRIPTION
Title
6 -PROJECT STRUCTURES -DEVIL CANYON STAGE II (**)• •••
Page No.
A-6-1
6.1 -General Arrangement (**)· · · ·
.A-6-1
6.2 -Arch Dam (**)· ·· ·
A-6-2
6.3 -Saddle Dam (**)· · · · ·
. .A-6-4
6.4 -Diversion (**)·A-6-6
6.5 -Outlet Facilities (**).· ·
·A-6-8
6.6 -Spillway (**)· ·
· ·· · · ·
.A-6-10
6.7 -Emergency Spillway ·· ·· · · ·
..A-6-12
(This section deleted)
6.8 -Power Facilities (*)· ·
·· · ·
A-6-12
6.9 -Penstocks (**)· ···A-6-13
6.10 -Powerhouse and Related Structures (**)A-6-14
6.11 -Tailrace Tunnel (*)·· · ·
A-6-17
6.12 -Access Plan (**)·· · ·
A-6-17
6.13 -Si te Fad li ties (*)···A-6-18
7 -DEVIL CANYON RESERVOIR STAGE II (*)·... . . ....A-7-1
8 ~TURBINES AND GENERATORS -DEVIL CANYON STAGE II (**)
8.1 -Unit Capacity (**)
8.2 -Turbines (**)
8.3 -Generators (0)••••••
8.4 -Governor System (0)
9 -APPURTENANT EQUIPMENT -DEVIL CANYON STAGE II (0)••
·.
·.
A-8-1
A-8-1
A-8-1
A-8-1
A-8-2
A-9-1
9.1 -Miscellaneous Mechanical Equipment (0)•
9.2 -Accessory Electrical Equipment (0)•••
9.3 -Switchyard Structures and Equipment (0)••
10 -TRANSMISSION LINES -DEVIL CANYON STAGE II (**)..• •
A-9-1
A-9-3
A-9-6
A-lO-l
11 -PROJECT STRUCTURES -WATANA STAGE III (***)
11.1 -General Arrangement (***)
11.2 -Dam Embankment (***)•••••••
11.3 -Diversion (***)•••••
11.4 -Emergency Release Facilities (***)
•••·..A-ll-1
A-11-1
A-1l-3
A-1l-5
A-1l-6 J
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851014 ii
SUMMARY TABLE OF CONTENTS (cont'd)
EXHIBIT A
PROJECT DESCRIPTION
Title Page No.
11.5 -Outlet Facilities (***)· ···A-11-6
11.6 -Spillway (***).· ·
··A-11-7
11.7 -Power Intake (***)··· ····A-11-8
I 11.8 -Power Tunnel and Penstocks (***)A-11-ll
11.9 -Powerhouse (***)· · ··· ·
.··A-11-11-I 11.10 -Trailrace (***)·· ·
··· ·
A-11-13
11.11 Access Plan (***)A-11-13 -.....",-··· ·· ·11.12 -Site Faci li ties (***)·· ·
..·A-ll-13
11.13 -Relict Channel (***)·· ·
.···A-ll-13
12 -RESERVOIR DATA -WATANA STAGE III (***)•• ••·•• •
A-12-1
13 -TURBINES AND GENERATORS -WATANA STAGE III (***)
13.1 -Unit Capacity (***)•••••.
13.2 -Turbines (***)••••••
13.3 -Generators (***)
13.4 -Governor System (***)
14 -APPURTENANT MECHANICAL AND ELECTRICAL EQUIPMENT -
WATANA STAGE III (***)•••••••••••••
14.1 -Miscellaneous Mechanical Equipment (***)•
14.2 -Accessory Electrical Equipment (***)•.•
15 -TRANSMISSION FACILITIES -WATANA STAGE III (***)
• •
A-13-1
A-13-1
···A-13-1
···A-13-1
A-13-1
·• •
A-14-1
A-14-1
A-14-1
•• •
A-15-1
15.1 Transmission Requirements (***)· ·
··A-15-1
15.2 switching and Substations (***)· ···A-15-1
16 -LANDS OF THE UNITED STATES (**)•.••·• • ••·•A-16-1
17 -REFERENCES •.•.• • •
.•• •
. .•• •·•••A-17-1
851014 iii
SUMMARY TABLE OF CONTENTS (cont'd)
EXHIBIT B
PROJECT OPERATION AND RESOURCE UTILIZATION
Title Page No.
1 -DAMSITE SELECTION (***)• • • •.0..• ••·....B-l-l
4.1 -Plant and System Operation Requirements (***)
4.2 -Power and Energy Production (***)•••
3.1 -Hydrology (***)•••••••••••••••••
3.2 -Reservoir Operation Modeling (***)•••••••
3.3 -Operational Flow Regime Selection (***)
5.1 -Introduction (***)••.•.•••••••.•••
5.2 -Description of the Railbelt Electric Systems (***)
5.3 -Forecasting Methodology (***)••'
5.4 -Forecast of Electric Power Demand (***)
1.1 -Previous Studies (***)
1.2 -Plan Formulation and Selection Methodology (***).
1.3 -Damsite Selection (***)•.••••••.••
1.4 -Formulation of Susitna Basin Development
Plans (***)• . • • • • •
1.5 -Evaluation of Basin Development Plans (***)•
B-l-l .
B-1-4
B-1-5
B-1-12
B-l-17
B-2-1
B-2-1
B-2-1
B-2-22
B-2-48
B-2-60
B-2-67
B-2-83
B-2-131
B-3-1
B-3-1
B-3-6
B-3-20
B-4-1
B-4-1
B-4-10
B-5-1 --I
I
B-5-1
B-5-l !B-5-17
B-5-47
B-6-1 d
B-7-1 I
..
·....
·....
·.. .....
· . ... . . .
·..
. ...... ... . ... . . ... .
2.1 -SusitnaHydroelectric Development (***)
2.2 -Watana project Formulation (***)••••..•••
2.3 -Selection of Watana General Arrangement (***)
2.4 -Devil Canyon Project Formulation (***).
2.5 -Selection of Devil Canyon General
Arrangement (***)• • • • ..'.• • • • .
2.6 -Selection of Access Road Corridor (***)
2.7 -Selection of Transmission Facilities (***).
2.8 -Selection of Project Operation (***)•••.
2 -ALTERNATIVE FACILITY DESIGN,PROCESSES AND
OPERATIONS (***).......• • • • • • •
3 -DESCRIPTION OF PROJECT OPERATION (***)
5 -STATEMENT OF ,POWER NEEDS AND UTILIZATION (***)
7 -REFERENCES
4 -POWER AND ENERGY PRODUCTION (***)• • •
6 -FUTURE SUSITNA BASIN DEVELOPMENT (***)
851014 iv
SUMMARY TABLE OF CONTENTS (cont'd)
EXHIBIT B -APPENDIX Bl
MAN-IN-THE-ARCTIC PROGRAM (MAP)
TECHNICAL DOCUMENTATION REPORT
STAGE MODEL (VERSION A85.I)
REGIONALIZATION MODEL (VERSION A84.CD)
SCENARIO GENERATOR
.Title
Stage Model
l I.introduction ...····· · ··.··· · ·2.Economic Module Description ···)3.Fiscal Module Description ·· ···4.Demographic Module Description ·5.Input Variables .· ·
··········6.Variable and Parameter Name Conventions
7.Parameter Values,Defini tions and Sources ··..
8.Model Validation and Properties ···· ·9.Input Data Sources ··· · · ····. .
10.Programs for Model Use · · · · · ·II.Model Adjustments for Simulation ·12.Key to Regressions · ····13.Input Data Archives ·· · ·
·· ·
.· ··· ·
Regionalization Model
Page No.
1-1
2-1
3-1
4-1
5-1
6-1
7-1
8-1
9-1
10-1
11-1
12-1
13-1
I.
2.
3.
4.
5.
6.
7.
8.
9.
10.
Model Description • • • •
Flow Diagram
Model Inputs • . •
Variable and Parameter Names • •
Parameter Values •
Model Validation • • • • • •
Programs for Model •
Model Listing • • • •
Model Parameters • • ••••
Exogenous,Policy,and Startup Values
· ..· .
1
5
7
9
13
31
38
39
57
61
Scenario Generator
Introduction • • • • • • • • • • • • • • • • • • • • 1
1.Organization of the Library Archives • •••1
2.Using the Scenario Generator • • • • • • • • • • 8
3.Creating,Manipulating,Examining,and
Printing Library Files •• • • • • •••14
4.Model Output • • • • • • • • •••• • • • • •••22
851014 v
SUMMARY TABLE OF CONTENTS (cont'd)
EXHIBIT B -APPENDIX B2
RAILBELT ELECTRICITY DEMAND (RED)MODEL
TECHNICAL DOCUMENTATION-REPORT (1983 VERSION)
Title Page No.
1 -INTRODUCTION • •1.1
2 -OVERVIEW • •
2.1
6 -THE BUSINESS CONSUMPTION MODULE
7 -PRICE ELASTICITY • • • • • • • • •
5 -THE RESIDENTIAL CONSUMPTION MODULE
8 -THE PROGRAM-INDUCED CONSERVATION MODULE
3.1
4.1
5.1
6.1
7.1
8.1
9.1
10.1
11.1
12.1
13.1 I
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.. .......
.. .
. . .
.. .. ... .
.........
. ..
V1
• • •II
3 -UNCERTAINTY MODULE •
11 -THE PEAK DEMAND MODULE
10 -LARGE INDUSTRIAL DEMAND
13 -MISCELLANEOUS TABLES
12 -MODEL VALIDATION
4 -THE HOUSING MODULE • •
9 -THE MISCELLANEOUS MODULE
851014
1 -INTRODUCTION
4 -BUSINESS SECTOR
Title
5 -PEAK DEMAND
1.1
3.1
5.1
2.1
6.1
4.1
Page No.
vii
SUMMARY TABLE OF CONTENTS (contrd)
EXHIBIT B -APPENDIX B3
RAILBELT ELECTRICITY DEMAND (RED)MODEL
CHANGES MADE JULY 1983 TO AUGUST 1985
2 -RED MODEL PRICE ADJUSTMENT REVISIONS •
3 -RESIDENTIAL CONSUMPTION MODULE
6 -EFFECT OF THE MODEL CHANGES ON THE FORECASTS
851014
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SUMMARY TABLE OF CONTENTS (cont'd)
EXHIBIT C
PROPOSED CONSTRUCTION SCHEDULE
Title Page No.
1 -WATANA STAGE I SCHEDULE (**)• • • •e • • • ••• • •
C-1-1
3.1 -Access (***).. ..···· ·· ·
·3.2 -Site Facilities (***)·· ·
··."·· ····3.3 -Dam Embankment (***)·· ···3.4 -Spillway and Intakes (***)· ·······3.5 -Powerhouse and Other Underground Works (**)
3.6 -Relict Channel (***)··········3.7 -Transmission Lines/Switchyards (***)
3.8 -General (***)....· ·
· ·· ····
C-I-2
C-1-2
C-1-2
C-1-2
C-1-3
C-1-3
C-1-3
C-1-3
C-1-3
C-2-1
C-2-1
C-2-1
C-2-1
C-2-1
C-2-2
C-2-2
IC-2-2
C-2-2
C-3-1 I
C-3-1
C-3-1
)C-3-1
C-3-2
C-3-2
IC-3-2
C-3-2
C-3-2 -I
C-4-1
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•••
• •
·.
·. .
·.
• •
••
· .
• •
·.. .......
·..
1.1 -Access (*)•••••••••••
1.2 -Site Facilities (**)••••
1.3 -Diversion (**)•••••••••
1.4 -Dam Embankment (**)•
1.5 -Spillway and Intakes (**)• • • •••••••
1.6 -Powerhouse and Other Underground Works (**)
1.7 -Relict Channel (**)• • • • • • • • • • • •
1.8 -Transmission Lines/Switchyards (*)••••
1.9 -General (**)•••••••••••••
2.1 -Access (**)•••••
2.2 -Site Facilities (**)•••••
2.3 -Diversion (*)• • • • • •••••••
2.4 Arch Dam (**)•• • •
2.5 -Spillway and Intake (*)••••••
2.6 -Powerhouse and Other Underground Works (0)
2.7 -Transmission Lines/Switchyar~s (*)
2.8 -General(*)••••••••
2 -DEVIL CANYON STAGE II SCHEDULE (**)•
3 -WATANA STAGE III SCHEDULE (***)•
4 -EXISTING TRANSMISSION SYSTEM (***)
851014 viii
SUMMARY TABLE OF CONTENTS (cont'd)
EXHIBIT D
PROJECT COSTS AND FINANCING
D-l-1
D-1-6
D-1-7
D-I-I0
D-l-l
D-l-11
D-l-12
D-1-12
D-l-13
D-1-13
Page No.
. .
... ..........
1.1 Construction Costs (**)
1.2 -Mitigation Costs (**)•
1.3 -Engineering and Administration Costs (*)•.••
1.4 -Operation,Maintenance and Replacement Costs (**)
1.5 -Allowance for Funds Used During
Construction (AFDC)(**)•••••••~•
1.6 -Escalation (**).•.•••••••.•••
1.7 -Cash Flow and Manpower Loading Requirements (**).
1.8 -Contingency (*)•..••..••...•.•..
1.9 -Previously Constructed Project Facilities (*)
Title
1 -ESTIMATES OF COST (**)
J
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2 -EVALUATION OF ALTERNATIVE EXPANSION PLANS (***)•...D-2-1
2.1 -General (***).••.••••••
2.2 -Hydroelectric Alternatives (***)•••..
2.3 -Thermal Alternatives (***)
2.4 -Natural Gas-Fired Options (***)•
2.5 -Coal-Fired Options (***)•..••
2.6 -The Existing Railbelt Systems (***)
2.7 -Generation Expansion Before 1996 (***)
2.8 -Formulation of Expansion Plans Beginning in
1996 (***)•.•..•..•••.•.•
2.9 Selection of Expansion Plans (***)
2.10 -Economic Development (***)•••••
2.11 -Sensitivity Analysis (***)•••.
2.12 -Conclusions (***)•....•••
D-2-1
D-2-1
D-2-10
0-2-10
0-2-19
D-2-24
D-2-27
0-2-28
D-2-33
0-2-39
0-2-44
0-2-46
3 -CONSEQUENCES OF LICENSE DENIAL (***).. . ... . ..0-3-1
3.1 -Statement and Evaluation of the
Consequences of License Denial (***)......
3.2 -Future Use of the Damsites if
the License is Denied (***).
4 -FINANCING (***)• • • • • • • • • • • • • • • • • •••
0-3-1
D-3-1
0-4-1
4.1 -General Approach and Procedures (***)
4.2 -Financing Plan (***).•••••.•
4.3 -Annual Costs (*~k).
0-4-1
0-4-1
D-4-3
851014 ix
SUMMARY TABLE OF CONTENTS (cont'd)
EXHIBIT D
PROJECT COSTS AND FINANCING
Title
4.4 -Market Value of Power (***)•
4.5 -Rate Stabilization (***)
4.6 -Sensitivity of Analyses (***)
.. ... .... . .
Page No.
D-4-4
D-4-4
D-4-4
5 -REFERENCES (***)
851014
• • • • ••• • •0 • • • • • • • • •
x
D-5-1
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SUMMARY TABLE OF CONTENTS (cont'd)
EXHIBIT D -APPENDIX Dl
FUELS PRICING
2 -WORLD OIL PRICE (***)
Title
1 -INTRODUCTION (***). .................
...... . ........
Page No.
01-1-1
Dl-2-l
2.1 -The Sherman H.Clark Associates Forecast (***)
2~2 -The Composite Oil Price Forecast (***)
2.3 The Wharton Forecast (***)••••.
3 -NATURAL GAS (***).. . ..... ..• • •••••..
01-2-1
Dl-2-2
01-2-5
DI-3-1
3.1 -Cook Inlet Gas Prices (***)
3.2 -Regulatory Constraints on the Availability of
Natural Gas (***)• . • • • • • • • • • • • •
3.3 -Physical Constraints on the Availability of
Cook Inlet Natural Gas Supply (***)•
3.4 -North Slope Natural Gas (***)
Dl-3-1
DI-3-10
DI-3-12
DI-3-20
DI-4-1
DI-4-1
D1-4-3
D1-4-4
D1-4-10
. .
... ...................
-Resources and Reserves (***)
-Demand and Supply (***)• • .
-Present and Potential Alaska Coal Prices (***)
-Alaska Coal Prices Summarized (***)
4.1
4.2
4.3
4.4
4 -COAL (***)
5 -DISTILLATE OIL (***).................D1-5-1
5.1 -Availability (***)
5.2 -Distillate Price (***)
D1-5-1
D1-5-1
6 -REFERENCES ............. .........D1-6-1
851014 xi
SUMMARY TABLE OF CONTENTS (cant'd)
EXHIBIT E -CHAPTER 1
GENERAL DESCRIPTION OF THE LOCALE
Title
1 -GENERAL DESCRIPTION (*)• •
1.1 -General Setting (**)
1.2 -Susitna Basin (*)••
........
. ..
•• •e • •
II • • •
•II • •
Page No.
E-1-1-1
E-1-1-1
E-1-1-2
2 -REFERENCES
3 -GLOSSARY
851014
• • • • • • • • • •~• 0 • •e 8 • • • • •
••••• 0 • • • • • • • • • •"• • • • • • •
xii
E-1-2-1
E-1-3-1
I
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SUMMARY TABLE OF CONTENTS (cont'd)
EXHIBIT E -CHAPTER 2
WATER USE AND QUALITY
Title
1 -INTRODUCTION (**)• • • • •
2 -BASELINE DESCRIPTION (**)•
..
• • • • 0 • •
·... ..
...
Page No.
E-2-1-1
E-2-2-1
·. .
2.1 -Susitna River Morphology (**)•••••••
2.2 -Susitna River Water Quantity (**)
2~3 -Susitna River Water Quality (**).
2.4 -Baseline Ground Water Conditions (**)
2.5 -Existing Lakes,Reservoirs,and Streams (**)
2.6 -Existing Instream Flow Uses (0)•
2.7 -Access Plan (**)
2.8 -Transmission Corridor (**).
E-2-2-3
E-2-2-12
E-2-2-19
E-2-2-46
E-2-2-49
E-2-2-50
E-2-2-63
E-2-2-64
3 -OPERATIONAL FLOW REGIME SELECTION (***)• •·.. .••E-2-3-1
3.1 -Project Reservoir Characteristics (***)
3.2 -Reservoir Operation Modeling (***)••
3.3 -Development of Alternative Environmental
Flow Cases (***)•••.•.••••••••
3.4 -Detailed Discussion of Flow Cases (***)•...•
3.5 -Comparison of Alternative Flow Regimes (***).
3.6 -Other Constraints on Project Operation (***)
3.7 -Power and Energy Production (***)•.•••
E-2-3-1
E-2-3-2
E-2-3-6
E-2-3-17
E-2-3-37
E-2-3-43
E-2-3-53
4 -PROJECT IMPACT ON WATER QUALITY AND QUANTITY (**)...E-2-4-1
4.1 -Watana Development (**)••••••
4.2 -Devil Canyon Development (**)•.•
4.3 -Watana Stage III Development (***).
4.4 -Access Plan (**)•••••••••
5 -AGENCY CONCERNS AND RECOMMENDATIONS (**).......
E-2-4-7
E-2-4-110
E-2-4-160
E-2-4-211
E-2-5-1
6 -MITIGATION,ENHANCEMENT,AND PROTECTIVE MEASURES (**)•
6.1 -Introduction (*).••...•...•....•
6.2 -Mitigation -Watana Stage I -Construction (**)•
6.3 -Mitigation -Watana Stage I -Impoundment (**).
E-2-6-1
E-2-6-1
E-2-6-1
E-2-6-5
851014 Xlll
SUMMARY TABLE OF CONTENTS (cont'd)
EXHIBIT E -CHAPTER 2
WATER USE AND QUALITY
Title Page No.
I
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E-2-6-15
E-2-7-1
E-2-6-7
E-2-6-13
E-2-6-13
E-2-6-13
E-2-6-16
E-2-6-16
E-2-6-18
E-2-8-1
• • 0 •
• • • • • • • • e • • • • • • • • • • • • •
• • • • • • • • • • D • ••• • •0 • •~• •
6.4 -Watana Stage I Operation (**)•
6.5 -Mitigation -Devil Canyon Stage II -
Construction (**)•••••••
6.6 -Mitigation -Devil Canyon Stage II -
Impoundment (**)•••••••
6.7 -Mitigation -Devil Canyon/Watana Operation (**)
6~8 -Mitigation -Watana Stage III -
Construction ('k**)•••••••
6.9 -Mitigation -Watana Stage III -
Impoundment/Construction (***)••••••
6.10 -Mitigation -Stage III Operation (***)
6.11 -Access Road and Transmission Lines (***)••••
7 -REFERENCES
8 -·GLOSSARY
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851014 xiv
SUMMARY TABLE OF CONTENTS (cont'd)
EXHIBIT E -CHAPTER 3
FISH,WILDLIFE,AND BOTANICAL RESOURCES
Title
1 -INTRODUCTION (0)
2.1 -Overview of the Resources (**)•••••
2.2 -Species Biology and Habitat Utilization
in the Susitna River Drainage (*)•••
2.3 -Anticipated Impacts To Aquatic Habitat (**)•
2.4 -Mitigation Issues and Mitigating Measures (**)
2.5 -Aquatic Studies Program (*)•••••.••
2.6 -Moni toring Studies (**)• • • • •••• •
2.7 -Cost of Mitigation (***)••••••••
2.8 -Agency Consultation on Fisheries Mitigation
Measures (**)• • •
J
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1.1 -Baseline Descriptions (0)
1.2 -Impact Assessments (*)•••••••••
1.3 -Mitigation Plans (*).•••
2 -FISH RESOURCES OF THE SUSITNA RIVER DRAINAGE (**)•..
5.1 -Introduction (***)• • • •••.
5.2 -Existing Conditions (***)••••••
5.3 -Expected Air Pollutant Emissions (***).
5.4 -Predicted Air Quality Impacts (***)••
....... . .......
3.1 -Introduction (*)••••••
3.2 -Baseline Description (**)••
3.3 -Impacts (**)•.••.••.
3.4 -Mitigation Plan (**)
4.1 -Introduction (*)
4.2 -Baseline Description (**)
4.3 -Impacts (*)..
4.4 -Mitigation Plan (**)••••
•••
.. ...
...
..
...••
• •• •
......••(**)••••
3 -BOTANICAL RESOURCES (**)
5 -AIR QUALITY/METEOROLOGY (***)•
4 -WILDLIFE
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851014 xv
SUMMARY TABLE OF CONTENTS (cont'd)
EXHIBIT E -CHAPTER 3
FISH,WILDLIFE,AND BOTANICAL RESOURCES
Title
5.5 -Regulatory Agency Consultations (***)•
Page No.
E-3-,5-3
.... . ..........
6 -REFERENCE •
7 -GLOSSARY
APPENDICES
...... . .... ... ........ .
• • ••
E-3-6-1
E-3-7-1
E1.3
E2.3
E3.3
E4.3
E5.3
E6.3
E7.3
E8.3
E9.3
EI0.3
Ell.3
851014
FISH AND WILDLIFE MITIGATION POLICY
ENVIRONMENTAL GUIDELINES MEMORANDUM
(THIS APPENDIX HAS BEEN DELETED)
PLANT SPECIES IDENTIFIED IN SUMMERS OF 1980 AND 1981
IN THE UPPER AND MIDDLE SUSITNA RIVER BASIN,THE
DOWNSTREAM FLOODPLAIN,AND THE INTERTIE
PRELIMINARY LIST OF PLANT SPECIES IN THE INTERTIE
AREA (THIS SECTION HAS BEEN DELETED AND ITS
INFORMATION INCORPORATED INTO APPENDIX E3.3.)
STATUS,HABITAT USE AND RELATIVE ABUNDANCE OF BIRD
SPECIES IN THE MIDDLE SUSITNA BASIN
STATUS AND RELATIVE ABUNDANCE OF BIRD SPECIES
OBSERVED ON THE LOWER SUSITNA BASIN DURING GROUND
SURVEYS CONDUCTED JUNE 10 THE JUNE 20,1982
SCIENTIFIC NAMES OF MAMMAL SPECIES FOUND IN THE
PROJECT AREA
METHODS USED TO DETERMINE MOOSE BROWSE UTILIZATION
AND CARRYING CAPACITY WITHIN THE MIDDLE SUSITNA BASIN
EXPLANATION AND JUSTIFICATION OF ARTIFICIAL NEST
MITIGATION (THIS SECTION HAS BEEN DELETED)
PERSONAL COMMUNICATIONS (THIS SECTION HAS BEEN
DELETED)
EXISTING AIR QUALITY AND METEOROLOGICAL CONDITIONS
XV1
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SUMMARY TABLE OF CONTENTS (cont'd)
EXHIBIT E -CHAPTER 4
HISTORIC AND ARCHEOLOGICAL RESOURCES
E-4-1-1
Page No.
E-4-2-1
E-4-2-12
E-4-2-13
E-4-2-1
E-4-2-2
E-4-2-10
E-4-1-4
E-4-1-4
..
..
•••
II • • •
•••...
..........
..
1.1 -Program Objectives (**)
1.2 -Program Specifics (**)
2.1 -The Study Area (**)•••••••
2.2 -Methods -Archeology and History (**)•
2.3 -Methods -Geoarcheology (**)••••
2.4 -Known Archeological and Historic
Sites in the Project Area (**)
2.5 -Geoarcheology (**)•.•••••••••••
Title
1 -INTRODUCTION AND SUMMARY (**)•
2 -BASELINE DESCRIPTION (**)• •
)
I
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3 -EVALUATION OF AND IMPACT ON HISTORICAL
AND ARCHEOLOGICAL SITES (**)•••••• •••••••E-4-3-1
3.1 -Evaluation of Selected Sites Found:
Prehistory and History of the Middle
Susitna Region (**)• • • • • • • • • • • • •••E-4-3-1
3.2 -Impact on Historic and Archeological Sites (**)•E-4-3-4
4 -MITIGATION OF IMPACT ON HISTORIC AND
ARCHEOLOGICAL SITES(**)• • • • • • •.......E-4-4-1
4.1 -Mitigation Policy and Approach (**)
4.2 -Mitigation Plan (**).••••
E-4-4-1
E-4-4-2
5 -AGENCY CONSULTATION (**)•• ••••• ••• •••••E-4-5-l
6 -REFERENCES .. ........ .....•••E-4-6-1
7 -GLOSSARY .... ...... . ........ . ...E-4-7-1
851014 xvii
SUMMARY TABLE OF CONTENTS (cont'd)
EXHIBIT E -CHAPTER 5
SOCIOECONOMIC IMPACTS
Title
.....• •
1 -INTRODUCTION (**)• • • • •
Page No.
E-5-2-l
E-5-1-1·...
·.....
.. .
. .......BASELINE DESCRIPTION (**)• •2
2.1 -Igentification of Socioeconomic
Impact Areas (**)• . • • • • • • . • • • • • • •E-5-2-l
2:2 -Description of Employment,Population,Personal
Income and Other Trends in the Impact Areas (**)E-5-2-1
3 -EVALUATION OF THE IMPACT OF THE PROJECT (**)......E-5-3-l
E-5-3-39
E-5-3-35
E-5-3-2
E-5-3-32
E-5-3-49
E-5-3-65
E-5-3-59
..
3.4
3.1 -Impact of In-migration of People on Governmental
Facilities and Services (**)••••••.•.•
3.2 -On-site Worker Requirements and Payroll,
by Year and Month (**)••••••••.
3.3 -Residency and Movement of Project Construction
Personnel (**)• • • • • • • • • . •
Adequacy of Available Housing in
Impact Areas (***)• . • •
3.5 -Displacement and Influences on Residences and
Businesses (**)• • . . • • • • • • • • •
3.6 -Fiscal Impact Analysis:Evaluation of
Incremental Local Government Expenditures
and Revenues (**)• • • • •••• • • •
3.7 -Local and Regional Impacts on
Resource User Groups (**)• • •
4 -MITIGATION (**)• •. ......... ....·.• •
E-5-4-l
..
4.1 -Introduction (**)•••
4.2 -Background and Approach (**)
4.3 -Attitudes Toward Changes
(This section deleted)
4.4 -Mitigation Objectives and Measures (**)
E-5-4-l
E-5-4-1
E-5-4-2
E-5-4-2
851014 xviii
SUMMARY TABLE OF CONTENTS (cont'd)
EXHIBIT E -CHAPTER 5
SOCIOECONOMIC IMPACTS
Title
5 -MITIGATION MEASURES RECOMMENDED BY AGENCIES(**)....
Page No.
E-5-5-1
5.1 -Alaska Department of Natural Resources (DNR)(**)
5.2 -Alaska Department of Fish and Game (ADF&G)(*)
5.3 -u.s.Fish and Wildlife Service (FWS)(*)
5.4 -Summary of Agencies'Suggestions for Further
Studies that Relate to Mitigation (**)
E-5-5-l
E-5-5-1
E-5-5-2
E-5-5-2
6 -REFERENCES
851014
••••••0 ••••••••••••••••
xix
E-6-6-1
SUMMARY TABLE OF CONTENTS (cont'd)
EXHIBIT E -CHAPTER 6
GEOLOGICAL AND SOIL RESOURCES
Title Page No.
1 -INTRODUCTION (**)
2 -BASELINE DESCRIPTION (*)...
· ..
• 0 • • • • • • •·...
E-6-1-1
E-6-2-1
·. . . ..
2.1 -Regional Geology (*)•••
2.2 -Quarternary Geology (*)• • • • • • .•.•
2~3 -Mineral Resources (0)•••••••
2.4 -Seismic Geology (*)• • • • • • ••••
2.5 -Watana Damsite (**)• • •••
2.6 -Devil Canyon Damsite (0)••••
2.7 -Reservoir Geology (*)•.••
E-6-2-1
E-6-2-2
E-6-2-3
E-6-2-4
E-6-2-11
E-6-2-17
E-6-2-23
3 -IMPACTS (*)• •......·..·..• •• • ••·.E-6-3-1
3.1 -Reservoir-Induced Seismicity (RIS)(*)····•E-6-3-1
3.2 -Seepage (*)....······ ··•E-6-3-4
3.3 -Reservoir Slope Failures (**)·······E-6-3-4
3.4 -Permafrost Thaw (*)·· · ·· ·······E-6-3-11
3.5 -Seismically-Induced Failure (*)·········E-6-3-11
3.6 -Reservoir Freeboard for Wind Waves (**)E-6-3-11
3.7 -Development of Borrow Sites and Quarries (**)E-6-3-12
4 -MITIGATION (**)• •
.•.••• • • •••·•·• ••·E-6-4-1
·... .
4.1 -Impacts and Hazards (0)••
4.2 -Reservoir-Induced Seismicity (0)
4.3 -Seepage (**)••••••••••
4.4 -Reservoir Slope Failures (**)•
4.5 -Permafrost Thaw (**)
4.6 -Seismically-Induced Failure (*)
4.7 -Geologic Hazards (*)•
4.8 -Borrow and Quarry Sites (*)
-..
E-6-4-1
E-6-4-1
E-6-4-2
E-6-4-2
E-6-4-3
E-6-4-3
E-6-4-4
E-6-4-4
5 -REFERENCES
6 -GLOSSARY
851014
...........
............
xx
·......
·......
·...
·...
E-6-5-1
E-6-6-1
I
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SUMMARY TABLE OF CONTENTS (cont'd)
EXHIBIT E -CHAPTER 7
RECREATIONAL RESOURCES
Title
1 -INTRODUCTION (**)• • •. . . . ....••....
Page No.
E-7-1-1
1.1 -Purpose (**)
1.2 -Relationships to Other Reports (*)
1.3 -Study Approach and Methodology (**)••
1.4 -Project Description (**)••••.•
2 -DESCRIPTION OF EXISTING AND FUTURE RECREATION
WITHOUT THE SUSITNA PROJECT (**)••••••••...
E-7-1-1
E-7-1-1
E-7-1-1
E-7-1-3
E-7-2-1
2.1 -Statewide and Regional Setting (**)
2.2 -Susi tna River Basin (**)••••••
3 -PROJECT IMPACTS ON EXISTING RECREATION (**)••••..
E-7-2-1
E-7-2-8
E-7-3-1
3.1 -Direct Impacts of Project Features (**)
3.2 -Project Recreational Demand Assessment •••
(Moved to Appendix E4.7)
E-7-3-1
E-7-3-12
4 -FACTORS INFLUENCING THE RECREATION PLAN (**).....E-7-4-1
4.1 -Characteristics of the Project Design and
Operation (***)• • . . . • . . . • . • • .
4.2 -Characteristics of the Study Area (***)•
4.3 -Recreation Use Patterns and Demand (***)••••
4.4 -Agency,Landowner and Applicant Plans and
Policies (***)•••.•••••••••
4.5 -Public Interest (***)••••••••••
4.6 -Mitigation of Recreation Use Impacts (***)•••
E-7-4-1
E-7-4-2
E-7-4-2
E-7-4-3
E-7-4-12
E-7-4-13
5 -RECREATION PLAN (**). ... .............E-7-5-1
5.1 -Recreation Plan Management Concept (***)
5.2 -Recreation Plan Guidelines (***)
5.3 -Recreational Opportunity Evaluation
(Moved to Appendix E3.7.3)
5.4 -The Recreation Plan (**)
E-7-5-1
E-7-5-2
E-7-5-4
E-7-5-4
6 -PLAN IMPLEMENTATION (**)
851014
......... ... ...
XXI.
E-7-6-1
SUMMARY TABLE OF CONTENTS (cont'd)
EXHIBIT E -CHAPTER 7
RECREATIONAL RESOURCES
Title Page No.
6.1 -Phasing (**)••••••••••
6.2 -Detailed Recreation Design (***)
6.3 -Operation and Maintenance (***)••
6.4 -Monitoring (**)•••••••••
E-7-6-1
E-7-6-1
E-7-6-2
E-7-6-3
7 -COSTS FOR CONSTRUCTION AND OPERATION OF THE PROPOSED
RECREATION FACILITIES (**)••••••••••••••E-7-7-1
7.1 -Construction (**)•••
7.2 -Operations and Maintenance (**)
7.3 -Monitoring (***)••••••••
..E-7-7-1
E-7-7-1
E-7-7-2
8 -AGENCY COORDINATION (**). . .............E-7-8-1
10 -GLOSSARY • •
PROJECT RECREATIONAL DEMAND ASSESSMENT
RECREATION SITE INVENTORY AND OPPORTUNITY EVALUATION
EXAMPLES OF TYPICAL RECREATION FACILITY DESIGN
STANDARDS FOR THE SUSITNA PROJECT
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E-7-10-1
E-7-8-1
E-7-8-1
E-7-8-1
E-7-8-2
E-7-9-1
....•• ••• • ••••.......
ATTRACTIVE FEATURES -INVENTORY DATA
DATA ON REGIONAL RECREATION FACILITIES
.............. ........
-Agencies and Persons Consulted (**)•••
-Agency Comments (**)•••
-Native Corporation Comments (***)
-Consultation Meet~ngs (***).•••••••
8.1
8.2
8.3
8.4
E2.7
E1.7
E5.7
APPENDICES
E4.7
E3.7
9 ~REFERENCES
E6.7
851014
PHOTOGRAPHS OF SITES WITHIN THE PROJECT RECREATION
STUDY AREA
xxii
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SUMMARY TABLE OF CONTENTS (cont'd)
EXHIBIT E -CHAPTER 8
AESTHETIC RESOURCES
Title
1 -INTRODUCTION (**)•...••....·.••·..••
Page No.
E-8-1-1
1.1 -Purpose (*)••••••••••
1.2 -Relationship to Other Analyses (*)
1.3 -Environmental Setting (**)••••
E-8-1-1
E-8-1-1
E-8-1-1
....... ........
... ...........4 -PROJECT FACILITIES (*)
2 -PROCEDURE (*)• • • •
3 -STUDY OBJECTIVES (*)
... ......· .
· .... ..E-8-2-1
E-8-3-1
E-8-4-1
...
4.1 -Watana Project Area (*)•
4.2 -Devil Canyon Project Area (*)••••.••.
4.3 -Watana Stage III Project Area (***)
4.4 -Denali Highway to Watana Dam Access Road (*)
4.5 -Watana Dam to Devil Canyon Dam Access Road (*)
4.6 -Transmission Lines (*)
4.7 -Intertie ••••••••••••••
(This section deleted)
4.8 -Recreation Facilities and Features (*)
E-8-4-1
E-8-4-1
E-8-4-1
E-8-4-l
E-8-4-2
E-8-4-2
E-8-4-2
E-8-4-2
5 -EXISTING LANDSCAPE (**)•..........·..E-8-5-1
5.1 -Landscape Character Types (*)
5.2 -Notable Natural Features (**)
E-8-5-1
E-8-5-1
......... . . ... . . . . . ...6 -VIEWS (**)
6.1 -Viewers (***)
6.2 -Visibility (***)
7 -AESTHETIC EVALUATION RATINGS (**)
· .
... .·.
. ..
·..
E-8-6-1
E-8-6-1
E-8-6-1
E-8-7-1
7.1 -Aesthetic Value Rating (*)
7.2 -Absorption Capability (*)••
7.3 -Composite Ratings (**)
E-8-7-1
E-8-7-1
E-8-7-2
851014 xxiii
SUMMARY TABLE OF CONTENTS (cont'd)
EXHIBIT E -CHAPTER 8
AESTHETIC RESOURCES
Title Page No.
I
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8 -AESTHETIC IMPACTS (**)• • • • • • • • • •e _ • • • •E-8-8-1
E-8-8-1
E-8-8-2
E-8-8-3
E-B-8-4
E-B-8-5
E-B-8-6
·..... ..
-Mitigation Planning of Incompatible
Aesthetic Impacts (Now addressed in Section 9)
-Watana Stage I (***)• • •
-Devil Canyon Stage II (***)• • • • •
-.Watana Stage III (***)
-Access Routes (***)• •
-Transmission Facilities (***)•
8.1
8.2
8'.3
8.4
8.5
8.6
9 -MITIGATION (**)• • • • • • •• • • ••••••• •••E-B-9-1
9.1 -Mitigation Feasibility (**)E-8-9-1
9.2 -Mitigation Plan (***)· · ·
.·E-B-9-2
9.3 -Mitigation Costs (**)·. . .•·E-B-9-l1
9.4 -Mitigation Monitoring (***).. .··E-8-9-12
10 -AESTHETIC IMPACT EVALUATION OF THE INTERTIE
(This Section Delected)
.....E-B-IO-l
11 -AGENCY COORDINATION (**)•·....•••• •· ...E-8-11-1
SITE PHOTOS WITH SIMULATIONS OF PROJECT FACILITIES
11.1 -Agencies and Persons Consulted (**)
11.2 -Agency Comments (**)••••
EXCEPTIONAL NATURAL FEATURES
E-8-12-1
E-B-ll-1
E-8-11-1
E-B-13-1• •
...
·.
•••
• •••
••••• •
·.....
•• •• •
•• •
• • • •
• •••• •
E1.8
E2.B
13 -GLOSSARY
APPENDICES
12 -REFERENCES •
E3.8 PHOTOS OF PROPOSED PROJECT FACILITIES SITES
E4.8 EXAMPLES OF EXISTING AESTHETIC IMPACTS
851014 xxiv
SUMMARY TABLE OF CONTENTS (cont'd)
EXHIBIT E -CHAPTER 8
AESTHETIC RESOURCES
Title
APPENDICES (cont'd)
Page No.
E5.8
E6.B·
E7.B
EB.B
E9.B
EXAMPLES OF RESERVOIR EDGE CONDITIONS SIMILAR TO THOSE
ANTICIPATED AT WATANA AND DEVIL CANYON DAMS
PROJECT FEATURES IMPACTS AND CHARTS
GENERAL AESTHETIC MITIGATION MEASURES APPLICABLE TO THE
PROPOSED PROJECT
LANDSCAPE CHARACTER TYPES OF THE PROJECT AREA
AESTHETIC VALUE AND ABSORPTION CAPABILITY RATINGS
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851014 xxv
SUMMARY TABLE OF CONTENTS (cont'd)
EXHIBIT E -CHAPTER 9
LAND USE
Title Page No.
4 -IMPACTS ON LAND USE WITH AND WITHOUT THE
PROJECT (***)•••••••••••••••
AREA (***)• • • • • • • ••.••• • • •c CI • •e • •
..
3 -LAND MANAGEMENT PLANNING IN THE PROJECT
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E-9-4-1
E-9-5-l
E-9-3-l
E-9-2-l
E-9-6-1
E-9-2-l
E-9-2-1
••
..
•••
• ••• •
· .
• •
•••
·..
..
•• •
• ••
·..CI • • •
..
•• • • ••
• • •e••
....
•• • •
2.1 -Historical Land Use (***)
2.2 -Present Land Use (***)
1 -INTRODUCTION (***)• • • • • • • • • •
2 -HISTORICAL AND PRESENT LAND USE (***)
5 -MITIGATION (***)•
6 -REFERENCES
851014 xxvi
SUMMARY TABLE OF CONTENTS (cont'd)
EXHIBIT E -CHAPTER 10
ALTERNATIVE LOCATIONS.DESIGNS.AND ENERGY SOURCES
. . .
Title
1 -ALTERNATIVE HYDROELECTRIC SITES (*)• • • • • • •
1.1 -Non-Susitna Hydroelectric Alternatives (*)
1.2 -Assessment of Selected Alternative
Hydroelectric Sites (***)•••••.
1.3 -Middle Susitna Basin Hydroelectric
Alternatives (0)•••••••••
1.4 -Overall Comparison of Non-Susitna
Hydroelectric Alternatives to the
Proposed Susitna Project (***)•.•
...
Page No.
E-I0-l-l
E-I0-l-l
E-I0-1-2
E-I0-1-17
E-I0-1-32
2 -ALTERNATIVE FACILITY DESIGNS (*).. .......E-I0-2-1
2.1 -Watana Facility Design Alternatives (*)
2.2 -Devil Canyon Facility Design Alternatives (0)
2.3 -Access Alternatives (0)••••••••
2.4 -Transmission Alternatives (0)
2.5 -Borrow Site Alternatives (**)
E-I0-2-1
E-I0-2-3
E-I0-2-4
E-I0-2-24
E-I0-2-53
3 ~OPERATIONAL FLOW REGIME SELECTION (***)......• •
E-I0-3-1
3.1 -Project Reservoir Characteristics (***)•••••
3.2 -Reservoir Operation Modeling (***)••
3.3 -Development of Alternative Environmental
Flow Cases (***)••.••••••••.•
3.4 -Detailed Discussion of Flow Cases (***)•
3.5 -Comparison of Alternative Flow Regimes (***)
3.6 -Other Constraints on Project Operation (***)
3.7 -Power and Energy Production (***)••
E-I0-3-1
E-I0-3-2
E-I0-3-6
E-1O-3-17
E-I0-3-38
E-1O-3-43
E-I0-3-53
4 -ALTERNATIVE ELECTRICAL ENERGY SOURCES (***)•.....E-1O-4-1
4.1 -Coal-Fired Generation ~lternatives (***)
4.2 -Thermal Alternatives Other Than Coal (***)
4.3 -Tidal Power Alternatives (***)••..
4.4 -Nuclear Steam Electric Generation (***)
4.5 -Biomass Power Alternatives (***)
4.6 -Geothermal Power Alternatives (***)••
E-lO-4-1
E-lO-4-27
E-lO-4-39
E-lO-4-41
E-lO-4-42
E-lO-4-42
851014 xxvii
SUMMARY TABLE OF CONTENTS (cant'd)
EXHIBIT E -CHAPTER 10
ALTERNATIVE LOCATIONS,DESIGNS,AND ENERGY SOURCES
Title Page No.
• • • • • 0 • • • • • • • • • • • • • • • •
4.7 -Wind Conversion Alternatives (***)
4.8 -Solar Energy Alternatives (***)••
4.9 -Conservation Alternatives (***)
5 -ENVIRONMENTAL CONSEQUENCES OF LICENSE DENIAL (***)
6 -REFERENCES
II •..
E-lO-4-43
E-IO-4-44
E-IO-4-44
E-IO-5-1
E-IO-6-1
7 -GLOSSARY
851014
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2~1 Technical Workshops (***)••••••••••
2.2 -Ongoing Consultation (***)•••••••••
2.3 -Further Comments and Consultation (***)
SUMMARY TABLE OF CONTENTS (cont'd)
EXHIBIT E -CHAPTER 11
AGENCY CONSULTATION
Title
E-1l-2-1
Page No.
E-ll-1-1
E-1l-2-1
E-1l-2-1
E-1l-2-2
•• • •
• • •0 • • • 0 •
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xxix
1 -ACTIVITIES PRIOR TO FILING THE INITIAL
APPLICATION (1980-February 1983)(***)
2 -ADDITIONAL FORMAL AGENCY AND PUBLIC
CONSULTATION (***)• • • • • • • • •
851014
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SUMMARY TABLE OF CONTENTS (cont'd)
EXHIBIT F
SUPPORTING DESIGN REPORT (PRELIMINARY)
2 -PROJECT DESIGN DATA (**)
Title
1 -PROJECT DATA (***)...................
...............
Page No.
F-1-1
F-2-1
• • •e
2.1 -Topographical Data (0)
2.2 -Hydrological Data (**)
2~3 -Meteorological Data (*)•
2.4 -Reservoir Data (0)
2.5 -Tai1water Elevations (0)
2.6 -Design Floods (**)
. ..
3 -CIVIL DESIGN DATA (*)• •
..·.• •
.0 .
....
· . .
• •• • •
F-2-1
F-2-1
F-2-1
F-2-1
F-2-1
F-2-2
F-3-1
... ...........
Standards (0)
5 -HYDRAULIC DESIGN DATA (**)
4 -GEOTECHNICAL DESIGN DATA (**)F-4-1
F-5-1
F-3-1
F-3-1
F-3-6
F-3-9
F-4-1
F-4-10
....
.. .
• •
.....
• • • • •e
· ..
·.
(0)
3.1 -Governing Codes and
3.2 -Design Loads (**)•
3.3 -Stability (*)•..
3.4 -Material Properties
4.1 -Watana (**)•••
4.2 -Devil Canyon (**)
. . ... .
·. . . .
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F-6-1
F-5-1
F-5-1
F-5-1
F-5-2
F-5-2
F-5-3
F-5-3
F-6-1
F-6-2
•••·.• •
· .
5.1 -River Flows (**)
5.2 -Design Flows (**)••• • • •
5.3 -Reservoir Levels (**)•
5.4 -Reservoir Operating Rule (**)
5.5 -Reservoir Data (**)
5.6 -Wind Effect (**)••••
5.7 -Criteria (***)
6.1 -Design Codes and Standards (*)
6.2 -General Criteria (*).•.•.
6 0
-EQUIPMENT DESIGN CODES AND STANDARDS (**)
851014 xxx
SUMMARY TABLE OF CONTENTS (cont'd)
EXHIBIT F
SUPPORTING DESIGN REPORT (PRELIMINARY)
xxxi
SUMMARY AND PMF AND SPILLWAY DESIGN FLOOD ANALYSES
F-7-1
F-6-4
F-6-6
F-6-6
F-6-8
F-6-9
F-6-12
Page No.
. .
· . .
·. .·..
••• • • • • • • • • •••••••••• •
WATANA AND DEVIL CANYON EMBANKMENT STABILITY ANALYSES
THIS APPENDIX DELETED
-Diversion Structures and Emergency Release
Facilities (*)•••••• • • • •
Spillway (**)• ••••••
Outlet Facilities (*)• ••••••••
Power Intake (*)••• • •••
Powerhouse (**)• • • • • • • • • • •
Tailrace Tunnels (**)• • • • • • • •
6.3
6.4 -
6.5 -
6.6 -
6.7 -
6".8
Title
F3
APPENDICES
7 -REFERENCES
Fl
F2
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APPENDIX 82
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SUMMARY
This report describes the 1983 version of the Railbelt Electricity Demand
(RED)model,a partial end-use/econometric model for forecasting electricity
consumption in Alaska's Railbelt region through the year 2010.It cQntains
complete documentation of the modeling approach,structure of the equations,
and selection of parameter values.In addition,information is presented on
the data bases used,supporting research,model output,and the Battelle-
Northwest residential energy-use survey conducted in the Railbelt during March
and April,1981.This survey was used to help calibrate the model.
RED has several unique capabilities:a Monte Carlo simulator for analysis
of uncertainty in key parameter values,a fuel price adjustment mechanism that
incorporates the impacts of fuel prices on demand,and the capability to
explicitly consider government subsidized investments in conservation
measures.The 1983 version contains the following features:
•an aggregate business electricity consumption forecasting
methodology that is based on the model's own forecast of commercial,
light industrial,and government building stock
o calibration of the Residential sector end uses,appliances
saturation,and fuel mode splits on actual data
•a variable price elasticity adjustment mechanism to faithfully
reflect consumer response to electricity,gas,and fuel oil prices
in both the Residential and Business Sectors
• a Housing Module that transforms a forecast of the total number of
regional households into forecasts of the occupied and unoccupied
housing stock by four types of housing units
•parameters updated to reflect 1980 Census information and
construction and energy market activity between 1980 and 1982,as
well as additional energy research performed in several other parts
of the country
•two load centers,Anchorage-Cook Inlet and Fairbanks-Tanana Valley
iii
•a report-writing module that reports price elasticities and price
effects on consumption (price-induced conservation and fuel switch-
ing),as well as households served,saturation of appliances,elec-
tricity consumption by sector,peak demand,and the sensitivity of
forecast results to variation of key model parameters.
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TABLE OF CONTENTS
CONTENTS
SUMr1ARY.••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••iii
Vacancies •••••••••••••••••••••••••••••••••••••••••••••••••••
~·1EcHANIsr·1 •••••••••••••••••••••••••••••'••••••••••••••••••••••••••
r~;l;tary Households •••••••••••••••••••••••••••••••••••••••••
INPUTS AND OUTPUTS ••••••••••••••••.••••••••••••••••••••••••••••••
1.1
2.1
2.3
2.4
2.4
2.5
2.6
2.7
2.7
3.1
3.1
3.1"
3.2
3.3
4.1
4.1
4.1
4.1
4.8
4.8
4.9
4.11
4.16
.......................................................
.......................................................
OVERVIEW
INTRODUCTION •••••••••••••••••••••••••••••••••••••••••••••••••••••
Household Size and Demographic Trends •••••••••••••••••••••••
Historic and Projected Trends in Demand for Housing •••••••••
UNCERTAINTY MODULE •••••••••••••••••••••••••••••••••••••••••••••••
THE HOUSING MODULE •••••••••••••••••••••••••••••••••••••••••••••••
UNCERTAINTY MODULE ••••••••••••••••.•••••••••••••••••••.••••••••••
RESIDENTIAL CONSUMPTION MODULE •••••••••••••••••••••••••••••••••••
BUSINESS CONSUMPTION MODULE ••••••••••••••••••••••••••••••••••••••
PEAK DEMAND MODULE •••••••••••••••••••••••••••••••••••••.••••••••••
PROGRAM-INDUCED CONSERVATION ~10DULE ••••••••••••••••••••••••••••••
MISCELLANEOUS CONSUMPTION MODULE •••••••••••••••••••••••••••••••••
MODULE STRUCTURE •••••••••••••••••••••••••••••••••••••••••••••••••
MECHANISM
PARAMETERS •••••••••••••••••••••••••••••••••••••••••.•••••••••••..
MODULE STRUCTURE •••••••.•••••••••••••••••••••••••••••••••••••••••
INPUTS AND OUTPUTS •••••.•••••••••••••••••••••••••••••••••••••••••
PARAMETERS ••••••••.•••••••.•••••••••.••••••••••••••••••.•••••••••
THE HOUSING MODULE •••••••••••••.•••••••••••••••••••••••••••••••••
1.0
2.0
3.0
4.0
v
Pric e E1 a5 tic;tie s ..............................•......•.....
pARAr~ETERS •••••••••••••••••••••••••••••••••••0 •••••••••••••••••••
r~ECHAN ISM •••••••••••••••••••••••••••••••••••••••••••••••••••••••
Appl i ance ·Surviva 1••••••••••••••••••••••••••••••••••••••••••
4.17
4.19
5.1
5.1
5.2
5.2
5.10
5.11
5.26
5.28
5.33
5.36
5.36
5.36
6.1•••••••0 •••••••••••••••••••••••••
Appliance Saturations •••••••••••••••••••••••••••••••••••••••
Base Year Housing Stock •••••••••••••••••••••••••••••••••••••
Depreciation and Removal ••••••••••••••••••••••••••••••••••••
Fuel Mode Splits ••••••••••••••••••••••••••••••••••••••••••••
Consum~tion of Electricity per Unit •••••••••••••••••••••••••
Electrical Capacity Growth ••••••••••••••••••••••••••••••••••
Household Size Adjustments ••••••••••••••••••••••••••••••••••
INPUTS AND OUTPUTS •••••••••••••••••••••••••••••••••••••••••••••••
THE RESIDENTIAL CONSUMPTION MODULE •••••••••••••••••••••••••••••••
MODULE STRUCTURE •••••••••••••••••••••••••••••••••••••••••••••••••
THE BUSINESS CONSUMPTION MODULE
5.0
6.0
MECHANISM •••••••••••••••••••••••••••••••••••••••.••••.••.••••.••6.1
pARAr~ETERS •••••••••••••••••••••••••••••••••••••••••••••••••••••••
MODULE STRUCTURE ..•...•.............•.•.•............•...........
INPUTS AND OUTPUTS •••~•••••••••••••••••••••••••••••••••••••••••••
Fl aor Sp ace St ack Eq uat ion s •••••••••••••••••••••••••••••••••
Business Electricity Usage Parameters •••••••••••••••••••••••
Business Price Adjustment Parameters ••••••••••••••••••••••••
6.1
6.2
6.7
6.8
6.16
6.20
7.1................................................PRICE ELASTICITY7.0
THE RED PRICE ADJUSTMENT MECHANISM...............................7.1
LITERATURE SURVEy................................................7.3
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8.0
SELECTION OF RED PRICE ADJUSTMENT MECHANISM STRUCTU~E
AND PARAMETERS ••••••••••••••••••••'••••••••.••••••••••••••••••••••
Sector Division •••••••••••••••••••••••••••••••••••••••••••••
Va ria b1 e El as tic i t Y•••••••••••••••••••••••••••••••••••••••••
Adjustment Over Time ••••••••••••••••••••••••••••••••'••••.•••
Cro ssP ric e El as tic it ies ••••••"••••••••••••••••••••••••••••-••
Parameter Estimates •••••••••••••••••••••••••••••••••••••••••
DERIVATION OF RED PRICE ADJUSTMENT MECHANISt~EqUATIONS •••••••••••
GLOSSARY OF SYMBOLS •••••••••••••••~••••••••••••••••••••••••••••••·
THE PROGRAM-INDUCED CONSERVATION MODULE ••••••••••••••••••••••••••
7 .10
7.10
7 .12
7.12
7.13
7.14
7 .15
7.22
8.1
MECHANISM •••••••••••••••••••••••.•••_............................8.1
INPUTS AND OUTPUTS •••••••••••••••••••••••••••.••••••••••••.••••••
MODULE STRUCTURE ••••••••••••••••••••••••••.••••••••••••••••••••••
8.S
8.S
Scenario Preparation (CONSER Program).......................8.7
Conservat ion •Residential ...................................8.10
9.0
Business Conservation ••••••••••••••••••••••••••••••••••••••
Peak Correction Factors •••••••••••••••••••••••••••••••••••••
PARAMETERS •..••......•........•...••••..•••......•.••.•..........
THE MI SCELLANEOUS t~ODULE •••••••••••••••••••••••••••••••••••••••••
8.12
8.16
8.16
9.1
MECHANISM •••••••••••••••••••••••••••••••••••••••••••••••••••••••9.1
INPUTS AND OUTPUTS...............................................9.1
MODULE STRUCTURE.................................................9.1
PARA~1ETERS.• •• •• • •• • •• • • • •• ••• ••• •• •• •• • ••• • •• •• •• •• • • • •••• • •• •••9.3
10.0 LARGE INDUSTRIAL DEMAND .10.1
MECHANISM,STRUCTURE,INPUTS AND OUTPUTS •••••••••••••••••••••••••
PARAMETERS ...•.......••........•.................................
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10.1
10.2
11.0 THE PEAK DEr~AND r~ODULE •••••••••••••••••••••••••••••••••••••••••••
r1ECHANISM •••••••••••••••••••••••••••••••••••••••••••••••••••••••
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11.2
INPUTS AND OUTPUTS ••••••••••••••••••••••••••••••••••••••..•••••••11.1
MODULE STRUCTURE ••••••.••••••••••••••••••••.•••••••••••••••••••••11.2
PARAMETERS •••••••••••••••••••••••••••••••••••••••••••••••••••••••11.5
Quantitative Analysis of Trends in Load Factors
in the Railbelt •••••••••••••••••••••••••••••••••••••••••••••11.6
Qualitative Analysis of Load Factors ••••••••••••••••••••••••11.10
12.0 MODEL VALlnATION ••••••••••.•••••••••••.••••••••••••••••••••••••••
ASSEssr~ENT OF RED 1 S ACCURACy •••••••••••••••••••••••••••••••••••••
REASONABLENESS OF THE FORECASTS ••••••••••••••••••••••••••••••••••
13.0 MISCELLANEOUS TABLES •••••••••••••••••••••••••••••••••••••••••••••
12.1
12.1
12.3
13.1
REFERENCES
APPENDIX A:
SURVEY
.......................................................
BATTELLE-NORTHWEST RESIDENTIAL SURVEy ••••••••••••••••••••
DESIGN ••••••••••••••••••••••••••••••••••••••••••••••••••••
R.l
A .1
A.2
SAr~PLE SIZE AND Cm~POSITION......................................A.2
MAILING PROCESS AND COLLECTION OF RESULTS........................A.5
OUTPUT
APPENDIX B:
.......................................................
CONSERVATION RESEARCH ••••••••••••••••••••••••••••••••••••
A.6
B.l
PACIFIC NORTHWEST POWER PLANNING COUNCIL •••••••••••••••••••••••••
BONNEVILLE POWER ADMINISTRATION ••••••••••••••••••••••••••••••••••
CALIFORNIA ENERGY COMMISSION •••••••••••••••••••••••••••••••••••••
WISCONSIN ELECTRIC POWER COMPANy •••••••••••••••••••••••••••••••••
ALASKAN RAI LBEL T•••••••••••••••••••••••••••••••••••••••••••••••••
APPEN DIXC:RED /10 DEL 0UTP UT••••••••••••••••••••••••••••••••••.••••••
LIST OF TABLES •••••••••••••••••••••••••••••••••••••••••••••••••••
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B.3
B.4
B.6
B.10
B .13
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1.1
2.1
3.1
4.1
5.1
6.1
8.1
9.1
11.1
11.2
A.1
A.2
FIGURES
The Railbelt Region of Alaska •••••••••••••••••••••••••••••••••••
Information Flows in the RED tbdel ••••••••••••••••••••••••••••••
RED Uncertainty Module ••••••••••••••••••••••••••••••••••••••••••
RED Hous i ng Modul e••••••••••••••••••••••••••••••••••••••••••••••
RED Residential Consumption Module ••••••••••••••••••••••••••••••
RED Business Consumption Module •••••••••••••••••••••••••••••••••
RED Program-Induced Conservation Module •••••••••••••••••••••••••
RED Miscellaneous Module ••••••••••••••••••••••••••••••••••••••••
RED Peak Demand Module ••••••••••••••••••••••••••••••••••••••••••
Daily Load Profile in the Pacific Northwest •••••••••••••••••••••
Battelle-Northwest Survey Form ••••••••••••••••••••••••••••••••••
Saturati on of Freezers in Anchorage-Cook Inl et Load Cente r ••••••
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1.2
2.2
3.3
4.3
5.4
6.3
8.2
9.2
11.3
11.12
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TABLES
3.1 Inputs and Outputs of the RED Uncertainty Module................3.2
3.2 Parameters Generated by the Uncertainty ~ndule..................3.4
4.1 Inputs and Outputs of the RED Housing Module ••~.................4.2
4.2'Number of Military Households Assumed to Reside on
Base in Railbelt Load Centers...................................4.8
4.3 Household Size Western U.S.and Railbelt 1950-1980 ••••••••••••••4.9
4.4 Forecast Size of Households,Railbelt Load Centers..............4.10
4.5 Impact of Householder Age and Household Size on Housing
Mix and Total Utility Sales,Anchorage-Cook Inlet...............4.13
4.6 Single-Family Housing as Proportion Year-Round Housing
Stock by Type,Ra i 1belt Load Centers,1950-1982.................4.14
4.7 Probability of Size of Households in Railbelt Load Centers......4.15
4.8 Regional Frequency of Age of Household Head
Divided by the State-Wide Frequency.............................4.16
4.9 Housing Demand Equations:Parameters'Expected
Value,Range,and Variance ••••••••••••••••••••••••••••••••••••••4.17
4.10 Assumed Normal and Maximum Vacancy Rates by Type of House.......4.18
4.11 Assumed Five-Year Housing Removal Rates in Railbelt
Region,1980-2010 •••••••••••••••••••••••••••.••••••.••••.••••..4.18
4.12 Railbelt Housing Stock by Load Center and Housing Type,1980....4.19
5.1 Inputs and Outputs of the RED Residential Module................5.3
5.2 Percent of Households served by Electric Utilities in
Railbelt Load Centers,1980-2010................................5.11
5.3 Appliance Saturation Rate Survey...............................5.12
5.4 Market Saturations of Large Appliances with Fuel Substitution
Possibilities in Single-Family Homes,Railbelt Load Centers,1980-2010.......................................................5.14
5.5 Market Saturations of Large Appliances with Fuel Substitution
Possibilities in Mobile Homes,Railbelt Load Centers,
1980-2010.......................................................5.15
x
5.6 Market Saturations of Large Appliances with Fuel Substitution
Possibilities in Duplexes,Railbelt Load Centers,1980-2010.....5.16
5.7 Market Saturations of Large Appliances with Fuel Substitution
Possibilities in Multifamily Homes,Railbelt Load Centers,
1980-2010.............................................•.....•...5.17
5.8 Market Saturations of Large Electric Appliances in
Single-Family Homes,Railbelt Load Centers,1980-2010...........5.21
5.9 Market Saturations of Large Electric Appliances in
Mobile Homes,Railbelt Load Centers,1980-2010..................5.22
5.10 Market Saturations of Large Electric Appliances in
Duplexes,Railbelt Load Centers,1980~2010......................5.23
5.11 f1arket Saturations of Large Electric Appliances in
Multifamily Homes,Railbelt Load Centers,1980-2010.••••••••••••5.24
5.12 Percentage of Appliances Using Electricity and Average
Annual Electricity Consumption,Railbelt Load Centers...........5.27
5.13 Growth Rates in Electric Appliance Capacity and Initial
Annual Average Consumption for New Appliances...................5.29
5.14 Comparison of Appliance Usage Estimates from selected Studies •••5.30
5.15 Electric New Appliance Efficiency Improvements 1972-1980........5.34
5.16 Percent of Appliances Remaining in service Years After
Purchase,Railbelt Region.......................................5.37
5.17 Equation s to Dete nni ne Adjus tment s to El ectri city
Consumption Resulting from Changes in Average
Household Size..................................................5.38
6.1 Inputs and Outputs of the Business Consumption Module...........6.2
6.2 Calculation of 1978 Anchorage Commercial-IndustrialFloorSpace.....................................................6.5
6.3 1978 Commercial-Industrial Floor Space Estimates ••••••••••••••••6.6
6.4 Comparisons of Square Feet,Employment,and Energy
Use in Commercial Buildings:Alaska and U.S.Averages ••••••••••6.10
6.5 Business Floor Space Forecasting Equation Parameters............6.13
6.6 Original RED Floor Space Equation Parameters....................6.14
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6.7 Predicted Versus Actual Stock of Commercial-Light
Industrial-Government Floor Space,1975-1981....................6.15
6.8 Business Consumption Equation Results...........................6.17
6.9 Electricity Consumption Per Employee and Square Foot and
Square Footage Per Employee for Greater Anchorage and
Fairbanks,1974-1981............................................6.19
7.1 Residential Electricity Demand Survey...........................7.6
7.2 Residential Survey Parameter Estimates..........................7.8
7.3 Commercial Electricity Demand Survey............................7.11
7.4 Commercial Survey Parameter Estimates...........................7.12
7.5 Parameter Values in RED Price Adjustment Mechanism..............7.14
8.1 Inputs and Outputs of the Conservation Modul e.........••••••••••8.6
8.2 Payback Periods and Assumed Market Saturation Rates for
Residential Conservation Options................................8.17
9.1 Inputs and Outputs of the Miscellaneous Module..................9.1
9.2 Parameters for the Miscellaneous Module.........................9.4
11.1 Inputs and Outputs of the Peak Demand Module ••••••••••••••••••••11.2
11.2 Assumed Load Factors for Railbelt Load Centers ••••••••••••••••••11.5
11.3 Computed Load Factors and Month of Peak Load Occurrence
for Anchorage and Fairbanks 1970-1981 •••••••••••••••••••••••••••11.7
11.4 Time Period of Peak Demands in Anchorage and Fairbanks ••••••••••11.13
11.5 Percentages of Total Forecasted Railbelt Electrical
Consumption Comprised by Individual Customer Sector •••••••••••••11.14
11.6 Conservation ~~asures Most Likely to be Implemented
in the Residential Sector of Alaska •••••••••••••••••••••••••••••11.14
12.1 Comparison of Actual Base Case,and Backcast Electricity
ConslJ11ption (GWh)1982..........................................12.2
12.2 1982 Values of Input Variables ••••••••••••••••••••••••••••••••••12.3
12.3 Comparison of Recent Forecasts,1980-2000 •••••••••••••••••••.•••12.5
xii
13.1 Number of Year-Round Housing Units by Type,Railbelt
Load Centers,Selected years ••••••••••••••••••••••••••••••••••••
13.2 Railbelt Area Utility Total Energy and System Peak Demand •••••••
13.3 Anchorage-Cook Inlet Load Center Utility Sales and Sales
Per Customer,1965-1981 ••••••.•.•.•••.•.•....•••••••-••.••..•••.•
13.4 Fairbanks-Tanana Valley Load Center Utility Sales and Sales
Per Customer,1965-1981 ••••••••.•••.•.•••••••••.••••••.••.••••.•
13.5 Adjustment for Industrial Load Anchorage-Cook
Inlet,1973-1981 ••••••••••••••••••••••••••••••••••••••••••••••••
A.l Customers,Number Surveyed,and Respondents for the
Residential Survey Battelle-Northwest •••••••••••••••••••••••••••
A.2 Weights Used in Battelle-Northwest Residential Survey •••••••••••
B.1 PNPPC Likely Conservation Potential at 4.0 Cents/kWh by
the Year 2000 •••••••••••••••••.•••••••••••••••••••••••••••••••••
B.2 BPA Budgeted Conservation Program Savings •••••••••••••••••••••••
B.3 CEC Conservation Programs Electricity Savings in
the Year 2002 •••••••••••••••••••••••••••••••••••••••••••••••••••
B.4 CEC Potential Energy Savi ngs by End-Use Secto r by
the Year 2002 ••••••••••.••••••.••••••••.••.••••••••.••••.••.••••
B.5 WEPC Conservation Potential by the Year 2000 ••••••••••••••••••••
B.6 Average Annual Electricity Consumption per Household
on the GVEA System,1972-1982 •••••••••••••••••••••••••••••••••••
B.7 Progerammatic Versus Market-Driven Energy Conservation
Projections in the Ar~L&P Service Area •••••••••••••••••••••••••••
B.8 Programmatic Energy Conservation Projections for AML&P ••••••••••
Appendix C has a special list of tables •••••••••••••••••••••••••••••••
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13.3
13.4
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1.0 INTRODUCTION
This document describes the 1983 version of the Railbelt Electricity
Demand (RED)model,a computer model for forecasting electricity consumption in
Alaska1s Railbelt region through the year 2010 (see Figure 1.1).The original
version of this model was developed by Battelle,Pacific Northwest Laboratories
(Battelle-Northwest)as part of the Alaska Railbelt Electric Power Alternatives
Study (Railbelt Study).The Railbelt Study was an electric power planning
study performed by Battelle-Northwest for the State of Alaska,Office of the
Governor and the Governor1s Policy Review Committee between October 1980 and
December 1982.
In March 1983,Battelle-Northwest was asked by the Harza-Ebasco Susitna
Joint Venture of Anchorage,Alaska to review the RED model structure,to make
appropriate changes,to document the changes,and to validate the model.Dur-
ing the update,Harza-Ebasco assisted and guided in the It.Ork performed.The 1983
version of the RED model is used as one of a series of linked models to produce
updated forecasts of electrical power needs in the Railbelt over the next
30 years.The other models used in the 1983 update foecasting methodology are
the State of Alaska's PETREV petroleum revenue forecasting model,the
University of Alaska Institute of Social and Economic Research's MAP economic
and population forecasting model,and the Optimized Generation Planning (OGP)
model for planning the Railbelt electricity generation system and for estimat-
ing electricity costs.Separate documentation is available for those models.
The outcome of the RED update process is contained in this documentation
report.The report contains complete documentation on the model,information
on data bases used in model development,and a section on model validation.
The RED forecasting model documented in this report is a partial end-
use/econometric model.Initial estimates of total residential demand are
derived by forecasting the number of energy-using devices and aggregating their
potential electricity demand into preliminary end-use forecasts.The model
then modifies these preliminary forecasts,using econometric fuel price elas-
ticities,to develop final forecasts of total residential energy consumption.
The model thus uses both technical knowledge of end uses and econometrics to
1.1
FAIRBANKS-TANANA
VALLEY C"':
~~~))O()~
"".,.):;:;::::':.:.-ANCHOIiA:~'l ~..si;"/
........::M~::::::fL
.....:~j1j~j~jjjjjj1j~i;:::gL::....
SCALE
• :.:::::::::::::::::::::::::::::::::::::::::::::::::::0 50 100 MILES::::::::::::::::::::::::::::::::::::::.....::::::::.:::::::::::::::::::::::::::::::::::::::::::::::::::::
FIGURE 1.1.The Railbelt Region of Alaska
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produce the residential forecast.The business sector (commercial,small
industrial,and government load)is treated similarly.However,because little
information is available on end uses in the business sectors in Alaska,pre-
liminary demand is estimated on an aggregated basis rather than by detailed end
use.Miscellaneous demand is based on the demand of the other three sectors,
while large industrial load and military load is forecasted exogenously by the
model user.
Other important features of the model are a mechanism for handling
uncertainty in some of the mo~el parameters,a method for explicitly including
government programs designed to subsidize conservation and consumer-installed
dispersed energy options (i .e.microhydro and small wind energy systems),and
the ability to forecast peak electric demand by load center.The 1983 version
of the model recognizes two load centers:Anchorage-Cook Inlet (including the
Matanuska-Susitna Borough and the Kenai Penninsula)and Fairbanks-Tanana
Valley.The model produces annual energy and peak demand forecasts for every
fifth year from 1980 to 2010,and then linearly interpolates to derive annual
energy and demand forecasts for years between the five-year forecasts.
To produce a forecast,the model user must supply the model with region-
specific estimates of total employment and total households for each forecast
period.A few statewide variables are also required,such as forecasts of the
age/sex distribution of the state's population.All of these variables are
produced by the University of Alaska Institute of Social and Economic Research
MAP econometric model;however,they can be derived from other sources.The
user must also supply price estimates for natural gas,oil,and electricity.
The estimates used in the 1983 update are consistent with input and output data
of the other models used in the forecasting methodology.Finally,the model
user may select either ranges or default values for the model·s parameters and
may run the model in either a certainty-equivalent or uncertain (Monte Carlo)
mode.The model then produces the forecasts.
This report consists of 13 sections.In Section 2.0 an overview of the
RED model is presented.In Section 3.0 the Uncertainty tvbdule,which provides
the model with t10nte Carlo simulation capability,is described.Section 4.0
describes the Housing t~dule,which forecasts the stock of residential housing
1.3
units by type.These forecasts are used in the electricity demand forecasts of
the Residential Consumption Module,discussed in Section 5.0.Forecasts of
demand in the business sector are produced by the Business Consumption Module,
which is described in Section 6.0.The price adjustment mechanism is the
subject of Chapter 7.0.The effects of government market intervention to
develop conservation and dispersed generation options are covered by the
Program-Induced Conservation Module,Section 8.0.Section 9.0 discusses mis-
cellaneous electricity demand (street lighting,second homes,etc.).Large
industrial demand is covered in Section 10.0.The Peak Demand Module,Section
11.0,concerns the relationship between annual electricity consumption and
annual peak demand.Section 12.0 covers model validation,and Section 13.0
provides miscellaneous statistics on Railbelt electrical demand.The report
also includes appendices on the Battelle-Northwest residential electric energy
survey used to calibrate RED,conservation research conducted by Battelle-
Northwest in support of the study,and model output for the 1983 update.
1.4
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2.0 OVERVIEW
The Railbelt Electricity Demand (RED)model is a simulation model designed
to forecast annual electricity consumption for the residential,commercial-
light industrial-government,heavy industrial,and miscellaneous end-use
sectors of Alaska's Railbelt region.The model also takes into account
government intervention in,the energy markets in Alaska and produces forecasts
of system annual peak demand.In the 1983 version of RED,forecasts of
consumption by sector and system peak demand are produced in five-year steps
for two Railbelt load centers:
~Anchorage-Cook Inlet (including Anchorage,Matanuska-Susitna Borough
and Kenai Peninsula)
•Fairbanks-Tanana Valley (including the Fairbanks-North Star Borough
and Southeast Fairbanks Census Area).
Between these five-year steps,the model linearly interpolates to estimate
annual energy and peak demand.When run in Mbnte Carlo mode,the model
produces a sample probability distribution of forecasts of electri~ity
consumption by end-use sector and peak demand for each load center for each
forecast year:1985,1990, 1995,2000, 2005, 2010.This distribution of
forecasts can be used for planning electric power generating capacity.
Figure 2.1 shows the basic relationship among the seven modules that
comprise the RED model.The model begins a simulation with the Uncertainty
Module,selecting a trial set of model parameters,which are sent to the other
modules.These parameters include parameters to compute price elasticities,
appliance saturation parameters,and regional load factors.Exogenous
forecasts of population,economic activity,and retail prices for fuel oil,
gas,and electricity are used with the trial parameters to produce forecasts of
electricity consumption in the Residential Consumption and Business Consumption
Modules.These forecasts,along with additional trial parameters,are used in
the Policy-Induced Conservation Module to model the effects on electricity
sales of subsidized conservation and dispersed generating options.The revised
2.1
consumption forecasts of residential and business (commercial,small indus-.
trial,and government)consumption are used to estimate future miscellaneous
consumption and total electricity sales.Finally,the unrevised and revised
consumption forecasts are used along with a user-supplied est~mate of large
industrial load and trial system load factor forecast to estimate peak
demand.The model then returns to start the next Mbnte Carlo trial.When the
model is run in certainty-equivalent mode,a specific "default"set of
parameters is used,and only one trial is run.
The RED model produces an output file of trial values for electricity
consumption by sector and system peak demand by year and load center.This
information can be used by the Optimized Generation Planning (OGP)model or
other generation planning model to plan and dispatch electric generating
capacity for each load center and year.
The remainder of this section briefly describes each module.Detailed
documentation of each of the modules is contained in Sections 3.0 through 11.0
of thi s report.
UNCERTAINTY MODULE
The purpose of the Uncertainty Module is to randomly select values for
individual model parameters that are considered to be key factors underlying
forecast uncertainty.These parameters include the market saturations for
major appliances in the residential sector;the parameters used to compute
price elasticity and cross-price elasticities of demand for electricity in the
residential and business sector;the market penetration of program-induced
conservation and dispersed generating technologies;the intensity of
electricity use per square foot of floor space in the business sector;and the
electric system load factors for each load center.
These parameters are generated by a Mbnte Carlo routine,which uses
information on the distribution of each parameter (such as its expected value
and range)and the computer's random number generator to produce sets of
parameter values.Each set of generated parameters represents a "trial."By
running each successive trial set of generated parameters through the rest of
the modules,the model builds distributions of annual electricity consumption
2.3
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and peak demand.The end points of the distributions reflect the probable
range of annual electric consumption and peak demand,given the level of
uncertainty.
The Uncertainty Module need not be run every time RED is run.The
parameter file contains IIdefault ll values of the parameters that may be used to
conserve computation time.
HOUSING MODULE
The Housing Module calculates the number of households and the stock of
housing by dwelling type in each load center of each forecast year in which the
model is run.Using regional forecasts of households and total population,the
housing stock module first derives a forecast of the number of households
served by electricity in each load center.Next,using exogenous statewide
forecasts of household headship rates and the age distribution of Alaska's
population,it estimates the distribution of households by age of head and size
of household for each load center.Finally,it forecasts the demand for four
types of housing stock:single family,mobile homes,duplexes,an~multifamily
units.
The supply of housing is calculated in two steps.First,the supply of
each type of housing from the previous period is adjusted for demolition and
compared to the demand.If demand exceeds supply,construction of additional
housing begins immediately.If excess supply of a given type of housing
exists,the model examines the vacancy rate in all types of houses.Each type
is assumed to have a maximum vacancy rate.If thi s rate is exceeded,demand is
first reallocated from the closest substitute housing type,then from other
types.The end result is a forecast of occupied housing stock for each load
center for each housing type in each forecast year.This forecast is passed to
the Residential Consumption Module.
RESIDENTIAL CONSUMPTION MODULE
The Residential Consumption Module forecasts the annual consumption of
electricity in the residential sector for each load center in each forecast
year.It does not,in general,take into account explicit government
2.4
intervention to promote residential electric energy conservation or self-
sufficiency.Such intervention is covered in the Program-Induced Conservation
Module.The Residential Consumption Module employs an end-use approach that
recogni zes nine major end uses of el ectricity,extra hot water for two of these
appliances,and a II sma ll appliances"category that encompasses a large group of
other end uses.For a given forecast of occupied housing,the Residential
Consumption Module first forecasts the residential appliance stock and the
portion using electricity,stratified by the type of dwelling and vintage of
the appliance.Appliance efficiency standards and average electric consumption
rates are applied to that portion of the stock of each appliance using elec-
tricity.The stock of each electric appliance is then multiplied by its
corresponding consumption rate to derive a prel iminary consumption forecast for
the residential sector.Finally,the Residential Consumption Module receives
exogenous forecasts of residential fuel oil,natural gas,and electricity
prices,along with "trial ll values of parameters used to compute price elastic-
ities and cross-price elasticities of demand from the Uncertainty Module.It
adjusts the preliminary consumption forecast for both short-and long-run price
effects on appliance use and fuel switching.The adjusted forecast is passed
to the Program-Induced Conservation and Peak Demand Modules.
BUSINESS CONSUMPTION MODULE
The Business Consumption Module forecasts the consumption of electricity
by load center in commercial,small industrial,and government uses for each
forecast year (1980,1985,1990,1995,2000, 2005,2010).Direct promotion of
conservation in this sector is covered in the Program-Induced Conservation
Module.Because the end uses of electricity in the commercial,small
industrial and government sectors are more diverse and less known than in the
residential sector,the Business Consumption Module forecasts electrical use on
an aggregate basis rather than by end use.
RED uses a proxy (the stock of commercial,small industrial floor,and
government space)for the stock of electricity-using capital equipment to
forecast the derived demand for electricity.Using an exogenous forecast of
regional employment,the module forecasts the regional stock of floor space.
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Next,econometric equations are used to predict the intensity of electricity
use for a given level of floor space in the absence of any relative price
changes.Finally,a price adjustment similar to that in the Residential
Consumption r~dule is applied to derive a forecast of business electricity
consumption (excluding large industrial demand,which must be exogenously
determined).The Business Consumption fvbdule forecasts are passed to the
Program-Induced Conservation and Peak Demand Modules.
PROGRAM-INDUCED CONSERVATION MODULE
Because of the potential importance of government intervention in the
marketplace to encourage conservation of energy and substitution of other forms
of energy for electricity,the RED model includes a module that permits
explicit treatment of user-installed conservation technologies and government
programs that are designed to reduce the demand for util ity-generated electric-
ity.This module was designed for analyzing potential future conservation
programs for the State of Alaska and was not used in the 1983 updated
forecasts.The module structure is designed to incorporate assumptions on the
technical performance,costs,and market penetration of electricity-saving
innovations in each end use,load center,and forecast year.The module
forecasts the aggregate electricity savings by end use,the costs associated
with these savings,and adjusted consumption in the residential and business
sectors.
The Program-Induced Conservation Module performs estimates of payback
period and penetration rate of commercial sector and residential sector
conservation options.In the residential sector,the model user supplies
information to the module on the technical efficiency (electricity savings),
electricity price,and costs of installation.The module then calculates the
internal rate of return on the option to the consumer,as well as the option1s
payback period for technologies considered lIacceptableli by the user.The
module's payback decision rule links the payback period to a range of market
saturations for the technologies.The savings per installation and market
saturation of each option are used to calculate residential sector electricity
savings and costs.In the business sector,the model user must specify the
2.6
technical potential for new and retrofit energy-saving technologies.The user
must also specify the range of conservation saturation as a percent of total
potential conservation.The Program-Induced Conservation Module then calcu-
lates total electricity savings due to market intervention in new and retrofit
applications and adjusts residential and business consumption for each load
center and forecast year.
MISCELLANEOUS CONSUMPTION MODULE
The Miscellaneous Consumption Module forecasts total miscellaneous
consumption for second (recreation)homes,vacant houses,and street
lighting.The module uses the forecast of residential consumption (adjusted
for conservation impacts)to predict electricity demand in second homes and
vacant housing units.The sum of residential and business consumption is used
to forecast street lighting requirements.Finally,all three are sunmed
together to estimate miscellaneous demand.
PEAK DEMAND MODULE
The Peak Demand Module forecasts the annual peak load demand for
electricity.A two-stage approach using load factors is used.The unadjusted
residential and business consumption,miscellaneous consumption,industrial
demand and load center load factors generated by the Uncertainty Module are
first used to forecast preliminary peak demand.Next,displaced consumption
(electricity savings)calculated by the Program-Induced Conservation Module is
multiplied by a peak correction factor supplied by the Uncertainty Module to
allocate a portion of/electricity savings from conservation to peak demand
periods.The allocated consumption savings are then multiplied by the load
factor to forecast peak demand savings,and the savings are subtracted from
peak demand to forecast revised peak demand.
The following sections describe each module of the model in greater
detail.
2.7
3.0 THE UNCERTAINTY MODULE
RED's Uncertainty Module allows the forecaster to incorporate uncertainty
in key parameters of the RED Model forecast.In other words,the impact of
uncertain parameter values can be reflected in the forecast values.
RED allows generation of key subsets of the full set of parameters.It is
not practical to allow all parameters to vary on all runs of the model,because
the total number of such parameter values required for a single pass through
the model is greater than 1000.For example,if the user wanted to generate 50
values for every uncertain parameter,over 50,000 values would have to be
produced.While this exercise is within RED's capabilities,the cost is very
high.
MECHANISM
A Monte Carlo routine uses the host computer's pseudo random number
generator to translate user-supplied information on a parameter,such as its
expected value,its range,and its subjective probability distribution,into
random trial parameter values.By producing simulations using several such
randomly generated values of the parameter,the model will yield electricity
consumption forecasts that incorporate each parameter's uncertainty.
INPUTS AND OUTPUTS
ranges,and (if required)the
Table 3.1 provides a summary of
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The Uncertainty Module requires three basic
•the number of values to be generated
• a selection of parameters to vary
•the parameter file.
The parameter file contains the default values,
expected value and variance of each parameter.
the inputs and outputs of the module.
3.1
inputs:
TABLE 3.1.Inputs and Outputs of the RED Uncertainty Module
MODULE STRUCTURE
The next step is to choose the number of values to be generated for each
parameter.This is the number of times the remainder of the model will be run,
each time wi th a different generated value for each parameter.Next,an
arbitrary seed for the random number generator is entered.
An overview of information flows within the Uncertainty Module is given in
Figure 3.1.First,the program asks whether the user would like to generate a
parameter.If the answer is no,then the default value (from the parameter
file)for each parameter is assigned.If a random parameter value is to be
generated,then the user is queried as to which parameters will be allowed to
vary.
(a)Inputs
Symbol Variable
N Number of Values
to be Generated
(see Table 3.2)Parameter's Range,
Variance,and
Expected Values
(b)Outputs
Symbol Variable·
(See Tabl e 3.2)Random Parameter
Values
·N Number of Times
r-bde 1 is to be Run
In put From
User Interface
Parameter Fi 1 e
Output To
Other ~dul es
Model Control Program
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Next,the computer generates a random number for each value to be pro-
duced.Thi sis accompl i shed by call i ng the computer l s "pseudo"random number
generator,which generates a random number between 0 and 1.From the parameter
fil e,the information on the range of the parameter,or (for parameters with a
normal distribution)the range,expected value,and variance is used to
3.2
ASSUMED RANGE
EXPECTED VALUE
START
SELECT PARAMETERS
TO BE
GENERATED
RANDOMLY
SELECT NUMBER
OF VALUES TO
BE GENERATED
(N)
COMPUTER
GENERATES N
RANDOM
NUMBERS
TRANSFORM
RANDOM NUMBERS
TO
PARAMETER
VALUES
OUTPUT
PARAMETER
VALUES
NO
ASSIGN DEFAULT
VALUE OF
UNSELECTED
PARAMETERS
FIGURE 3.1.RED Uncertainty Module
construct cumulative probability functions for each parameter.The random
values for each parameter are then generated by applying the random numbers to
these functions.
PARAMETERS
Table 3.2 provides a list of the parameters that can be generated by the
Uncertainty Module.Where information exists on parameter distributions from
3.3
NameSymbol
TABLE 3.2.Parameters Generated by the Uncertai nty ~~odul e(a)
Statistical
Distribution
3.4
(a)Values of these parameters (except CONSAT,which varies by case)are found
in Tables 4.9,5.4 through 5.11,6.8,7.5,and 11.2.
econometric results,the distribution of values is assumed to be normally
distributed.Where no information exists on the shape of the parameter
distribution,all values within the range are considered equally likely and the
distribution is assumed uniform.
SAT
A;B;A;OSRR.;
GSRR.
BBETA
CONSAT
LF
Housing Demand Coefficients
Saturation of Residential Appliances
Residential,Business Parameters for
Own-,Oil-Cross and Gas-Cross Price
adjus tment
Floor Space Consumption Parameter
Saturation of Conservation Technologies
Load Factor
No rma 1
Uni form
Norma 1
No rma 1
Uni form
Uniform
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4.0 THE HOUSING MODULE
The consuming unit in the residential sector is the household,each of
which is assumed to occupy one housing unit.The Housing Mbdule provides a
forecast of civilian households and the stock of housing by dwelling type in
each of the Railbelt's load centers.The type of dwelling is a major deter-
minant of energy use in residential space heating.Furthermore,the type of
dwelling is correlated with the stock of residential appliances.This module,
therefore,provides essential inputs for the Residential Consumption Module.
MECHANISM
The Housing Module accepts as input an exogenous forecast of the regional
population and number of households to forecast household size.The total
households forecast is adjusted for military households and is then stratified
by the age of the head of household and the number of household members.The
housing demand equations then use this distribution of households by size and
age of head to predict the initial demand for housing by type of dwelling.The
initial demand for each housing type is compared with the remainin~stock,and
adjustments in housing demand and construction occur until housing market
clearance is achieved.
INPUTS AND OUTPUTS
Table 4.1 presents the data used and generated within this module.
Exogenous forecasts of regional households,population,and the state-wide
distribution of households by age of head are needed as input,while the module
passes information on the occupied and vacant housing stock to the remainder of
RED.
MODULE STRUCTURE
The Housing Module1s structure is shown in Figure 4.1.The module begins
each simulation with a user-supplied forecast of households and population for
the load center.The assumed number of households for each load center is
first adjusted for military housing demand and multiplied by a decimal fraction
4.1
where
4.2
TABLE 4.1.Inputs and Outputs of the RED Housing Module
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(4.1)
Variable Variable Input From
Regi ona 1 Household Forecast Forecast Fi 1 e
Variable Variable Output From
Occupied Housing Stock by Type Residential Mbdule
State Households by Age Group Forecast File
Housing Demand Coefficients Uncertainty Mbdule
(a)Inputs
Symbol
THH
HH Ata
b,c,d
(b)Outputs
Symbol
HD Ty
to obtain a forecast of households served by utilities.Total households are
then stratified by age and size of household,and then used to generate an
estimate of demand for each type of housing (TY).Demand;s compared to the
initial stock,resulting in new construction or reallocation of demand as
appropriate.The end result is a set of estimates of occupied and unoccupied
housing units by type.Finally,the housing stock is reinitialized for the
next forecast period.
The first step in the Housing Module is to find the number of civilian
households in a given Railbelt load center.
CHH =total number of civilian households
BHH =military households residing on base (exogenous)
THH =total households (exogenous)
i =region subscript
t =forecast period subscript.
On-base military households are subtracted out because they do not signifi-
cantly affect off-base housing.In addition,since the military supplies
NEW
CONSTRUCTION
OF TYPE TY
•AGE DISTRIBUTION
OF HOUSEHOLD
HEADS
•SIZE DISTRIBUTION
OF HOUSEHOLDS
FILL VACANCIES
TYWITH
COMPLEMENTARY
DEMAND
REGIONAL
FORECAST
•POPULATION
•HOUSEHOLDS
CALCULATE
DEMAND FOR
HOUSING UNITS
BY TYPE TY
STRATIFY
HOUSEHOLDS BY
AGE OF HEAD
SIZE OF HOUSEHOLD
FORECASTS OF OCCUPIED,
UNOCCUPIED HOUSING
BY TYPE
4.3
FIGURE 4.1.RED Housing Module
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REINITIALIZE
HOUSING
STOCKS
DEMAND
PARAMETERS
(UNCERTAINTY
MODULE)
INITIAL HOUSING
STOCK TY
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electricity to them,on-base households have no impact .on the residential
demand for utility-supplied electricity.(a)
4.4
(a)Military purchases of electricity from the utility system are handled as
industrial loads.
Once the total number of civilian households in the load center has been
obtained,they are stratified by the s;"ze of the household and the age of the
household head.To obtain the distribution of households by size of household,
the total number of households is multiplied by the probabilities of four size
categories derived from information provided in the 1980 Census of Popula-
tion.To estimate the distribution of households by the age of head,the 1980
Census ratio between the regional and state relative frequencies of age of head
is assumed to remai n constant.The user suppl ies forecasts of the statewide
age distribution of heads of households from a forecasting model or by some
other method.Using the state relative frequency distribution,therefore,and
applying the constant ratios of regional to statewide frequencies,the model
obtains forecasts of the regional distribution of households by age of head.
The joint distribution by size of household and age of head is obtained by
multiplying the two distributions:
HH =number of households in an age/size class
THH =total number of households
CHH =total civilian households
A =subscript denoting aggregate state variable
P =regional household size probability (parameter)
R =ratio of the regional to state relative frequency of age of
household head (parameter)
a =age of head subscript
s =household size subscript.
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(4.2)
HH Ata=CHH it x THH x Pits x Ria
Ata
HH itas
where
(4.6)
(4.5)
4.5
HDOPit =CHHit -HO SFit -HO MFit -HO MHit
HO =housing demand
SF =index for single f ami ly
Ssit =a~l HH itas ;s =1,2,4
Aait =S~l HH itas ;-2,3,4a=
MF =index for mu 1 t if am i 1y
r~H =index for mobil e home
OP =index for duplex
(4.4)
(4.3)
The demand for a particular type of housing -single family,multifamily,
mobile home,or duplex -is hypothesized to be a function of the size of the
household and the age of the head (which serves as a proxy for household
wealth).Equations projecting demand for three of the types of housing (single
family,multifamily,mobile homes)were estimated by the Institute of Social
and Economic Research (ISER)from Anchorage data collected by the University of
Alaska's Urban Observatory (Goldsmith and Huskey 1980b).The remaining
category (duplex)is filled with the remaining households.
The demand for a parti cul ar type of housi ng is gi ven by the foll owi ng
equations:
where
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a =index denoting the age of houehold head
a =1 <25
a =2 25-29
a =3 30-54
a =4 55+
s =index denoting the size of household
s =1 <2
s =2 3
s =3 4-5
s =4 6+
b,c,and d are parameters from the Uncertainty Module.Expected values
and ranges of these parameters are presented in Table 4.9.
The model then adjusts
housing market is cleared.
previous period's stock net
the housing stock and housing demand so that the
Initially,the housing stock is calculated as the
of demolition:
where
HSTYit =HSTYi(t-l)x (1 - r t )(4.7)
HS =housing stock
TY =index denoting the type of housing (SF,MF,MH,and OP)
r =period-specific removal rate (parameter).
Net demand for each type of dwelling is defined as the demand minus the housing
stock:
where
NO =net demand.
NOTYit =HO TYit -HS TYit (4.8)
If net demand for all types of housing is positive,then enough new construc-
tion immediately occurs to meet the net demand plus an equilibrium amount of
vacancies required to ensure normal functioning of the housing market:
4.6
4.7
The equi 1i bri urn vacant hous i ng stock is the "norma 1"vacancy rate times the
stock of housing.
If the net demand for a particular type of housing is negative,however,
then the vacancy rate for that type of housing has to be calculated:
(4.9)
(4.10)
HD TYit=1 -HS TYit
NC TYit =NDTYit +VTy x (HSTYit +ND Tyit )
NC =new construction
V =normal vacancy rate (parameter).
AV =actual vacancy rate.
where
1.The number of excess vacancies within a type is calculated by subtracting
the housing demand from one minus the maximum vacancy rate,times the
stock.
2.The number of substitute units available to fill the excess supply is
given by subtracting one minus the normal vacancy rate,times the close
substitute stock from the close substitute demand.
where
If the actual vacancy rate is greater than its assumed maximum,then the excess
supply of that particular type of housing is asslJl1ed to drive down the price of
that type of dwelling.Individuals residing in other dwellings co~ld be
induced to move to reduce mortgage or rent payments.An adjustment to the
distribution of housing demands,therefore,is appropriate.
Substitution first occurs,if possible,within groups of housing that are
close substitutes (single-family and mobile homes;duplexes and multifamily).
If not enough excess demand exists from the close substitutes to fill the
depressed market,then substitution occurs from all types.The procedure is as
follows:
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3.The minimum of lor 2 is subtracted from the complementary housing .demand
and added to the depressed demand.
4.If excess supply persists (the actual vacancy rate is above its assumed
maximum),then the above procedure is repeated;only the number of housing
units available is now calculated using maximum vacancy rates and all
types of housing where the actual vacancy rate is less than their assumed
maximum.The available units are then allocated based on normalization
weights of the number available by type.
The final outputs of this module are occupied housing by type (HD Tyit )and
unoccupied housing:
VH it =L HS TYit -HD TYitTY
where
VH =total vacant dwelling units.
PARAMETERS
Military Households
(4.11)
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Fa i rbanks
3,062
Supplied by ISER.
The number of on-base military households,presented in Table 4.2,is
assumed to remain constant over the forecast periods.The level of military
activity in Alaska has stabilized,and little indicates that a major shift will
occur in the future.
TABLE 4.2.Number of Military Households Assumed to Reside
on Base in Railbelt Load Centers
Anchorage
3,212
Sou rce:
4.8
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4.9
TABLE 4.3.Household Size western U.S.and Railbelt 1950-1980
(Persons per Occupied Unit)
Table 4.3 shows how the size of households has changed in the United
States and in the Railbelt since 1950.The table indicates that the average
number of persons per housing unit has declined dramatically in both the U.S.
and the Railbelt during the period.Since 1970,the size decline has been more
(a)Obtained by dividing total resident population by
total households.Includes only urban places of
10,000 persons for Alaska locations.
Sources:U.S.Department of Commerce 1982;Goldsmith and
Huskey 1980b;Harrison 1979;and U.S.Bureau of
the Census 1960.
=
Fai rbanks-
Tanana Valley
3.3(a)
3.6
3.4
2.9
Anchorage-
Cook In 1et
3.4 (a)
3.4
3.4
2.9
United
States
3.5(a)
3.3
3.1
2.7
1950
1960
1970
1980
Household Size and Demographic Trends
A key factor in the residential demand for electricity is the number and
type of residential customers.The number of customers approximately equals
the number of households served by electricity,with the difference b~ing
caused by such factors as yacant housing with electrical service.Thus,it is
important in forecasting the demand for electricity to forecast the number of
households.The number of households in a load center is,in turn,a function
of the size of the population and the rate of household formation.Household
formation depends on the number of persons of household formation age;certain
economic factors that may influence household formation,such as potential
household income,price of housing,interest rates;changing tastes for mar-
riage and housing;and government housing programs.
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rapid in the Railbelt than in the nation as a whole,resulting from increasing
numbers and proportions of young,single adult householders and childless
couples.This trend toward smaller households headed by young adults probably
has a practical 1 imit somewhere near the Western Census Region 1980 average
household size of 2.6.However,recent revisions have been made to the Univer-
sity of Alaska's MAP economic and population model to forecast the number of
households based on the household formation rates implicit in the 1980 census
figures.These imply that the lower 1 imit may not be reached.Table 4.4 shows
the MAP forecast size of households in the Railbelt ,for the years 1980-2010 for
a typical economic scenario.The average size of households is relatively
insensitive to the scenari~used,depending almost entirely on the age distri-
bution of population.
Household formation rates are thought to depend on the income of potential
householders,the price of housing,and borrowing costs implied by interest
rates.Unfortunately,Alaska economic data do not include time series on
Railbelt household income or housing prices;therefore,it has not proved
possible to estimate househ91d formation rates based on these variables.
The RED model formerly estimated the number of households in each Railbelt
load center from a MAP model estimate of statewide households and the
TABLE 4.4.Forecast Size of Households,Railbelt Load Centers
Year Anchorage-Cook Inlet Fairbanks-Tanana Valley
1980 2.91 3~0
1985 2.73 2.89
1990 2.69 2~5
1995 2.67 2.81
2000 2~4 2.79
2005 2.63 2.76
2010 2.62 2.71
Source:University of Alaska Institute of Social and
Economic Research,case HE.6,FERC 0%Real
Growth in Oil Prices
4.10
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relationship between the age distribution of the population in each load center
and the age distribution of Alaska1s population.The 1983 version now simply
accepts a MAP model forecast of the number of households in each load center.
The number of households served by electric utilities is estimated by multiply-
ing the numbers of households times a constant to reflect the proportion of
households served byelectricity.(a)The number of households served by
utility-generated electricity is virtually 100%in Anchorage.Rural areas of
the Matanuska-Susitna Borough and Kenai Peninsula Borough have a few residences
not served (mostly seasonal homes),but the Fairbanks.North Star Borough and
Delta Junction areas have many year-round dwellings not served by utilities.
Historic and Projected Trends in Demand for Housing
The demand for a particular type of housing--single family,multifamily,
mobile home,or duplex--is hypothesized to be a function of the size of house-
hold and the age of the household head.The economics literature generally
also includes price of housing and household income in the demand for hous-
ing.However,Alaska economic information does not include time series on
family income and housing prices that could be used to forecast housing demand
by type.Cross-sectional data on household income do exist for Anchorage in
1977 by type of housing (Ender 1978);however,the lack of historical time
series on household income prevent the estimation of household income as a
function of economic growth over time in the Railbelt.However,the age of the
head of household serves to some extent as a pro~for household income,with
older household heads generally more wealthy and able to afford larger homes.
Larger households also require more space and larger homes.These factors are
included in the demand equations for individual types of houses contained in
the RED model.
Government Program Effects
ISER performed an analysis of State of Alaska housing programs in 1982
(ISER 1982)with the following findings.Alaska Housing Finance Corporation
(a)Although this calculation is actually performed in the Housing Module,its
description is included in this doucment with the discussion of
residential electricity demand in Section 5.0.
4.11
(AHFC)operates several different housing programs on behalf of the state in
which it acts as a secondary lender to provide mortgage loan money at the
lowest possible interest rates.Between July 1980 and December of 1982,AHFC
had a substantial negative impact on mortgage interest rates in Alaska,ranging
from'2.5 percentage points in July,1980 to slightly more than 4 percentage
points in December 1981.Average loan volume repurchased by AHFC increased
5 times between 1979 and 1981,and accounted for 85%of all Alaska home loans
from July 1980 to October 1981.Much of the activity was due to the special
Mortgage Loan Purchase program enacted in June 1980.ISER found that the State
of Alaska's low interest housing loan programs caused construction of new homes
statewide to be about one thousand units higher (or one third higher)than it
would have been without the program and caused conversion of about 300 units
from rental to sales units.The other substantial effect was on the qual ity of
housi ng purchased •.New homes buil t duri ng 1980-1981 were an average $25,000
more expensive than existing homes.The proportion of multifamily construction
was not clearly affected one way or the other by the loan programs.In 1980
and 1981 new multifamily construction in Anchorage was only 30%of total units
built,whereas it had been 50%or more every year from 1974 through 1979.
However,opposite effects were found in Fairbanks.Loan program impacts were
confounded with the levels of rents.These were depressed between 1979 and
1981 and failed to support the construction of new multifamily rental units.
Compared to a situation without large-scale interest subsidies,ISER1s
findings suggest that continuation of these large-scale subsidies would result
in the following:1)more first-time home buyers and more expensive units
being built (though it is not clear that these would necessarily be single-
family detached houses rather than condominiums);and 2)downward pressure on
rents,reducing the incentive for building multifamily rental units.Depending
on people's tastes for single-family detached units versus condominiums and the
builder's cost of providing units of each type,government programs could cause
si ngl e-fami ly construct i on to increase.£!..decrease as a proport i on of the
total.In the RED model,government programs are assumed to have no long-term
net effect on housing mix by type.
4.12
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TABLE 4.5.Impact of Householder Age and Household Size on Housing Mix
and Total Utility Sales,Anchorage-Cook Inlet
Housing Demand by Type of Housing
Table 4.5 compares the demand for types of housing in the Anchorage-Cook
Inlet load center with and without the influence of household age and household
size as reflected in the RED model structure.With the influence of household
size and age,relatively more households occupy single-family homes,which have
a lower electric fuel mode split than multifamily housing.By the y~ar 2010,
residential electricity demand is about 3%lower with the effects of size and
age of households on housing mix than without these effects.As revealed by
the table,even fairly large differences in the proportions of households in
the various types of dwellings have little impact on electricity consumption
forecasts.
4.13
Source:REO Model Runs,Case HE.6,FERC 0%Real Price Increase.
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Single Family Proportion
of Served Households:
With Age and Size Effects
Without Age and Size Effects
Multifamily Proportion of
Served Househol ds:
With Age and Size Effects
Without Age and Si ze Effects
Mobile Home Proportion of
Served Househol ds:
With Age and Size Effects
Without Age and Size Effects
Duplex Proportion
of Served Households:
With Age and Size Effects
Without Age and Size Effects
Residential GWH Sold by Utilities:
With Age and Size Effects
Without Age and Size Effects
1980
0.496
0.496
0.284
0.284
0.115
0.115
0.105
0.105
979.5
979.5
1990
0.549
0.461
0.245
0.383
0.126
0.097
0.080
0.059
1336.1
1382.2
2000
0.549
0.461
0.261
0.383
0.127
0.097
0.063
0.059
1599.6
1656.4
2010
0.545
0.461
0.264
0.383
0.129
0.097
0.063
0.059
1883.9
1955.0
TABLE 4.6.Single-Family Housing as Proportion Year-Round Housing
Stock by Type,Rai1be1t Load Centers,1950-1982
After an initial adjustment,Table 4.5 also shows a slight downward trend
in the proportion of single-family households as the size of households
declines between 1990 and 2010.This is consistent with the falling historical
trend in the proportion of single-family houses in Rai1be1t communities from
1950-1980,as shown in Table 4.6.Although a short-term reversal of the
historical trend may have been occurring since 1980,especially in Fairbanks,
high vacancy rates and depressed rents probably explain the high proportion of
single-family homes constructed since 1980.In particular,the very high pro-
portion of single-family construction in Fairbanks since 1980 can be attributed
to high vacancy rates in multifamily units between 1977 and 1980.Vacancy
rates for multifamily dwellings in Fairbanks ranged upward from 0.5%in May
1976 to 13.5%in June 1980.The vacancy rates have fallen dramatically since
(to 1.7%by June 1982),and building permits for new multifamily units have
recovered,increasing by over 50%in the North Star Borough from 1981 to 1982
(Community Research Quarterly,Winter 1982).
Tables 4.7 and 4.8 present the parameters used to derive the joint distri-
bution of households by size and age of head.The baseline figures for the
1950(a)
1960
1970
1980
1982(a)
Proportion Sing1e-
Family Housi ng
Built 1980-82
Anchorage -
Cook Inlet
0.592
0.628
0.471
0.462
0.472
0.539
Fai rbanks -
Tanana Vall ey
0.713
0.518
0.389
0.450
0.472
0.781(b)
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(a)Urban Anchorage and Fairbanks only.
(b)Fairbanks-North Star Borough only.
Source:Tab1 e 13.1.
4.14
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TABLE 4.7.Probability of Size of Households
in Railbelt Load Centers
Year Si ze Anchorage Fairbanks
1980(a)<2 0.476 0.455
3 0.190 0.210
4-5 0.291 0.287
6+0.042 0.048
1985 (b)<2 .489 .468
3 .188 .208
4-5 .282 .278
6+.042 .048
1990(b)<2 .502 .481
3 .185 .205
4-5 .272 .268
6+.041 .047
1995(b)<2 .515 .494
3 .182 .202
4-5 .262 .258
6+.041 .047
2000(b)<2 .528 .507
3 .180 .200
4-5 .253 .249
6+.041 .047
2005 (b)<2 .541 .520
3 .178 .198
4-5 .244 .240
6+.041 .047
2010(b)<2 .554 .533
3 .175 .195
4-5 .234 .230
6+.041 .047
(a)Source:Battelle-Northwest End-Use
1
Survey.
(b)The Anchorage initial distribution
reaches the western U.S.regional
average by 2010 (Bureau of the
1 Census 1977).The Fai rbanks di s-
tribution is assumed to have the
same rate of change as Mchorage.
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TABLE 4.8.Regional Frequency of Age of Household Head
Divided by the State-Wide Frequency
Age of Head Anchorage Fairbanks
<25 1.064 1.108
25-30 1.013 1.103
31-54 1.018 0.988
55+0.867 0.842
Source:1980 Census of Population
General Population Charac-
teristics:Alaska PC80-1-B3.
distribution of size parameters were derived from the Battelle Northwest end-
use survey.Those parameters were adjusted to approximately approach the 1977
Western Regional average household size of 2.6 (Bureau of Census 1977)by the
year 2010 in Anchorage in constant linear increments.Fairbanks uses the same
increments and converges to a household size of about 2.7.The ratio of
regional to statewide frequency of age of head was derived from the 1980 Census
of Population for Railbelt locations.These ratios are assumed to remain
constant over the forecast period.
The housing demand parameters were originally estimated by ISER using a
linear probability model.The expected values in Table 4.9 are the estimated
coefficients reported by ISER.The ranges were calcul ated as the width of the
95%confidence intervals;the variance was backed out of the reported
F statistics.
Vacancies
Table 4.10 presents the assumed normal and maximum vacancy rates by type
of house.ISER derived the normal vacancy rates by taking the ten-year U.S.
averages of vacancy rates for owner and renter units (Goldsmith and Huskey
1980b).Single-family and mobile homes have the owner rate;multifamily homes
have the renter rate;and duplexes are the average of owner and renter rates.
For the maximum vacancy rates,Anchorage multifamily rates were available.The
relationship between the normal rates for multifamily and all other types was
used to derive the maximum rates.
4.16
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TABLE 4.9.Housing Demand Equations:Parameters'Expected Value,
Range,and Variance
Parameter Expected Value Range Variance
bo 0.461
ba1 -0.303 0.142 0.001
ba2 -0.175 0.152 0.001
ba4 0.080 0.230 0.003
b2s 0.182 0.205 0.003
b3s 0.317 0.182 0.002
b4s 0.380 0.226 0.003
Co 0.383
cal 0.225 0.124 0.001
c a2 0.086 0.133 0.001
ca4 -0.090 0.202 0.003
c2s -0.203 0.180 0.002
c3s -0.280 0.159 0.002
c4s -0.352 0.198 0.003
do 0.097
da1 0.068 0.101 0.001
da2 0.039 0.109 0.001
da4 0.014 0.159 0.002
d2s 0.008 0.152 0.001
d3s -0.020 0.130 0.001
d4s -0.016 0.162 0.002
Sou rce:Go 1dsmi th and Huskey 1980b,Table B.6.
Depreciation and Removal
Housing demolition rates (Table 4.11)are a function of the age of the
housing stock and the demand for housing.ISER found that approximately 1%of
the housing stock was removed between 1975 and 1980 in Anchorage and Fairbanks
(Goldsmith and Huskey 1980b).As the existing stock ages,the removal rate is
assumed to grow toward the U.S.average,which has been estimated to be between
2 and 4%per forecast period (5 years).
4.17
TABLE 4.10.Assumed Normal and Maximum Vacancy Rates
by Type of House (Percent)
1980-1985
1985-1990
1990-1995
1995-2000
2000-2005
2005-2010
Norm~1)Maxi~g~
Type Rate a Rate
Si ng1 e Family 1.1 3.3
t1>b i1e Home 1.1 3.3
Dupl ex 3.3 10.0
Multifamily 5.4 16.0 )
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Author Assumption.
Imputed by ISER from Bureau of
the Census (1980a).
Imputed by ISER from Anchorage
Real Est imate Research Committee
(1979)•
Years
(a)
(b)
Source:
Assumed Five-Year Housing Removal Rates in Rai1belt
Region,1980-2010 (Percent of Housing Stock at
Beginning of Period Removed During Period)
Removal
Rate (percen t)
1.25
1.50
1.75
2.00
2.25
2.50
TABLE 4.11.
The professional economics literature has devoted some attention to
depreciation rates in housing.In an article in the Review of Economics and
Statistics,Leigh (1980)used a perpetual inventory method of calculating the
national stock of efficiency-adjusted residential housing units and checked
these estimates against the Census of Housing for 1950,1960,and 1970 as well
as other authors'estimates.The various sources sited in Leigh1s article show
values for economic depreciation/replacement ranging from 0.4 to 2.35%,with
most estimates grouped around 1.0 to 1.5%.Leigh herself calculates about 1%
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for the period 1950 through 1970.ISER calculated an approximate five-year 1%
rate of removal for Anchorage and Fairbanks housing units by comparing the
estimated number of units in 1970 and 1979 with cumulative building permits
data.Because the housing stock ages and new houses provide more "services"
than 01 d houses,the rate of economi c depreci ati·on for a gi ven area is assumed
to be larger than the rate of physical depreciation.Consequently,housing
units are physically replaced less frequently than 1%per year.The U.S.
average physical depreciation rate was calculated by de Leeuw (1974)at between
2 and 4%per five-year period or 0.4 to 0.8%per year.It is assumed that as
the Alaska housing stock ages,the very low current removal rate of 1.0%per
five years will approach the national lower bound rate,2.0%by 2000 and 2.5%
by the year 2010.
TABLE 4.12.Railbelt Housing Stock by Load Center ~nd
Housing Type,1980 (nunber of units)a).
Base Year Housing Stock
The base-year housing stock figures displayed in Table 4.12 are the counts
of year-round housing stock from the 1980 Census of Housing for Alaska.i
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Hou sing Type
Si ngl e Fami ly
Mobile Home s
dupl exes
Mul ti fami ly
Total
Anchorage
40,562
10,211
8,949
27,980
87,702
Fairbanks
10,873
2,175
2,512
8,607
24,167
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(a)A unit is occupied by one household.Thus,
a 4-plex is considered four housing units.
Source:1980 Census of Housing,STF3 Data Tape.
4.19
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5.0 THE RESIDENTIAL CONSUMPTION MODULE
The Residential Consumption Module provides forecasts of electricity
consumption for the Residential sector.The forecasts of the residential
sector's needs do not include the impacts of conservation produced by market
intervention by government.The potential for and impacts of such conservation
activities are handled in the Program-Induced Conservation Module (see Chapter
8.0).Furthermore,the module's forecast of residential requirements is the
amount of electricity that needs to be delivered to the residential sector -it
does not include allowances for line losses.
The Residential Consumption Module estimates the amount of electricity
residential consumers use,with explicit consideration of the impacts of
electricity price changes and fuel switching among electricity,gas,and oil.
Impacts of fuel switching to and from other fuels (such as wood)are handled in
the Program-Induced Conservation Module.
MECHANISM
The Residential Consumption Module employs an end-use approach.In an
end-use analysis,the first step is to identify the major uses of electric-
ity.Future market saturations of the uses are forecasted so that the future
stock of electricity-consuming devices is defined.The next step is to esti-
mate the amount of electricity demanded to meet a future demand for the ser-
vices of the devices.The forecast of average consumption of the appliance
stock,therefore,reflects both the trend in the size of the device and its
utilization rate,as well as projected increases in the efficiency of the
device.Once the stock of major electricity-consuming devices and their
corresponding average annual per-unit consumption of electricity are forecast,
the future consumption of electricity by device type is obtained by multiplying
the number of devices by their predicted annual average consumption of
electricity.Using the same procedure for miscellaneous residential uses and
summing over all end-uses yields an aggregate forecast of electricity
requirements.
5.1
One major problem of the end-use approach is that the impacts of changes
in fuel prices (both electricity and alternatives)and income on electricity
usage are usually treated directly through the forecaster's judgment.The RED
Residential Consumption Module addresses this problem differently.Byadjust-
ing the aggregate residential consumption figur~with variable price and cross-
price adjustment factors computed in the model from actual consumption data and
prices,RED accounts for price change and fuel-switching impacts in the resi-
dential sector.These adjustments can be interpreted as electricity conserva-
tioninduced by changes in fuel prices.
INPUTS AND OUTPUTS
Tabl e 5.1 presents the inputs and outputs of the modul e.The number of
households by dwelling type is the number of occupied civilian dwelling units
served by electricity predicted in the Housing Module.The price adjustment
parameters,as well as the appliance .saturations,are generated in the Uncer-
tainty Module.The output of the module is preliminary residential sales of
el ectri city.
MODULE STRUCTURE
The Residential Consumption Module identifies the following major uses of
electricity in the residential sector:
1.Water Heating
2.Cooking
3•Re f rig erat ion
4.Freezi ng
5.Clothes Washing (and additional water heating)
6.Clothes Dryi ng
7.Dishwashing (and additional water heating)
8.Saunas-Jacuzzi s
9.Space Heating
In addition,several other uses of electricity by households are captured by a
small appliance category.Small appliances include televisions,radios,
lighting,head-bolt heaters,kitchen appliances,heating pads,etc.The basic
5.2
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TABLE 5.1.Inputs and Outputs of the RED Residential Module.
5.3
premise of this module is that the household is the primary consumer of elec-
tricity,not the individual.However,the number of individuals in the house-
hold significantly affects the consumption of energy for clothes washing,
clothes drying,and water heating.Therefore,an adjustment is included in the
model for changes in the average household size to recognize the impact of such
changes on the usage of these appliances.
For the nine major uses of electricity,the end-use approach is used (see
Figure 5.1).Figure 5.1 shows the calculations that take place in the Residen-
tial Consumption Module.Beginning with a regional estimate of occupied hous-
ing stock by type,the module uses appliance market saturation parameters to
estimate the stock of each of the major appliances recognized by the model.
The module then calculates the initial fuel mode split for multifuel appli-
ances,calculates preliminary electric consumption for each appliance type
(including small appliances),and then sums these estimates together into a
preliminary consumption estimate for the residential sector.Price forecasts
for gas,oil,and electricity and IItrial ll -specific own-price and cross-price
adjustments are used to adjust the preliminary forecast.The adjustments are
described in Section 7.0.
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(a)Inputs
Symbol
HD Ty
A,B ,A ,
OSR ,GSR
SAT
(b)Outputs
Symbol
RESCON
Variable
Electrically Served Households
by Type of Dwelling
Price Adjustment Coefficients
Appliance Saturations
Variable
Residential Electricity
Requirements
From
Housing Stock Module
Uncertai nty t10dul e
Uncertainty Module
To
Miscellaneous,Peak Demand
and Conservation Modules
FORECAST OF
OCCUPIED HOUSING
STOCK BY TYPE
(HOUSING MODULE)
CALCULATE STOCK OF
LARGE APPLIANCES
BY END USE,
DWEL,LING TYPE
CALCULATE INITIAL
SHARE OF EACH
APPLIANCE USING
ELECTRICITY '
CALCULATE AVERAGE
ELECTRICAL USE IN
LARGE APPLIANCES
BY APPLIANCE
CALCULATE TOTAL
PRELIMINARY LARGE
APPLIANCE USE
BY
APPLIANCE
APPLIANCE
SATURATIONS
BY HOUSING TYPE
(UNCERTAINTY
MODULE)
FUEL MODE
SPLIT
1980
eFFICIENCY
STANDARDS
CALCULATE
PREUMINAY
SMALL APPLIANCE
USE OF
ELECTRICITY
PRICE FORECASTS
(EXOGENOUS)
SUM PREUMINARY
CONSUMPTION FOR
ALL APPLIANCES
PRICE AND
CROSS·PRICE
ADJUSTMENTS
RESIDENTIAL
CONSUMPTION
PRIOR TO
CONSERVATION
ADJUSTMENT
PRICE
ADJ.PARAMETERS,
RESIDENTIAL SECTOR
(UNCERTAINTY
MODULE)
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FIGURE 5.1.RED Residential Consumption Module
5.4
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Once the number of electrically served households by type of dwelling is
known,the applicance stock can be estimated.The saturation rate for an
appliance is the percentage of households residing in a certain type of dwell-
ing and having the appliance in question.By multiplying the housing-type-
specific saturation rate by the number of households residing in that type of
housing and then summing across housing types,the model forecasts appliance
demand in each future forecast period t:
Results from the Battelle-Northwest (BNW)end-use survey (see Appendix A)
show significant differences in the saturations of these nine end uses by the
type of dwelling in which the household resides.The module,therefore,uses
the number of occupied housing units of each type of dwelling (single family,
multifamily,mobile home,and duplex)as predicted by the Housing Module as one
of the inputs to estimate the stock of appliances.
The Housing Module predicts the number of occupied primary(a)residences
by type in a given region served by electric utilities.By multiplying the
number of occupied housing units by type by an assumed percentage served,the
Housing Module forecasts the number of primary occupied housing units served:
(a)Excluding second or recreation homes.
5.5
(5.1)
(5.2)SATTYitk x HHS TYit
4
AD.=
ltk TY=!
i =region subscript
t =forecast period (t =1,2,3,•••,7).
HHS TYit =SE it x HD TYit
TY =denotes the type of dwelling
SE =proportion of households served by an electric utility
HO =stock of occupied dwellings from the Housing Module
HHS =households served
where
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=L (SAT TYitk x HHSTY;t)
TV=1
(5.2)
where
AD =appliance demand
SAT =saturation rate (parameter)
k =end-use appliance.
Next,the model calculates the number of future additions to the stock.Assum-
ing demand is fully met,the nlJT1ber of new appliances in period t;s found by
calculating the stock of appliances surviving from all previous periods and
subtracting this surviving stock from appliance demand:
where
NA =number of new appliances
The future appliance stock,therefore,can be stratified by vintage.Next,the
model calculates the initial stock of electricity-consuming appliances by mul-
tiplying the number of appliances in each vintage by the percentage using
electricity:
ASiok =initial stock of ap~iances (1980)
mdtk =vintage specific scrap rate in period t;for vintage m
(parameter)(m =1,2,3,•••,7).
Equation 5.3 can be rearranged so that the stock equals the demand:
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(5.5)
(5.4)EAS iok =FMS ik x AS iok
ENA imk =FMS ik x NAimk
5.6
where
EAS =initial stock of electric appliances
FMS =fuel mode split
ENA =additions to the electric appliance stock
EAD =total electric appliance stock.
The Residential Consumption Module next calculates the average annual
electricity consumption of each major appliance.Different vintages of
appliances use different amounts of electricity,so the average consumption
must reflect the vintage composition of the stock.Furthermore,industry
energy efficiency standards for appliances could change in future years.The
future vintage specific consumption rate can be derived by multiplying the
current (1980)consumption rate by a growth factor and adjusting for any
changes in efficiency standards.By weighting these figures by the proportion
of the stock they represent,the average consumption of each appliance type in
a forecast year is derived:
ACitk'=average consumption of appliance k in period t (parameter)
AC iok =average consumption of appliance k in the beginning period
(pa rameter)
Z =length of forecast periods t and m in years (parameter)set
equal to 5 for this study.
g =growth rate of appliance k consumption (parameter)
(5.7)
(5.6)
x (l+g
k
)(m-1)x Z
5.7
EAS.(l_d o ) t ~AC.x 'ok x t k +L AC .
,ok EAD
itk
m=l'ok
ENA imk (l_.d
m
tk))x (l-csmk)x -~~---..-....;~
EAD itk
ACitk =
where
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cs =conservation standards target consumption reduction
(paramete r)•
Finally,the preliminary consumption for each major appliance can be
calculated by multiplying the stock of each appliance by its calculated average
consumption:
where
CONSitsa =L HHSTY't x [AC·os +(ACG't x t x Z)](5.9)TY 1 1 a 1 sa
The Residential Module makes no distinction among the various types of
appliances in the small appliance category.The requirements for these units
are simply the product of the number of households in the region,the initial
consumption level,and a growth factor in consumption over time:
CONSitk =EAO itk x ACitk x AHSitk
where
CONS =preliminary consumption of electricity prior to price
adjustments
AHS =household size adjustment parameter for clothes washing,
clothes drying,water heaters only.
ACG =growth factor in small appliance consumption
sa =index denoting small appliances.
Total preliminary residential consumption is found by summing across end
uses:
9
RESPREit =L CONS itk +CONS itsak=l
where
RESPRE =total preliminary residential consumption.
5.8
(5.8)
(5.10)
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RESPRE it reflects mainly the physical characteristics of the stock of
electrical appliances and household income.Consumers,however,can respond
dramatically to changes in the prices of electricity and alternative fuels.
The own-and cross-price adjustment factors measure the responsiveness of
consumers to price changes.Specifically,the own-price adjustment factor is
the ratio of the percentage of change in the quantity taken of electricity
during a five-year period to the weighted percentage change in price of
electricity relative to the prices of other goods during the period.
Similarly,the demand for electricity is also a function of the prices of
alternative fuels.For example,the cross-price adjustment factor for gas
measures the responsiveness of the quantity of electricity taken with respect
to change in the price of natural gas.In other words,the cross-price adjust-
ment factor predicts the percentage change in the quantity of electricity taken
for a one-percentage change in the relative price of an alternative fuel.
If the cross-price effect is positive,then the fuels are said to be
substitutes.As the price of another fuel rises,the quantity taken of elec-
tricity rises.For example,natural gas and electricity are substitutes.If
the price of gas rises enough relative to the price of electricity,then some
natural gas customers will switch to electricity.If the cross-price effect is
negative,the fuels are complements,implying that increases in the price of
the alternate fuel will cause reductions in the amount of the electricity that
is taken.
The RED model distinguishes between short-run and long-run responses to
price.In the short run,or the immediate future,consumers cannot alter their
usage as much as over longer periods of time,since their stock of appliances
is fixed.Over a longer period of time,they can replace elements of their
stock with devices that use less electricity,or perhaps use another fuel
source.Therefore,the speed with which consumers adjust from the short-run to
the long-run is important.
The price effects generated in RED are aged over the forecast period from
their short-run values to their long-run values,thus explicitly modeling con-
sumers'changing the pattern of use in the short run and fuel switching in the
long run.The Uncertainty Module generates both the short-run values of the
5.9
The actual calculation of the price adjustment of residential consumption
is as fo 11 ows:
price effect for specific trials and the coefficient of the speed of consumer
response.Chapter 7.0 discusses both the economic theory and literature under-
1y~ng the estimation of the own-price effect and cross-price effects of gas and
oil on electricity consumption,as well as the manner in which the effects are
calculated.
RESCON it =RESPRE it x (1 +OPA tt ) x (1 +PPAit)
x (1 +GPAit)(5.11 )
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5.10
PARAMETERS
RESCON is the predicted electricity consumption in the residential.sector
before adjustments for program-induced conservation.This figure is passed to
the Peak Demand and Program-Induced Conservation Modules.Note that RESCON is
a single number.The Residential Consumption Module does not report price-
adjusted consumption of electricity by end use.
The percentage of households served by an electric utility (Table 5.2)is
an important parameter.ISER has estimated that only 91%of the occupied
housing in Fairbanks was connected to an electric utility (Goldsmith and Huskey
1980b).Due to the high emphasis the Alaska state legislature and governor
have placed on energy,the extension of electrical service to all who would
like service is highly probable.Therefore,electrical services are assumed to
be extended to the entire stock of housing in the Fairbanks load center by
1995.The Anchorage-Cook Inlet load center is assumed to be 100%served.
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consumption of electricity in the residential sector
own-price adjustment for electricity
cross-price adjustment for fuel oil
cross-price adjustment for natural gas.
where
RESCON =
OPA =
PPA =
GPA =
5.11
TABLE 5.2.Percent of Households Served by
Electric Utilities in Railbelt
Load Centers,1980-2010
Market penetration rates for many appliances in Alaska are already outside
the bounds of lower forty-eight state experience and have been increasing over
time.However,many of the major appliances will likely never reach 100%
market saturation for a variety of reasons,such as transient population,the
convenience of substitutes such as laundromats,small housing units with
Appliance Saturations
Because historical growth and comparison with the lower forty-eight states
provide only 1 imited guidance on both current and future market saturations of
major appliances,somewhat arbitrary maximum penetration rates have been esti-
mated.The estimates were made by comparing recent utility saturation rate
studies by San Diego Gas &Electric (SOG&E)in 1982 and Southern California
Edison (SCE)in 1981 (realizing their 1 imited relevance in estimating Alaska
saturation rates),information from 1980 Census of Housing for Alaska,
information from the Battelle-Northwest end-use survey,and other related
literature.Wide bands of uncertainty should be presumed for all appliances
examined since saturation rate data in the literature were not consistent.
Table 5.3 summarizes saturation rates examined.
91
93
96
100
100
100
100
Fairbanks
100
100
100
100
100
100
100
Anchorage
Source:Goldsmith and -Huskey 1980b,
Table C.13, C.14,0.4,0.5.
The state is assumed to extend
electrical service to all residents
by 1995.
(a)
(b)
Year
1980(a)
1985(b)
1990(b)
1995(b)
2000(b)
2005(b)
2010(b)
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TABLE 5.3.Appliance Saturation Rate Survey (table values in percent of households)
SCE (1981)
SOG&E (1982)(a)
(range of va 1ues
observed in
Appl i ance (total market area)market area)(b)
Clothes nrier --71.1-81.2
Refrigerator 97.5 96.2-96.6
Freezer 26.2 9.1-33.5
Hot Tub/Jacuzzi/11-39 1.3-19.4Saunas
Water Heater --92.3-97.7
Cooking Range 96.2 98.3-99.5
<..11.Oi shwasher 55.4 41.2-58.0~
N
Clothes Washer 68.9 75.6-89.3
Microwave Ovens 34.5 17.9-38.9
Space Heating 94.6
Railbelt:Housing
Census (1980
(range of
values:lowest,
highest area)
92.0-97.7
99.5-99.9
99.9
Railbelt BNW End-Use
Survey (1981)
(range of values:
lowest to highest
area and building type)
61.0-90.2
99
57.2-94.8
2.5-16.9
86.9-100.0
95.7-100.0
23.3-78.2
63.8-92.5
(a)Average values for all customers.
(b)By building type.Types were single family,apartments/condominiums/town houses,and mobile homes.
(c)Areas were Anchorage (Anchorage,Matanuska-SUsitna,and Kenai Peninsula Boroughs)and Fairbanks
(North Star Borough plus Southeast Fairbanks Census Area).Fairbanks was the lower value.
(d)Building types were single family,mobile home,multifamily,and duplex.See Tables 5.4-5.11.
Sources:See reference list.
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inadequate space for some appliances,changing consumer perferences,etc.The
saturation rate estimates assumed in the RED model reflect a compromise between
1)rapid historical growth in appliance stocks in Alaska,2)approaching
boundaries on market saturation and 3)comparable saturation data from other
sources.
Tables 5.4 through 5.7 show the default value and range for future market
saturations of major appliances that can use one of several fuels in normal
home installation.The table values are the expected percentages of housing
units of a given type that will own the appliance in a given year (having'
access to and owning an appliance may result in different saturation rates)and
market area,and the subjective uncertain range that can be used instead of the
default value if the Monte Carlo option is chosen.The table title indicates
the type of housing.The assumptions for each type of appliance are given
below.
Hot Water
Hot water was available in nearly 99%of single-family homes in the
Anchorage market area,according to the Battelle~Northwest end-use survey.It
is assumed that 99%is a maximum for two reasons:the market saturation of hot
water in the western U.S.was 99%in the 1970 census (Bureau of Census 1970);
and Alaska can be expected to have rural cabin-like structures with limited
electric service for some time to come.In the Fairbanks market area,single-
family saturations are projected to increase to the Anchorage level by 1990.
The end-use survey and 1970 Census both show saturations in the vicinity of 90%
in this area.Increasing urbanization in Fairbanks and better electric service
should increase this percentage.
The other types of structures in the Battelle-Northwest survey showed
market saturations of nearly 100%in all market areas.The exception was
multifamily housing.However,the wording of the question in the survey upon
which this calculation is based may have been interpreted as asking whether the
respondent had a hot water tank in his unit rather than (as was intended)
whether he had hot water available.A 100%market penetration for hot water in
duplexes and multifamily buildings was assumed.Mobile homes were considered
the same as single-family units.
5.13
TABLE 5.4.Market Saturations (percent)of Large Appliances with Fuel Substitution
Possibilities in Single-Family Homes,Railbelt Load Centers,1980-2010
Water Heater Clothes Dryers Range (cook i n9)Saunas-Jacuzzi s
Load Center Year Defaul t Range Defaul t Range Default Range Default Ra~e
a.Anchorage 1980 98.6(a)--90.2 --99.9(a)--14.1
1985 98.8 95.,.100 91.2 88-94 100.0 100-100 16.3 13-19
1990 99.0 98-100 92.5 89-95 100.0 100-100 18.7 14-22 .
1995 99.0 98-100 93.7 90-96 100.0 100-100 21.0 16-26
2000 99.0 98-100 95.0 92-98 100.0 100-100 23.4 18-28
<.J1 2005 99.0 98-100 95.0 92-98 100.0 100-100 25.7 20-30.......
.po.2010 99.0 98-100 95.0 92-98 100.0 100-100 28.1 23-33
b.Fai rbanks 1980 86.9(a)--81.4 --99.5(a)--7.9
1985 93.0 91-95 84.0 80-88 100.0 100-100 8.9 6-12
1990 99.0 98-100 87.5 82-92 100.0 100-100 10.0 6-14
1995 99.0 98-100 92.5 87-97 100.0 100-100 11.2 6-16
2000 99.0 98-100 95.0 92-98 100.0 100-100 12.4 7-17
2005 99.0 98-100 95.0 92-98 100.0 100-100 13.6 8-18
2010 99.0 98-100 95.0 92-98 100.0 100-100 14.8 9-19
(a)For hot water and cooking,missing values in the Battelle-Northwest survey were not counted.
_1-
TABLE 5.5.Market Saturations (percent)of Large Appliances with Fuel Substitution
Possibilities in Mobile Homes,Railbelt Load Centers,1980-2010
Water Heater Clothes Dryers Range (cooking)Saunas Jacuzzi s
Load Center Year Defaul t Range Defaul t Range Default Range Defaul t Range
a.Anchorage 1980 98.2(a)--79.0 --95.7(a)--6.1
1985 99.0 98-100 80.0 79-81 100.0 100-100 6.9 3-11
1990 99.0 98-100 82.0 80-84 100.0 100-100 7.8 4-12
1995 99.0 98-100 84.0 82-86 100.0 100-100 8.7 5-13
2000 99.0 98-100 85.0 83-87 100.0 100-100 9.6 6-14
2005 99.0 98-100 90.0 85-95 100.0 100-100 10.5 6-14
U1.2010 99.0 98-100 95.0 91-99 100.0 100-100 11.4 7-15......
U1
b.Fairbanks 1980 99.0(a)--92.3 --98.6 (a)--2.5
1985 99.0 98-100 94.0 91-97 100.0 100-100 2.8 1-5
1990 99.0 98-100 95.0 92-98 100.0 100-100 3.1 1-7
1995 99.0 98-100 95.0 92-98 100.0 100-100 3.5 1-8
2000 99.0 98-100 95.0 92-98 100.0 100-100 3.8 1-8
2005 99.0 98-100 95.0 92-98 100.0 100-100 4.2 1-8
2010 99.0 98-100 95.0 92-98 100.0 100-100 4.5 1-9
(a)For water heat and cooking,missing values in the Battelle-Northwest end-use survey were not
counted.
..
TABLE 5.6.Market Saturations (percent)of Large Appliances with Fuel Substitution
Possibilities in Duplexes,Railbelt Load Centers,1980-2010
Water Heater Clothes Dryers Range (cooking)Saunas Jacuzzi s
Load Center Year Default Range Default Range Defaul t Range Defaul t Range
a.Anchorage 1980 100.0(a)--90.0 --96.4 --16.9
1985 100.0 100-100 91.0 90-92 100.0 100-100 19.0 16-22
1990 100.0 100-100 92.5 90-95 100.0 100-100 21.2 17-25
1995 100.0 100-100 93.0 91-96 100.0 100-100 23.4 18-28
2000 100.0 100-100 95.0 92-98 100.0 100-100 25.6 21-31
2005 100.0 100-100 95.0 92-98 100.0 100-100 27.6 23-33
<.11 2010 100.0 100-100 95.0 92-98 100.0 100-100 29.8 25-35.......
100.0(a)85.5(b)0)b.Fairbanks 1980 100.0 8.2------
1985 100.0 100-100 91.0 90-92 100.0 100-100 9.2 6-12
1990 100.0 100-100 92.5 90-95 100.0 100-100 10.3 6-14
1995 100.0 100-100 93.0 91-96 100.0 100-100 11.4 6-16
2000 100.0 100-100 95.0 92-98 100.0 100-100 12.5 8-18
2005 100.0 100-100 95.0 92-98 100.0 100-100 13.5 9-19
2010 100.0 100-100 95.0 92-98 100.0 100-100 14.6 10-20
(a)Values for Battelle-Northwest end-use survey were adjusted to 100 percent for water heaters in
1980.For explanation,see text.
(b)1980 clothes dryer penetration in Fairbanks for 1980 adjusted downward by one to match the number of
washers in duplexes •
TABLE 5.7.Market Saturations (percent)of Large Appliances with Fuel Substitution
Possibilities in Multifamily Homes.Railbelt Load Centers.1980-2010
Water Heater Clothes Oryers Range (cooking)Sa unas Jacuzzi s
Load Center Year Oefaul t Range Oefaul t Range Oefaul t Range Oefa ul t Range
a.Anchorage 1980 100.0(a)--75.7 --98.2 --13.6
1985 100.0 100-100 83.0 82-84 100.0 100-100 15.0 12-18
1990 100.0 100-100 83.5 82-85 100.0 100-100 16.4 12-20
1995 100.0 100-100 84.0 82-86 100.0 100-100 17.7 13-23
2000 100.0 100-100 85.0 83-87 100.0 100-100 18.9 14-24
(J1 2005 100.0 100-100 90.0 85-95 100.0 100-100 19.9 15-25........
-.....J 2010 100.0 100-100 95~0 92-97 100.0 100-100 20.9 16-26
b.Fai rbanks 1980 100.0(a)--61.0 --100.0 --5.7
1985 100.0 100-100 65.0 61-69 100.0 100-100 6.3 3-9
1990 100.0 100-100 70.0 65-75 100.0 100-100 6.9 3-11
1995 100.0 100-100 80.0 75-85 100.0 100-100 7.5 3-13
2000 100.0 100-100 85.0 80-90 100.0 100-100 8.0 3-13
2005 100.0 100-100 90.0 85-95 100.0 100-100 8.5 4-14
2010 100.0 100-100 95.0 92-97 100.0 100-100 8.9 4-14
(a)Water heat survey numbers adjusted to 100 percent for 1980.For explanation.see text.
Clothes Dryer
The Battelle-Northwest survey and 1970 Census both show Railbelt market
saturations for clothes dryers far above the U.S.average (Bureau of Census
1970).Information available from the 1980 U.S.Statistical Abstract for 1979
shows that about 61.5%of electrically served housing units have an electric or
gas dryer (up from 44.6%in 1970)(Bureau of Census 1980b).In contrast,the
Battelle survey showed market saturations ranging from 61%in Fairbanks multi-
family structures to over 90%in other types of housing.Single-family dryer
saturations ranged from 81%in Fairbanks to 90%in Anchorage.'Becaus~Alaska
already has such high saturations,the forecast is outside the bounds of
historical experience.A reasonable estimate is that no more than 95%of
single-family homes,mobile homes,and duplexes will ever have dryers because
of the availability of laundromats and because of the room taken up by washer-
dryer combinations in small housing units.For multifamily units,penetration
is assumed to be much slower because of the space problem.Since washers and
dryers are now installed in pairs in most new housing,market saturations for
dryers (which are now about 2%below those for washers in most areas)will
approach that for washers as old housing stock is replaced.In general,the
lower the existing saturation,the greater is the uncertainty concerning its
future growth rate.
Cooki ng Ranges
Several data sources were examined to arrive at market saturation rate
estimates.The Battelle-Northwest end-use survey indicated that between 96 and
100%of all households surveyed had a range available.SDG&E (1982)reported a
96.2%saturation rate while SCE (1981)ranged from 98.3%for multi-family units
to 99.5%for single-family units.The substitution of hot plates,broiler
ovens (1979 estimated national saturation rate of 26%)and microwave ovens
(1979 estimated national saturation rate of 7.6%)may account for the differ-
ence between 90 and 100%.Therefore,100%of all housing units currently are
assumed to have cooking facilities available by 1985.This percentage holds
throughout the period.
5.18
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Saunas,Jacuzzis,Etc.
These units are a relatively new phenomenon in private homes,almost all
having been installed since 1970.The Battelle-Northwest end-use survey found
market saturations ranging from 2.5 to 17%,SDG&E (1982)11 to 39%,and SCE
(1981)1.3 to 19.4%,all depending upon market area and housing type.Accord-
i ng to the survey,14%of Anchorage si ngl e ,family househol ds reported havi ng
one of these units,compared to 10.4 and 11.0%,respectively,for SCE and
SDG&E.Among single-family homes built since 1975 in Anchorage,the saturation
was 21%,while among single-family homes built since 1980 in the SDG&E survey
area,the saturation was 23.8%.To arrive at saturation rate estimates,a
target rate sl ightly larger than both was asslJl1ed for newly constructed singl e-
family homes in Anchorage to allow for the increasing popularity of saunas-
jacuzzis.Additional allowances were made for the existing stock of housing to
acquire saunas-jacuzzis.The additional allowances changed over time based on
the belief that saturation growth rates would fall as the newness of the item
wore off.This phenomenon may happen with any relatively new technology.Once
it has reached that segment of the population initially desiring to own a sauna
or jacuzzi,additional growth will be slower since a lower maximum·penetration
rate,when compared to other appliances,is aSSlJl1ed.Additional supportive
evidence for a lower maximum penetration rate is found from California.There,
saturation rates are lower than in Alaska and growth rates are slowing down.
One additional impact on the willingness of those individuals initially not
strongly desiring to own a sauna or jacuzzi may be the relatively high price,
at least when compared to other major appliances.Also,installation costs may
be higher in Alaska since poorer weather would necessitate that the unit be
enclosed.However,the inflation-adjusted cost of saunas and jacuzzis,whirl-
pools,etc.is expected to drop somewhat as it does with any new appliance
type.This could raise future market saturations above current levels.By
weighing these factors,and considering economic growth prospects for the
subregions,the estimated default values were chosen.They are presented in
Tables 5.4 through 5.7.
One potential problem exists in Table 5.7.The Battelle-Northwest end-use
survey created a slight ambiguity in terms of appliance ownership for
5.19
multifamily homes by not asking residents of this type of housing whether they
actually owned or had access to a sauna or jacuzzi.In some apartment
complexes,a central recreation building houses a sauna or jacuzzi that all
residents may use.If every individual in the apartment complex claims they
each have a sauna or jacuzzi when in fact only one exists,the saturation rate
is overstated.This phenomenon is brought out in the SCE (1981)data,where
19.4%of all apartment/condominium/townhouse occupants claimed a hot tub/-
jacuzzi.However,only 6.7%of that total had their own private hot tub/-
jacuzzi.A level of 19.4%gives an incorrect representation of the penetration
rate for saunas and jacuzzis and an overestimate of electricity consumption.
To correct for this problem,default values and ranges in Table 5.7 have been
adjusted downward for slower future growth.
Tables 5.8 through 5.11 indicate default market saturations and ranges of
values for large household appliances that are almost always electric.These
include refrigerators,freezers,dishwashers,and clothes washers.The table
title indicates the housing type,and the table values show an expected market
saturation for each appliance by market area and year.The ranges shown in the
tables reflect the degree of uncertainty attached to the default value.The
wider the range,the greater is this subjective uncertainty.The assumptions
supporting the table values are given below by appliance.
Refrigerators
The Battelle-Northwest end-use survey found that virtually 100%of all
households had a refrigerator.This is in agreement with several other studies
such as SDG&E (1982)at 97.5%,SCE at 96.2 to .96.6%,and the national Residen-
tial Energy Consumption Survey (RECS)at 99.8%.The California Energy Commis-
sion (CEC)found in 1976 that enough housing units had second refrigerators to
raise total California market saturation to 113-116%.ISER,in their report to
the Alaska State Legislature,assumed that this high percentage would likely
not prevail in Alaska because of the cooler cl imate (Goldsmith &Huskey
1980b).Therefore,a default value of 99%was chosen throughout.In the RED
model,the ISER assumption is modified to permit a range of values from 98 to
100%.
5.20
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TARLE 5.8.Market Saturations (percent)of Large Electric Appliances in Single-Family Homes,
Railbelt Load Centers,1980-2010
Refri gerators Freezers Dishwashers Clothes Washers
Load Center Year Defaul t Range Defaul t Range Default Range Default Range
a.Anchorage 1980 99.0 --88.3 --78.2 --91.7
1985 99.0 98-100 90.0 85-95 85.0 80-90 92.0 90-94
1990 99.0 98-100 90.0 85-95 90.0 85-95 92.5 90-95
1995 99.0 98-100 90.0 85-95 90.0 85-95 93.7 91-96
2000 99.0 98-100 90.0 85-95 90.0 85-95 95.0 92-98
In•2005 99.0 98-100 90.0 85-95 90.0 85-95 95.0 92-98N......
2010 99.0 98-100 90.0 85-95 90.0 85-95 95.0 92-98
b.Fai rbanks 1980 99.0 --84.9 --53.8 --84.9
1985 99.0 98-100 88.0 86-90 79.0 75-85 86.0 84-88
1990 99.0 98-100 90.0 85-95 90.0 85-95 87.5 85-90
1995 99.0 98-100 90.0 85-95 90.0 85-95 92.5 90-95
2000 99.0 98-100 90.0 85-95 90.0 85-95 95.0 92-98
2005 99.0 98-100 90.0 85-95 90.0 85-95 95.0 92-98
2010 99.0 98-100 90.0 85-95 90.0 85-95 95.0 92-98
TABLE 5.9.Market Saturations (percent)of Large Electric Appliances in Mobile Homes,
Railbelt Load Genters,1980-2010
Refri gerators Freezers Dishwashers Clothes Washers
Load Center Year Default Range Default Range Defaul t Range Default Range
a.Anchorage 1980 99.0 --94.8 --43.9 --80.6
1985 99.0 98-100 92.0 90-95 67.6 62-72 85.0 80-90
1990 99.0 98-100 90.0 85-95 90.0 85-95 90.0 85-95
1995 99.0 98-100 90.0 85-95 90.0 85-95 90.0 85-95
U'l 2000 99.0 98-100 90.0 85-95 90.0 85-95 95.0 92-98.
N 2005 99.0 98-100 90.0 85-95 90.0 85-95 95.0 92-98N
2010 99.0 98-100 90.0 85-95 90.0 85-95 95.0 92-98
b.Fairbanks 1980 99.0 --73.0 --48.6 --92.3
1985 99.0 98-100 82.0 75-89 71.4 66-76 93.0 91-95
1990 99.0 98-100 90.0 85-95 90.0 85-95 92.5 91-96
1995 99.0 98-100 90.0 85-95 90.0 85-95 94.0 92-96
2000 99.0 98-100 90.0 85-95 90.0 85-95 95.0 92-98
2005 99.0 98-100 90.0 85-95 90.0 85-95 95.0 92-98
2010 99.0 98-100 90.0 85-95 90.0 85-95 95.0 92-98
-.L
TABLE 5.10.Market Saturations (percent)of Large Electric Appliances in Duplexes
Railbelt Load Genters,1980-2010
Refrigerators Freezers Dishwashers Clothes Washers
Load Center Year Defaul t Range Defaul t Range Defaul t Range Default Range
a.Anchorage 1980 99.0 --66.5 --76.5 --92.5
1985 99.0 98-100 75.0 70-80 85.0 80-90 93.0 91-95
1990 99.0 98-100 85.0 80-90 90.0 85-95 95.0 92-98
1995 99.0 98-100 85.0 80-90 90.0 85-95 95.0 92-98
U1 2000 99.0 98-100 85.0 80-90 90.0 85-95 95.0 92-98.
Nw 2005 99.0 98-100 85.0 80-90 90.0 85-95 95.0 92-98
2010 99.0 98-100 85.0 80-90 90.0 85-95 95.0 92-98
b.Fai rbanks 1980 99.0 --75.2 --57.4 --85.5
1985 99.0 98-100 80.0 75-85 85.0 80-90 91.0 90-92
1990 99.0 98-100 85.0 80-90 90.0 85-95 92.5 90-95
1995 99.0 98-100 85.0 80-90 90.0 85-95 93.0 91-96
2000 99.0 98-100 85.0 80-90 90.0 85-95 95.0 92-98
2005 99.0 98-100 85.0 80-90 90.0 85-95 95.0 92-98
2010 99.0 98-100 85".0 80-90 90.0 85-95 95.0 92-98
TABLE 5.1l.Market Saturations (percent)of Large Electric Appliances in Multifamily Homes,
Railbelt Load Genters,1980-2010
Refri gerators Freezers Dishwashers Clothes Washers
Load Center Year Defaul t Range Defaul t Range Default Range Default Range
a.Anchorage 1980 99.0 --62.5 --73.3 --76.5
1985 99.0 98-100 65.0 60-70 85.0 80-90 85.0 80-90
1990 99.0 98-100 70.0 65-75 90.0 85-95 90.0 85-95
1995 99.0 98-100 70.0 65-75 90.0 85-95 92.0 90-94
U1 2000 99.0 98-100 70.0 65-75 90.0 85-95 95.0 92-98.
N
~2005 99.0 98-100 70.0 65-75 90.0 85-95 95.0 92-98
2010 99.0 98-100 70.0 65-75 90.0 85-95 95.0 92-98
b.Fairbanks 1980 99.0 --57.2 --23.3 --63.8
1985 99.0 98-100 65.0 60-70 34.0 30-39 68.0 63-72
1990 99.0 98-100 70.0 65-75 50.0 45-55 70.0 65-75
1995 99.0 98-100 70.0 65-75 74.0 70-79 80.0 75-85
2000 99.0 98-100 70.0 65-75 90.0 85-95 85.0 80-90
2005 99.0 98-100 70.0 65-75 90.0 85-95 90.0 85-95
2010 99.0 98-100 70.0 65-75 90.0 85-95 95.0 92-98
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Freezers
The end-use survey found market area-wide saturations of freezers ranging
from about 80%in Fairbanks to over 90%in Anchorage.These figures are 10 to
20%higher than assumed by ISER for 1980 for these areas,about 40%a~ove 1970
Census values for the Rail~elt,and 30 to 40%above the U.S.average.In other
words,area-to-area comparisons and historical experience are not very helpful
for predicting future saturations.For single-family homes and mobile homes,
the maximum saturation has been assumed to have been just about reached because
with better shopping facilities and increased urbanization,fewer freezers will
be necessary for long-term food storage from bulk buying.
For duplexes and multifamily units,the percent of saturation should
remain significantly lower.The tenants in such units tend to be more
transient and are probably less involved in Alaskan hunting,fishing,and
gardening pursuits than most Alaskans.Consequently,they would have less
demand for freezers.Second,rental units tend to be smaller.Consequently,
renters might tend to substitute rented commercial cold-storage locker space
for a freezer to conserve scarce living space in duplexes and multifamily
units.The range of uncertainty is shown to be quite broad,since market
penetration has been rapid in the last 10 years,but the maximum appears to
have been reached in some cases.
Dishwashers
The Battelle-Northwest end-use survey found market saturations for dish-
washers well above the existing U.S.average.In the U.S.as a whole,the 1979
saturation was about 41%of homes served by electricity (Bureau of Census
1980b),but this percentage ranged from 50%in Fairbanks to 75%in Anchorage
survey homes.Saturations have increased by about 50 percentage points in both
Railbelt load centers since 1970,again outside the range of historical experi-
ence.(Using this experience,ISER (Goldsmith and Huskey 1980b)projected 1978
market saturations of 50%in Anchorage and 36%in Fairbanks.)The rate of
increase in market saturation was very rapid in the 1970s,but further
increases in saturation in Anchorage in particular may be 1 imited since a high
proportion of some types of housing units already have dishwashers.A maximum
saturation of 90%was assumed for all homes.The annual rates of saturation
5.25
growth for the 1970s were then projected for each region:9%per year for
Anchorage,and 8%per year for Fairbanks.Except for Fairbanks multifamily,
where historical growth rates are assumed,90%maximum saturation is assumed to
occur in 1990.The growth rate was then ass umed to fall to zero.A wide range
of uncertainty is assumed for dishwasher saturations because of the tenuous
nature of the required assumptions.
Clothes Washers
The Battell e-Northwest end-use survey found tha.t area-wi de clothes washer
saturations ranged from about 84%in Fairbanks to 89%in Anchorage.These
figures are well above the 73%reported for the u.s.in 1979 in the 1980
Statistical Abstract (Bureau of Census 1980b).It also represents about 10 to
15 percentage points growth since the 1970 Census.The rate of saturation
increase did not slow down appreciably in the 1970s compared to the 1960s;
consequently,market saturation may not have yet approached its maximum.For
forecasting,the maximum penetration is assumed to be 95%.Different types of
housing reach this maximum at different rates.In particular,since single-
family homes are already 85 'to 90%saturated,they reach 95%slowly,achieving
this level by the year 2000.Some markets are closer to being completely
saturated.Even at low rates of growth they reach 95%somewhat earlier.In no
case is clothes-washer saturation allowed to be below that for clothes
driers.The Battelle-Northwest survey generally found that washer saturation
was one to two percentage points higher than that for dryers •.Where this was
not the case (e.g.,duplexes in Fairbanks)the difference appears to have
occurred because of the small number of households in the category.The market
saturations for washers and driers gradually converge,since they are now
usually installed in pairs.Multifamily saturation of washers and driers grows
the slowest,reaching 95%by 2010 in Fairbanks.
Fuel Mode Splits
The fuel-mode splits presented in Table 5.12 were also derived from the
Battelle-Northwest end-use survey and 1980 Census of Housing with the exception
noted below.These parameters are assumed to remain fixed over the forecast
period,as the cross-price elasticity adjustment handles fuel switching.
5.26
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TABLE 5.12.Percentage of Appliances Using Electricity and Average Annual
Electricity Consumption,Railbelt Load Centers
Anchorage Fairbanks
Percentage Using Electricity(a)Annual kWh Percentage Using Electricity Annual kWh
Appl i ance SF MH DP MF Consumption SF MH DP MF Cons ullflt ion
Space Heat (Existing Stock)
Single Family 16.0 NA NA NA 32,850 9.7 NA NA NA 43,380
Wlb i1e Ilome NA 0.7 NA NA 24,570 NA 0.0 NA NA 33,210
Duplex NA NA 22.8 NA 21,780 NA NA 11.7 NA 28,710
Multi Family NA NA NA 44.4 15,390 NA NA NA 14.8 19,080
Space Heat (New Stock:1985)
Si ng 1e Family 10.0 NA NA NA 40,100 9.7 NA NA NA 53,000
I-bb il e Home NA 0.7 NA NA 30,000 NA 0.0 NA NA 40,600
Oupl ex NA NA 15.0 NA 26,600 NA NA 11.7 NA 35,100
Multi Family NA NA NA 25.0 18,800 NA NA NA 14.8 23,300
Water Heaters (Existing)36.5 50.4 44.0 60.9 2,800 33.1 42.8 43.1 26.2 3,300
Water Ileaters (New:1985)10.0 50.4 15.0 25.0 3,000 33.1 42.8 43.1 26.2 3,475
U1 Clothes Dryers 84.3 88.1 81.3 86.6 1,032 96.2 94.6 94.4 100.0 1,032.
N........Cooking Ranges 75.8 23.2 85.2 88.2 850 79.0 48.2 95.0 97.1 850
Sauna-Jacuzzi s 93.5 100.0 93.7 81.8 1,600 61.8 100.0 60.8 100.0 1,600
Refrigerators 100.0 100.0 100.0 100.0 1,636 100.0 100.0 100.0 100.0 1,636
Freezers 100.0 100.0 100.0 100.0 1,342 100.0 100.0 100.0 100.0 1,342
01 shwashers 100.0 100.0 100.0 100.0 250 100.0 100.0 100.0 100.0 250
Additional
Water Heating (Existing)36.5 50.4 44.0 60.9 799 33.1 42.8 43.1 26.2 799
Water Heating (New:1985)10.0 50.4 15.0 25.0 799 33.1 42.8 43.1 26.2 799
Clothes Washers 100.0 100.0 100.0 100.0 90 100.0 100.0 100.0 100.0 90
Additional
Water Heat ing (Exi sting)36.5 50.4 44.0 60.9 1,202 33.1 42.8 43.1 26.2 1,202
Water Heating (New:1985)10.0 50.4 15.0 25.0 1,202 33.1 42.8 43.1 26.2 1,202
Miscellaneous 100.0 100.0 100.0 100.0 2,110 100.0 100.0 100.0 100.0 2,466
(a)SF =single family;Mil =mobile homes;OP =duplexes;I·IF =multifamily •
Discussions were held with several Anchorage area home builders,the staff
of Anchorage Municipal Power and Light,ISER,and two real estate management
firms in Anchorage concerning incremental fuel mode splits for new housing
stock.The consensus was that very few units are being constructed in the
Anchorage area in 1983 with either electric heat or electric hot water where
gas is available because electric thermal units are considered to have
unattractively high operating costs.This is believed to be a phenomenon
caused by past electricity price increases and is therefore not accommodated by
the RED price adjustment coefficients after 1980.Accordingly,the 1983
version of the model judgmentally imposes reduced incremental electric fuel
mode splits in space heating and water heating for new housing units built in
the Anchorage-Cook Inlet load center since 1980.The fuel mode splits are kept
above zero to reflect construction in portions of the Anchorage-Cook Inlet load
center not served by gas..Where incremental fuel mode splits are shown,elec-
tricity use rates for both the new and old stock are shown in Table 5.12.
Post-1985 use rates for all appliances appear in Table 5.13.
Comparison of Census and Battelle Northwest end-use survey results for the
percentage of water heaters using electricity in Fairbanks in 1980 revealed
lower values in the Census.The assumption was made that the Census results
were more accurate and additional time went into a further analysis of the
Battelle Northwest end-use survey.As a result of this and a study of the
methodology employed in the Census,original end-use survey fuel mode split
values have been scaled downward by a correction factor of 0.6 for hot water.
After the correction factor,the figures now reported in Table 5.12 are
believed to be accurate.
Consumption of Electricity per Unit
The average kilowatt hour consumption figures are primarily based on
values summarized from other studies presented in Henson (1982)and also SDG&E
(1982).Below is a brief discussion of each parameter.Studies reviewed are
shown in Table 5.14.
5.28
TABLE 5.13.Growth Rates in Electric Appliance Capacity and Initial Annual
Average Consumption for New Appliances
Average Annual
kWh Consumption for Growth Rate in
New Appliances (1985)Electric Capacity
Appl i ance Anchorage Fai rbanks Post-1985 (annual)
Space Heat
Si ngl e Family 40,100 53,000 0.005
ttlbi 1e Homes 30,000 40,600 0.005
Duplexes 26,600 35,100 0.005
Multifamily 18,800 23,300 0.005
Water Heaters 3,000 3,475 0.005
Clothes Dryers 1,032 1,032 0.0
U1.Cooking Ranges 1,200 1,200 0.0N
1.0
Saunas-Jacuzzi s 1,750 1,750 0.0
Refri gerators 1,560 1,560 0.00
Freezers 1,550 1,550 0.00
Dishwashers 230 230
Additional Water Heating 740 740 0.005
Clothes Washers 70 70 0.0
Additional Water Heating 1,050 1,050 0.005
Small Appliances and Lighting 2,110 2,466 (a)
(a)Incremental growth of 50 kWh per customer in Anchorage per 5-year period;
70 UJh in Fairbanks.
TABLE 5.14.Comparison of Appliance Usage Estimates from Selected Studies (measured in kWh)
Scanlon Partl &
SRI(b)MR I (b)CEC(b)Appliance Hoffard(a)Parti ESC George AHAt1 SDG&E
Refrigerators -- --
----1.270 1.665
Frost Free 2.177 1.624 --1.455 1.523 --1.858 2.250 1.880
Standard 869 684 --681 933 --893 1.500 906
Freezer --1.084 1.622 1.294 1.478 1.342 1.316
Frost Free 2.252 ---- --------1.820 1.210
Standard 1.881 ------------1.190 811
Electric Range 1.024 804 1.083 753 1.180 782 674 700 671
Clothes Washer ------ --
98 88 70 103 259
Clothes Dryer --1.051 1.363 1.170 990 1.032 950 993 808
Washer/Dryer
Combination 2.680
Ul Water t1eater 3.021 4.535 2.628 --4.490 4.046 3.826 4.219 2.581.
w Dishwasher 1.539 538 360 149 250 363 259a----
Color Television 639 613 --726 490 --420
Space Heating 11.966 3.441 7.301 5.876 14.i53 2.258 9.834 --2.486 SF(c)
785 MF}
1.152 Mil
Central Air
Conditioning 1.505 1.809 1.596 2.183 5.494 3.573 2.924
/1i sce 11 aneous 2.127 1.865 1.882 1.950 ----1.259
(a)Results of final (7th)iteration.
(b)Engineering estimates.
(c)SF denotes single family units.MF multifamily units.and MH mobile.homes units.
...-.1 ~-..Sources for Tahle 5.13:
7·,.1'·~.-1)The Christian Science Monitor.1981.pp.15.
2)san-niego Gas and Etectrlc 19R2.
3)Scanlon and lIoffard 1981.
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Space Heat
For space heating in the existing housing stock,the average annual
consumption figures derived by ISER are used (Goldsmith and Huskey 1980b).
These figures were derived based on heating degree days,floor space,and
average consumption of all electric homes within the Railbelt region and were
adjusted downward by 10%to allow for additional conservation in the building
stock since ISER's study.
Water Heaters
The average consumption for water heaters is based on the California
Energy Commission's (CEC's)estimates and several engineering studies sum-
marized in Henson (1982).The figure separates out consumption for clothes
washers and dishwashers and has been adjusted upward by 15%to account for the
colder-water inlet temperature in Alaska.Anchorage values were also adjusted
downward for some heating of municipal water supplies (see Tillman 1983).
Clothes Dryers
For clothes dryers,average consumption is the figure reported by the
Midwest Research Institute (MRI).ISER (MRI 1979)picked a lower estimate
based on household size,but the colder climate in Alaska should also raise the
estimated use of dryers.This is reflected in high saturation values for this
appliance.
Cooking-Ranges
This category is broadly interpreted as production of heat for cooking
purposes.The figure reported was derived by averaging the values from several
reports.
Saunas-Jacuzzis
The authors informally contacted several suppliers of saunas,jacuzzis and
hot tubs and were told that the consumption of these devices ranged from
100-3000 kWh annually.Hunt and Jurewitz found 1300 kWh annual consumption for
new additions to the stock.However,SDG&E (1982)reported annual average con-
sumption at approximately 2700 kWh.A conservative consumption figure of
5.31
1600 kWh annually was chosen to reflect the presence of bathtub whirlpools and
other small units as well as larger units.
Re fri gerators
An average value from SDG&E (1982)was used,allowing for a 75%saturation
of frost-free units in the Railbelt,as revealed by the Battelle-Northwest
resid~ntial survey.
Freezers
This figure showed little variation among Merchandising Week,MRI,and
ISER.The MRI figure was chosen.
Dishwashers
The value assumed for dishwashers is the mean of several engineering
studies cited in Henson (1982)and SDG&E (1982).Additional water heating
associated with dishwashing has been separated out.
Di shwasher and Clothes Washer Water
These values are from the CEC,adjusted upward to account for colder water
inlet temperatures in Alaska.
Miscellaneous Appliances
For miscellaneous appliances,estimates of consumption were originally
prepared by ISER by subtracting estimated large appliance electricity consump-
tion for 1978 from total 1978 consumption/residential customer (Goldsmith and
Huskey 1980b).Lighting was inferred from national statistics and increased to
1000 kWh/year/customer.The remainder was charged to small appliances.
Research for the RED Model checked ISER's work by assuming:1)televisions
(rated at 400 kWh/year)are included in small appliances;and 2)the ISER
estimate of 480 kWh/year/customer for headbolt heaters is replaced with load
center-specific estimates derived from load-center specific utilization data
produced by the Battelle-Northwest end-use survey and National Oceanic and
Atmospheric Administration (NOAA)data on normal minimum temperatures (NOAA
1979);and 3)1000 kWh/year lighting.The revised estimates for block heaters
5.32
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are as follows:Anchorage,459 kWh/year/customer;Fairbanks,1127 kWh/year/-
customer.Because the results were broadly consistent with ISER's figures,
ISER's totals were used (Goldsmith and Huskey 1980b).
Electrical Capacity Growth
Table 5.15 presents average annual kWh consumption for new appliances in
1985.Revised numbers are presented reflecting the authors'belief that
improved efficiency ratings for appliances coming onto the market will largely
offset future increases in energy use brought about by increases in appliance
size.This is not merely a phenomenon of Alaska fuel prices;rather,it
reflects national energy market trends.Alaskans have little choice concerning
the purchase of more efficient appliance technologies since the available
appliance mix is dictated by national markets.
Little information is available on changes in appliance efficiencies in
the absence of price effects in the Alaska market.However,the appliance
manufacturers associations and the U.S.Department of Energy (DOE)have
developed estimates of appliance efficiency for several types of new appliances
(see King et ale 1982).The major source for the efficiency ratings on new
appliances was a DOE survey of appliance manufacturers (Form CS-179)that asked
actual energy efficiency information on current models of appliances for 1972
and 1978.In addition,manufacturers were asked to make projections of new
appliance efficiency for 1980.The Association of Home Appliance Manufacturers
has since revised some of the estimated efficiencies of the 1980 (sometimes
1981)models and has found that estimated efficiencies have improved more than
was anticipated at the time of the CS-179 survey.In fact,refrigerators
freezers,dishwashers,and clothes washers have improved enough in average
efficiency to offset the effects of product size increases and new energy-using
features (such as the frost-free option on refrigerators),leading to a sig-
nificant net reduction in average kilowatt-hours used in the new models.(a)
Table 5.15 summarizes the findings of the CS-179 survey and appliance
manufacturers.
(a)Personal Communication,Jim McMahon,Energy Analysis Program,Lawrence
Berkeley Laboratory,May 24,1983.
5.33
TABLE 5.15.Electric New Appliance Efficiency Improvements 1972-1980.
(percent i mp act 0 n en er gy use,1972 bas e)
CS-179 Findings(a)Appliance Manufacturers(b)
Appl iance 1972-1978 1972-1980 1972-1980
1.Water Heat
Effi ci ency -1.1 -1.9 NA
Si ze Increase NA NA NA
Other Features NA NA NA
\
Net Energy Use NA NA NA
2.Ranges
Efficiency -15.7 -20.1 NA
ISizeIncreaseNANANA
Other Features NA NA NA
Net Energy Use NA NA NA
I3.Clothes Dryers
Efficiency -0.0 -4.2 -3.1
Size Increase NA NA 0.4
IOtherFeaturesNANA0.4
Net Energy Use NA NA -2.7
4.Refrigerators IEfficiency-20.5 -34.3 -45.6
Size Increase NA NA 8.0
Other Features NA NA 11.6 ~Net Energy Use NA NA -26.0
5.Freezers
Effi ciency -24.7 -32.8 -48.0 ( )(.
Si ze Increase NA NA -10.0 c
Other Features NA NA 18.5
Net Energy Use NA NA -39.5
!6.Oi shwashers
Effi ci ency NA NA -45.0(d)
Size Increase NA NA 1i4.0 (d)
fOtherFeaturesNANA
Net Energy Use NA NA -3LO(d)
7.Clothes Washers -51.6(d)IEfficiencyNANA
Size Increase NA NA II s 1 ightu(d)
Other Features NA NA (d)
I12.1(d)
Net Energy Use NA NA -39.5
NA =Not Available
(a)Source:King et ale 1982.
(b)Source:McMahon 1983.
(c)Net decrease in average size.More compact models sold.
(d)1972-1981.
5.34
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Even in the absence of further changes in Railbelt energy prices,residen-
tial consumers in the region are expected to have access to increasingly effi-
cient models of major appliances.In the recent past,efficiency improvements
have more than offset increases in the size of these appliances.For the
future,consumers are assumed to adopt more efficient available models to just
offset increases in size of new models for the years after 1985.Two excep-
tions are allowed.Table 5.15 shows that water heaters have not improved
significantly in efficiency.Once properly installed (and then only if in an
unheated space),the limits of efficiency improvements will have been reached
on existing designs.From there on,further improvements are possible from
redesign of water-using appliances,tankless point-of-use water heating,and
significant behavioral changes of household residents,but these are unlikely
without further price increases in the Railbelt.Thus,as household incomes
rise,it is assumed that hot water usage increases and efficiency improvements
do not offset these increases in the absence of price changes.A similar
factor is assumed to be at work in space heating.Rising household incomes are
assumed to increase the average size of the housing stock and comfort demands
at a faster rate than efficiency improvements can reduce demand in the absence
of energy price changes.
Prior to 1985,a mix of influences is expected to be operating on energy
use.Water heaters and space heating systems are assumed to increase in size
with little or no offsetting conservation effects in the absence of fuel price
increases.Clothes dryers are assumed to have about the same energy use as in
1980,with small increases in size offset by small improvements in effi-
ciency.New ranges are assumed to increase in size and in energy-using fea-
tures over the existing stock to surpass the existing upper bound usage in
Scanlon and Hoffard (1981)single-family homes.Refrigerators have gained
radically in energy efficiency historically and are assumed to continue to do
so between 1980 and 1985,offsetting size and energy-use increases.1980
refrigerator energy usage rates already reflect a large proportion of frost-
free units.(Battelle-Northwest survey results show about 75 to 80%frost-free
units in the Anchorage load center,65 to 70%frost-free in Fairbanks.)Thus,
little increase in energy use can be expected from penetration of frost-free
units.Although nationally freezers have become more efficient,additional
5.35
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penetration of frost-free models in the Railbelt is assumed before 1985,lead-
ing to a small increase in average energy use.Clothes washers and dishwashers
are assumed to continue their recent historic trend toward greater efficiency
and conservation of hot water before 1985.After that,water use increases
while efficiency improvements just offset increased capacity and use.Sauna
and jacuzzi 1985 energy use reflects additional market penetration of slightly
larger units than comprise the 1980 stock.
Appliance Survival
Table 5.16 presents the percentage of appliances remalnlng in each five-
year period after their purchase.These figures were derived by ISER based on
Hausman's work (1979)with implicit discount rates for room air conditioners.
Hausman found that the stock of a particular vintage of ai r conditioners was
fairly well approximated by a Weibull distribution.By substituting differing
lifetimes (EPRI 1979)for alternative appliances,ISER used his results to
derive the figures in Table 5.16.For saunas and jacuzzis,RED assumes the
appliance lifetime was comparable to refrigerators.
Household Size Adjustments
Clothes washers,clothes dryers,and water heaters are used more inten-
sively by large families.Relying on a 1979 Midwest Research Institute study
of metered appliances and family size (Midwest Research Institute 1979),ISER
researchers calculated an adjustment factor for usage of electricity in clothes
washers,clothes washer water,clothes dryers,and water heaters (Goldsmith and
Huskey 1980b).As household size declines,so does energy use in these appli-
ances,other things equal.Table 5.17 shows the equations used.ISER annual-
ized the equations (which were based on daily use),normalized them to an
average household size of three persons,and calculated a ratio to adjust
calculated electricity consumption for average household size.
Price Elasticities
The final parameters used in the Residential Module are the parameters
used to compute the price effects described briefly in the module structure
section of this chapter.Because of the complexity of the algebra involved,
5.36
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1 TABLE 5.16.Percent of Appliances Remaining in Se rvi ce Years After
Purchase,Railbelt Region
I a.Old Appliances 5 10 15 20 25 30
Space Heat (All)0.90 0.80 0.6 0.3 0.1 0.0
I Water Heaters 0.6 0.3"0.1 0.0 0.0 0.0
Clothes Dryers 0.8 0.6 0.3 0.1 0.0 0.0
I Ranges-Cook i ng 0.6 0.3 0.1 0.0 0.0
0.0
Saunas-Jacuzzi s 0.8 0.6 0.3 0.1 0.0 0.0
I Refri gerators 0.8 0.6 0.3 0.1 0.0 0.0
Freezers 0.9 0.8 0.6 0.3 0.1 0.0
1
Dishwashers 0.6 0.3 0.1 0.0 0.0 0.0
Clothes Washers 0.6 0.3 0.1 0.0 0.0 0.0
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b.New Appl i ances
Space Heat (All)0.89 0.73 0.56 0.42 0.3 0.1
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Water Heaters 0.75 0.35 0.1 0.0 0.0 0.0
Clothes Dryers 1.00 0.75 0.35 0.1 0.0 0.0
(.
Ranges-Cook i ng 0.75 0.35 0.1 0.0 0.0 0.0
Saunas-Jacuzzi s 1.00 0.75 0.35 0.1 0.0 0.0
Refri gerators 1.00 0.75 0.35 0.1 0.0 0.0
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Freezers 1.00 1.00 0.75 0.35 0.1 0.0
Dishwashers 0.75 0.35 0.1 0.0 0.0 0.0
!Clothes Washers 0.75 0.35 0.1 0.0 0.0 0.0
1 Source:ISER (Goldsmith and Huskey 1980b)except for saunas-jacuzzis,
which is author assumption.
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(a)AHS =Adjustment factor.
(b)AHH =Average household size (Based on 3.0).
TABLE 5.17.Equations to Determine Adjustments to Electricity
Cons umpt i on Res ul t i ng from Changes in Average
Hou seho 1d Si ze
the discussion of this topic has been given its own chapter (Chapter 7.0),
where the parameters are reported.The values for the parameters came from
Mount,Chapman,and Tyrell (1973).
Appliance
Clothes Washer
Clothes Washer Water
Clothes Dryer
Water Heater
Equation
AHS(a)=1 x AHH(b)
AHS =0.25 +0.75 AHH
AHS =0.25 +0.75 AHH
AHS =0.51 +0.49 AHH
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6.0 THE BUSINESS CONSUMPTION MODULE
The Business Module forecasts the requirements for electricity in the
commercial,light industrial,and government sector of the Railbelt economy.
The figures predicted here do not consider the impacts of explicit program-
induced conservation.Program-induced conservation is handled in the Program-
Induced Conservation Module.Heavy industrial use is forecasted exogenously,
as described in Section 10.0.
MECHANISM
The structure of the forecasting mechanism in the Business Consumption
Module is dictated by the availability of data that can be used to produce
forecasts.Unlike many Lower 48 utility service areas,"the Railbelt has a very
weak data base for estimating and forecasting commercial,light industrial,and
government electricity consumption.No information exists for consumption of
electricity by end use in this sector,so RED produces an aggregate forecast of
business electricity consumption.The Business Consumption ~~odule uses a
forecast of total employment for each load center to forecast business
(commercial,light industrial,and government)floor space.The module then
uses this forecast of the stock of floor space (a proxy for the stock of
capital equipment)to predict an initial level of business electricity
consumption.This initial prediction is then adjusted for price impacts to
yield a price-adjusted forecast of business electricity consumption.
INPUTS AND OUTPUTS
Table 6.1 presents the inputs and outputs of the Business Consumption
Module.Load-center-specific forecasts of total employment are exogenous to
RED.Currently these come from forecasts of the ISER Man in the Arctic Program
U~AP)model.The elasticity of use per square foot of building space and price
adjustment parameters are assigned in the Uncertainty Module.The output of
the Business Consumption Module is the price-adjusted forecast of electricity
requirements of the business sector before the impacts of program-induced
conservation are considered.
6.1
"
TABLE 6.1.Inputs and Outputs of the Business Consumption Module
a)Inputs
Symbol
TEr1P
BBETA
A,B,;\,OSR,GSR
b)Outputs
Symbol
BUSCON
MODULE STRUCTURE
Name
Total Regional Employment
Electricity Consumption Floor
Space Elasticity
Price Adjustment Coefficients
Name
Price-Adjusted Business
Co nsumpt ion
From
Forecast Fil e (exogenous)
Uncertainty Module
(paramete r)
Uncertainty module
(paramete r)
To
Miscellaneous,Peak Demand
and Conservation Modules
Figure 6.1 presents a flow chart of the module.The first step is to use
employment forecasts to construct estimates for the regional stock of floor
space by five-year forecast period.The predicted floor space stock is then
fed into an electricity consumption equation that is econometrically derived to
yield a preliminary forecast of business requirements,which is then adjusted
for price impacts.
After investigating several alternative methods for forecasting business
fl 00 r space,Battell e-Northwest researchers dec i ded to use a ve ry s impl e
formulation of the floor space forecasting equation in the 1983 version of
REO.The floor space per employee in .Anchorage and Fai rbanks is ass umed to
increase at a constant rate to levels about 10%and 15%,respectively,above
today's 1evel s by the year 2010.Thi s takes into account both the evidence of
historic increase in floor space per employee in Railbelt load centers and the
historic lower levels of floor space per employee in Alaska compared with the
nation as a whole.The assumption is still quite conservative,since Alaska's
commercial floor space per employee is far below the national average.Th'e
forecasting equation is shown as equation 6.1.
6.2
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PRICE
FORECASTS
(EXOGENOUS)
FORECAST
EMPLOYMENT
CALCULATE
BUSINESS/
GOVERNMENT/
LIGHT INDUSTRIAL
FLOOR SPACE
CALCULATE
PRELIMINARY
BUSINESS
ELECTRICAL
CONSUMPTION
PRICE AND
CROSS·PRICE
ADJUSTMENTS
BUSINESS
CONSUMPTION PRIOR
TO
CONSERVATION
ADJUSTMENTS
PRELIMINARY
BUSINESS USE
COEFFCIENTS
(UNCERTAINTY
MODULE)
PRICE
ADJ.PARAMETERS
BUSINESS SECTOR
(UNCERTAINTY
MODULE)
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FIGURE 6.1.RED Business Consumption Module
where
STOCK =floor space in business sector
a =initial (1980)floor space per employee
b =annual growth factor (1 plus growth rate)in floor space per
employee
TH1P =total employment
=index fo r the regi on
t =time index,t=1,2,3,•••,7
k =time index,k=1,2 ,3,•••,31.
6.3
(6.1)
The controlling data series for the commercial forecast is an annual
estimate of commercial floor space,which is derived for the period 1974 to
1981.The beginning point is an estimate of commercial floor space in the two
locations developed by ISER (Table 6.2 and Table 6.3)that shows the 1978 stock
of energy-using commercial floor space in Anchorage to be about 42.3 million
square feet (from which 860 thousand square feet of manufacturing floor space
were subtracted to yield 41.4 million)and in Fairbanks about 10.8 million
square feet.This estimate was adjusted backwards and forwards for the period
1974 to 1981 using a predicted construction series (Equation 6.4)to produce a
stock series for the two locations.
Once the forecast of the stock of floor space is found,the module then
predicts the annual business electricity requirements before price adjustments,
based on a regression equation:
PRECON it =exp[BETA i +BBETA i x 1n(STOCK it )](6.2)
where
PRECON =nonprice adjusted business consumption
BETA =parameter equal to regression equation intercept
BBETA =percentage change in business consumption for a one percent
change in stock (floor space elasticity).
exp,ln =exponentiation,logarithmic operators
t =index fo r the forecast yea r (1980,1985,•••,2010).
Finally,price adjustments are made with the price adjustment mechanism
identical to that in the Residential Consumption ~dul e.
where
BUSCON =price-adjusted business requirements (MWh)
OPA =own-price adjustment factor
PPA =cross-price adjustment factor for fuel oil
GPA =cross-price adjustment factor for natural gas.
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TABLE 6.2.Calculation of 1978 Anchorage Commercial-Industrial Floor Space
10 3ft2
AMATS Survey (Anchorage Bowl,1975)
Minus Non-energy Using (parking lots,
cemeteries,etc.)
42,067
18,918
Energy Using Floor Space
20 Percent Adjustment for Underreporting
23,149
4,630
27,779
Sectors not Included in Survey:
1.Girdwood/Indian(a)
2.Eagle River/C?Uyiak(b)
3.Hotels/Motels c
4.Assorted Cultural Buildings(d)
53
300
1,000
500
29,632
Item:(e)
6.5
1978 Non-Manufacturing Floor Space,Anchorage
Source:Adapted from Goldsmith and Huskey (1980b).
7,400
37,000
36,140
2,663
1,405
706
7,331
6,148
3,722
3,528
3,131
25,120
5,000
4,520
1,500
860
Genera 1
Educat i on
Warehousing
Hotel s
Manufacturi ng
Retail Trade
Warehous i ng
Education
Wholesale Trade
Transport-Communication-
Public Utilitites
Government
Manufacturi ng
Other
Growth Between 1975-1978(f)(about 25 %)
1978 Estimated Commercial-Industrial Floor space(g)
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TABLE 6.2.(contd)
Twenty-five businesses in 1975 acording to telephone book.Assume 2,500
square feet/business.
Based on the ratio of the housing stock in 1978 between Eagle River/Chugiak
and Anchorage.
Assumes 2,000 rooms at 500 square feet/room.Based on Jackson and Johnson
1978,p.40.
Forty-six establishments identified in 1975 telephone book.Average size
assumed to be 10,000 square feet.
Detail does not add to total in original.Total was assumed correct.
Thi sis based upon two indicators.The fi rst is the growth in employment
between 1974-75 and 1978.Civilian employment was as follows:1974-
58,700,1975 -69,650,and 1978 -76,900.Employment growth was 31%in the
period 1974 to 1978 and 10%in the period 1975 to 1978.(State of Alaska,
Department of Labor,Alaska Labor Force Estimates by Industry and Area,
various issues.)The second is the growth in the appraised value of
buildings over the period 1975 to 1978.After adjusting for inflation,the
increase was 48%.Based on the assumption that the rapid employment
increase in 1975 resulted in undersupply of floor space in that year,we
assume a 25%growth in floor space between the summer of 1975 and 1978.
Independent estimates of floor space in 1978 in the educational category
and the hotel/motel category were available from the Anchorage School
District and Anchorage Chamber of Commerce,respectively.The remaining
growth was allocated proportionately among the other categories.
(a)
(b)
(c)
(d)
(e)
(f)
(g)
Greater Anchorage Area
Anchorage
Kenai-Cook Inlet
Matanuska-Susitna
Seward
Greater Fairbanks Area
Fairbanks
Southeast Fairbanks
Source:Adapted from Goldsmith and Huskey (1980b).
6.6
41.4
36.1
3.2
1.5
0.6
10.8
10.4
0.4
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The price-adjusted business requirements are then passed to the Program-
Induced Conservation and Peak Demand Modules.
PARAMETERS
As described in the subsection on MECHANISM,the data base available in
the Railbelt for forecasting business electricity consumption is very weak.
Among the principal problems in forecasting for this sector are the following:
•No information on electricity consumption by end use exists for this
sector in the Railbelt.
•Many of the Railbelt's large commercial users of electricity
(considered industrial users in many electricity demand forecasting
models)are primarily commercial users.In addition,many
government offices are in rented commercial space.This makes it
impossible to use employment by industry to forecast electricity
consumption separately for commercial,industrial,and government
end-use sectors since the Standard Industrial Classification (SIC)
codes in which employment is typically reported do not at all.
correspond to the traditional end-use sectors of electricity-demand
models.
•While an estimate exists for the stock of business floor space in
the Railbelt ln 1978 and can be used to estimate the intensity of
commercial electricity use,the only comprehensive data base on
commercial (including industrial and government)building
construction available to estimate changes in stock is subject to
tight copyright controls.It was necessary,therefore,to estimate
historic construction to derive historic series of the stock of
business floor space.
These problems made it reasonably clear that forecasts by end use or even
end-use sector were impossible.However,it was unclear whether stock or
employment was a better predictor of business electricity consumption.
The approach used to resolve the issue consisted of three steps.First,
the historical relationships of electricity consumption per employee and per
6.7
square foot of commercial floor space were examined to determine the most
appropriate relationship on which to base the forecasts.Second,equations
developed for related work were applied to the two locations and examined as to
the plausibility of their forecasts.Finally,a less sophisticated forecasting
methodology was devised due to data limitations.This methodology took maximum
advantage of the existing Railbelt data base.
The historical relationships of electricity consumption per square foot
and per employee in the commercial sector were examined to determine whether
one or the other of the two relationships was more appropriate as a basis for
consumption forecasting electrical energy consumption.This examination,
reported in the subsection on consumption below,concluded that floor space was
theoretically superior and a slightly more stable predictor of electricity
consumption.
Floor Space Stock Equations
Several different methods were used in an attempt to forecast commercial
building stock in the Railbelt.These methods included adapting forecast
equations from related work performed by Battelle-Northwest in the-Pacific
Northwest and the nation as a whole.It was not possible to directly estimate
building stock equations for the Railbelt due to copyright restrictions on the
use of the data used to estimate the Pacific Northwest and national equations.
The forecast method used a relatively unsophisticated approach to develop
floor space forecasts.Commercial sector energy consumption and building stock
figures for Anchorage and Fairbanks were compared to similar estimates in the
Lower 48.These comparisons then formed the basis for the method used for
forecasting floor space.
Data on "actual"floor space in the commercial sector are scarce;this
1 imited the comparison to one year (1979 for U.S.figures;1978 for
6.8
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Alaska).(a)Some Lower 48 multistate regional estimates,but no independent
state-wide estimates,were available.Table 6.4 summarizes the results of
these comparisons to Railbelt estimates for a variety of sources.
An average 531 square feet per employee existed in commercial buildings in
the U.S.in 1979 (using Energy Information Administration data on square foot-
age and total U.S.employment,less mining and manufacturing employment).
Broken out by region,the figures ranged from 364 to 751.The highest space-
per-employee ratio occurs in the North Central region,and the smallest is in
the West.Comparable figures for 1978 in the Railbelt fall at the lower end of
that range.For comparison,the table shows estimates from a survey performed
by the Bonneville Power Administration (BPA)by commercial building type:
trade employees use 891 ft 2 ;services employees use 1194 ft 2 ;and office
employees use 305 ft 2 •Figures for the distribution of commercial square
footage by building type in the U.S.do not exist,but if the square footage
estimates in Table 6.4 are accurate,they may indicate a relatively higher
proportion of offices in the Railbelt on average than in the U.S.
Estimates for the Railbelt from historical data (1978)and the RED model
(1980)fall below the U.S.national average for square footage per employee.
The estimates are reasonable,however,and the differences largely reflect
differences in the precise definition of employees (U.S.Department of Commerce
or State of Alaska definition)in the available data used in the denominator.
The reasonableness of the square-footage-per-employee figure in the
Railbelt can also be evaluated by examining comparable figures for kWh/employee
and kWh/ft 2 in Table 6.4.The 1979 national average energy use shown is 7303
kWh per employee.Regional averages range from 4468 kWh in the West to 9997 in
the North Central region.With California's moderate temperatures (low heating
(a)F.W.Dodge,a division of McGraw-Hill,Inc.,markets local historical
estimates of residential and nonresidential construction by building type,
from which estimates of historical building stock may be generated.
However,copyright restrictions on these data prevented their direct use
in RED model development unless they were purchased for use in the
project.Tests of the data base in other projects persuaded us that the
expense of purchasing the F.W.Dodge data set for use in RED Model
development was not justified.
6.9
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I19.57
20.80
8,407
7 ,496
kWh/Emp 1 oyee kWh/ft2
7,303 13.75
7 ,310 13.02
9,997 13.31
7,358 15.45
4,468 12.27
7,851 20.9
7 ,550 22.5
10.21
13.02
11.16
15.15
16.80
22
(range 5-65)
16
36
45
Retail/Wholesale 18.16
Offi ce 7.75
Wa rehouse 5.34
I-ealth 24.31
375
336
531
562
751
476
364
429
360
891
1,194
305
7000+HOO(e)
5.5-7000 HOD
4-5,500 HOD
<4000 HOD
<4000 HOD
Trade
Servi ces
Offi ce
BPA (1980)(h)
TABLE 6.4.Comparisons of Square Feet,Employment,and Energy Use
in Commercial Buildings:Alaska and U.S.Averages
ft2/Emp 1oyee
EIA(a,b)
U.S.(1979)
NE
NC
S
W
Al aska(1978)(c)
Anchorage
Fairbanks
Climate Zone(a,b)
<2000 COO(d)
<2000 COO
<2000 COD
<2000 COD
>2000 COD
PG&E (1981)(f)
Power Council (1983)(g)
Warehouse
Offi ce
Hospi tal
RED Al aska (1980)(i)
Anchorage
Fairbanks
(a)EIA 1983.
(b)U.S.Bureau of the Census 1980b.
(c)Goldsmith and Huskey 1980b.
(d)COD =cooling degree days
(e)HOD =heating degree days
(f)Pacific Gas and Electric Co.1981.
(g)Northwest Power Planning Council 1983.
(h)Bonneville Power Assocation 1982.
(i)RED Model Run Case HE.6--FERC 0%Real Increase in Oil Pri ces (Employment
Alaska Department of Labor basis from MAP model).
6.10
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and low cooling load)in the West,and the large heating load in the North
Central,these figures are reasonable.Alaska's figures of 7851 and 7550 kWh
per employee are slightly higher than the national average,which follows,
given Alaska's hours of winter dayl ight and temperatures.No independent
utility survey-based estimate could be found.
The RED model (1980)predicts 8,407 and 7,496 kWh per business sector
employee in Anchorage and Fairbanks,respectively.~e definition of employees
differs between the two estimates for the Railbelt,but a figure 10 to 15%
higher than the NC region for an area such as the Railbelt that has large
heating,lighting (due to shortened days),and a reasonable cooling load is not
unacceptable.
The national average kilowatt-hour use per square foot in commercial
buildi.ngs shown in the table is 13.75 kWh/ft 2•~e regional averages vary from
12.27 kWh/ft 2 in the West up to 15.45 kWh/ft 2 in the South.Alaska's figures
are almost double the Western regional average.~is reflects the relatively
high consumption per employee and low square footage per employee.First
assumptions might attribute this to the relatively high heating load,but a
comparison of regions by climate zone [that is,by heating-degree (HOD)and
cooling-degree-days (COD)]does not support this hypothesis.Moving from the
coldest to the warmest climate,kWh/ft 2 figures basically increase.Assuming
Alaska belongs to the coldest cl imate classification,Railbelt averages might
be expected to fall at the bottom end of the range.Also,the Railbelt commer-
cial building stock is predominantly heated with gas or oil,which ought to put
the Railbelt at the bottom of the range,not the top.
An alternate explanation would examine the mix of commercial building
types within the regions.In all cases,warehouses are the least energy
intensive,while restaurants,grocery stores,and health facilities are
relatively energy intensive.Estimates by Pacific Gas and Electric (PG&E)
(1981)ranged from 5 to 65 kWh/ft 2 ,with an average of 22.A report prepared
for the Pacific Northwest Power Planning Council (1983)showed existing
commercial stock consumption at 16 kWh/ft 2 in warehouses,36 kWh/ft 2 in
offices,and 45 kWh/ft 2 in hospitals.BPA estimates (1982)show consumption in
warehouses around 5.5 kWh/ft 2 ,offices at around 8,retail facilities around
6.11
18.25,and health facilities at 24.5 kWh/ft 2 •As shown in Table 6.3,non-
energy using commercial space has been el iminated to the extent possible in the
Railbelt figures.These figures suggest (as in the ft 2/employee case)that the
Alaska mix of commercial buildings may lean relatively more heavily toward more
energy-intensive space like offices,restaurants,and hospitals.In addition,
the Alaska consumption data include some industrial sector consumption and
therefore inflate the estimates of kWh/ft 2 •
Lack of data in the area of square feet of stock of commercial buildings
severely limited the depth of these comparisons.The comparisons that were
performed are only as good as the data from which they were derived,which
varied considerably in quality.However,figures for square foot,energy,and
employee ratios estimated from available data suggest that estimates from the
RED model are fairly reasonabl.e,especially considering the level of
sophistication of the model and the quality of available data.
Given the problems reported below with a satisfactory statistical rela-
tionship for predicting floor space,a rather simplified approach to fore-
casting commercial floor space was used.This approach is that square footage
per employee will grow from its current low level to reach current Lower 48
values by the end of the forecast period,2010.Although this is not a very
satisfying alternative,professional judgment suggests this to be more appro-
priate than the other options.It recognizes a direct relationship between
floor space and employment and permits fairly easy use of sensitivity analysis.
This simplified formulation is derived by assuming that floor space per
employee grows by 10%in Anchorage by the year 2010 and by 15%in Fairbanks.
This is a conservative assumption since best estimates put Anchorage growth in
stock per employee at about 11%for the 1970s,and Fairbanks·growth at 46%.
The year 2010 stock-per-employee estimates (U.S.Department of Commerce
definition of employment)would then be 412 square feet and 386 square feet per
employee in Anchorage and Fairbanks,respectively.This brackets the 1979 U.S.
western regional average.These growth rates are then applied to the 1980
estimates of Railbelt load center floor space per employee (Alaska Department
of Labor employment definition).This provides commercial floorspace forecast
equations for the two cities as follows:
6.12
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Anchorage
Fai rbanks
429.5(1.0033)k x Employment
360.4(1.0046)k x Employment
where k is the forecast period in years.The only change necessary for
forecasting was to convert the annual growth rates into five-year forecasts.
The coefficients are shown in Table 6.5.
TABLE 6.5.Business Floor Space Forecasting
Equation Parameters
Load Center
Anchorage
Fairbanks
Other 1v'ethods Tri ed
Parameter
ai
429.5
360.4
Values
b i
1.0033
1.0046
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In previous versions of the RED model,the parameters used to forecast the
annual change in floor space stock were extracted from work at Battelle-
Northwest for BPA.Staloff and Adams developed a theoretical and empirical
formulation of a stock-flow model for the demand and supply of floor
space.(a)Using three-stage least squares multiple regression,they estimated
their system of equations using pooled cross-section/time-series data for the
years 1971-1977 for the 48 contiguous states and tested the equation on Alaska
data,among other regions.
In their formulation,the percentage change in the stock of floor space is
a function of the changes in the following:the annual change of the nominal
interest rate,the annual percentage changes of the Gross National Product
(GNP)deflator,the annual percentage change in regional income,and the annual
percentage change in regional population,as well as some cross-product terms:
(6.4)
(a)Staloff,S.J.and R.C.Adams.1981 (Draft).
6.13
where
Stock =floor space stock
61-69 =parameters
~=symbol for the first difference (annual change)
GNPDEF =gross national product price deflator
POP =population
INC =income
i =index for the region
t =index for the year
II =symbol for the annual percentage change
r ::nominal interest.
(6.4 )
contd
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The Anchorage Consumer Price Index (CPI)was used as a proxy for the GNP
price deflators.It is assLalled (as historically revealed)that the nominal
interest rate was approximately three percentage points above the measure of
inflation.A proxy for regional income was derived by multiplying regional
employment by the statewide average wage rate.Parameter values are shown for
equation 6.4 in Table 6.6.
TABLE 6.6.Original RED Floor Space Equation Parameters
).
\
Parameter Coefficient
-0.1291
1.2753
0.3553
-0.113
0.1929
-0.0947
-0.0078
-0.0116
-0.0412
Standard Error
0.00345
0.2566
0.0302
0.0037
0.0355
0.0078
0.0008
0.0253
0.0061
6.14
T-Stat i st ic
-3.75
-4.97
11.76
-3.04
5.43
-12.09
-9.92
-0.46
-6.68
Table 6.7 shows how well the stock-flow floor space relationship performed
in Anchorage and Fairbanks historically.Although the stock-flow equation
performs fairly well on backcast and could be used to predict stock of commer-
cial space for the historical period,in forecasts of future years it predicted
virtually no growth in square footage per employee in Fairbanks and vigorous
growth in building stock per employee in Anchorage.Since Fairbanks·actual
commercial stock per employee grew faster between 1974 and 1981 than Anchor-
age's stock per employee,this forecast result appeared incorrect.For fore-
casting purposes,the equation was replaced with a simpler formulation that
trended square footage per employee from existing levels in the Railbelt to
near the current western average.
TABLE 6.7.,Predicted Versus Actual Stock of Commercial-Li~ht
Industrial-Government Floor Space,1975-1981,\.)
(million square feet)
Fo recast Error Forecast Error
.A1lchorage as Percent of Fai rbanks as Percent of
Year Predicted Actual (%)Predicted Actual (%)
1975 31.2 -7.2 6.6 -3.8
1976 33.8 -9.3 7.2 -18.1
1977 37.0 -6.9 7.8 -23.0
1978 40.5 -2.4 8.2 -24.1
1979 42.3 -1.1 9.4 -16.0
1980 43.8 -0.7 9.9 -13.3
1981 44.7 -0.4 10.4 -9.2
(a)Because of the double lag structure of equation 6.1,only 1975-1981
can be compared.
Source:Unpubl ished test results of Staloff and Adams (1981 Draft).
Several other equations estimated for related national commercial
buildings work at Battelle-Northwest were also applied to the Railbelt to
determine their ability to forecast floor space.The equations used were
estimated using pooled Lower 48 Standard Metropolitan Statistical Area (SMSA)
and non-SMSA level data.The magnitude of the units of the independent
6.15
variables (primarily the population,employment,and construction activity
variables)was within an order of magnitude of those in Alaska.However,the
magnitude of population,employment,and construction activity in the Railbelt
is still small compared to those in the U.S.data us·ed to estimate the equa-
tions.This may partly explain why building stock equations estimated with
Lower 48 data do not perform well when appl ied to Alaska.
Annual additions to commercial floor space were estimated with several
linear,logrithmic,and difference forms as a function of the following:
•lagged commercial building stock additions
•AAA bond rate in two forms--current and first differences
•population,both lagged and first difference
•employment,both 1 agged and first difference
•income,both lagged and first difference.
The equations IIfit li the data on which they were estimated reasonably well,
with R-square values generally above 0.9 and significant t-values on all
coefficients.However,the equations did not perform well when applied to the
two Alaska locations.All of the equations,in fact,produced negative levels
of construction in forecasts.As mentioned above,this may be partly due to
the magnitude of the units of the independent variables in relation to those
used to estimate the equations.Mbre importantly,the special behavior of the
Alaskan economy may not be adequately described by equations estimated using
data from the Lower 48 states.
Business Electricity Usage Parameters
These parameters were estimated with regression analysis.Using predicted
historical floor space shown in Table 6.7(a)and using historical commercial-
light industrial-government electricity consumption,the following regression
equations were estimated:
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In(CON it )=BETA i +BBETA i x In(STOCK it )+eit (6.5)
(a)Copyright restrictions precluded the combining of lI ac tual ll data--that is,
estimated construction based on FW Dodge construction data and 1978 building
stock estimate produced by ISER.Predictions of historical floor space were
done with equation 6.4.
6.16
where
TABLE 6.8.Business Consumption Equation Results
CON =historical business sector consumption (MWh)
BETA =intercept
BBETA =regression coefficient
STOCK =predicted stock of floor space,hundreds of square feet
g =stochastic error term.
Table 6.8 presents the results of the regression analysis.(a)The
parameters BBETA are allowed to vary within a normal distribution,truncated at
the 95%confidence intervals in Anchorage and 90%in Fairbanks ••
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BETA
standard error
t-statistic
BBETA
standard error
.t-statistic
GAMMA
standard error
t-statistic
THETA
standard error
t-statistic
R 2
Anchorage
-4.7963
0.6280
-7.6368
1.4288
0.0491
29.1159
0.9906
Fa i rbanks
-0.9611
3.6314
-0.2647
1.1703
0.3293
3.5538
0.1629
0.0535
3.0444
-0.0028
0.0024
-1.1547
0.9121
The estimating equation (equation 6.5)was modified with dummy variables
for Fairbanks to capture and remove the effects of a rising trend in Fairbanks
electricity prices after 1974 and the effects of the pipeline boom on consump-
tion from 1975 to 1977.The regression equation estimated for Fairbanks is as
follows:
(a)Regression intercept was adjusted to calibrate consumption in the business
sector to its actual 1980 value for forecasting purposes.
6.17
In(CON t )=BETA +BBETA x In(STOCK t )+GAMMA x V
+THETA x DT +£t
with CON t ,BETA,BBETA,and £defined as above and where
o =Dummy variable (1974 through 1981 =1)
V =Dummy variable (1975 through 1977 =1)
T =Time index for T =1,•••,9.(1973 through 1981)
GAMMA,THETA =regression coefficients.
(6.6 )
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The dummy variables were held at zero in forecasting.
The historical electricity consumption data were obtained from FERC Form
12s for the Railbelt utilities (supplied by ISER)and from Alaska Power
Administration.These data lump together commercial and industrial sales by
si ze of demand and there is no rel iabl e way to disaggregate these two types of
consumers.This is felt to be a significant shortcoming of the data series.
Commercial and industrial loads should be separated because the typical
characteristics of industrial demand for electricity are different from the
demands of commercial and government users.Part of past Railbelt.industrial
load identified by subtracting commercial consumption for users over 50 KVa
from the Homer Electric Association (HEA)service area load and assuming this
load was mainly industrial.(a)Historical loads are shown in Section 13.0.
Historical electrical consumption per square foot of estimated commercial
floor space and per employee,and estimated floor space per employee are
displayed in Table 6.9.The consumption per estimated square foot in Anchorage
shows a 2.0%annual increase for the period,while Fairbanks shows an annual
decrease of 3.1%.The actual cause of thi s decrease in Fai rbanks is unknown,
but may be due to declines in space heating,or to priced-induced conservation,
or to growth in warehouses as a proportion of commercial stock.The floor
space is low at the beginning of the period on a per-employee basis relative to
Anchorage (as well as other known estimates)but then increases at a faster
(a)The major industrial users in HEAls service area include Union Oil,
Phillips Petroleum,Chevron U.S.A.,Tesoro-Alaskan Petroleum Corp.,and
Collier Chemical.Other large commercial (non-industrial)users are
included in HEAls over-50 KVa figures,but could not be separated.
6.18
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TABLE 6.9.Electricity Consumption Per Employee and Square Foot and
Square Footage Per Employee for Greater Anchorage and
Fairbanks,1974-1981
kWh/ft 2 kWh/Emp 1oyee ft 2/Employee
Year Anchorage Fai rbanks Anchorage Fairbanks Anchorage Fai rbanks
1973 19.9 27.7 6612 6631 332.6 217.8
1974 19.5 26.8 6414 5399 329.8 201.1
1975 21.1 31.7 6341 5368 300.0 169.1
1976 22.8 30.5 7044 5641 309.1 185.2
1977 22.9 30.8 7445 6922 325.5 224.1
1978 21.9 29.6 7847 7550 359.1 255.1
1979 20.8 23.5 7663 6858 369.2 292.4
1980 22.9 21.7 8644 6913 377.6 318.3
1981 23.3 21.5 NA(a)NA NA NA
(a)Not applicable.
rate.Once the floor space per employee estimates for Fairbanks reach similar
levels to those in Anchorage,the kWh/ft 2 figures for Fairbanks appear to
stabilize.
The energy consumption per employee figures show increases over time of
3.4%and 0.5%annually for Anchorage and Fairbanks,respectively.(a)These two
series show some instability with slight decreases in 1975 and 1979.The
growth rates are too high,too unstable,and too disparate for long-term appli-
cation,reflecting a period of extreme growth within the state.With more
disaggregated data,employment may prove to be a suitable argument for
industrial electricity consumption.However,with a rather limited Railbelt
industrial sector,forecasts of industrial demand are better handled on a
scenario building basis;i.e.,identify industry expansion plans case by case.
Several regression equations were estimated in an attempt to develop a
theoretically satisfying relationship to predict electricity consumption
(a)No data are available on consumption of electricity by SIC industry
code.Multiple regression techniques proved unsuccessful in determining
the separate effects of each subsector1s employment on commercial demand,
due to high colinearity among explanatory variables.
6.19
separately in the commercial,light industrial,and government sectors.All
failed most normal statistical tests.The aggregate nature of the electricity
consumption data and employment data,the rather high trend exhibited for per-
employee consumption,and the limited data series prevented statistical
estimates of consumption on a per-employee basis.No further attempt was made
to estimate a statistical relationship between electricity consumption and
emp 1oyment.
Business Price Adjustment Parameters
The parameters used in the price adjustment mechanism are an important
part of the business electricity forecasting mechanism.As in the Residential
Consumption Module,the parameter default values and ranges were picked from
r~oun.t,Chapman,and Tyrell (1973).Chapter 7.0 discusses these parameters and
their use in the price adjustment mechanism.
6.20
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7.0 PRICE ELASTICITY
This section describes the price adjustment mechanism employed in the RED
model.In both the Residential and Business Modules,this mechanism modifies
preliminary estimates of electricity consumption generated elsewhere in the
model.Changes in consumption are made to account for changes over time in
electricity,natural gas,and oil prices.The changes in electrical consump-
tion computed by the price adjustment mechanism can be considered price-induced
conservation of electricity.(a)Outputs from the price adjustment mechanism
are the final RED electricity consumption estimates for each sector,region,
and time period.
The remainder of this section is divided into four parts.A brief general
introduction to the RED price adjustment mechanism is given in the next sub-
section.This is followed by a survey of economic literature on electricity
demand.In the third part,the structure and parameters selected for the RED
price adjustment mechanism are discussed.Implementation of the selected
structure and parameters is described in the final subsection.
THE RED PRICE ADJUSTMENT MECHANISM
The RED price adjustment mechanism is motivated by economic theory,which
hypothesizes the following:consumption of any commodity is determined both by
"sca l e "variables such as population,income,and employment,as well by the
prices of the particular commodity,its substitutes,and its complements.
Elsewhere in the RED model,preliminary estimates of electricity consumption
are generated,with consideration only of "sca l e "variables.The price adjust-
ment mechanism described in this section completes the analysis of consumption
determinants suggested by economic theory.
The mechanism works in the following manner.Preliminary,non-price
adjusted estimates of electricity consumption by region,sector,and time
(a)Of course,with falling electricity prices or increases in gas and oil
prices,the price adjustments could result in increased electricity
consumption or "nega tive conservation ll of el ectricity.The price
adjustments include fuel switching.
7.1
period are introduced into the model.These preliminary estimates were
generated under the assumption that 1980 price levels are maintained through
the year 2010.
The price adjustment mechanism accounts for the fact that prices in any
forecast period K are not necessarily the same as prices in 1980,even in real
(inflation-adjusted)terms.If real electricity prices increase (decrease)in
any region and sector between 1980 and period K,economic theory suggests that
electricity consumption in that region and sector would decrease (increase)
relative to its non-price-adjusted pre1 iminary estimate.Conversely,if real
natural gas or oil prices increase (decrease)in any region and sector between
1980 and period K,electricity consumption in that region and sector would
increase (decrease)relative to its non-price-adjusted preliminary estimate
because natural gas and oil are substitutes for electricity.Thus,the RED
price adjustment mechanism scales preliminary estimates of electricity
consumption upward or downward based on changes in real electricity,natural
gas,and oil prices.
The amount by which preliminary period K consumption is scaled upward or
downward depends on three general factors:1)the percentage change in real
electricity,natural gas,and oil between forecast period K-1 and forecast
period K,as well as price changes occurring prior to period K-1;2)the short-
run elasticities of electricity demand with respect to the three prices;and
3)the speed with which final consumers of electricity move toward their long-
run equilibrium consumption levels when these prices change,which is
represented by a IIl agge d adjustment coefficient",or alternatively,the long-
run demand elasticity.Short-run elasticities of demand are defined as the
percentage change in consumption in year t caused by a one percent increase in
price in year t.Own-price elasticities refer to changes in electricity
consumption caused by changes in electricity prices;cross-price elasticities
refer to changes in electricity consumption associated with changes in either
natural gas or oil prices.Short-run elasticities represent the instantaneous
adjustment that consumers make when prices change.Of course,-in the case of
electricity,a significant period of time may pass before consumers have fully
responded to a price change in year t:time is required to change old habits,
7.2
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to replace old appliances with more energy-efficient ones,to weatherize
residences or commercial/industrial buildings,and to switch to other energy
sources.The lagged adjustment coefficient represents the rate at which
consumers move toward their final equilibrium consumption level;the higher
this coefficient,the more current consumption depends on past consumption,and
thus the slower conslJ1lers respond to current price changes.In fact,simple
algebra can show that the long-run demand elasticity (either own-or cross-
price),which is defined as the percentage change in electricity consumption in
year t +~caused by a one percent change in price in year t,can be defined in
terms of the lagged adjustment coefficient and the short run elasticity.The
formula for the long-run elasticity ELR is given by
ELR ESR=1-A
where ESR is the short-run elasticity and A is the lagged adjustment
coefficient.
(7.1 )
Alternatively,a set of long-run price elasticities can be entered into
the mechanism.These elasticities describe the change in consumptl0n caused by
a price change once the consumer has reached a point of equilibrium with that
pri ce change.
LITERATURE SURVEY
Si nce the lIenergy cri ses ll of the early 1970s,an extensive economi c/
econometric literature on the demand for energy,and electricity in particular,
has been generated.A survey of this 1 iterature was performed with two primary
objectives:first,to identify possible structures of the RED price adjustment
mechanism;second,given the structure,to identify potential parameter values
for the mechanism.These objectives center around the concepts of elasticity
and adjustment coefficients.In performing the survey,the objectives led to
the following questions.
•Should the RED Residential and Business Sectors be combined or
modeled separately?
7.3
•Should the own-price elasticity be a constant or a function that
depends on the price level?
•Should both natural gas and oil cross-price elasticities be included
in the mechanism and should these elasticities be constant or vary
by the price levels of the two fuels?
•Should the relationship between short-run and long-run price elas-
ticities (both own-and cross-)be modeled explicitly by including
lagged adjustment coefficient in the mechanism,or should the two
types of elasticities be included in the mechanism separately?
•Once the structure is selected,what are the most appropriate values
for the parameters of the mechanism?
All of the studies surveyed were econometric in nature,in which electri-
city demand functions were estimated using statistical techniques.A variety
of data bases was used in these studies,and the fuctiona1 forms,independent
variables,and estimation techniques employed varied substantially as well.
All but a few of the studies modeled residential,commercial,and industrial
electricity demand separately;in many studies,only one of these sectors was
considered.Many of the studies estimate price elasticities that do not vary
according to price levels;this is accomplished by regressing the natural
logarithm of consumption on the natural logarithms of the prices and other
independent variables.The coefficients of the price terms can then be
interpreted as elasticities.Non-constant elasticities were estimated in a few
studies,using a variety of functional forms.One method of estimating
variable price elasticities is to regress the natural logarithm of quantity on
the natural logarithms of the prices,the natural logarithms of the other
independent variables,and the reciprocals of the prices:
log Q =a +b log P +++cliP +++(7 .2)
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where 1I10g 11 denotes natural logarithm,Q is consumption of electricity and P
its price,a,b,c are parameters to be estimated,and 11+++11 denotes the other
price and independent variables in the equation.In this specification,the
own-price elasticity is equal to b -clp,which depends on P.
7.4
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Several studies include only natural gas as a substitute for electricity,
a smaller number include only oil,and some studies include both.The substi-
tute commodities included in an equation depend on the intentions of the
researcher and the type of data used:neither oil nor natural gas prices
typically vary much in cross-sectional samples,so their effects on electricity
consumption are difficult to discern when using this type of data.
Finally,the type of elasticity estimated (short-run,long-run,both)
varies across the studies survey.In studies using time-series data,the
coefficients on prices and the other independent variables are typically inter-
preted as short-run elasticities.An exception to this occurs when lagged
consumption is included as an independent variable in the estimation equation;
then,the coefficients in the prices represent short-run elasticities,and the
long-run elasticity is given by equation 7.1 with A the coefficient on lagged
consumption.In equations estimated using cross-sectional samples,the
coefficients are typically interpreted as long-run elasticities.Pooled time-
series --cross-section samples pose a bit more of a problem;the estimated
coefficients contain both long-run and short-run effects.However,when lagged
consumption is included as an explanatory variable,the price coefficients
again represent short-run elasticities and long-run elasticities are again
given by equation 7.1.
Table 7.1 summarizes the econometric studies of residential electricity
demand surveyed.For each study,the type of elasticity estimated (constant,
variable),the time period for which it is relevant (short-run,long-run,
both),and the type of data used (cross-section,time-series,pooled cross-
section --time-series)are presented.A1 so shown are the substitutes'prices
and non-pri ce factors cons ideredi n each st udy.The own-and cross-pri ce
elasticities estimated in each study are presented in Table 7.2.For those
studies in which lagged consumption was included in the equation,its coef-
ficient,the lagged adjustment coefficient,is also presented.
Est imates of the short-run own-pri ce e1ast i ci ty va ry cons iderab 1y.In
absolute values,the minimum estimate is 0.101,while the maximum is 0.3.Many
of these differences can be attributed to the data used in the estimation;
estimates based on national data would be expected to differ from estimates for
7.5
TAflE 7.1.Residential Electricity Oanand Survey
1,Ype of ether Demnd
Auttx>r Elasticity Tille Frare Type of Data Substitute Prices ~temri nants(a)
J'fiderson,K.P.(1972)<bnstant Long run O'oss-section Average Il"ice
Residential Danand for 1969,states of Natural Gas
Electricity:Ei:onmetric
E'stilT8tes For G11ifomia
and the lhited 9::ates.
The ~nd <bqDration,
Santa tbnica,CA
lin de rson,K.P.(1973)Constant 910rt run Cross-section Fuel oil,V,HS,SHJ,NU,
I€s identi a1 Energy Use:long run 1969,states bottled gas,W,S
lin Econmetric lInalysis R-coal
1297-NSF.lhe ~nd Corp.,
Santa fvbnica,CA
-....J.BaLghnan,MJ....,Qmstant 910rt run Tille series Enerw price Vi,N,Mf,LT,0'1 Joskcw,P.L.,Dilip,K.P.long run 1968-1972 index Pi
1979 Electric Po.-.er in the 48 states
lhited 9::ates:Hxlels
and FUlicy J'fialysis.
MrT Press,Cartridge,MA
Bl attenberger,G.R.,Constant 9l0rtrun Tille series tbrgi nal price npe,fce,x,
Taylor,L.D.,long run 1960-1975 natural gas,ddh,ddc
Rennhack,R.K.1983,states fi xed charge
lIt-atural Gas Availability natural gas,
and the Residential Danand price of fuel
for Energyll.The Energy oil
Journal.4(1):23-45
Ha 1vorsen,Robert.1976 Constant Long run Cross-section Average price cr 'P~,V*,J,
1I0emn d For Electric 1969 .per thenn for 0,I,,H,E
Energy in the United states all types of
States ll
•SoJthern Econ gas purchased
Journal.42(4):61G-625.by sector
-L_
TAItE 7.1.(contd)
Type of Other Danand
Moor Elasticity Tine Frare Type of Illta ~titute Prices IEtenninants(a)
I-B 1vorsen,Robert.1978 Constant Long run Pooled Averaje real PR,y~A,0,
EConometric Nbdels of U.S.1961-1969 gas lTice for J,U,,HA, T
Enerw [)amnd.D.C.Heath 48 states all types of
and Co.,Lexi ngton,t4A gas in cents
per thenn
Hirst,Eric,and ~m~,(bnstant 9nrt run o-oss-sect i on HT,HSA,C,TI,
Janet.1979.liThe ORNL long run 1970 BJ,U
Residential Energy-Use
tvbdel:St ructure and
Results ll
•land Econo-
......mics.55(3):319-333.......
Ibuthakker,H.S.and Constant 910rt run Tine series 9t-l'~'pTaylor,L.D.1970.
Consuner DEm'lnd in the
United States.I-Brvard
lhiv.Press,Carbridge,MA
tvbunt,T.D.,Chapnan,Variable 910rt run Cross-section Price of gas-Popul ation,per
L.0.,and Tyrrell,T.J.long run 1~7-1970 inc 1Ldes capita incane,
1973.El ectricity OEm'lnd States natural,1iquid avg.electricity
in the lhi ted .9:ates:M IEtroleun,lTice,lTice index
Econometric Analysis.manufactured for appl i ances,
and mixed gas.rrean Janua ry
t8lperature
(a)For s}ffibols,see glC6sary at end of section.
TABLE 7.2.Residential Survey Parameter Estimates
Snrt-l\In LDng-1Ol U:gged Gis Oil
0...0 Price Uo6l Price A:ljus1Irent O"os s-pri ce O"oss-price
Moor Elasticity Elasticity (})efficient (A)Elasticity Elasticity
Jlnderson (1972)---0.91 --0.1l.
hlderson (1973)-0.3 -lJ2 0.732 O.3Q 027L
Bau:lhnan,et a1 (1979)-0.19 -1.00 0.842 0.055,0.17L 0.015,0.00a..
Blattenberger,et al (1983)-OJOl -1J)52 0.904 oJl)25,0 J)Hl
K:11vorsen (1976)---0.97 --O.la
........K:11vorsen (1978)---lJ4 --0J)!i..
00 Hi rst,CarnE¥(1979)-0.16 -0.83 --0.025,0.2Q 0.005,0.04L
I-b.rt:hakker,Taylor (1970)-OJ3 -1.89 0.873
M:>unt,O1apnan,Tyrrell -0.14 -1.21 0.884 0.025,0.21L
(1973)
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individual states,and estimates for more recent periods would be expected to
differ from older estimates.The functional forms used and the set of indepen-
dent variables considered also appear to playa role.However,in neither case
does a clear relationship appear.
The long-run own-price elasticities display even greater variation,
largely because two methods of estimating these elasticities exist:1)using a
cross-sectional sample,or 2)using a time-series or a pooled sample and
including a lagged en,dogenous variable.For the studies surveyed,the second
approach generally leads to larger (in absolute values)estimates of the long-
run own-price elasticity.
As expected,in studies in which both long-and short-run elasticities are
estimated,the long-run elasticity is larger in magnitude than the short-run
elasticity.The relationship reflects the fact that consumers can manage only
a 1 imited response to price changes in the short run,when their housing and
appliance stocks are fixed,but respond more fully over time when these stocks
can be varied.
Estimates of the lagged adjustment coefficient do not vary as much as the
other parameters;most estimates are about .85.Oil and natural gas price
elasticities vary much less than the other parameters of interest,but quite a
lot relative to their magnitudes and are considerably smaller than the own-
price elasticities.
Most of the literature surveyed considered commercial and industrial elec-
tricity demand separately.Industrial demand elasticities are typically larger
than those in the commercial sector because of the large amounts of electricity
used for purposes in which oil,natural gas,and coal serve as very good subs-
titutes.In the commercial sector,most electricity consumption is for light-
ing and cooling,uses in which fuel-switching is not as easy.
The RED Business sector is a combination of industrial and commercial
sectors.Most business concerns in the Railbelt,however,are commercial or
light industrial.Therefore,the industrial electricity demand elasticities
were deemed inappropriate to the Railbelt,and only the commercial electricity
demand literature was surveyed.
7.9
Only two studies that deal exp1 icit1y with the commercial sector were
found.These two studies are summarized in Tables 7.3 and 7.4,which parallel
Tables 7.1 and 7.2.Even among these two studies the estimated price elasti-
cities vary considerably;the two short-run own-price elasticities are -.03 and
-.29.The cross-price elasticities again vary considerably less,and are much
smaller in magnitude than the own-price elasticities.
For both the residential and commercial sectors,the hypothesis that own-
price elasticities are constant was statistically tested and rejected by Mount,
Chapman,and Tyrrell (1973)(MCT).In that study,own-price elasticities were
found to increase in magnitude as the level of electricity prices increased.
Thus,the absolute value of the own-price elasticity of electricity demand is
higher in regions with high electricity prices than in areas with lower elec-
tricity prices and increases (decreases)over time as the real electricity
price increases (decreases)over time.In both sectors,oil and natural gas
were each found to significantly affect electricity consumption,and long-run
elasticities were found to be larger than short-run elasticities.However,the
parameter estimates do vary according to sector;Mount,Chapman,and Tyrrell,
who estimated models for both sectors,found significantly greater"price
responsiveness in the short run and long run in the commercial (Business)
sector,with approximately equal lagged adjustment coefficients.
SELECTION OF RED PRICE ADJUSTMENT MECHANISM STRUCTURE AND PARAMETERS
On the basis of the literature surveyed in the previous section and consi-
deration of the non-price modules of the RED model,the RED price adjustment
mechanism was specified in the following manner.
Sector Division
Separate price adjustment mechanisms are used for the two end-use sectors.
In the only study surveyed in which both sectors were considered,MCT found
that the electricity demand elasticities for the two sectors were considerably
different.Thus,specifying a single mechanism to be applied to both sectors
would lead to biased estimates of the price adjustments in each sector.How-
ever,each of the two mechanisms has the same structure;only the parameters
and the price changes considered differ.
7.10
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TAIl.E 7.3.CcmTercial Electricity Damnd &1rvey
1,Ype of Cther OaTBnd
Author Elasticity Tine Frare Type of Data SUbstitute Prices Determi nants(a)
~ierlei n,Jares G.,Dunn,(bnstant 9lJrt-run O"os s-sect i on N:l.tural gas,~'PEj ,Jares W.,IvtConnon,lOll;}-run tine series fuel oil it-1jJaresC.1981.liThe 1967-1917
Danand for Electricity regional NE
and N:l.tural G3s in the
Northeastern United
9:atesll
•The Revi~of
-.I Econoni cs and Statistics •........AUgJst 1981,pp.403-408 •.......
MJunt,T.0.,O1apnan,Variable 91ort-run Cross-section Gas Y,P,PE,Qt-1
L.D.,and Tyrell,T.J.long-run 1947-1970
1973.Electricity OBlBnd States
in the lhited 9:ates;ftll
EConaretric fua lysi s.
(bntract No.W--7405-eng-
26.ORNL,Oak Ridge,
Tennessee
(a)For s)ffibols,see glossary at end of section.
TABLE 7.4.Commercial Survey Parameter Estimates
Moor
Bierlein,et.a1.(1981)
M)lIlt,et.a1.(1973)
Variable Elasticity
9"ort-1W
(),.,n Price
Elasticity
-0.03
-0.29
Long-IW
0Hn Price
Elasticity
-0.37
-1.36
Legged
Pdjustrrent
O:>efficient (X)
0.9167
0.8724
Gas
Cross-price
Elasticity
0.045,0.48..
0.015,O.oa
Oil
Cross-price
Elasticity
-0.095,-1.09..
The own-price elasticity in each sector is not constant,but varies with
the level of the real electricity price.In the only study surveyed in which
variable elasticities were estimated,MeT rejected the hypothesis that own-
price elasticities were constant.Furthermore,a considerable amount of
variation was found in the estimated own-price elasticities during the litera-
ture survey.This variation could be caused in part by variations in the
estimating samples·price levels.
These factors would be unimportant if the level of electricity prices in
the Railbelt region were fairly similar to the mean level of prices used in
estimating the constant elasticity equations,if the levels of electricity
prices within the Rai1be1t were uniform,and if real electricity prices in the
Railbe1t were not expected to change during the forecast period.In such a
case,the estimate from a constant-elasticity model might provide a reasonable
approximation to the true elasticity in the Railbe1t.Even if the true
elasticity were variable,when evaluated at the mean level of prices,it would
be similar to a constant elasticity estimated with the same data.Unfortu-
nately,none of these conditions hold;the average level of Rai1be1t electri-
city prices in 1980 was significantly below U.S.average electricity price;
within the Railbe1t,the level of Anchorage electricity prices was less than
half the level of Fairbanks prices in 1980;and in several of the RED price
scenarios,electricity prices increase rapidly enough that by the year 2000
they are 50 to 100%high.er in real terms than they were in 1980.
Adjustment Over Time
Long-term price elasticities are not entered explicitly into the mecha-
nism;instead,short-run elasticities and a lagged adjustment coefficient are
7.12
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employed.Thus,long-term elasticities appear explicitly in the mechanism via
the rel ationshi p given above.Thi s choice was made for three reasons.Fi rst,
the explicit short-run elasticities are consistent with the implicit long-run
elasticities;that is,the elasticity estimates can be taken from the same
study,estimated with a lagged adjustment coefficient.If the long-run
elasticity were entered explicitly,it could not be taken from the same study
as the short-run elasticity because it is impossible to obtain both elasti-
cities from one equation except via the lagged adjustment coefficient.Second,
since the 'lagged adjustment coefficient did not vary much across the studies,
whereas the long-run elasticities did,choosing a value for A was more
straightforward.Third,and most importantly,by including the lagged adjust-
ment coefficient the impact of price changes in year t on consumption in year t
+1,t +2,•••,t +10 can be assessed directly;because t +1,•••t +10 is
neither the short-run nor the long-run,with only the two sets of elasticities
and no lagged adjustment coefficient these impacts cannot be directly measured,
but only crudely guessed.This is particularly important in RED because it
forecasts electricity consumption at five-year intervals;price changes in the
first-year of the five-year period obviously have neither a long-run nor short-
run impact on consumption in the fifth year of the period,but an intermediate
impact.
Cross Price Elasticities
.Short-and long-run natural gas and oil cross-price elasticities are
included in the mechanism.In several of the studies surveyed,one or the
other fuel was found to be a substitute for electricity,although due to data
limitations they were only considered simultaneously in a handful of studies.
Thus,the effect of oil and gas price changes on electricity consumption,
although small in relation to the effect of electricity prices,cannot be
ignored.It is important to include these prices in the RED price adjustement
mechanism for the following reasons.Much of the own-price elasticity of
electricity demand can be attributed to IIfuel switching.1I As real electricity
prices increase,some households and businesses will,the mechanism predicts,
II sw itch ll from e1 ectricity to natural gas or oil for heating and other energy
uses.However,if real oil and gas prices are also increasing,the extent of
7.13
this fuel switching will be diminished.The cross-price elasticities are
employed in RED to account for this.One would think that the amount by which
this fuel switching is diminished because of rising gas and oil prices would be
a function of the level of oil and gas prices;in other words,that these
cross-price elasticities are not constant with respect to their corresponding
prices.Unfortunately,none of the studies surveyed employed variable cross-
price elasticity models;thus,the cross-price elasticities in each of the two
price mechanisms are constant.
Parameter Estimates
TABLE 7.5.Parameter Values in RED Price Adjustment Mechanism
The parameter estimates for each of the two price adjustment mechanisms
were taken from the study by t1>unt,Ghapnan,Tyrrell (1973).Oil cross-price
elasticities,which were not estimated in the MGT study,were based on profes-
sional judgment and values taken from the literature survey.The parameter
values used in RED are presented in Table 7.5.The MGT parameter values were
used in RED for two reasons.First,their models were most consistent with the
structure selected for the RED price adjustment mechanisms;there are separate·
equations for the residential and business sectors,variable own-price elasti-
cities are employed,lagged adjustment coefficients are estimated,and a cross-
price elasticity (gas)is included.Second,the elasticities estimated by MGT,
when evaluated at 1980 Anchorage and Fairbanks prices (in real 1970 dollars,as
in MGT),appear reasonable.In the residential sector,calculated short-run
elasticities were -.1462 in Anchorage and -.1507 in Fairbanks;calculated
(a)Measured in mills per KWH,1970 dollars.
Short-Run 8asticities
Own-Price
Natura 1 Gas
Oil
Lagged Adjustment
Residential
Sector
-.1552 +.3304/p(a)
.0225
.01
.8837
Business
Sector
-.2925 +2.4014/p(a)
.0082
.01
.8724
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long-run elasticities were -1.2571 and -1.296,respectively.The short-run
elasticities are slightly below the average of the estimates presented in
Table 7.2;since average prices are rather low in the Railbelt,this result is
satisfactory.The long-run elasticities are slightly above the average of the
studies surveyed,since the MCT lagged adjustment coefficient is at the high
end of the range of those surveyed.This is satisfactory for the Railbelt
because el ectricity compri ses a 1arge share of consumers'budgets due to the
cl imate and winter hours of darkness and because in the past residents of the
area have been conservation-minded.The business sector short-run own-price
elasticities evaluated at 1980 prices are -.2270 in Anchorage and -.2600 in
Fairbanks,and the respective long-run elasticities are -1.7788 and -2.0378.
The short-run estimates are a little below the average MCT calculated,due to
below-average Railbelt prices,and the long-run elasticities are at the high
end of the range found in the survey.
DERIVATION OF RED PRICE-ADJUSTMENT MECHANISM EQUATIONS
The final outputs from the RED price adjustment mechanism are price-
adjusted consumption of electricity for each sector,region,and tlme period,
denoted RESCON iK and BUSCON iK •Each of these is equal to preliminary estimates
of consumption,denoted RESPRE iK and PRECON iK ,multiplied by a series of price
adjustment factors:
RESCONiK =RESPRE iK •(1 +OPAiKt)•(1 +PPA iKt )•(1 +GPAiKt)(7.3)
BUSCON iK =PRECON iK •(1 +OPA ikt )•(1 +PPA iKt )•(l +GPAiKt)(7.4)
where
=regi on index
K =time period index
t =sector index (=1 residential,= 2 business)
OPA =own-price adjustment factor
PPA =oil (petroleum)-price adjustment factor
GPA =gas-price adjustment factor and denotes multiplication.
7.15
Thus,final consumption in a sector is equal to preliminary,non-price
adjusted consumption scaled upward or downward depending on the signs and mag-
nitudes of the three corresponding adjustment factors.These factors combine
information on price changes in periods K,K-1,.,own-and cross-price elasti-
cities in periods K,K-1,•••,and lagged adjustment coefficients in the fol-
lowing manner.First,denoting electricity,oil,and natural gas prices by
PEiK~'POiK~'and PGiK~'(define the five-year percentage change in prices):
PE i K-1 o)/PEi K-1 ~"...."
POi K-1 ~)/POi K-1 ~""
(7.5 )
(7.6)
7.16
where 11**11 denotes exponenti ation.Thus,duri ng each of the years between K-1
and K,prices increase an average of 100 •PCPEAi~'and 100 •PCPOAiK~'and
100 •PCPGAiK~percent.
The impact of a change in the price of electricity in the first year of
the five-year period on consumption in the fifth year of the period can be
analyzed in steps.First,the impact of the price change on consumption in the
first year (denoted t)is given by
PCPGiK~=(PGiK~-PG i ,K-l,~)/PGi ,K-1,~.
Then calculate the average annual percentage change in price during the
five-year period:
PCPEAiK~=(1 +PCPEiK~)**.2 1
PCPOAiK~=(1 +PCPOiK~)**.2 1
PCPGAiK~=(1 +PCPGiK~)**.2 - 1
(7.7)
(7.8)
(7.9 )
(7.10)
(7.11)
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Similarly,the change in year t + 2 consumption is equal to the sum of two
component s:
where %6 denotes percentage change,0t ·is consumption in year t,sector t,
region i,Pit1 is the price,and ESRit1 is the short-run own-price of
electricity.Equation 7.9 states that consumption in year t falls (increases)
in percentage terms by an amount equal to the price increase (decrease)scaled
by the own-price elasticity (which is negative).The effect of the price
change in year t on consumption in year t + 1 is the sum of two components.
First,lagged consumption has fallen by %6Qit1'so this period l s consumption
falls by X%6Qitt.Second,the price change which occurred in year t persists
(the price did not go back to its year t-1 level)so consumption in year t + 1
falls by ESR i ,t+1,1 •r06PiU.Thus,the change in year t + 1 consumption of
electricity caused by a price change in year t is given by
This process can be carried out to year t +4,the final year of the
five-year period:
(7 .12)
(7 .15)
(7.13)
(7.14)
(7.16)
7.17
+ESR i t+4 1), ,
2+X ESR i ,t+2,1 +X ESRi,t+3~
4 3
=%6 Pitt •(X ESRi U +XES Ri ,t+1 ,1
=(X ESRit1 +ESR i t+1 1)•%6P itt,,
%60 i ,t+4,t
106Qi ,t+1,t =XMQiU +ESR i ,t+1,1 •%6P itt
%6Q i,t+2,1 =X%Oi t+1 1 +ESR i t+21 •%6P itt""
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which gives the percentage change in year t + 4 consumption resulting from the
price change %~Pit~in year t.Similar price changes occur in year
t +1 (%~Pi ,t+l,~),t + 2 (%~Pi ,t+2,~),t + 3 (%llP i ,t+3,~),and
t +4 (%~Pi,t+4,~),with equal percentage price changes assumed during each of
the five years.That is:
%~PiU =%~Pi,t+1,~=%~Pi,t+2,~=%~Pi,t+3,~=%~Pi,t+4,~=PCPEAik~(7.17)
(7 .18)
The impact of these individual price changes on consumption in year t + 4
can be derived in a manner similar to that used to obtain equation 7.16.The
sum of the impacts of the five annual price changes is given by equation 7.18:
UO;,t+4,l =PCPEA;k1 •(A 4
ESR;1:1
The combined total impact of the five annual price changes in t,t+l,t+2,
t+3, t+4,on consumption in period t+9 (period K+l)is given by
3 2+2).ESR i ,t+1,~+3).ESR i ,t+2,~
+4).ESR.t+3 n + 5 ESR i t+4 ~)
,"N "
Equation 7.18 accounts for price changes which occur between period K-1
and K;price changes which occurred before K-l also influence consumption in
period K,just as price changes in period t affect consumption in,for example,
period t +9:
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(7.19)
).ESRi,t+8,~+ESRi,t+9,~)
).5 ESR i t+4 ~+).4 ESR i t+5 ~
""
+ +
+•••+
7.18
Extending this analysis forward,combining terms,and rearranging,one
obtains the percentage change in any five-year period K as a function of
average annual price changes between K-l and K,K-2 and K-l,etc:
2+3:\ESR i ,K3 ,t +4;\ESR i ,K4 ,R.
+ S ESR i ,KS ,t}
Where the subscripts K1",K5 denote,respectively,the first year in the period
between K-1 and K,the second year in the period between K-l and K,etc.The
summation over past price changes takes into account that these price changes
persist:that once prices have increased,the increase and its effects are
permanent,until and unless future price decreases offset them.
5
%t:.Q i Id.=;\'Yo t:.Qi ,K-l,R.
+(L PCPEAim~
,('4 ESR 1 ,Kl,t +2;.3 ESR i ,K2,t
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5=;\'Yot:.Qi t +4 R.,,
+PCPEA 1kt (;.4 ESRi,t+S,t +2;.3 ESR i ,t+6,t
2+3;\ESR i ,t+7 ,R.+4;\ESR i ,t+8,R.
+SESR i ,t+9,t)
(7.20)
(7.21)
Equation 7.17 defines OPA i k t as the percentage adjustment to electricity, ,
consumption which must be made because of real electricity price changes.
Restated,
7.19
_ 5
->..OPA i ,K-l,t
+(I PCPEA imt )·(>..4 ESR i kl tm=l ' ,
(7.22)
3 2 ESR.+>..ESRi ,K2,t +>..1 ,K 3 ,t
+A ESR i ,K4,t +ESR j ,K5,t)
Similarly,price adjustment factors for oil and natural gas price changes can
be derived,with one simplification -the oil and gas cross-price elasticities
are constant.Thus,
5
PPA iKt =>..PPAi,K-l,t (7.23)
+(I PCPOA.mt)m=l 1
•OSR t
\I .
I
5GPAikt=>..GPA i K-l t, ,
(7.24)
+ (I PCPGA imt)
m=l
•GSRt
•(>..4 +2>..3 +3>..2 +4>..+5)
where OSRt is the short-run oil cross-price elasticity in sector t and GSRt is
the short-run gas cross-price elasticity in sector t.
7.20
All that remains is to attach values to ESR i ,Kj,t.In the MeT study,
short-run elasticities are defined by
ESR =a -b/P.(7.25)
Implementation of this requires calculating the average elasticity for a given
year Kj,so that
(7.26)
where Pi ,Kj-1,t is the price at the end of the year before Kj,and Pi,Kj,t is
the price at the end of year Kj.
7.21
Y
HS
SHU
NU
W
S
Yi
N
Pi
MT
LT
mpe
fce
x
ddh
ddc
Cr
Prm
y*
J
D
Z
R
H
E
PR
YH
A
U
M
HA
T
HT
GLOSSARY OF SYMBOLS
=income per household
=average family size
=single detached housing units (fraction of total)
=nonurban housing units (fraction of total)
=mean December temperature
=mean July temperature
=income per capita (67 dollars)
=population density
=energy price index relative to cpr (dollars per Btu)
=average temperature of warmest three months of year (OF)
=average temperature of coldest three months of year (OF)
=marginal price of electricity
=fixed charge for electricity
=total personal income
=heating degree days
=cooling degree days
=number of residential customers
=marginal price of electricity
=per capita personal income
=average July temperature
=heating degree days
=population per square mile
=percent rural population
=percent of housing units in single-unit structures
=number of housing units per capita
=average real price of residential electricity,in cents per kwh
=average real income per capita,in thousands of dollars
=index of real wholesale prices of selected electric appliances
=percentage of population living in rural areas
=percentage of housing units in multiunit structures
=average size of households
=time
=stock of occupied housing units
7.22
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C
T1
EU
U
gt-1
Xt
p
Yj
PE·J
Qit-1j
Y
P
PE
Qt-1
L
=average size of housing units
=the fraction of households with a particular type of equipment
=thermal performance of housing units
=average annual energy use for the type of equipment
=usage factor
=lagged personal consumption expenditure for electricity per capita
in 1958 dollars.
=total personal consumption expenditure per capita in 1958 dollars
=implicit deflator for electricity/implicit d~flator for peE (1958=100)
=value of retail sales
=average deflated price per KWH of electricity
=lagged per capita fuel consumption
=income per capita
=population
=price of electricity (mills per KWH)
=lagged demand in millions of KWH.
=long run
7.23
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8.0 THE PROGRAM-INDUCED CONSERVATION MODULE
The purpose of the Program-Induced Conservation Module is to account for
the electricity savings that can be obtained with a given set of consumer-
installed conservation technologies and government policies,together with the
associated costs of these savings.The peak demand or capacity savings of the
technologies set are calculated in the Peak Demand Module.
The module forecasts only those portions of conservation that are not
market-or price-induced.The module was developed as part of Battelle-
Northwest's Alaska Railbelt Electric Power Alternatives Study in 1981 and was
designed as a tool to enable the State of Alaska to analyze the impact of
potential large-scale conservation programs.The future of such programs in
Alaska is in doubt (Tillman 1983)and the data on the savings and costs of
existing programs are uncertain.The Program-Induced Conservation Mbdule was
not used in the 1983 updated forecasts,but a description of the module is
given below.
MECHANISM
The fuel price adjustments in the Residential Consumption and Business
Consumption ~~dules account for market-induced technology-related conservation
impacts,as well as reductions in appliances use and changes in the way in
which they are used.The Program-Induced Conservation Mbdul e analyzes
government attempts to intervene in the marketpl ace to induce conservati on vi a
loan programs,grants,or other policy actions.The module accounts for the
effects of this program-induced conservation on demands for electric energy and
generating capacity.
RED separates conserved energy into two parts:energy saved from the
actions of residential consumers and energy saved from reduced energy use in
the business and government sectors.Figure 8.1 provides a flow chart of the
process employed.
A separate,interactive program developed with RED (CONSER)is called by
RED to prepare a conservation data file.This file contains information on the
8.1
START
CONSER LOAD DATA FILE
).
SUM OVER
USES
•SAVINGS
o COSTS
ADJUST
REOUIRE',IE:-lTS
FOR SUBSIDIZED
CONSERVATION
CALCULATE
•SAVINGS
•COSTS
IN NEW AND EXISTING
USES
OUTPUTS
.ELECTRICITY SAVEO
.COST OF SAVINGS
.PEAK CORREC TION
FACTOR
SUM OVER
OPTIONS
•SAVINGS
o COSTS
RESIDENTIAL
REOUIREMENTS
(RESIDENTIAL
MODULE)
ADJUST
REQUIREMENTS
FOR SUBSIDIZED
CONSERVATION
TECHNICAL INPUT
oPEAK CORRECTION
oFAC TOR (PCF)
TECHNICAL INPUT
o UNSUBSIDIZED
INSTALLED COST
GO TO NEXT
CONSERVATION
OPTION
TECHNICAL INPUTS
o SUBSIDIZED
INSTALLED COST
oO&M COST
TECHNICAL INPUTS
oELECTRICITY SAVED
oLIFETIME
o ELECTRICITY
PRICES
.TECHNICAL INPUTS
o MAXIMUM
SATURATION
o PAYBACK RULE
BUSINESS INPUTS
(NEW tEXISTING USES)
·I'OTENTIAL SAVINGS
.PROPOR T ION SAVED
·PEAK CORRECTION
FACTOR
.COST OF SAVINGSI
MWH
NO
SELECT
RESIDENTIAL
CONSERVATION
OPTION
CONSERVATION
DATA FILE
WRITE
o SATURATION
o PCF
TO CONSERVATION
FILE
FIGURE 8.1.RED Program-Induced Conservation Module
sector,CONSER queries
to ten options may be
costs,energy savings,
installed conservation
user for the technical
and the level of market acceptance of
options.For the residential
parameters 0 f eac h option (u p
various conSlBller-
the
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included).Based on a user-supplied forecast of electricity prices and the
costs associated with each option,CONSER calculates the internal rate of
return on each technology.The user compares this rate to a bank passbook
savings rate as a very loose minimum test of acceptability.If the user
decides,based on this comparison,that the option should be included in the
analysis,CONSER calculates the payback period for each option.CONSER then
writes the default values and range of values for the option's market-
saturation rate to an output data file.The user is then queried for the
market saturation of electricity in the use that the conservation option
offsets (e.g.,electric water heating).Thi s market saturation is also written
to the output data file.
Government residential conservation programs primarily reduce the
effect i ve purchase pri ce of conservat ion opti on s to the cons ume r.Therefore,
CONSER next requests the user's estimate of consumer purchase and installation
costs for each option with and without government subsidization.,The
saturation of each technology with and without subsidization is calculated and
is written to the output data file.
For the business sector,CONSER requests the potential proportion of
predicted electricity use that might be saved through conservation,the
estimated proportion of these potential conservation savings that are realized,
and the costs per kWh for conservation savings in existing and new buildings.
These values are also written to the output data file,which now becomes an
input data fil e for the Conservation f'<bdul e.
RED uses the residential conservation information in the CONSER data file
to account for the impacts of the conservation technologies under
consideration.First,the amounts of conservation occurring in the residential
sector with and without government subsidization are calculated by multiplying
together the electric use saturation rate,the conservation saturation rate,
and the number of households.Next,the level of program-induced conservation
is calculated by subtracting the nonsubsidized conservation savings from the
subsidized figure.Finally,this figure is subtracted from the price-adjusted
residential requirements to derive the utilities'total residential sales.
8.3
The business conservation calculation separately addresses the sales to
new and existing uses,and two potential pools of electricity savings are
calculated.For simplicity,existing uses are defined as the previous forecast
periods'electricity requirements,whereas new uses are defined as the
difference between the previous period's requirements and the current period's
requirements.The two potential pools of savings are the sales to new uses and
retrofits times user-supplied potential savings rates (for new uses and
retrofits).The predicted 1evel of savi ngs in each case is found by
multiplying the potential pools of savings times user-supplied conservation
saturations with and without government intervention.Finally,the total
program-induced savings are derived by subtracting the savings without
government intervention from sales with government intervention for both new
and existing uses.Total price adjusted requirements,minus program-induced
business conservation,equals utilities'total sales to business.
The economi c costs of the resi denti a1 conservati on technology package are
found by multiplying together the government subsidized conservation saturation
rate,the electric saturation rate,the number of households,and the cost to
consumers per installation without government intervention for each
conservation option,and summing over options.For the economic costs of
business conservation,the total megawatt hours saved by government-subsidized
conservation is multiplied by the cost per megawatt hour saved.
Finally,the Conservation MOdule helps calculate the effect of
conservation on peak demand.Unfortunately,not all conservation technologies
can be given credit for displacing the demand for peak generating capacity.
Therefore,CONSER queries the user for a peak correction factor,a variable
that takes on a value between zero and one if the option receives credit for
producing some portion of its energy savings during the peak demand period;
otherwise the value is zero.These peak correction factors for each option are
aggregated in RED.First,they are weighted by the proportion of total
program-induced electricity savings each option represents during a given
forecast period.Next,the weighted correction factors are summed together.
The resulting aggregated peak correction factor is sent to the peak demand
model to calculate the peak savings of the set of conservation technologies.
8.4
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INPUTS AND OUTPUTS
The inputs and outputs of the Program-Induced Conservation Module are
summarized in Table 8.1.The potential market for the conservation option is
de.fined by the total number of households served (HHS)and the saturation of
the electrical devices (ESAT)whose use of electricity can be displaced by
investment in a particular conservation option.ESAT equals the total market
saturation of the appliance times the fuel mode split.The total number of
households served is calculated in the housing module,while ESAT is
interactively entered by the user.RCSAT,the penetration of the potential
market by the conservation technology,is determined within the CONSER
parameter routine.The technical energy savings and the costs of residential
conservation devices (both installation and mainten~nce)are interactively
specified within CONSER by the user.
The business segments of CONSER also que~the user for the potential and
actual saturations of electricity conservation in the business sector and the
costs per megawatt hour saved for business investments in conservation.
Finally,the correction factors are decimal fractions that are
interactively supplied by the user to CONSER and that reflect the extent to
which conservation options receive credit for peak savings.
The outputs of the Program-Induced Conservation Module are the final
electricity sales to the business and residential sectors,and the electricity
savings of the conservation technology set considered in a given run of the RED
model.
MODULE STRUCTURE
The price adjustment mechanisms used in the Business and Residential
Consumption Modules employ price elasticities derived from studies that did not
I
distinguish among the impacts of conservation technologies and other effects of
energy price changes.Since conservation of electricity is argued to be
induced either by energy price changes or by market intervention designed to
encourage conservation,the treatment of conservation in RED was cautiously
developed to eliminate the possibility of double counting energy savings and
costs.
8.5
TABLE 8.1.
a'Inouts
Inputs and Outputs of the Conservation Module
SYmbol
HHS
TECH
COST!
COSTO
RCSAT
ESAT
PRES
RESCON
CF
PPES
BCSAT
COST'
BUSCON
b)Outouts
Symbol
TCONSAV
TCONCOST
ADRESCON
ADBUSCON
ACF
Name
Total households served
Technical energy savings
Installation and purchase cost
of the residential conservation
device
Ooeration and maintenance costs
of the residential conservation
devi ce
Residential saturation of the
device (with and without govern-
ment intervention)
Residential electric use
saturati on
Expected residential electri-
city price
Price-adjusted residential
consumption
Peak correction factor
Potential proportion of elec-
tricity saved in business in
new and retrofit uses
Business conservation saturation
rate (with and without govern-
ment intervention)
Cost per megawatt hour saved
in busi ness
Business price-adjusted
consumption
Name
Total electricity saved
(business plus residential)
Total cost of conservation
(business plus residential)
Adjusted residential consumption
Adjusted business consumption
Aggregate peak correction factor
8.6
From
Residential Module
CONSER,Interactive Input
CONSER,Interactive Input
CONSER,Interactive Input
CONSER,Interactive Input
CONSER,Interactive Input
CONSER,Interactive Input
Residential Module
CONSER,Interactive Input
CONSER.Interactive Input
CONSER,Interactive Input·
Uncertainty Module
CONSER,Interactive Input
Business Module
To
Report
Report
Miscellaneous and Peak
Demand Modules
Miscellaneous and Peak
Demand Modules
Peak Demand Mode 1
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In RED's formulation,the Program-Induced Conservation Module serves
primarily as an accounting mechanism that tracks the impacts of a given set of
technology options in the residential sector and the aggregate level of
conservation in the business sector.However,since government policies and
r
programs could have a significant,direct impact upon the level of conservation
adopted,and since the incremental impacts of these actions are not
incorporated in the price adjustment process of the Residential and Business
Consumption Modules,the Program-Induced Conservation ~bdule explicitly
calculates these impacts and accordingly adjusts the forecasted sales to
cons umers.
Scenario Preparation (CONSER Program)
The calculations of the Conservation Module require scenarios of the
saturation of conservation options,the expected electricity savings,and their
associated costs.To reduce the amount of data entry in scenario preparation
and to facilitate the use of a broad set of conservation technologies and
government policy options,a separate program (CONSER)queries the user for
information necessary to calculate the saturations,savings,and costs.These
parameters are then written to a data file where they can be accessed by the
remainder of the Conservation ttldul e.Two steps are required:1)determining
if an option will achieve market acceptance;and 2)calculating market
saturations for options gaining acceptance.
The first step is to determine whether a specific conservation option will
achieve market acceptance.For the residential sector,the way RED identifies
acceptable options is to compare them with other investments available to the
consumer.Conservation is an investment with a financial yield that can be
calculated and compared with other investment options.By comparing the
internal rate-of-return (IRR)of a conservation option with the market rate of
interest,one can determine whether conservation options'return is sufficient
to encourage market acceptance.
The market rate of interest to which RED compares the internal rate-of-
return is the standard commercial bank passbook interest rate.Passbook
accounts have several characteristics:
1.They are virtually risk free.
2.They are extremely liquid.
8.7
,The IRR is calculated with the following formula:
3.They have trivial requirements as to the size of the initial deposit.
4.They are readily available to everyone.
The value of electricity savings is based on the energy prices the consumer
expects.It is calculated by querying the user for price forecasts and the
electricity savings (in kWh)for each option and multiplying:
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(8.1)_
(8.2)
8.8
=dollars per kWh in load center i
=annual kWh savings in region i per installation of device k.
PRESi
TECH ik
Investments in conservation technologies,however,are characterized by
the following:
1.risky
2.difficult to liquidate
3.(sometimes)require a large initial payment.
where
T =lifetime of the device (maximum of 30 years)
p =internal rate-of-return
t =subscript for the year.Takes on values 1 to 30
ES =value of electricity saved
C =total cost of the option in the year
=subscript for the load center
k =subscript for the option
These factors would cause most homeowner-investors to require a higher rate of
return on conservation than those on passbook accounts to invest in
conservation.Therefore,a conservation option can pass the internal rate market
interest test even though it might not be adopted.Such a comparison insures that
every option that could achieve market acceptance is included in the portfolio of
conservation technologies to be considered.
where
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The cost (Citk)is the 1980 dollar installation and purchase cost in the year
the device is purchased and the annual maintenance and operating 1980 dollar
costs in all remaining periods.
Recognizing that initial cost is a major barrier to conservation,the
Congress has provided incentives for in~ividuals to install energy-conserving
equipment.Furthermore,the State of Alaska has also instituted several
programs aimed to promote installation of conservation equipment.Because the
main impact of these programs is to reduce the initial cost of conservation,
CONSER uses the subsidized installation and purchase costs of the device to
forecast whether a device will achieve additional market acceptance over an
unsubsidized case.
As previously stated,CONSER requests the expected electricity price
forecast for each year,the operating and maintenance costs,the kWh savings
and the government subsidized purchase and installation costs of the device for
each region.CONSER calculates the internal rate of return of the option,
prints this information,and asks the user if the option is to be used.If it
is,then the unsubsidized costs of purchasing and installing the option are
also requested.
If the scenario to be considered does not include government intervention,
the installation and purchase costs entered for the subsidized and unsubsidized
cases should be the same (and equal to the unsubsidized costs).
The next step of scenario preparation is to determine the market
saturation rat~of each conservation option.RED employs a payback decision
rule to determine the default value and the range of the conservation
saturation rate.Since the expected value of electricity savings probably is
not constant across time,the payback period is calculated by dividing the
installation and purchase costs by the cumulative net value of electricity
savings (value of energy savings minus operating and maintenance costs),
starting with the first year and continuing until the ratio is less than one.
The number of years required to drive the ratio to less than one is the payback
period.
The payback period is calculated for both the subsidized and nonsubsi-
dized cases.Since the subsidized case usually will have lower installation
8.9
and purchase costs,the payback periods for the subsidized case will usually be
lower and the conservation saturation rates will usually be higher.
CONSER also requests the name of the conservation option,a forecast of
the market saturation rates for electric devices from which the option
displaces consumption,and the peak correction factor for each conservation
option.The saturation of electric devices is used within the Conservation
Module to define the potential market of the conservation option,whereas the
peak correction factor indicates the extent to which the option dis~aces
electricity consllTIption at the peak.This information,as well as the costs
and saturation of the conservation option (for the unsubsidized and subsidized
cases),is written to a data file for later access by the remainder of the
Program-Induced Conservation Module.
Funding constraints in the Railbelt Alternatives Study prohibited the
development of detailed cost and performance data for business conservation
applications.CONSER,therefore,requires the user to provide the following
for both new and retrofit uses:the potential proportion of electricity that
conservation technology can displace and an estimate of the proportion of those
potential savings actually realized for subsidized and unsubsidized cases.
CONSER also requests the cost per megawatt hour saved for both cases and the
peak correction factor for new and retrofit uses.
This business sector information is also written to CONSERls output data
file.By running CONSER with several different technology packages and
government policy packages,conservation scenario files can be easily
constructed for later analysis within RED.
Residential Conservation
Using the information from the data file that CONSER creates,the
calculation of electricity saved by the set of technologies is
straightforward.By multiplying the electric device saturation and the
incremental nunber of households served,the total nunber of potential
applications of the conservation device is found.The incremental number of
households served in the first forecast period (1980)is zero,since the
current consumption rates already include the current level of conservation.
8.10
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By next multiplying the potential number of uses by the savings per
installation and the saturation of the conservation option,the amount of
electricity saved is derived:
CONSAV itkj =RCSATikj x TECHik x
(ESAT itk x HHSit -ESAT i (t_1)k x HHS i (t_1)(8.3)
where
CONSAV =electricity saved (kWh)
RCSAT =conservation saturation rate
TECH =electricity savings per installation (kWh)
ESAT =electric device saturation rates
HHS =total households served
t =denotes the forecast period (1,2,3,•••,7)
j =denotes subsidized (j=1)or nonsubsidized (j=o).
The total electricity displaced through the residential conservation set
considered is found by summing across the options (subscript k):
K
RCONSAV it1 =k:1 CONSAV itk1 (8.4)
where
RCONSAV =residential electricity conserved (kWh)
K=total number of residential options considered.
Since the price adjustment mechanism does not account for government-
induced conservation,the model next adjusts residential sales by the
incremental conservation attributable to government programs:
ADRESCON it =RESCON it -(RCONSAV it1 -RCONSAV;to)(8.5)
where
ADRESCON =final electricity requirements of residential consumers
RESCON =price-adjusted residential consumption.
8.11
The electrical device saturation and the incremental number of households
define the nUllber of potential applications.The cost of purchasing and
installing the option is calculated by multiplying the potential number of new
uses by COSTI (the installation and purchase costs per option).Next,by
multiplying COSTO .(annual operations and maintenance costs per option)by the
cumulation of previous forecast periods'potential uses,the operating and
maintenance costs are found.Finally,by summing all these components,the
total annual costs associated with conservation savings in a given forecast
period can be found.During any forecast year,the annual costs are equal to
one year's total installation costs,plus operating costs associated with all
previous additions to stock:
conservation option
h =forecast period subscript.Can take on values 1 to t.
By summing over the options,the total costs of the residential conservation
set is found.
where
CONCOST itkj =[COSTI ikj x RCSAT itkj x (ESATitk x HHS it -
ESATi(t_l)k x HHSl(t_l))/S +COSTOik x ~:lRCSATikj x
(ESAT ihkj x HHS ih -ESAT ihkj x THHS ,(h-l))]
where
CONCOST =the option 's total annual cost
COSTI =unit cost in 1980 doll ars for purchasing and installing the
conservation option
COSTO =unit cost in 1980 dollars of operating and maintaining the
K
RCONCOST,'tJ'=E CONCOST'tk'
k=l 'J
RCONCOST =present value of the total costs of the set of
residential conservation options.
8.12
(8.6 )
(8.7)
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Business Conservation
The total costs of conservation are the unsubsidized total costs
(RCONCOST ito )'consumers pay the subsidized costs (RCONSAV it1 ),and government
pays the difference (RCONCOSTito -RCONCOST it1 ).
For business conservation impacts,funding constraints prohibited
collection of detailed cost and performance data.Fortunately,a 1 imited
number of studies have estimated the potential energy savings and associated
costs for aggregate conservation investments in new and existing buildings.
RED separates the conservation impacts for the business sector into two
parts:those arising from retrofitting existing buildings,and those arising
from incorporating conservation technologies in new construction.As in the
residential segment of the Program-Induced Conservation Module,the potential
pool of electricity that can be displaced must be identified for both new
construction and retrofits.Thi s "poo l"is determined by the state of
conservation technology and is supplied to the conservation module from the
CONSER output file.The actual amount of conservation that occurs depends upon
the price of electricity and competing fuels and upon the cost and performance
characteristics of the options available.This is also supplied by CONSER.
In RED,the potential pool of displaced electricity for businesses is
derived by first separating business sales into sales to existing structures
and sales to new structures.For simplicity,the change from the previous
periods'business requirements as calculated by the Business Consumption ttJdule
is assumed to be the sales to new buildings:
(8.8 )
8.13
SALNBit =BUSCON it -BUSCON i (t_1)
SALNB =sales to new buildings
BUSCON =business consumption prior to conservation adjustments.
Therefore,the sales to existing buildings are the sales in the previous
period:
where
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SALEXit =BUSCONi(t_l)(8.9)
SALEX =sales to existing buildings.
To find the potential pool of electricity use displaced through retrofits and
.incorporation of conservation options in new buildings,the Program-Induced
Conservati on Modul e multi pl i es the di saggregated sal es fi gures times the
potential percentage of electricity saved in new and retrofit buildings:
where
POTNB it =SALNB it x PPES itN
POTEXit =SALEX it x PRES itE
(8.10a)
(8.1 Ob)
POTNB =potential amount of displaced electricity in new buildings
PPES =proportion of electricity that technically can be displaced via
retrofit or incorporation of conservation options in new
buildings.I
POTEX =potential amount of displaced electricity in existing buildings
E =subscript for existing buildings I·
N =subscript for new buildings.
These figures,however,only provide the technically feasible amount of I
electricity that could be displaced.Market forces determine what level of the
potential electricity savings will be achieved.I
In the residential segment of the Program-Induced Conservation Module,RED
used an internal rate-of-return test and a payback period decision rule to I
determine first,whether an option would achieve market acceptance,and second,
what level of acceptance it would achieve.As mentioned above,the information
available for business conservation does not permit such an analysis.
Therefore,the model user is required to assume a level of potential market
saturation.The saturation rates (one for retrofits,one for new buildings)
must reflect the prices of fuels (including electricity),the costs of the
package of options employed,and the electricity savings expected for
subsidized and nonsubsidized cases.
8.14
The saturation rates are obtained from the data file CONSER creates.The
displaced electricity can be found by multiplying the total saturation rates by
the total potential pool of electricity savings:
where
BCONSAV itNj =BCSATitN x POTNBitj
BCONSAVitEj =BCSATitE x POTEX itj
(8.l1a)
(8.l1b)
BCONSAV =electricity savings
BCSAT =saturation rate for conservation options in business.
As in the residential sector,the business requirements must be adjusted
for the incremental impact of government programs:
where
ADBUSCON it =BUSCON it -(BCONSAV itN1 -BCONSAV itNo )
-(BCONSAV itE1 -BCONSAV itEo )
(8 .12)
ADBUSCON =adjusted business consumption.
The total cost of the conservation set in a given future forecast year is
given by multiplying the 1980 dollar cost per megawatt-hour saved by the
conservation savings in each use:
where
BCONCOSTitj -(BCONSAV itEj x COST iEj +BCONSAV itN1 )
BCONCOST =business conservation costs,future forecast year
COST =1980 dollar costs per megawatt hour saved •.
(8.13)
The total costs of the conservation in a future forecast year to "soc iety"is
the nonsubsidized costs (BCONCOST ito )'whereas the value of the subsidy in that
year is (BCONCOSTito -BCONCOST it1 ),and businesses bear only the subsidized
costs (BCONCOST it1 ).
8.15
Peak Correction Factors
The last item to be calculated is the aggregate peak correction factor for
the incremental impact of government conservation programs on peak demand.
This factor is calculated by weighting each option's peak correction factor by
the option's proportion of incremental conservation:
K (CONSAV itk1 -CONSAV itko ) x CF k
ACF it =k:1 (RCONSAV it1 -RCONSAV ito )+(BCONSAV it1 -BCONSAV ito )
(BCONSAV itE1 -BCONSAV itEo ) x CF E +(BCONSAV itN1 -BCONSAV itNo ) x CF N
+(RCONSAV it1 -RCONSAV ito )+(BCONSAV it1 -BCONSAV ito )
where
(8.14)
ACF =aggregate peak correction factor
CF =option-specific peak correction factor,equal to the proportion
of the electrical demand of displaced appliances that can be
displaced at the peak demand period of the year (e.g.,January).
PARAMETERS
One of the requirements of the Alaska state program whereby homeowners
request state money to install conservation measures is that the payback period
for the measure be less than seven years.Therefore,if a conservation
option's payback period is assumed to be greater than seven years,the options
market penetration will be very limited,effectively zero.However,if the
option pays for itself within the first year,then the option would penetrate
the entire potential market immediately.The relationship between payback
period and penetration rate for payback periods between zero and seven years is
assumed to be linear.A range of 15%on these values is arbitrarily assumed •.
Table 8.2 presents these market penetration parameters.
8.16
TABLE 8.2.Payback Periods and Assumed Market Saturation
Rates for Residential Conservation Options
Payback
Period
(years)
a
1
2
3
4
5
6
7
8
As sumed
Saturation
(%)
100.0
87.5
75.0
62.5
50 .0
37.5
25.0
12.5
a
Assumed
Range
(%)
80-95
67 .5-82.5
55-70
42.5-57 .5
30-45
17 .5-32.5
5-20
0-5
Source:Author Assumption
8.17
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9.0 THE MISCELLANEOUS MODULE
MECHANISM
The Miscellaneous Module uses outputs from several other modules to
forecast electricity used but not accounted for in the other modules,namely,
street lighting,second homes,and vacant housing.
INPUTS AND OUTPUTS
This module uses the forecasts of electrical requirements of the residen-
tial and business sectors and the vacant housing stock.The only output is
miscellaneous requirements.Table 9.1 provides a summary of the inputs and
outputs of thi s modul e.
TABLE 9.1.Inputs and Outputs of the Miscellaneous Module
a)Inputs
Symbol
ADBUSCON
ADRESCON
VACHG
b)Outputs
Symbol
MISCON
MODULE STRUCTURE
Name
Adjusted Business Requirements
Adjusted Residential Requirements
Vacant Housing
Name
Miscellaneous Requirements
From
Program-Induced
ConservatiDn Module
Program-Induced
Conservation Module
Housi ng rvbdul e
To
Peak Demand rvbdule
Figure 9.1 provides a flowchart of this module.For street lighting,the
requi rement s are ass umed to be a constant proportion 0 f conservat ion-adjusted
business and residential requirements:
SRit =sl x (ADBUSCON it +ADRESCON it )
9.1
(9.1)
RESIDENTIAL
PLUS
BUSINESS
CONSUMPTION
.-•
CALCULATE CALCULATE CALCULATE
SECOND HOME STREET LIGHTING VACANT HOUSING
CONSUMPTION REQUIREMENTS CONSUMPTION
~
SUM FOR
MISCELLANEOUS
CONSUMPTION
~
I MISCELLANEOUS
CONSUMPTION I .
FIGURE 9.1.RED Miscellaneous ~1odu1e
where
SR =
ADBUSCON =
ADRESCON =
i =
t =
sl =
street lighting requirements
business requirements after adjustment for the incremental
conservation investments
final electricity requirements of residential consumers
subscript for load center
forecast period (1,2,3 •••,7)
street lighting parameter.
i .
For second-home consumption,RED cal cu1 ates the number of second homes as
a fixed proportion of the total number of households.A fixed consumption
factor is then applied:
SHR it =sh x CHH it x shkWh
9.2
(9.2)
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where
VHR =vacant housing requirements
VACHG =number of vacant houses
vh =assumed consumption per vacant dwelling unit.
Total miscellaneous requirements are found by summing the three components
above:
SHR =second home requirements
CHH =total number of civilian households
sh =proportion of total households having a second home
shkWh =consumption factor.
Finally,the use of electricity by vacant housing is a fixed consumption
factor times the number of vacant houses:!
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where
VHR it =vh x VACHG it (9.3)
MISCON it =SR it +SHR it +VHR it
where
MISCON =miscellaneous electricity consumption.
PARAMETERS
(9.4)
Table 9.2 gives the parameter values used for the Miscellaneous Module.
These parameters are all based on the authors'assumption because no other
source of information is available.Tillman (1983)found that Anchorage
Municipal Power and Light has a conservation program in place to convert city
street lights from mercury vapor lamps to high-pressure sodium lamps,resulting
in some savings of electric energy.This is considered to be a one-shot
success whose total impact grows proportionately to street lighting demand.
Even since this program was instituted,miscellaneous demand has continued
to grow.It is assumed that the effects of additional requirements for
street lighting will partially offset the effect of conservation,and that
9.3
TABLE 9.2.Parameters for the Miscellaneous Module
this component of miscellaneous demand will continue to be about proportional
to residential and business use in the future.
(a)1980 ratio of street lighting to business plus residential sales.
(b)O.Scott Goldsmith,ISER,personal communication.
(c)Author assumption.Reflects reduced level of use of all
appl iances.
Symbol
Sl
sh
shkWh
Vh
Name
Street lighting(a)
Proportion of households having a second home(b)
Per.unit second-home consumption(b)
Consumption in vacant housing(c)
Value
0.01
0.025
500 kWh
300 kWh
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10.0 LARGE INDUSTRIAL DEMAND
Large industrial demand for electricity in the RED model is not provided
by the model itself;rather,the model provides for a data file called EXTRA
OAT,which is read by the program each time a forecast is made.The model user
supplies a "most likely"default value forecast of electricity energy and
demand at system peak to the EXTRA OAT file for each load center he wishes to
include in the model run.If he wishes to develop a t'-bnte Carlo forecast,he
must also supply forecasts for higher and lower probability conditions.These
exogenous estimates can be assembled from any source;however,they should be
consistent with the economic scenario used in any given model forecast.This
was done for the 1983 update.
The EXTRA OAT data set has other uses.Although military demand for
electricity in the Railbelt historically has been self-supplied,the model user
could test the effect of military demand on utility sales or total Railbelt
demand by adding military annual energy and peak to the exogenous forecast for
each load center•.self-supplied industrial energy can be handled in a similar
fashion.Finally,EXTRA OAT can be used to account for cogeneration of
electricity and for utility load management.The model user only needs to
estimate the effect of such projects for 1980,1985,1990,etc.on annual
energy sales and load at the time of year when the electrical system peak load
occurs.He then subtracts these estimates from his estimates of large indus-
trial (plus military)annual energy and demand at system peak and enters the
difference in EXTRA OAT for each forecast period and load center.This data
file will accept negative numbers showing net conservation.Other types of
conservation or demand that cannot be analyzed in detail in other sectors of
the model can also be handled here.Examples might include agricultural and
transportation demand for electricity or the impacts of district heating
systems on electrical consumption.
MECHANISM,STRUCTURE,INPUTS AND OUTPUTS
The user supplies data for the file EXTRA OAT for each load center and
forecast period on net total industrial,military,agricultural,transportation
10.1
·annual energy demand at system peak (net of cogeneration effects)for each load
center for cumulative probabilities of 0.75,0.5 (default value),and 0.25 that
demand will be greater than or equal to the value specified.The model then
adds these estimates to the appropriate reports in the forecast results.
Inputs and outputs are identical.Outputs are supplied to the Peak Module (to
calculate system peak demand)and to the report writing routines.
PARAr1ETERS
There are no parameters in the RED model large industrial demand
calculations.
10.2
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11.0 THE PEAK DEt1AND MODULE
Up to this point,only the method to forecast the total amount of electri-
city demanded in a year has been considered.However,for capacity planning,
the maximum 'amount of electricity demanded (or peak demand)is probably more
important.Peak demand defines the highest rate of consumption of electric
energy during the year.As identified in RED,it does not include losses of
energy in transmission.
MECHANISM
Unlike the Lower 48,where utilities frequently have done extensive cus-
tomer time-of-day metering and other analyses to estimate peak demand by
customer type and end use,the Railbe1t utilities have virtually no information
on peak demand by type of customer and end use.Consequently,the RED model
does not forecast peak demand by end use;instead the Peak Demand Module uses
regional load factors to forecast peak demand.The load factor is the average
demand for capacity throughout the year divided by the peak demand for capacity
in the year.RED first calculates the peak demand without the peak savings of
program-induced conservation.Next,the peak savings of the incremental pro-
gram-induced conservation are calculated,taking into account the mix of con-
servation technologies being considered.Finally,by netting out the peak
savings,RED calculates the peak demand the system must meet.
INPUTS AND OUTPUTS
Table 11.1 provides a summary of the inputs and outputs of the Peak Demand
Module.The load factors (LF)are generated by the Uncertainty tIodu1e,whereas
the aggregite peak correction factor (ACF)comes from the Conservation
Module.The business,residential,and miscellaneous requirements (BUSCON,
RESCON,and MISCON)come from the Business,Residential,and Miscellaneous
Modules,whereas the conservation-adjusted requirements (ADRESCON and ADBUSCON)
come from the Conservation r1odu1e.The outputs of this module are 1)the peak
demand in each regional load center at the point of sale to final users,and
2)the incremental peak savings of subsidized conservation.
11.1
MODULE STRUCTURE
Figure 11.1 provides a flow chart of this module.First,the peak demand
without subsidized conservation is calculated.This is done by dividing the
total electricity requirements in each region by the product of the load factor
times the number of hours in the year.Next,the same operation is performed
using energy requirements adjusted for the energy savings resulting from sub-
sidized conservation investments.This yields the prel iminary peak savings.
RED then adjusts the peak savings by multiplying the aggregate peak correction
factor times the peak savings.The corrected peak savings are then subtracted
from the peak demand calculated in the first step to derive the regional peak
demand at the point of sale.
TABLE 11.1.Inputs and Outputs of the Peak Demand Module
Incremental peak savings Report
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From
Residential
Cons umpt i on MJdul e
Uncerta i nty ~dul e
Name
Regional load factor
Residential requirements prior to
adjustment for subsidized conservation
Aggregate peak correction factor Conservation Module
Business requirements prior to adjustment Rusiness
for subsidized conservation Consumption MJdule
Name To
Peak demand Report
Business requirements adjusted for sub-Conservation Module
sidized'conservation
Residential requirements adjusted for Conservation Module
subsidized conservation
RESCON
a)Inputs
Symbol
LF
BUSCON
ADRESCON
ADBUSCON
ACF
b)Outputs
Symbol
FPD
PS
The first step is to calculate the total electricity requirements without
subsidized conservation by adding the residential,business,and miscellaneous
requirements:
11.2
LOAD
FACTORS
(FROM UNCERTAINTY
MODULE)
•ANNUAL SAVINGS
DUE TO SUBSIDY
•PEAK CORRECTION
FACTOR
(FROM CONSERVATION
MODULE)
CALCULATE
PEAK
SAVINGS
CALCULATE
PRELIMINARY
PEAK DEMAND
ANNUAL ELECTRICITY
REQUIREMENTS
•RESIDENTIAL
•BUSINESS
•MISCELLANEOUS
LARGE
INDUSTRIAL
DEMAND
PEAK
DEMAND
FIGURE 11.1.RED Peak Demand Module
(11.1)TOTREQB it =BUSCON it +RESCON it +MISCON it
11.3
where
TOTREQB =
BUSCON =
RESCON =
~1I SCON =
i =
t =
total electricity requirements before conservation adjustment
(MWh)
business requirements before conservation adjustment (MWh)
residential requirements before conservation adjustment (MWh)
miscellaneous requirements (MWh)
index for the load center
index for forecast period (t =1,2,•••,7).
Next,the Peak Demand Mbdule calculates the peak demand without accounting
for the incremental conservation due to subsidized investments in conservation
by applying the load factor:
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TOTREQB it
LF it x 8760 (11.2)
where
PO =peak demand (MW)
LF =load facto r
8760 =number of hours in a year
p =index denoting prel iminary.
To calculate the peak savings due to subsidized conservation investments,
RED first must find the incremental number of megawatt hours saved:
TOTREQSit =BUSCON it -AOBUSCON it +RESCON it -AORESCON it (11.3)
11.4
TOTREQS =incremental megawatt hours saved by subsidized conservation
i nves tments
AOBUSCON =business requirements after adjustment for the incremental
impact of subsidized conservation
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(11.4)
(11.5)
TOTREQS·t
PS it =ACF it x LF it x 8760
PS =peak savings (MW)
ACF =aggregate peak correction factor.
where
where
AORESCON =residential requirements after adjustment for the incremental
impact of subsidized conservation.
Next,peak savings are found by multiplying the incremental electricity
saved by the aggregate peak correction factor and applying the load factor:
Finally,by subtracting the peak savings from the preliminary peak demand,
the final peak demand for each region is derived:
where
11.5
TABLE 11.2.Assumed Load Factors for Rai1belt Load Centers
PARAMETERS
FPD =index denoting final peak demand.
Load Facto r (%)
Defaul t Range
55.73 49.2-63.4
50.00 41.6-59.1
Load Center
Anchorage
Fai rbanks
The only parameters in the Peak Demand Module are the system load factors
assumed for the Anchorage and Fairbanks load centers.These load factors are
shown in Table 11.2.
Simple trend-line fitting and more complex ARIMA time series modeling were
used in an attempt to develop quantitative forecasts for future load factors
for the Anchorage and Fairbanks load centers.A qualitative analysis was also
In the REO model,peak electricity demands are estimated as a function of
the seasonal load factors (average energy demands/peak energy demands)for the
major load centers in the Railbelt.Thus,identification of appropriate load
factors is crucial in determining the need for peak generating capacity for a
given amount of forecasted electrical energy demand.
Forecasting future load factors and thus,peak electrical energy demands,
is a difficult process because of the interaction among many factors that
determine the relationship between peak and average electrical demands.The
analysis conducted in support of the parameter estimates in Table 11.2 quanti-
tatively and qualitatively evaluated annual load factors for the Anchorage and
Fairbanks load centers.The impacts of the diversity between the two load
centers in the timing of the occurrence of peak loads is also briefly discussed
below.
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conducted of the impacts of conservqtion programs,changes in customer mix,and
other variables as they may affect future load factors for the two load
centers.
The central conclusion arlSlng from the analysis is that no scientifically
defensible basis for projecting that future load factors for the Anchorage and
Fairbanks areas will either increase or decrease could be developed within the
resources of the study.(a)Thus,average load factors for the period 1970-1981
of 0.56 for Anchorage and 0.50 for Fairbanks were used as default values in
developing peak demand estimates.Historic minimum and maximum values of the
load factors of individual utilities in each load center were examined.The
lowest and highest of these in each load center were used as the minimum and
maximum load factor values for the load center.
Quantitative Analysis of Trends in Load Factors in the Railbelt
Trend analysis is not a preferred approach to forecasting future electri-
cal load factors and peak loads in the Railbelt.Ideally,the methodology for
forecasting future load factors over a long-range planning horizon (in RED,
30 years is the planning horizon)should incorporate information on structural
variables that determine the load factor.Examples of such structural vari-
ables are the forecasted demands of different customer classes (i.e.,residen-
tial,commercial,and industrial)and the forecasted patterns and saturation
rates of appliances.
Developing a structural econometric model of load factors and/or peak
loads is a complex task.In addition,while Anchorage Municipal Light and
Power has conducted very 1imited metering of residential sector customers,in
general there is no data base in Alaska that associates patterns of residential
electrical use with appliance stock and socioeconomic characteristics.Even
less data are available on the commercial sector.Thus,the data necessary for
building a structural time-of-use model are not available for the Railbelt
(a)This is consistent with Anchorage Municipal Light and Power findings of no
trend in load factor (personal communication,Max Foster,AMLP economist,
to Mi ke Ki ng,June 11,1981).
11.6
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area.Thus,in this study,quantitative analysis of Anchorage and Fairbanks
load factors was 1 imited to trend analysis.
Simple Trend Analysis
Table 11.3 presents estimates of the annual load factors for areas
approximating the Anchorage and Fairbanks service areas and the month in which
the peak load occurred in the period 1970-1981.The load factors presented in
Table 11.3 were estimated by the following equation:
REG
PMW*8.76
where
REG =regional energy generation for Anchorage or Fairbanks areas in
gigawatt hours
PMW =largest monthly peak regional energy demand for Anchorage or
Fairbanks areas in megawatts.
TABLE 11.3.Computed Load Factors and r10nth of peCJk)Load Occurrence
for Anchorage and Fa;rbanks 1970-1981l a
Anchorage Fa i rbank s
Year Load Factor Peak Load MJnth Load Factor Peak Load t1Jnt h
1970 0.524 December 0.445 December
1971 0.575 January 0.443 December
1972 0.562 December 0.486 January
1973 0.585 January 0.505 January
1974 0.589 December 0.446 December
1975 0.495 December 0.474 December
1976 0.583 December 0.555 January
1977 0.548 December 0.466 December
1978 0.576 December 0.553 January
1979 0.593 December 0.574 January
1980 0.541 December 0.488 December
1981 0.559 December 0.511 Decembe r
(a)Computed from data presented in DOE/APAdmi n (1982).
11.7
All data for estimating the load factors were obtained from tables
developed by the Alaska Power Administration (APAdmin)(DOE-APAdmin 1982).The
area designated as the "Southcentral"region in the APAdmin statistics is
assumed to be representative of the Anchorage service area in the Railbelt and
the area designated as the "Yukon"is assumed to be representative of the Fair-
banks area.
The information presented in Table 11.3 clearly shows that the period when
Railbelt peak loads occur (and thus,when annual load factors are determined)
is in the winter,coinciding with the timing of coldest winter weather and
max imum hours of da rknes s.It is des;rab 1 e fo r forecast i ng purpose s to stan-
dardi ze for weather-re 1ated impacts on the load factor.Incl ud i ng weather-
related impacts in the trend analysis could lead to erroneous conclusions if a
nonrepresentative mix of weather patterns occurred over the period of the time
series data.In addition,weather is such a random variable that it is almost
impossible to forecast.
Assuming that a strong correlation between non-weather-related load fac-
tors and time could be identified,future non-weather-related load.factors
might be reasonably forecast using the coefficient in the time trend
equation.To correct the load factors for weather-related influences,the
annual load factors for each year presented in Table 11.3 were multiplied by
the nlJ1lber of heating degree days in each corresponding year.The resulting
adjusted load factors for Anchorage and Fairbanks were then regressed against a
time variable using the following simple equation:
Y =a +bx
where
Y =load factor multiplied by heating degree days
x =time.
The explanatory power of time in explaining changes in the adjusted load
factor was low for both Anchorage and Fairbanks.The R2 values for the regres-
sions were 0.39 for Anchorage and 0.02 for Fairbanks,respectively.Both the t
and F values for time in the Anchorage equation were significant at 95%levels
11.8
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of confidence.The time coefficient was negative,indicating that Anchorage's
weather-adjusted load factor was declining over time.For reasons that will be
discussed later,it does not appear that forecasting a declining load factor in
either Anchorage or Fairbanks is realistic.In any case,the level of explana-
tory power provided by the time trend equations was too low to base any fore-
casts of future load factors upon the results.
Trend Analysis Using an ARIMA Model
A more complex method of using time series data to forecast future load
factors in an ARIMA model (Autoregressive Integrated Moving Average)was also
attempted.The first step in this process was to calculate load factors by
month for the period 1970-1981.These monthly load factors were calculated in
a manner similar to that used in calculating the peak load factors presented in
Table 11.3.Calculating load factors for each month in the 12-year period pro-
vided a data base of 144 observations,which was more than sufficient for dev-
eloping an ARIMA model.
The next step was to attempt to identify the correct spec~fication of the
ARIMA model in terms of the lag operators to be used and the degree of differ-
encing to be employed.The objective in identifying the model is to obtain a
stationary historical time series that will consistently represent the para-
meters underlying the trends in the time series.
The appropriate lag operators for the model were specified to be 1 and
12.That is,the load factor in a parti cul ar month shoul d be correl ated with
the load factor in the previous month and the load factor in the previous
year.Computation of autocorrelation coefficients for the data using lag
operators of one and 12 and various levels of differencing revealed that using
first differences on both lag operators produced a stationary time series with
small random residuals in a relatively short time for both Anchorage and Fair-
banks.
Thus,the ARIMA model for load factors was identified as the following:
11.9
=
where
random error term ("white noise")
1ag operator
sequential autoregressive parameter for the first difference
on the load factor of the previous month
8 1 =sequential moving average parameter for the first difference
on the load factor of the previous month
812 =seasonal moving average parameter for the first difference on
the load factor of the previous year
Yt =load factor in a particular month.
This model specification is similar to the one developed by Uri (Uri 1976)for
forecasting peak loads using an ARIMA time series model.
The model was applied to the monthly load factor data and relatively low
residual sum of squares (i.e.,unexplained variation in the data)were
obtained.The coefficients of the ARIMA model were then input into an ARIMA
forecasting routine that uses the most recent historical data and the coeffi-
cients to generate forecasts for specified forecasting periods.
The forecasts generated by the ARIMA forecasting model predicted that the
load factor for Anchorage over the next 30 years would increase from 0.56 to
0.66,whereas the load factor for Fairbanks would decrease from 0.51 to 0.42.
However,project resources were insufficient to permit validation and refine-
ment of the ARIMA coefficients and the resulting forecasts.In addition,
qualitative analysis of the factors influencing load factors does not support
the conclusion that Fairbanks load factors are likely to decline over time.(a)
Qualitative Analysis Of Load Factors
Although peak load forecasting has received a substantial amount of
research attention,the relationship between peak loads and average energy
(a)Whether the load factor is computed on a monthly basis,as in Table 11.3,
or on an annual basis,as in Table 13.2 it appears that Fairbanks'load
factor is increasing slightly.In any event,0.42 appears unrealistically
low.Note also that simple trend analysis showed opposite results.
11.10
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demands has not received the same degree of attention.Locating research
literature on the relationship between peak loads and average loads and on the
factors that influence this relationship proved to be a difficult task.In
addition,it is questionable how applica.ble the results of studies from other
areas are to the Railbelt because of the unique characteristics of the area and
the fact that load factors tend to be unique to each utility system.
The following discussion represents an attempt to synthesize available
information into a useful form for evaluating potential changes in Anchorage
and Fairbanks load factors.Much of the discussion is somewhat subjective,and
empirical results on these topics are unavailable.Consequently,there was not
a strong enough basis for concluding that load factors will change substan-
tially from present levels in the major load centers of the Railbelt.
Impacts of Changes in the Customer Load Mix on the Load Factor
The customer mix,which can be measured by the proportion of total power
demands comprised by the residential,commercial and industrial sectors,is a
crucial factor in determining the load factor of an electrical service area.
The analysi s of power demands by customer is important.If it coul d be
demonstrated that the demands of particular customer classes are the primary
cause of Railbelt system peak demands and that changes in the current mix of
customer demands are likely to occur in the future,future changes in the Rail-
belt system load factor could be evaluated.
In general,residential power demands have the greatest degree of vari-
ation both by time of day and by season of the year.Commercial power demands
demonstrate slightly less variation over time.Industrial power demands are
the most constant type of power demand over time.
A typical Lower 48 load pattern for residential,commercial,and indus-
trial customers on a peak day is shown by a daily load profile in the Pacific
Northwest in Figure 11.2.Note the substantial amount of variation in residen-
tial power demands by time of day relative to other sectors.The pattern of
demand illustrated in Figure 11.2 is typical for most utilities,
11.11
LOAD (1000 MW)
30 I-TOTAL
INDUSTRIAL---COMMERCIAL RESIDENTIAL
•••••••••••••••
.....
15 [••••
...........
N ••••
10
----
•••••••••.--..............,'".....'".''...'.''.....'"•••••••••~'~.••••••..••..••••••••..••.'...•••
•••••••••••••••
•----------..".,.--.....
/"./"".,.----------------/~-------,--
----,--------
25 I-
20 I-
51-
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o I I I I I I I I I I I I I I I I I I
12 2 4 6 8 10 12 2 4 6 8 10
AM PM
FIGURE 11.2.Daily Load Profile in the Pacific Northwest
L--.
11.13
The late afternoon timing of the occurrence of peak demand in the Railbelt
generally indicates that both residential and commercial demands are likely to
be important in determining the occurrence of peak demand.Thus,it does not
appear that the load factor of the Alaska power system would be particularly
sensitive to changes in the relative mix of residential and commercial power.
(a)Source:r~morandum from Myles C.Yerkes of the
Al aska Power Authority to the Committee on Load
Forecasts and Generation,Alaska Systems Coordi-
nati ng Counci 1-
(b)Includes Anchorage ~1unicipal Power and Light and
Ch ugach El ectri c As soc i at ion.
(c)Includes Fairbanks Municipal and Golden Valley
Electric Association.
The percentages of total Railbelt forecasted power consumption comprised
by individual sectors for various future time periods are presented in Table
11.5.The information presented in this table demonstrates that in the case
examined there is no clear trend in the share relationship between commercial
and residential demand.Thus,even if Railbelt residential and commercial use
had different load patterns,it is not clear that this would result in any
5 p.m.
5 p.m.
4 p.m.
4 p.m.
Time Period of Peak Demand
Time Period of Peak Dem~nds in
Anchorage and Fairbanks~a)
December 29,1981 January 2,1982ServiceArea
Anchorage(b)
Fai rbanks (c)
TABLE 11.4.
since sectoral load patterns in most utility service areas will reveal substan-
tially greater variation in residential loads over time than for other sectors.
Data on load patterns by type of customer in Alaska were not available.
However,a 1 imited amount of data on total utility system loads was avail-
able.An analysis of these data shows that highest power demands in Alaska
occur in the late afternoon and early evening.This is illustrated by the data
presented in Table 11.4 for two peak days during the winter of 1981-1982.
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(a)Source:1983 Al aska Long-Term Energy Pl an
TABLE 11.6.Conservation ~"easures MJst Likely to be
Implement~d)in the Residential Sector
of Alaska~a
TABLE 11.5.Percentages of Total Forecasted Railbelt
Electrical Consumption ComDr1sed by
Individual Customer Sector\a)
clear trend in system load factor.Industrial demand could change the load
factor,but industrial demand is handled separately in REO (see section 10.0).
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55.0
50.8
48.2
48.6
Fai rbanks
44.8
49.2
51.8
51.4
Residential Commercial
Level
R-38
R-11
Storm Window Installation
Doors and windows
Blankets and Wraps
47.2
51.9
52.1
53.9
52.8
49.1
47.9
46.1
Measure
ceiling Insulation
Wall Insul ation
Glass
Weatherstripping
Water Heater Improvement
Yea r Res ident ia 1 Commerc ia 1
Anchorage
1980
1990
2000
2010
(a)Sectors add to 100%(excludes miscellaneous and
industrial demand).
Source:REO Model Run,Case HE6--FERC 0%Real
Growth in Price of Oil.
11.14
In summary,it appears that future conservation efforts in the Rai1belt
will result in positive,but very small,improvements in the power system load
factors.A successful program to increase lighting energy efficiency could
significantly increase the positive impacts of conservation upon the system
load factor.
Load Center Diversity
The diversity in the timing of peak electrical demands is important in
determining how changes in demand will affect the system load factor.The
impacts of demand diversity between Fairbanks and Anchorage will be particu-
larly important after the two load centers are intertied in 1984.
The measures listed in Table 11.6 are generally related to the overall
gDa1 of improving thermal energy efficiency in the residential sector.Thus,
one would expect that the implementation of most of these conservation measures
would result in greater energy demand reductions in the winter than the average
demand reduction for the entire year.
However,it should be noted that electricity is used for space heating in
only a small_percentage of the Railbe1t 1 s residences and businesses.Thus,the
impact of improvements in thermal efficiency on the total electrical power
system load factor may not be large.(a)
Electrical demands for lighting are probably the major causal factor in
creating the large disparity between peak and average electrical demands in
Alaska.Currently,according to the 1983 Alaska's Long-Term Energy Plan,
lighting is not targeted as an area for future conservation efforts in
Alaska.Without a sustained conservation effort in lighting,it appears
unlikely that conservation will result in a signfficant change in the annual
load factor in the Railbe1t.
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(a)Note also (from Section 5.0)that the incremental electric fuel mode
in space and water heat for the Anchorage service area is very low.
means that over time the measures shown in Table 11.6 will grow less
less effective in saving electricity,other things being equal.
11.15
spl it
Thi s
and
Data on demand diversity among customer classes in Alaska were not avail-
able.A limited amount of data on demand diversity among untilities was avail-
able.These data,collected by the Alaska Systems Coordinating Council (Yerkes
1982),reveals that the diversity among utilities in the timing of peak demands
is not great.The ratio of the highest peak demand for the Alaska power system
as a whole (the coincident peak)to sum of the peaks for the individual utili-
ties (the noncoincident peak)was 0.98 for selected peak days in December,1981
and January,1982.
This high coincidence factor,which equates to a low level of diversity
among the various utilities in the timing of peak demands,implies that future
shifts in the mix of demand among the various load centers will have little
impact on over~ll peak demand.A primary cause of peak power demands that
occurs in Alaska is high-pressure Arctic weather systems that generally tend to
increase the demand for electric power in almost all areas of Alaska.Thus,
diversity in demand among utilities has little impact on total system peak
demand,although more research would be necessary to reach the same conclusion
for the various customer classes.
11.16
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12.0 MODEL VALIDATION
The purpose of a model validation is to assess the accuracy and plausi-
bility of the model·s forecasts.In engineering or physical systems,this can
be accomplished via controlled experiments,where a system can be character-
ized,simulated,and compared to experimental results.
Unfortunately,demand forecasting models attempt to describe the inter-
actions of physical systems,individuals,and the environment.It is impos-
sible,therefore,.to conduct the type of validation that typically accompanies
physical science models.
Validation of integrated economic/engineering models typically consists of
two tests:the ability of the model "come close"to historical figures when
the actual inputs are used,and the "reasonableness"of the forecasts.This
section applies both of these tests to the RED model.
ASSESSMENT OF RED'S ACCURACY
In order to assess the accuracy of a simulation model,the usual procedure
is to substitute historical values for the inputs or "drivers"of the model,
produce a backcast,and compare the predicted and actual values.Unfortun-
ately,the period for which this type of exercise can be produced is relatively
brief.
End-use forcasting models are very data intensive,and RED is no excep-
tion.Much of the data necessary to run the model (including fuel mode split
and appliance saturations)required a primary survey of the population.His-
torical data for these critical parameters is incomplete;therefore,the
accuracy tests which can be performed on the model are limited.
A partial validation of RED's accuracy,therefore,was performed by taking
the linearly interpolated forecast values from the case.
The linearly interpolated forecasts were then compared with the actual
consumption levels in 1982.Table 12.1 presents a cross tabulation of these
values.
12.1
TABLE 12.1.Comparison of Actual Base Case,and Backcast Electricity
Consumption (GWh)1982
An cho rage-Cook Inlet Fairbanks-Tanana Valley
Base (b)Base(b)
Actual Case Backc ast Ac tual Case Backcast
Residential 1,146 1,060 1,097 178 205 208
Business(a)1,072 1,118 1,170 269 243 254
Other 23 25 23 5 7 6
Total 2,241 2,203 2,290 452 455 468
%Difference from Actual -1.7%2.2%0.6%3.5%
(a)Including Industrial Demand.
(b)Sherman Clark No Supply Disruption.This value is a linear interpolation
between the 1980 and 1985 forecast values.
Even though RED is designed to be a long-run model,it produces an inter-
polated forecast with an error of only 0.6%in Fairbanks,and an error of only
-1.7%in Anchorage when compared to actual data in the most recent year avail-
abl e.
The model was also run using best estimates of 1982 economic drivers and
fuel prices shown in Table 12.2.These results are shown in Table 12.1 as the
Backcast case.The results are also very close to the actual values in most
cases for the individual sectors;the forecast of total consumption was within
3.5%of the actual value in both load centers.Given that the model is a long
run model,that forecasts of actual households and employment and to be used in
place of unknown actual data,and that the 1980 fuel mode splits,appliance
saturations,and use rates had to be used in place of 1982 values (which are
not available)the backcast performance for 1982 is very good.
The remaining discrepencies in the forecasts for the individual sectors
appear to be related to the quality of the input data.In general,however,
there are insufficient data available to determine whether the "actual"eco-
nomic data are correct until about two to three years after the fact.Alaska
"actual"data periodically undergo substantial revision.Therefore,the per-
formance of individual sectors for a short-term forecast of this type should
12.2
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TABLE 12.2.1982 Values of Input Variables
Anchorage Fai rbanks-
Cook-Inlet Tanana Valley
Househo 1ds (a)83,677 22,922
Employment(a)120,533 33,500
Electricity Prices ($/kWh)(b)
Resi denti al 0.45 .100
Business 0.42 .095
Natural Gas Prices·($/mcf)(b)
Residential 1.84 12.53 (c)
Business 1.61 11.08
Fuel Oil Prices ($/gall 0 n)(b)
Residential 1.19 1.21
Business 1.12 1.17
(a)Forecasts by MAP model for Sherman Clark NSD case.Consis-
tent estimates of households and total employ-
ment are not available for 1982 from official sources.
(b)All prices are in nominal dollars.
(c)Propane price.
considered less important than the forecasts'long-term plausibility.The next
subsection covers the subject of long-term plausibility of the forecasts.
REASONABLENESS OF THE FORECASTS
In order to test the reasonableness of RED·s long-term forecasts,we com-
pared the base case used in the 1983 update with three comparable long-term
forecasts.The three forecasts were:forecasts by Pacific Northwest Power
Planning Council (PNPPC)and Bonneville Power Administration for the Pacific
Northwest,an area with large electric space heat loads and rising prices;and
a forecast by Wisconsin Electric Power Company (WEPCO)for Wisconsin and Upper
r1ichigan,an area with relatively stable electric prices and low electric space
heat penetration.The intent was to compare forecasts from areas similar to
the Railbelt Region.The Pacific Northwest forecasts were selected because of
12.3
the low electricity prices the region shares with the Anchorage load center,
while the Wisconsin area closely corresponds to the cl imate and fuel mode split
exhibited in the Railbelt.
The Pacific Northwest Power Planning Council created by an act of Congress
to coordinate and direct acquisition of generation resources in the Pacific
Northwest,prepared a twenty-year forecast of electricity demand in the North-
west.PNPPC modelled four alternate load growth scenarios (low,medium low,
medium high,and high)for the purposes of generation planning.We chose the
medium high scenario for comparison because it corresponds more closely to the
economic conditions expected to occur in the Railbelt.
The Bonneville Power Administration (BPA)is the marketer of all federal
power in the Pacific Northwest.BPA,due to its adversarial relationship with
the PNPPC,recently completed construction of their own forecasting tools.We
chose to examine BPA's medium scenario as it represents their assessment of the
most probable situation.
The Wisconsin Electric Power Company markets power to Milwaukee-Kenosha-
Racine Standard t-'Etropolitan Statistical Area,plus selected count.ies in cen-
tral and northern Wisconsin and upper Michigan.Unlike the two Pacific North-
west organizations,WEPCO markets to a service area with relatively little
electric space heating.As in the southern Railbelt,the primary fuel source
is natural gas,with electricity supplying only 4 to 5 percent of total energy
used.Consequently,there are fewer the opportunities for savings of electric
energy in conservation of building heat than exist in the Pacific Northwest.
In contrast to the Pacific Northwest,where annual resiClent~i-alelectric
consumption in 1980 averaged 17,260 kWh per household,and 11,000 to 13,000 in
the Railbelt WEPCO customers averaged 7,240.The fact that the electric load
in the WEPCO area is mostly not related to the thermal shell of the building is
reflected in the much higher growth rates of electricity consumption than in
the Pacific Northwest or the Railbelt.This increasing power forecast is also
caused by the assumption by WEPCO that electricity rates would rise at only 0.3
percent per year in real terms through the end of the century,much 1ess than
in the Pacific Northwest or the Railbelt.In WEPCO·s service area,it was
12.4
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assumed electricity would capture a high (40-65 percent)share of new
residential units due to its projected cost advantage over oil and gas.
Table 12.3 presents a decomposition of two commonly used metrics for the
BPA,PNPPC,WEPCO and RED forecasts:the annual growth rate in use per
employee and use per household.The RED forecasts both exhibit higher growth
rates than either of the Paci fi c Northwest forecasts,but lower than the rates
in the WEPCO forecast.
TABLE 12.3.Comparison of Recent Forecasts,1980-2000
J
Pacific Northwest Power Council
Bonneville Power Administration
Wisconsin Electric Power Company(a)
RED
Anchorage
Fa i rbank s
Average Percent
Growth Rate,
Use Per Household
-.64
-.64
1.41
-.36
0.98
Average Percent
Growth Rate
Use Per Employee
.14
-.31
3.97
1.04
.0.93
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(a)For Wisconsin Electric Power Company,the residential forecast is use
per customer.
This is the expected relationship of the forecasts.The BPA and PNPPC
forecasts assume vigorous conservation programs and rising electricity prices
in a region characterized by high market penetration of electric space heat and
water heat in both the residential and commercial sector.Furthermore,because
Pacific Northwest electricity prices have been low historically,there are many
opportunities available for cheaply saving large amounts of electricity.In
contrast,the Railbelt and WEPCO regions do not have as many inexpensive
opportunities to save large amounts of power,since most thermal requirements
are being met with natural gas.Furthermore,the rate of increase in
electricity prices is expected to remain low in the WEPCO region,reducing
incentives to conserve.The RED forecasts occupy a middle ground,both in
terms of base year consumption and in terms of the rate of increase in
12.5
consumption.With moderate rates of electricity price increases and fewer
inexpensive conservation opportunities,RED shows lower rates of conservation
than the Pacific Northwest.In comparison with the WEPCO area,the Railbelt is
expected to have a declining electric share in space heat and water heat,so
the rate of increase in use per customer would be less.In addition,since
Railbelt customers on the average use more electricity than WEPCO customers and
are facing higher projected rates of electricity price increases,the
forecasted rate of increase in the rate of electricity consumption should be
lower.Based on this comparison,the results of the RED forecast,therefore,
seem to be in line with what other forecasters are predicting.
12.6
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13.0 MISCELLANEOUS TABLES
Abbreviations Used
APA =Alaska Power Authority
AP&T =Alaska Power and Telephone (TOK)
AP Admin =Alaska Power Administration
.CEA =Chugach Electric Association
GVEA =Golden Valley Electric Association
GWH =Gigawatt Hour
HEA =Homer Electric Association
kWh =Kilowatt Hour
KVa =Kilovolt
MEA =Matanuska Electric Association
MW =Megawatt
MWH =Megawatt Hour
FMUS =Fairbanks Municipal Utility System
SES =Seward El ectri c System
SQ FT =Square Foot
13.1
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TABLE 13.1.Number of Year-Round Housi ng Units by Type,
Rail belt Load Centers,sel ected Years
Single Mobi 1e
Fami ly Du pl ex Multi fami 1y Home Total
Anchorage-Cook Inlet Load Cente r:
(U rban)1950(Aa)3,325 964 1,128 202 5,619
1960(b)19,195 1,552 8,033 1,783 30,563
1970(c)21,935 3,981 14,259 6,403 46,578
1980f d~40,562 8,949 27,980 10,211 87 ,702
1982 e 47,610 9,899 31,893 11,379 100,781
Fa i rbanks-Tanana Vall ey Load Cente r:
(Urban)1950(a)1,295 166 352 2 1,815
1960 (b)6,527 671 4,547 853 12,598
1970(c)5,335 1,068 6,072 1,254 13,729
1980f d~10,873 2,512 8,607 2,175 24,167
1982 e 12,218 2,551 8,927 2,193 25,889
Ra i 1be 1 t:
1950(a)4,620 1,130 1,480 204 7,434
1960(b)25,722 2,223 12,580 2,636 43,161
1970(c)27,270 5,049 20,331 7,657 60,307
1980f d~51,435 11 ,461 36,587 12,386 111 ,869
1982 e 59,828 12,450 40,820 13,572 126,670
(A)Excludes Kenai-Cook Inlet Census Division,Seward Census Division,
Matanuska-Susitna Census Division.
(a)U.S.Department of Commerce Census of Housing 1950;Alaska,General
Characteristics,Table 14.These are all dwelling units.
(b)U.S.Department of Commerce Census of Housing 1960:Alaska,Table 28.
These are all housing units.
(c)U.S.Department of Commerce Census of Housing 1970:Alaska,Table 62.
These are all year-round housing units.
(d)U.S.Department of Commerce Census of Housing,1980:STF3 data tapes.
All year-round housing-units.
(e)1980 Census,plus estimated 1980-1982 construction from Mr.Al Robinson,
economist,U.S.Department of Housing and Urban Development,Anchorage.
13.2
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TABLE 13.2.Railbelt Area Utility Total Energy and System Peak Demand
I Anchorage-Cook Inlet Fairbanks-Tanana Valley
Annua 1 Peak Load An nua 1 Peak Load
I
Energy (GWh)Demand (MW)Facto r Energy (GWh)Demand (MW)Factor
1965 369 82.1 0.51 98 24.6 0.45
1966 415 93.2 0.51 108*26.7 0.46
I 1967 461 100.8 0.52 NA NA NA
1968 519 118.0 0.50 141*42.7 0.38
j 1969 587 124.4 0.54 170*45.6 0.43
1970 684 152.5 0.51 213 57 .1 0.43
J 1971 797 .166.5 0.55 251*70.6 0.41
1972 906 195.4 0.53 262 71.2 .0.42
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1973 1,010 211.5 0.55 290 71.5 0.46
1974 1,086 225.9 0.55 322 89.0 0.41
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1975 1,270 311.7 0.47 413 108.8 0.43
1976 1,463 311.0 0.56 423 101.0 0.48
1977 1,603 375.4 0.49 447 117.5 0.43
I 1978 1,747 382.8 0.52 432 95.8 0.51
1979 1,821 409.6 0.51 418 100.7 0.47
I 1980 1,940 444.4 0.50 402 95.4 0.48
1981 2,005 444.7 0.51 422 93.1 0.52
j 1982 2,254 471.7 0.55 452 94.4 0.55
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TABLE 13.3.Anchorage-Cook Inlet Load Center Utility Sales and I
Sales Per Customer,1965-1981
1ResidentialCommercial-Industrial-Government
Sal es Sal es Per Sal es Sa 1es Pe r
(GHH)Customers Customer (kWh)(GWH)Customers Customer (kWh)I196517427,016 6,425 189 3,994 47 ,235
1966 ,194 28,028 6,937 215 4,147 51,909
)1967 208 30,028 6,941 241 4,363 55,206
1968 233 34,443 6,766 277 4,804 57,715
I196926237,653 6,971 316 5,125 61,656
1970 309 41,151 7,517 363 5,784 62,713
1971 369 43,486 8,487 415 6,006 69,057 I
1972 419 47,707 8,788 473 6,420 73,704
1973 457 49,433 9,239 539 6,693 80,557
1974 494 54,606 9,044 577 7,232 79,791
1975 592 58,326 10,147 659 7 ,750 85,073
1976 675 62,413 10,817 769 8,789 87,598
1977 739 71 ,275 10,375 846 9,860 85,753
1978 841 76,999 10,928 884 10,219 86,542
1979 845 76,494 11 ,047 878 10,368 84,684
] .1980 936(a)77,743 12,040 1 002(a)10,629 94,270,
1981 916(b)80,089 11 ,437 1 030(b)11,021 93,458,
Annual Growth IRate1965-81
10.9%7.0%3.7%11.2%6.5%4.4%
f
(a)1979 data used for SESe
!(b)Based on 1980 MEA,1979 SES data.
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TABLE 13.4.Fairbanks-Tanana Valley Load Center Utility Sales
and Sales per Customer,1965-1981
Residential Commercial-Industrial-Government
Sales Sa 1es Per Sa 1es Sa 1es Per
(GWH)Customers Customer (kWh)(GWh)Customers Customer (kWh)
1965 39 8183 4,804 55.198 1,318 41,880
1966 47 8170 5,712 59.376 1,467 40,474
1967 NA NA NA NA NA NA
1968 61 9,344 6,569 77 .906 1,469 53,033
1969 77 10,023 7,672 91.212 1,579 57,766
J 1970 91 10,756 8,418 118.560 1,888 62,797
1971 106 11,184 9,515 133.056 1,929 68,977
1972 121 11 ,487 10,529 135.873 2,002 67 ,869
)1973 133 11,825 11,233 150.823 2,054 73,429
1'974 154 13,261 11,600 161.615 2,242 72,085
I 1975 190 13,877 13,719 210.759 2,342 89,991
1976 194 15,419 12,561 219.175 2,530 86,630 .
I 1977 198 17,197 11,500 240.463 2,834 84,849
1978 178 17 ,524 10,153 242.668 2,854 85,027
1
1979 169 18,070 9,344 219.335 2 795(a)78,474,
1980 160 18,054 8,890 214.263 2,737 78,283
1981 159 19,379 8,219 224.354 2,942 76,259
Annual Growth
Rate 1965-81
I 9.2%5.5 3.4 9.2%5.1 3.8
I (a)Includes 1979 estimated 70 customers for AP&T.
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TABLE 13.5.Adjustment for Industrial Load Anchorage-Cook Inlet,1973-1981
Total Achorage Homer Electric 7W~Anchorage Anchorage
Comm-Ind-Govt MWH Demand Industri al Load a IICommercial ll Sq Ft.(b)
1973 540,476 56,130 484,346
1974 579,068 58,298 520,770 29,660,900
1975 661,192 62,806 598,3R6 33,471,800
1976 771,054 72,063 698,991 37,049,800
1977 846,939 83,989 762,950 39,618,900
1978 896,072 82,984 813,088 41,440,000
1979 904,851 R7,955 816,896 42,733,800
1980 988,957 99,103 889,854 44,042,700
1981 1,030,753 130,318 900,435 44,817,400
MWH Use/Sq Ft.kWh/SO FT %From Previous Yr
1973 0.0179 17.9
1974 0.0176 17.6 -107
1975 0.0179 17.9 1.7
1976 0.0189 18.9 5.6
1977 0.0193 19.3 2.1
1978 0.0196 19.6 1.6
1979 0.0191 19.1 -2.6
1980 0.0202 20.2 5.8
19R1 0.0201 20.1 -0.5
(a)Commercial-Industrial Load over 50 KVA (commercial users included)
(b)Predicted value.See Chapter 6.0.
13.6
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REFERENCES
R.1
Anchorage Municipal Light and Power (AML&P).1982.Electric Utility
Conservation Plan.Anchorage Municipal Light and Power,Anchorage,Alaska.
Anchorage Community Planning Department.1980.1980 Census Profile -
Municipality of Anchorage.Anchorage Community Planning Department,
Anchorage,Al aska.
Acherman,J.D.and D.A.Landgren.1981b.20-Year Demand and Energy
Forecast:1981-2000.Wisconsin Electric Power Company,Milwaukee,
Wisconsin.
• I
1979.Electric Power in the
The MIT Press,Cambridge,
Anchorage Real Estate Research Committee.1982.Anchorage Real Estate
Research Report.Volume VIII,Anchorage Real Estate Research Commmittee,
Anchorage,Alaska.
Anchorage Real Estate Research Committee.1979.Anchorage Real Estate
Research Report,Volumes II and III.Anchorage Real Estate Research
Committee,Anchorage,Alaska.
Appl ied Economics Inc.et ale 1981.State of Al aska -Long Term Energy
Plan.Prepared for Department of Commerce and Economic Development,Division
of Energy and Power Developnent,Anchorage,Alaska.
Acherman,J.D.and D.A.Landgren.1981a.Econometric Model Forecast:1981-
2000.Forecast 2:Popul ation and NlJl1ber of Customers Forecast.Wi sconsi n
Electric Power Company,Milwaukee,Wisconsin.
Alaska Power Administration/U.S.Department of Energy.1982.Alaska Electric
Power Statistics 1980-1981.'Alaska Power Administration/U.S.Department of
Energy,Washington,D.C.
Baughman,M.L.,P.L.Joskow and D.P.Kamat.
United States:r-bdels and Policy Analysis.
~'1assachusetts •
Bonneville Power Administration.1978.Draft Environmental Impact Statement,
Proposed 1979 Wholesale Rate Increase,DOE/EIS-0031-D,Bonneville Power
Administration,Portland,Oregon.
Bonneville Power Administration.1982a.Forecasts of Electricity Consumption
in the Pacific Northwest,1980-2000.Bonneville Power Administration,
Portland,Oregon.
Bonneville Power Administration.1982b.Economic/Demographic Projections -
Inputs to BPA Energy Forecasting MJdels.Appendix I to Forecasts of
Electricity Consumption in the Pacific Northwest,1980-2000.Bonneville
Power Administration,Portland,Oregon.
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Bonneville Power Administration.1982c.Technical Documentation of Final BPA
Energy Forecasting Models.Appendix II to Forecasts of Electricity
Consumption in the Pacific Northwest,1980-2000.Bonneville Power
Administration,Portland,Oregon.
Booz-Allen and Hamilton,Inc.et al.State of Alaska -Long Term Energy
Plan.Executive Summary Prepared for Department of Commerce and Economic
Development,Division of Energy and Power Development,Anchorage,Alaska.
California Energy Commission (CEC).1976.Analysis of Residential Energy
Uses.California Energy Commission,Sacramento,California.
California Energy Commission.1982.California Energy Demand -·1982-2002 •.
Volume I:Technical Report,Prepared for Consideration in the Biennial
Report IV Proceedings,California Energy Commission,Sacramento,California.
California Energy Commission.1983.1983 Electricity Report P104-83-001,
California Energy Commission,Sacramento,California.
Community Research center.1983.A Review of Socia-Economic Trends.
Community Research Quarterly,Fairbanks North Star Borough,Community
Research center,Fairbanks,Alaska.
Criterion,Inc.1982.Economic and Demographic Data and Forecasts of CFM
IV.Prepared by Criterion,Inc.for San Diego Gas and Electric,San Diego,
California.
Cronin,F.J.1982.IIEstimation of Dynamic Linear Expenditure Functions for
Housing.1I The Review of Economic and Statistics.64(1):97-103.
Department of Commerce and Economic Development.1983a.1983 Long Term Energy
Plan (Working Draft).Division of Energy and Power Development,Department
of Commerce and Economic Development,Alaska.
Department of Commerce and Economic Development.1983b.1983 Long Term Energy
-Plan -Appendix.Division of Energy and Power Development,Department of
Commerce and Economic Development,Alaska.
Department of Commerce and Economic Development.1983c.1983 Long Term Energy
Plan -Appendix II.Division of Energy and Power Development,Department of
Commerce and Economic Development,Alaska.
Electric Power Research Institute.1977a.Elasticity of Demand:Topic 2.
Electric Utility Rate Design Study,Volume 12,Electric Power Research
Institute,Palo Alto,California.
Electric Power Research Institute.1977b.Elasticity of Demand:Topic 2.
Electric Utility Rate Design Study,Volume 13,Electrlc Power Research
Institute,Palo Alto,California.
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El rick and Lavidge,Inc.1980~The Pacific Northwest Residential Energy
Survey.Prepared by Elrick and Lavidge Inc.for the Bonneville Power
Administration and the Pacific Northwest Utilities Conference,Portland,
Oregon.
Harrison,S.D.1979.Alaska Population Overview.Alaska Department of
Labor,Juneau,Alaska.
Halvorsen,R.1978.Econometric Models of U.S.Energy Demand.Lexington
Books,Lexi ngton,r1assachusetts.
Ender,R.L.1977.liThe Opinions of the Anchorage Citizen on Local Public
Policy Issues."Anchorage Urban Observatory,Anchorage,Alaska.
Henson,S.E.1982.An Econometric Analysis of the Residential Demand for
Electricity in the Pacific Northwest.Department of Economics,University of
Oregon.
An Analysis of Changes in Residential Energy
PNL-4329,Pacific Northwest Laboratory,Richland,
R.3
Gi 1 bert/Commonwea lth.1981.Fe as i bi 1i ty St udy of El ectri ca 1 Interconnecti on
Between Anchorage and Fairbanks.Jackson,Michigan.
Goldsmith,S.,and L.Huskey.1980a.Electric Power Consumption for the
Railbe1t:A Projection of Requirements.Institute of Social and Economic
Research,Anchorage -Fairbanks -Juneau,Al as ka.
Institute of Social and Economic Research.1982 •.A Study of Alaska's Housing
Programs.Institute of Social and Economic Research,University of Alaska,
Anchorage,Alaska.
Goldsmith,S.,and L.Huskey.1980b.Electric Power Consumption for the
Railbe1t:A Projection of Requirements -Technical Appendices.Institute of
Social and Economic Research,Anchorage -Fairbanks -Juneau,Alaska.
Ender,R.L.1978.1978 Population Profile,Municipality of Anchorage.
Anchorage Municipal Planning Department,Anchorage,A1asksa.
Energy Information Administration (EIA).1983 •.Nonresidential Buildings
Energy Cons umpt i on Survey:Part 1:Natural Gas and E1 ectri city Cons umpti on
and Expenditures.Energy Information Administration,Washington,D.C.
Hunt,P.T.,Jr.,and J.L.Jurewitz.1981.An Econometric Analysis of
Residential Electricity Consumption by End Use.Southern California Edison
Company,Los Angeles,California.
King,r1.J.'et al.1982.
Consumption 1973-1980.
Washington.
Jackson,J.J.and W.S.Johnson.1978."Commercial Energy Use:A
Disaggregation of Fuels,Building Type and End Use."Oak Ridge National
Laboratory,Oak Ridge,Tennessee.
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King,M.J.and M.J.Scott.1982.RED:The Railbelt Electricity Demand
r-1odel Specification Report.Volume VIII,Battelle,Pacific Northwest
Laboratories,Richland,Washington.
Leigh,W.A.1980."Economic Depreciation of the Residential Housing Stock of
the United States,1950-1970."The Review of Economics and Statistics
62(2):200-206.
Maddala,G.S.,W.S.Chern and G.S.Gill.1978.Econometric Studies in
Energy Demand and Supply.Praeger Publishers,New York,New York.
Midwest Research Institute.1979.Patterns of Energy Use by Electrical
Appliances.EPRI EA-682,Electric Power Research Institute,Palo Alto,
California.
Municipality of Anchorage.1982.1982 Population Estimation Methodology.
Planning Department,Municipality of Anchorage,Anchorage,Alaska.
National Oceanic and Atmospheric Administration.1979.Local Climatological
Data.National Climatic Center,Asheville,North Carolina.
Northwest Power Pl anning Council.1983.Regional Conservation and El ectric
Power Plan 1983.Northwest Power Planning Council,Portland,Oregon.
Pacific Gas and Electric Company.1980.1979 Residential Appliance Saturation
Survey.Economics and Statistics Department,Pacific Gas and El~ctric
Company~·San Francisco,California.
Pacific Gas and Electric Company.1981.Commercial Business Energy Use
Survey.Pacific Gas and Electric Company,San Francisco,california.
San Diego Gas and Electric Company.1982.1981 Residential Energy Survey
(MIRACLE V).San Diego Gas and Electric Company,Policy and Communication
Research Department,San Diego,California.
Scanl an,T.and D.Hoffard.1981."A Conditional Demand Approach to Appl iance
Usage Estimates for Si ngl e-Family Homes in the Paci fi c No rthwest."
Bonneville Power Administration,Portland,Oregon.
Smith,G.R.,and G.W.Kirkwood.1980.Forecasting Peak Electrical Demand
for Alaska's Railbelt.Prepared by Woodward-Clyde Inc.for Acres American,
Buffalo,New York.
Southern California Edison Company.1981.1981 Residential Electrical
Appliance Saturation Survey.Southern California Edison Company,Rosemead,
Cal i forni a.
Taylor,L.D.1975.liThe Demand for Electricity:A Survey.1I The Bell
Journal of Economics.6(1):74-110.
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Tillman,D.A.1983.The Potential for Electricity Conservation in the
Railbelt Region of Alaska.Harza-Ebasco,Anchorage,Alaska.
The Christian Science tvnnitor.tv1arch 18,1981.IIFind the Real •Culprits'in
Savi ng Energy at Home."
u.S.Bureau of Census.1960.General Population Characteristics.Final
Report PC(1)-3B,U.S.Bureau of Census.U.S.Department of Commerce,
Washington,D.C.
U.S.Bureau of Census.1970.Census of Housing.Bureau of Census,U.S.
Department of Commerce,Washington,D.C.
U.S.Bureau of Census.1977.Annual Housing Survey.Bureau of Census,U.S.
Department of Commerce,~ashington,D.C.
U.S.Bureau of Census.1980a.Housing Vacancies:Fourth Quarter 1979.
Bureau of Census,U.S.Department of Commerce,Washington,D.C.
U.S.Bureau of Census.1980b.1980 U.S.Statistical Abstract.Bureau of
Census,U.S.Department of Commerce,Washington,D.C.
U.S.Bureau of Census.1980c.Population and Households by States and
Counties.Bureau of Census,U.S.Department of Commerce,Washington,D.C.
U.S.Department of Commmerce.1977.Projections of the Population of the
United States:1977 to 2050.Available from the U.S.Government Printing
Office,Washington,D.C.
U.S.Department of Commerce.1981.BEA Regional Projections.Volume
Economic Areas.U.S.Department of Commerce,U.S.Government Printing
Office,Washington,D.C.
U.S Department of Commerce.1982.1982 State and Metropolitan Area Data
Book.U.S.Department of Commerce,Bureau of Census,Washington,D.C.
Wisconsin Electric Power Company.1982.IIpost Hearing Reply Brief of
Wisconsin Electric Power Company on Matters Other Than Rate of Return.1I
Public Service Commission of Wisconsin,Milwaukee,Wisconsin.
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APPENDIX A
RESIDENTIAL SURVEY
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APPENDIX A
BATTELLE-NORTHWEST RESIDENTIAL SURVEY
To calibrate an end-use model of electricity demand,the initial number of
appliances that use electricity must be known.At the time the RED model was
undergoing initial development (1981),there was no adequate information
available in the Railbelt concerning either residential appliance stock and
fuel mode split or uses of electricity in the commercial sector.While it did
not appear possible to collect significant useful information on the commercial
sector within project resource constraints,BNW researchers concluded that a
residential survey was both possible and desirable.This initial evaluation.
was reinforced when it became clear that data would not be available from the
1980 Census of Housing on detailed housing characteristics until 1982 at the
earliest,and that reporting on appliances would be less complete than in
1970.Accordingly,plans were made to survey the residential sector.
Although a lot of new information of good quality was developed in the
survey,there were several constraints on the survey process.First,the
resources available to design,test,run,and analyze the survey were extremely
limited.This precluded in-person interviews,large samples,or follow-up of
non-respondents.Second,it was not possible to stratify the survey sample,
both because there was no accurate information on types of dwellings in any
Ra il be 1 t community except Anchorage and because uti 1 ity customers coul d not be
matched to dwelling types or demographic characteristics.To conserve project
resources for analysis,we chose to do a blind mailing of the survey instrument
with no follow-up to random samples of each utility's residential customers.
Where possible,the random mailings were done by the utilities themselves.
Where Battelle-Northwest did the mailings,random subsets of customers or
complete customers lists were supplied by the utilities to Battelle-Northwest.
A.1
SURVEY DESIGN
Because budget limitations precluded follow-up interviewing as a means to
improve survey response rate and to check errors,it was very important to have
a survey instrument that required minimal respondent effort and time,gathered
only the least controversial and highest priority information,and was easy to
understand.Questions considered controversial items (income),questions
difficult to understand (insulation values or energy efficiency of appliances)~
and questions requiring substantial respondent effort (estimates of annual
electrical bills)were dropp~d.The highest priority questions concerning
appliance stock and fuel mode split were retained.A draft of the question-
naire was sent to the Railbelt utilities and other interested parties in
Alaska,and was reviewed by several senior Battelle-Northwest researchers.
Based on their comments and the results of a pretest with uncoached clerical
staff,the questionnaire was simplified to the point that it required the
average test respondent only two to five minutes to answer all questions.A
copy of the survey form is shown in Figure A.l.
SAMPLE SIZE AND COMPOSITION
Because of the high labor costs of selecting respondents,addressing the
mailings,and key punching and verifying the survey results,it was decided
that an acceptable level of accuracy for survey results would be plus or minus
6 percent with 95 percent confidence on the entire sample for a load center.
In order to obtain utility cooperation in mailing the questionnaire,we
considered it necessary to achieve this level of accuracy for each utility's
service area to provide them with usable data.Thus,accuracy of survey
results for load centers that contain more than one utility is somewhat greater
than the sampling error for each utility would suggest.Because of the care
taken in survey design to maximize response rate,we believed that an average
response rate of 50 percent was possible with no follow up.The desired number
of respondents was therefore doubled to obtain the nunber of mailings in each
utility service area.A total of 4,000 questionnaires were sent to the respon-
dents,of which 1764 usable responses were received,for an average response
A.2
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~~Banelle~~I·_
Pacific Northwest Laboratories
P.O.Box 999
Richland,Washington U.S.A.99352
Telephone (509)
Telex 15·2874
Alaska Railbelt Electric Power Alternatives Study
Dear Alaskan:
Battelle,Pacific Northwest Laboratories is working under contract to the
State of Alaska to help determine the future needs for electricity in the
Railbe1t Region,and the best way to meet those needs.
Many individuals believe that the Susitna hydroelectric power project is
the best way.Others think that these needs can be better met by employing
coal,conservation,or some other means.First,however,we need to estimate
future electric energy needs in the Railbelt.We can only do this properly if
we know how people in the region use electricity.
That's where you can help us.
Please take a few minutes to complete the questionnaire on the other side--
it is only one page long and will take only 5 minutes or so to answer.
Why should you help?First,the information you provide will be vital in
decisions your state government will make over the next year and a half to
build or not build the Susitna project.Either way,your electricity bill will
be affected.Second,whether or not the Susitna project is built,the
confidential information you provide will help your local utility plan ways in
which to meet your future electricity needs.
Since this is an issue of such importance to you and Alaska,every response
is vital.All responses will be strictly confidential.There will be no way
anyone can tell who you are from your response.The results of this survey
will be published in your local newspaper.
Please respond as accurately as you can.Thank you for your cooperation.
Sincerely,
Michael J.King
Research Economist
P.S.In order for us to consider your response,you will need to return the
questionnaire within three weeks.For your convenience,you will find a
postage paid envelope enclosed.
FIGURE A.1.Battelle-Northwest Survey Form
A.3
------
Please complete the following questionnaire and return It In the enclosed
envelope.It you have already completed and returned a questlonnalre,please
disregard this request.
1.What type of building do you reside Inl
()single family home ()duplex
()mobile home ()multifamily (3 or more units)
2.Number pf persons In your household (please respond In each category):
g.\lhat type of heating distribution system do you usel
8.\:hat.proportion of your heating needs are lIIet by:
0-114 !!4-112 ![2-3/4 3/1..-a 11
main fuel ()()()()
second fuel ()()()()
other fuels ()()()()
()radlant or convection ()hot water or steam.()forced air
Children Under 5o-1--2--T-rGr more
() () () () ()
Children 5-18o-i-i 3 or more
()()()()
AdulLs )8t
o 1 2-34 or more
()()()() ()
3.How many rooms are In your res idencel How many bedroomsl _
4.Approximate square feet of living space (Just your estimate):
6.\lhat Is the main fuel used for heating your homel
()natural gas (!electricity
()propane-butane (coal or coke
()fuel all,kerosene,or coal oil wood
()solar collectors 1 district heating system
()passive solar (check one:()south facing windows ()custom solar design)
()no
()second or vacatIon home.
How many vehicles do you usually plug-Inl ()1 ()2 ()3 or more
Do you plug the vehicle(s)in:()overnight ()just In the mornlngl
At approximately what temperature do you start plugging theln In1 ___
()primary residence
13.The uses described above are for my:
Do you have an electric refrl~eratorl ()yes
Jf yes,is ~t frost freel ()yes ()no
12.If you use plug-ins for vehicles:
11.
10.Please Indicate the fuel your appliances use:
t'I-
QJ a
>.......U r-QJ
.c ....r-,QJ ....c:
I-...QJC aQJ.......I-C ...I-'"-c u ~...0-"0 .-...r-0
QJ ....'"+-!a 0 ....-QJI-
a .-IlQ ftS :J s..a a 0 ~QJ
-a QJ co>.aa.:J u '"'I--"
water heater ()()()()()()()()
range/stove ()()()()()
sauna/jacuzzi/etc.()()()
clothes dryer ()()()()
clothes washer ()()
freezer ()()
dishwasher ()()
(I electric I ty
(coal or coke
(wood
(district heating
i1
1601-2000
2001-2400
greater than 2400
()1970-1974
()1975-1980
()before )950
()1950-1959
()1960-1969
II
less than 700
701-1000
1001-1300
1301-1600
In what year was your house (building)bulltl (just your estlmate)
In addition to your main fuel,what additional fuels do you use to heat
your homel
()none(I natura l gas
(propane-butane
(fuel oil,kerosene,or coal 011
(solar collectors
()passive solar (check one:()south facing wIndows ()custom solar design)
7.
+:0
5.
».
FIGURE A.1.(contd)
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rate of 44.1 percent.Table A.1 shows the total number of residential
customers in each utility,the number and percent surveyed,the number and
percent responding.
RES IDENTIAL
TABLE A.I.Customers,Number Surveyed,and Respondents for
the Residential Survey Battelle-Northwest
1980 Year End Customers Surveyed Customers Respondi ng
Ut il ity(a)Customers(b)Number Percent Number Percent
Chugach Electric (CEA)42,567 530 1.2 222 41.9
Anchorage Municipal (AMPL)13,744 522 3.8 214 41.0
Seward El ectri c (SES)1,090 424 38.9 185 43.6
Homer Electric (HEA)8,620 518 6.0 249 48.1
Matanuska Electric (MEA)11 ,722 520 4.4 268 51.5
Goblen Valley (GVEA)13,591 524 3.9 252 55.0
Fairbanks Municipal (FMUS)4,463 504 11.3 156 31.0
Copper Valley (CVEA)1,588 458 28.8 252 55.0
Total 97 ,385 4,000 4":T 1,798 "44:9
Tota 1 Used 97,385 4,000 4.1 1,764 44.1
(a)CVEA is not part of the interconnected Railbelt,since it serves
Glennallen and Valdez.This utility and load center were eventually
dropped from the analysis.
(b)Source:Al aska Power Administration.1979 customer totals were used for
CVEA,HEA,and GVEA.Residential customers only.
MAILING PROCESS AND COLLECTION OF RESULTS
The survey questionnaire was administered in one of three ways.In some
cases the utilities randomly selected a list of residential customers and
performed the mailing.In these cases,Battelle-Northwest provided the utility
an appropriate number of mailings,consisting of the questionnaire and pre-
stamped,self-addressed return envelope.To ensure confidentiality,the ques-
tionnaire was stamped only with the initials of the utility,providing identi-
fication of the service area.No other identification of the respondent was
possibl e from the survey form or the return envelope.When Battell e-Northwest
performed the mailings,the utilities provided either a random sample of
A.5
customer addresses or their complete mailing list of residential customers,
from which a random sample was drawn.No known geographic bias was introduced
by the sampling technique.Finally,Fairbanks Municipal Utility System (FMUS)
provided neither a mailing list nor mailing services to the project.In this
case,the Fairbanks telephone directory was used as a source of customer
addresses.Although an attempt was made to exclude addresses outside the City
of Fairbanks served by Golden Valley Electrical Association,unknown biases
were probably introduced into the Fairbanks sample by the sampling procedure.
The response rate was also signficantly lower for the FMUS sample.
As the survey forms were received,they were coded,keypunched and veri-
fied.The raw card image data file was recorded on magnetic tape and loaded
into an SPSS data file,organized by subfiles corresponding to each utility.
The results for each utility were weighted according to the total number of
residential customers in each load center in 1980,the last year's count
available at the time the file was assembled.The weights are shown in
Tabl e A.2.
TABLE A.2.Weights Used in Battelle-Northwest Residential ·Survey
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OUTPUT
Util ity
Chugach
Anchorage Municipal
Seward El ectri c
Homer El ectri c
Matanuska Electric
Go 1den Vall ey
Fairbanks Municipal
Copper Vall ey
Weight
2.81
1.17
.06
.45
.54
1.21
.67
1.00
The output of the survey was organized in SPSS files and printed in
frequency distributions and standard SPSS CROSSTABS tables.An example of
typical output is shown in Figure A.2 for freezer saturation.In the figure,
712 out of 807 Anchorage area single family households are shown to have
A.6
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STATISTICAL PACKAGE FOR THE SOCIAL SCIENCES
__I __I
',.._..~..~.....-----......_··_'...,....--,..!"I';.- --.-...-._.
07/28/81
FILE ENDUSE.D (CREATION DATE =06/17/81)
SUBFILE ClA AMLP SEA HEA MEA
****** ******** ****C R 0 SST ABU L A T ION 0 F * ****)
FF FHEEZER FUEL B~TYPE
**** ******•************ *•**** * **** ** *******I
COUNT
ROW pcr
COL PCT
'1'OT PCT
TYPE
1
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1
SINGLE F HOBILE H DUPLEX
AMILY OMF~
-1.1 1.1 2.1
MUl,1'J FA,..
1 LY
3.1 4.1
ROW
TOTAL
Ji'Ji'---·--~-I------·-I-·------L·--~---·I--------I--------I
-1-------·1--------1--------1--------1--------1
o I
0.7 I
6.7 I
0.0 I
3 I
2.4 I
4.5 I
0.3 1
)::-
•-.I
HISSING
DO NOT HAVE
-1.
o.
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0.4
8.1
0.0
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36 I
52.8 I
4.4 I
3.1 I
59 I
46.8 I
7.3 I
5.2 I
o
().7
0.7
0.0
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11
16.0
9.8
0.9
26
20.7
23.7
2.3
J
1
1
1
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20
29.9
13.1
1 • B
37
29.7
24.4
3.3
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b7
5.9
126
11.0
-I------~-I------··r·~-.--·-I--·----~I---·----I1.I Ii I 712 I .62 I 73 1 96 I 949
HAVE 1 0.6 I 75.1 1 6.5 I 1.7 I 10.1 I 83.1
I 85.2 I 88.3 I 94.8 I 66.5 I 62.5 I
I 0.5 I 62.4 I 5.4 I 6.4 I 8.4 I
-I--------I-----~·-I-~·---·-I--~----·l--------ICOLUMN780765110153 1142
TOTAL 0.6 70.6 5.7 9.b 13.4 100.0
CHi SQUARE =91.30715 WITH 8 DEGREES OF FREEDOM SIGNIFICANCE =0.0000
FIGURE A.2.Saturation of Freezers in Anchorage-Cook Inlet Load Center
Figure Note:Subfiles for each surveyed utility were combined and-weighted by weights
in Table A.2.Seven households were unidentified by type of house and were ignored.
freezers (missing values were cQunted as "do not have").~e computer shows
this as 88.3 percent saturation of single family households.~;s percentage
was used in Table 5.8.In practice,these computer estimates were usually
modified with professional judgment;however the Battelle-Northwest survey
supplied the raw data on which the judgment was made.
A.8
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APPENDIX B
CONSERVATION RESEARCH
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APPENDIX B
CONSERVATION RESEARCH
The Railbelt area has 1 imited ability to adopt conservation measures that
would result in large-scale electricity savings.According to Ti'llman (1983),
past conservation in load centers like Fairbanks has been largely the result of
price increases for electricity.In addition,Railbelt utility managers
believe that future electrical conservation will be largely the result of
price,not conservation programs.The impact of conservation programs in the
Railbelt has been taken into account in the fuel mode splits,use rates,and
price effects incorporated in the 1983 update.In addition,selected conserva-
tion programs in the Lower 48 states were analyzed to determine if anything
could be learned about program impacts in the Railbelt.
An attempt was made to compare conservation of electricity in the Railbelt
wlth conservation effects as forecasted by four policy-making bodies elsewhere
in the United States.The goal was to obtain a range of potential energy sav-
ings due to price-and program-induced conservation and determine if such esti-
mates would be applicable (and to what degree)in Alaska.The four pol icy-
making bodies chosen were the Pacific Northwest Power Planning Council,the
Bonneville Power Administration,the California Energy Commission and the Wis-
consin Electric Power Company.The first three entities were chosen because
they represented regions in the western U.S.and because conservation programs
played a signficant role in their regional planning.Wisconsin Electric Power
Company was chosen as an example of a utility in a colder cl imate where natural
gas was the predominant fuel source.However,Wisconsin has its peak demand
for electricity in the summer when natural gas cannot fuel air conditioning.
It became clear upon examination of the various programs that direct com-
parison of the forecasts was not possible at the end-use level nor was it pos-
sible to compare the assumptions supporting the forecasts (e.g.,heating/cool-
B.1
ing degree days,appliance standards,etc.).The following list touches on
some of the differences among forecasts which made either direct or indirect
comparison difficult.
•Definitions of conservation differed.
o Variables were not consistent across regions.
•Programs were not consistent across regions.
•Some documentation showed a lack of internal consistency in report-
ing values.
•One entity reported savings in peak capacity while the others
reported both capacity and energy forecasts.
•Direct comparison of baseline,high,and low load growth scenarios
was not possible because of the level of conservation implied in the
forecasts;i.e.,in a low demand case more conservation is assumed
than in the high demand case,or conservation instead may be assumed
in a sensitivity case.
•Savi ngs coul d be projected ei ther by program,or appl i ance,or end-
use sector.
In addition,each of the four Lower 48 entities quantifies the components
of conservat i on effects di fferent ly.The Northwest Power Counc ill s approach is
to assume no change in technological efficiency;therefore,there is no price-
induced conservation.Conservation is treated as an energy resource.A
separate supply function (with price and program components)determines the
value of potential conservation.The difference between the forecast demand
and the supply function is the value of conservation potential.The program
and price components of the conservation increment cannot be readily sepa-
rated.Potential savings are reported at the appliance level.
The California Energy Commission also forecasts a conservation increment
in which price and program shares are not easily discernible.Part of the
program-induced savings has been quantified and double counting of price-
induced conservation is subtracted by a 20%implicit reduction in savings
estimates.The Bonneville Power Administration forecast has both technological
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change and price response imbedded in their model,but only part of their pro-
gram-induced conservation is quantifiable.
The Wisconsin Electric Power Company lacks the more sophisticated end-use
models used by the other three and focuses more on the peak demand savings
potential.Trend analysis driven by population projections is used to estimate
capacity requirements.There is some conservation implicit in the demand
growth estimated by the model.For example,air conditioning efficiency
improvements are assumed,and three "adjustments"are made to total demand for
rate structure reform,solar water heat,and solar space heat;but in general,
only fragments of the conservation response are quantified.
The literature provides some idea of the energy use attributable to bud-
geted and proposed programs,however.The foll owi ng subsection di scusses the
separate definitions of conservation adopted by the four policy-making bodies,
the forecasts of program-induced energy savings,and the methods adopted to
avoid double counting of competing programs and double counting of price and
program effects.The last subsection looks at current estimates for Alaska and
determines whether the conservation program savings have relevance to Alaskan
forecasts.
PACIFIC NORTHWEST POWER PLANNING COUNCIL
The Pacific Northwest Power Planning Council (PNPPC)was created in 1981
in accordance with the Pacific Northwest Electric Power Planning and Conserva-
tion Act (the Act)to encourage conservation and the development of renewable
resources in the Northwest and to assure an adequate and economical power sup-
ply.Conservation is defined by the PNPPC as the more efficient use of elec-
tricity by the consumer through replacing existing structures with electricity-
saving technologies or the use of new,more energy-efficient devices and pro-
cesses in the residential,commerical,industrial,and agricultural sectors.
The PNPPC assessments do not distinquish between price-induced conservation and
program-induced conservation.The forecast power supply estimates are based on
the high market penetration rates the PNPPC assumes for each conservation pro-
gram available under the Act.A conservation measure is assumed cost-effective
at costs below 4.0 cents per kilowatt-hour (roughly the cost of power from
B.3
regional coal plants).Not all of the economically achievable savings can be
realized,however,due to constraints such as consumer resistance,quality con-
trol,and unforeseen technical problems.The PNPPC believes that given the
wide range of measures permitted by the Act.,over 75%of the economically
achievable levels are possible (ranging from 56%for residential appliances to
100%in the industrial sector).Table B.1 lists the likely conservation sav-
ings at a cost equal to or less than 4.0 cents per kilowatt hours by the year
2000.Most of the savings in the residential sector come from building shell
or hot water tank improvements.Electricity has a larger share of space and
water heating loads in the PNPPC region than it does in the Railbelt.Thus,
many of the conservation savings of electricity in the PNPPC could not be
achieved in the Railbelt.
The PNPPC decided that all technically achievable conservation estimated
for the industrial sector could be realized since the savings represented less
then 10%of the region's current industrial electricity demand.This level was
considered a reasonable goal for the industrial sector.
Including all conservation along with other available resource choices can
avoid double counting of conservation induced by prices in the demand model and
conservation counted as potential resources on the supply side.This implies
that price-induced efficiency improvements within the end-use sectors and elec-
tricity uses where conservation programs are proposed are included in resource
potential,not demand reductions.In the residential and commercial sectors
technology efficiencies were frozen at 1983 levels so that the PNPPC models
forecast future energy use as if no efficiency improvements were made.Unfor-
tunately,once a conservation program or measure is available,savings in
response to price changes cannot be separated from those derived from the pro-
gram.Running the PNPPC demand model for individual programs will quantify the
impact for each measure under a given fuel price and supply scenario.
BONNEVILLE POWER ADMINISTRATION
The Bonneville Power Administration (BPA)supplies about half of the elec-
tric power production in the Pacific Northwest.Its service area is
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TABLE B.1.PNPPC Likely Conservation Potential at 4.0
Cents/kWh by the Year 2000
Residential (kWh/household)
Exi st i ng Space Heat 854
New Space Heat 1404
Water Heating 1364
Air Conditioning a
Refrigerators 259
Freezers 108
Cooking 15
Lighting 150
Other 229
4383
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Commercial (kWh/employee)(a)
Existing Structure 1199
New Structures 825
2024
Industrial (kWh/employee)(a)
$1000-3000 subsidy/kW 655-3282
(a)Includes federal,state and local government,
transportation,communication,public utilities,
wholesale and retail trade,finance insurance,
real estate,services.
(b)Includes mi ning,manufacturing,and construction.
Source:Pacific Northwest Power Planning
Council,1983.
roughly equivalent to the area covered by the PNPPC power planning efforts
(Oregon,Washington,Idaho,Western Mbntana).Long-range electricity demand
forecasts are made by 6PA to assist in utility power planning.Projections are
expressed as a baseline case to which alternative cases are added for a high-
low range of electricity consumption.Forecasts made by 6PA covering the
regi on defi ned by the Paci fi c Northwest El ectri c Power Pl anni ng and Conserva-
tion Act of 1980 (P.L.96-501)were done primarily to assist regional decision
making until the publication of the PNPPC official 20-year energy forecast and
plan in the spring of 1983.
6.5
BPA estimates of conservation potential savings include price-induced sav-
ings and savings from existing governmental,utility,and BPA conservation pro-
grams.Conservation programs that have yet to be initiated or budgeted are not
included.Some improvements in technology efficiencies are implicitly included
as part of the consumer price response.
The types of programs represented by the base,low,and high forecasts
include the following:
•home energy efficiency improvement
•commercial energy efficiency improvement
•street and area lighting efficiency improvement
•institutional building efficiency improvement
•utility customer service system efficiency improvement
•support of direct application renewable resources projects.
The BPA currently sponsors weatherizing of electrically heated dwellings
(primarily retrofit of existing housing),wrapping electric water heaters,
encouraging the distribution and use of shower water flow restraints,and
installing faucet flow control devices,low-flow shower heads,and solar hot
water/heat pump water heater conversions.Table B.2 summarizes the savings
estimates by program for residential and commercial sectors.Currently,there
are no budgeted programs in the Industrial sector.
BPA's Office of Conservation estimated the savings from conservation
measures that could not be explicitly modeled and subtracted that amount from
computed demand.To avoid double counting of price-induced conservaton,the
measure-specific savings were reduced by 20%.Again,most savings were found
in space conditioning and water heating.
CALIFORNIA ENERGY COMMISSION
The California Energy Commission (CEC)is required by the Warren-Alquist
Act of 1974 (Publ ic Resources Code,Section 25309)to "identify emerging trends
related to energy supply demand and conservation and public health and safety
factors,to specify the level of statewide and service area electrical energy
demand for each year in the forthcoming 5-,12-,and 20-year periods,and to
provide the basis for state policy and actions in relation thereto •••".In
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4,933
4,933
435
400
1,532
600
270
2,200
13,771
Water Heater Wrap
Shower Flow Restrictor
Residential Flow Control
Shower Heads
Faucet Heads
Solar/Heat Pump Water
Commerc ia1 (k Wh/employee)(a)
Publ ic
Heat i ng
Cooling
Water Heating
Lighting
Other
Private
Heating
Cooling
Water Heating
Lighti ng
Other
(a)Includes local and state government,trans-
portation and utilities,trade,finances,
insurance,real estate,services and con-
struction.High growth figures were used
for total number of employees.
Source:Bonneville Power Administration.
1982a.Table 5.6 and Appendix II,Table
23.
TABLE B.2.BPA Budgeted Conservation Program Savings
(annual kWh savings by the year 2000)
Residential (kWh/household)
Region Wide Weatherization
Low Income Weatherization
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compliance with the code,the CEC prepares a biennial report containing updated
energy supply/demand projections and a supplemental electricity report.Infor-
mation in this section reflects the fourth and most recent report (1983)in the
series.
The CEC has adopted the following definition of conservation.
IIConservation savings from local,utility,state,and Federal
programs in place or approved,and savings resulting from private
utilization of conservation measures in response to prices,and sav-
ings from programs on which analytical work is well advanced and for
which there is a substantial likelihood they will be in effect by
January 1985.II
The code requires the CEC -to include all conservation that is reasonably
expected to occur based on credible evidence within the framework provided by
their definition.Conservation programs and savings are categorized into three
classes:1)conservation reasonably expected to occur,2)additional achiev-
able conservation,and 3)conservation potential.Savings in Category 1 are
used to reduce the demand estimate.Those in Category 2 are considered to have
a moderate probability of occurring because of a higher uncertainty factor.
Category 3 includes both 1 and 2 and any other conservation thought to be cost
effective when compared to new generation sources.All conservation savings
reasonably expected to occur must be included in the CEC's adopted forecast.
Quantifying additional achievable conservation can help to establish new con-
servation programs.Table B.3 summarizes the savings reasonably expected to
occur for each program or measure.Table B.4 1 ists the savings by end-use sec-
tor.
The CEC feels that because programs are the causative agent for many
measures adopted,forecasts should report savings by program.Double counting
of programs is eliminated by analyzing how specific conservation measures
affect end uses of energy and reconciling competing programs·influence on each
measure.A_lI sharing ll structure is set up which includes effects of programs
and price fluctuations.Price-and program-induced conservation becomes IIdis-
jointed.1I For example,in general the residential sector model does not have
price-induced savings from consumer choice of more efficient appliances,
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TABLE B.3.CEC Conservation program(Electricity
Savi ngs in the Yea r 2002 a)
Sector Demand(GWH)
Res ident ia1 kWh/househo 1d
Existing Retrofit and
Programs 391 34
1975 HCD Buil di ng Standards 2,292 201
1978 CEC Building Standards 644 57
1982 CEC Bu i1ding Standards 5,108 449
1978 CEC Appliance 6,069 533
011-42 Programs ° °
Other Retrofit Programs 301 26
Load Management Cycling 1,160 102
15,965 1,403
Commerc ia1 kWh/employee
1978 CEC Building Standards 6,011 549
1983 CEC Bui 1ding Standards 1,083 99
1983 CEC Equipment Standards 1,057 97
Schools and Hospitals 234 21
Load Management Audits 1,683 154
at her Comme rc ia1 1 ,846 169
11,914 1,088
Industrial
1978 CEC Building Standards 323 97
(a)Reasonably expected to occur.Street lighting and agriculture sectors
exc 1uded.
Source:California Energy Commission 1983,Table 3-IV-1,2,3.Household
and employment projections used were taken from U.S.Department of Com-
merce,Bureau of Economic Analysis,1980 Regional Projections.Households
at 11,377,270:commercial employment at 10,950,677;industrial employment
at 3,321,917.
B.9
TABLE B.4.CEC Potential Energy Savings by End-Use
Sector by the Year 2002
B.10
WISCONSIN ELECTRIC POWER COMPANY
Source:California Energy Commission,Volume I Technical Report,1982,
Table 3-7.Agriculture not included.
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2,049
1,173
145
86
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GWh
23,313
12,849
1,593
983
o
4,985
o
43,723
Sector
Residential
Commercial Bldg
at her Comme rc ia 1
St reet Li ght i ng
Process Industry
Assembly Industry
Extraction Industry
Total
but estimates savings based on mandatory standards.In the commercial sector,
CEC loan management audits compete with price to motivate customers to make
efficiency improvements.However,as more programs are introduced this separa-
tion becomes more difficult.Once again,heavy reliance is placed on building
shell improvements to achieve conservation of electricity.
The Wisconsin Electric Power Company (WEPC)is an investor-owned utility
servi ng the Mi lwaukee,Kenosha,and Raci ne Standard Metropol itan Areas,Centra 1
and Northern Wisconsin,and the Upper Peninsula of Michigan.Wisconsin1s pri-
mary fuel source (70%)has been natural gas since 1977.Electricity accounts
for only 4 to 5%of total energy used.WEPC has adopted a very broad defini-
tion of conservation,covering not only more efficient end use of electricity
but also energy saved at the supply and conversion levels,e.g.,fuel switch-
ing,time-of-use rates,load management,etc.,although load management was not
modeled.It should be noted that there is currently an on-going debate between
WEPC and the Wisconsin Public Services Commission regarding this definition.
Basically the problem centers around WEPC's desire to raise rates to pay for
programs they define as conservation measures.The Commission uses the defini-
tion of improvement in efficiency of energy end use by the customer.The Com-
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mission feels that WE PC emphasizes load management over incentives to the cus-
tomer and thereby serves the company objectives first.(a)WEPC counters with
the following argument:
"Staff has been critical of Wisconsin1s Electric's perspective
on conservation.It is true that Wisconsin Electric has viewed con-
servation in context of the over-all planning process.That process
seeks to anticipate and influence load patterns in nrder to maximize
efficiency ,and.maintain financial strength with the ultimate purpose
of insuring that reliable service can be delivered at the lowest
reasonable cost.The encouragement of efficient end-use of el ectri-
city contri butes to the achi evement of pl anni ng goals to the extent
that peak use is constrained.It may be detrimentalbto the extent
that it results in inefficient plant utilization."{)
Two points about this controversy are important to this study.First,
total state or regional energy planning will be less efficient until a unified
policy position is adopted.Such a situation occurred in the past between BPA
and PNPPC and was resolved through guidelines provided by the Regional Power
Act.Second,the WEPC conservation forecasts will include end-use efficiency
improvements,price-induced and program-induced conservation,and energy sav-
ings from fuel switching.
WEPC uses trend analysis to estimate peak demand.The WEPC system is pri-
marily concerned with providing adequate capacity and their modeling effort
refl ects that concern;there is very 1 ittl e di saggregati on -at the end-use
level.The energy forecast is 'derived directly from demand and contains some
conservation from an implicit reduction for improved air conditioning effi-
ciencies.Then,adjustments in hourly energy use for rate structure reform and
solar water and space heat are made.These adjustments are summed for monthly
and annual energy forecasts.The adjustments were allocated to each sector in
the following manner:
(a)Post Hearing Brief on Docket 6630-ER-14.
(b)Hearings before the Public Service Commission of Wisconsin Docket 6630-
ER-14."Application of Wisconsin Electric Power Company for Authority to
Increase Rates for Electric Service Based on Projected 1983 Operations,"
1982.
B.11
•rate structure reform to general secondary (commercial)
•solar to residential
•air conditioning efficiency improvements to residential and general
secondary according to the percent of the efficiency reduction at
summer peak demand attributable to each sector (62%residential,38%
c om me rc ia 1)•
TABLE B.5.WEPC Conservation Potential by the Year 2000 (Base Case)
Table B.5 presents the energy savings by customer for the year 2000.
Energy savings per household or employee were not available.
Sector
Residential
General Secondary
(c omme rc ia1)
Savi ngs
13 kWh/custome r
447 kWh/customer
Source:Number of customers from
Response to Item 7 of the Publ ic Ser-
vice Commission of Wisconsin Docket
6630-ER-14 Regarding Conservation.
Estimated savings from Wisconsin Elec-
tric Power Company 20-year Demand and
Energy Forecast 1981-2000,
Table 2-1.2.Air Conditioning load
reduction developed from Table 1-3.1
and Table 2-1.4.
These conservation estimates represent only part of the total potential.
Although the air conditioning component includes price response,the solar and
rate structure components do not.The forecast does not include reductions for
improved efficiency in other appliances.Double counting occurs in adjusting
for improved appliance efficiency resulting from federally mandated standards
and the associated response to the econometric pricing assumptions.WEPC
avoided double counting (or rather discounted for it)by not quantifying
separate adjus tment s fo r basel oad and wate r heat i ng effi c ienc ie s.
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ALASKAN RAILBELT
The State of Alaska,various 'utilities in the Railbelt region,and the
Municipality of Anchorage have implemented energy conservation programs that
include measures for conserving electricity that have already reduced electri-
city consumption.
Major conservation programs currently available in the Railbelt incJude
the State Division of Energy and Power Deve.lop11ent energy audit and loan (DEPD)
program;the Golden Valley Electric Association program (primarily education in
support of the market place);similar education programs by the Chugach Elec-
tric Association and the Fairbanks Municipal Utility System;and the City of
Anchorage Program involving audits,weatherization,and educational efforts.
The Golden Valley program was partly responsible for a reduction of electricity
use in this Fairbanks service area from 17,332 kWh/household in 1975 to 9303
kWh/household in 1982 (see Table B.6).In the past,however,the DEPD program
has been the most extensive with an estimated 24%of all Railbelt houses having
had an energy audit performed.The program has saved an estimated average of
1,582 kwh/year of electricity per Alaska household,with electricity equaling
about 18%of total energy savings from the program.No reliable data on DEPD
program electricity savings are available in the Railbelt load centers.
According to Ti llman (1983),almost all of the Railbelt programs have been
aimed at the residential sector,with conservation in the commercial and indus-
trial sectors being accomplished primarily through market conditions.Price-
induced conservation is then more easily distinguishable in those two
sectors.In the AML&P program,total conservation potential through 1987 has
been disaggregated into program-and price-induced components (see Table B.7)
with approximately a 40 and 60%share,respectively.For a breakdown by pro-
gram,see Table B.8.
Tillman indicates that price-induced electricity conservation will be more
important in the future than programmatic conservation for the following
reasons:
B.13
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TABLE B.6.Average Annual Electricity Consumption per
Househol d on the GVEA System,1972-1982
Annual Monthly
Cons umpt ion Cons umpt i on Percent
Year (kWh)(kWh)Change
1972 13,919 1,160 +5.6
1973 14,479 1,207 +4.0
1974 15,822 1,319 +9.3
1975 17,332 1,444 +9.5
1976 15,203 1,267 -12.3
1977 14,255 1,188 -6.2
1978 11,574 965 -18.8
1979 10,519 877 -9.1
1980 9,767 814 -7.1
1981 9,080 757 -7.0
1982 9,303 775 +2.5
Source:GVEA,as reported by Tillman (1983).
•It has the dominant share of impacts.
•Subsidized audits and investments programs for residences are being
phased out.
o Practical impact limits are being achieved in institutional build-
ings and systems programs.
•Current plans for future programs are predominantly educational pro-
grams designed to support price or market-induced conservation.
Tillman (1983)notes that two miscellaneous AML&P programs are expected to
save considerable electric energy by the year 1987.These are street lighting
improvements,whose impact is taken into account in Section 9.0,and heating of
the Anchorage municipal water supply to reduce the electricity use of water
heaters.The water heater impact is factored into the use rates for Anchorage
water heaters in Section 5.0
In attempting to determine the level of conservation potential,the ques-
tion arises as to whether further investment in energy-savings programs
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TABLE B.7.Programmatic Versus Market-Driven Energy Conservation
Projections in the Ar~L&P Service Area
Programma t i r Market Drivrg Tot a1(a)Conservat i on a)Conservat ion )
Year (MWh)(%of Tota 1)(MWh)(%)(MWh)(%)
1981 12,735 39.5 19,558 60.5 32,294 100
1982 19,609 34.9 27,243 65.1 46,853 100
1983 20,896 37 .1 35,374 62.9 56,289 100
1984 27,619 41.1 39,560 58.9 67,133 100
1985 30,195 40.4 44',536 59.6 74,730 100
1986 32,614 40.6 48,133 59.4 81,015 100
1987 35,421 41.0 50,940 59.0 86,363 100
Cumulative 179,089 40.3 265,344 59.7 444,677
(a)Detail does not add to total in the orginal.1981 programs
included:
Re si dent ia1
weatheri zat ion
St ate Programs
Wa t er Flow Re st ric tor
Water Heat Injection
MWh/yr
586
879
200
3,921
5,586
kWh/Cu stome r
42
63
14
281
400
Industri a1
Boiler Feed Pumps 7,148 2298
Planned conservation programs include hot water
wraps in the residential sector and street light
conversion and utility transmission conversion in
the commercial sector.The number of customers was
provided by the 1982 Al as ka El ectri c Power Stat i s-
tics of the Alaska Power Administration.
(b)1981 Price elasticity effects equaled 19,558 MWh/yr.
Sou rce:AML&P 1982.
B.15
TABLE B.8.Programmat ic Energy Conservation Projections for Ar~L&P (MWh/yr)
Program 1981 1982 1983 1984 1985 1986 1987
Weatherization 586 762 938 1,114 1,290 1,466 1,641
State Programs 879 1,759 2,199 2,683 3,078 3,518 3,737
Water Flow 200 464 464 464 464 464 464
Rest ri ct.i on s
Water Heat 3,922 3,922 3,922 3,922 3,922 3,922 3,922
Injection
Hot Water NA NA 249 249 249 249 249
Heater Wrap
St reet Light 0 555 1,859 3,307 4,788 6,306 7,861
Convers i on
Transmission 0 0 4,119 8,732 9,256 9,811 10,399
Convers i on
Boi 1er Pump 7,148 7,148 7,148 7,148 7,148 7,148 7,148
Conversion
TOTAL 12,735 14,609 20,896 27,619 30,195 32,614 35,421
%Change From NA 14.7 43.0 32.2 9.3 9.8 8.6
Previous Year
Sou rce:Ar~L&P ,as reported by Tillman (1983).
would be cost effective.An investigation of program-induced versus price-
induced conservation forecasted by other regions could indicate if current mar-
ket penetration levels in the Railbelt are realistic.Unfortunately,as we
have seen,total separation of price and program effects forecasted by PNPPC,
BPA,CEC,and WEPC has not yet been achieved.We have some indication that
these forecasts do show programmatic contributions by the year 2000 in residen-
tial commercial,and industrial sectors.However,the extent to which techni-
cally achievable conservation limits can be approached in Alaska through
programs and what proportion would be due to market actions is not clear.In
general,because of differences in housing stock,fuel mode splits,fuel
prices,climate,and other factors,forecasted program savings for other
regions may have only limited relevance for the Railbelt.
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APPENDIX C
RED MODEL OUTPUT
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APPENDIX C
RED MODEL OUTPUT
This appendix displays selected RED model output produced for the 1983
update.Included in the following tables are information on the number of
households served by electricity in each load center,housing vacancies,fuel
price forecasts,electricity used per household and pe~employee,as well as
summaries of price effects and programmatic conservation,annual electricity
requirements by sector and load center,and total peak demand.The figures
presented in these tables are at the point of sale and include estimates
supplied by Harza-Ebasco of military and industrial demand.They do not
include an adjustment for transmlssion losses.However,for the 1983 update of
the alternative generation plans these reported figures were adjusted for
transmission losses.
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LIST OF TABLES
H-12--SHERMAN CLARK NO SUPPLY DISRUPTION ••••••••••••••••••••••••••••••••••C.II
Households Served,Anchorage -Cook Inlet ••••••••••••••••••••••••••••C.13
Households Served,Greater Fairbanks •••••••••••••••••••••••••••••••••C.14
Housing Vacancies,Anchorage -Cook Inlet ••••••••••••••••••••••••••••C.IS
Housing Vacancies,Greater Fairbanks •••••••••••••••••••••••••••••••••C.16
Fuel Price Forecasts Employed,Electricity ($/kWh)•••••••••••••••••••C.I?
Fuel Price Forecasts Employed,Natural Gas ($/MMBtu)•••••••••••••••••C.18
Fuel Price Forecasts Employed,Fuel Oil ($/MMBtu)••••••••••••••••••••C.19·
Residential Use Per Household (kWh)(Without Adjustment
for Price),Anchorage -Cook Inlet •••••••••••••••••••••••••••••••••••C.20
Residential Use Per Household (kWh)(Without Adjustment
for Price),Greater Fairbanks ••••••••••••••••••••••••••••••••••••••••C.21
Business Use Per Employee (kWh)(Without Large Industrial)
(Without Adjustment for Price)••••••••••••••••••••••••••••••~.••••••••C.22
Summary of Price Effects and Programmatic Conservation in
GWh,Anchorage -Cook Inlet ••••••••••••••••••••••••••••••••••••••••••C.23
Summary of Price Effects and Programmatic Conservation in
GWh,Greater Fairbanks •••••••••••••••••••••••••••••••••••••••••••••••C.24
Breakdown of Electricity Requirements (GWh)(Total Includes
Large Industrial Consumption),Anchorage -Cook Inlet ••••••••••••••••C.25
Breakdown of Electricity Requirements (GWh)(Total Includes
Large Industrial Consumption),Greater Fairbanks •••••••••••••••••••••C.26
Total Electrical Requirements (GWh)(Net of Conservation)
(Includes Large Industrial Consumption)rv1edium Range (PR =.5)•••••••C.2?
Peak El ectri c Requi rements (MW)(Net of Conservation)
(Includes Large Industrial Demand)rv1edium Range (PR =.5)••••••••••••C.28
HE3--DOR AVG SCENARIO •••••••••••••:•••••••••.•••••••••••••••••••••••••••••C.29
Households Served,Anchorage -Cook Inlet ••••••••••••••••••••••••••••C.31
Households Served,Greater Fairbanks •••••••••••••••••••••••••••••••••C.32
C.3
Housing Vacancies,Anchorage -Cook Inlet ••••••••••••••••••••••••••••C.33
Housing Vacancies,Greater Fairbanks •••••••••••••••••••••••••••••••••C.34
Fuel Price Forecasts Employed,El ectricity ($/kWh)•••••••••••••••••••C.35
Fuel Price Forecasts Employed,Natural Gas ($/MMBtu)•••••••••••••••••C.36
Fuel Price Forecasts Employed,Fuel Oil ($/MMBtu)••••••••••••••••••••C.3?
Residential Use Per Household (kWh)(Without Adjustment
for Price),Anchorage -Cook Inlet •••••••••••••••••••••••••••••••••••C.38
Residential Use Per Household (kWh)(Without Adjustment
for Price),Greater Fairbanks ••••••••••••••••••••••••••••••••••••••••C.39
Business Use Per Employee (kWh)(Without Large Industrial)
(Without Adjustment for Price)•••••••••••••••••••••••••••••••••••••••C.40
Summary of Price Effects and Programmatic Conservation in
GWh,Anchorage -Cook Inlet ••••••••••••••••••••••••••••••••••••••••••C.41
Summary of Price Effects and Programmatic Conservation in
GWh,Greater Fairbanks •••••••••••••••••••••••••••••••••••••••••••••••C.42
Breakdown of Electricity Requirements (GWh)(Total Includes
Large Industrial ConsLDllption),Anchorage -Cook Inlet •••••••~••••••••C.43
Breakdown of Electricity Requirements (GWh)(Total Includes
Large Industrial ConsLDllption),Greater Fairbanks •••••••••••••••••••••C.44
Total Electrical Requirements (GWh)(Net of Conservation)
(Includes Large Industrial Consumption)Medium Range (PR =.5)•••••••C.45
Peak Electric Requirements (MW)(Net of Conservation)
(Includes Large Industrial Demand).Medium Range (PR =.5)••••••••••••C.46
HE9--DOR 50%••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••C.47
Households Served,Anchorage -Cook Inlet ••••••••••••••••••••••••••••C.49
Households Served,Greater Fairbanks •••••••••••••••••••••••••••••••••C.50
Housing Vacancies,Anchorage -Cook Inlet ••••••••••••••••••••••••••••C.51
Housing Vacancies,Greater Fairbanks •••••••••••••••••••••••••••••••••C.52
Fuel Price Forecasts Employed,El ectricity ($/kWh)•••••••••••••••••••C.53
Fuel Price Forecasts Employed,Natural Gas ($/r1MBtu)•••••••••••••••••C.54
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Fuel Price Forecasts Employed,Fuel Oil ($/Mr~Btu)••••••••••••••••••••C.55
Residential Use Per Household (kWh)(Without Adjustment
for Price),Anchorage -Cook Inlet •••••••••••••••••••••••••••••••••••C.56
Residential Use Per Household (kWh)(Without Adjustment
for Price),Greater Fairbanks ••••••••••••••••••••••••••••••••••••••••C.57
Business Use Per Employee (kWh)(Without Large Industrial)
(Without Adjustment for Price)•••••••••••••••••••••••••••••••••••••••C.58
Summary of Price Effects and Programmatic Conservation in
GWh,Anchorage -Cook Inlet.,••••••••••••••••••••••••••••••••••••••••C.59
Summary of Price Effects and Programmatic Conservation in
GWh,Greater Fairbanks ••••••••••••••••••••••••••••••••••••••••••••••o C.60
Breakdown of Electricity Requirements (GWh)(Total Includes
Large Industrial-Consumption),Anchorage -Cook Inlet ••••••••••••••••C.61
Breakdown of Electricity Requirements (GWh)(Total Includes
Large Industrial Consumption),Greater Fairbanks •••••••••••••••••••••C.62
Total Electrical Requirements (GWh)(Net of Conservation)
(Includes Large Industrial Consumption)Medium Range (PR =.5)•••••••C.63
Peak Electric Requirements (MW)(Net of Conservation)
(Includes Large Industrial Demand)Medium Range (PR =.5)••••••••••••C.64
HIO--DOR 30%•••••••••••••••••••••••••••••••••••••••••••••••••••••••••.••••C.6S
Households Served,Anchorage -Cook Inlet ••••••••••••••••••••••••••••C.67
Households Served,Greater Fairbanks •••••••••••••••••••••••••••••••••C.68
Housing Vacancies,Anchorage -Cook Inlet ••••••••••••••••••••••••••••C.69
Housing Vacancies,Greater Fairbanks •••••••••••••••••••••••••••••••••C.70
Fuel Price Forecasts Employed,Electricity ($/kWh)•••••••••••••••••••C.71
Fuel Price Forecasts Employed,Natural Gas ($/MMBtu)•••••••••••••••••C.72
Fuel Price Forecasts Employed,Fuel Oil ($/MMBtu)••••••••••••••••••••C.73
Residential Use Per Household (kWh)(Without Adjustment
for Price),Anchorage -Cook Inlet •••••••••••••••••••••••••••••••••••C.74
Residential Use Per Household (kWh)(Without Adjustment
for Price),Greater Fairbanks ••••••••••••••••••••••••••••••••••••••••C.75
C.5
Business Use Per Employee (kWh)(Without Large Industrial)
(Without Adjustment for Price)•••••••••••••••••••••••••••••••••••••••C.76
Summary of Price Effects and Programmatic Conservation in
GWh,Anchorage -Cook Inlet ••••••••••••••••••••••••••••••••••••••••••C.ll
Summary of Price Effects and Programmatic Conservation in
GWh,Greater Fa;rbanks •••••••••••••••••••••••••'••••••••••••••••••••••C.78
Breakdown of Electricity Requirements (GWh)(Total Includes
Large Industrial Consumption),Anchorage -Cook Inlet ••••••••••••••••C.79
Breakdown of E1 ectri city Requi rements (GWh)(Total Inc1 udes
Large Industrial Consumption),Greater Fairbanks •••••••••••••••••••••C.80
Total Electrical Requirements (GWh)(Net of Conservation)
(Includes Large Industrial Consumption)~dium Range (PR =.5)•••••••C.81
Peak Electric Requirements (MW)(Net of Conservation)
(Includes Large Industrial Demand)~dium Range (PR =.5)••••••••••••C.82
H13--0RI SCENARIO •••••••••••••••••••••••••••••••••••••••••••••••••••••••••C.83
Households Served,Anchorage -Cook Inlet ••••••••••••••••••••••••••••C.85
Households Served,Greater Fairbanks •••••••••••••••••••••••••••••••••C.86
Housing Vacancies,Anchorage -Cook Inlet ••••••••••••••••••••••••••••C.87
Housing Vacancies,Greater Fairbanks •••••••••••••••••••••••••••••••••C.88
Fuel Price Forecasts Employed,Electricity ($/kWh)•••••••••••••••••••C.89
Fuel Price Forecasts Employed,Natural Gas ($/MMBtu)•••••••••••••••••C.90
Fuel-Price Forecasts Employed,Fuel Oil ($/MMBtu)••••••••••••••••••••C.91
Residential Use Per Household (kWh)(Without Adjustment
for Price),Anchorage -Cook Inlet •••••••••••••••••••••••••••••••••••C.92
Residential Use Per Household (kWh)(Without Adjustment
for Price),Greater Fairbanks ••••••••••••••••••••••••••••••••••••••••C.93
Business Use Per Employee (kWh)(Without Large Industrial)
(Without Adjustment for Price)•••••••••••••••••••••••••••••••••••••••C.94
Summary of Price Effects and Programmatic Conservation in
GWh,Anchorage -Cook Inl et ••••••••••••••••••••••••••••••••••••••••••C.9 5
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Summary of Price Effects and Programmatic Conservation in
GWh,Greater Fairbanks •••••••••••••••••••••••••••••••••••••••••••••••C.96
Breakdown of Electricity Requirements (GWh)(Total Includes
Large Industrial Consumption),Anchorage -Cook Inlet ••••••••••••••••C.9?
Breakdown of Electricity Requirements (GWh)(Total Includes
Large Industrial Consumption),Greater Fairbanks •••••••••••••••••••••C.98
Total Electrical Requirements (GWh)(Net of Conservation)
(Includes Large Industrial Consumption)Medium Range (PR =.5)•••••••C.99
Peak Electric Requirements (MW)(Net of Conservation)
(Includes Large Industrial Demand)Medium Range (PR =.S)••••••••••••C.IOO
HE4--FERC +2%••••••••••..••••••••••••••••.••••••••.••••.•.•••••••••••••••oC.IOI
Households Served,Anchorage -Cook Inlet ••••••••••••••••••••••••••••C.103
Households Served,Greater Fairbanks •••••••••••••••••••••••••••••••••C.I04
Housing Vacancies,Anchorage -Cook Inlet ••••••••••••••••••••••••••••C.lOS
Housing Vacancies,Greater Fairbanks •••••••••••••••••••••••••••••••••C.I06
Fuel Price Forecasts Employed,Electricity ($/kWh)•••••••••••••••••••C.IO?
Fuel Price Forecasts Employed,Natural Gas ($/MMBtu)•••••••••••••••••C.I08
Fuel Price Forecasts Employed,Fuel Oil ($/MMBtu)••••••••••••••••••••C.I09
Residential Use Per Household (kWh)(Without Adjustment
for Price),Anchorage -Cook Inlet •••••••••••••••••••••••••••••••••••C.110
Residential Use Per Household (kWh)(Without Adjustment
for Price),Greater Fairbanks ••••••••••••••••••••••••••••••••••••••••C.l11
Business Use Per Employee (kWh)(Without Large Industrial)
(Without Adjustment for Price)•••••••••••••••••••••••••••••••••••••••C.112
Summary of Price Effects and Programmatic Conservation in
GWh,Anchorage -Cook Inlet ••••••••••••••••••••••••••••••••••••••••••C.l13
Summary of Price Effects and Programmatic Conservation in
GWh,Greater Fairbanks .•.•••.••.••••.•••••••..•.•••••.•...••••,•...•••C.114
Breakdown of Electricity Requirements (GWh)(Total Includes
Large Industrial Consumption),Anchorage -Cook Inlet ••••••••••••••••C.llS
Breakdown of Electricity Requirements (GWh)(Total Includes
Large Industrial Consumption),Greater Fairbanks •••••••••••••••••••••C.116
C.?
Total Electrical Requirements (GWh)(Net of Conservation)
(Includes Large Industrial Consumption)~'1edium Range (PR =.5)•••••••C.117
Peak Electric Requirements (MW)(Net of Conservation)
(Incl udes Large Industri alDemand)Medi urn Range (PR =.5)••••••••••••C .118
HE6--FERC 0%•••••.•••••..••••.••.•••••••••...••......•••••••.•.•.•.•..•.•.C.119
Households Served,Anchorage -Cook Inlet ••••••••••••••••••••••••••••C.121
Households Served,Greater Fairbanks •••••••••••••••••••••••••••••••••C.122
Housing Vacancies,Anchorage -Cook Inlet ••••••••••••••••••••••••••••C.123
Housing Vacancies,Greater Fairbanks •••••••••••••••••••••••••••••••••C.124
Fuel Price Forecasts Employed,Electricity ($/kWh)•••••••••••••••••••C.125
Fuel Price Forecasts Employed,Natural Gas ($/MMBtu)•••••••••••••••••C.126
Fuel Price Forecasts Employed,Fuel Oil ($/MMBtu)••••••••••••••••••••C.127
Residential Use Per Household (kWh)(Without Adjustment
for Price),Anchorage -Cook Inlet •••••••••••••••••••••••••••••••••••C.128
Residential Use Per Household (kWh)(Without Adjustment
for Price),Greater Fairbanks •••••••••••••••••••••••••••••••~••••••••C.129
Business Use Per Employee (kWh)(Without Large Industrial)
(Without Adjustment for Price)•••••••••••••••••••••••••••••••••••••••C.130
Summary of Price Effe~ts and Programmatic Conservation in
GWh,Anchorage -Cook Inlet ••••••••••••••••••••••••••••••••••••••••••C.131
Summary of Price Effects and Programmatic Conservation in
GWh,Greater Fairbanks •••••••••••••••••••••••••••••••••••••••••••••••C.132
Breakdown of El ectricity Requi rements (GWh)(Total Incl udes
Large Industrial Consumption),Anchorage -Cook Inlet ••••••••••••••••C.133
Breakdown of Electricity Requirements (GWh)(Total Includes
Large Industrial Consumption),Greater Fairbanks •••••••••••••••••••••C.134
Total Electrical Requirements (GWh)(Net of Conservation)
(Includes Large Industrial Consumption)Medium Range (PR =.5)•••••••C.135
Peak Electric Requirements (MW)(Net of Conservation)
(Includes Large Industrial Demand)Medium Range (PR =.5)••••••••••••C.136
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HE7--FERC -1%••..•.•...•..•.••.•••.•••...•.•...•••.••.•••••....•••...•....C.137
Households Served,Anchorage -Cook Inlet ••••••••••••••••••••••••••••C.139
Households Served,Greater Fairbanks •••••••••••••••••••••••~•••••••••C.140
Housing Vacancies,Anchorage -Cook Inlet ••••••••••••••••••••••••••••C.141
Housing Vacancies,Greater Fairbanks •••••••••••••••••••••••••••••••••C.142
Fuel Price Forecasts Employed,Electricity ($/kWh)•••••••••••••••••••C.143
Fuel Price Forecasts Employed,Natural Gas ($/MMBtu)•••••••••••••••••C.144
Fuel Price Forecasts Employed,Fuel Oil ($/MMBtu)••••••••••••••••••••C.145
Residential Use Per Household (kWh)(Without Adjustment
for Price),Anchorage -Cook Inlet •••••••••••••••••••••••••••••••••••C.146
Residential Use Per Household (kWh)(Without Adjustment
for Price),Greater Fairbanks ••••••••••••••••••••••••••••••'••••••••••C.147
Business Use Per Employee (kWh)(Without Large Industrial)
(Wi thout Adjustment for Pri ce)•••••••••••••••••••••••••••••••••••••••C.148
Summary of Price Effects and Programmatic Conservation in
GWh,Anchorage -Cook Inlet •••••••••••••••••••••••••••••••••~••••••••C.149
Summary of Price Effects and Programmatic Conservation in
GWh,Greater Fairbanks •••••••••••••••••••••••••••••••••••••••••••••••C.IS0
Breakdown of El ectri city Requi rements (GWh)(Total Incl udes
Large Industri al Consumption),Anchorage -Cook Inlet ••••••••••••••••C.151
Breakdown of Electricity Requirements (GWh)(Total Includes
Large Industrial Consumption),Greater Fairbanks •••••••••••••••••••••C.152
Tota 1 El ectri ca 1 Requi rements (GWh)(Net of Conservati on)
(Includes Large Industrial Consumption)Medium Range (PR =.5)•••••••C.153
Peak Electric Requirements (MW)(Net of Conservation)
(Includes Large Industrial Demand)Medium Range (PR =.5)••••••••••••C.154
HE8--FERC -2%••...•••••••••..•••••..••......•....•...••••..........•.••..C.ISS
Households served,Anchorage -Cook Inlet ••••••••••••••••••••••••••••C.157
Households Served,Greater Fairbanks •••••••••••••••••••••••••••••••••C.158
Housing Vacancies,Anchorage -Cook Inlet ••••••••••••••••••••••••••••C.159
C.9
Housing Vacancies,Greater Fairbanks •••••••••••••••••••••••••••••••••C.160
Fuel Price Forecasts Employed,Electricity ($/kWh)•••••••••••••••••••C.161
Fuel Price Forecasts Employed,Natural Gas ($/MMBtu)•••••••••••••••••C.162
Fuel Price Forecasts Employed,Fuel Oil ($/Mt1Btu)••••••••••••••••••••C.163
Residential Use Per Household (kWh)(Without Adjustment
for Price),Anchorage -Cook Inlet •••••••••••••••••••••••••••••••••••C.164
Residential Use Per Household (kWh)(Without Adjustment
for Price),Greater Fairbanks ••••••••••••••••••••••••••••••••••••••••C.165
Business Use Per Employee (kWh)(Without Large Industrial)
(Wi tho ut Ad jus tme nt for Pric e)•••••••••••••••••••••••••••••••••••••••C.166
Summary of Price Effects and Programmatic Conservation in
GWh,Anchorage -Cook Inlet ••••••••••••••••••••••••••••••••••••••••••C.167
Summary of Price Effects and Programmatic Conservation in
GWh,Greater Fairbanks •••••••••••••••••••••••••••••••••••••••••••••o.C.16B
Breakdown of E1 ectri city Requi rements (GWh)(Total In c1 udes
Large Industrial Consumption),Anchorage -Cook In1et ••••••••••••••••C.169
Breakdown of Electricity Requirements (GWh)(Total Includes·
Large Industrial Consumption),Greater Fairbanks •••••••••••••••••••••C.170
Total Electrical Requirements (GWh)(Net of Conservation)I
(Includes Large Industrial Consumption)Medium Range (PR =.5)•••••••C.171 .
Peak E1 ectri c Requi rements (MW)(Net of Conservation)I
(Includes Large Industrial Demand)Medium Range (PR =.5)••••••••••••C.172
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j
I
l
I
I
1 __I
SCENARIO,MEO ,HIZ ••SHERMAN CLARK NO SUPPLY OISRUPTION ••6/24/1q83
HOUSFHOLoS SfRVEO
ANC~OAAGE •COOK INLET._......~.-.~..-......
YEAR SIt.GLE FAMILy MULTIFAMILY MOIUl.E HOliES nUPLEXES TOTAL............•................•-_..~..-...........................••....
1980 35 l1 n.211314.14l!l1)•741H.I.11501.
0.000)(0.000)(0.01)0)(o.OOC))(0.000)
IQBS 4b22 11 •2b200.IOQ58.85b1.QIQ51~
('")(0.00/)(o.ono)(0.000)(0.000)r 0.000).
.......IQqo 58140.2b34Q.13'50')•811bO.1010511.w
0.0011)(0.000)(0.000)(0.000)r 0.000)
19q5 64779.Z9 Q31.11.1941.8333.It HBII.
0.0011)(0.001)(0.000)(0.000)(0.000)
2000 69822.H2SQ.Ib201).802l.12,102.
0.001\)(0.000)(0.000)(0.000)(0.000)
2005 75177 •3'.I17~•1174Q.8738.138blll.
O.I)UO)((1.1)1)0)(0.000)(0.000)(0.000)
,Q7j!l.
.
9649.1531211.ZOIO 8H43.400 I , •
0.1)01)(0.000)(0.000)(0.000)(0.000)
SCEN4RIO,~IEO ,HI2--SHER~AN CLARK NO SUPPLY DISRUPTIOH--6/24/1Q8]
HOlISEHnLns SERVEO
GREATER FAIARAHKS........-..~.._~._....
VEAR SINGLE FAMILY I4UL TlFAMILY MUBlLE HOliES nUPLEXES TOTAL............•.•.................•.•....•....•......•..••....•.•.......
1980 ?liO.5287.lt8~.1617.15113.
0.000)(0.000)(0.0(0)(0.(00)(0.000)
t985 111646.5 Ab7.2130.1765.201107 •
0.000)(O.flont (0.000)(0.000)(0.000)
n.19~0 ti7l8.7 9 bO.2270.237!.211132.
t-'(0.000)(0.(011)(1).000)(0.000)C 0.0(1)+::0
1995 ,lInb.71'41.3328.2139."112 4 11.
(0.000)(0.000)(1).000)(0.000)c 0.000)
2000 Ib5l8.HOl.384'5.2298.30]74.
o.oo/)(0.000)(0.(00)((\.000)(0.000)
2005 17 9 51.8bll'.IIno.lut.329n.
0.000)c 0.000)(0.000)(0.000)(0.000)
2010 t 9lt75.9b12.11673.2]]4.3bi911.
0.0(0)(0.000)(0.0(1)(0.000)(0.0(\/)
~il
-1_
SCENARIO.MED •HIZ--SHER14AN CLARK NO SijPPLY DISRUPTION.-6/2q/I~Sl
HOUSING VACANCIES
ANC~ORAGE ~COOK INLET..•.•.....•.~•.••....•
YEAR Slt~GLE FAMILY HULTJFA'41LY 14091LE "'OMES DUPLEXES TOT~L..-.......•...•.....••.•.....•..........•....•....•.•.•..•..•........
1~80 508q.7&6b.I enl.1461.IUOlJ.
0.000)(0.000)(0.000)(0.(00)(0.(00)
Iq85 1509.IlIqb.li!l.zea2.llllJ •
("")(0.000)(0.000)(0.000)(0.0001 (0.0011).......lQqO &46.1005.141'1.281'1.20alJ.U1
0.000)(0.00(1)(0.000)(0.000)(0 ••000).
11'11'15 713.IbU.16".284.2771.
O.OUO)(0.000)(O.OUO)(0.000)(0.0(0)
2000 708.Ino.17e.1145.3181.
0.(00)(0.0(0)(0.000)(0.(00)(0.000)
2005 83 4 •11'164.lQ5.288.~281.
0.000)(0.000)(0.000)(0.(00)(0.000)
2010 caP.2182.217 •319.1&]11.
0.11110)(0.110(1)(0.000)(0.000)(0.(00)
..................................
ere "-0 ~o "",c "'e _0 ere
11'0 _0 «10 Ne.:FO NO oDC'
«Ie 11'0 oDe ...0 ...e ~O IOC
-'«I •..,···· ··oC C 0 e 0 c c c:
I-
0
to-
.-.-......-...............
It\C Ne._0 00 «10 0'0 ...0
en CPC'NO 100 CI;IO ....0 00 ...e
'"'w 100 ....0 e C 0 NO Cl
«I x •·•·• •·)CP w Cl 0 ~Cl 0 Cl 0--'
"Q,
::r :;,
N 0....
oD•...•zc CD...I&l en en ...........-.-.-......-
t-...¥~..oCl :FO lI'O ...0 Ne...00 -e.
0..u Z Z 100 NO NO ,",e ::re :FO 11'0
:;,:.4 0 CPCl Cl Cl e Cl 0 Cl
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a:~'"'·N ·0 ··••.....~Cl 0 0 0 Cl 0
-'~
u .......
Z -'
C :;,
%X
II:
I&l
%
C•>-...........-............
•oJ ,",e ClCl CPO NO Ale "'Cl oDO
N ...11'0 _0 NO ..00 11)0 CPCI _0
%~'e _0 _0 _0 _e -e NO
:a::.......· ·
•··••
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l&l
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SCENARIO,~ED'~'2 ••SHERMA~CLARK NO SUPPLY OISRUPTloN ••6/2QJlq81
FUEL PRICE FORECASTS·EHPLOYF.O
ELECTRICITY (S J KWH)
ANCHORAGE •COUK INLET GREATEA FAIRRANKS......-.......-.•..••.•..._-_...~.~....~.......•...••._.•.......•.........
YEAR RESIDENTIAL BUSINESS RESIDENTIAl.RIIS I NESS........•......•....••.....•...•...-........
,Q80 1).031 0.034 0.0'15 I).OQO
nss 0.048 O.l)QS O.O'lS o.OQO
,Q90 0.052 o.04Q 0.1)'12 0.087
n95 0.058 D.OS!0.0'14 0.089
2000 lI.ob2 n.oSQ G.OcH»O.OQt
2005 O.(lbS n.ob2 0.0'18 O.OQ]
lOto O.Ob'l'1).1)«14 o.tOO o.OqS
n
I-'
OJ
~
SCENARIO.MEn.HIZ-.SHERHAN CLARK tlO SUPPLY DISRUPTIO~-.b/24/1~83
rUEL PRICE FORECASTS EHPLoYEn
NATURAL GAS (~/HMBTU)
ANCHORAGE •'COOK INLET GREATER FAtR6ANKS.........•.••....•••.•.........•.••....••.•••.............................
YEAR RESlOENTUl BUSINF.SS RESIDENT!AL QUSJNfU..........•..••.............•.•.............••...
1980 1.130 1.500 12.740 11.290
1985 1.9S0 I.flO IO.bOO '1.150
1990 2.1180 2.b50 11.240 ~.1'10
1995 4.01)0 3.820 13.0]0 11.'580
2000 4.29(1 ".l'lbO 1'5.110 13.UO
2005 4.~bl'l ".7.50 17 .52(1 u.no
2010 5.31'1'l 5.ISO 20.310 18.RbO
n
......
~
~~I'_,-,L...-..-~
SCENARIO,~EO,HIZ-.8HERHAN CL~RK NO SUPPLY OISRUPTION ••6Il4/IQ83
FIIEL p~tCe;FQR[CASTS EHPLOYF.D
FUEL OtL (S/MH8TU)
.NCHOR~GE •COOl<tNLET GREATER 'AJRRANKS....-...._...•....••.._..........._.~.............................•.......
YEAR RESIDENTIAL BUSINESS RESIDENTUL PollS!Nf sa..............•.............•.••...•...•....•....
1980 7.750 7.200 7.830 7.1j(1)
U85 6."50 1j.900 6.510 6.180
19 9 0 6.8110 6.lqB 6.910 6.~80
19Q5 1.930 '.580 1l.010 7.680
2000 ".1 9 0 8.640 '.no 8.'bO
2005 1O.6~0 tn.I 00 IO.UO 10.4110
lOIO Ii.J511 11.800 12.1180 12.150
SCENARIO'~EO,HI2--SHERMAN CLAR~NO SUP~LV nISRUPTION--6/24/IQ83
RE81nENTIAl USE PEA HOUSEHOLD (KWH)
,wtT~OUT AOJUSTHENT 'OR PRICE)
ANCHO~AGE •COOK INLET•••..•..•..••...-.....
SHALL LARGE SPACE
VEAR APPLIANCES "PPLIANCES HEAT TOTAL..........••.•.••.....••............•••..•..
IQ80 llIO.nO 6500.1»3 5U8.52 Ub99.15
0.1)00)C 0.000)(0.000)(O~OOO)
n 1985 2IfJO.OO 6151./1'4821."3 UIH.33.
(0.000)(0 ..000)(0.000)(0~000)N
0
1990 2210.00 601".76 4584.35 IZ814.U
(o.oon)C 0.000)(1).000)C 0.000)
1995 22bO.OO 5959'..31 4515.56 12734.87
(0.(100)C 0.000)(I).noo)C 0.000)
2000 2310.1)0 5 q8 9.J8 4 11 SJ.8Il U753.ll
0.(00)«0.000)«o.oon)(0.000)
2005 23/)n.oo 60'59~12 41120.04 128J9.17
0.(00)(0 ..000)(0.000)(O~OOO)
lOU 2410.00 61ll.98 4443.55 U977.52
0.(00)r 0.1)00)r 0.000)«0.000)
___J
SC£NARIOIHED I H'2··SH!~~AN CLAR~NO SUPPLY DISRUPTION ••b/2Q/1Q81
RESIDENTIAL USE PER HOUSEHOLD (KWH)
(WITHOUT AOJUSTMENT FOR PRICE)
GREATER FAIRBANKS........-.~...•.......
SHALL LA~GE SPACEYEARAPPLIANCES,lPPLIANCES HEAT TOT AL \.......•...•••......................•.•...••.
1981)2 1l bO.00 51lq .5i!lU 3.bO liSt q.18
0.000)(0.000)(0.(00)(0.000)
("")
'''85 2515.q 9 "178.9"lUb.lt 12321.21l.
N (0.000)(0.000)(0.(00)(0.000)~
lCJ90 2bOo.00 b1l53.56 3812.52 lnlZ.0111.000)(0.000)(0.000)(0.000)
'995 2U".00 "bU·.aT 4050.111 t HU.000.000)(0 ..000)(0.000)(0.000)
2000 214b.00 U9S.II'5 11110.10 Ue!t.150.000)(O.OOOl (0.(00)(O~OOO)
20P5 21'1".00 6B18~ab 4515.80 111190.b.
0.000)(0 ..000)(0.0(0)(0.000)
2010 28Ub.OO 6B87~'85 IIbS5.Qb 1 11 1129.810.000)(O~OOO)(0.(00)(0.000)
('""')
N
N
._-1-
SCENARIO'HEO,HIZ--SHERMAN CLA~K NO SUPPLY DISRUPTIO~--6IlQ/198]
BUSINESS USf PER EHPLOYEE (KWH)
(WITHOUT LARGE INDUSTRIAL)
(WITHOUT ADJUSTMENT FnR PRICE)
YEAR ANCHORAGE -COOk INLET GREATER FAIRRANKS.....••.....•...•......••...•.•.................
19S0 8Q07.0Q 711 Q 5.70
0.000)(0.000)
1985 Q580.18 7972.11
0.000)(11.000)
1990 10355.Db 8127 .35
0.0011)(0.000)
1995 10918.QS 8b62.27
0.000)(o.UOO)
2000·IIQI6.QO 8957.9C!
0.0011)(0.0011)
2005 12089.67 9308.0]
0.000)(0.001/)
2010 129]2.U 9711.tl5
0.000)(0.000)
-.J_
SCENARIOI MEO I HI2--SHERMAN CLARK NO SUPPLY DISRUPTION--~/2q'I~8]
8UM~ARY OF PRICE EFFECTS AND PAOGRAHATTC CONSEAVATION
IN GWH
~NCHOqAGE -COOK INLET
RESIDENTIAL RUSINESS..........•.•..••...••
OWr·I-PR I CE PRUGRll1-1 NDIICFO CROSS-PRICE OWN-PAIC!PROBIUI4-1 NDIJCED eROSS-PRICEYEA~REDUCTION CONSE"R~-"~IOII ..AED.UCTI,Cl.N "RE9,I.!C_:qo~..Co~~~~~~!!g~..~,REDUCTION..............................4040.40 .........40 .......40 ...............................................................
1980 o.oon 0.0/1/1 0.000 0.0011 0.000 0.000
UBI 6.169 0.000 -/1.'561 9.]21 1).000 0.'532198212.]]7 0.0011 -1.\35 18.&'51 0.0011 1.0&]1981 18.SOb 0.000 -1.102 21.q80 0./100 1.'5951981121l.e.711 0.00/1 -2.211)]7.]01 0.000 2.12&
198'5 ]0.Bin O.OO/)-2.837 1I&.~n o.noo 2.658
198&38.1176 O.no/l -10.645 58.180 0.000 -0.35&1987 46.109 O.nno -18.1154 &9.726 0.000 -3.3101988Sl.1Q2 0.000 -2&.2&2 81.213 0.000 -6.3851989bl.375 11.000 -]u.n71 ~2.819 0.000 -9.]99
1990 fJ 9 .00a 0.000 -111."79 1011.1&&0.1100 -12.413n.19q1 11'S.01l&O.non -91.191 IIQ.9110 0.000 -19.0&0Nw1992hl.oall O.oon -1,110.'515 13~.'5IU 0.000 -25.7117199]207.121 0.000 -189.831 151.088 0.000 -32.]5319911253.159 0.1'00 -239.150 166.&63 0.000 -]CJ.OOO
1995 299.197 0.000 -288.1I68 182.237 (1.000 -1£5.&117
199/:1 2]4.0IQ o.oon -225.008 198.218 0.000 -52.58819971&8.842 0.00/1 -1&1.5111 21 11 .120 0.000 -5CJ.5](I199810].&6'S 0.(100 -Q8.086 nO.3bl 0.000 -66.11711999]II.IIS8 o.oon -311.&2&211b.1I01 0.1100 -13.1112
2000 -26.689 0.000 28."3'5 2bl.III1U 0.000 -80.1511
2001 -7.502 o.non e..1170 282.IIS9 0.000 -Qo.21152/102 11.&85 0.000 -1'5.8CJS 302.'53'5 0.000 -100.137ZOO]30.872 0.006 -3A.2bO ]22.~lJo 0.000 -110.02112001150.05 9 o.noo -bO.&l5 1"~.~2S 0.000 -IICI.CJ20
2005 b9.2116 0.000 -8i.Q 90 ]b2.b70 0.000 -UCJ.1I11
200/:1 '18.151 0.000 -CJt;.9011 3811.132 0.000 -tlll.3]~2001 IH .055 (1.000 -I'18.fll"lIt3.5 Q S 0.000 -156.8&1120/18 9S.9bO 0.000 -121.733 IIH.oS1 o.noo -nO.HI2009101l.8b1l o.oon ~IJlI.6117 IIbll.'520 o.oon -183.911
20.10 111.7b 9 o.noll -ll1T.5&e!1189.982 0.000 -U7.1l1i1i
SCENARIO'HEO I HI2-.8HERMAN ClAR~~o SUPPLY DISRUPTION.-6/24/19a3
SUMMARY OF PRICE E'FECTS AND PROGRAMATIC CONSERVATION
IN GWH
........•.•
GREATER FAIRRANKS
FlESlOENT Ul
(")
N+=-
YEAR........
1980
1981
1982
1981
19811
1985
198&
1987
1988
1989
1990
1991
1992
1991
199/1
'995
1990
1997
1998
1999
2000
2001
2002
2003
2004
2005
200&
2007
i008
2009
2010
----.lJ
OWN.PRICE
REDUCTION
....
0.000
(l.000
0.000
(1.000
0.000
0.000
-0.200
..0.4011
-0.600
-0.800
..1.000
-1.008
-1.016
-1./)24
..I./)]]
-1.041
-0.8b A
-0.69'5
-0.522
-0.34 0
-0.176
0.129
0.431
0.738
1.042
1.347
1.772
2.19~
2."24
1.049
'!.4n
PROliIUtl-INDUCED
CONSERVATIUlj...-....
0.000
0.1100
0.000
11.000
0.000
0.000
0.(1)0
n.ooo
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
o.nOIl
0.000
0.1l00
0.000
0.000
o.oon
0.000
I).(le)O
0.000
o.oon
o.oon
CROSS-PRltE
REDUCTION
....
0.000
0.758
t .'516
2.274
].032
].789
4.184
4.578
11.912
'S.h7
'S.hl
S.IU
4.592
4.01)8
3.424
2.U 9
1.350
-0.1110
-l.UO
-l.1l9
-4.600
-6.el5
-9.042
-11.2'38
-U.lln
-n.nl
·18.662
-21.613
-211.6011
-27.'575
-lO.'5lfll
OWN.PRICE
AED~C.U~N_.....
0.000
0.000
0.000
0.000
0.000
0.000
.0.342
.0.685
-1.027
.1.369
.1 •.,.2
-1.61]
-1.6311
.1.595
-1.556
-1.517
-1.247
_0.°78
.0.70S
.n."30
.0."&0
0.297
0.7b3
1.228
1.6 eHJ
2.lbO
2.819
1.1117
11.136
11.70'5
!i.1I54
BUSINESS.••.••.....
PROGRAM-INDUCED
CON~~YHJgN __.,_....
0.000
0.000
0.000
0.0011
o.noo
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.0011
0.000
0.1100
0.000
0.000
0.1100
0.000
0.1100
0.000
0.000
0.000
0.000
0.000
0.000
0.000
o.noo
0.000
CROSS-PRIer
REDUCTION
...
0.1100
0.5111
1.028
1.'5112
2.056
2.570
2.758
2.946
1.1311
3.323
3.'5lt
3.0811
2.657
P.UI
1.8011
1.378
0.556
-(1.26'5
-1.086
-1.IJ07
-2.721J
-3.010
-'5.0lJl
-6.271
-7.1152
-S.6H
-10.235
-11.836
-13."38
-1'5.0]0
-16.6111
--L
SCENARIO,HEO ,HI2--SHER"AN CLA~K NO SUPPLY OISRUPTION.-6/24/Iq83
BREAKDOWN O~ELECTRICITY REQUIREMENTS (GWH)
(TOTAL INCLUDE5 LARGE INDUSTRIAL CONSUHPTION)
ANCHn~Ar.l -conK INLET........-....•........
MEDIUH RANGE (PR_.5).......-_......-..~.
RESIOEIITIAL BUSINESS Io1I8CEl.LANEOUS EXOG.INDUSTRIALYEARRlQIJIRF.:IIENTS RE'QIJlREMENTS REQUUEHENU LOAD TOUl..-...........•••.•••...•.••.•..•••...•..•...•.........•...•...•...-..•....-....-...~......._.
1980 979.53 875.~6 24.31 811.00 IQ63.IQ
1981 1019.55 Q46.5S 211.611 Q2.01l 2082.821982l059~57 1017.13 2/,1.98 100.16 2202.4';JlJ8!lOQ9."0 1068.92 25.31 108.24 HU.n198111119.02 Iho.1I 2S.6!IU.12 24111.7ft
(l~8S 1179"b4 12ll.30 25.Cl8 UII~40 !561 ~32
198b 121l ..t15 1l80.n U.S]137 .~9 2658~U1987UUS.bS IHO.28 U.b7 151.38 n54.991988Il76.b6 IH9.71 28.51 IbII.1I8 2851.8&'
("")
1989 UII.U 1429.26 n.u 17~.H ~QIl8.66.
UI ~86N19QoIlIl4....,11178.75 30.20 30 11 5.119U1
1991 15711.10 ISI0.lIb 10.88 195.13 1110.56199ii!1111)].52 1542.17 11.56 19 8."0 3U5.bll199]1432.94 15H.87 32.24 201.66 32 4 0,72U9414"2.36 1605.158 32.92 204.Q]3305.n
1995 11191.78 un.29 33.60 208.20 3370~87
199b ISI7.'70 l6b1.01l 34.16 214.111 1421:1.04(991 IS4].b2 1688.80 34.71 220.118 1487,2219981569.5]1714.55 3S.29 226.nz 15 45.110199q1595.11111 1740.11 ]5.8b 231.9b 3603.57
2000 Ih21.lt-17&b.Ob ...1b.42 217 .90 l&61.7'5
2001 loSS.8S lIl1i?6lJ 17 .27 lllll.l:Ib 3750.1620021090.n If\5 Q .31 18.11 252.02 3819.782003Inll.8'1905.QQ 38.1:1"251:1.08 H28.71:1200l!1759.30 lCJS2.151 39.80 266.14 4017.81
2005 17q3.7~tt~9q.i?f)40.65 271.20 410&.82
2000 l1l19.iLl ii!Obl:l.1l2 41.87 281.58 4232.482007lA1l4.bS 1140.'1';43.08 28CJ.CJ b 4358.152008IHO.'09 22lt.08 44.30 298.34 11/183.81200qIlJ7S.53 U81.71 45.52 306.12 IIb09.48
2010 20C'0.Qb 23si?.llt 4b.74 ~I5.10 4715.III
SCENARIO'MEO ,HI2--SHERHAN CLARK NO SUPPLY DISRUPTION--6/24/1~81
BREAKnOWN OF ELFCTRICITY REQUIREMENTS (GWH)
(TOTAL INCLUDES LARGE INDUSTRIAL CONSUHPTION)
GREATER FAIRBA~KS....._-.-.......--_...
MEDIUM RA~GE (PR ••5).•...•.•.....•••••.•
RESJIlENTIAL RUSINESS MISCELLANEOUS E_OG.INDUSTRIALYEARREQl1IRf.HENTS REQUIREMENTS REQUJREHWTS LOAD TOTAL.................•.••..-.._._......~-...••__w ••_~•••••_•••..-•..••.••...•..•.-.•..•.•...•.••..
I~ao 1UI.19 217.14 6.18 0.00 1100.31
1981 190.64 229."11 6.75 0.00 427.2!IU2 201l.~l)2112.55 6.11 0.00 451l.1S1983219.15 255.25 (a.tl7 0.00 481.0719811iH.lln 207.9t1 6.61 0.00 507.99
1985 247.65 280.66 0.59 0.00 5311.91
198b 200.10 2eq.1I5 6.65 10.(10 566.201987272.55 298.211 6.70 20.00 597.501988285.00 301.011 6.1'5 10.00 628.7919892(11.45 115.83 6.80 110.00 6bO.08n.1990 ]09.9/)1211.6i!0.80 5/).00 b91.38N
0'\
1991 323.22 112.83 7.118 50.no 713.141992lUI.5.'141.n5 7.11 50.00 734.89199]349.85 349.27 7.54 5n.oo 756.b'!i19911]U.lo 157.Il Q 7.17 50.00 778.41
1995 376.47 lb5.70 7.99 50.00 800.17
1996 380.28 171.79 8.16 50.00 816.231991,\90.09 377.117 8.32 50.00 812.291998IIOS.90 38!.96 8.49 50.00 8118.31119991115.11 19".011 8.b5 50.no 864.40
2000 1125.52 ""396.12 8.8?50.00 8110'.lib
2001 lI:!b.86 1105.61 9.04 50.00 901.5220024118.21 illS.10 9.27 50.00 922.5820034';9.Sf»4211.59 9.50 50.00 9113.f»520041170.(11 1134.08 9.72 50.00 9bll.71
200S 482.25 44].!§7 9.915 50.00 985.71
2006 II'~S.96 057.05 10.22 50.00 tOI3.nlOO1509.f»7 470.'5]10.'50 50.no 10110.702008521.37 484.01 10.78 50.00 10bB.lo2009511.06 a97.Q Q 11.05 50.00 1095.02
20111 551).79 510.en 11.31 50.00 I tin.09
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SCENARIO,~ED,HI2 ••9H~RHAH CLARK NO SUPPLY OISRlIPTION ••6/2q/l~8J
TOTAL ELECTRICITY REQUIREMENT8 IGWH)
fNET OF CONSERVATION)
(INCLUDES LARGE INOUSTRIAL CON8U~PTIO")
MEDIUM PANGf IPP ••5).............•........
YElR ANCHORAGE •COOk INLET QREATER ,AIRRANkS TnUL..................••....~........_.•..••.....•••.......-~.....•....-..
IUO l~n.19 lIOO.JI B6J~S'
UBI 2082.82 lI27.23 25t(1~0519822i!01.IIS 11511.15 2b'!ib.fIO19111n22.01 1181.01 2803.14lUll2/l1I1.7ft 507 •99 29qQ.69
19B5 2S61.31 ~]1I.91 Jo9&~2'
1986 21158.U 5.6.20 3li'4~J61987275".99 S9T.58 ]JS2~"9.,B8 i1851.82 628.JCI 111110.61191192U8.U 660.0B 3608.711
Ino 10"5.119 6 9 1.18 1J1b~81
19 9 1 311O.S6 713.111 382]~701992117s.e.1I 73 4 .89 HIO,S11991HIlO.7l 156.65 199 1,31199Q])05.79 17".111 on811.2/)
1995 JUo.e?1100.17 QITI ~04
1996 J/l29.0II 816.23 112I1S~21In7JIIII7.22 812.29 111191'511998)5"S.IIO 811(11.14 IIJ 9 1.11119993601.S7 8611.110 1I/167~q7
2000 1e.&1.n (1180.4&45"Z·.21
ZOOI 171)0.76 901.52 116!12.282002JIlJ9.78 922.58 11762~J62001J9'-8.79 911J.65 111\12./111
200"/1017 .81 9611.11 119112°.51
2005 "11)&.82 985.17 '!i8 9 2.5 9
2006 1J2J1.Q9 I nl3.U 5i'1I5~n2007II.5S8.15 10"0.70 5J98.8Q20081l/J-].81 IOb8.'1l '!i5';1.'J7200911689."8 1095.&2 5705°.10
lOIO /lnS.llI Iln.(l9 -;"511.i!3
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SCENARIO'~EO.HI2 ••8tIERHAN CL&RK NO SUPPLY OISRUPTION ••6/24/!981
PEAK fLfCTRIC REQUIREMENTS (~W)
(NfT OF CONSERVATIO~)
(INCLUDES LARGE IUOliSTRUL DEMAND)
HEDIU~RANGE (PR ••5)•.•......•.........•••
YfAR ANCHOR4GE ~COOK INLET GREATER '&IRB1NK8 TOTH.....u .••••....••••••.••••.•..•...•........•..•..•...•••..•...•.......
1980 3 9 6.51 91.40 4R7~9/)
1981 420.b8 97.54 5t1i~2J19l'2 q4(j.8~101.&9 -5411.5~14J83 4b9.011 In.83 578.67I'UII 119].21 115.98 6n9~19
1985 SH.H U2.13 619.52
198b 537 .82 129.21 bb7~081987558.24 136.41 b911.6S19(\8 578.'''7 1113.55 722'.22
1989 S99.1I)150.b'1 749.79
19IJO 619.51 15'.83 777~36
1991 bll.n lb2.~O 79!5~551992b1l5.97 167.71 813 p 741993b'i9.19 In.'II 811.9219911b'72.'H 177.70 8!i0.1I
1995 b8S'-bJ 162.67 IJb8~3()
IIJ'l6 ben .ll .186.34 R1J3~651997708.'lQ 191).00 898.9IJ
1'l98 720.b7 ,l'l].&7 914.31119q9732.35 19 7 .1 "929'.68
2000 7411.01 >'Ol.BO 945~01
2001 7b2.00 205.81 9&7.812002779.'lb i!tl).bl 9 1UI.58
2003 797.91 21'5.43 10tl.3b2QOII815.90 220.24 103#)'•.11
2005 833.86 225.05 10~8~91
200b 859.29 2.51.32 1090~bO2007884.71 2 J7.59 1122.30
2008 91/).14 2H.lh 115l~qQ
2009 q35.56 i!5/).t1 11115'.69
2010 9&0.98 256.110 1211~31\
..J-
HE3--DOR AVG SCENARIO
C.29
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SCErUR I0I "'ED I HE1 ••DOA AVG SCENARIO ••bI2q/1983
HOUSEHOLDS SF.RVED
ANCHOPAGE •COOK INLET...•..•..•...•........
YEAR SINGLE FAMILY HUL TJFAHILY ,",OFilLE HmlES DUPLEXES TOTAL..--..•••...•..•....•.•.••-_................................••.•.•..•...
1980 15473.""114.8210.7 0 86.11501.
0.000)«0.000)«0.000)(0.000)t 0.000)
1985 4SU5.2b200.10857.8561.9U01.
«0.00 0 )«n.ooo)(1'.000)(0.000)(0.001')
n.1990 1.;'52Q9 •25817 •12721.84bO.102351.w......(1'.000)«o.noo)(0.000)(o.noo)(o.nno)
1995 61 089.271:129.14066.8331.111111.
0.000)«n.ooo)(0.000)C 0.000)(o.oon)
2000 "b029.!U825.151l'!.8181.1201bfl.
0.000)(n.ooo)«0.000)(0.000)(0.000)
2005 1 U Q 6.3 1H1ti7.lb822.8283.13JJb8.
0.000)((1.000)(0.000)(0.000)(0.000)
i!olO 7901:10.383.,1.1871'S.91S Q •145291~
(n.ooo)(0.000)t o.noO)(0.000)t 0.00/))
SCENARIO,MEO ,HE3·.nOR AVG SCENARIO·.6/24/1Q83
HOUSEHOLDS SERVED
GREATER FAIRBANKS......~........•...••.
YEAR SItIGLE FAMILY HUI.TI'F-AUILY MOBILE HOMES DUPL£lCES TOUL_.-..............•........•........--......•....•..•.....•..•..•...••
1980 7220.5281.1 t 89.1611.15]13.
(0.1)00)(0.000)(0.000)(0.000)(0.(00)
1985 I 0lt46.568(1.2130.1120.20180.
(o.noo)(0.(100)(0.000)(0.000)(0.000)
n.sq90 10852 •7900.2101.2315.232QO.
w
N (0.(00)(0.0110)«0.000)(0.000)«0.(00)
1995 134 Q l'..,841.2697.2)39.26)75 •
o.nOIl)(0.000)(0.000)(0.000)(ft.OOO)
2000 1501ft.7701.](104.2298.28(11I3.
(0.000)(0.000)(0.000)(0.000)«0.(00)
aoo,;10802."8 Q5.]960.2252.30975.
0.000)(0.000)(0.000)(0.000)(0.000)
2010 18520.9051.4401.2198.34169.
0.000)(0.000)(0.(00)(0.000)(0.000)
--U -L-
SCENARIO.MED •HE3 ••nOR AVO SCENARIO •••/~4/198]
HOUSING VACANCIES
ANCHORAGE •COOK INLET....••.•.•............
YEAR SINGLE FAMILY MUlTlFAMILV MOUlE HOMF.:S DUPLEXES TOTAL..-.................................•.•.........•.••....•........-.....
Iq80 5089.711o~.Il'1ql.14b3.h20~.
0.000)(O.(lO(\)(0.000)(0.00(\)(0.000)
Iq85 50~.14 q 6.IU.2fJZ.241l)•
('""')(0.000)(0.0(0)(0.000)(0.000)(0.000).
ww lqqO 60 R•1477 •140.289.2514.
0.000)(n.ooo)(o.onO)(0.0(0)(o.oon)
IqqS b72.14q2.155.2154.2603.
o.noo)(0.000)(0.000)(O.~OO)(0.000)
2000 726.lbb~.169.a79.Z839.
o.noo)(0.000)(0.000)(n.ooo)(0.000)
2005 790.lRbI •18~.III,2850.
(0.000)(0.000)(0.000)(0.000)(0.0(0)
2010 870.2071 •201,.302.3441'1.
(0.01)0)(o.onn)(0.000)(0.000)(0.000)
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C.34
SCENARIO,MED'HE3 ••0nR AVG 8CENARIO ••6/24Jl~8]
FUEL PRICE FO~ECASTS EHPLOY£D
ELECTRICITY (5 I KWH)
ANCHORAGE .·COOK INLET GREATER FAYRBANKS.....•........~-.•...•••..•.•..•.•.••...•.~-..•.••.•._--.
n
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YEAR RESIDENTIAL BUSINESS RESIDENTIAL BIJSIN£SS...........•............••..•...................
1980 0.1137 0.031,1 0.099 0.n90
1985 0.n4~0.045 0.090 0.085
1990 (l.051 0.01,18 0.090 0.08'3
1995 0.051,1 O.OIJI 0.090 0.08'5
~OOO 0.057 0.056 0.090 0.0815
2005 0.061 0.058 0.092 0.087
2010 0.Ob3 O.ObO 0.09'5 0.090
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SCENARro.MEO.HE3 ••00P AVO 8C£NARIO ••6/2~/1~8]
FUEL PRICE FORECASTS E~PLOY£D
NATURAL GAS (S/HH8TU)
ANCHfJAAGE -COOk It~LET ORfATER FAIRAAN~S........•.......•.•.•.•...•..•..............-....~.-....•...••..•--_.-....
YEAR RESIDENTIAL RlIS I t~F.5S RESIDENTIAL BUSINESS
••••.........•..•.........•..........•••••••••••
1980 I.no 1.500 12.740 11.290
1985 1.9bO I.no 9.810 8.3bO
1990 l.710 2.4RO 9.1bO 8.110
1995 1.250 1.0l0 10.371)8.920
2000 1.410 3.180 11.220 9.710
lO05 3.'5bO ]~]JO 11.970 1Il.!i20
iOlO 3.710 1.Q'30 12.770 11.320
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SCENARIOaMED a HE3--00R AVG SCENARIO--~/~4/1'81
'UEL PRICl 'ORECASTl EMPLOYED
'U!L OIL (S/HHBTU)
ANCHORAGE •COOl<INLET GREATER FAYRIUNI<S-.....•..........•.................••.....•...•...........................
YEAR RESIDENTIAL BUSINESS RESIDENTIAL BUSIN!SS_._....•..•.•.........~.....•.•.•.....•.........
1980 1.750 7.l00 1.810 1.500
1985 IJ.~HO 15."21)~.Olo 15.100
1990 5.941)5.190 6.000 5.610
1995 b.ll0 5.1bO 6.]10 6.nao
2000 b.830 6.280 6.890 6.560
2005 1.~90 6.1110 7.]b0 1.030
2010 1.180 1.no 7.850 1.520
SCENARIU,MED ,HE3·.00~AVG 8CENARJO ••6/24/1983
RESIOENTIAL USE PER HOUSFHOLD (KWH)
(WITHOUT AI)JllSTfo1ENT FOR PRICE)
ANCHORAGE •COOK INLET..••......••..••..••.•
SMALL I..&RGE SPACE
YEAR APPLIANCES APPL I At-ICES HEAT TOTAL........-..~..-....•.........-..-....•.•...•.
1980 211".OU ,,5 0 0.U 5088.52 U1l99.15
0.000)(0.'000)(0.000)(0'.000)
1985 llbll.OO 61511.71 '1811.81 13146.51
(o.noo)(0.000)(0.000)(0.000)n.
1990 2211).00 602b.18 lt6i!J.92 12860.10w
(».0.1)00)(0.000)(0.000)(0.000)
1995 22bO.OO 5958.98 4511.98 U710.IU.
0.000)(0.001)(0.000)(0.000)
2000 2110.00 5988.97 111I41.i!9 12140.26
0.000)(o..ono)(0.000)(o.ono)
2005 21bO.OO 6060.87 ""21.11 12841.98
(0.000)(0.000)(.0.000)(0.000)
2010 C!lI lQ.OO 6126.81 "440.62 12971.114
(0.001)(0.'000)(0.000)(0.000)
_il _1--L
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SCENARIO.MEO •HF1·.D~R AVG SCENARIO--b/2Q/1981
RESIDENTIAL USE PER HOUSEHOLD (KWH)
(WITHOUT ADJUSTMENT FOR PRICE)
GREATER FAIRBANKS.-......-..~.....-....
8M4LL LARGE SPACE
YEAR APPLIANCES APPLIANCES HEAT TOTAL.......•..•................•.•...•......••...
1980 j!Qbf>.OO 5n9~5~Bl1.bh 1151lJ.lft
(O.OOO)(0.(00)(O.OOO)(0.000)
1985 251b.OO blBI.3Q 3593.90 12311.23n(o.noo)(0.000)(0.0(0)(0.000).
W
I.D 1990 2Mb.OO 61.1l~0.61 3848.67 12895.29
(o.noo)(0 ..0(0)(0.0(0)(0.000)
1995 2676.01 6656.15 4088.11 13420.2J
(0.0(10)(o.noo)(0.0(0)(0.000)
2000 l746.00 bn3.05 4320.70 13859.75o.oon)(0.01l/)(0.000)(0.000)
2005 2 8 16.(10 b853.5b 4507 .50 11&177.06
0.000)(o.ono)(0.001))(0,,000)
2010 2 A8 b.no b8 9 3.Jb 4b5b.97 14416.320.1)00}(0.000)r 0.000)(0.000)
SCENARIO'MED'HEJ ••OQR AVG SC£NA~tO ••6/24JI~8]
RUSINESS USE PER EMPLOYEE (KWH)
(WITHOUT LARGE INDUSTRIAL)
(WITHOUT ADJUSTMENT FO~PRICE)
YEAR ANCHORAGE •COOK INLET..-......•..••.•....-.~...
1~80 1\4 07.0Q
0.000)
1~85 ~518.78
('")(0.000).
+=-0 1990 10089.60
(1).000)
1995 1060a.92
0.000)
2000 IlIn.aq
0.000)
2005 11850.11
0.000)
2010 ti!67S.13
0.0(0)
GREATEP 'AIPBANKS.......~..-.--
7495.70
0.000)
7947.41
(0.0(0)
AZ49.74
0.1)00)
8559.84
0.000)
8Ull.7S
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9227 .92
0.0(0)
9&28.13
(0.000)
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C.41
SCENAlUOI HEO I Hf3·.nOR AVG SCEN-RIO ••~/24/1983
SUMMARY OF PRICE EFFECTS AND PROGRAMATIC CONSERVATION
IN G\tlH
GREATER r_IR84NKS
ftES JOWl!AL I!IUUNf!S.•..-...................OIllN.PRICE PROGRAH·lNDlICfO CROSS.PRICE nWN-PAICE'PROGRlH-PlOlICE'O CIIOSS-PRICEYEAAREllUCTtONCUt~SFRV ~TlOII REDUCTION AEOUC.TlON CON~~~Y~!~~N ._.PEDUCTION................................................................................................................................................
1980 0.000 (I.noo 0.000 0.000 (1.000 0.000
lUI -0.2bb 0.000 I.')fll .0.493 0.000 0.7241982.O.53~0.000 2.1l1 00(1.486 0.000 1.IJS71983.0.1 4 1 0.000 3.182 .1.479 o.noo 2.t861984ool.ObJ 0.000 4.243 .1.q7~n.ooo 2.911J
19S5 ·1.:J29 1/.000 5.]04 .~.lIb'5 0.000 3.6113
19Sfl .1.'560 0.000 ~.2411 oo~.AOS o.oon 1J.1511IU1001.HI 9.000 '.185 001.145 0.000 1J."6b1988.2.1)22 0.00/1 8.US .1.485 n.ooo 5.11A1989·2.251 0.000 9.0bfl .'.R2b 0.000 ~.6qO
1990 002.Q8Q ".000 10.006 .1I.1b1.0.000 6.~0?n.
1991 002.b85 0.000 10.Qfl4 .0.1135 0.000 1..185+:>
N 1992 ..&'.88b O.noo 10.Q22 -4.'70/1 0.000 b.'5&71993·3.087 0.000 11.380 -4.912 o.noo b.7S0199Q..1.2114 O.nOO II.A38 .5.241 o.oon b.4]3
1995 -3.4'10 0.000 Il.il9b .S.510 0.000 '.115
1996 ·3."38 0.000 U,llfl _0.97A 0.00(1 6.21151991·3.787 0.000 1I.931 -1I./lQ6 0.000 5,3751998·3.93&0.(01)1t.757 001.915 0.•110(1 4.5051999.11.(\84 0.000 11.578 00].383 o.oon ].b]S
2000 0011.231 0.000 11.198 .2.851 0.0011 2.7&5
2001 0011.175 0.000 10,890 ..l.33S 0.000 1.0502002.4.117 0.000 10.382 .3.'319 o.oon 3.13520010011.059 "./)00 9.1175 .4.JO~0.000 ].fl\920011..tl.ooo 0.00'1 9.]~7 ..1I.78b 0.000 ~.90tl
2005 -1.9tl2 0.(100 .~.aS9 ..~.210 0.000 1I.la9
200b .1.623 0.1\0/1 8,Obll 001l.841 0.01\/1 3.7942007-3.30!'o.nlJO 7.lE,4 -".lIlt 0.000 3.1I0A2008002.986 11.000 b.lI7J 003.9l\'-0.000 J.0182009..2."b7 ".1\00 5.b7B ..3.'i52 0.1100 2.628
2010 002.311A lI.oon 11.8113 001.12J 0.000 2.231'
__I _1-I-~
SCENARIO'"'EO •HE3-·00R AVO·SCENARrO--&/211/1983
BREAKDOWN 0'ELECTRICITY REQUIREMENTS lQWH)
(TOTAL INCLUDES lARGE INOUSTRIAL CONSUMPTION)
4NCHORA~E •COOK r~LET..•....•.•.-.•.•..•...
"EDIUM RANGE (PR ••S)..•.••...•.....•..•.
RESIDENTIAL BI.ISltlfSS HI8C!LLANEOUS EXOG.INDUSTRIALYEARREIWIREHENTSRfQlIlREHENTSREQUIREMENTSlOAn TOTAL•..•....••.••...••...._......-...~...•.....•••.....•-....~......-................-........
IUO 979.53 875.3&211.31 811./10 PHd.I q
19S1 1011.74 940.811 24.56 9l.0S l075.23USC!105'5.91)1006.33 24.82 10 0.1&&'187.l6198310911.17 1/171.81 25.08 10".211 2299.301984IIn.38 1I37.Z9 25.311 116.32 2411.3J
19S5 1170.59 1202.78 25.M 1211.40 25ll'].]7
198~119l.97 1232.72 2&.15 137 .89 2589.731987lZIS.311 12&2.65 2b.1l 151.38 2&5&.0919881231.72 1292.59 27.27 IbII.B8 &'722.11519891260.09 1322.53 27~83 \Ttl.l7 27R8.81
n 1990 12R2.117 1152.1I&28.311 '91.8&P.8S5.17.
~w 1991 130l.'H UU.'57 28.89 19'5 .•U 2906.5519911323.117 11106.68 2 q .l'I 198.40 2IJ57.93199313C13.9 7 11131.711 29.89 201.6&3009.311994BU.II?llIbO.89 30.110 2011.93 3060.69
1995 13114.911 11187.99 30.90 208.20 '3112.07
199&U08.59 1518.20 31.1111 2U.U 317l.1I11997'ClU.ZI 15411.112 32.05 220.(18 3232.76199811155.82 1576.63 32.&3 2&'6.02 3293.101999III79.tIQ 16011.811 33.20 211.9 6 H53.lIli
2000 1503.011 UH.O&H.711 237.90 111t3.79
2001 1532.P 1&83.35 311.Sq 2 11 11.9&11195.02lOOl15&I.l9 1721.611 35.31)252.02 1510.21120031510./10 117'.93 36.06 259.08 1~57.1I1200111&19.5i 181&.22 3&.83 2&6.1/1 3738.70
lO05 Ib1J6.bJ 18bO.51 31.59 ?73.20 111'9.91
2006 168b.90 192Q.IS 38.&8 181.58 3911.3020011125.17 1987.19 39.77 ?89.Q&110112.&821)08 1703'-113 2051.43 110.86 198.311 ClISII.06200918nt.·70 2115.0b /11.95 10b.72 11265.113
lOlO 1819.91 2118.10 113.011 :U5.tO 1117b.8'
("")
+=-+=-
SCE~ARIOI "EO I HE1--DOA AVO SCENARIO--6/24/1q83
8REAKDOWN OF ELECTRICITY REQU1R!HENTS (GWH)
(TOTAL INCL"D~S LARGE INDUSTRIAL CON8UHPTION)
GREATER FAIRBANKS..............•.......
~EDIUH RANGE (PR8.5)..•........•.•...•..
RESlDE~nIAL.AUSINESS ~1SCF:LLANEOUSYEARREQIJI!lEHFNT6 REQUIAE~E~TS REQUIREHENTS-.-..•••.•..•..•..••.............-._.......--_..•....•...•
1980 1111.19 217.14 6.78
Iq81 IqO.OI 22".93 6.741982201.6i1'241).71 6.701983217.2t1 25~.50 6.66Iqllll210.'815 2611.29 6.62
1985 21111.tll 276.011 6.58
1986 2511.11 l81.U 6.561987263.80 281.27 6.5319118213.117 292.80 6.51Iq89281.111 29S.115 6.IIQ
1990 2Q2 ..80 104.04 6.4ft
1991 303.27 31 0.23 6.611lq92313.1t1 316.(12 6.al
1993 3i4.21 Jil2.61 6.QQ
19911 1311.68 3211.110 7.17
1995 3115.15 !3~.OO 7.3(1
1996 353.53 ]111.71'7.50
1997 361.91 1411.56 7.66
1998 110.a Q 355.H 7.82
\1999 17t1.67 ~b2.11 7.97
2000 387.05 It>>8.B9 8.13
2001 3911.41'377.'71 8.3020021I0S.Q2 386.52 8.117
2003 '''5.35 HS.3Il 11.114
i004 111.11.71'1I01J.1S 8.81
2005 IIH.i!l 412.97 8.98
2006 tillS.52 U211.75 9.21120071150.83 u36.5'9.512008IIb8.13 tl48.31 Q.71
2009 117'J.II Il IlIJO.08 10.03
2010 /l o U.7/J 1171.BCI 10.30
,--
EXOG.JtlDUSTRUL
LOAD TOTAL•.•.........••••••....••............
0.00 1100.:51
0.(1)1125.611
0.00 1151.011
(l.OO 476.110
~.OO 50 I.J7
0.00 527.13
10.00 552.37
20.00 577.60
10.00 602.811
40.00 1:128.07
50.00 1:153.30
50.00 UO.III
50.00 686.98
50.1)0 703.82
50.00 720.6!)
50.00 737.119
50.00 752.81
50.00 768.12
!lo.oO 783.44
50.00 7 Qa.76
50.00 8111.07
50.00 8H.49
50.00 850.91
50.00 869.33
50.00 88T~75
50.00 90ft.U
50.00 929.51
50.00 952.86
50.00 91ft.2t
50.00 qq9~5b
50~on 9022.90
-'-----
n.
~
U1
-~-
SCENARIO.MED.HE3-.00R ~VG SCENARIO ••61111/1983
TOTAL ELECTRICITY REQUIREMENTS (GWH)
(NET 0'CONSERVATION)
(INCLUDES LARGE INDUSTRIAL CONSUMPTION)
MEDIUM R~NGE (PR ••5)••...•••.•.•.•.••.....
YEAR ANCHORAGE -tOOK INLET GREATER FAIR8ANKS TOTAL......•.••..•..••.••.•........•.•.•...•••..•••....••.....•••..••......
1980 19b3.U 1100.]1 ~]"3~51
U81 2075.21 1125.b8 251)0~91)
1982 2187.26 1.151.011 26111.3019832299.10 1176.110 n75~701981121111.3]'301.77 2Q11.IO
1985 2523.37 527.13 1051)'.50
19h i'589.73 552.37 1t1l2~10198726'5b.09 577.bO 1233.&919882722.115 &02.84 3]~5~291989271111.81 628.07 ll1lb~8"
1990 2855.17 b'B.30 ]Sn8~118
1991 nOb.55 670.14 1~7b~b919922957.93 686.98 ]6411~91199]30 0 9.31 701.82 1713.13199q30&0.&9 72'l.b5 17RI~311
1995 1112.07 737.119 31\1I9~5b
lfl9b 3172.4t 752.81 ]925~2219973B2.7b 7bll.12 11000.88
1'~98 32 9 3.10 781.1111 1I07b~51J19993353.qll 798.h 4152.20
2000 ]1113.79 8111.07 11227~8"
2001 3 119 '5.02 832.119 11]27.51
2002 1576.211 850.91 1IIl27~15
2003 3657.47 Ilb9.H 1I';:!b'.80
20011 1738.70 887.75 1I&~6·.1I11
2005 1819.93 90&.\&"726.09
200&lHI.30 9a9.51 11860.AI
2007 110 1'2.68 952.8&IIQ95.5q
2008 41511.0&97&.21 1i130.2b
2009 112&5.q'J Q99.5b li2M.99
2010 1137b.81 l n 22.90 liJQq'.71
n
~
0\
~CENARIOI ~ED I HE1-.00R AVG SCE~ARIO ••6/211/198J
PE~K ELECTRIC REQUIREMENTS (HW)
(NET OF CONSERVATION)
(lNCLUOES LlRI;F.INDUSTRIAL DEHAND)
HEDIU~RANGE (PR ••5)....-----.......••.•••
YEAR ANCHOR ACE -COOK INLET GREATfR 'lIRBlNkS TOUL•.......•..........•.•.........•...•............-..~......-._-....
1980 3'16.51 91.lI O IIn~90
1981 1119.U 97.19 'H6~32I'ISi 11111.75 IOi.'Ie 544~1J1'183 11611.37 108.n sn,.lll198111I86.99 I to.56 bOl.S'S
1'185 509.62 120.35 U'l~97
1986 523.80 U6.11 b1l9~911987537.99 Ill.n U9.8S198855i.11 137.62 689~801989566.36 1111.38 '70'1.H
lIa90 580.54 149.til 7l9~U
1'191 Sq O.96 152.98 743~911199if:lOI.H 156.83 758,.201991bll.n 160.67 772,.116I9qq622.20 1611.52 786.72
1995 &l2.U 16f1.U 800:98
199&/:l1l1l.711 171.86 81&~&01991656.87 175.36 fl32.2i11'198 b~8.9q 178.85 S1I7~84Iqqq6111.11 182.35 8U.46
2000 6'1].211 185.85 8n~08
lOOI 7 0 9.61 190.05 8q9~U2002725.98 191.1.26 Q20.24
2003 742.35 198.11&q1l0~81200Q758.n 202~U Q61·.39
2005 175.'10 206.87 98".97
200&797.b('l 212.20 IOO9~802007820.0'1 211.53 1037,.63
lO08 e1l2.59 222.8b 1065.1152009111>5.09 i!28.19 IOqJ~24
2010 881.59 231.52 1121~tt
I
)
I
i
1
I
I
i I
>
I
I
j
HE9--DOR 50%
C.47
I
I-
I
!
1
I
)
I
I
I
SCENARIO.MEO •HE9 ••DDOR 50X ••6/~4'1'8]
~OUSEHOLD8 S[RVED
ANCHORAGE •COOK INLET
••••••••••••••••••••••
YEAR SINGLE FAMILY MULTIFAMILY MOBILE ~OHES DUPLE XU TOTAL..-.•••••••••••••••••••••••••••••••••••••••.................•.•.......
1980 154U.lOll".8230.1486.11503.
0.000)(0.000)(0.000)(0.000)(0.000)
1985 45685.2620".t085'.85b7.9nl'§.o.noo)(0.000)(0.000)(0.000)(0.000)
n 1990 55038.25811.12b6'•81160.102036..(0.000)(0.000)(0.000)(0.000)(0.000)~
~
1995 59941 •26890.13789.8131.108 Q 59.
(0.000)(0.000)(0.000)(0.000)(0.000)
2000 64111.19755.14 9 10.8187.1t!163.
/).000)(0.000)(0.000)(0.000)(0.000)
a005 695711.BJbl.16295.8024.1i!725l.i •
(11.000)(0.000)(0.000)(0.000)(o.oon)
aolO 763bO.37012.18072.8845.11I028R.
0.(l00)(n.ooo)(0.000)(0.000)(0.000)
_I
SCENARIO.~ED •HE9 ••DonR 50X.·~/24/Iq83
HOUSEHOLDS SERVED
GREATER FAIRBANKS
••••••••••••••••••••••
YEAR SIUGLE FAMILY HllLT1FA"'ILV HOBILE HOMES DUPLEXU TOTAL.........•.......••••••••••••••••••••••••••••••••••••••••••••••••••••
1980 7220.5287.t 189.161'•15313.
(0.000)(0.000)(0.000)(0.000)(0.000)
1985 10646.$68".cHlO.1721.201815.
(""')(0.000)(0.000)(0.0(0)(0.000)(0.000).
01
0 1990 11)125.79bO.2103.2115.2]1 &'3'.
0.000)(0.000)(0.000)(0.000)(0.(00)
1995 12geo.7841.2573.2]39.257H.
0.000)(0.01)0)(0.000)(0.000)(0.000)
2000 1'132 4 •7703.l194.2298.21'120.
(0.0(0)(0.000)(0.000)(0.(00)(0.000)
2005 lUO~.7549.3808.2252.H8IS.
0.000)(0.000)(0.000)(0.000)(0.000)
2010 Inn.8661.4213.lIO".12186.
(o.oon)(0.0(0)(0.000)(0.0110)«0.(00)
----
SCENARIO.MEO •HE9--0UOR ~OX--6/~Q/198]
HOUSING VACANCIES
ANCHORAGE -COOK INLET-....••...••......••..
YEAR SlNGL!'AMILv MULTIFAMILY MOBILE HOMES DUPLnES TOTAL
•••••••••••••••••••••••••••••••••••••••••••...•...........••.••..•...
19M 508CJ.hbb.1991.l£lb3.1b209.
(G.OOO)(0.000)(0.000)(0.000)(0.(00)
nBS 503.14 cHI.120.292.~410.
("'")(0.(00)(0.(00)(0.000)(0.000)(0.(00).
<..n......1990 b05.t 471.139.28CJ.21StO.
0.000)(0.000)(0.000)(0.000)(0.0011)
1995 659.SCI.152.284.tlQ Q •
(0.000)(1).000)(0.000)(0.000)(0.000)
2000 101.lbOl.Ib4.279.27511.
0.000)(0.000)(0.(00)(0.000)(0.000)
200S 7b5.lBO~.179.27'1.3020.
0.000)(0.000)(0.000)(0.000)(o.oon)
lOla 13£10.1999.19 9.2 9 2.]329.
0.000)(0.000)(0.000)(0.000)(0.(00)
_1
ICEN4RIOI MED I HE9 ••DOOR SOX·.6/ZlI/1Q81
HOUSINQ V4CANCIES
QREATER 'lIR8lN~S
••••••••••••••••••••••
YEAR SINGLE FAMILY MULTIFAHILY MOBILE HOHU DUPLEXES TOTAL
••••••••••••••••••••••••••••••••••••••••••••••••••••••••......•...•..
1980 ]653.H2O.qh.eqs.885Q.
(o.non)(0.1'00)(0.000)(0.000)(0.(00)
n85 118.2R33.2Q.766.~74t •n.ooo)(0.000)«0.000)(0.(00)(0.001')
('")U90 tlR.1I511.2].81..671..
01 (1'./)00)«0.000)«0.000)(0.000)(n.oon)
N
1995 1ll3.QlIR.28.80.6QQ.
(0.000)«0.(00)(0.000)(0.000)'(0.000)
2000 158.aliO.]§.78..71 t •
0.000)(0.000)(0.(00)(0.000)(0.000)
Z005 178.4]1.42.77.,728.
(0.000)(0,(00)«(1.000)«0.000)(0.000)
2010 19&.abq.46.lU.,R78.
0.000)(0.(00)(0.000)(0,000)(o.noo)
~-
SCENARIO,HED I HE9 ••DOOR 50X.·~/24/1q8]
'UEL PRICE FORECASTS EMPLOYED
ELECTRICITY ($J KWH)
(""')
U1
W
ANCHORAGE •COOl(INLET GREATER FAIRBANKS.....•........•...•...........•••.....•....••.•...........••...•..........
YEAR RESIDENTIAL BUSINESS RESIDENTIAL BIJSINFSS..--.........••.......~......................•...
1980 0.031 0.030 o.n'!)O.OlJl)
1985 0.048 0.045 0.095 0.090
19lJO 0.049 0.04t1 0.090 0.081i
"tiS 0.050 0.1)117 0.090 0.085
2000 0.051 0.048 0.090 0.085
2005 0.051 0.048 0.090 0.0813
2010 1).1)51 0.048 0.090 O'.08'!i
•••••••••••••••••••••••••••••••••••••
SCENARIO.MED.HE9--DOOR 50X--6/24/1981
ANCHORAGE -COUK INLET
FUEL PRICE FORECASTS EMPLOYED
NATURAL OA8 (S/HMBTU)
GREATER FAiRBANKS
•••••••••••••••••••••••••••••••••••••
('""')
01
.,J::>
YEAR RESIOENTUL BUSINESS RESIDENTIAL BUSJNESS.................•••••••••••••••••••••••••••••••••
n80 t.730 t.500 1i!.140 It.2QO
t9ltS 2.001'1 1.770 10.660 9.210
t990 2.630 2.ClOO 9.090 ".640
1995 2."10 2.580 8.120 6.610
2000 2.11 0 2.481)1.6U 6.210
201'15 2.UO I.ClOO 1.210 5.820
2010 2.'51»0 2.330 6.890 5.4110
SCENARIO.MED.HE9 ••DOOR 50X ••b/24JI98]
FUEL PRICE FORECASTS EMPLOYED
FUEL OIL (S/MMBTU)
n
<.n
<.n
ANCHORAGE •COOK INLET GREATER FAIRBANKS
•••••••••••••••••••••••••••••••••••••..•••.•..••..•........•..•.•.-_......
YEAR RESIDENTIAL BUSINESS RESIDENT!AL BUSINESS....••••••••••••••••••••••...........•••••••••••
1980 7.750 .,.200 '7.830 1.50 11
n8S 11.1190 S.QIIO 6.550 6.220
1990 5.530 11.1:180 5.51:10 5.2&0
11:195 11.950 a.1I00 4.1:190 11.660
2000 11.6&0 11.110 11.110 11.180
2005 4.1130 3.880 4.1160 11.130
2010 11.200 3.650 11.2110 3.l:Iln
SCENARIO'"ED ,H£9 ••nOOR 50~.·6/24/1'83
RESIDENTtAL USE PER HQUS[HOLD (KWH)
(WITHOUT ADJUSTMENT 'OR PRICE)
ANCHORAGE •COOk INLET..•.•.•.-•......-.....
SMAll LARGE SPACE
YEAR APPLIANCES APPLIANCES
HEAT TOTAL....••••••••••••••••••••••••••••••••••••••••
1980 2110.00 6S(lO~U 5088.52 11699.1!l
(0.000)(0;(00)((l.0{)0)(0.000)
1985 2160.00 6154.64 4931.62 13141,.27
(0.0(0)(0.'000)(0.000)(O~OOO)
n.U90 l210.00 6021,.17 4~27.A2 U8~4.60<.n
0'1 (0.000)(0.'0(0)(0.000)(0.0(0)
1995 22~0.OO S9S8~47 450'.39 un7.87
0.(00)(0.000)(0.0(0)(0.000)
2000 2\1').00 5988.l5 4436.47 U7~q.bl
(n.noo)(0.000)(0.0(0)(0,000)
2005 i3bO.00 6060.94 4421.47 U8~l.40
n,OOo)(0.000)(0.000)(0.0(0)
2010 2410.00 1,127.57 443 9 .13 U4J76.10
(0.000)(0.'0(0)(0.000)(0.000)
SCEN~RIOI HED I HE9 ••DOOR 50X--6/24/198!
RESIDENTIAL USE PEA HOUSEHOLD (KWH)
(WITHOUT AOJUSTMENT 'OA PRICE)
GREATER 'AIRBAN~8
••••••••••••••••••••••
SMALL LARGE SPACE
VElA APPLIANCES APPLIANCES HEAT TOTAL
••••••••••••••••••••••••••••••••••••••••••••
1980 2406.00 57]9.5;»HU."6 11519.18
0.000)(0.000)(0.000)(O~OOO)
1985 2535.99 6181~26 3594.14 12311.40
n f 0.000)(0 ..000)(0.000)(0.000).
U1
"'-J 1990 2606.01 643q~31 3840.88 12886.20
0.000)(0 ..000)(0.000)(0.000)
1995 2U6.01 6651~89 4081.97 11404.87
0.000)(0.000)(0.000)(0.000)
2000 2146.01 U 9 O.89 4]25.95 13862.85
0.(00)(0.'000 )(0.000)(0.000)
2005 2 8 16.00 6858.32 "497.119 14171.81
(0.000)(0.000)(0.000)(0.000)
2010 2885.99 6895~94 4656.78 1 IUI38.72
0.(00)(0.001)(0.000)(0.000)
SCENARIO.HED •HEq··OOOR SOX.·6/2q/l~8J
BUSINESS USE PER EHPLOYEE (KWH)
(WITHOUT LARGE INDUSTRIAL)
(WITHOUT ADJUST~ENT FOR PRICE)
YEAR ANCHORAGE •COOK INLET GREATER FAIRBANKS............~.............••••••••••••••••••••••
,
1980 8a1l7.011 7QQS.70
(0.000)(0.000).
un q51~.~b 7947.9]
(0.000)(0.000)
n.1990 10059.611 un .2101
00 (0.(00)(0.000)
-
1995 10482.60 851S.0S
(0.(00)(0.000)
2000 11024.92 8822.88
(0.000)(0.000)
2005 11680.8b 9169.82
(1).000)(0·.000)
2010 12481.9 7 9Sbt".117
(/).000)(0.000)
--
SCENARIO,MEO ,HE9--000R 50X--6/~4/1q83
SUMMARY OF P~ICE EFfECTS AND PROGRAHATIC CONSERVATJON
IN GWH
ANCHORAGE -COOK INLET
RESlOEtHI1L BUSINESS......•.....••••......OilN-PRICE PROGRAM-INDUCED C~OSS-PRICE OWN-PRICE PROGR1M-t~DUC!D CROSS-P~Ir.EYEARREDUCTlOIIICONSERVATIONREDUCTIONREnUCTlON.H'NSERVAIHPL _AEnUCTlON...............................................................................................................................
1980 0.000 0.000 0.000 0.000 0.000 0.000
1981 Cl.145 0.000 -0.96Q 9.139 0.000 (I.3l~1982 12.290 0.0011 -1.928 18.277 ".000 0.b53198318.4J'3 0.000 -2.892 27.416 0.000 0.919198424.580 0.000 -3.856 36.554 0.000 1.306
1985 ]!I.He;0.000 .4.820 4S.bt3 0.000 1.632
198b 35.681 0.(101).8.771 52.1180 0.000 1.2881987411.b41 0.000 -&2.121 59.266 0.000 0.943198845.sn 0.000 -U.Ul fIb.OS3 0.0011 0.599198950.551 0.000 -20.6ill 72.8110 0.000 0.255
1990 5'5.515 0.000 -211.571 79.621 0.000 -0.090n,.
III 9 I 59.11115 0.000 -n.1I1O ell.9bO 0.000 o.ll'23U"1
I.D 1992 113.314 0.000 ·:10.246 90.194 0.000 0.51b19q307.211 0.000 -n.on 95.627 0.000 I).A4'Iq9q 71.lll 0.000 -35.91 9 100.961 0.000 1.Ib2
1995 7e;.012 0.000 .18.155 106.2911 (\.000 1.475
Iqqll 78.44'-0.000 -39.5119 I U .1188 0.000 2.51919q761.871 0.000 .1I0.11lS 117.881 0.000 1.681lq98B'3.100 0.000 .111.131 123.617 0.000 1I.78b1,q9 88.729 0.000 .111.9]1 129."71 0.000 5.8qO
2000 92.1511 0.000 -42.72S Ui!I.lb5 0.000 6.99]
2001 q'5.0BI 0.000 -42.5110 140.~B5 0.000 8.6b3200298.11011 0.000 -"2.155 146.70e;0.000 10.1322001100.927 0.000 -42.170 152.1126 0.000 li!.0022004103.850 0.000 -41.9('5 15fl.11I6 0.000 Il.nl
2005 106.774 0.1.'00 -41.800 161."66 0.000 15.3111
200&10 q .1b1l 0.000 -41.008 170.170 0.000 11.7b7lO07112.755 0.000 .110.215 171.6711 0.000 20.1911200B115.7Qb 0.000 -lq.lI21 \I\1I.51 fl 0.000 22.6212009118.7]7 0.000 -]B.Ul IQ,.IIH2 0.000 21i.01l8
2010 121.728 0.000 -]7.838 IQ""J8h 0.000 21.1174
SC!No\RIO,HED ,HE9 ••DOOR SOX.-6/24/198J
SUMMARY OF PRICE EFFECTS AND PROGRAHATIC CONSERVATION
IN GWH
GREATER FAIR8A~KS
RESIDENTIlL llUSlNESS•...........•.........DWN-PPICE PROGR.I1-INDUCED CROSS-PRICE OWN-PRICE:PROGRAM-INDUCED CROSS-PRICEYEARREDUCTIONCONSERVATIONREDUCTIONREnUCTlON.~QN..SE.R~nIQN ~EDUr.TlON.....,-................................................................................................................................................
1980 o.ono 0.000 0.000 0.0110 0.000 Cl.OOO
1981 O.OUII 0.000 11.126 0.000 0.000 0.48819820.000 0.1)00 1.452 0.000 0.000 0.Q7!i19113o.OUO o.oon 2.178 0.000 0.000 I.IIU19840.000 0.000 2.904 n.ooo 0.000 1.950
1985 0.000 0.000 3.630 0.000 0./100 2.4 38
19h -0.JI9 0.000 S.Ol9 -0.1532 0.000 3.2501987-0.&38 o.noo '.IIl?-1.0611 0.000 4.0621988-0.957 0.000 7.8l'.t.~96 0.000 4.8nU89.1.176 0.000 9.22S .2.129 0.000 !i.U5
("'")1990 -'.595 0.1)011 10.&24 -2.661 0.000 6.491.
Q)llJ91 -1.846 0.000 ll.HS .2.998 0.000 1.39501992-2.097 0.000 111.127 -3.ns 0.000 8.292UQ3-2.31l8 o.oon 15.R78 -1.611 0.000 9.1891994-2.599 0.000 11.no _4.008 0.000 10.087
1995 ·2.1150 0.000 19.381 _4.145 0.000 10.984
.199&-J.031 0.000 20.996 -4.588 0.000 II.~H1997-1.211 0.000 22.&11 _11.832 0.000 lZ.&951998-l.H?0.000 24.226 -5.OTS 0.000 11.551U99·J.572 0./100 2S.8110 .15.118 0.000 111.407
2000 -1.151 0.000 21.455 .15.'5&1 0.000 15.2U
2001 -1.905 0.000 29.126 -S.179 0.000 16.1982002-C1.0S11 n./)Oo 31).797 _1§.q97 11.000 17.1342001.4.211 0.000 32.468 -6.21&0.000 18.070200Cl-C1.3bJ lI.noo H.U 9 -&.1114 0.000 19.00.
2005 -1I.'B6 0.000 35.Bl 0 -b.652 0.000 19.qll~
20Gb -tl.e.b~0.001)37.HS -6.892 0.(100 21.0912007·Q.1\20 0.001)H.6Qt -1.132 0.000 22.nq2008-4.Q73 0.000 AI.55b -7.371 0.009 21.1882009-5.t25 0.000 til.tI 72 _1.612 0.000 illl.516
2010 -5.211 n.llull 45.188 -7.•852 0.1100 2'5.6815
SCENARIO.MED •HE9 ••DOOR 501.-~/24/1981
6REAKDOWN OF ELECTRICITY REQUIREHENTS (GWH)
(TOTAL INCLUDES lARGE INDUSTRIAL CONSUMPTION)
ANCHORAGE •COOK INLET-_......-.._-.~.-.....
MEDIUH RANGE (PRa.'5)..•.•..•.•~...-.....
RESIllENTlAL BUSINESS MISCELLANEOUS EXOG.INDUSTRIALYEARREQUIREMENTSREUlIIREMENTSREQUIREMENTSLOAD TOTAL.......~...........•.......•••...................-......~...•.•....--..........•...••.....•.•.
1980 979.53 875.1&211.31 811.00 tC'i~3.ICI
19111 1018.53 C'iIU .~O 211.sa 92.08 207~.7C'i19821051.511 ltlOl.81 211.85 100.16 2190.3I!1981 109".511 10711.07 25.13 108.211 2303.981981111J5.50 1140.11 25.40 ll~.U 2417.51
19(15 1114.55 1206.55 25.68 1211.40 2531~ll
19Rb 1195.98 U311.lJ9 26.20 tl7.89 25911.&11987un..111 Ubi.64 26.73 151.38 2658.1~1988 I 218.t'1l 1290.&8 27 .26 1611.88 2721.~61989U60.28 BIR.73 27.79 178.37 27115.16
("")
13116.77 28.31 19(.8b U1l8.b5.1990 1281.710'1......
1991 1~C'iS~1I8 136S.61 28.58 195.13 2884.791992IJ09.25 IJ811.1I11 28.811 I C'ia.1lO 2920.C'il199)13U.02 111 03.28 29.10 201.66 29'57.061994I3lb.7C'i 11122.11 29.36 201l.9J 29H.20
1995 USO.56 IlIlln.95 29.&2 208.20 3029.]]
1C~9b 13&8.cn 11171.17 30.23 214.111 30811.50199713El7.3'1'501.39 30.83 220.08 1139.&El19981005.111 U31.bi!u.ln 226.02 11lJII.8S19'19 I0211.IC'i 1561.84 U.04 231.96 3250.02
2000 IOlli.S CI 1592.0b 32.bO 237 .110 3305.1'1
2001 11Ib1.9J 1635.87 H.37 2 01l.lJ6 ]]82.1020021493.27 1679.69 34.10 252.02 1459.0820031518.&1 1721.50 34~84 25CJ.(IS 351&·.03200111503.95 17~1.32 35.57 26b.1II 3lIt2.u
2005 l'itl Q .29 1811.0 3&.30 273 .lO lbl!9.C'i2
ioo~1&(12.15 lRllI.OO H.li 281.58 H95.U2001Ib3b.ll 1936.8&38.33 28CJ.9&]9111.3620081611'1.11'1999.73 39.3 4 298.14 11007.0820091703.13 l062.5'1 110.36 306.72 IIlli.80
2010 I 73b.SC'i 21c?5.lI b 1I1.n 315.10 11218.52
SCENARIO.MEO •HE9 ••nOOR 501--6/24/1981
BREAKDOWN Of ELECTRICITY REQUIRE~ENTS (GWH)
(TOTAL INCLUDES LARGE INDUSTRIAL CONSUMPTION)
GREATER FAIR8ANKS..........•....•..•••.
MEDIUM RANCE (PR_.S)....................
RESIOE~nl·L BUSlNf5S MISCELLANEOUS EWOG.INDUSTRIALYEARREQUIREMENTSREQUlfH.'-4ENTlI REQUIREMENTS LOAD TOTAL..-.....•...........•.....••.........-~..•••..•.......•..........•....•...•....••.........•..•
1980 I7b;19 "17.111 6.18 0.00 400.31
1981 190.'09 2l8.70 b.H 0.00 425.S11982lOJ.7"240.211 b.70 0.00 "'0.71J1981211.48 251.82 b.b6 0.00 415.96 '1984 231.18 2U.18 6.61 0.00 SOlitl
1985 2114.87 2H.95 6.57 0.00 SU~]lt
1980 l53.79 219.77 6.53 10.00 550.0919872112.70 l84.l§9 6.49 20.00 5J3.n191'8 271.112 289.111 6.46 30.00 597.1191989280.54 2 fU.23 6.42 110.00 621.18
n 1990 2A9.1Ii!5 299.115 6.38 50.00 bllll'.88.
0'1 1991 ~97 .27 JU.90 6.50 50.00 b56.118N
InC!305.09 306.75 lI.b3 SO~OO 668.4111993312.91 110.M 6.75 50.00 6811.26ln4320.71 U4.115 6.1!8 50.00 6"2~06
1995 328.51J 318.10 1.00 50.110 703.85
1996 n4.110 3i!~.55 7~12 50.00 715~061997)40.25 328.110 7.ll 50.00 72b,2819983Qb.10 UlI.OS 7.3!i 50.(10 7]7.501999351.95 339.30 7.116 511.00 7118.71
lOOO ]';7.80 3411.55 7.58 50.00 759.93
2001 lfl4.ll9 3SI.Rb 1.73 50~1I0 7711.012002371.1~)59.11 7.67 511.00 788.222003377.8b lb6.llll 8.02 50.00 8112.37200113AlI.51)lH.79 8.17 50.00 81b.51
2005 ]91.211 ]8t.IO 8.31 50.00 '8]0.6b
2006 ]99.1>5 191.31 8.52 50.00 8119.11820071108.0'5 /I01.!j2 8.72 50.00 868.10Z008ql~.4b 1111.74 8.qi'50.00 881.12200942/1.87 IIZI.QS 9.12 50.00 Q05.911
2010 qB.i11l IIJi!.Ib 9.31 50.00 q211.76
o
O'l
W
SC!NARIO.MED I HEq ••DOOR 50X ••6J~4Jlq8J
TOTAL ELECTRICITY REQUIREHENTS (GWH)
INET 0'CONSERVATION)
(INCLUDES LARGE INDUSTRIAL CONSUMPTION)
MEDIUM RANGE CPA ••5)....•..•...........•.•
YEAR ANCHORAGE •COOK INLET GREATER FAIR8ANKS TOTAL..-...........•...•.•....•.••.................•...........•......•.-..
1980 I'H'3.19 lIOO.31 nU~51
1qU 2(11b.79 425.5J 15(1j!~32Iq82iI9O.38 1150.14 2ft II 1.1319812303.98 1.lT5.lIb 27H~911lq811i1l11.57 5U 1.18 2qt8~n
1985 2531.17 526.J9 301S7~56
1q8b 25911.&7 550.09 31114~U1987i«>'58.1«>573.n ~231,.95191182721.«>6 597.119 3319,lS19892185.16 621.18 3406.311
1990 28118.65 MII.B8 Jn3~511
1991 2884.79 656.b8 3501~1I719921920.93 6613.117 3589,1919932957.06 b80.2b U37,1219911299J.20 692.0b U1l5.25
1995 3029.33 703.115 1733".18
199b 30811.50 715.0b 3799".571997:UH.«>8 7Zb.28 38b5.9b199831911.85 73 7.50 393a.31119993250.02 74 A.71 1998~73
2000 H05.19 75 9 .93 1I0b5~Ii
2001 HRl.11I 7711.07 III'5b,2120021115~.08 788.22 11207 ,30
2003 35J&.03 802.37 11138,.39200113«>12.97 81b.5t IIlIi9.118
2005 3b8'J.92 810.bb 115<!O~5~
200b 3195.&11 11II9.4a 1I&1I5~li2007390I.30 8b8.30 47&9.6&2008 4007.08 !l87.12 G8911~202009111'2.80 905.94 S018.74
2010 11218.52 92 11 .7&51113.2 R
n
0'1
~
SCENARIO'HED'HE9 ••000R 50'••~/24/1q8]
PEAK ELECTRIC REQUIREHENTS rMW)
(NET OF CONSERVATION)
(INCLUDES LARGE INnUSTRI4L DEMAND)
MEDIUM RANGE (PR ••5).....•..........•.....
YEAR ANCHORAGE •COOK INLET GREATER FAIRBANKS TOTAL.......•....•......•..•.•.....•......•...........••.•.•••..•...•......
1980 HfI.51 91.110 487.90
1981 419.45 91.15 5U~1I019824I1a.39 10l.91 545~]0198]4115.H 10R.n 574~0019844R8.27 1111."2 602.11)
1985 511.21 120.18 &]I ~39
19811 S211.BI 125.59 1I50~401987538.41 131.00 U9.411988552.Ul 11lI.110 68".411989565.bl 1111.81 '7n7~1l2
1990 SH.21 14'7.22 '72&~4]
19 1H 51'1&.51)149~91 13l1~/UIf,92 5 9 3.79 152.60 '7411~4019'1l bOI.09 155.30 756.31319911b08.]1'I 157.99 76,,~n
1995 bI5.b'7 11IO.U '7'71i~]5
199b f12f1.73 IU.Z4 789'.98
1997 U7.80 Ifl5.80 fIl)].bO
1998 &48.1I6 Ib8.36 817~2]1999 bli9.93 110.92 830.85
2000 b70.9"IH.Q8 844.48
ZOOI b Rb.1I9 1711.11 8U:202002101.98 In.94 881:93
2003 717.41)183.1'7 9110'.b5
2004 132.97 186.40 1119:38
201)5 7118.47 189.U 918".11)
200b 7119.81 ICH.U 961'.742007791.15 19R.23 qI)9~31
2008 Btl.4R 202.52 1015~Ot
2009 '133.82 206.82 IOIl0~&4
2010 855.1&211.12 I01l6~2~
L
I
I
I
[
1
I
i
i
!
(
(
i
I
I
r -
(
i
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HIO--DOR 30%
C.65
I
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SCENARIO.MEO •HIO ••DUR )OI••4/24/1~83
HOUSEHOLDS SERVED
ANCHORAGE •COOK INLET
••••••••••••••••••••••
YEAR SINGLE FAHIlY HUL lIFAMILY ,",ORILE HOMES DUPLEXES TOTAL
•••••••••••••••••............................•.....•.•.•...•...••....•.
1980 35413.20]1".82lo.7486.11S0J.
0.000)(0.(100)(0.000)(1'.000)(0.001"1)
1985 45H8.2blOll.10801.8567.90951.n (0.000)(0.000)(0.000)(0.000)(0.000).
C'\
'-I 1990 53135.25877.12l87.U60.n958.
0.000)(0.000)(0.000)(0.000)(0.000)
1995 58J22.i5"91.13 u 07.UU.105956.
(0.000)(0.000)(0.000)(0.000)(0.001'1)
2000 62565.28711.14505.8181.11;J975.
0.000)(0.000)(0.000)(0.000)(0.000)
2005 61 1l9 O.li!568.15906.1833.12~1'17.
0.000)(0.000)(0.000)(0.000)r 0.001'1)
2010 1417 9 •lb272.17105.8667.137422.
0.000)(0.000)(0.000)C 0.000)(0.000)
SCENARIO,MED ,Hl0 ••00R 101 ••~/l4/'~R3
HOUSEHOLDS 8ERVED
GREATER FAJRRANKS
••••••••••••••••••••••
YEAR SINGLE !l'AIolJlY MULTIFAMILY HDBIlE HOMES DUPLEXES TOTAL_.-.-.............••••••••••••••••••••••••••.............................
1980 722n.5287.1189.litl?•I~lU.
o.ono)«0.000)«0.000)«0.000.)(o.oon)
1985 10646.55n.il]O.U9].20042.n (11.1100)(0.000)(0.000)«0.000)(0.000).
0"1
OJ 1990 !OSB.1743.ll03.2197.li!56.
(0.000)«0.000)«0.000)(0.000)(0.(00)
1995 122 q 2.784'.2410.2319.2/t88!•
(0.(00)(0.000)«0.000)(0.000)(0.000)
2000 13631.7103.3006.2298.2664!.
0.000)(o.oon)«0.000)(0.000)(0.000)
2005 15550.7549.3638.l252.2!!990.
o.noo)(o.noo)«o.oon)«0.000)«o.noo)
2010 17358.8483.4126.lObi.32028.
o.noo)(0.000)«0.000)(0.000)(n.oon)
SCENARIO.HEO •HIO--DOR 10X--&/24/198J
HOUSING VACANCIES
ANCHORAGE -COOK INLET.........•.........•..
YEAR SlNGL£FAMILY HUL TIF AHILY HORILE HOMES DUPLEXES TOTAL...............•.-...~................................•••..............
1980 5089.1666.1991.14&3.Ib20Q.
(0.000)(0.000)(0.000)(B.OOO)(".000)
1985 499.1496.119.292.2i106.
.(0.000)(0.1'00)(0.000)(0.000)(0.000)
n.19QO lI587 •1471.135.289.~488.(J)
lD (0.000)(0.000)(0.000)(0.000)(0.000)
19q5 642.1050.147.284.2124.
0.000)(0.000)(1).000)(0.000)(0.000)
2000 688.1551.160.in.2618.
(0.000)(0.(00)(0.000)(0.000)(0.001)
lO05 7Q7.17S Q •175.IIU.3144.
0.000)(0.000)(0.000)(0.000)(0.000)
2010 823.1959.195.2U ,3261.
0.000)(o.noo)(n.ooo)(n.ooo)(0.000)
SCENARIO'"'ED ,HIO--DOR 10X.-b/24/IQR]
HOUSING VACANCIES
GREATER ~'IP.8AN~S
••••••••••••••••••••••
YEAR SINGLE ~AHILY MULTJ,.,HILY HOULE HOMES DUPLEXES TOTAL....••••••••••••••••••••••••••••••••••••••••••••••••••••.•.......•...
1980 Jb51.3320.086.895.8854.
0.1)00)(0.000)(0.000)(0.000)C 0.000)
1985 tl8.2 0 QA.iQ.79Q.3884.
CJ (o.noo)(0.000)(0.000)C 0.000)(o.noo).
.......a 1900 117.611.21.259.1070.
0.000)(0.000)o(0.000)(0.000)(0.000)
1995 135.41 48.27.80.680.
(0.000)(0.000)(o.nOn)(0.000)C 0.0011)
2000 150.4 /10.]3.78.701.
0.000)(0.000)(0.000)(0.000)(0.000)
2005 171.4]1.40.77.719.
0.000)(0.000)(0.000)(0.000)(o.oon)
2010 191.45 A•QI5.216..9111.
0.000)(0.000)(0.000)(0.000)(0.000)
SCENARIOI ~ED I MlO--DOR 10~.-6Ilq/1983
'UEl PRICE 'ORECA8TS EHPLOYF.D
ELECTRICITY (,I KWH)
n.
'-J
---'
ANCHORAGE -COOK INLET GREATER FAIRBANKS
•••••••••••••••••••••••••••••••••••••..•..••...•..•..................-..-.
YEAR RESIDENTIAL BUSIUESS -RESIDENTIAL RLJSlNESS....••••••••••••••••••••••••••••••••••••••••••••
1980 0.037 0.0]4 0.09~0.090
1985 0.048 0.0115 0.095 0.090
1990 0.049 1).046 0.090 0.085
1995 0.050 0.047 0.0 9 0 0.085
2000 0.050 0.041 0.090 0.085
2005 0.050 0.047 0.090 0.08t;
2010 0.050 0.041 0.090 0.085
SCENARIO."ED.HIO ••OOR 101 ••6/24/198]
FUEL PRICE FORECASTS EMPLOYfO
NATURAL GAS (i/MMBTU)
n.
'-J
N
ANCHORAGE .'COOK INLET GREATER FAIRBANKS
••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••
YEAR RESIOENTUL BUSINESS ~E81DENTIAL RUSINESS
•••••••••••••••.....~.....•••••••••••.•.........
1980 I.no 1.500 12.740 It.290
1985 1.911)1.100 9.090 7.640
1990 2."80 2.250 1.1bO 6.110
1995 2.530 ~.]OO 6.HO 5.~9D
2000 2."50 ~.2aO 6.290 ".840
2005 2.]61)2.130 5.820 4.370
2010 2.26n l.D30 5.390 3.940
SCENARIO'MED'HIO-_DDR ]OX--b/24/1983
FUEL PRICE FORECASTS EMPLOYED
~UEL OIL ($/MMBTU)
n
........w
ANCHORAGE •COOK INLET GRFAT!R FAIRBANKS
•••••••••••••••••••••••••••••••••••••...•.••..............•••.......--....
YEAR REUOEHYUL BUSINESS RESIDENTIAL BUSINESS•.--............••••••••••••..••..•.•.•••••••••••
1980 7.750 7.200 7.830 7.~00
1985 15.530 4.980 5.5t10 5.200,
1990 4.73')4.180 4.770 4.440
1995 4.110 1.!bO 4.1 ilO 3.810
2000 3.830 1.280 1.8&0 3.1530
2005 3.550 3.000 3.580 3~250
Zo10 1.280 1.730 ].]10 2.980
SCENARIO'MED I HIO ••OUR 101 ••6J24JI9as
RESIOENTIAL USE PER HOUSfHOLD (KWH)
(WITHOUT A~JUSTHENT FOR PRICE)
~NCHORAGE •COOK INLET
-~.......••...~.......
SHALL LAROE SPACE
YEAR APPL tA~CES APPLlA~ICES HEAT TOTAL
••••...~...................•.•..••...•........
1980 211(1.00 etSOO.U 5088.52 13699.ts
0.0(0)(0:(00)(0.(00)(0 .•0(0)
n 1985 2160.00 ~Ub.51 118l1.~1 13153.715.(0.(00)«0 ..0(0)t 0.000)(0.0(0).......
~Ino 2210.00 6010.91 11651.U 128'11.34
(0.(00)(0.'000)«0.000)(0.000)
Ins 2260.00 5958.55 11'07.71 12726~Z5
(0.000)«0:0(0)(0.(00)(0.000)
2000 2110.00 5988 ~11 QOn.b9 12130.82
(0,(00)(0.'0(0)(0.000)(0.0(0)
2005 2J60.0U 60b2~U 4 4 21.68 12844.811
n.ooo)(0,000)(0.(00)(0'.0(0)
Jato 2411).00 bU9~56 1108.60 12977.96
(0.0(0)(0.'000)(0.0(0)(0'.0(0)
___I
SCENARIO'"'ED ,HIO--DOR 3~X.-b/24/1~83
RESIDENTIAL USE PER ~OUSEHOLD (KWH)
(WJTHO~T ADJUSTMENT 'OR PRICE)
GREATER FAI~8ANKS
••••••••••••••••••••••
SIiALl LARGE SPACE
'tEAR APPL J4NCES APPLIANCES HUT TOTAL....•..•..•.•.......•...•....•.........•.•..
1980 24b6.00 S739.52 13lJ.66 11519.18
0.000)(.0.000 )(0.000)(0.000)
n 1985 25]~.q~bl"2.ln 3586.15 12305~07.(0.(01))(0.0(0)(0.000)(O~OOO)
'-I
U1
1~90 2606.00 6434.60 3822.(1]12862~n
0.000)(0.000)(0.000)(0;"000)
19~5 2b76.00 66117~01 4075.11 133~8.12
1).000)(0.000)(0.(00)(0.000)
2000 27116.00 b7e9~50 4329.f>7 U8~5.18
0.000)(0.1/)00 I (0.000)(0.000)
2005 2&16.00 6859".08 450l.21 1411 7 .30
(0.(00)(0.'0(0)(0.(00)(0.000)
2010 2e86.01 68~9·.46 4655.1'11 1"441.45
(1).1)00)(0.'000)(0.000)(0.000)
SCENARIO,"'ED I HtO--OOR 301--6/24/'~83
8USIN!SS USE PER EMPLOYEE (KWH)
(WITHOUT LARGE JNDUSTRIAL)
(WIT~OUT ADJUSTMENT FOR P~ICE)
YEAR ANCHORAGE -COOK INLET QREATER "IR81~KS
••••••••••••••••••••••••••.•.••.........•~......
1980 8407.1)4 749S.10
(0.1100)(0.000)
n 14'85 9482.69 7932.11.c 0.000)c 0.000)
.......
0\1990 9938.71 81"2.36
C 0.000)(0.000)
1995 10147.91 'UUI7.5"
(0.000)(0.000)
ZOOo 10908.131 "1~2.72
0.(100)(0.000)
2005 Il'Ul.IJO 9137.18
(0.000)(0.(00)
2010 12397.Ii!9536.33
1).000)(0.000)
8CEtURIO."'ED •HIO ••OOR 101 ••bJ2UJI9R3
SUHHARY or PRICE E"ECTS ~ND PROGRAHATIC CONSERVATION
IN GWH
ANCHORAGE •COOK INLET
RESIDENTIAL BUSINESS••...••..•.....~._..-.
O"'N.PRICE PROGRAH-INDUCED CROSS·PRICE OWN-PRICE PROGRAM-INDUCfD CROSS·PRlCEYE~R REDUCTION CONSERVATION REDUCTION REDUCTION CONSERVATION REDUCTION.;...............-~--..---:-.~t=",,;.f;-':~t:............;•••;1;;;;....;:.....................i-..........................................
IUD 0.000 0.000 0.000 0.000 0.000 0.000
lUI e..OSG 0.000 0~Q8q 8.982 0.000 I.'SUI1982U.h?0.000 0.918 11.9 0 ll 0.000 3.151198J18.251 0.000 1.468 20.Q 40 0.000 4.12?19811 2 11 .US 0.000 1.957 35.928 0.000 0.101
1985 30.418 0.000 2.4110 tIIl.911 0.000 7.819
1980 34.989 0.000 0.021 51.t15 0.000 8.5191987H.Soo 0.000 -2.1104 57.319 0.000 9.100198844.HI 0.(100 -/1.829 61.5211 0.000 9.800198948.702 0.000 -7.255 0 9 .728 0.000 10.4111
1990 53.213 0.000 .9.680 ?1Ji.on1 0.000 11.082n.
-....J 1991 56.610 O.(I/)/)-10.438 80.910 0.000 12.591-....J 19 9 2 59.9bO 0.000 -11.195 "5.887 0.000 IU.IOI1993b3.103 0.000 ·11.953 90.803 0.000 15.ott1'''''bb.oll1 0.000 -12.711 95.840 0.000 11.120
Ins 09.990 (1.000 -13.40 9 100.817 0.000 18.030
1990 n.021 0.000 -12.9/19 tOIl.40n 0.000 20.780199775.251 0.000 -12.428 109.983 0.000 2Z.9112199877.882 0.000 ·11.908 1111.565 0.000 25.098199980.512 0.000 -1I.38?119.1118 0.000 2?ZSU
iOOO tl3.11l2 0.000 -10.(\67 t23.731 0.000 29.1110
2001 85.032 (1.000 -9.329 l21l.b97 0.000 32.1171200288.122 0.000 -7.791 133.b04 0.000 15.53220039(1.b1Z 0.(100 -0.254 118.030 0.000 38.593200119].IOZ 0.000 .4,,110 143.597 0.000 111.0511
2005 9S.S9Z 0.000 -3.178 tIl8.So]0.000 1I11.?I!!
2000 98.2b7 0.1'100 .0.011 154.705 0.000 119.1502007100.9113 (1.(100 1.952 IbO.Rllo 0.000 53.585200810].018 0.000 11.517 106.QS7 0,000 58.021200910b.C'Q]0.(100 1.0S2 173.12R 0.000 b2.450
2010 108,QbQ 0.0011 9.e.1l7 I1Q.270 0.000 6b.891
SC[NARIO,MED I ~IO-_OOR 10~--bI24/198]
8UHHAR1 OF PRICE EFFECTS AND PROGRAMAT!C CONSERVATION
IN GWH
GREATER FAIRBANKS
RESIOENTUL RUSINESS....-.................
OWN-PRICE PROGRAH-INOUCED CROSS-PRICE OWN-PRICE PROGRAM_INDUCED CROSS-PRTCEYEARREDUCTlOtlClmSF~VA TI 011 REDUCTION RE~l!.!;..T I QN __CONSFRVAUON REDucTION..............-.:..t:t:'.:....~+...:.:........~t:..~,..._..-..................................................................................
IUD n.oOIl o.noo 0.000 0.000 0.000 n.oon
1981 o.noo 0.n90 1.]]8 o~ooo 0.000 0.8'19Uu0.000 0.000 2.611»n.ooo 0.000 I.nll1'183 0.000 0.000 4.014 o.oon o.noo 2.69719840.000 0.11 110 5.352 o.oon 0.000 ].5'16
1915 0.000 0.000 6.fl91 0.000 0.000 4.4'115
1986 -0.310 o.oon 8.527 -O.lill 0.000 5.5261987-/).620 0.000 10.161 -1.022 0.000 6.'5'571988-o.'no 0.0011 12.19q -1.531 0.000 7.581119n-1.2110 0.000 14.035 _2.044 0.000 8.619
("")1990 -1.5511 0.000 15.872 -"'.1555 0.000 '1.650.
""-J Iqql -I.lql 0.000 18.09]-il.B78 0.000 111.183(Xl U9i!-2.031 0.000 20.]15 _].200 0.000 II.q16U9]-2.211 0.000 22.536 -].522 0.000 11.04Q1'1'14 -2.512 11.000 211.758 _1.844 0.000 111.183
1995 -2.752 0.000 26.979 _".166 0.000 15.3U
1996 -2.919 0.000 29.014 -11.407 0.000 U.42]Uq1 -3.104 0.000 31.049 _11.648 0.000 17.530UqB-3.280 0.000 ]3.083 -1I.88 Q 0.000 IB.U11999.3.115&0.000 35.118 -5.13n n.ooo 1'1.7411
2000 -1.612 0.000 37 .151 -5.111 0.000 20.851
2001 -1.184 0.000 19 .361 -!!i.5ql 0.000 22.1282002-3.935 /).000 1I1.57n .5.811 0.000 21.405200]-4.087 0.1100 43.718 -b.031 0.000 211.68220011-4.239 0.000 45.q!lb .6.251 0.000 25.'15'1
2005 .4.391 0.000 41'.195 .6.471 0.000 27.2311
20Gb .11.54]0.000 50.n7 _~.712 0.000 28.8482007-4.bq6 0.111)0 53.19'1 .6.'152 0.000 30.4602008-4.84(1 0.(100 56.01)1 _7.1'13 0.000 32.0722009-5.nOI 0.000 SA.MII .7.1131 o.oon ]].684
lOID -5.1511 O.lllJP bl.i'Ob _1.67 4 0.000 15.2Q"
SCENARIO 1 I4EO 1 HIO·.OOA JOX ••&/211/198J
8~EAI(DOWIl OF ELfCTRltfTY REQUIREMENTS (GWH)
(TOTAL INCLUDES LARGE INDUSTRIAL CONSUMPTION)
ANCHORAGE •COOK INLET......_.-............•
MEDIUM RANGE (PR_.5).......~.._.__......
RESIDENT!'L BUSINESS IotISCELLANEOUS [VaG.INDUSTRIALYEARREQUIREMENTSREQUIREME"4TS REQUIREMENTS LnAD TOTAL............~.....•......•......................•.•.••••....•••......••.••...•.....•••.._....•
1980 q79.53 87S.3&24.31 811.00 UU.1lI
1981 lou,.n 931.25 24.51 92.08 2010.11198Z1053.U 999.14 24.72 100.16 2177.14198310119.92 IOU .03 24.91 108.24 2284.111984112&.71 1 ta2.92 25.13 116.J2 2lCJ1.oe
1985 1163.S'1184.81 25.14 12'''.40 2498.0b
1986 1119.81 1204.43 25.7J lJ1~U 2547.921987tIqb.l2 12211.0b 26.12 151.38 2597.7919881212.511 lZ1IJ.69 2b.SO IU.U 2b1l7.6519891228.911 1261.32 26.S9 17S.!?2bq7.52
n 1990 Ii!U.9S U.2S 191.86 27117.J!l.1245.30........
1.0 1991 12n.IS 21.5]195.1]'U7b.1I31254.b2
19 9 2 12U.911 1315.36 27.78 198.40 2805.1171991t273.2b 133t.57 28.02 201.66 28]/1.5'19911 1i!l:\2.5'1 13117.78 28.27 204.91 Ub3.511
Ins t2ql.qO 1361.99 28.52 208.20 2892.61
199b 131)9.27 lnS.llo 29.06 2111.14 29117.871997132b.61 1112b.82 n.60 220.08 ]003.1]1998 13113.99 II1S8.23 30.14 226.02 J01J8.lCJ1999U61.]"148Q.U 30.68 231.96 31 B.65
2000 1378.72 1521.07 31.23 231.90 lU8.91
2001 Il.o].SIi I'H,Ii.3S 31.97 2114.Q6 32I1S.83200211128.38 I bOQ.63 32.n 252.112 3322.752003IIIS.5.21 16S3.'1 33.116 2S9.08 nQQ.6720041478.011 1698.20 34.20 2b6.t4 1Il1b.SQ
2005 1502.88 17112.118 34.95 273.20 :n5:J.50
200b 1535.27 18011.20 3S.93 281.58 365b.ge20071567.b6 18bS.en 3b.91 289.96 3760.4620081600.06 1927.65 37 .8'298.34 3863.9420091632.115 1989.38 38.81 306.72 H67.42
20ln 1&6/~.811 2051.111 39.Sf,315.10 11070.90
SCENARIO,M~D,HIO.-OOR JOI--6/24/1983
BREAKDOWN 0'ELECTRICITY REQUIREMENTS CGWH)
(TOTAL INCLUDES LARGE INOUSTRIAL CONSUMPTION)
GREATEP FAIRBANKS..•.•........•..•..•..
MEDIUH RANGE (PR_.5).•••••......•.......
RESIDENTIAL BUSINESS MISCELLANEOUSYEARRE.QUIREMENTS REQUIREMENTS REQUIREMENTS•.....•......•.•....•..•...•.•.•...••..•........•......•..
1980 17b.39 217.U b.78
lUI 189.10 221.53 b.151981lOI.eo i!l1.9].~67198]2111.51 i!ll8.li!6.6219811227.22 2511.12 b.56
1985 an.92 2b9.11 b.51
1986 2117.10 272.22 b.451987a511.i!~275.n b.39
lua 261.11&2711.43 b.3319892et8.b3 281.54 6.27
n.Ino 17S.81 284.64 b.21CO
0
b~291991281.47 288.03
1992 289.14 291.41 b.371993a9S.80 294.79 b.4b
1994 3112.47 29~.t7 b~54
1995 309.13 301.55 b.62
199b lU.1I8 ]0&.81 b.7lI1997J19.82 3I2.Gb b.85
In8 325.17 317.12 b.9&1999 nO.52 321.58 7.07
2000 315.116 127.811 7.18
2001 H2.U US.tl9 7.322002]48.40 ]42.34 7.4&
2003 1511.bb 149.59 1.bl200113&0.91 156.84 7.715
2005 31>7.20 3b4.08 7.89
200b 375.OS 173.911 8.09
2007 382.91 383.H 8.21l
2008 ]90.7&393.bll 8.48
2009 39~.&2 1101.50 8.b8
20to qllb.48 413.15 8.87
---,
E~OG.INDUSTRIAL
LOAD.-....•...
o~oo
o~oo
0.00
0.00
0.00
0.00
10.00
20.00
30.00
40.00
50.00
50.00
50.00
50.00
50.00
50.00
50.00
50.00
50.00
50.no
50.00
50~l)0
50.00
50.00
50.00
50.00
50.00
50.no
50.00
50.00
50.110
TnUL.......•......-...
400.31
U3.16
44b.40
IIn.4lS
1192.50
515.54
535.77
555.QQ
57b.2t
59&.411
blb.&b
b26.79
bU.92
bll1.n
Ul.1S
6117.31
618.02
b88.73
U9.115
7IO.lb
720.88
7111.511
7118.20
7bl.85
175.51
7811.17
807.08
8211.Qe
811Z.8Q
860.7Q
8'78.70
n.
ex>
--I
SCENARIO.MEO.HIO-~OOR 301.-0/24/19113
TOTAL ELECTRICITY REQUIREMENTS (GWH)
(NET OF CONSERVATION)
(INCLUDES LARGE INDUSTRIAL CONSUMPTION)
MEDIUM RANGE (PR ••5)...••....••.....••....
YEAR ANCHORAGE •COOK INLET GREATER FAIRBANKS TOTAL_.-......•.....................•........•..........~••................
1980 UU.19 1100.]1 23U~SI
1981 2070.17 42].Jb ~/191~5219822117.14 1146.110 i'6~3~511198322811.11 4&9.45 &'753.5619842391.011 492.50 ,2M]~58
1985 2498.011 !its.511 ]013~&O
1986 ~5q7.92 535.77 308]~&9198725n.79 555.99 3153,7819882bIl7.65 576.21 3223,8719892697.52 596.114 3293.911
1990 27117.38 "'6.66 n611~O'i
1991 2776.113 620.n 31103~2219922805.117 036.92 311112~39199128]11.52 6117.05 31181.57199112863.56 "57.IS 3520.711
1995 28 9 2.111 6117.31 35'59~92
199&2947.87 678.02 3fl25~891991100J.13 688.711 3691.87199830'58.39 699.115 11!i7~811199911ll.b5 ?tn.16 18U.8i'
2000 11b8.91 720.88 18M~n
2001 H1I5.8J 7311.5q 39BO~3120021322.7Ij H8.20 lI070.9520033399.07 7&1.85 III'"~52200111117&.59 775.51 11252~10
2005 355]~50 789.t7 lI]/12.b8
200b Jb'5b.91l 801.08 lIlUJlI~Ob2007J1bO.'UI 8211.98 11585.411200836U.911 8112.89 1170b,8J200919b7."2 860.79 11828.21
2010 4010.90 878.70 119I1q~b(l
n.
co
N
8CEN~RIOI HED I Hto ••nOR 10¥••oJ24/19S3
PEA~ELECTRIC RF.QUIREMENTS (MW)
(NET OF CONSERVATIO~)
(INCLUDES L~RGE I~DUSTHIAL DEMAND)
MEDIUM RANGE (PR ••5).....••...........•...
YEAR ANCHUR1GE •COOK INLET GREATER fAIRB~NK8 TOTAL....•.....••.•-....~....~....-....-..~...........•...••..••••.....
1980 3110.5t 91.40 1181~90
11181 418.09 96.ob 514~1519821U9.08 101.92 541.0019B31101.26 107.18 5b8~4111981111112.85 .112.44 SQS.29
1985 5011.113 111.10 b22~13
198&St5.211 t22.32 U1~5b1981520.011 126.9]652 11 981988516.85 131.55 UB,.1I019895117.be1 136.10 683.82
1990 558.40 1110.17 U9~211
1991 564.30 111].09 707.391991570.14 Ilf5.110 115.5419113575.98 1117.11 123 11 70199115"1.82 150.03 nl.85
1995 51!11.bet 152.]/1 7110~00
1911&591t.75 15 0 .J8 751~531997009.83 157.21 7&71'061998020.91 159.68 7110.591999b12.00 1b2.12 794'.12
2000 0113.01'1011.57 807~U
2001 058.57 107.69 826,252002b70.00 110.81 8411 .•8b2003089.55 173.92 863.47200111(15.00 t77.04 88i.(II'
2005 720.53 IBlI.lb 900~69
2006 7 0 1.111 1811.25 925~65201117b2.28 188.JII 9r;0,u200B781.tb 192.lI2 975.5920098011.011 196.51 tooO~56
2010 8211.92 200."0 102":52
j
I
j
i
)
I
!
]
)
H13--DRI SCENARIO
C.83
I -
)
j
I
I, I
,l
j
I
SCEtURIOI MEn I HI3--0Rl SCENARIO·.6/24/1~8]
HOUSE~OLOS SERVED
ANCHORAQE •COOM INLET
••••••••••••••••••••••
YEAR SINGLE FAMILY MULTIFAMILY MORILE ~O"'ES DUPLEXES TOTAL..-.................•........•.•.....•.....•.........•........•......
1980 351171.2011 /!.8230.7486.7150].
0.000)(O.O?O)(0.000)C 0.000)t 0.(00)
1985 '1b2C!1.2b204.10957.8567.91~50.n (11.01)('1)(0.000)(0.000)(0.000)C 0.000).
OJ
U1 19~0 57890.2587'7.IllO I.811U.105528.
0.000)(0.000)(0.000)(0.000)C 0.000)
1995 65471.301124.15120.8331.It~154.
0.000)(0.000)(0.000)(0.000)c 0.000)
2000 739~q.35115,-.17215.8532.135167.
(0.000)C 0.000)(0.000)(0.000)C 0.000)
2005 83157.1I02b7.19580.96411.1528111.'.
".000)(0.000)(1).000)(0.0001 t O~OOO)
2010 95227 •110455.li!589.It 057.175127.
0.000)(0.(00)(11.000)(0.000)C 0.000)
SCENARIO'HEO ,HI3 ••DRI SCEN.RIO ••bla4/1~8J
HOUSEHOLDS SERVED
GREATER FlIAQlH~8...••.•..•_....•.••..•
YEAR SINGLE FAMILY MULTIFlMILY M081LE HOME8 DUPLE liES TOTAL..-.........•....•••••••••••••••••••••••••••••••••••••••......•......
1980 1220.'5287.1189.1617,15111.
(0.001))(0."00)r 0.001)(0.000)(0.1)00)
1985 10646.5866.inO.1764.20406.
n (0.(00)(0.011/'1)(D.ono)«1).000)(0.(00).
OJ
0'\1990 11458.7960.2204.2315.n997.
0.(00)(0.00/'1)(0.000)(0.000)(0.(00)
1995 1/.1936.78 tH.3192.2339,28507.
(0./'100)(0.000)(0.000)(0.1'100)(0.000)
2000 '7blG.8212./.Il11.2298,12292.
(0.000)(0.0(0)(0.000)(0.000)c 0.000)
2005 19820.9f.l3f.l./.IU2.2349.361117.
0.001'1)(0.1)00)(0.000)«0.000)(0.000)
2010 l257 9 •11088.5375.2686.41H!•
f).000)(0.000)(0.1'100)(0.000)C n.ooo)
SCENARIO,HED ,~tl ••ORI 8CENARI0 ••~/~4/tq81
HOUSING VACANCIES
ANCHOP.AGE •COOK INLET
••••••••••••••••••••••
YEAR SINGLE FAMILV MULTIFAMILY MORILE HOH£S DUPLEXES TOTlL..-..•.•.................................................-.••.••......•
tq80 'SOBq.7bbf>.tqqt.1463.Ib201;l.
o.non)(o.oon)(O.OUO)(0.000)(o.oon)
('")tCJSS '50 A•141;1".IZt.2f12.24t1..(0.000)(0.(00)(0.000)(0.000)(11.(00)
ex>.......tIJCJO b3".14"7.Illb.281;1.2!t4Q.
0.000)(0.000)(0.000)(0.000)(0.000)
tIJfl5 121l.1.,4J.tbb.284.2814.
0.0(11)(0.(00)(0.0(0)(0.000)(0.000)
zooo 814.l 1J 14.,SIJ.282.3tIJIJ~
0.000)(0.000)(".000)(0.000)(o.oon)
2005 eliT.ll7 tJ •2tS.318.3b25.
1'1.0(0)(1).000)(0.000)(0.01'10)(o.oon)
20to 104".2501J.24IJ.165.41bQ.
o.l'Ion)(0.000)(0.1'100)(0.0(0)(0.0(0)
SCEN_RIO.MED •HU--I)RI SCEIIARIO ..b/24J1QR]
~OUSING VACANCIES
G~EATER FAIRBANKS..•...............•..•
YEAR SINGLE FAMILY "'UL TI Futi LV HORtlE HOMES DUPLEXES TOTAL.................•••••••••••••...•...•.....•••••••••••••..•.........•
11:180 lbSl.HaO.QSb.895."1'54.
0.000)(0.01l0)(1).1)00)(0.000)«1\.001\)
11:185 t 1 R.2b55.24.72l.151 q •n (0.1)00)(0.000)(0.000)(0.000)«0.000).
co
CO lQqO tab.(1511.24.8t.b8b.
0.1)(11))(0.0(0)(0.000)(n.ooo)«0.000)
19q5 tb4.448.:H.80..129.
0.0(0)(0.000)(0.(01))(n.oOO)«0.000)
lOOO t9U.447.liS.78.7&11.
n.ooo)(0.0(0)(0.(00)(0.000)«0.000)
2005 iH8.520.St.1ft.8b7.
0.000)(0.000)(O.(H)O)«0.1)00)«o.OOtl)
20to 24~.l§qQ.sq.89.Q9S.
0.0(0)(0.01)0)(0.0110)(0.000)«0.(00)
__I
---~----,---"-
SCENARIO,HED.Ht3--0RI SCENARIU--b/24/1Q6J
FUEL PRICE FOREC~STS EMPLOYED
ELECTRICITY (5 I KWH)
n.
CO
1.0
ANCHORAGE •COOK INLET GRf~TER FAIRBANKS.....•.......•..••.•..........•.........•...........•...............•.....
YEAR RESlOENTUl BUSIN£SS RESIDENTIAL RUSINESS..-.•.....•••.•............•..•.......•....••.•.
1"80 0.037 0.014 0.095 (1.090
19S5 O.OIIS o•lUI 5 0.095 0~090
1990 0.054 (1.051 0.09~0.087
1995 O.OU O.ObO 0.09Q 0".oe9
2000 (l.Ob9 O.l)bb 0.09b 0.091
2005 0.072 8.0b9 0.098 0.093
~010 0.075 (I.0 l'i!0.100 0.095
n.
lOa
acE~ARIOI MEn I HIl ••ORr aCEN~RIO ••6/l4/1q8J
'UEL PRICE 'OREC~aTS EMPLOY!O
NATURAL GAS (S/M~ATU)
ANCHORAGE •COOK INLET GREATER 'AIRBANKS..•...••..•...•••••.•..•.•....•..~.....••.•...•......••..................•
YE~R RESIDENTUL FUJSINESS RESlDENTI AL BUSINESS..........•................•••••••••••...........
Iq80 1.131)1.500 12.no 11.2QO
Iq85 2.030 1.800 II.IIQO 111.240
IQqo J.450 ).2211 16.010 Ill.~60
lqQS 5.10n 11.'370 lQ.840 18~]qO
2000 15.750 5.1520 23.120 21 ~670
2005 boOIO ~.780 i!4.HO n~1l20
20tO b.lllO fJ.110 26.230 24.7&n
n
l.O
--'
SCEN~RIOI MEO I H1J ••DRI SCENARIO-.b/24/1983
FUEL PRICE FORECASTS EMPLOYED
FUEL OIL ('/~MBTU)
ANCHORAGE •COOK JlJL ET GRE"TER FAYRBANKS••.....•...•..•••••.•........._...............................•...•........
YEAR RESIOEt-tTlAL &lISHIESS RESIOENTUL RlISlNESS...................................•.........•....
U80 1,150 7,200 1,830 1,1500
1985 1,120 6,570 1,180 6,8'50
1990 9,151)Q,200 9.8QO 9.'J1O
1995 12,080 II.'5]n 12.190 11".860
lOOO 1~,n80 11,530 14.l10 13.8811
lOOS III.qOO 14.350 15.040 1",110
2010 15.970 1!!ii,lIiD If»,120 15,190
SCENARIO'"ED I HI3 ••0PI SCENARtO ••bI24/1981
RESIDENTIAL USE PER HOUSEHOLD (KWH)
(wtT~OUT ADJUSTMENT fOR PRICE)
ANC~ORAGE •COOK INLET.............•........
SHALL LARGE SPAC!
YEAR APPLI,NCE8 APPLIANCES HEAT TOTAL_.-.........•.............•......•..•....•...
U80 2t1O.00 tl50U.U 5088.52 13f1lJ9.t5
0.001))(0.000)(0.000)(0'.000)
("")
1985 b151~49.21bO.00 4821.87 11133.37I.D t n.ooo)«0.000),0.000)(0.000)N
1990 221/).00 bOZO.51 4586.63 12811.14
(0.000)(0 ..000)(0.0(0)(0.000)
U 9 5 22&0.00 5960.28 4518.86 un9.14o.oon)(0 ..000)(0.000)(0.000)
2000 2510.00 S Q9 1.14 ''''53.51 12756.65
0.1)00)(0.000)(0.000)(0.0001
2005 2360.00 6062.51 4 11 Z2.21 12844.U
0.00/))(0.000),0.000)(0.000)
2010 2410.00 6127".20 4450.64 12981.8lI
0.000)(0 ..000)(0.000)(0.000)
SCENARIO,HED ,HI3--0RI SCEH'RIO-·6/~4/19Bl
RESIDf~TI4L USE PER HOUSEHOLD (KWH)
(WITHOUT ADJUSTMENT 'OA PRICE)
GREATER fAIRBANkS........--...._.......
SHALL LARGE SPACE
YEAR APPL IANtES APPLIANCES HFAT TOTAL..-...........................•..••••••••••••
1980 i!4bb.OO 51Jq~51 311J.U 115".18
(0.000)(0.000)r 0.001)(0.000)
t985 ~53t1.00 U7S'.9a 3606.28 11321.25
("")(0.000)(0.'000)(0.000)(0.000).
lOw 1990 2606.00 6448.88 38t11.13 12922.2t
r o.oon)(0.'000)(0.000)(0.000)
1995 2616.00 66t19.21 4051.13 11l91.00
0.001l)(0.'000)(0.000)(0.0(10)
1000 ~146.01 6792~911 HU.tS 13875.10a.ooo)(0.'0(0)(0.000)(0'.000)
2005 C!8115.99 6818.54 4541.84 14198.38n.ooo),(0 ..000 )(0.(100)(0.000)
2010 2866.01 6886.16 4659.68 1114]2.116
0.00(1)(a ~·OOO)(0.000)(0 ..000)
SCENARIO'MED I HI]-.nRI StEHARIO.-6/2q/1~81
BUSINESS USE PER EMPLOYEE (~HH)
(WITHOUT LARGE INDUSTRIAL)
(WITHOUT AOJUST~ENT FOR PRICE)
••••••••••••••••••••••
ANCHORAGE •COOK INLET ..•..•...•..•.•..••...GREATER 'AIR8ANKS
~b95.07
0.0/)01
9500 ••H
0.000)
9968.76
0.000)
7971.01
0.000)
9088.00
0.000)
8300.29
0.000)
7495.70
0.0001
(
(
(
8407.04
0.000)
13"£11.57
o.nOI)
12748.53
o.nol')
11855.8q
0.000)
9500.13
0.000)
102bl.tl
0.(100)
11037.1'1
0.000)
t
(
(
(
(
YEAR....
1980
1985
("").
~
~1990
19~5
2000
2005
2010
SCENARIO,"'EO I HI)--ORI SCE~ARIO--~/24/1'8]
SU~lIlARY OF PRICr.EFFECTS 4NO PROGRlfolATlC cnNSERV1TtON
IN GWH
ANCHUR4GE -COO~INLET
REStDENTI~L •I'U9tNESS............-..~.......Owtl-PR I CE PROGRAH ...I~DlICED CROSS-PRICE OWlj-PRICE PROGR "'t_r NOucrO CROSS-PRIcrYEARREOllCTIONCONSERVHIOltREDUCTIONRE~I.!~r I.Ott.CON~~R.Y~!HI~.___REDUCTION..................,-.-.....-~-'-"_.................................................................................................................................
1980 0.000 0.000 (1.000 O.OOll 0.000 0.000
1981 b.215 O.lIOO -1.1&3 '.]59 0.000 -lI.398198212.1129 0.000 ...].525 18.119 0.000 -n.79b198318.b44 0.(100 -5.288 28.078 0.000 -1.194198421l.1158 0.000 -1.051 ]J .4]"o.non -1.'592.
1985 31.1171 0.000 -8.814 U.797 0.000 -1.990
1986 12.161 0.000 '5.970 60.349 0.000 -9.2041987-6.710 o.noo 20.75]73.900 0.000 -lb.1l181988-a'5.bOI 0.000 3'5.536 87.452 0.000 -23.b311989-qll.4']0.000 50.11'tOI.00 4 0.000 -30.84'5
1990 -63.3131l 0.000 &5.102 114.555 0.000 -38.0'59('").
1.0 1991 -34.229 0.000 30.178 l]~.q9S 0.000 -50.751tnt992-s.on 0.000 -4.746 lbl.4II 0 0.000 -6].44]1993 a4.063 0.000 -3 9 .670 187.882 0.000 -16.13'5199453.23 11 O.OO/).74.'594 212.124 0.000 -88.821
1995 62.394 11.000 -109.518 236.766 0.000 -tOI.518
1996 8R.9bl O.oo/)-120.0lt i'61.9t7 O.OOll -116.816199791;.528 0.000 -130.505 n 9 .Ob8 0.000 -112.1111998t02.095 0.000 -11l0.998 1311.219 0.000 -1117.4101999108.662 0.000 -151.491 3&1.310 0.000 -162.707
2000 115.22°0.000 -161.9115 192.521 0.000 -118.004
2001 li!O.4U 0.000 ·'69.698 4 21.317 0.000 -194.]062002125.597 0.000 -IH.lIlt 4b2.lll 0.000 -210.608200]130.781 0.000 -185.1211 119b.909 O~(!OO -22b.91120011IH.9b/J 0.000 -192.B38 'i3t.705 0.000 -2113.211
2005 III I •III 9 0.000 -200.551 I;b".5fl2 0.000 -2SQ.515
2006 11I6.bO"(1.000 -208.410 "11.0b8 0.000 -280.9122007ISl.ot-1I 0.01l0 -?16.108 bS9.blQ 0.000 -]0?'.3102008151.S2Q 0.000 -2l".Hl7 70b.201 0.000 -123.7072009lb2.'H\Q 0.000 -,B2.0~5 752.7b7 0.000 -]45.100;
2010 IbR./lllq 0.000 -2H.91l1l 799.]311 0.000 -366.502
SCENARIO,"ED I Hll-_O~1 SCENARIO--bI14/IQ81
8UM'IA~y OF PRICE E'fECTS AND PAOGRAHATIC CONSERVATION
IN GWH
GAEATER FAIRBANKS
RESIDENTIAL ~USINESS.•..........••..•.•..•
OWN-PPICE PAOGRAH_INDUCED CROSS-PRICE OWN-PAICE PAOGRiH-INnUCED CROSS-PAIC!YEAR REDUC"ON CONSERVATION REElUC TlO_~._REDUCTI.O.~CON~~~Y~JJ.QN .__r REDUCTION_...-.............................................................................................................
Iq80 0.00l)o.non 0.0011 0.0011 o.oon 0.000
1981 0.000 0.000 0.351 0.000 0.000 (I.2t1]1982 0.000 ".000 0.113 0.000 0.000 0.tl85lq830.000 0.000 1.1170 0.000 0.000 0.128198t10.000 0.000 I.llal 0.00l)0.1100 0.971
1985 0.000 o.oon t.780 0.000 0.000 1.213
Iq8t.-0.t97 0.000 0.014 -0.333 0.000 0.31161987-0.39 11 0.000 -0.q56 -0.6b5 0.000 -0.5211988-0.590 n.ooo -l.H5 -0.998 0.000 -1.3881989-0.781 0.0110 -3.6'5 -1.330 0.000 -2.256
1990 -0.9110 0.000 -5.0bi!!-1.661 n.ooo -3.123n.IqQI -0.997 0.000 -7.697 -1.657 0.000 -0.580~m 19Q2 -1.010 0.000 -to.13(1 -1.651 0.000 -6.006199]-1.023 0.000 -12.962 -1.6115 0.000 -1.50719Q4-1.036 0.0110 -15.595 -1.639 0.000 -8.968
19 Q5 -I.OIIQ o.noo -18.228 -t .612 0.000 -10.0]0
19 cH»-0.877 0.000 -21.578 _t.]43 0.000 -12.2091997-0.7011 0.000 -ZIl.Q29 -1.054 0.000 -13.9891998-0.1532 0.000 -Z8.280 -0.7611§0.000 -15.7681999-0.3bO 0.11011 -31.631 -0.076 0.000 -u •5 tIS
2000 -o.UU 0.(100 -311.981 -0.t87 0.000 -19.327
2001 11.11111 0.000 -38.208 0.348 0.000 -21.050200Z0.
'
11111 0.000 -41.555 0.883 0.000 -22.773200]0.(120 0.000 -IU.81ll 1.418 0.000 -20.096200111.lse;(1.000 -118.128 t.954 0.000 -26.219
2005 1.'JQt Il.001l -'i1.4111 2.089 o.noo -27.942
20Gb 1.992 0.00(1 -5'3.t68 3.300 0.000 -]0.031120072.11911 /).000 -58.922 11.112 0.000 -]2.12620082.99C;1l.1I00 -b~.676 11.924 0.1100 -311.2t120093.119b 11.000 -bb."]1)5.1H 0.000 -36.]09
2010 'J.Q9R 11.000 -70.18]b.SII?0.000 -111.1101
SCENARIO.MED I HI3--0RI SCENARIO.-6/211/19S1
BREAKDOWN O~ELECTRICITY REQUIREHENTS (GWH)
(TOTAL INCLUD~S LARGE INDUSTRIAL CONSUMPTION)
ANCHORAGE -COOK INLET........-......•......
MEOlllfo1 RANGE (PH_.5).•.•.........•....••
R~SIDF.NTlAL BUSINUS MISCELLANEOUS EWO~.INDUSTRIALYEARRE.QUIREfolft-JTS REQUIREMENTS REQUIREMENTS LOAD TOTAL................••.•.•..••.•.......•..•....•.......•...•....••.........•.....-•••............•
1980 9H.5J 875.30 211.31 84.00 1961.1'
1981 1020.10 9111."j!211.00 92.08 20811.8619811001.86 1019.4a 25.02 100.16 2206.5219811101.02 1091.55 25.37 108.211 2328.1819811111111.19 11U.61 25.13 116.32 211 119 .85
1985 IUS :35 U15.b1 20.08 1211."0 ~511.51
198b 1218 ..45 1211.98 l6.88 137.89 2601.2119811251.5'5 112/J.30 21.'"151.38 2750.91198812114.05 1362.61 28.111 164.88 28110.611989BI1.7S 1401J.U 29.21 118.31 2930.30
n 1990 1350.85 111111.21 ]0.06 191.flb 3020.00.
1.0
-......fq 9 1 139".20 11198.51 11.02 195.13 3UII.S619921429..55 UIl9.79 31.98 198.110 3209.71199]1468.89 1601.01 32.93 201.eto 33011.57199111508.211 1652.10 33.89 204 ••3 !399.lIiP
1995 15111.59 1703.64 H.B5 208.20 311911.£18
19 9 0 1592.28 1101.1)9 35.911 2111.tll HOII.2S1991Ib30.97 I Alo.15 31.01 220.0B 31111.231998Ib"l.b"18711.110 38.ti!226.02 18211.211999IHI).3 '•19U.oo 39.22 231.96 ]9311.18
2000 1771.03 19911.9j!110.31 2]7.90 1I01l1l.U
2001 1821.31 20&7.29 111.&1 21111~90 11175.2220021811.79 21 H.bo IIi!.90 252.02 1130b.282001IQ22..03 2~12.03 1I11.i!0 25Q.08 111137 .31120/)11 197Z.3b i!2811.111 115.50 2hll.14 1I5b8.1I1
2005 202i!.b Q 2356.78 IIb.19 273.20 11699.111
lO0b 201'7.81\i!1I6i!.IQ 118.59 281.58 118M~21lO072153.0b 2Sb7 .59 50.38 289.9b 50U.0020082218.25 lbB.no 52.18 2 Q8.)1I 52111.712009228].,n 2771'.111 53.97 301..72 SII2i!.53
2010 2.3118.&1 Cl883.112 55.71 liS.'/)Sb03.30
SCENARIO't4ED ,HI1--O~1 SCF.NARln--b/24/19A3
BREAKDOWN OF ELECTRICITY REQUIREMENTS CGWH)
(TOTAL INCLUD!S LARGE INDUSTRIAL CONSIH1PTlON)
GREATER FAIABAN~S.......--._..•..•..•..
MEDIuM RANGE (PR ••IIi)•...•..••..••..•.•.•
RE S IUF.NTJ AL BUSINI!SS MISCELLANEOUS EXOC.INDUSTRIALYE4RREQlIIREI1E/lTS RF.QllIREl1tNTS REQUIREMENTS lOAD TnUl.............•••••••.•.....••..••...--........••••.....•.•.........••.•••.•..-.•••..•........•
1980 17b.39 i!17.I11 6.n o~oo 400.31
1981 Iql.OIl 230.11 6.1!1 0.110 427.90198220S.b 9 24].08 6.1i!0.00 1155.501983220.~4 256.05 b.69 0.00 1I1'l.OQ1981123ll.9'l 269.03 b.66 0.00 510.611
1985 249.61j 282.00 b.b3 0.00 5]8.21
1986 262.9'S no .lIn b.68 10.00 570.031987216.211 218.79 b.74 20.00 601.781988289.S11 307."6.80 30.00 631.531989302.811 315.59 6.8'5 40.00 U5.2S
("").1990 31b.11I 323.'n 6.91 50.00 697.03~ex>
313.15 7.22 50.00 727.001991336.63
1992 HO.III 349.29 7.53 !O.OO 756.981993367.17 361.94 7.84 50.00 786.9'S199113811.18 3711.59 8.15 50.00 8UIi'll
.1995 401.111 3111.25 8.4.SO.OO 8116.8Q
19 9 b 1117.59 40 n .54 8.7J 50.00 87b.90199711]11.00 IIIl.II l 9.08 50.0/)906.901998450.41 4;!'.11 9.38 50.00 936.9119994ltb.II'4110.00 9.69 50.00 966.91
2000 U83 ..22 1153.69 10.00 50.00 996.9£1
2001 500.15 1161l.65 10.34 50.00 1029.132002517.07 1183.60 10.67 50.00 1061.3/12003533.99 lI911.55 11.0 I 50.00 locn.562004550.Q2 SU.SI 11.311 50.00 I 1£15.77
2005 5U.8U i!l8.116 .
11.68 50.00 tl'S7.IJ!
2006 5117.96 511~.7f1 12.10 50.00 I1Q8.822001b08.07 564.05 12.53 50.00 1239.65200!628.1 9 i!89.35 I2.Q5 5/).on 1280.1192009b1lA.31 609.l!Q 13.38 50.00 1321.33
2010 bbB.lli!f,i!9.94 B.8O 50.00 1362.17
("").
1.0
1.0
SCENARIo,MEO,HI]-.DRI SCE~ARIO-••'lIl/1983
TOTAL ELECTRICITY REQUIREME~TS (OHH)
(NET OF CONSERVATION)
(INCLUDES LARGE INDUSTRIAL CONSUMPTION)
MEDIUM P1HGE CPR ••5).....•••.....••..••..•
YEAR ANCHORAGE -COOK INLET GREATER FAIRBAN~S TOTAL..................•.........•.•....•....•............••...•.•.....•...
1980 I 9U.I~1100.31 nb]~51
1981 20811.86 1127.90 2'S12~161982li!06.52 1155.50 2662,.02198)U28.19 1183.n 2811,271981124119.85 'iIO.68 lqU.51
1985 2511 .5t 538.21 ]IOq~79
IUb 2bbl.21 570.03 U31 ~21119812150.1'1 601.18 H52.6q198828110.61 6H.53 311711~1319892930.3!l 665.28 nQ5.58
Ino ]OZO~OO "'1.03 3717~0]
1991 31111.811 U7.00 ]8111~8bIn2U09.1I 156.98 Hbb.69
1993 ]]011.51 786.n 4'091.52
1994 3H9.112 eU.Q2 11216.311
1995 ]Q.;lIl.2 A 846.89 11]11(.11
1996 3b04.25 8H.U 111I81~15InlHIII.23 90b.90 llllll,tl199&18211.21 91#.1.It 1 a161,11UQ9]9:U.IA Itb6.91 11901.0Q
200l)1101111 ..16 996.92 50 lit ~07
2001 11115.22 In2Q.I]52011.35
2002 4306 ..28 1061.34 5U7~b2
2003 11437 .311 1093.56 55H~90
2004 11568.111 112'5.'77 56QIl~17
2005 11699.Q7 lt5l.Q&58'S'-.45
2006 1.18 9 0.23 1198.82 6019~05
20111 'S061.00 12H.bS 6100.65
2008 521.11.17 1280.119 1I5'-~·.26
20119 51122.53 1321.3]67a3.86
2010 'SbOJ.31l 1362.lfJ 6965~1I6
n.
--'
oo
SCENARIO'~EO,HI1 ••DRI SCE~ARIU ••6/lll'lq81
PEAK ELECTRIC REQUIREMENTS (HW)
'NET OF CONSERVATION)
(INCLUDES L'RGE IN~USTRtAL DEHANO)
M~OIU~RANGE (PR ••5)...............•..•...
YEAR ANCH~RAGE •COOK INLET GREATER FAIRBANKS TOTAL...............••••••..•.•........................•.•...•.•...........
1980 396.'51 91./10 1~87.90
lUt 421.10 97.69 !itll~801982445.7<'103.99 5119~691983470.29 110.2'5~0.5819844911.-88 116.59 611~1I8
1985 1319.(18 I Z2.89 6112'.37
1986 518.1111 130.I 4 U8·.561987557.41 137.]9 e.9a~7"1988 516.37 1411.63 721.011989505.311 151.88 7117~Z2
1990 6111.31 159.12 7n~1I3
1991 633.63 165.97 799~591992652.9~172.111 825~7619H67Z.l1 179.65 851~9219911691.59 186.50 878.011
1995 710.91 I en.14 901l~25
1996 733.20 200.19 q13~191991755.119 207.011 9621'511998777.78 213.89 991.611999800.1)1 220.711 t020~8t
ZOOO '52Z.36 Z27.59 '0Ilq~95
ZOOI 848.94 234.95 I083~892002875.52 242.30 1117.82200]902.lD 249.b5 tlljl~7520049C18.68 251.01 1 U5~69
2005 955.26 26 11 .36 121ll'.62
2006 ll91.91 273.69 t265.662007IOZ8.68 283.01 1311 ..6 92008IOb5.39 292.33 1357.7120091102.10 "01.6b U03'.16
2010 t118.81 310.ll8 14119~80
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SCENARIO'HED ,HE4~~'ERC +2X--6/24/1Q83
HOUSEHOLDS SERVED
~NCHORAGE -COOK INLET...•..........•..•.•.•
YEAR SINGLE 'AMllY HUL TIF UlILV MOBILE HmlES DUPLEXES TOTAL..-..•......•.•.••......................•.•.......................-.....
1980 35473.l031 a •8230.74"6.7.503.
0.000)(o.oon)(/).000)(0.,000 )(0.0001
1985 UQ087.26201l.11 4 q2.8567.QS350.
o.noo)(0.(00)(0.000)C 0.000)(o.oon)
n IqQO bOil?.27154.ll8lS.84t/O.109610 •.
-.I (0.000)(0.000)C 0.000)(0.000)(0.000)aw
1995 6803".Utl];».15710.1838.1111018.
0.01)0)(1).000)(0.000)(0.000)(0.000)
2000 719bl.37 4 1S.181'57.9000.ltl2519~
(0.000)(0.000)("./l00)(0.000)(0.000)
200S tllb8".1I0234.U60Q.9652.153183.
((l.O')O)(0.000)(0.000)(0.000)(0.000)
2010 891~4.II]Q45.21114.10314.1611816.
0.01)0)(1'.1)00)C (l.OOO)C 0.000)(0.000)
SCEN~RJOI "4ED I ~E4 ••'ERC +2X ••~/24/1~83
HOUSEHOLDS SERVED
GRE~TER 'AIRRA~kS..•.•.•.•••.......•••.
YE~R SINGLE '''Io4ILY MlJL TtFAMILY MORILE HO~lES DUPLEXES TOTAL-.-..••..•.....•........•.....---...............•...........••..•....
1980 7?20.5287.1189.1 b17.15113.
".000)(0.000)(0.000)(o.oon)(0.(00)
1985 lOb46.SfUIl'.2110.17b!i.20401'.
0.000)(0.000)(0.000)(0.(00)(0.(00)
n 1990 111171.7960.2~08.2375.24013..(0.001)(0.000)(0.000)(0.1'00)(0.000).....
0
.J:>,1995 IQQj4.7841.:nQl.2139.28505.
0.000)(0.00(1)(0.000)(o.oon)(0.(00)
2000 17 RS9.843i!.4173.2298.]2762.
0.(00)(0.000)(0.000)(0.000)(o.oon)
2005 1 9 11 8 •9257.4496.225~.15129.
0.(00)(o.oon)(0.000)(o.oon)(n.noo)
2010 20455.9916.4852.2422.17705.
0.000)(n.noo)(0.000)(1).000)(0.(00)
~
SCENARIO."'EO •HEq·.FEQC t2X·.6/?4/IQ83
HOUSIIIG VACANCIES
ANCHORAGE •COOK INLET
••••••••••••••••••••••
YEAR SINGLE FAMILY IolULT!FAHtLY MO~tLE UOMES DUPLEXES TOTAL-.-....................-..............••............•.•...............
t980 50A9.76b6.U 9 1.IlIb3.tbi!!OQ.
(1).000)(0.000)(0.000)(0.(01)r 0.000)
t985 540.t49b.126.292.2455.
0.(00)(0.000)(0.000)(0.000)r I\.oon)
n t990 bb~•200.152.289.IJOJ..
~(0,1100)(0.000)(0.000)(0.000)(o.OOn)
a
U1 t995 Tql!•PSt.17].780.145~.
0.(00)(0.000)(0.000)(0.(00)r 0.0(0)
2000 as".2020.200.297.3375.
o.nOo)(0,000)(0.000)(0.000)(0.000)
2005 92t.21B.216.319.3627.
1).1100)(0.000)(0.0(1)(0.000)(0.(00)
2010 988.2146.U1.342.3909.
0.1'00)(o.OOn)(0.(00)(1).000)r 0.(00)
SCENARIO'"'ED ,HElI ••FERC +2X ••~/lq/t98]
HOUSINQ VACA~C'ES
GREATER FAIRBANKS..........•..•..-.....
YEAR SINGLE FAMILY HUL TlF AMILY HOltlLE HOMES DUPLEXES TOTAL.........•.....••......••......•.••.•..•....•.•......................
1980 3flS3.3320.98~.89'.8 A 5t1.
o.noo)(n.ooo)(0.0.00)(0.000)(o./lO(l)
1985 118.i65].24.722.3~1'7.
0.000)(0.000)(0.0001 (0.000)(1).000)
n 1990 Ill».til';CI •2/1,81.686..(0.000)(0.000)(0.000)(0.000)(0.000)--'
0en IlJlJ5 l~lI.lItI".37.80.729.
0.000)(0.000)(0.000)(0.000)(o.noo)
lOOO Iqb.lI55.lib.78..771»~
((1.000)(0.000)(0.000)(0.000)(0.00/))
lO05 210.son.50.70..'no.
0.000)(0.1)00)(0.000)C 0.000)(0.000)
lolO 225.'i3 9 •53.80.aQ7.
0.09 0 )(o.oon)(0.000)(0.000)(0.000)
-L
SCENARIO.~ED.HEq ••FERC t2X··6/24/1~8J
FUEL PRICE FORECASTS EMPLOYED
ELECTRICITY (I I KWH)
ANCHORAGE.COOK INlET GREATER FAIRBANKS•.••.....•......•••..•••...•........-•........•....•.•..•....-~.
YEAR RESIDENTIAL BUSINESS RESIDENTIAL RIISINESS..........•.•..................•............-....
1980 0.n}7 0.034 0.095 O.(lcU
("")1985 (\.048 0.Otl5 0.095 0.090.
-'a 0.092.......1990 (\.053 0.050 0.087
1995 0.058 n.OS5 0.094 0.089
2000 0.1)(.2 0.059 0.096 O.ocH
2005 0.065 0.062 0.098 0.091
2010 0.Ob7 0.06 11 0.100 0.095
SCENARIOI ~ED 1 HF4 ••FERC +~X ••6/i"/lq81
FUEL PRICE FORECASTS EMPLOYED
NATURAL GAS (S/"HBTU)
n
......
a
ex>
ANCHORAGE •COOl<INLET GREATER FAIRBANKS
•••••••••••••••••••••••••••••••••••••.•.•.•...••...........•...•••.•...•••
YEAR RESIDENTIAL 8lJ8INF.SS RESIDENTIAL fWSINESS.....•........••••••••••••.•....•••..............
1980 1.730 1.500 12.1110 11.290
Iq85 2.030 1.800 U.OIIO 1t.6110
1990 3.190 ~.~60 111.]QO 12.850
1995 11.260 4.030 15.890 111.190
2000 11.590 ".UO 17.511(1 15.670
2005 ".950 11.7l0 19.370 17.]00
2010 S.lIlO S.IIO 21.390 19.100
·-L-
SCENARIO I MED I ~EQ-.FERC t2X--6/2Q/198]
FUEL PRICE FORECASTS EMPLOYF-O
FUEL OtL (S/MMBTU)
ANC~ORAGE -COOK INLET GREATER FATRRANKS.....~....-.........••........•.•.........._..•...•...•....•..•.....•..•..
n........
a
lD
YEAR RESIDENTIAL 8USINESS RESIDENTIAL RUSINESS.._..•.•.•....•..••.....••.................•.•...
1980 7.'750 7.200 7.8]1)7.500
1985 '7.911(1 7.lIaO 8.010 '7.730
1990 R.'7ClO 1J.190 8.840 8.530
1995 9.#.)8(1 °.011(1 9.'7~0 0.1120
2000 10.1180 0.9AO 10.780 10.400
2005 11.790 11.020 11.900 11.480
2010 13.020 12.I 70 1].140 12.680
SCENARIO'MED ,Hf4-_FERC +2X-.6/lq'I~81
RESIDENTIAL USE PER HQUS!HOLD (KWH)
(~tTHOUT ADJUSTMENT FOR PRICE)
ANCHORAGE •COOK INLET..•.•......•...•......
SH_LL L-RGE SPAC!YEAR APPLIANCES APPLIANCES HHT TOTAL•..............•.•............•.............
USO 2110.00 6500.61 5088.52 Utl~9.15
1).000)(0./)00)t 0.000)(0.000)
1985 2160.00 6092.53 4771.61 U024.1CI
(0.000)(0.000)(0.000)(0.000)
n 1990 2210.00 5975.94 4579."6 12765.4(\.......(0.000)(0.000)(0.000)(O~OOO)......
a
U95 2?bO.OO 5~2".JO 45]1.47 127,111.71o.oon)(0.000)t 0.000)(0.000)
2000 2~tn.OO 5957 ••n ""47.b4 127111'.86
(0.000)(0.000)(0.000)(0.000)
2005 21&0.1)0 6020.37 4409.15 12789.53
0.000)(0.1)00)(0.000)(0.000)
2010 241 n.OO 60 82.00 4436.52 12918.52
(0.000)(0.000)(0.000)(0.000)
--L
SCENARIO'MEO ,HEq·.FERC +2X ••6J2Q/1981
RESInENTIAL USE PER HOUSEHOLD (KWH)
(~ITHOUT ADJUSTMENT 'OR PRICE)
GREATER 'AIRBANKS....•....•........•...
SHALL LARGE spaCE
YEAR APPL IANeF.S APPLIANCES HEAT TOTAL..........••••.•...•......•....••...........
1980 a4U.OO 5119.5l HU.6b 115~9.1"
0.0011)(0.0(0)(0.000)(0.000)
1985 2S35.99 6178.9l 3606.37 123~1.28
(0.000)(0.0(0)(0.000)(0.000)
n 1990 2Mb.OO 64(19~0l 3 8 6'7.59 12922.62.
--'(0.(011)[1).000)(0.000)(0·.000)--'
--'
1995 2676.01 6609.22 4051.72 llJ9b.9S
(0.(00)(O~OOO)[0.000).(0.000)
2000 2'711S.Q 9 67 9 2.90 43111.48 13882.37
(0.(00)[0,000)[0.000)(0.000)
2005 2816.01 b811l.8 Cf 4530.64 Itl18I.51
(0.000)[0.0(0)[0.000)(0.000)
2010 2886.00 6 88 2.97 11649.81 144.18.78
11.000)(0.0(0)(0.000)(0.000)
SCEN4RIOI Mf.D I HE4·.FERC +~X ••6/24/1q81
BUSINESS USE PEA EMPLOYEE (KW~)
(WIT~OUT LARGE INDUSTRIAL)
(WITHOUT AOJUSTMENT FOR PRICE)
YEAR
••••
nAO
tq85
("").
--'
--'
N
Iq90
tq95
2000
2005
2010
ANCHORAGE •COOK I~LET..............•....••.
8407.0"
0.000)
lJS80.61
0.000)
102&'5.00
(0.000)
110Jl.7So.oon)
tI9&2.09
(0.001))
1240".03
C f).oon)
13012.'H
(O.nOO)
GREATER ~AIR8ANKS....••••.•...••..•..••
1495.70
0.0(0)
7972.U
0.000)
830t."7
0.000)
861'4.21
0.000)
9116.49
(0.000)
93q6.87
(0.000)
9734.70
0.0(0)
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SCENo\RIOI HED I HE4-_FERC t2X.-b/2Q/1981 ITfRATJONS I:
SUMMARY OF PRICE EFFEtTS AND PROGRAMlTIC CONSERVATION
IN GWH
r,RE1TfR F1IR~ANKS
REUtlFNT!lL AUSIt,JESS......~................
OWN-PRICE PROGR U1.PIDUCEIl CROSS.PRICE O"'N-PRIC~PAOGRAM-INOLJCED CROSS·PAIC!YEAR REDUCTION CONSfRV AT 10t4 REDUCTION RE~IJCTtON CONSERV~TIt)N REDUcTTON.......................................t:...........'....-............................................................................................
1980 !l.noo 0.000 0.01)0 0.000 0.000 0.000
1981 0.000 0.000 .0.097 -0.097 0.000 -(\.08019820.(100 0.000 .0.IClS -0.194 0.000 -0.15919830.01)0 0.000 .0.292 .0.292 0.000 -0.2H198110.000 n.ol)o .0.]90 .0.]8q 0.000 -0.119
1985 0.(100 0.1100 -0.1187 -0.1186 0.000 -0.]98
198&-O.I'H 0.000 ·1.095 _0.886 0.1100 -0.7501987-0.3111 o.noo -1.702 -t.'-86 o~ooo -1.102tq88-0.591 o.oon -2.310 -1.686 n.ooo -1.4531989-0.7AlI 0.000 -2.918 -C1.086 o.noo -1.80'5
n t990 -0.9 84 I).oon -].525 -2.486 0.000 -2.157......t991 -n.997 9.000 -11.72]-2.543 n.ooo -2.7811......
-1=:0 199C!-1.010 0.01)0 -5.921 -2.'599 0.000 -3.11111In3-1.023 0.000 -7.119 -2.6'55 0.000 -11.043Inll-1.03b o.oon -8.117 _2.711 o.non -4.b72
1995 -1.049 o.Ol)n -9.'HS _2.167 0.000 -';.]01
1996 -0.877 0.000 -11.313 -LI.5111 0.000 -6.21101997-0.705 0.000 -U.II0 -2.315 o.nOo -7.17 91998-0.5311 0.000 -111.9 08 -2.08Q 0.000 -8.1171999-0.3&?0.000 -16.705 -1.862 0.000 -9.056
2000 -0.190 0.000 -18.503 -1.636 0.000 -Q.9911
iOOl O.I 3S o.oon -20.5113 -1.160 0.000 -10.QI92002O.lIbO 0.000 -l2.li8i!-0.6811 0.000 -11.81111200]0.784 0.000 -ZIl.62i -0.207 0.1)00 -ILI.76Q20011t.11)9 o.oon -l6.b~2 0.2b9 0.000 -13.6Q4
2005 1.11311 0.0011 -28.702 0.711'S 0.000 -11I.6IQ
200b 1.8bQ 0."00 -31.132 1.366 0.01)0 -1'i.78120072.3011 0.01)"-]3.562 1.981 0.000 -16.Qll7'2008 2.1H 0.000 -35.992 l.601 1).000 -18.1122009].IH 0.000 -38.1122 3.228 o.oon -19.216
lo10 3.6(18 o.oon -qO.~52 1.849 0.000 -20.440
SCENARIO'I4EO ,HEq··'E~C +2X ••~/24/IqA3
8RE4KDO~~OF ELECTRICITY REQUIREI4ENTS (QWH)
(TOTAL INCLUDES LARGE INOUSTRIAL CONSUI4PTION)
GREATER ,AIQ8&NKS-........•............
MEDIUM RANGE (P~••5)•••........•.--.....
REStDfNTIAL BUSINESS MISCELLANEOUS EWOG.INOUST~IALYEARREQlJtREllEIHSRfQIJlPEMENTSREQUIREHENTSLOAD TOTAL.._............•.••.••......•...............••..•......•........................••..•.••......
1980 17b.3 q 2\7.14 &.18 0./10 QIIO.31
1981 191.50 nO.511 &.16 0.00 428.BO198220&.&1 24J.94 tI.74 o.no 11'57.29198]221.72 257 .311 tI.72 0.00 1I~'5.77198112Jb.83 270.711 6.69 0.00 5 til.26
1985 251.94 2811.111 6.67 0."0 5112.115
198&264.52 292.14 6.72 10.00 S13~311987277.0 ct 300.1"6.76 20.00 6011.00198821l9..b7 301'.til &.81 30.00 6111.62
n 1989 302.'21i 116.15 &.85 110.00 6bS.25......1990 3111~8l 3211.IS &.90 50.(10 695.87.....
0'\
1991 310.35 335.92 7.18 5(1.00 723.11519921115.87 31'7.&9 7.117 50.00 751.011993)1)1.40 359.46 7.76 50.00 7JA.61199,.316.'91 371.23 8.04 51'.00 806.19
1995 392."11 383.00 8~33 50~00 83]'.77
1996 /108.&6 397.115 8.&5 50.00 8611.75199711211.81 lIlt .90 8.97 50.00 895.71119U/1111.08 1126.15 9.29 50.00 92&".7219991157.30 alll)."O 9.61 511.00 9~7.70
2000 IIH.51 aS5.25 9.91 50.00 9A~.69
2001 IIn.91)1160.l9 10.0'50.110 10"/1.28200211911.2q /IbIS.H 10.26 50.no 1019.87200350ll.U 1170.17 10.42 50.(10 1035.tl720011515.0t>1175.41 In.5'!50.00 1051.06
2005 525.115 1180.4 6 lO.JIS 50.(1(\1066.65
2000 51b.SIl /lSq.<»8 1O.9t>5n.no ,n86.7820075111.b3 /lcH~.10 11.11 50.110 1106.9020085513.12 506.q 3 11.H 5n'.Oil 1127.032009ShQ.lIl 15115.75 11.5.50.no '1117.1!'
2010 SRO.9/l '5211.58 It .130 Iio.no 1167.213
SCENARIO.MEO.HE4-.FlRC +1X ••6/24/198]
TOTAL EL£CTRICITY REQUIREHENTS (GWH)
(NET OF CONSERVATION]
(lNClIJOES LARGE II~OUSTRIAL CONSUMPTION]
MEDIUM R_NGE (PA ••5]-~--
_J_
n.............
'..J
YEAR ANCHORAGE.COOK INLlT GREATER FAIRR4~KS TOTAL-.-.........•..•.•.....•••.......-...-.......~........-•.••.••..•.....
1980 19U.19 1101).11 2]b3~51
1981 20 9 3.15 1128."0 252 t".9!;19112 2223.11 457.29 26~1)~40198323'53.07 1185.77 2"'1".8419842483.03 514.26 19C17~29
'98S lbll.99 511~.75 ]1~5~74
1986 21/12.91 S73.37 ~276.21119872792.83 604.00 3H6~8]1988 28132.n b34.62 ]517.3119892972.61 665.25 3U7.92
tq90 30b2.59 69'.1'17 1758~46
19 q 1 ]ISq.68 723.45 3883~13UQ23i!5b.7b 751.1)3 41)07,.7 9
1993 335J.85 778.U 111321'4619Q4]4'511.91 "'06.19 /1257.13
lQ95 J548.0i!813.77 /l3~1.79
1996 3619.12 864.75 115113~871997]1)10.21 IIQS.llI 4705.9'51998]941 •.J!926.72 1l'-68~0319994072.t1t 957.70 1;0]0 .11
2000 1121)3.50 988.b9 S192~IQ
2001 112b4.02 100".28 526"'~3020024]etll.5J 1019.87 53(14~402003IIJII5.011 1035.(17 ~1I20.51200444115.51.1 1051.01,151196.62
2005 11506.07 11)66.b5 5572~7J
200b 115 9 6.11 1086.78 568l~q52007Qb8b.21.l 1106.90 5793.1112008(l7'7b.3"It 27.(1]'59(.13.Ql20091I1;\6b."""47.15 6013.b1
1010 11956.')1\Ilb7.28 6123.8b
n.............
CO
SCENARIO.MED.~EII ••FE~C +2X ••6/2Q/lQ8]
PEAK ELECTRIC REQUIREMENTS CMW)
(NET OF CON8F.RVATIO~)
CI~CLUDES LARGE INDUSTRIAL DEMAND)
MEDIUM ~ANG~CPR ••5)..-...•.........•.....
YEAR ANCHORAGE •COO~INLET GREATER FAIRBANKS TOTAL..............•••......•..•....•...•...............•...................
11;80 ]96.51 91.40 IIR7:91)
1981 lIi!i.8n 97.90 '52n~70198i!IIl.I9.n 1011.110 5~]~501983475.19 11/).91 586~i9198115(l1.6~117.111 619~09
1985 527.97 123.91 6~1.8q
198b 5 Gb.'IS 130.90 677~8q198756b.00 137.89 7031'891988585.01 14 11 .88 7&19.891989bOIl.02 151.87 755~89
1990 623.03 158.8e.7111.89
19'11 bl.ll.81 IblJi.lb 807~96199i6bi.58 171."5 8311.041993-bai!.lb 177.75 8e-1>'.1119911702.111 1811.05 8~&~ICJ
1995 nl.'Ii!l'JO.34 912:2&
199b 748.53 t'll.lIi!tl45~qlJi19Q7775.15 i!Ol.l.1I9 nCl~6111998eoI.77 211.56 1 0t3~3J1999828.38 218.64 11)1I7~02
2000 855.00 225.71 1080~71
2001 8U.U i!Z9.27 109&.110200i!879.2b 23l.83 It 12.092003891.n 236.H 111.7.7'!o200119(,13.5),239.Q5 11113:117
2005 915.65 143.51 It~q·.16
200b Q3].H 2118.tl lIel ~8q20079SI.9l 252.70 12011.63
2008 970.0b 257.311 121(.3#.12009Ql'8.20 2bl.89 11.15/).10
2010 100&.311 2&&.119 t272~f1~
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C.119
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SCENARIO'!oIED ,HEb ••FEAC 01 ••bI2"/198]
HOUSEHOLDS SERVED
~NCHORAGE •COO~INLET....••.•..•..•••...•••
VEAR SINGLE FAMILy MULTIFAMILY 104081LE HOMES DUPLEXES TOTAL_.-.•......••.....................•....••...•••••••••••••..•..-........
1980 35111l.203111.82l0.1lI8b.'11)03.
(0.000)(0.001')(0.0/)0)(0.001))(0.001')
("").
1985 46227.26204.10958.8567.q195b.--'
N (0.001')(0.00/)(0.001)(0.000)(O.l)on)--'
1990 ~7q06.25 8 77.13305.U60.105548~
(0.000)(o.o~()(0.(l00)(n.ooo)(0.000)
1995 661)9 11 •31l A lO.15261.8U3.120'504~
0.00/))(0.000)(1).000)(0.001')(0.0(0)
2000 69b68.HI qo.16151.79 9 6.U695!i~
1).00")(1\.000)(0.000)(0.000)(0.000)
2005 7tl~07.3SfJ8Q.17432.8579.1 ]6207~
1).000)(1\.1)0n)(0.001)(0.000)(0.00")
2010 80911].19158.l cltH.9UO.1485911.
(0.000)(n.IlO?)(0.000)(0.(01))(1).000)
SCENARIO,MED I HEb-_FEAC Ql ••bll4it9~]
HOUSEHOLns SERVED
GRE'TER FAIRBANKS.............••.....••
YEAR SINGLE FAMILY MUl TIFAt1ILY HORIlE HonES DUPLE XF.S TOTAL.......•................•.•...-_..•.•.....•.....•........•.•.••..•...
IqeO 72l0.5t18'.1189.lftU.15]1~.
0.(00)(0.0(10)(0.000)(0.000)C o.noo)
1985 10bltb.5Flb7 •2130.17~5.2040'.
0.000)(o.ono)(0.000)(0.000)(0.000)
n.1990 1140].HbO.220ft.ai7s •2/1n01.--I
N (0.1)09)(0.1)01')(0.000)(0.000)(0.1)00)N
1995 15ll R•7Alll.1448.2Hq.287b6.
(o.noo)(0.001\)(0.000)(0.0(0)c 0.(00)
2000 Ibl8.,.7701.3807.2298.lpt q i.'.
(0.(100)(0.000)(0.000)(0.0(0)C 0/000)
2005 17555.8293.4121.2252.]~221.
(0.000)(0.0(0)(0.000)(0.(00)C (I.noo)
20to 1897f,.q~ln.450].2249.]4981.
(n.ool'l)(o.oon)(0.0(0)(0.(00)c 0.1)00)
~
SCENARIO'MED ,HEb--FEAC OX--6/iQ/t983
HOUSING VACANCIES
ANCHORAGE -COO~INLET.....•.......••...•.~.
YEAR SINGLE FA~ILY MULTIFAMILY MOBILE HOMES DUPLEXES TIlTAL...............•...................•.....•..•••••••••••••..•..........
1980 5089.1666.1 9 91.14bl.1&209.0.(100)(0.000)(0.000)(o.onn)(o.noo)
("")1985 ~nR.1496.t 21.292.~417 •.(0.0(0)(n.ooo)(0.000)(0.01)0)(o.oon)--'
Nw 1990 b37 •1477 •14b.289.2549.0.0(0)(0.(01))(o.noo)(0.000)(0.(00)
1995 727.1664.108.284.21141.
11.(00)(o.noo)(0.000)(0.000)(0.1)01')
2000 7t1&.11 9 0.178.471.3i!04~O.llon)(o.oon)(0.000)(n.ooo)(n°.ooo)
2005 820.1 9 27.192.283.322~.
0.000)(0.000)(0.001))(0.000)(o.Qon)
2010 890.211 '5.211.309.~5211.(o.oon)(0.001))(o.noo)(0.000)(o.noo)
SCENARIO.ME£l •HEb--'ERC OX ••b/l4/t983
HOUSING VACANCIES
GREATER FAIRAANKS...•.....•....•.......
YEAR SINGLE 'AMIlY MULTIFAMILY Iot081LE HOMES DUPLEXES TOUL..................•••••••••••••....•........•••••••••••••.......-.....
1980 3&51.312 n•98b.89li.8854.
(0.000)(o.oon)(0.000)(0.000)(0.000)
n.1985 tl8.l65tJ.24.122.]'BR •.......
N (0.001l)(".000)(0.000)(0.000)(0.(100)+=-
1990 126.1151,1.24..81,b86.
(0.000)C 0.000)(o.OQn)(0.000)r 0.000)
1995 Ib7.44R.38.80.lU.
o.noo)(0.000)(0.000)(o.oon)(0.1100)
2000 180.CillO.42.18.140.
o.(lon)((1.000)(0.00(1)(0.000)(n.ooo)
2005 '9J.""".'t'i.77.16f.
(n.oon)(n.I)OO)(1).000)(0.000)(0.00(1)
2010 ?O9.SOll.50.28..1R6.
0.000)(o.oon).(0.000)(0.000)(o.oon)
8CEN~RIOI ~ED I HEb ••~ERC OX.·6/l4/1983
_-L_
FUEL PRICE FORECAST8 EHPLOYED
ELECTRIC TTY (I I KWH)
n
.....
N
<.11
ANCHORAGE •COOl<INtET GREATER FAIRBANKS
•••••••••••••••••••••••••••••••••••••......~•.•..•••..•.•••.•.......-.....
YEAR RESIDENTIAL BUSlN!SS RESIDENTIAL RUSHIESS..........•.......................•....•••.......
1980 0.037 n.03a 0.095 0.090
1985 O.OGA 0.04S 0.091)o.olin
lli90 0.052 (\.049 O.OliO n.085
1995 0.057 I).(lsa (l.(l9(l 0.085
2000 0.n59 n.056 0.090 O.oSl§
2005 O.Obl 0.058 0.n90 0.(185
2010 0.06]0.1)6(1 0.090 0./185
SCENARIo,HED'HE~••FERC OX ••6/ZU/I'83
fUEL PAICE FORECASTS EMPLOYED
.NATURAL GAS (S/HHBTU)
n
--'
N
O"l
ANCHORAGE •COOK INLET GREATER 'AJRBANKS
•••••••••••••••••••••••••••••••••••••............•.......•.•...•..........
YEAR RESI/)ENTUL BUSINESS RE81 DENTl AL BUSINESS
••••...•.•..................•••••••••••............
1980 '.130 1.1S00 12.140 1'.290
1985 2.('10 t .180 12.!I]O 11.'90
1990 2.9bO 2.7]0 12.11;]0 II.t90
1995 'J.bOO 1.J70 U .I§]O 11.190
2000 '.bon J.J70 U.5]0 tt.'9n
2005 J.bOO 1.170 12.530 1l.190
20.0 3.60 0 J.]70 12.530 1I.19n
SCENARIO.HED •HEb-_FERC 01--6/20/1983
~
FUEL PRIC!FORECASTS EMPLDYEn
FUEL OIL (t/HHBTU)
ANCHOR ARE •COOK INLET
•••••••••••••••••••••••••••••••••••••
GREATER FAIRBANKS...•..•.....•..........•..........•..
n
---I
N
'-J
YEAR RESlDENTI AL 8USJN!8S RESIDENT!AL RIJSHIESS..-.......•••.•..............•........-.-...-.-.-
1980 7.750 1.200 7.830 7.1500
1985 7."30 7.130 1.100 7.03 0
1990 1.&30 7.130 1.100 7.030
1995 7.b30 1.130 1.100 7.4]0
2000 7.tt30 ".130 7 .100 '.030
2005 1.630 7.130 1.100 7.030
2010 7.630 '.130 7.700 '."10
SCENARIO,"'ED ,HEb--FERC OX--6/ZQ/t98]
RESIDENTIAL USE PER HOUSEHOLD (KWH)
(WIT~OUT ADJUSTHENT 'OA PRICE)
ANCHORA~E -COOk INLET..~•........•.•...••..
SMALL LARGE SPACE
YEAR 4PPLI ANtES APPLI At-lCES HEAT TOTAL
••••.........•••••••••••••••••••••••••••••••
("")1980 21 tn.no 1151\0.61 5088.152 136~9 .15.
--'(o.OOn)(o.ono)(0.000)(0.000)N
ex>
1985 21btl.OO 61 151 '.lUI 4821.78 13133.24
(n.flOG)(o.lnno)(0.000)(0.000)
1990 2~IO.00 60?0~q8 Q586.40 128'6.88
(1.000)(0:000)(0.000)(0.000)
1995 22btl.OO 59110.98 4'519.96 127~0.94
(n.ooo)(0.000)t 0.000)(0.000)
2000 21JO.OO 15988.1)6 4448.08 127~f.I.15
(0.(00)(0.000)(0.001\)(0.1)00)
2005 2'&0.00 6058.14 4418.19 12 8 '6.73
(0.000)(0.10(0)(0.000)(0.000)
2010 2 4 10.00 11123.90 4 4112.09 129!S.OQ
(0.000)(o.ono)(0.(00)(0.000)
SCENARIO'HED I HE6--FlRC OX--6/aU/19~]
RESIDENTIAL USE PER HOUSEHOLD (KWH)
(WITHOUT .DJUSTH~NT FOR PRICE)
GREATER FAJRBAN~S..~_..•......•.••....•
SHALL LARGE SPACE
YEAR APPLIANCES APPLI ANeES HEAT TOTAL..........._...................•.....~.......
('")1980 2461.1.00 ~719.5~1l1l.66 11519.18.
---'(0.(00)(0.(00)r 0.(00)(0.000)N
U)
1985 253').QQ 1t178~96 3406.31 12321 ~260.0(0)(0.'0(0)((I.OOO-l (0'.000)
1990 2Mb.OO 6408~8CJ 38U.ua 129~2.310.(00)(0.'0(0)(0.(00)(0.(00)
19135 2676.01 6b11~50 4053.13 114~O.810.0(0)(I).:ono)(0.(00)(~.OOO)
2000 27116.00 67q3~16 11105.71-13844.1)0
0.(00)(0 ..000)(0.000)(0.001)
2005 2 8 16.00 684 Ii '.7(1 4517.20 1 4 11 8 •9 0(0.(00)(0.'000)(0.(011)(0.000)
2010 j!R86.no 68A7~911 1I65b.67 1114'0.tll
(0.(00)(0.'0(0)(0.0(0)(O.OOOl
SCEN~RIOI HED I HEb-.FERC 0'.-6/24/t983
BUSINESS USE PER EHPLOYEE (KWH)
(WITHOUT L4RGE INDUSTRIAL)
(WITHOUT 'DJUSTHE~T FOR PRICE)
YEAR ANCHORAGE •COOK INLET GREATER 'AJR8~NKS
••••...........•........••-_.••......•.•.•.•••..
1980 BUO?nu 7495.70
(C).noo)(0.(00)
n.1985 Q580.53 7972.14--'w r n.oon)(0.0(0)
0
1990 IO~bl.f\2 6]1)0.55
(0.0(0)(0.000)
1995 11/)8'5.'J2 8707.76
r 0.0(0)(0.0(0)
2000 lU5C1.10 8913.11
(o.oon)(o.noO)
2005 I Jl~2q.05 9252.''''(n.ooo)(0.000)
2010 12707.1b 9Ub.33
(n.oon)(0.000)
1-
SCENARIO'MED ,HEb--FERC OX--b/2Q"983
SUM~ARV OF PRIce EFFECTS AND PAOGRAHATIC cnN8ERV1TtON
IN GHH
ANCHORAGE -COOK INLET
RESIDENTIAL flUUNESS.............•.•.......OWtf-PR I CE PROGRAH-INOUCED CROSS-·PRICE OWN-PRICE PROGR~M-INDUCED CROSS-PRICEVEARREDUCTIONCONSERVATIONA~DUPI0.!t ..RE~lJq ION__CONS.~~Y~HQ~.REDUr.TtON................................~..~....;t:-.:..................................................................................................
UBo /).000 0.000 0.000 0.000 0.000 o.oon
19BI fa.~3n 0.000 -2.058 9.38n 0.000 -n.8fa1198112.lIbO 0.000 -/&.US 1".lbl 0.000 -1.13 1l1983IIl.b91)0.000 -b.17)28.141 0.0011 _~."nl1984211.921 n.noo -B.231 ]7 .'52'n.noo -1.1Ifa8
1985 31.151 /).000 -to.~R9 IIb.Cl OI 0.000 -a.33'5
198~39.&B 0.000 -19.595 I)1.Q65 f).OOO -7.9~111987Q8.111I 0.(01)-28.901 &CI.028 o.nOI)-It .SIIl1988Sb.59b 0.000 -38.201 811.1)91 0.111111 -15.10111989b5.011t 0.1100 -47.5U 41.1511 o.ono -IR.nll
n 1990 11.5bO o.noo -5b.A1Q 102.21'7 0.000 -22.284.......1991 91.bCla o.noo -81.1198 \18.11]7 o.ono -21.~~2w1992121.82'7 o.noo -106.177 135.IISb n.ooo -H.I&n......
199)11I5.9bl 0.000 -131).856 152.015 0.000 -3'7.1)9819911110.0 9 5 0.000 -155.535 Ibll.b9'5 0.000 -1l~.03"
1995 1911 .221\0.1100 -I Bll.l!!"'85.31 1l 0.000 -11".9111
U9~~11I.AII9 0.000 -194.427 1911.b4a 0.000 -1I9.2I1B1991235.110 9 0.001l -219.&110 203.Qn 1I.000 -S1.bOI199825".oeCl 0.000 -il'3 C1 .353 2U.1O)o.noo -53.91111999nb.'709 0.001l -259.0b7 <'22."31 o.noo -Sb.n1
2000 "Cll.330 o.nOll -278.781.'tJ31.CI&l 0.(\00 -51t.~1l1
(1001 ~0(l.'5a5 o.noll -il79.425 ;t44.b10 o.nnn -"1.0722002'03.1bn n.ooo .~81.010 i'51.177 n.noo -b'.U3~00l 30&.Cl 71\o.non -;.182.216 nO.0811 o.non -&10.13112nOllJln.18Q 0.000 -283.1&1 i.!A2.'791 n.nno -bll.&"'5
2005 H 3.110"o.oon -2811.'!\Ob i'9!!i.1l9A n.(\oo -11.19"
lO06 31&.619 o.noo -4'811.681 '12.72 q 0.000 -711.2822007'lq.IlB o.non -2Bll.85b ~2q.q60 o.oon -7'7.1bAlOORJH.OIl1 0.000 -2B5.cno ~1I1.lqO o.noo -8n.453~009 H&.2bt I).noo -285.l0C;'b ll .1I21 n.ooo -8'.53~
2010 :\29.117&0.00(\-i!85.]fJO 18'.b52 0.1)00 -Bb."25
SCENARIU."'EO •HE6 ••FE~C OX ••6JZGJI9RJ
9U~~ARY OF PRICE E'FECTS A~D PROGRAMATIC CONSERVATION
IN GWH
GREATER FAIRBANKS
RESIDfNTI_L RUSINE!I.IS.....•••••....•..•.•..OWN.PRICE PROGIUM·lNDUCEll CROSS.PRICE OWN.PRICE PROr.R AM.I NDIJCFD cROSS·PRYCrYEARPEDUCTIONCotlSF,R.Y.A !.ION REDUCTION ..REDU(:UON CON~ERYAHQN __REDUr.TTON••••........................................................................................................................
1980 0.000 0.000 0.000 0.000 0.000 0.000
t981 ·".2b7 0.1100 1l.010 0.000 0.0011 0.l)2Q198Z-0.lin 0.11011 0.1110 0.000 0.0011 0.OG81981-".AOO 0.000 0.209 ".000 0.0011 0.072198G·'.066 0.090 0.219 0.000 O.OlJO O.OCH.
1985 .1.Bl 0.000 0.3119 0.000 11.000 0.120
198b -I.S7~0.000 0.•1I 12 -n.552 0.0110 O.13b1987lOt.812 o.oon 0.4711 -t.tOti n.ooo 0.1531968·Z.051 0.000 0.531 .1.b57 0.000 0.17"1989 .Ct.291 0.000 O.~9'.2.210 0.000 n.18b
n 1990 -2.~JO 0.000 0.6b2 ..1.'61 0.000 0.203.
--'
Il.i?lqW199,·2.772 0.000 0.72'5 ..3.201 0.000N1992..3.1I11 0..000 0.188 .,3.640 11.000 0.'-3111991..1.25 11 0.01)1)0.8S1 .G.079 0.1100 0.'51,\1'911 .~.1I9b 0.000 0.9111 .1.1.517 0.0110 n.i'''''
1995 ·3.137 1).1100 0.9 11 ..1.1.956 0.000 0.i'81
1990 -1.869 0.000 1.On .'5.147 0.000 0.21'71997.4.001 0.000 1.1146 -S.HII 0.000 0.2921998.lI.131 (\.000 1.081 .5.~2q 0.000 0.2971999.G.2"1)o.oon I.tl 5 .~.720 0.000 o.,OJ
2000 .4.196 0.000 1.150 _li.cll t 0.000 O.lOR
2001 .lI.li21)0.000 1.182 -6.109 0.000 0.'"'52002..G."1I3 0.000 1.21 11 .".306 0.(1)0 0.3232n01-1I.7bb 0./100 1.246 .6.liOG o.oon o.HI20011.1I.A8R o.noo I.na .".701 o.oon 0.138
2005 .'5.nll 0.000 1.!U .&.1198 o.ono n.'Gb
20(10 -"i.IlIO 0.000 1.3114 .7.13t 1'1.000 0.3562007-li.2b9 0.000 1.117 .1.364 0.000 0.~f.l72008.."i.199 o.noo 1.1.111 .7.1\'6 o.noo n.3772009.S.lilA n.ooo 1.'''''5 .7.1'2'n.ooo 0.3R7
2010 .S.bl)7 0.01)0 1.117e,.R.Ob~0.000 O.J'7
_1-
SCENARIO'~EO,HEb-.FERC 0%••6/24/1q~3
BAEAKOOW~0'ELfCTRtCITY REQUIREMENTS (GWH)
(TOTAL INCLIIOES LAR~E I~OUSTRIAL CONSUMPTION)
ANCHORAr.E •COO~INLET_._-..._..-.~..-._....
HEDI~H RANGE (PR ••5).....•..............
RESIDEIlTIAl BUSINESS MISCELLANEOUS EXOG.INDUSTRIALYEARRE~IIJRF.:'1f.NTS PEQUIREHENTS REQUIREMENTS LOAD TOTAL.........~......••.•.-..•..........•.•••.•.....•......•........-.....••.•.•........••..•..-•..
1980 979.51 e75.U 211~31 811.00 191)1.19
1981 1020.99 9111.90 211.67 92.08 ;tORS.011198210"2.45 t020.115 25.03 100.10 il208.091983'103.90 1091.00 25.11(1 108.211 "BO.51l19811'1IlS.31s IlbS.55 25.7~Ilb.12 illl~2.q9
1985 lllib.lIi!1218.1'1 9 2b.12 1illl.IlO il5115.111
19ab '216.07 IC!7Q.11)2b.811 137.89 i!f."0.7 1l19811241.1.51 t120.51 27 .03 151.38 '.1116.0111988'276.3"1361.72 2".38 Ibll.88 i'RJ1.31l1989130b.21 11102.91 '.9.13 178.37 i.l910.I)II
n 1990 1310.06 1111111.14 29.89 '91."6 ~O/)l ~911.
--'w 19 9 1 13 1 3.'1 ,1500.8?....30.88 195.13 '099.911w1992'410.16 l!i57.S1 31.87 19".1l0 11 9 7.91119Q1111117.21 1614.19 12.afl 201.66 ~2Q5.9319911'4114.27 1610.88 33.ab JII04.93 n9J.QJ
1995 'S21.3i'1127.56 111.85 '-08.10 311 9 '.9'
1996 ISU.98 112 Q .9S 35.07 ~14.",3510.1"19')7 151i2.6l!i ll'H.1S 35.28 220.oa 'SIIO.36199a15bl:\.31 1734.711 15.51)22b.02 15611.571999ISH.en UJT.t3 ]5.72 i!3'.96 '5111l.79
2000 'SQ'J.6IJ l1H.!§]35.Q II 137.QO 'f113.0j)
1001 16i.l3.b2 1171.72 36.'55 ~IUI.96 ~b78~84200Z'&117.&0 Ifl07.91 37 .15 i'5&1.02 171111.682001Ib11.54 1842.1)9 37.7b 259.(18 11110.5(1200/1 1095.51 1117".28 38.36 i!66.14 1f17b.J6
2005 1719.5'5 1910.117 38.97 i.l13.20 '942.20
ZOOo 1711iZ.1I1 1968.J111.1 J9.9(1 281.lia 40llZ.17(1007 17f1S.30 2(1Z6.01 110.88 ).89.90 41"2.1510081"18.1~2083.78 111.811 298.111 112112.112009'''51.05 21 lit .'5/1 42.79 '(1".72 113112.1'
1010 '81l].Q?i!199.11 113.7'S 115.'0 4/14i.l°.oa
n
--'
w
.J:>,
SCENARIO.HED.HEb-.'ERC 0'••6/21.1/1983
BREAKDOWN 0'ELECTRICITY REQUIREMENTS (GWH)
(TOTAL INCLUDES LARr.r.INOUSTRIAL CONSUMPTION)
QREATF.R FAIRBANKS.....•••••-.....-.....
HEDIUM RANGf (PRs.'S)..-_.•......•...••.•
RESIDENTIAL 8U8INESS MISCELLANEOUS EXOG.INDUSTRIALVEARREQIJlRFHEIlTS'REQUIREHEIlTS REQUIREMENTS LOAD-.--..-.........•............-•...........••......••...•......•..•..........
1980 lh.3 9 ll7.l11 6.78 0.00
1981 19 1.60 230.33 fl.16 0.0019lt220fl.81 2113.5]fl.74 0.001983222.01 256.7]fl.71 0.001984211.;!2 .269.93 b.&9 0.00
1985 2~2.1I1 281.12 6.67 o.no
UAb 26q .'315 290.86 fl.70 10.001987i!7b.27 298.60 6.711 20.0019882118.1 9 30&.34 6.77 311.001989300.12 1111.08 6.81 110.110
1990 312.'01.1 121.82 6.8/1 50.00
1991 327.28 )]1.1.14 7.U 50.00199234~.52 1116.55 7.11]50.1101993357.7b 358.91 7.12 50.0019QQ371.01 171.27 8.01 50.00
1995 31\11.;25 383.U 8.30 50.00
199fl 3 911.8'5 18Ci.23 8.38 50.0019971I01.4t;18fl.82 8.117 50~00199f1/108.05 188.1.11 8.5e.50.0019991111I.b5 390.00 8.611 50.00
lOOO 1121.2S 191.'!~8.73 50.00
2001 429.0!198.1.17 8.88 50.00200211'0.99 1I05.1fl 9.011 50.1102001IIQII.R'5 1112.25 9.19 50.002001111'52.U 1119.13 9.311 50.00
20115 4&0.59 112".02 9.'io 50.00
2006 1110.21 IIU.tO 9.7t 'io.on2001II19.94 UIl.17 9.93 50.002008'JAil .'ft:!1159.2'i 1O.1lJ 51).no2009QQIl.30 1170.H 10.3&'5o.no
2010 'i08.9~481.41 ,n.'iS '50.00
_1-
TOTAL....•...••.•.....•
/l00.31
1128 ~6.,
1I'j7.07
IlR5.lIS
5t3.81
'j42.21
571.ql
601.61
Ul.31
6e.!.01
,qO.7'
718.fln
746.50
771l.39
IIo2.2q
830'.'R
en.lII,
tlIl6.74
8';5.01
8U.2Q
All.IIi'
81'6."1
q/)t.3R
916.21l
Q31.20
946.1'
967.014
q811.0~
IOOQ.o~
10~q.99
'(1)0.9"
n
--I
W
U1
SCE~lRIOI MEO I HEb-_rERC 0¥.-6/2u/1983
TOTAL ELECTRICITV AEqIJIREMf~TS (GWH)
(~ET OF CO~SERVATION)
C1NClIIOE!LARGE INOUSTRIAL CONSUMPTION)
MEDIUM RANGE CPR ••5)........-.....•...••.•
YEAR ANCHORAGE •COOK INLET GREATER FAIRBlN~S TOTAL_.-.-....--............•.........-.....•.••_•.........................
1980 1963.19 1100.31 2JU~51
1981 2085."b4 1128.69 25tll~H19822208.0 9 /157.07 '-'665.1619831330.511 1185.115 "81"~91)198/1 ~lIc;2.99 513.83 l!l1)66.8l
1985 ~575.jn ~1I'-.21 ]1 t7~b5
1986 '-'060.7/1 571.91 1232~0519872711b.OIl 601.61 H1I7.6519882831.311 UI.31 U62".6519A92916.&/1 bol.ftl 1577'.05
1990 1001.9Q 690.71 161)2".65
191)1 J099~,qll 718.60 l!l18~5/11992JI97.911 7116.50 1911/11'11]1C~9J 12 Q'5.93 7711.H 11070.31Iq911HQ~.9'/102.29 11196'.22
Iq9S JU91.9~830.18 1I]"2~II
1996 1516.1/1 1'38."0 a]511~00199715110.36 8/1f••711 11187.091998J501)/I.S7 855.(\1 11/11 9 :.5919993'51'8.79 IIbJ.29 111152.08
2000 3013.00 A71.57 /l1I"/I~S7
lOOt 'b18.81l MO./l7 /l565~H
2002 17 4 11.611 QOI.38 U611b~Ob
2003 3810.5'-916.19 /172&.81
20011 J81b.36 931.i!0 4An7'.SII
2005 ]9112.20 1)/16.11 /l8I1A.30
2006 110112.17 1)07.08 '5001)~25
2007 111/12.1"98/1.(15 -;110.(1)
2008 /12112.13 101)9.02 52c;l~IS
2009 /1]/12.11 11\2 9 .99 c;372.01)
2010 411/12.08 1050.Q6 l)41)3~Otl
n
--'wen
.-.1-
SCENARIO.HEO.HE6 ••rEAC OX ••6/ZQ/1983
PEAK ELECTRIC ~EijUIAEHENTS (HW)
'~ET OF CONSERVATIO~)
(INCLIJIlES LARGE INOUSTRUL DEMAND)
MEOIUM RANGE (PR ••5).............•........
YEAR ANCHORAGE •COOK INLET GREATER FAIRBANKS TOTAL.........................•............................~.•••...•._.....
1980 .596.51 91.40 1187~90
1C~81 1121.26 97.87 519~11I198211116.0i!1011.15 '550~]7.1983 47o.n 111).83 I\RI~6119841195.51 II1.Jl 6t2~811
19R5 520.28 Ill.n 6114:07
1986 5U.35 136.117 668~9'1987 51\&.111 1]7 .15 69].7619885711.48 11111.1]'18."019~9 592.54 150.90 743.1111
1990 610.61 157.6~'b8~29
Iq91 610.57 1&11.05 nll~6219926'50.53 17IJ.4l!"20 ..951'~91 &70.5/1 176.79 8117.291C~9q &9(1.116 181.15 811'.62
199$710.111 18 9 .52 M9:9'5
1996 7 1 5.l'5 Iql.lIt 906~$61997719.87 '91.10 911.181998724.&11 19l§.19 ell 9~7919997'9.]2 197.n8 926~40
2000 7111.011 19".97 913~02
2001 1 11 7.2"202.]11 9a9~&a20027&0.41!205.76 966.26206377].70 '0 9 .18 Q"2~1I92001.1 7"6.9 2 212.59 qqq:51
2005 ROO.III 215.99 10'6~11
2006 8i10.31 2Z0.78 101l1~081007840.47 22;.57 '06b~nl2008flbO.&l i'lO.15 IOQ'l~98200968(1.7 0 215.111 I I I 'i .•en
i!oto 9 0 0.9&239.93 tl40~811
HE7--FERC -1%
C.137
(
I
i -
\
(
(
!
I
-)
SCENAHIOI !olED I HE7··'ERC .1~••bllq/t98]
HOUSFHOLOS SERVED
ANCHORAGE •COOK INLET.......--..•..........
YEAR SINGLE FAMILY MULTIFAMILY MOBILE HOMES DUPLEXES TOTAL...............••.............................•.......•...•.•.........
1980 ]5473.lO]14.8230.U81a.7~50J.0.000)«o.noo)«f).000)«0.000)C n.noo)
n
11502.65U.
.1985 49138.~6201l.9'1I1~•--0 «0.000)«o.noo)(0.000)(0.000)«0.0/)0)w
\0
1990 bOH7.~72S7 •13865.811bO •10~929.0.000)«0.000)(0.(00)(0.000)«o.non)
19Q5 66718.11004.1'5372.8133.12142b.0.000)«n.ooo)(0.0110)«0.000)«0.(00)
2000 70748.H&OA.1&]9].RillS.1288b3.0.000)«o.noo)«0.(00)«0.001)r n.noo)
2005 75730.3&261.17719.87&!1.1381.1U~0.000)«0.000)«o.nOn)«0.000)«o~oon)
2010 82347.19840.194b9.9526.151t81.0.(00)«0./)00)(0.0(0)(0.(00)«0.(00)
SCENARIO.HED •HE7--FERC -1¥--6/211/198]
HOUSEHOLDS SERVED
ORF.iTER F4IAB'N~S
••••••••••••••••••••••
VEAR SINGLE FAMILY IotUL TIF lMILY 140B I LE HOl1E S DUPLEXES TOTAL....••••••••••••••••••••••••••..............•••••••••••••.•.......•...
1980 7220.5~8'7.1189.161'7.IS311.
0.000)(0.0(0)(0.000)(0.000)(o.nOO)
1985 106116.5880.2130.1768.201J211.
(0.000)(0.000)(0.000)(0.000)(0.000)
("").19C1O 111jH.79bO.2222.2]75.211090.-'
+:0-(o.noo)(0.000)(0.000)(0.000)(0.000)0
1995 1411~'7.7841.3l36.2339.27823.
(0.000)(0.000)(o.oon)(0.000)(0.(00)
2000 15712.710l.3631J.22Cl8.2Cl348.
0.000)(n.OOO)(0.000)(0.000)(0.00(1)
2005 171011.8020.11011.2252.3lJe,]•
(0.1)00)(0.000)(0.000)(0.000)t o.oon)
"
2010 18520.ClO31.11391.2t96.34150.
(o.noo)(0.000)(0.000)(0,000)(0.001)
1_
SCENARIO'"'ED I HE7 ••F ERC .IX··6/24/1981
~OUSrNG VACANCIES
ANCHORAGE •COOK INLET
••••••••••••••••••••••
YEAR SINGLE FAt4ILY MULTIFAMILY MOBILE HOMES DUPLEXES TOTAL..-............•.•••••••••••••.•.•...•....••••••••••••••.............
IQ80 5089.7666.Iq91.1463.1620~.
n (0.00 0 )(0.000)(0.000)(0.000)(0.000).
--'1985 Sill.1496.121.292.2455.+=0
--'(0.000)(0.000)(0.000)(o.oon)(0.000)
IIlQO 66 4 •en.151.289.1202~
(0.00 0 )(0.000)(0.000)(0.000)(0.000)
IIlQ5 1111.16711.1611.284.2861.
o.noo)(0.0011)(o.oon)(lI.noo)(o.non)
2000 178.1815.18n.152.~126.
0.000)(0.000)(0.000)(0.(00)(0.000)
2005 833.IQS8.195.288.32111.
0.(0 11)(0.000,)(0.000)(0.000)(0~000)
lOIO lJOf).2151.214.1111.1586~
0.000)(O.f)O~)(o.noo)(O.OOn)(0.000)
SCENARIO.HED •HE7-_FERC -IX--6/24/IQ81
HOUSINB VACANCIES
OREATfR FAIRBANKS
••••••••••••••••••••••
YEAR SINGLE FAMILY HUL TlFAIotILV ,",OBILE HOti£S OUPLElCES TOTlL..-.•••••••••••••..............•••••••••••••...........................
1980 3~5J.H2O.q 8 6.8QS.8854.
0.1'00)(0.1'00)(0.000)(o.oon)f o.non)
n.I'US 118.26Ql.24.uq.150~.--I
~(I).oon)(0.000)e 0.001)e 0.000)(o.non)N
19QO 127.45".25.81.681.e 0.000)(0.000)e 0.000)c 0.000)f o.oon)
1995 159.441'.36.80.72~.
(o.noo)(0",000)(0.000)C 0.000)(o.noo)
2000 173.440.40.78.73(~
0.000)(o.noo)(0.000)(0.000)(0.000)
ZOOS 188.431.40.17.lai.
0.000)(".000)(0,000)(0.000)f o.noo)
2010 200."RR.48.81.821.
0.000)(0.000)(0.000)(0.000)(0.000)
_1-
SCENARIO'MED'HE1·.FERC .IX••b/24J1geJ
FUEL PRICE FORECASTS EMPLOYED
ELECTRICITY (I I KWH)
ANC~ORAGE •COOK INLET GRfATER FAIRBANKS
••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••
n.
--'
.po.
w
YEAR RESIDENTIAL BUSINESS RESIDENTIAL BUStN~S!...........................•••••••••••.•.•......•
1980 0.037 0•.,34 0.09!0.090
1985 0.0118 0.01l!0.095 0.090
1990 1).052 0.04'0.090 0.085
1995 0.0511 0.051 0.090 0.08!
2000 0.055 0.052 0.090 0.085
2005 0.051 0.0511 0.090 0.085
iOl0 0.059 O.OSb 0.090 O~085
8CENARtOI MED I HE7 ••FERC .IX••6/24/1'81
FUEL PRICE FORECASTS EMPLOYfD
NATURAL GAS (S/MMBTU)
("")
-'
+>-+>-
_1-
ANCHORAGE •COOK !tRET GREATER FAIRRANKS
•••••••••••••••••••••••••••••••••••••..•..............•.•..............•..
YEAR RESIDENTIAL BUSINESS RESIDENTIAL BUSINF.SS
•••••.........•••••••••••••••••••••••....•....•.
1980 1.7]0 I.S00 12.1110 11.290
1985 2.000 1.170 12.280 1ft ~~80
n90 2.1170 ~.&40 11.680 10.430
1995 3.120 3.090 it.UO 9.920
2000 3.0&0 2.830 1O.5eao 9.1130
2005 2.9bO 2.730 10.040 fI.970
2010 2.Reao 2.UO '.550 A.530
SCENARIO'"ED,HE7-_FERC -tX--6/2Q/1981
FUEL PRICE 'OAECASTS EMPLOYFn
'UEL OIL (S/H~BTU)
ANCHORAGE -COOK INLET GREATER FAIRBANKS
•••••••••••••••••••••••••••••••••••••.......••.•........•...•...•........•
n
--'
+:>
U1
YEAR RE8I[1Et~TlAL BIJSJNESS AEunENTI AL BUSINESS....•••••••••••.......~........•......•.........
IQ80 7.750 7.200 1.830 7.500
1985 '.Q80 6.990 1.550 '.280
IQ90 '.110 6.650 1.t80 6.9](1
1995 6.761)6.120 6.820 6~5qO
2000 6.Q3n 6.010 6 o QCJO 6~l60
2005 6.un 1).120 6.110 !~9bO
2010 5."20 15.(140 S.870 §~660
---.._--
SCENARIO'MED ,HE7··FERC .IX ••6/24/19a]
~E8IOENTIAL USE'PER HOUSEHOLD (KWH)
(14ITHOIJT AI)JUSTI1F.tn 'OA PRICE)
ANCHORAGE •COOK INLET
••••••••••••••••••••••
SHALL LARGE SPACE
YEAR APPLIANCES APPLIANCES HEAT TOTAL-._............-........••••••••••...........
1980 21to.DO "500~b]5088.52 13699.15
0.(00)(0.,000)(0.000)(0'.000)
("').U85 2IbO.OO 60q2~3t1 Q770.7I UOB.ot;~
.,.:::.(0.000)(0.000)t 0.000)(O~OOO)m
1990 2210.00 5975~bO 4579.19 1276tl.7 q
(0.000)(o~'l)O!)t 0.000)(0.000)
U9S 2'.bl).OO Sqlq~57 4513.35 12692.q?
0.001)(0.0(10)(0.000)(0.000)
2000 2310.00 5949.22 44IU).9Q U70b.t6
(0.000)(0'.000)(0.000)(0.000\
2005 2JbO.OO "019~U 4 4 16.38 t27QS.51
(0.000)(0;,000)r 0.000)(0.000)
2010 2410.00 itOI'll.0'7 444O.be 11.934.75
0.000)(0.0(10)(0.000)(O~.:OOO)
_1-
SCENARIOI HEO I HE1·.FERC .IX ••~/24/1q8J
RESIDENTIAL USE PER HOUSEHOLD (KWH)
(WITHOUT ADJUSTMENT FOR PRICE)
GREAT~R FAIRBANKS......................
SMALL LARGE SPACE
YEAR APPLUNCES APPLIANCES HEAT TOTAL..-....•......•...........•.....•...........
IUO 24~~.no 5nq~S2 3113.66 llStq.IB
(0.(00)(0.0(0)(o.non)(O~OOO)n.
--'IqB5 2'33S.Q9 61'8.18 3607.23 12322.00-Po (o.oon)(0.'0(0)(o.noo)(0~000)--..J
1990 2toOb.no 64119.91 3868.80 12924.71
(0.(00)(0.0(0)(0.(00)(O~OoO)
1995 2676.01 6661l~68 11048.]3 13389.02
(0.1'00)(0.0(\0)(0.000)(n.oo/"
2000 27t16.01 b7ql~01 4308.'18 138~7.06
(0.000)(0.0(0)f ".000)(0.000)
2005 2816.00 664q~00 4510.tO I I1 P5.1O
((l.000)(0.(00)(0.000)(0.000)
2010 2886.00 68R9~70 4656.39 11111'2~Oq
0.(00)((1.;000)(0.(00)(0.000)
___I
acENtRIO.~EO I HE7·.FERC .11••~/24/1q83
BUSINESS USF PER EMPLOYEE (KWH)
(WITHOUT L.~GE JNDUSTRIAL)
(WITHOUT ADJUSTMENT fOR PRICE)
YEAR ANCHOR4GE •COOK tHLET•......~..•........•••••••
1980 840'7.04
0.(00)
1985 9SAlJ.43
t 0.000)n.
--'1990 104'H.3e..J:>o
00 (0.(00)
19Q5 10"23.18
t 0.(00)
2000 llC'i3.18
t 0.(00)
2005 11 8 2 Q.69o.noo)
2010 12611.95o.noo)
__L
GR,.ATER 'AIRBANKS
••••••••••••••••••••••
7US.70
0.000)
nn.7S
(0.000)
8304.16
0.000)
8b2b.08
(O~OOO)
8889.85
(0.000)
Q~IQ.O~
0.000)
9b05.75
0.000)
SC[NARIOI HEO I HE1 ••FERC .IX••~/l4JI98]
RESIDENTIAL USE PER HOUSEHOLD (KWH)
(WITHOUT ADJUSTMENT FDA PRICE)
GREAT!R FAIRBANKS.............•......•.
SMALL LARGE 8PACE
YEAR APPLIANCES APPLI ANCES HEAT TOTAL..-............•..•....•...........•••.....•
U80 21l~~.00 5139~52 3313.~1l U5U.18
(0.000)(0.000)(o.oon)(O~OOO)n.......1985 2c;35.Q9 61'8.18 3607.23 123C!2.00~(o.oon)(0.'000)(0.000)(O~OOO).......
19QO 2~Ob.no b4C19.91 38b8.80 12924.71
(0.000)(0.000)(0.000)(O~OOO)
U95 2b7b.Ol U6ll~b8 110118.33 13389.02
(o.tlOO)(0.0('0)(0.000)(0.0(0)
2000 21l1b.Ol n92~07 tnoll.98 138~7.0b
(0.000)(0.000)f 0.000)(0.000)
200S 281f/.00 66119·.00 4510.10 ups.to
(1.'.000)(0.000)(0.000)(0.000)
2010 2886.00 6889~7(1 4656.3Q 1411~2~09
(0.000)C.0.;000)(0.000)(0.000)
8CEN4RIO.to4EO I HE7··FERC -11--6/Ztl/1983
BIISYNESS USF PER EMPLOYEE (KWH)
(WITHOUT L4AGE INDUSTRIAL)
(WITHOUT ADJUSTMENT FOR PRICE)
YEAR 4NCHORAGE •COOK IHLET GR~ATER 'AIRBANKS•......~............•.•....•........•..•....•..•
1980 8110'7.04 7US.70
(0.(00)(0.000)
1985 9S~S.1I3 7973.75
(0.(00)(0.000)n.
~1990 IOlH.3f1 8304.16~
00 (0.(00)(0.000)
1995 10"23.18 8Utl.08
(0.(00)(0.000)
2000 11223.18 8889.85
0.(00)(0.000)
2005 1182 Q .b9 9il Q .02
(0.0(0)(0.000)
2010 12613.95 QU5.7S
(0.(00)(0.000)
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C.150
---saNAR"fO'-HEO -'-HE1--;FERC -lX--bnQ/lnl
BRE_KDOWN OF ELECTRICITY REQUIREMENTS (GWH)
(TOTAL INCLUDES LARGE INDUSTRIAL CONSUHPTION)
ANCHOPAGE -COO~INLET......•...............
MEDIU~RANGE (PR8.S).....-~.
n
--'
11l
--'
YEAR....
1980
1981
"82
"8]
"81£
,,85
"8b
"87
1988
1989
"90
1991
"92
U9]
19911
,,95
"9f1
1997
1998
,,99
zooo
2001
2002
2003
20011
2005
200b
2007
2008
2009
2010
RESIDENTIAL
REQUIREMENTS..........•..•....
9H~5]
Ion .'b'5
107S.'7ft
112].8"
1111.9'
IlZ0.1 I
12'52.U
128q .1'5
13tfl.1?
131£8.19
B80.2t
1408.]1
14]0.54
tllftq.71
11£'2.88
1521.0S
1518.05
1555.01i
1512.0'§
1589.05
I bOo .'0'5
tb28.U
Ib51.47
lo74.1?
Ib9b ..88
17t Q .S9
1750 .'ft I
1781.bl
IAI2'-bo
1~1I3.bA
t~1II.70
BUSINESS
RErJUIREHENTS.•.....•.....•..•.
871).30
94".17
to2n."
10lJ].80
Ilbft.fli
123'.4]
1280.011
1121.RS
U61.06
11104.27
1II4'5.''9
1/I7b.8b
1508.211
1539.61
1'570.9 9
1602.3b
1619.311
U3ft.31
IU1.29
IUO.2b
1687.24
17211.28
1701.33
1198.37
11I35.Q2
II\U.46
I Q 29.'!i7
IQ86.b1
211 In.77
2100.88
llSl.Qa
HIICHUNEOUS
REQIJIREHEt~TS..•..•.•.........•
211.]1
211.75
25.19
25.U
!b.n
2b.52
27 .22
27.91
28.00
2'.30
29.'9
30'.72
]1.1£11
32.16
]2.89
H.ot
33.98
34.lb
311.11
35.11
35~1I8
3b.11
lb.74
37.37
]8.00
38.b]
]9.50
110.50
1£1.4]
112.3ft
1I3.2 Q
EXOG.INOUSTRtAL
LOAD...•...••.........
80.00
92.08
100.10
108.24
llft.12
124.00
U7.89
151.18
lO4.B8
178.37
191.h
1 q5.11
198.110
20t.flb
~OIl.91
208.20
2111~ta
220~08
226.02
231.96
2n~90
2411.9&
252.02
2SQ~08
26b.I4
273.20
281.'58
289.9b
298.111
10b.12
115.10
TOTAL..•••.••••.......•
I9U.19
2092.b5
22n.10
2]5t.'55
21181.01
21110.40
20lJ7.88
n8S.29
2872.71
2QbO.1]
10117.511
]111.08
]I1q.bt
1218.15
HOI.&8
'Bbli.2i!
1405.51
]445.80
3486.09
1526.38
356ft.67
]614.11
3701.50
37&9.00
381b.41l
HO].88
1I001.]2
11098.70
01'Jb~20
112 Q 3.bO
41]91.08
n
~
01
N
SCENARIOI ~EO I Hfl·.rERC .'X••b/211/1~8J
BREAKOOWN OF ELECTRICITY REQUIREHENTS CGWH)
(TOTAL INCLIIDES LARGE INDUSTRIAL CONSUMPTION)
GREATER ,AIRBANKS..•.......•......__...
MEDIUM RANGE CPR_.5)..•............•.•..
RfSIDENTUL 81lS[I~Fsa MISCELLANEOUS E~OG.INDUSTRIALYEARRf.QUI~PtfNTS REQIIIREMENTS RE~UIREHENTS LoAn TOTAL..................••••....••....•..•............~..-...........................•....••.••••...
'980 17b.3~211.14 11.78 0.00 11110.3'
U81 ,91.l~2311.lb 6.7b 0.00 428.1111982lOb.19 "113.58 6.73 0.00 11511~51'981 221.09 2Sb.81 11.70 0.110 1184.601984215.99 271).11]b.b?0.00 512.711
'985 250.89 283.1.6 f.l~65 0.00 5110.80
19 8 0 2b2.h 290.9]11.68 10.00 570·.37'981 Z1/J.63 298.59 b.72 lO.OO 599.911198828b.50 30b.26 b.75 10 .•00 629.5219892q~.]7 111.9]b.79 40.00 6 '!Ii 9 •Q9.
1990 ]10.211 321.60 b.83 50.110 ""8~U
U91 322.0 9 ]2~.U 7.03 50.00 707.7AU92313.'"]]5.73 7.23 50.00 ,12b.9019931115.'80 3112.~O 1.11]50.00 1110.0219911]';7.&5 3Q9.llb 1.b3 50.00 7115.111
1995 309.50 35b.91 7.83 50.00 7811.26
199&]75.6"lbll.]8 7~93 50.00 H].9919q7]81.-8&3&3.83 8.03 50.00 80].'721998]88.011 3b7.28 8.13 50.00 811.IIS1999]94.21 170.73 8.23 5n.oo 82J.t8
2000 1100.39 374.18 B.33 50.00 812.91
200,q01.3'381.13 8.118 5n.oo 811&.92200211111.i!3 3811.08 8.112 50.00 860.9]2003 1121.U 195.0]8.71 50.00 87l1.9lJ20/)11 1126.'06 1I01.qa 8.91 5n.oo lIAS.96
lO05 1134.98 1109.93 9.05 50.00 9/)2·.97
200&11111.'52 1119.112 9.26 50.00 922.202001IIS?.06 112 11 .91 9.116 50./10 9111.1122008IIbO.5Q 1I111).3Q 9.&&50./10 960.blI2009116Q.13 1150.fl8 9.86 50.00 979.81
2/)10 1177.U bllt.Jb 10.06 50.(10 999.0"
("")
01
W
SCENARIO,HEO I HE7 ••FERC .IX ••~/2q/198]
TOTAL EL!CTRICITV REQUIREMENTS (GWH)
tNET OF CONSERVATION)
(INCLUDES LARGE INDUSTRIAL CONSUMPTION)
'1EDIII'1 RANGE (PR ••li)...••....•..••..•..•.•
VEAR ANCHORAGE •COOk INLET GREATER ,AIRBANkS TUT lL........._.~..........•..••..•.....•......•.•...•......•.........•.....
U80 1CJ~].19 400.31 un.51
1981 2092.'b5 (128.41 2521~O~1982 22n.l0 456.51 U7a.bl198323'51.55 484.60 2816~1619842481.01 512.70 2Q93.71
1985 2611).1~6 541).80 lUI ~2b
1986 2b97.88 570.17 ]2~8~2519872785.29 599.94 1385.21119882872.71 ~29.52 lSlli!.2119892960.1]659.09 1619.22
1990 30111.511 U8.60 ln6~21
1991 1111.08 701.18 1818~8&1992 3174.6'726.9 0 1901,521993321l3.-15 14&.02 19811,17199111301.68 7&5.t4 406&.82
1995 13&5.22 7811.h 111119".118
19 9 6 3405.51 793.99 4199'.50199711~1I5.80 803.72 4249.521998\IIflb.09 8U.1I 5 4299~54199935h.H 823.18 11349".S~
2000 llibb.&7 "H.91 11199~58
20°1 1034.1 ,8tlb.92 4q81~0320023701.56 8bO.9]4S62.1.19200331~9.01)874.95 q&43~942004381b.411 "88.96 IIHS'.40
2005 H03.8"902.97 48nb~86
2006 41101.32 lJ22.20 49'-3,.5220011109(\.76 9111.112 '0 11 0.18200811196.20 9&0.1111 li1So.84200911293.&1.1 97 9 .81 52T3~5t
2010 11391.0A 991).09 -;391)~l'7
n
--'
01
~
-1-_
SCENARIO'HED'H£7 ••FE~C .IX••6/24/198]
PEAK ELECTRIC Rf.QUIREMENTS (HW)
(NET OF CO~SERVATIONJ
(INCLUD~S LARGE INDUSTRIAL DEMAND)
MEDIUM RANGE (PR ••5).....•......•.........
YEAR ANCHORAGE •COOK INLET GREATER FAIR8ANKS TOTAL...~......•....••••.•.•.•.....•.•.••....•...••••.•••.•••.••...........
UsO 396.51 91.110 1I"7~90
1981 1Ii!2.70 97.81 S20~511982448.S9 1011.23 5'5]~1119831175.08 110.64 '585~7219811501.27 117 .05 618.32
Iq85 527 .lIb 123.117 6'50~91
US6 5115.95 130.22 b76~171981564.115 13fI.97 701.11219885112.95 14l.7i!726 .•671ge9bOI.1I5 151'1.46 751.91
1990 619.95 157.21 777.16
Inl b32.S5 It>>t.58 7911~4](l~q2 64S.7b 165.9q 8tl,7 0
1993 658.66 170.31 828,97199/1 b71.57 17 4 .61 811b.24
IqqS b ll 4.'117 179.011 8b3~51
199b b92.49 181.26 873~75Iq97700.50 IS].48 81'3.991998708.52 185.70 8911~22199971b.54 187.92 9n4~4b
2000 7211.55 190.t5 9111:70
2001 US.IO t9J.]1I oll~45
2002 751.b5 196.511 9t18~1920037b5.20 199.74 961l,qtl
20011 778.75 202.4111 981.b9
2005 H2.3D 206.111 4I4I1l~1l11
2006 811 ~9q 21/).53 1022~412001831.58 211l.qj!IOtlb.50
200S 1\51.2l 219.11 1010~532009870.87 223.70 10'HI.5b
2010 "90~5i 228.09 11111'.1&0
1
1 HE8--FERC -2%
1
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SCENARIO,HED ,HE8 ••FERC .ZI••&/24/!QR]
HOUSEHOLns SERVED
ANCHORAGE •COOK INLET
••••••••••••••••••••••
YEAR SINGLE FAMILy MULTIFAMILY 1040BILE HOMES DU"LUES TOTAL................................•...........•••••••••••••......•...••.
1980 lS4U.20314.8230.11186.11501.0.000)(0.000)(0.000)(o.oon)t 0.000)
n 1985 4908&.~b2n4.11492.85b1.95349.
U1 (0.000)(0.1)0(1)(0.000)(0.000)t O.OOn)
.........
1990 604b9.27341.13891.8460.tlot11.0.(00)(('l.OOI'll (0.000)(0.000)t 0.000)
1995 652 45 •10061.15018.8333.tlB659~(0.0(0)(0.0(0)(0.000)(0.000)(0.000)
2000 6 9 29&.12901.hOSS.79 48.12&20l~(0.000)(0.000)(0.000)(0.000)t n.ooo)
2005 14~6b.35513.11384.8557.135800.(0.000)(0.000)(0.000)(0.0(10)(O~OOO)
2010 80 9 12.3 9 156.1 913a.9363.la~5&5.(0.000)(0.000)(0.000)(0.00(1)«0.000)
_~I
SCEN4RIO.MED I HES--FERC -2X.-6/24/1983
HOUSEHOLDS SERVED
GRE"TER F"IRB"NKS
••••••••••••••••••••••
YEAR SINGLE FAMILY HUL TI,.4MIL Y MOBILE HOMES DUPLUES TOTAL....••••••••••••••••••••••••••.......•........•............-..•••.•..
1980 n20.5281.1189.1611.IIBn.
n (0.0(0)(0.~0(l)(0.000)(0.000)C 0.(00)........1985 tOf/46.5~b1.2130.I 76~.lOO07.c..nco (0.001.')(0.000)(0.000)(0.000)(0.0(0)
tno tt575.7~60.2233.2175.2~1~~.
0.0(0)(o.noo)(0.(00)(0.000)(0~000)
n9S tn8f/.784t.3083.2]]~.21l11Q.o.noo)(·0.000)(0.0(0)(0.000)C o.oon)
2000 15152.7703.3481.2n8.~86qO.
0.000)(0.000)(0.(00)(0.000)(n.ooo)
2005 16727.7794.3~2~.2252.30702.
0.(00)(0.(00)(0.000)(0.000)(0.001))
2010 18155.8 R5S.4310.2t53.H~n.
0.000)(0.000)(0.000)(0.000)(0.01)0)
SCENARIO,HED ,HE8--FERC -2X--&/24/1983
HOUSING VACANCIES
ANCHORAGE -COOK INLET
••••••••••••••••••••••
YEAR SINGLE FAMILY HUL TlFAHJLV MOBILE HOMES DUPLf.lCE8 TOTAL............•..•••••••••••••••••••••••••••••....•....................
n 1'80 508'.hU.1"1.111&).1620lJ..(0.000)(0.000)(0.000)(0.0(0)(o.oon)
~
U1
t.O •'85 1)110.lalJ6.126 •2'2.?Q5'5.
0.(00)(0.0(0)(0.000)(0.000)(0.000)
"'0 66'5.'.153.28'.1 till.
(0.000)(o.oon)(0.000)(0.000)(0.000)
'''5 118.1623.ttls.2811.27'0.
0.000)(0 ..(00)(0.000)(0.000)(0.(00)
2000 162.1777.117.519.12J1i •
(0.(00)(0.(00)(1).000)(0.000)(0.0(0)
2005 811.1 9 21.t91.282.321~.
(0.(00)(0.000)(0.000)(0.00(1)(0.(00)
2010 8'0.it 15 •.Ul.10'.\3'i211.
0.(00)(0.000)(0.000)(0.000)(0.(00)
-----L
SCENARIO,IolEC ,HES ••'ERC .2X--6/2IlJ1QS3
HOUSING VACANCIES
G~EATER FAIRBANKS..........~...........
YEAR SINGLE fV'ILY MULTIFAMILY ,",DAILE HOMES DUPLFXU TnTAl
••••••••••••••••••••••••••••••..............•••••••••••••.•...•.......
lQSO US].3320.Q86.89~.81'54.
(0.000)(0.000)(0.000)(0.000)C 0.01)0)
("").
--I 1985 llR.2654.24.'722.''il''.0'\a (0.000)(0.000)(0.000)(0.000)(o.oon)
1990 127.454.25.81.687.
(0.000)(0.000)(0.000)(0.001)(0.000)
1995 153.4118.]4.SO.714.
(0.000)(0.000)(0.000)(0.000)(0.000)
iOOO 167.4110.lA.1~.123.
0.000)(0.000)(0.000)(0.000)(0.000)
2005 t81l.187.Ill.17.49t.
0.000)(0.000)(.0.000)(0.000)(0.000)
2010 20 n•In'!.47.124.8qQ.
0.000)(0.000)(o.noo)(0.000)(0.000)
seEN_RIO,HED'HE8 ••FEAC -aX ••bI2Q/1983.
FUEL PRICE 'OAECASTS EMPLOYED
ELECTRICITY ($I KWH)
ANCHORAGE •COOK I~I.ET ~REATE~FAIRBANKS
_I
•••••••••••••••••••••••••••••••••••••...•..•.....•.......-.•..............
("")
~
m
~
YEAR RESIDENTIAL BUSI~IESS REunENTlAL 8USINESS...........•......................•..........-..
1980 0.OJ7 /).034 0.0915 0.1\1)0
1985 0.0118 n.01l5 0.09'5 1\.01)0
1990 0.051 0.048 0.090 /).0815
1995 0.053 0.050 0.090 0.085
2000 O.llS\!!0.052 0.090 0.085
2005 0.056 0.053 0.090 0.085
2010 0.057 0.n54 0.090 O.O~15
SCENARIO.HEn.HE8 ••FERC .ZX••6/24/Iq83
FUEL PRICE FORECASTS EMPLOYED
NATURAL QAS (S/HMBTU)
n
.......
en
N
ANCHORAGE •COOK INLET GREATER FATRR4NKS........•.......•.•••.•.....•....•.•~•..•.•.........•.....................
YEAR RESIDENT IAL BUSINESS RESIDENTIAL AUSINES9..-..•.....•..........-...................•.....
1980 1.730 1.';00 12.530 11.2QO
1985 , •Q80 1.750 12.030 10.750
Ino 2.770 i.'!!i40 10.880 Q.710
1995 3.070 2.840 9.830 8.780
zooo 2.8 80 ~.650 8.890 7.Q40
lOOS i!.720 2.490 8.030 7.170
2010 ~.5bO i.UO 7.2bO 6.480
SCENARIO.~ED.HEB ••FERt .ZX··6/2Q/19ftJ
FUEL PRICE FORECASTS EMPLOYED
FUEL OIl (l/HMBTU)
_I
("")
--'m
<.oJ
ANCHORAGE •COOK INLET GREATER FAtRF.lANK9
••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••
VEAR RESIDENTIAL BUSINESS RESIDENTIAL RUSYNESS....•••••••••••••••••••••••••••••••••••.....•.•.
1980 ".751t ".200 1.830 ".I§on
1985 7.320 6.850 7.390 7.110
1'190 6.620 6.190 6.680 6.450
1995 'i.990 S.flOO fI.OIIO 'i.A30
2000 5.410 5.060 S.llflO 15.210
iOOS 4.890 4.510 1I.940 .,4.160
2010 4.420 lI.130 iI.4f10 4.310
SCENARIO'MED ,HEB ••FERC .2X••6/~q/198]
RESJnENTtAL liSE PER HOUSEHOLD (KWH)
(WITHOUT AnJUsTHENT 'OA PRICE)
ANCHORAGE •COOK INLET.........•...•..•..•.•
SMALL a.·ARGE SPACE
YEAR APPLY VICES APPLIANCES HEAT TOTAL.....•..•...••••••••••••..........••••••••••
n 19BO 2tl0.(10 651)0'.6]5088.52 136n.u.
--'(o.noo)("..000)r 1).000)(0'.000)en+=-
1985 11t/n.oo bO Q Z.53 11771.63 13024.17
(0.000)(0.'01)1))t O.OO/)(O~OOO)
1990 ~210.0U 59'7b~22 IIsn.27 127~5.IIQ
(0.000)(0.0(0)(f).000)(0.0(0)
1995 2~bO.OO ~qt8~SQ 4510.05 12688.64
0.000)(0.0(0)(0.000)(0.0(0)
2000 2110.00 59 4 9.30 11 4 51.13 U710~4]
(0.001)(0-'000)r 0.(00)(0.000)
2005 2360.1)0 &019~52 4417.03 12796.S!I
(0.(00)(0.000)t 0.0(0)(0',000)
2010 2410.00 60R5~02 4/~40."21 129~5.22
(0.(\00)(0.1)00)(0.000)(0.000)
SCENARIO'"'ED I HE8-.FERC ·~~··6/24Jl~8]
~ESIDENlUL liSE:PER "'OUSEHOLD (KWH)
(WITHOUT ADJUSTMENT FOR PRICE)
GREAT£R FAIRBANKS
••••••••••••••••••••••
S"1ALL LARGE SPACE
YEAR APPLI4NCES APPLIANCES liEU TOTAL
••••...........••••••••••..-.......••••••••••
n
2466.00.1980 5739~5t'3113.66 11519.18.......
m (0.000)(0 ..000)(0.000)(0.000)
U1
1985 2535.9Q 61'78'.96 3606.32 123~1.26
f 0.000)(0.'000)(0.000)(0.000)
1990 2606.00 6450.94 38b9.59 129~6.53
n.ooo)f 0.000)f 1.\.000)(0.000)
1995 2676.01 61l60.15 4045.01 Ulel.2J
0.000)(0.0(0)f 0.000)(0.000)
2000 2746.00 67 9 1".29 4311.59 138~8.88
(0.1)00)(0.'000)f 0.000)(0.000)
2005 2816.00 6852'.56 4504.19 14172.94
(0.°00)(0.'0(11)f (1.000)(0.000)
2010 2886.00 6891~75 4656.59 144'4.35
(/J.OOO)(0.'000)(I).non)(0.000)
-L
SCfNARIOI MED I HE8 ••FERC -2¥--'/24J19Ul
BUSINESS USE PER EMPLOYEE (KWM)
(WITHOUT LARGE INDUSTRIAL)
(WITHOUT ADJUSTMENT FOR PRICE)
YEAR ANCHORAGE -COOK INLET GREATER FAIRBANKS
••••••••••••••••••••••••••.........~..........•.
U80 81107.011 1495.70
0.000)(0.000)
n IUS 9580."8 7912.'".(0.000)(o.ono)--'
0'1
0'1 1990 1010 /.51 8313.01
0.001)(0.000)
Ins 10690.4b 8585.26
(0.001)(o.noo)
2000 IIIH.&5 88159.71)
(O.~OI)(0.000l
2005 117S2.'H "191.17
(0.(00)(0.000)
2010 12539.23 9581.36
f 0.000)(0.000)
SCEIURIO,Io4EO I HES--FERC -2X--6/24'1981
SUMMARV OF PRICE EFfECTS AND PROGRAHATIC CONSERVATION
IN CWH
ANCHORAGE -COOK INLET
REUl.lfNTIAL AUSINEU.......................
OWN-PRICE PROGIUM-INDUCED CROSS-PRICE OWN-PRICE PROGRAM-I"'OUCF.D t:ROSS-PRtt:!YEAR REDUCTION CONSERVATION REDUCTION REDUCTION CONS~,!~A!IQN ...__AfDUt:TtON••••..............................................................................................
1980 0.000 0.000 0.000 0.000 0.000 0.000
1981 &.3B2 0.000 -1.&16 ~.36'§0.000 -0.5ll198212.7U 0.000 -3.232 18.730 0.000 -1.024198319.1115 0.000 -4.847 28.095 0.000 -1.lJ35198425.526 0.000 -6.4&1 37.4&0 0.000 -2.047
1985 31.908 0.000 -8.079 U."2lJ 0.0110 -il.5'§9
198&39.052 0.000 -14.&89 5&.'&"0.000 -".&8419871I&.19b 0.(100 -21.299 67.t10 0.00/1 -&.1109198851.139 o.oon -27.909 77.252 0.000 -8.9351989bO.1I83 o.ouo -311.'519 87.195 0.000 -11.0&0
("")1990 b7 .627 0.000 -41.129 97.537 0.000 -11.tl!5.
--I
m 1991 7 11 .1171 o.OUO -47.290 105.367 0.000 -111.388........1992 81.:515 (1.000 -53.1151 113.196 o.oon -15.590199388.115'0.1\00 -59.612 121.026 0.000 -1&.7921991195.(103 0.000 -65.771 128.1\56 11.000 -t7.9 ca
1995 101.8111 0.000 -71.9H 136.685 (1.000 -19.196
Iq9b 10&.611 0.000 -72.9112 fIl4.'nl 0.0(1(1 -18.5921991111.1127 0.000 -U.9119 152.458 0.000 -17.988199811b.211 0.000 -14.95&16/1.3114 0.001\-17.1831999121.007 0.(100 -75.'&1 168.230 0.000 -16.719
2000 125.797 0.000 -7&.9111 t76.116 0.(100 -16.175
2001 129.90 1 0.000 -n.7a5 IBII.9/15 0.000 -111.111920021311.018 1).(100 -711.117 9 191.7111 0.000 -12.18112003138.128 0.000 -73.2311 202.602 0.000 -11.0882004IlIl.iIl8 0.000 -71".988 111.1131 0-.noD -9.392
2005 1116.3119 0.0011 -70.7113 22(1.260 0.000 -7.696
200&ISO.CJ 75 n.lloo -&8.217 i'll.bOl 0.000 -11.8372007155.1;>01 0.000 -65.b91 242.~1I2 0.000 -1.9782008lbO.221 0.000 -&3.165 25 ll .<'83 "0.00(1 0.881
2009 1611.1\51 0.000 -00.638 265.&215 0.000 3.740
2010 Ib9.ll79 (1.000 -SA.112 <'lb.9b6 0.00l)b.599
SCENARIO,HED ,HE8··'EPC .rX••6/24/1983
SIJHHARY OF PRICE EFFECTS AND PROORAHA,IC CONSERVATION
IN Gl'lH
GREATER FAIRBANKS
RESlllfNTUL RUSINESS•...........•.•....•.•
O...~l.PRICE PROGRAI1-INOUCED CROSS.PRICE OWN.PRICE PROGRAH-INDUCED eROSS-PRIC[YEAR ~EDUCTJON CONSERVATION RE.DUCTION REDI!~TION CONS£RVA TI ON REOUrTlON••••.................................................................;...:;e;;~;.;...;;;....................
19S0 0.000 0.000 0.000 0.000 0.000 0,000
nSI 0.000 0.000 0.192 0.000 11.000 0.130US20.000 0.000 0.385 n.ooo 0.000 0.25919830.(100 0.000 0.577 o.ono 0.0011 0.389US40.000 0.1'00 0.1&9 o.oon 0.000 0.5U
1985 0.00'1 0.000 0.9b2 0.000 0.000 0.648
nSf!-0.334 0.000 l.bU .0.495 0.000 ".97lt1981.0.6&9 1).000 2.3&2 .0.990 0.000 1.3091985.1.003 o.oon 3.0b2 .'.4815 1).000 1.6391989·1.331 1).000 3.1bZ _1.98t 0.000 1.971'
n 1990 ·'.b72 n.ooo 4.4b3 ..2.476 0.000 2.300.
~
0'\U91 -1.93 9 /1.000 5.631 ..2.9Sb 1'.000 2.991001992·2.20b 0.000 b.199 -3.43&0.000 3.6931993·2.473 0.000 7.9&1 -3.916 0.001'4.39019911-Z.H9 0.000 9.135 -4.396 0.000 5.086
1995 -1.006 0.1100 10.303 -4.87b 0.000 Ii.T83
Uh ·3.186 0.000 1l.749 .5.066 0.000 6.4401997-3.3&6 0.000 13.195 -5.256 0.000 1.0971998-3.546 0.000 14.641 -5.441 0.000 1.1531999-3.12&0.000 16.087 .5.637 0.000 8.1110
1000 -3.90f.l o.oon 17 .533 .5.821 0.000 9.0&1
2001 .11.056 0.000 19 .33 11 -b.Oi!T 0.000 9.9bO200i!..4.20b 0.000 21.1l4 .b.Z27 0.000 10.853Z003.11.355 ".000 21.935 .6.426 0.000 11.11152004-4.505 o.noo 2 4 .135 .6.626 Il.oon 12.638
zoos .11.654 o.noo lb.SH .6.826 0.000 13.530
200b -".'301 ".000 28.184 .1.051 0.000 14.1172007.4.948 0.000 31.032 .1.288 0.000 15.904Z008·'i.094 0.000 33.279 -7.'Ho 0.000 17.0912009.5.241 o.non 35.521 -1.750 o.oon 111.218
2010 -5.3811 0.000 17.175 -7.QS2 0.000 19.4bS
SCENARIO'MEO ,HER--FERC -2X.-6/211/198J
BREAKDOWN UF ELECTRIC lTV REQUIR!HENTS (GWH)
(TOTAL INCLIIOES LARGE INOl.lSTRUL t:ONSUHPTlON)
GREATER FAIRRANKS.-..._~.-.............
MEDIUM RANGE (pn_.5)•......•..••....••••
RESIDENTIAL BUSJ NES!l MISCELLANEOUS EkOO.INDUSTRIALYEARREQUIREMENTSRfQUIREMENTSREQUIREMENTSUIAD TOTAL_.-....•...•......•.....•......•..........•...•.••......•..•.•...•.••.•.•....•................
1980 110.39 217.14 b.78 0.00 IIno.31
1981 1111.21 230.23 b.75 0.00 1128 ..111198220b.03 2113.32 b.7J 0.00 1156.071981220.1311 256.111 b.70 0.00 11113.9519811235.66 i'b9.50 6.67 0.00 511.133
1985 250.118 282.59 6.bG 0.00 519.11
198b 2b2.211 291).62 6.b8 10.00 569.51119872U.OO 298.65 b.72 20.00 5119.37
n 1988 285.76 306.68 b.75 311.00 629.20.1989 2 9 7.52 314.71 6.79 110.00 6'39.0!--I
........1990 ]/19.28 322.75 b.8]50.00 688.86a
1991 118.62 32&.78 0.97 50.00 702~S71992327.97 n".81 7 .1I 50.00 11 5.891993331.31 33 4 .811 7.26 50.00 729 ..110U9113110.U 138.87 7.40 50.00 7 4 2.92
1995 355.99 111'..90 7.54 50.00 7'36./1'1
1996 lbl.]9 ]11&.58 7.fill 50.00 765~6119971&6.80 350.2&7.73 50.00 174~7B1998J1l.21)351.cn 7.8]50.00 783.U1Q99377.00 ]S7.U 7.92 sn.oo 79].I II
2000 ]83.01 361.;»9 8.02 50.00 802.32
2001 389.06 ~67.·h 8.111 50.00 815.1'32002]9;.11 1111.b3 8.25 50.00 827.992003'UI.I&181.3n 8.37 50.00 840~llJ20nll1107.21 187.°7 8.49 50.00 8!U.61
2005 II t3.26 1911.64 8.bl 50.no 866.51
2006 1I?0.71o 11011.51 8.81 50.00 88/1.08200711~8.2b 4111.38 9.01 50.00 90t.e.520n81I1S.7~424.&'5 9.22 50.no 919.21Z009till).2&1I]/J.12 9.42 50.00 93b.80
lOIO 1150.7e.411'.99 9.6"50.no 9-;11.37
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.......
--a
8CENAR10.MEO.HE8 ••FERC .2X••b/24'196]
TOTAL ELECTRICITY REQUI~EHENTS (GwH)
(NET 0'CO~SERVATIOH)
(lNCLIJOES LARGE INDUSTRIAL CONSUMPTION)
H£OIUH RANGE CPR ••5)....................•.
YE4R ANCHORAGE •COOK INLET GREATER FAIRBANkS TOTAL............••......•••.••.•...•••.•....•........••....•.....•••......
11:180 ,qU."400.31 21U~5'
11:181 2091.ttO 428.19 2511:1~8019822210.02 IISb.07 2b7fJ ..0I:I198]n 48.113 483.95 21132,381984211h.811 SII.s]2988.b'7
19A5 2US.lS 539.11 JllIII~9b
198&2bl:lb.J'7 5b 9 .511 32bb~3119812188.29 S91:1.31 ]H7,bb19882879.81 b21:1.20 ]509.0111:I8q 21:171.33 b59.03 3b1O~3b
1990 1062.85 b8R.8b n'H~7'
Iq91 1100.711 702.37 3~1)3~1119923138.b'J 115.89 H511,51IIl1l33176.S2 '729.110 J90S,9i1991112111.111 '7112.1:12 31:157.32
1995 3252.30 75b.1I3 1I0"8~13
11:I9b ]2ln.Sl 165.&1 401j1:l~1I'719973nS.lIl 771.1.78 1.1110.221998lH7.00 783.l:Ib IItbO~qb199931118.57 793.14 G211.71
looo 1IIbo.11I 802.32 42b2~111§
2001 3526 ..111 81'!i.15 43111~bl2001351:12.t'l 821.99 111120~8020033bS9.'14 SlIO.83 1.11191:1.9720011HlS.1I8 853.67 1I579".15
lOOS J791.81 8bb.'51 4b58.31
200b 381\7.41 8811.0S 4771:55200711:1(13.11 901.b5 48811!,1920084078.19 919.23 4998.0220091Il1I1.QS 1:13b.80 1511 (.2li
lOIO 11210.11 q5 4 .17 !i2211:QI:l
-L.
n
......
-.....J
N
SCENARIO,MEO,HE8 ••'ERC .2X ••6/24/1981
PEAK £L!CTRIC REQUIREMENTS (MW)
tNET 0'CONSERVATION)
(INCLUDES LARGE INDUSTRIAL DEMAND)
H£DIUM RANGE CPR ••5).•...•...•.....•.....•
YEAR ANCHORAGE •COOk I~LET GREATER F.IRBANkS TOTAL.......•......••.•..••...........-.~.........-...••...........-..-....
1980 396.51 91.110 1l87~90
1981 422.48 97.1b 520.2/l19824118.4b 104.13 552.59198]414.111.1 110.49 5A4".9]
n811 500.41 11ft.h 611~21
1985 526.39 I i!l.22 6119.&1
198&545.73 130.03 US~7619815115.01 13ft.8/1 101.91119885811.40 1113.64 728~05198960].14 150."5 7511~19
1990 623.08 151.26 180~]1l
Inl 630.13 160.34 791~081991638.39 163.43 ,801.8219936/l6.011 166.51 1512~S5199465]~69 169.&0 821.29
Ins 661.3(1 172.69 nil'.03
1996 669.62 174.18 81111~1I0
1997 b17.90 176.88 854".77
1998 0 8 &.18 178.97 86;~15199969(1.115 18t .07 875~52
2000 102.7]181.lel 885.89
2001 716.05 186.09 902~152002729.31 189.02 918~1l0200]7112.70 191.95 934.b520011Ub.OO!1911.89 950.90
2005 769.311 197.82 961~16
200b 7AS.b2 201.8]990~IIS20018(17.9/1 205.154 1013.7112008821.17 209.85 1037~0]2009 8116.11'5 2l3.n t060.H
2010 Bb5.73 211.88 to8]~61
APPENDIX 83
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EXECUTIVE SlH1t·1ARY
The Railbelt Electricity Oemand (RED)t10del was utilized in July,1983 to
produce forecasts of electricity demand for the two Railbelt load centers of
southcentral and interior Alaska:Anchorage-Cook Inlet and Fairbanks-Tanana
Valley.These were contained in the July,1983 Susitna Hydroelectric Project
License Application to the Federal Energy Regulatory Commission (FERC).The
July,1983 version of the model has since undergone independent review by FERC
staff and by Dr.T.J.Tyrrell,a consulting economist from W~kefield,Rhode
Island whose 1973 electricity demand article provided part of the basis for
RED.As a result of this ongoing review and updating of the model a number of
refinements have been made.The following refinements of the RED model are the
most important:1)the mechanism utilized in RED to adjust electricity con-
sumption for future changes in the real prices of electricity,natural gas,and
fuel oil was simplified;2)some of the values utilized in RED for market satu-
rations,fuel-mode splits,and energy consumption in residential appliances
were adjusted;3)more refined data concerning the building stock and elec-
tricity consumption were used to project Railbelt electricity demand in the
commercial-light industrial-government sector;4)the Fairbanks-Tanana Valley
peak load factor was revised upward.The new,August 1985 version of the model
is known as RED85A.
Additional research and data collection has been undertaken as part of
this effort.In general,the research has confirmed the July,1983 approach
although certain computational details in RED have been changed to more closely
reflect Railbelt electricity demand conditions.The new version of the model
has also been run for several electricity demand cases contained in the July,
1983 license application and the forecasts compared to those produced in July,
1983.The overall effect on the July,1983 forecasts has been to decrease the
July,1983 reference case forecast for the year 2010 by 0.1%;some cases
featuring higher fuel prices are reduced;cases with lower price forecasts are
increased by a larger percentage.
iii
PRICE ADJUSTt1ENT
Our analysis of the RED price adjustment mechanism,additional model test-
ing,and Or.Tyrrell's evaluation of the model led us to the conclusion that
the price adjustment mechanism could be refined and simplified.Recent litera-
ture on the estimates of both short-and long-run price elasticities show that
in recent years,the demand for electricity has become less price responsive.
Accordingly,the price elasticities were reduced.Secondly,we concluded that
a simpler and improved method for including price elasticities in the REO model
woul~be more understandable to model users.Taken together,these combined
refinements,which have been included in the current RED85A version,increase
the price responsiveness of the REO model.Or.Tyrrell has confirmed the
approach and the range of elasticity values used in the model.
RESIDENTIAL PARAMETERS
The parameters of the Residential Consumption t10dule were reexamined to
confirm their consistency with known data concerning Railbelt electricity con-
sumption.In addition,we reviewed our assumptions to make certain that fore-
casted changes in market saturations of appliances,percentages of given appli-
ances using electricity (electric fuel mode splits),and electricity con-)_
sumption rates for each type of appliance were consistent with values we would I
expect if the rea 1 (i nfl at.i on -adj usted)fuel pri ces in the Rail be It di d not
change in the future.This is necessary because the Residential Consumption
Module first forecasts residential consumption in the absence of price changes,
then adjusts the forecist for price impacts.The detailed saturations,fuel
mode splits,and consumption rates must be selected to avoid double-counting of
price effects.
The review showed that increasing some preliminary electricity consumption
rates would eliminate an overcorrection for price effects in the July 1983
version of the model.r1inor adjustments were made in a few appliance market
saturations.No base year (1980)saturations,fuel mode splits or consumption
rates were changed as a result of the review.Taken together,these changes
increased residential consumption.-l
iv
BUSI NESS PARAt1ETERS
The structure and parameters of the"Business Consumption t10dule were also
reviewed for compatibility with data that became available to us during 1984
and 1985 concerning electricity consumption in the business sector in the
Railbelt and elsewhere.We determined in the course of our ongoing model
review that the national floorspace per employee estimate utilized by the model
in 1983 contained categories of employment and floorspace not present in the
Railbelt,so that a comparison of national and Railbelt floorspace per employee
would be misleading.The national estimate was refined so that it only
included those categories of floorspace and employment actually present in the
Railbelt.The Railbelt estimate was double-checked against the U.S.Department
of Energy's 1979 and 1983 national Nonresidential Building Energy Consumption
Survey and found consistent.
The Railbelt estimates of floorspace per employee'were previously assumed
to converge over time to the national estimate at a constant exponential rate;
however,a preferred procedure is that such estimates be based on conditions in
the Railbelt and to merely double-check the Railbelt estimates against national
estimates.This became possible in 1984 with our acquisition of additional
data on the Railbelt business sector.Consequently,the historical Anchorage
linear growth path was adopted in place of the previous exponential path for
the RED85A version and checked against national data.Floorspace per employee
was about the same,regardless of whether Anchorage or national data were used.
The final change to the Business Consumption t10dule was that the electric-
ity consumption equations were reestimated to take into account more refined
data that became available in 1984.Both load centers'historical data series
for business electricity consumption and business building space were revised
to incorporate the new data.The equation having the best statistical fit was
virtually unchanged from the July,1983 version.
PEAK LOAD
Recent utility data from the Railbelt show that the assumed value of the
Fairbanks-Tanana Valley load center's annual load factor was too low.It was
revtsed upward in RED85A to reflect the most recent load data available.
v
Table 1 shows the effect of all the changes,taken together,on the refer-
ence forecast for the Railbelt from the Susitna license application as accepted
by FERC in July,1983 (July 1983 Susitna license application).
Table 1 shows that the July,1983 reference forecast is affected very lit-
tle by the model changes.This finding tends to hold up for other cases as
well.Figures 1 and 2 show that for the DRI case,the highest fuel price run
in the July,1983 Susitna license application,the forecast changes are notice-
able but small.This is also true of the FERC -2%case,which contained the
lowest fuel price forecast.In summary,although the details of the forecast
change,the overall forecast is little affected.
TABLE 1.Comparison of July 1983 Reference Case Forecasts of
the RED Model,RED85A Versus July 1983
RED85A July 1983 %Difference
Total Consumption,Year 2010 5854 5858 -0.1
(GWh)
Total Peak Demand,Year 2010 (t-lW)1195 1217 -1.8
Total Residential Consumption,2403 2572 -6.6
Year 2010 (G~/h)I-Total Business Consumption,3028 2863 +5.8
Year 2010 (GWh)
)Total Other Consumption,423 423 +0.0
Year 2010 (GWh)
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2010
FERC -27-
JULY 83
2005
FERC -27-
RED85R
2000
DRI
JULY 83
1995
YERR
DRI
RED85R
1990
./
./
././//
......,~/
......,"/./',,/,,/~~------.------~--------_~........---~~---""---,,,,---..--.--.-_~_---.--.~"""'_~--------=,~._._._.
REFERENCE REFERENCE
RED85R JULY 83
---
(GWh)
7500
7000
6500
6000
5500
<5000..............
4500
4000
3500
3000
2500
2000
1500
1980 1985
FIGURE 1.Comparison of Railbelt Total Electricity Consumption Forecasts RE085A
Versus July 1983 Red Model
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EXECUTIVE SUt1~1ARY
1.0 INTRODUCTION
CONTENTS
••••••••••••••••••••••••••••••0 •••••••••••••••••••••
•••••••••••••••••••••••••••••••••0 ••••••••11 •••••••••
iii
1.1
2.0 RED MODEL PRICE ADJUSn1ENT REVISIONS ••••••••••••••••••••••••••••2.1
REVIEH OF PARAt1ETER VALUES ••••••••••••••••••••2.1
REsrnENTIAL SECTOR •••.••••.•••••_••••..•.8 ••00 ••••,...............2.5
cor1r1ERCIAL SECTOR •••••••••••••••••••••••••••••••••••••••••••••••2.11
STRUCTURE OF THE PRICE ADJUSTMENT MECHANISM •••••••••••••••:.....2.12
3.0 RESIDENTIAL CONSW1PTION ~1ODULE ••••••••••••••••••••••••••••••••••3.1
APPLIANCE SATURATIONS •••••••••••••••••••••••••••••••••••••••••••3.1
1980 ELECTRICITY CONSUMPTION ESTIMATES ••••••••••••••••••••••••••3.3
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4.0
Ten Percent Space Heat Conservation Adjustment •••••••••••••
Fifteen Percent Adjustment for Lower Water Temperature
in Water Heating ..•..•••..•••••.•.•...Q •••••••••••••••~••••
Cooking Ranges •••••••••••••••••••••••••••••••••••••••••••••
Fuel ~1ode Splits in Replacements and New Housing •••••••••••
Annual Consumption,1985 and After •••••••••••••••••••••••••
BUSINESS SECTOR •••..•...••••.•••••..•.••.••.•••.•.•••.••••.•.••.
3.8
3.9
3.10
3.10
3.12
4.1
STRUCTURE OF THE JULY,1983 BUSINESS CONSUMPTION ~10DULE •••••••••4.1
PREDICTING FLOORSPACE STOCK •••••••••••••••••••••••••••••••••••••4.3
PARAr1ETER VALUES .••...•..•.•.••.••.....•••.••••.•.••...••.••.•..4.4
PREDICTING BUSINESS CONSUMPTION •••••••••••••••••••••••••••••••••4.7
5.0 PEAK DEr1AND •••••••••••••••••••••••••••••••••••••••••••••••••••••5.1
6.0 EFFECT OF THE rIDDEL CHANGES ON THE FORECASTS ••••••••••••••••••••6.1
APPENDIX A -RAILBELT COMMERCIAL BUILDING STOCK AND
ENERGY USE DATA •...••.•.••.•..••••...•.....••.....••.•..A.I
ix
APPENDIX B -THE EFFECT OF F.W.nODGE CONSTRUCTION DATA ON
RAILBELT ELECTRICITY DEMAND FORECASTS •••••••••••••••••••B.1
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FIGURES
1.Comparison of Railbelt Total Electricity Consumption Forecasts
RED85A Versus July 1983 t10del •••••••••••••••••••••••••••••••••••vi i
2.Comparison of Railbelt Total Peak Demand Forecasts
RED85A Versus July 1983 RED r~odel vi i i
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TABLES
1.Compari son of July 1983 Reference Case Forecasts of the RED nodel
RED85A Versus.Jul y 1983 ••••••••••••••••••,.0 0 •••••••,.••e ,.••••,..• •vi
2.1 Comparison of Parameter Values for Residential Electricity Oemand,
Chern-Bouis Versus r10unt-Chapman-Tyrrell ••••••••••••••••••••••.•2.6
2.2 Effect of Holding Appliance Stock Constant on Elasticity
Estimates -IP •••••O..2.9
July 1983 ...••••.e,•••••••••••••••It ••••••••••••••••,.•••••••••••••
Calculation of 1979 National Square Footage Per Employee ••••••••
Differences in Equations for Business Electricity Consumption
Equations in Fairbanks-Tanana Valley,RED85A Versus
Post-1985 Annual Growth Rates in Electricity Consumption for
Residential Appliances ••••••••••'•••••••••••00 •••••••••••••••••00
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2.12
3.2
3.3
3.14
4.6
4.6
4.9
4.8
5.1
4.10
.......
Comparison of Parameter Values for Residential and Business
Electricity Demand,RED85A Versus July 1983 •••••••••••••••••••••
Changes in Market Saturations of Clothes Washers,RED85A
Versus July 1983 RED r10del ••••••••.••••••••••••••••••••••••••••••
Changes in t1arket Saturations of Clothes Driers,RED85A
Versus July 1983 RED Model ••••••••••••••••••••••••••••••••••••••
Parameter Values for Business Floorspace Equation,RED85A
Historical Annual Load Factors,Fairbanks-Tanana
Valley Load Center ••••••••••••••••••.•.•••••.••••••.••••••••••.•
Estimated Commercial Floorspace,Anchorage-Cook Inlet and
Fairbanks-Tanana Valley Load Centers,1973-1983 •••••••••••••••••
Differences in Equations for Business Electricity Consumption
in Anchorage-Cook Inlet,RED85A Versus July 1983 ••••••••••••••••
2.3
3.3
3.1
3.2
4.1
4.2
4.3
5.1
4.4
4.5
6.1 Comparison of Railbelt Total Electricity Consumption Forecasts,
RED85A Versus July 1983 RED Model...............................6.2
6.2 Detailed Comparison of Reference Case Forecasts,Year 2010,
RED85A Versus July 1983 ••••••......••.•••.•...•••.•••••....•..•.6.3
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6.3 Comparison of Price-Impacted Consumption,REDR5A Versus July
1983 Forecasts,Anchorage-Cook Inlet ••••••••••••••••••••••••••••6.5
6.4 Co~parison of Price-Impacted Consumption,RED85A Versus July
1983 Forecasts,Fairbanks-Tanana Valley.........................6.7
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CHANGES IN THE RAILRELT ELECTRICITY DEI1AND t10DEL,
JULY 1983 TO AUGUST 1985
1.0 INTRODUCTION
The Rai1be1t Electricity Demand (RED)Model was utilized in July,1983 to
produce forecasts of electricity demand for the two Rai1be1t load centers of
southcentra1 and interior Alaska:Anchorage-Cook Inlet and Fairbanks-Tanana
Valley.These were contained in the July,1983 Susitna Hydroelectric Project
License Application to the Federal Energy Regulatory Commission (FERC).The
July,1983 version of the model has since undergone independent review by FERC
staff and Dr.T.J.Tyrrell,a consulting economist from ~/akefie1d,Rhode
Island who provided an independent assessment of the RED model (Tyrrell 1984).
Dr.Tyrrell is a pioneer in the estimation of electricity demand models.In
addition to review,he provided key modeling insights and additional informa-
tion to the Battelle-Northwest staff.His review confirmed the modeling
approach and parameter values used in the August 1985 version of RED,called
RED85A.As a result of Dr.Tyrre11's review and other work,it w~s concluded
that some refinements could be made to the July 1983 version of RED.The
following refinements were the most important:1)the mechanism utilized in
RED to adjust electricity consumption for future changes in the real prices of
electricity,natural gas,and fuel oil was to be simplified and improved;
2)some of the values utilized in RED for market saturations,fuel-mode splits,
and energy consumption in residential appliances were to be adjusted;3)more
refined data concerning the building stock and electricity consumption were to
be used to project Railbe1t electricity demand in the commercial-light
industrial-government sector;4)the peak load factor in Fairbanks was to be
revised upward.
As a result of the ongoing review process since the July 1983 Susitna
license application,Battelle-Northwest researchers have undertaken the above
series of refinements to the July 1983 version of the Rai1be1t Electricity
Demand Model,both to improve forecasts of future electricity consumption in
the Rai1be1t load centers and to streamline the model.We developed a more
straightforward method to compute the mode1·s fuel price adjustment,and
1.1
modified values of mathe~atical constants contained in the fuel price adjust-
ment equations to reflect latest available studies of electricity demand.The
change has been included in the RED85A version,and is described in Chap-
ter 2.0.We also reviewed and in a few cases changed other parameter values in
the Residential Consumption Module.The results of the review and changes are
given in Chapter 3.0.The overall effect is to reduce forecasted residential
electricity consumption as discussed in Chapter 6.0.
Another change from the July 1983 version is a revised set of assumptions
concerning square footage of commerc1al-light industrial-government floorspace
per employee.We conducted additional data collection efforts in the Railbelt
on commercial building stock and electricity consumption and acquired and ana-
lyzed the F.W.Dodge Construction Potentials data set,the best available data
set on commercial building stock.We also reviewed the 1979 and 1983 National
Non-Residential Buildings Energy Consumption Surveys,published in t1arch 1983
and July 1985 by the U.S.Department of Energy.As a result of these reviews,
end year (2010)square footage per employee was adjusted upward fro~values
used in the July 1983 version of RED.At the same time,the growth path to
reach the end year value was adjusted from a constant (exponential)growth rate
based on national data to a linear rate based on Railbelt data,consistent with
gradual satisfaction of the de~and for commercial floorspace.The parameters
of the electricity consumption equations were also reviewed and adjusted
slightly.The adjustments are described in Chapter 4.0.The overall effect in
the Business Consumption Module is an increase in business electricity con-
sumption,discussed in Chapter 6.0.
Finally,the annual load factor in the Fairbanks-Tanana Valley load center
was increased by about 10%to reflect recent load data for this load center.
This is discussed in Chapter 5.0.Peak load in Fairbanks is reduced as a
result,as discussed in Chapter 6.0.
The remainder of this report is organized as follows.The next section
discusses changes made to the price adjustment mechanism and the reasons for
those changes.The third section deals with Residential Consumption t10dule
parameters,and the fourth section with changes to the Business Consumption
t1odule.The fifth section describes a minor change made to the Peak Demand
1.2
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t1odule.No changes were made to the other RED modules between July,1983 and
August,1985.A final section of the paper describes the impact of the model
changes on the forecasts.Appendix A describes the Railbelt data collection
effort.Appendix B discusses the analysis of the F.W.Dodge Construction
Potentials data.
1.3
REFERENCES
Tyrrell,T.J.1984.Review and Analysis of the Treatment of Price
Elasticities of Del'land in the Susitna Hydroelectric Project RED t10del
(1983 Verslon wlth Revlslons).Prepared for Rarza-Ebasco $usltna Joint
Venture,Anchorage,Alaska.
1.4
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2.rr RED MODEL PRICE ADJUSTMENT REVISIONS
The RED Model price adjustment mechanism was specified and documented in
July,1983,and was based on an empi rical study performed in 1973 by Mount,
Chapman,and Tyrrell.Since that time,Battelle-Northwest has continued its
process of internal and external model review,which has led to two conclu-
sions.First,recent empirical studies have showed sharply reduced price elas-
ticities in both the short run and long run compared to those in the Mount,
Chapman,and Tyrrell study and other studies of its vintage.Almost as sig-
nificant are the apparent reasons for these reduced estimates,which appear to
be particularly applicable in the Railbelt.Second,we concluded that a more
direct method for including price elasticities in the RED model would be more
understandable by model users.This chapter discusses these modifications.
REVIEW OF PARAMETER VALUES
The parameter values contained in the June 1983 model were taken from a
study performed by Mount,Chapman,and Tyrrell in 1973.Using annual data on
consumption,prices and other variables in the 48 contiguous states for the
1947-1970 period and multiple regression analysis,they estimated econometric
demand equations for the residential,commercial and industrial sectors.The
natural logarithm of annual state consumption in each sector was regressed on
the corresponding fuel prices and income (in logarithms and reciprocals),the
lagged consumption (logarithm),and several other variables (but not the appli-
ance/equipment stock).They obtained estimates of the short-run own-price and
cross-price elasticities,which represent the percentage change in this yearls
consumption caused by a 1%change in this year's electricity and other fuel IS
prices,respectively,where the change can be interpreted as over time or
across scenarios.They also obtained estimates of the lagged adjustment coef-
ficient,A,which represents the proportion of the complete,long-run adjust-
ment to a permanent electricity price change that occurs after the year of the
initial change (l-A represents the proportion of the long-run response occur-
ring in the first year,or the ratio of the short-run to the long-run elasti-
city).These estimates were obtained for each sector;given them,long-run
elasticities can be calculated as well.Since the appliance-equipment stocks
2.1
do not appear as independent variables in the Mount,Chapman,and Tyrrell equa-
tions,these stocks are not held constant,so the short-run elasticities
include the first-year effect of adjusting the appliance-equipment stock to
price changes/differences.
The effect of appliance stock changes is not a serious problem in estimat-
ing the short-run elasticities.Short-run elasticities primarily reflect
changes in the utilization of the existing stock of appliances.Very little
appliance/equipment/building stock adjustment occurs on the basis of current
prices (this is also reflected in the low values for cross-price elasticities
typically found in empirical studies of electricity demand),so the short-run
elasticities estimated when stocks are allowed to vary are similar to the esti-
mates that are derived when stocks are held constant •.
The long-run price elasticities,or the lagged adjustment coefficients,
are likely to be different when stocks are held constant.The value of A in a
model holding appliance stocks constant,for example,would be significantly
smaller than in a similar model in which stocks were not held constant because
equipment capacity could not be reduced and efficiency increased in response to
increased electricity prices.When appliance stocks are not significantly
altered,a greater proportion of the total,long-run response to price impacts
will occur in the year of the price change.The long-run effect of the price
change would still be larger in magnitude than the short-run effect,but the
ratio of the two would be smaller when there are few substitution
possibilities.
Long-run modification of the appliance stock has three components:
1)replacement of single appliances to increase the level of consumer services
per unit of fuel used (usually,by reducing fuel use per unit of service);
2)replacement of the stock in such a way as to reduce the amount of service
purchased (e.g.,by using smaller houses or less water heater capacity);and
3)changing the number of appliances using the fuel (e.g.,either through
reduced market saturations or through fuel switching).Except for fuel switch-
ing or reduced market saturation,the long run effects show up as reduced
lI utilization ll (e.g.,reduced fuel use)of the appliance stock rather than as a
change in the stock.
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There are two basic reasons that the RED model should consider changes in
the appliance stock separately from utilization of the stock.First,projected
price increases are not expected to result in much fuel switching beyond that
which would take place at current prices.
In the Railbelt region of Alaska,electricity historically has been about
two times more expensive than fossil fuel substitutes,even on a service (or
converted Btu)adjusted basis.The future energy price forecasts show elec-
tricity remaining significantly more expensive than fossil substitutes.The
net result of this is that there are few additional fuel substitution possi-
bilities in the existing stock from electricity to alternative energy sources
that would not be undertaken at existing relative prices;however,there are
possibilities for improving the efficiency of the existing electricity-using
capital stock.The appropriate long-run elasticity for the region,therefore,
lies between a long-run elasticity which allows fuel switching and an elas-
ticity which holds the appliance stock fixed in both number of units and
capacity.
Second,available national econometric studies that can be used to deter-
mine RED model price effects were performed on data that requires adaptation of
the study to the RED model structure and Railbelt economic conditions.For
example,econometric studies that do not adjust the estimates for changes in
the quality of appliances available may have overestimated price elasticity.
In addition,there are theoretical reasons for believing that price elastici-
ties measured for increases in price (future Railbelt conditions)would be less
than elasticities measured for decreases in price (true for much of the U.S.
during the period many national econometric studies were done).
The RED model distinguishes between those changes in the number and capac-
ity of appliances that take place because of changes in the cost of fuel and
those changes that occur because of other reasons,such as improvements in the
quality of appliances,changes in tastes,increased household incomes,etc.
Most econometric models in the literature do not make this distinction,and as
a consequence they appear to produce biased estimates of price effects on con-
sumption.In these models,for example,the effects on residential consumption
of increased appliance quality,convenience,and durability experienced in the
2.3
u.s.between 1950 and 1970 are mixed together with the compounding effects of
declines in the real prices of electricity and of electric appliances.If a
given econometric model estimated the effect of electricity prices on electri-
cal consumption primarily from data for this period (and most of these models
have),but did not hold constant the effects of increased quality,convenience,
and durability,then the estimated long run price elasticity will be too large
in absolute value,resulting in too large a price effect.Holding appliance
stock constant as measured by the number of appliance units to adjust for the
bias would result in too low a price elasticity,of course,since future elec-
tricity prices in reality could cause some changes in market saturations of
electrical appliances.However,since in national studies much of the measured
historical change in consumption was due to market penetration of new types of
appliances and new levels of service from electrical appliances,it is likely
that a long term price elasticity of demand estimated with appliance stocks
constant will be closer to current reality than one in which appliance stocks
are not controlled.It also suggests recent studies are more accurate than
older studies.
In addition,the literature has raised theoretical concerns about the pos-
sible asymmetry of consumer responses for price decreases as opposed to price
increases.According to this argument,when electricity prices decrease new
uses for electricity previously thought to be 1I1uxuriesll will be widely
adopted.When the price increases"many of these same uses,once experienced,
may tend to be viewed more as IInecessitiesll so people are more reluctant to
abandon them than to adopt them in the first place.Therefore,the demand for
electricity may be less elastic for price increases in the future than for
price decreases experienced during much of the historical period.The differ-
ence between the upward and downward elasticities can be approximated by hold-
ing appliance stocks constant to capture the effect of past price decreases on
market saturations of appliances.
In order to test the parameter values in July 1983 version of RED against
these assumptions,we performed an additional brief survey of the electricity
demand literature.Our focus was on studies which explicitly held the appli-
ance-equipment stock constant in the process of estimating the (long-run or
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short-run)price elasticities and lagged adjustment coefficients.Studies
which had been reviewed in the July 1983 parameter selection process were
included in this review,as were several studies which have since become avail-
able to us.
RESIDENTIAL SECTOR
Three recent studies of residential electrical demand appeared to be par-
ticularly relevant.The first was by Chern and Bouis in 1982.They economet-
rically estimated the structural change in the demand for electricity during
the period 1955 to 1980 for 48 states.The demand equation estimated by Chern
and Bouis was similar to that utilized by r10unt,Chapman,and Tyrrell in that
the equation form was multiplicative in its arguments and included many of the
same variables:logarithms of electricity and gas prices,income,population,
and lagged consumption.The equation was also estimated with pooled time -
series and cross-sectional state data,using state dummy variables to capture
the effects of left-out variables.The Chern-Bouis equation was somewhat dif-
ferent in that price elasticity did not explicitly vary with price of elec-
tricity and in that heating and cooling degree days were explicitly included in
the equation.However,it is likely that the Chern-Bouis findings on the
change in price elasticity over time would also hold for a variable elasticity
form of the equation.
The Mount-Chapman-Tyrre11 approach can be implemented in either a variable
elasticity form,where price elasticity varies with price,or in a constant
elasticity form,where elasticity does not vary with price.Virtually all
econometric studies of electricity demand other than the Mount-Chapman-Tyrrel1
1973 study use the constant elasticity form.Also,examination of the July
1983 output of RED revealed little variation in price elasticity over the range
of electricity prices expected to prevail in the Rai1be1t between 1980 and
2010,so the Chern-Bouis constant form appeared to be appropriate for use in
RED85A.
Chern and Bouis utilized their 24 years of data to perform 15 estimates of
the demand equation for successive la-year intervals.Statistically signifi-
cant and substantial decreases were found in both the long run and short run
2.5
TABLE 2.1.Comparison of Parameter Values for Residential Electricity Demand,
Chern-Bouis (1982)Versus r10unt-Chapman-Tyrrell (1973)
(a)Not significantly different from zero.
(b)In the last five periods,3 out of 5 observed values were not signifi-
cantly different from zero and two were negative.An average of the
5 short-run values is .025;long run is .094.
Chern and Bouis interpreted the observed decline in price elasticity as
being caused partly by increased penetration of more durable heating and cool-
ing electrical'appliances into the market place (reducing the speed of adjust-
ment and increasing A)and partly by the almost complete saturation of existing
elasticities of demand.For the period 1955 to 1964,for example,the esti-
mated long run elasticity of demand was -1.36 while the short run elasticity
was -.801.For the period 1969-1978,Chern and Bouis found that the short run
elasticity had become only -.133 (Mount-Chapman-Tyrrell had found about -.140
at the mean of U.S.electricity prices in 1971)and that A,the coefficient on
lagged consumption,was .733 (Mount-Chapman-Tyrrell had found .884).This
resulted in a long run elasticity for Chern and Bouis of -.498 for the most
recent period,compared to -1.21 in Mount-Chapman-Tyrrell.There was no clear
trend in the elasticity on natural gas price in Chern and Bouis's work.The
average value for the five most recent periods was about .02.Table 2.1 com-
pares Chern and Bouis's results with Mount-Chapman-Tyrrell.
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Chern-Bouis(1982)
1969-78 Peri od
-.801 -.133,
-1.360 -.498
.015(a).060(b)
.026 .224
.411 .733
Chern-Souis (1982)
1955-64 Peri ad
.8837
-.140
-1.21
Mount-Chapman-
Tyrrell (1973)
Price of Electricity:
Short Run
Long Run
Price of Natural Gas:
Short Run .0225
Long Run .193
Lagged Consumption
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markets for electric appliances.With higher durability and near-complete
saturation,recent changes in demand tend to reflect only relatively slow
changes in the average use by existing customers and increases in the customer
base rather than increased market penetration.This tended to reduce the esti-
mated price elasticity during recent periods.
Taking all of these factors together,the following conclusions are appar-
ent.First,the short-run price elasticity is clearly less in recent years
than it was during the periods used to calibrate the July 1983 version of the
RED model.Moreover,the rapidly declining use of electricity for space heat
in the Railbelt and the virtual absence of residential air conditioning means
that "thermostat adjustments"available nationally to conserve electricity as
price rises would be unavailable in the Railbelt,leaving less adjustable uses
such as water heating,clothes drying,lighting,and cooking as the end uses
which would have to be reduced in response to price increases in the short
run.This implies that the Rail~elt price elasticity in the short run is thus
less than the most recent national average in Chern and Bouis's work.The
RED85A version of RED therefore contains the slightly lower value of -.12 for
the residential sector.This is also within the range specified in Tyrrell
(1984)and is slightly less,in absolute value,than the average short run
elasticity of -0.152 in the 1983 version of RED.
Second,Chern and Bouis estimated a value for 1,the lagged consumption
coefficient,that was lower than that estimated by Mount-Chapman-Tyrrell (0.733
versus 0.8837).They also concluded that 1 has increased over time,due to
increased durability of appliances such as those for heating and cooling.
However,several calls to h~ating and cooling firms and building contractors in
the Railbelt indicated that market saturations of electric heat are apparently
declining in that region,and that air conditioning is not significant.
Therefore,the special mix of appliances in the Railbelt should tend to reduce
the effects of increased durability observed by Chern and Bouis,making 1 less
than they estimated.Accordingly,the RED85A version of RED contains the
slightly lower value of 0.7.The relatively high value of 1 in Mount-Chapman-
Tyrrell and other early work can be attributed to the increased long-term
2.7
market penetration of major new end uses of electricity during a period of
declining electricity prices.Market penetration effects should be much less
under current Railbelt conditions.
A second study was performed by Taylor,Blattenberger,and Rennhack of
Data Resources,Inc.,for the Electric Power Research Institute in 1982.Using
annual data for the 48 contiguous states for the years 1960-1974,they esti-
mated demand equations which explicitly held the appliance stock constant.
Independent variables included lagged consumption and the marginal price of
electricity.The estimated elasticities themselves are not of use because the
RED model utilizes average,not marginal prices (and elasticities with respect
to average prices tend to be higher than those with respect to marginal
prices).(a)What is interesting is the coefficient on lagged consumption vari-
able,which represents X:in the equation having the best statistical fit,its
value was .700,with a standard error·of 0.031.Using either the Mount,
Chapman,Tyrrell short-run elasticities evaluated at 1980 prices,or the Chern
and Bouis elasticities,this X value implies long-run elasticities in the
vicinity of -.40 to -.50 in Anchorage and in Fairbanks.These long-run elas-
ticities appear reasonable,given that they primarily represent responses in
utilization rates,with only modest fuel switching in response to price
changes.
We note also that stock-held constant short-run price elasticities
obtained by Taylor,Blattenberger and Rennhack are not very different from
those estimated from the Mount-Chapman-Tyrrell (1973)framework.This is shown
in Table 2.2.Taylor,Blattenberger and Rennhack obtained short-run elastic-
ities in their preferred equation similar to those of Mount-Chapman-Tyrrell
(a)Although economic theory states that both average price and marginal price
affect the demand for electricity,most researchers have encountered
serious econometric problems in attempting to use both in regression
equations.Halvorsen (1978,pp.9-12)demonstrates that by pairing a
demand equation of double-logarithmic form with an electricity price-
determination equation of the same form in a two-stage least squares
procedure,the average price and marginal price of electricity produce the
same estimate of own-price elasticity.In general,however,this is not
the case.Most researchers have used average price because of its
availability.
2.8
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2.9
(a)Price coefficients are for average prices in Mount-Chapman-Tyrrell,for
marginal prices in Taylor-Blattenberger-Rennhack.Marginal and average
series can be expected to be highly correlated over time in i~dividual
regions.
(b)The stock-held-constant equation reported here is the most exact comparison
with the lagged consumption model;however,a slightly better-fitting equa-
tion was used to derive A.
when using lagged consumption alone.When they held stock constant,the coef-
ficients on lagged consumption and price all decreased in absolute value,but
the short-run elasticities were relatively unaffected.
The third study also supports reduced long-run elasticity values.Moe,
Owzarski,and Streit of Pacific Northwest Laboratory estimated Pacific North-
west residential winter electricity demand equations in a 1983 study performed
for the Bonneville Power Administration.Using a sample of 1,437 individual
Northwest single-family residences with data on November 1976 through April
1977 consumption,prices,appliance stocks,and other variables,they estimated
a demand equation relating the logarithm of electricity consumption to the
logarithms of average price and appliance stock (measured in kilowatt hours of
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Price of
Electricity
Short Run
Long Run
Price of
Natural Gas
Short Run
Long Run
Lagged
Consumption
Lagged Consumption:
Mount-Chapman-
Tyrrell (1973)(a)
-.140
-1.21
.0225
.193
.8837
Lagged Consumption:
Taylor-Blattenberger-
Rennhack (1982)(a)
-.101
-1.052
.002
.018
.904
-.051
NA
-.00095
NA
.631
"normal"use)and obtained an own-price elasticity estimate of -.424,with a
standard error of .051.Both Pacific Northwest electricity prices and
Anchorage electricity prices are below the national average.These estimates
therefore may be applicable to the RED study region.
Interpretation of the elasticity estimates produced from cross-sectional
data,however,depends on the nature of cross-sectional differences between
variables.If differences in the explanatory variables across the cross-
sectional observations have existed for some time,then the cross-sectional
observations will all either be at equilibrium or the same point of disequilib-
rium.In either case,the observed differences in the capital stock of appli-
ances and utilization of that stock should reflect long-term differences
J
between cross-sectional observational units and estimated cross-sectional elas-
ticities can be interpreted as long run elasticities (Halvorsen 1978,
pp.11-12).Kmenta (1978,pp.114-117)has worked out the bias in the esti-
mated coefficients for a simple model where adjustment to the long run may be
incomplete.In the usual case,this amounts to a left-out variable problem,
where not much can be said about either the existence or direction of bias in
the estimated coefficients.The survey data for the Pacific Northwest used in
Moe,Owzarski,and Streit (1983)was collected for 1976 through 1977 prior to
recent rounds of sharp increases in fuel oil,natural gas,and electricity
prices that may have differentially affected individual customers.Thus,it is
likely that the individuals in the data set were,on average,equally adjusted
to long-run differences in their circumstances and that the coefficients esti-
mated in Moe,Owzarski,and Streit can be interpreted as long-run elasticities.
On the basis of these three studies,RED85A version of the RED model con-
tains a value of 0.700 for A in the residential sector.Several other studies
incorporating the appliance stock as an independent variable were considered.
In all cases,however,the estimates from these studies were deemed inappropri-
ate,either because of study date (Kaysen and Fisher 1962),estimation tech-
nique (Anderson 1974),or study region (McFadden,Puig,and Kirshner 1977).
Tyrrell (1984)confirms that 0~7 is a reasonable value.
2.10
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cmmERCIAL SECTOR
Our review of the commercial electricity demand literature revealed two
studies which provided a p~rspective on the value of the RED business sector
lagged adjustment parameter (A).We reviewed a study performed by Chern and
his associates at Oak Ridge National Laboratory in 1982 (Chern et al.1982),as
well as an update of the study in 1983 (Bouis,Brown,and Chern 1983).Using
annual data for the 48 contiguous states for the years 1955-1978,they esti-
mated separate demand equations for each of the nine U.S.Census Divisions.
The ~quations were estimated in double logarithmic form,with average price and
lagged consumption appearing as independent variables.Values of A range from
.07 to .88;the arithmetic average is .618 and values in relatively low-price
regions are generally below the average.These A estimates may~of course,
overstate the utilization-only price response,since equipment stocks are not
held constant in any of the nine equations.(a)They suggest,however,that a
value of .700 is appropriate in the commercial sector.
A short-run elasticity value for the commercial sector from Mount-Chapman-
Tyrrell (1973)had been used in the July 1983 version of RED to determine the
short-run elasticity of demand for electricity in the business sector.They
had found a commercial short-run price elasticity of about -.18 to -.20.A
strong result of the Chern and Bouis residential work is that both short-run
and long-run residential price elasticities have declined relative to those in
the period investigated by Mount-Chapman-Tyrrell and that recent periods show
lower values than the 1955-78 period as a whole because of structural changes
in demand.A parallel decline in price elasticity in the commercial sector
would be expected because of similar structural changes in demand.Because
there is little electric heating and air conditioning in the Railbelt,the
short run elasticity ought to be toward the lower end of the observed range.
The value of -.15 in the RED85A version of RED is in the lower part of Bouis,
Brown,and Chern's observed range,and is within the range in Tyrrell (1984).
(a)The review of the commercial electricity demand literature indicated that
there were no studies in which commercial equipment stocks were explicitly
held constant.
2.11
-It is consistent with the theoretical and empirical literature and appeared
appropriate for the business sector in the Rai1be1t.
Table 2.3 provides a comparison of price adjustment parameter values in
RED85A and the July,1983 versions of RED.In both the residential and busi-
ness sectors the average short-run and long-run price elasticity values are
lower in the RED85A version than in the July 1983 version.
2.12
STRUCTURE OF THE PRICE ADJUSTMENT MECHANISM
(a)Measured in mills per kWh,1970 dollars.
TABLE 2.3.Comparison of Parameter Values for Residential and
Business Electricity Demand,RED85A Versus July 1983
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July 1983
-0.1552 +0.3304/p(a)
0.0225
0.01
0.8837
-0.2925 +2.4014/p(a)
0.0082
0.01
0.8724
RED85A
-0.12
0.0225
0.01
0.700
-0.15
0.0082
0.01
0.700
Sector and Variable
Natural Gas
Oil
Lagged Adjustment (A)
Residential Elasticities
Short-Run Elasticities
Own-Price
Natural Gas
Oil
Lagged Adjustment (A)
Business Sector
Short-Run Elasticities
Own-Price
In the July 1983 version of RED,an approximate mathematical expression
was -used to estimate the change that would occur in a given future year in the
quantity of electricity demanded resulting from a change in price at an earlier
point in the forecast.Specifically,the mechanism employed in the July 1983
version approximated the percentage change in quantity of electricity consumed
in a forecast period K:(a)
5OPAiKi=A OPAi,K_l,i
(2.1)
2.13
3 2+A ESR i ,K2,i +A ESR i ,K3,i
+A ESR i ,K4,!+ESRi,KS,!]
where PCPEA imi denotes the annual percentage change in the price of electricity
in region i,time period m,and sector i,while ESR denotes the short-run vari-
able own price elasticity,calculated as:
(2.2)
5=A PPA i ,K-1,iPPAiKi
ESR BETA 5 GM1t~A +5 GAMr1Ai,K,i =i +.P • Pi,K,i i ,K-1i
where BETA and GAMMA were parameters estimated by f1ount-Chapman-Tyrrell.Simi-
1arly,price adjustment factors for oil (PPA)and natural gas price changes
(GPA)were derived,with one simplification -the oil and gas cross-price elas-
ticities were constant.Thus,
(a)There are several subscripts in RED denoting time periods.In Equa-
tion (2.1),K denotes a future forecast period 1985,1990,1995,•••,
2010.Small m also denotes a future forecast period,but is less than or
equal to K.K1,K2,etc.denote individual years within a forecast
period.Small t,which appears in Equation 2.5,denotes individual years
1981-2010.
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+~PCPGA i mR.[GSRR.
m=l
+I PCPOA i mR.[PSRR.m=l
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(2.3)
(2.4)
(2.5)
5=A GPA i ,K_1,R.
where OSRR.is the short-run oil cross-price elasticity in sector R.,GSRR.is the
short-run gas cross-price elasticity in sector R.and PCPOA and PCPGA are the
annual percentage changes in the prices of fuel oil and natural gas,
respectively.
This complex formulation of the own-price and cross-price effects was
adopted in the July,1983 version of RED to directly translate the Mount-
Chapman-Tyrrell price effects (estimated on an annual basis)into five-year
stepped electricity consumption differences for various price scenarios.A
more straightforward way of calculating the quantity adjustment index,however,
is to directly use a partial adjustment specification.(a)The required formu-
lation (with oil prices added)for a constant price elasticity is:
(a)At the same time,the model was modified from a variable elasticity form
(the more general form estimated by f10unt-Chapman-Tyrrell)to a constant
elasticity form (which is a special case)because data were not sufficient
to estimate changes in GAMMA in Equation 2.2.In addition,price elas-
ticities did not vary greatly with the f~ount-Chapman-Tyrell variable elas-
ticity formulation over the fuel price range expected to prevail in the
Railbe1t.
2.14
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where Qt is the quantity of electricity consumed in year t,Pt is the price of
electricity in year t,PG t the price of gas,POt the price of oil and e the
constant which is the base of natural logarithms.Lambda (A)is defined as
before,as is GSR and OSR;ai~is an estimated constant,and e is the short-run
electricity price elasticity.
Since Equation 2.5 is multiplicative in form,we can derive separate quan-
tity adjustment indices for each fuel.The total quantity adjustment index is
the product of the three indices:
(2.6)
where Qiki is the total price adjustment index in region i,time period k and
sector ~,and OPA,GPA,and PPA are the price adjustment indices for changes in
electricity,natural gas,and oil prices,respectively.The own-price adjust-
ment (OPA)index is derived by holding the gas and·oil price terms from Equa-
tion 2.5 constant at 1.0 and defining price of electricity as an index RP with
its 1980 value equal to 1.0:
AOPA=OPAiKi iK-H
B·RP i K~
(2.7)
Similarly,holding electricity prices constant in Equation 2.5,we can derive
quantity adjustment indexes for gas and oil.The prices are normalized into
relative price indices RPG and RPO,with 1980 equal to 1.0 in each case.
GPA i ,K,i =GPA~,K_1,i oRPG~:~,i (2.8)
A
PPA i ,K,i =PPA i ,K-1,i oRP09 SR
1 ,K ,i (2.9)
Preliminary consumption (sales in the absence of price changes,PRECON)is
finally adjusted by the product of the quantity indices derived above to deter-
mine predicted,price adjusted consumption,Qiki'
2.15
2.16
(2.10)
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REFERENCES
Anderson,K.P.,1974.The Price Elasticity of Residential Electricity Use.
Report P-5180.Rand Corporation,Santa Monica,California.
Bouis,H.E.,K.D.Brown,W.S.Chern.1983.Integration of the State-Level
Electricity Demand Forecasting Model and the Regional Electricity t1odel.
P-3132-SR.Electric Power Research Institute,Palo Alto,California.
Chern,W.S.and H.E.Bouis.1982."An Investigation of Structural Changes
in Residential Electricity Demand,"in 1982 Business and Economics Statistics
Section Proceedings of the American Statistical Association.
Chern,W.S.,et al.1982.An Integrated System for Forecasting Electric
Energy and Load for States and Utility Service Areas.NUREG/CR-2692.ORNL-
TM-7947.Oak Ridge National Laboratory,Oak Ridge,Tennessee.
Fisher,F.,and C.Kaysen,1962.A Study in Econometrics:The Demand for
Electricity in the United States.North-Holland,Amsterdam.
Halvorsen,R.1978.Econometric Models of U.S.Energy Demand.Lexington
Books,D.C.Health and Company,Lexington,Massachusetts.
Kmenta,J.1978."Some Problems of Inference from Economic Survey Data.11 In
N.K.Namboodiri,ed.Survey Sampling and Measurement,Academic Press,New
York,New York.
McFadden,D.,C.Puig,and D.Kirshner.1977."Determinants of the Long-Run
Demand for Electricity,"in American Statistical Association,1977."Deter-
minants of the Long-Run Demand for Electricity,"in American Statistical
Association,1977 Proceedings of the Business and Economics Statistics Sec-
tion (Part II),pp.109-117.
Moe,R.J.,S.L.Owzarski,and L.P.Streit.1983.Impact of Conservation
Measures on Pacific Northwest Residential Energy Consumption.PNL-4717.
Pacific Northwest Laboratory,Richland,Washington.
2.17
Taylor,L.D.,G.R.Blattenberger,and R.K.Rennhack.1982.Residential
Demand for Energy.Volume I:Residential Energy Demand in the United
States.EA-1572,Volume 1.Electric Power Research Institute,Palo Alto,
California.
Tyrrell,T.J.1984.Review and Analysis of the Treatment of Price Elastici-
ties of Demand in the Susitna Hydroelectric Project RED Model (1983 Version
with Revisions).Prepared for Harza-Ebasco Susitna Joint Venture,Anchorage,
Alaska.
)
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Mount,T.D.,L.D.Chapman,and T.J.Tyrrell.
the United States:An Econometric Analysis.
National Laboratory,Oak Ridge,Tennessee.
1973.Electricity Demand in
ORNL-NSF-3P-49.Oak Ridge
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3.0 RESIDENTIAL CONSUMPTION f~ODULE
In response to comments by FERC staff at a meeting in Anchorage in October
1983 and comments subsequently received from Dr.T.J.Tyrrell,we reexamined
several of the parameters of the July,1983 version of the "RED Residential
Consumption r10dule to confirm that the values of these parameters best
reflected Railbelt conditions.This review included appliance saturations,
1980 electricity consumption estimates,fuel mode splits in new and replacement
appliances,and electricity consumption growth in the absence of fuel price
changes.The actual changes to RED made as a result of the review are shown in
Tables 3.1,3.2,and 3.3.The results of the review are shown in the following
subsections.
APPLIANCE SATURATIONS
In his review of the July,1983 version of REO,Dr.Tyrrell noted that the
pattern of market saturation rates of appliances would ordinarily be expected
to follow the traditional S-shaped pattern,increasing at a decreasing rate as
the maximum saturation rate was approached.The saturations for a few appli-
ances in the July,1983 version of RED did not follow this pattern--clothes
driers in multifamily dwellings in both Anchorage and Fairbanks appeared to
follow an irregular pattern.Dishwashers in single-family housing in Fairbanks
followed an abruptly declining pattern while dishwashers in Fairbanks multi-
family housing followe~an almost linear pattern.
The assumptions underlying these saturation patterns were reexamined for
the RED85A,version of RED.Dishwashers'saturation patterns were not
changed.Historically,the rate of market penetration of dishwashers in
Anchorage and Fairbanks has been very rapid,as revealed by the 1970 Census and
the 1981 Battelle Railbelt residential energy survey.The existing ceiling of
90%saturation appeared to be appropriate,given the large number of two-worker
families in both Anchorage and Fairbanks.The historical growth rates in sat-
uration were simply projected forward until 90%was reached (in cases of low
initial saturation rates);or alternatively,90%was assumed to be achieved by
3.1
3.2
TABLE 3.1.Changes in Market Saturations of Clothes Driers,
RED85A Versus July 1983 RED Model
(%of served househol~s)
(a)Value used by the model when run in certainty mode (that is,
when the Uncertainty Module does not select random values for
model parameters).Default values were used in the forecasts.
1990 (a growth rate slower than historical rates in most cases),at decreasing
rates.In the case of dishwashers in Fairbanks,this results in a traditional
S-shaped curve.
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July 1983
Default(a)Range
RED85A
75.7 75~7
83.0 82-84 83.0 82-84
87.0 84-90 83.5 85-90
90.0 83-93 84.0 88-92
92.0 89-95 85.0 90-94
94.0 90-98 90.0 91-97
95.0 91-99 95.0 92-97
61.0 61.0
67.0 63-71 65.0 61-69
74.0 70-79 70.0 65-75
80.0 75-85 80.0 75-85
85.0 80-90 85.0 80-90
90.0 85-95 90.0 85-95
95.0 92-97 95.0 92-97
Default(a)Range
Anchorage
Mu ltifam i 1y :
1980
1985
1990
1995
2000
2005
2010
Fairbanks
r1u ltifamil y:
1980
1985
1990
1995
2000
2005
2010
In the July,1983 version of RED,clothes driers'saturation rates were
tied very closely to clothes washer market penetration in all housing types.
The time path for market penetration in the RED85A version in multifamily
housing has been smoothed as shown in Table 3.1.At the same time,clothes
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washer saturations in Fairbanks multifamily homes and mobile homes were
revised.These revisions are shown in Table 3.2.The impact of all these
changes on the forecast is expected to be insignificant.
TABLE 3.2.Changes in Market Saturations of Clothes Washers,
RED85A Versus July 1983 RED Model
(%of served households)
RED85A July 1983
Default Range Default Range
Fairbanks
Multifamily:
1980 63.8 63.8
1985 70.0 65-75 68.0 63-72
1990 75.0 70-80 70.0 65-75
1995 80.0 75-85 80.0 75-85
2000 85.0 80-90 85.0 80-90
2005 90.0 85-95 90.0 85-95
2010 95.0 90-100 95.0 92-98
Fairbanks:
r10bi 1e Homes:(one year changed on 1y.)
1990 93.5 91-96 92.5 91-96
1980 ELECTRICITY CONSUMPTION ESTIMATES
Electricity consumption estimates for residential space heat in the 1980
housing stock are referenced in July,1983 documentation of RED as coming from
the 1980 study of Railbelt electricity demand by Goldsmith and Huskey of the
University of Alaska Institute of Social and Economic Research.Electricity
use rates in space heating are up to four times greater than those reported in
other studies for the Lower 48.Therefore,these estimates were reviewed to
confirm their accuracy.In addition,a 10%downward adjustment had been
assumed for initial electricity conservation in the building stock between 1978
(Goldsmith and Huskey's base year)and 1980 (the base year for RED forecasts).
The July 1983 version also contained a 15%upward adjustment in RED for energy
used in water heating to allow for cold inlet water temperatures in Railbelt
3.3
locations.Both of these assumptions were reviewed in light of additional
data.Finally,we reexamined the estimate of electricity consumption in
cooking.
All the original assumptions are supportable and require no changes.We
deal with each of the issues below.
Space Heat.The difference between space heating electrical consumption
in the Railbelt and Lower 48 study areas is,as far as we can determine,real
and accurately portrayed in RED.The following was the procedure utilized by
Goldsmith and Huskey in deriving the original estimates of electricity
consumption.
Step 1.The beginning point of the analysis was 1970-1979 Alaska Gas
and Service Company (now ENSTAR)residential customer data
that show 1979 consumption per customer was 202.5 mcf.
Goldsmith and Huskey estimated that about 84%of the load
(the part that varied by temperature)was for space heat.
Alaska Gas and Service Company used national data to esti-
mate about 75%of the total was for space heat.The two
figures were averaged to yield 80%,or 162 mcf per customer
for space heat,averaged across single family,mobile home,
and duplex units.(Multifamily fall under commercial sched-
ules for gas.)
Step 2.The gas heat load was converted to kHh of electricity by
assuming 65%efficiency for gas,95%for electric heat.
This results in an average 32,400 kWh fo~single family,
mobile homes,duplexes.Using an assumed 1979 distribution
of structures,average floorspace per unit,and average heat
requirement per square foot based on surface-area-per-square
foot ratios and Alaska energy studies,Goldsmith and Huskey
worked out implied average space heating loads:
3.4
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The multifamily unit electricity requirements were also cal-
culated using average building surface area/floorspace
ratios.
Heating requirements per unit in other parts of the Railbelt
were next determined from Anchorage values using information
on relative size of units (smaller in Kenai Peninsula and
Matanuska-Susitna Boroughs)and relative heating degree-
days.Average unit size was assumed to be 20%smaller in
the Kenai Peninsula Borough and 15%smaller in the
Matanuska-Susitna Borough than in Anchorage,based on 1970
Census median number of rooms per ho~sing unit.Fairbanks
housing was assumed 8%smaller than Anchorage,based on an
actual 1979 Fairbanks Community Information Center energy
survey data of Fairbanks for single family,duplex,and
mobile home units.Heating degree days from 1977 and 1978
heating seasons were used to proportionately adjust
Fairbanks consumption per square foot upward from Anchorage
values.No heating degree day adjustments were made for
outlying areas of Anchorage-Cook Inlet.The results are
shown below for 1978.
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Step 3.
Si ngl e Famil y
Duplex
t10b i1e Home
Total
Average Floor
Space
1,480
1,085
820
1,350
Relative
Average Heat
Requirement
Per Squa re Foot
1.0
0.9
1.38
1.02
Average Load
(kWh)
34,823
22,976
26,626
32,400
3.5
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kWh
Single Family Duplex Multifamily Mobile Home
Anchorage-Cook Inlet
Anchorage 34,800 23,000 15,300 26,600
Kenai-Cook Inlet 27,800 18,400 12,200 21,300
Matanuska-Susltna 29,600 19,600 13,000 22,600
Seward 27,800 18,400 12,200 21,300
Fairbanks/Tanana Val ley
Falrbanks/45,900 30,400 20,200 35,100
Southeast Fairbanks
Step 4.The 1978 average consumption figures were then weighted by
the numbers of IIfi rst hornell uni ts of each type in each loca-
tion having electric space heat in 1978.The product of the
number of first homes of each type,the estimated 1978 fuel
mode split,and the use per household by type of house was
added across types and divided by the total estimated number
of households having space heat.The total was then
adjusted to a normal degree-day year.This results in con-
sumption figures as follows:
kWh
Anchorage-Cook Inlet
Fairbanks-Tanana Vailey
Single Family
36,500
48,200
Duplex
24,200
26,600
Multifamily
17,100
18,800
Mob!Ie Home
27,300
30,000
Step 5.This estimate of consumption in space heating can be inde-
pendently verified from three other sources.First,Axel
Carlson of the University of Alaska Cooperative Extension
Servi ce esti mated IItypi ca 1 11 house heati ng requi rements for
six types of houses in Fairbanks and one type in Anchorage
(Fairbanks North Star Borough Community Information Center
Special Report No.2,1978 and Special Report No.4,
1976).His findings were:
3.6
Anchorage Fai rbanks
1-Single Family 40,917 kWh 52,392 kWh
a.2300 square feet
(incl.daylight basement)
b.768 square feet 29,042
(closed crawl space)
c.768 square feet 26,620
(heated crawl space)
2.Mobile Home
a.768 square feet 34,873
(closed crawl space)
b.768 square feet 32,671
(heated crawl space)
Adjusted for housing size,these estimates are compatible
with the ISER estimates from Step 4.
A second sou rce of data for compa ri son was the IIheat.i ng
onlyll electrical consumption figures for Chugach Electric
from the Federal Power Commission's annual publication ~
Electric Homes,from 1963 to 1974.The average use rate in
all Chugach electrically space-heated homes was between
29 thousand and 30 thousand kWh,very similar to the ISER
estimates for single family and mobile homes.
A third source used by ISER also gave similar,but somewhat
higher estimates of consumption in space heating.When
national average electrical requirements from the U.S.
Department of Energy Office of Community Systems were
applied to Anchorage,the following estimates were obtained:
3.7
Type
SIngle Family Detached
Single Family Attached
Multifamily HIgh Rise
Multifamily Low Rise
Mobile Home
Thermal Requirement
(Btu/sq.ft/heatlng degree-day)
11.3
6.2
4.5
5.0
15.0
Average
SIze (sq ft)
1,570
1,370
900
800
720
Annual Anchorage
ElectrIcal RequIrement
(kWh)
56,716
27,154
12,947
14,386
34,526
Ten Percent Space Heat Conservation Adjustment
The base year electricity consumption used in the ISER predecessor to the
RED model was based on 1978,adjusted to normal heating degree days (10,911 in
Anchorage;14,344 in Fairbanks).The base year used in the RED model was 1980,
also adjusted to normal heating degree days.
As shown in the following table,combining the ISER 1978 space heating
estimates with the RED model's 1980 estimates for housing stock and fuel mode
split the model would produce a normalized estimate for electricity consumption
per household much higher than actual values for 1980.On the other hand,
adjusting for 10%conservation between 1978 and 1980 produces a value much
closer to the actual 1980 consumption per household.Given that some non-space
heat loads may also be weather-sensitive,and probably contribute to the dif-
ference between RED estimated and actual consumption,the 10%adjustment
appears appropriate and has not been changed.
Annual Use Rates in Residential Sector for
Actual 1980 Degree Days
(kWh)
Anchorage Fairbanks
1.With ISER 1978
use rates
2.With RED adjusted
use rates (ISER 1978
rates,minus 10%)
3.Actual consumption
per household
13,534
13,031
13,090
3.8
11,454
11,214
10,449
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Fifteen Percent Adjustment for Lower Water Temperature in Water Heating
We examined a number of studies that gave estimates of electrical consump-
tion in water heaters for Lower 48 locations.Other things equal (insulation
of the heater and water use rates),one would expect that lower inlet water'
temperatures would cause proportionately higher electrical use rates in Alaska.
The question is:how much higher?
The basic study used to calibrate the water heating estimate was the Cali-
fornia Energy Commission's (CEC)1976 estimate (See in Table 5.14 of Volume 2C
of the July 1983 Susitna license application).The original source was
adjusted upward by 15%to allow for lower Alaska water temperatures.Data
supplied by David Myers of Batte11e-Northwest 's Geosciences Research and Engi-
neering Department reveals that groundwater temperatures in the Anchorage area
average 2°to 3°C (36°to 37°F)and about 1°C (33°F)in the Tanana Basin.(a)
Peter Poray,of the Anchorage Municipal Energy Coordinator's Office,confirms
that surface water temperatures in Ship Creek (which supplies about half of
Anchorage's needs)average 33°F.(b)In contrast,ground water temperatures in
much of the U.S.are in the high 50°to low 700s (see Ground Wate~and Wells,
Johnson Division,UOP,Inc.,St Paul,Minnesota,1975,p.12).In particular,
this is true for CEC's region.
Even allowing for no difference in standby heat loss in water heaters
between Alaska and the CEC study,the 15%adjustment appears conservative,in
view of the 50%di fference in i n1 et temperature.The 500 KWh/year/uni t down-
ward adjustment of water heater electrical use made in the RED model in 1983 in
Anchorage to allow for heating of Anchorage's water supply at the Municipa-
1ity's power plant also appears conservative.Although the temperature at the
plant is increased by as much as 15°F,only half of the supply is heated;only
Anchorage (not the Matanuska-Susitna Borough or Kenai Peninsula)is involved,
and no studies have been done to see how far into the Municipa1ity's water sys-
tem the added heat penetrates.
(a)Telephone communication,David Myers,Battelle Northwest Geosciences
Research and Engineering Department to Michael Scott,May 17,1984.
(b)Telephone communication,Peter Poray,Municipality of Anchorage to Michael
Scott,May 18,1984.
3.9
Another sign that the water heat estimate is appropriate is a series of
Pacific Northwest submetering studies of domestic hot water system whose
results were reported in Appendix K to the Regional Conservation and Electric
Power Plan of the Pacific Northwest Power Planning Council in 1983.The
average use in 120 units in 8 studies was 6318 kWh per year,with a standard
devi ati on of 2600.In spi te of the warmer average inl et water temperatures in
these studies (e.g.,about 47°in Seattle),almost all the studies showed
higher use rates than was assumed for the Railbelt.Adopting the range of 3.5
to 5.6 kWh per day per household occupant in those studies and standardizing at
the Railbelt average household size of about 2.9 in Anchorage,the studies
(unadjusted for water temperature)would show a range of 3704 to 5928 kWh per
y"ear for Rai 1belt size households compared to the 4800 kWh actually fore-
casted.This supports the original 15%adjustment for cold water in the
Railbelt.
Cooking Ranges
Cooking ranges were assumed in the July 1983 license application to have
el ectri c consumpti on rates that were lithe average of several studies 0 II In
fact,the 850 kWh assumed is the simple arithmetic average of the rates shown
in Table 5.14 of Volume 2C,rounded to the nearest ten kWh.This was not
changed in the RE085A versi on of REO.
Fuel Mode Splits in Replacements and New Housing
In the July,1983 license application,incremental fuel mode splits for
space heat and water heat in Anchorage-Cook Inlet were set at low values
relative to the existing (1980)stock to reflect the fact of large-scale
movement to natural gas.This reflected information obtained from several
telephone interviews with Anchorage area builders and real estate firms in May
1983.At that time,the incremental water heating fuel mode splits were set
equal to the incremental space heating splits except in mobile homes,where
about half the existing units appeared to have electric water heaters even
though the space heat units were overwhelmingly gas.
Since July 1983,we have again reviewed the assumptions concerning the
incremental fuel mode splits.The incremental splits should reflect not only
3.10
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new construction practices but fuel switching at the retirement of existing
systems.Additional telephone interviews with Anchorage area plumbing and
heating firms have confirmed current large-scale conversions of both space heat
and water heat to natural gas in all types of housing in areas where gas is
available.Conversions are occurring on both relatively new systems as well as
old all-electric systems.Accordingly,there appears to be no reason to change
the incremental fuel mode splits from their conservative July 1983 values,
which continue to reflect some electric space and water heat in areas beyond
the reach of gas pipeline systems.It is still assumed that pipeline gas will
not be available in the Fai rbanks-Tanana Valley load center,so no adjustments
are made to existing fuel mode splits in that area.
A di fference between the 1981 Battell e resi denti al survey and the 1980
Census for Fairbanks fuel mode split in water heating was noted.The Fairbanks
water heating fuel mode split reported in the survey had been adjusted to a
figure much closer to the Census value,not because it IIdisagreed ll with the
1980 Census,but because the 1980 residential consumption calibration requi red
it.The Census did not report the fuel mode split for hot water by type of
housing.However,model calibration coincidentally brought the average
Fairbanks fuel mode split for water heating close to the Census figure.The
average fuel mode splits for the 1981 Battelle survey and 1980 Census are shown
below:
Electric Fuel Mode Splits (%)
Space Heat:
Anchorage-Cook Inlet
Fairbanks-Tanana Valley
\~ater Heat:
Anchorage-Cook Inlet
Fairbanks-Tanana Valley
Cooking:
Anchorage-Cook Inlet
Fairbanks-Tanana Valley
(a)Adjusted value.
3.11
1981
Battelle
23.0
10.9
45.5 ( )
30.5 a
73.3
84.3
1980
Census
20.2
11.6
36.2
36.1
68.6
83.4
The only place where the 95%confidence intervals of the Battelle and Cen-
sus estimates do not overlap is water heat in Anchorage.Even there,the
results of the Census sample and Battelle survey are in fairly close agree-
ment.The lower 95%confidence bound on the Battelle estimate is about 39%for
the electric fuel mode split.The upper bound on the Census electric fuel mode
split is estimated at 37%.Given that both the "Rattelle Census and estimates
are based on samples and that the Census does not report fuel mode splits by
type of dwelling,fuel mode split in Anchorage-Cook Inlet was not adjusted.(a)
Annual Consumption,1985 and After
The 1985 electricity consumption rates for residential appliances in the
July,1983 license application were also reviewed.Except in a few cases,the
1985 consumption rate assumptions are clearly stated in the July,1983 license
application and require no further explanation.Broadly stated,up to 1985 the
consumption rates of electricity by residential appliances reflect pre-existent
trends in the efficiency improvements that are partly or wholly offset by
increasing trends in size or energy-using features that are expected to prevail
in the Railbelt.The only appliance which exhibits an apparent large
unexplained change between 1980 and 1985 consumption is cooking (ranges).In
1980 the value for annual consumption of electricity in cooking is 850 kWh.
For replacement stock and new units it is 1200 k~Jh.This "rap id increase"is
designed to take care of two factors:1)The wide range of values for annual
consumption reflected in the various studies in Table 5.14 of Volume 2C of the
July 1983 license application reflects varying ages of appliance stock in the
studies and varying presence of features such as automatic timers and self-
cleaning ovens.It is assumed that most replacement and new stock will contain
these features,which will increase incremental energy use.2)There are
several convenience kitchen appliances,not directly accounted for in the RED
model,that are directly related to food preparation and could add to II coo king ll
energy use as their market saturation increases.Included are separate
(a)Had we adjusted to the Census value,the impact on the 2010 forecast of
changing the fuel mode split would have been about a 32 GWh decrease in
consumption,or approximately -.5 percent.
3.12
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The increases in stock size implied in Table 5.13 of Volume 2C of the
July,1983,license application are about 22%over the 1980 average stock
Electricity use in space heating reflects increasing average size of homes
being built in the Railbelt region.While the 1980 consumption rate was based
on 1980 average floorspace for the then-existing stock,1985 consumption rates
reflect the size of units being added.Table 3.3 shows the differences
electric ovens (Association of Home Appliance Manufacturers rated at 373 kWh
per year),electric cooktops (365 kWh per year)and microwave ovens (98 kWh per
year).In addition there are food processors,food waste disposers,and trash
compactors,all becoming more common in Railbelt kitchens.
revealed in the Battelle 1981 Railbelt residential survey (see Appendix A,
Volume 2C of the Susitna license application)between the size of housing units
added after 1975 and average unit size.The figures shown below do not account
~or several sources of size increase in new 1985 stock.The figures do not
reflect renovations and additions which also increase average size of dwelling
units built before 1975,nor post-1980 size increases in new units,nor average
size increases within specific classes of housing (single family,for
instance).In spite of this,the 1981 survey shows a clear trend toward larger
units in the post-1975 stock.
Fairbanks-Tanana Valley
Average Post-1975
11.3 10.2
21.1 17.3
19.3 16.5
13.1 15.0
16.6 18.1
9.8 12.6
8.8 10.2
Anchorage-Cook Inlet
Average Post-1975
8.1 6.9
18.1 14.1
14.2 17.6
12.9 12.2
20.8 21.5
14.6 14.6
11.1 12.9
Size of Houses in Railbelt Region
(%)
Size Class (sg ft)
1.Less than 700
2.701-1000
3.1000-1300
4.1301-1600
5.1601-2000
6.2001-2400
7.2401 and over
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3.13
TABLE 3.3.Post-1985 Annual Growth Rates in Electricity
Consumption for Residential Appliances
RED85A July 1983
Space Heat
Si ngl e Fami·ly 0.005 0.005
Mobile Homes 0.005 0.005
Dup 1exes 0.005 0.005
Multi fami ly 0.005 0.005
Water Heaters 0.0 0.005
Clothes Dryers 0.01 0.00
Cooki ng Ranges 0.01 0.00
Saunas-Jacuzzis 0.01 .0.00
Refri gerators 0.01 0.00
Freezers 0.01 0.00
Dishwashers 0.01 0.00
Additional Water Heating 0.005 0.005
Clothes Washers 0.01 0.00
Additional Water heating 0.005 0.005
Small Applitn}es and
Lighting:a
Anchorage 80 kWh 50 kWh
Fai rbanks 100 kWh 70 kWh
(a)Change per five-year period.
size.In Anchorage,for example,this implies new single family detached units
would be about 1830 square feet,versus 1500 assumed in the 1980 stock if
energy consumption per square foot were the same in 1980 and 1985 stock.Simi-
larly,new duplexes would be about 1300 square feet;new mobile homes,about
1000 square feet;new multifamily units,1100 square feet.In Fairbanks,the
new single family units would average about 1700 square feet versus 1350 in the
3.14
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existing 1980 stock,1000 square feet in new duplexes versus 800 in the 1980
stock;1000 square feet in new multifamily versus 850 in the 1980 stock;and
1100 square feet in new mobile homes versus 900 in the 1980 stock.In com-
parison,U.S.Department of Energy gives the following,generally larger,1981
average unit sizes for the nation:single family detached,2093 square feet;
single family attached,1946;2-4 units,1126;5 or more units,826;mobile
homes,880.(a)
In summary,there is no reason to change any of the 1985 electricity con-
sumption rates for the RED85A version of RED,since the 1985 consumption rates
still appear reasonable.
Post-1985 growth rates in energy consumption are changed between the July,
1983 and RED85A versions of RED.The July,1983 version assumed that increases
in post-1985 energy-using features of most major appliances would be just off-
set by increases in appliance efficiencies.The RED85A version no longer
assumes the post-1985 trends would be offsetting.There are two major
reasons.The first is that average consumption rates in new appliances in the
absence of electricity price changes in the Railbeltreflect the menu of appli-
ance choices available to-Railbelt residents after 1985.The efficiency of
this appliance stock will in turn reflect electricity prices in national
markets,while choices made from the menu will reflect local prices.April
1983 Energy Information Administration long-term forecasts of national elec-
tricity prices show modest national rates of increase (about 0.9%per year,
1980 to 1990;1.6%per year from 1985 to 1990).(b)Also,appliance energy
conservation has been so successful to date (see Table 5.15 of the Volume 2C of
July,1983 application)that further improvements in the available stock after
1985 may be difficult to achieve.These factors will tend to restrict somewhat
the choices in efficiency of appliances available for purchase in the Railbelt
after 1985.On the other hand,as incomes rise and the wealth of Railbelt
households increase,they will demand a greater level of services from their
(a)
(b)
U.S.Energy Information Administration,Residential Energy Consumption
Survey,Housing Characteristics,1981,August 1983,Table 5.
U.S.Energy Information Administration,.1982 Annual Energy Outlook,with
Projections to 1990,DOE/EIA-0383(82),April 1983,Table 4.
3.15
appliance stocks.Accompanying the demand for services growth will be a
general increase in the capacity and,hence,energy use,of appliances.Since
efficiencies are expected to change little in absence of price changes,the
average consumption rates should rise over the forecast period to reflect the
wea lth gai ns •
The second reason for increasing energy use is that long-term elasticities
estimated in national econometric studies such as those in the RED model meas-
ure,to some extent,the effect of national electricity price trends on tech-
nological change in the energy efficiency of the appliances available .for
purchase.However,for the RED model this effect must be held constant because
the market is too small to influence national appliance stock efficiencies.
Put another way,it is necessary in small markets like the Railbelt to offset
that porti on of the long-term pri ce el asti city effect measured in nati onal
studies that accounts for general price-induced technological change in
appliance efficiency.The July 1983 version of RED over-corrected for the
price-induced technological change by holding the growth rates in electricity
consumption constant,while including an elasticity that incorporated the same
effect.The RED85A versi on of RED adjusts for this by i ncreasi ng the post-1985
growth rates in appliance electricity consumption for all appliances except
water heaters and space heating units.These are assumed to be more influenced
by local conditions.A conservative growth figure of 1.0%per year is used,up
from 0.0%in the July,1983 version.
Miscellaneous appliances were also reviewed.A number of new appliances
became available in the late 1970s and early 1980s whose market penetration is
clearly increasing,but whose effect on total usage is not clear at this time.
These include video cassette recorders,home computers,video games,central
vacuum systems for cleaning,security alarm systems,and central air filtration
systems.In addition,Fairbanks area utility interviews in February 1984 con-
fi rmed that in Fairbanks additional emphasis has been given to controlling
environmental carbon monoxide pollution at low temperatures by the U.S.Envi-
ronmental Protection Agency and the North Star Borough government.This has
taken the form of an ordinance prohibiting cars and trucks from being left
3.16
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idling and mandating the plugging in of car engine block heaters at tempera-
tures as high as 20°F to improve engine performance.This could increase the
use of engine block heaters at warmer (0°to 20°F)temperatures than has been
the case in the past.The growth rate in miscellaneous consumption has been
increased slightly to account for these phenomena in Table 3.3.
3.17
REFERENCES
Association of Home Appliance Manufactures.No date.(But After 1981).
Factors on Major Home Appliance Energy Consumption and Efficiency Trends.
Association of Home Appliance Manufacturers,Chicago,Illinois.
Goldsmith,O.S.,and L.Huskey.1980.Electric Power Consumption for the
Railbelt:A Projection of Requirements.Institute of Social and Economic
Research,Anchorage,Alaska.
Pacific Northwest Power Planning Council.1983.Northwest Conservation and
Electric Power Plan.Volume II.Pacific Northwest Power Planning Council,
Portland,Oregon.
Tyrrell,To J.1984.IIReview and Analysis of the Treatment of Price Elastici-
ties of Demand in the Susitna Hydroelectric Project RED Model (1983 Version)
wi th Revi si ons.1I Prepared for Ha rza-Ebasco Susitna Joi nt Ventu re,Anchorage,
Alaska.
3.18
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4.0 BUSINESS SECTOR
The ongoing review of the REO model has generally supported the structure
of the model's Business Consumption t1odule.Additional data have been col-
lected from both Railbelt and national sources which have permitted further
refinements to both the structure and selected parameter values.(a)
This section contains a discu~sion of the changes made in the July,1983
version of the Business Consumption t1odule.He first review the calculations
in the July,1983 version.Next,we discuss the way in which floorspace per
employee is forecasted,and finally,discuss minor adjustments made to the
equations that determine preliminary electricity requirements prior to price
effects.
STRUCTURE OF THE JULY,1983 BUSINESS CONSUMPTION MODULE
Using regional employment,the Business Consumption Module first con-
structed estimates of the regional stock of floorspace by five-year forecast
period.The predicted floorspace stock was then fed into an electricity con-
sumption equation that is econometrically derived to yield a preliminary fore-
cast of business electricity requirements,which was then adjusted for price
impacts.
After an investigatio~of several alternative methods for forecasting
business floorspace,a simple formulation of the floorspace forecasting equa-
tion was used in the July,1983 version of RED.The floorspace per employee
was assumed to increase at an exponential constant rate to the 1979 national
level reported by the U.S.Energy Information Administration (1983)by the year
2010,or cumulatively increase about 10%and 15%in Anchorage and Fairbanks,
(a)During February and t1arch,1984 we conducted additional interviews with
Railbelt utility staff,state and local government planning groups,public
and private building managers,and realtors.The results are reported in
Imhoff and Scott,1984 included in this document as Appendix A.The data
we found suggests that the utilities themselves are oriented toward either
a per-customer or per-square foot approach to estimating business elec-
tricity consumption.Available data are consistent with both the 1980
building stock per employee and 1980 consumption per square foot data
contained in the July 1983 version of RED.
4.1
respectively.This took into account both the evidence of historic increase in
floorspace per employee in Railbelt load centers and the historic lower levels
of floorspace per employee in Alaska compared with the nation as a whole.This
assumption was still quite conservative,since Alaska's commercial floorspace
per employee is far below the national average and has been growing faster
historically than the projected rate.The forecasting equation is shown as
Equation 4.1.
STOCK"K=a·•(b·)t-1 •TEr1P't
.",
where
STOCK =floorspace in business sector
a =initial (1980)floorspace per employee
b =annual growth factor (1 plus growth rate)in floorspace per
employee
(4.1)
Once the forecast of the stock of floorspace was found,the module
then predicted the annual business electricity requirements before price
adjustments,based on a regression equation:
TEr1P =total employment
i =index for the regi on (Anchorage-Cook Inlet or Fairbanks-
Tanana Valley)
K=time index,K=1,2,3,•••,7 (forecast periods)
t =time index,t=1,2,3,•••,31 (years)•
PRECON iK =exp[BETA i +BBETA i x In(STOCK iK )]
4.2
(4.2)
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where
4.3
PREDICTING FLOORSPACE STOCK
PRECON =nonprice adjusted business consumption (MWh)
(4.3)BUSCON iK =PRECON iK •OPA iK •PPA iK •GPA iK
BUSCON =price-adjusted business requirements (MWh)
OPA =own-price adjustment factor
PPA =cross-price adjustment factor for fuel oil
GPA =cross-price adjustment factor for natural gas.
where
BETA =parameter equal to regression equation intercept
BBETA =percentage change in business consumption for a 1%change
in stock (f100rspace elasticity).
exp,ln =exponentiation,logarithmic operators
Finally,price adjustments were made with the price adjustment mechanism
structure identical to that in the Residential Consumption Module,however,
different in the parameters:
In summary,the forecast of electricity demand in the business sector was
directly tied to a forecast of f100rspace stock via a regression equation.
F100rspace stock,on the other hand,was derived by an explicit assumption of
the level of f100rspace per employee in the year 2010--f100rspace was then
derived by providing an exogenous forecast of employment.
As can readily be seen in Equation 4.1,the level of f100rspace per
employee in a given forecast year was assumed in the July,1983 version of RED
to exponentially approach the national 1979 level.The RED85A version assumes
that the past historical trend of square feet of business space per employee
will continue.This results in a year 2010 value for the Anchorage load center
that is slightly less than the national 1979 value.It also is less than the
1979-1983 national growth rate in business f100rspace per employee.(The 1983
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value was 676.7 square feet per employee compared to 623 in 1979.
difference was 53.7 square feet per employee,or 13.4 square feet
per year (U.S.Department of Energy 1985).The projected rate of
Anchorage is only 5.8 square feet per employee per year.
The national
per employee
increase in
PARAMETER VALUES
(a)As progressively more categories of commercial floorspace become available
in adequate amounts in Anchorage,we expect there will be fewer categories
growing rapidly,contributing to an overall decline in the growth rate.
ai =intercept (1972 value)square footage per employee in each load
center
b =growth rate parameter
t =forecast year (t=1,2,•••,38).
In the July,1983 version of RED,the national average number of square
feet per employee was derived using the 1979 Energy Information Administration
(EIA)Nonresidential Buildings Survey and U.S.Department of Commerce,Bureau
of Economic Analysis (BEA)definitions of total employment.In the Railbelt,
however,an alternative measure of total employment can be found using the
State of Alaska,Department of Labor data series as edited by the University of
Alaska,Institute of Social and Economic Research (ISER).Furthermore,since
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)STOCK it =(ai +b •t)•TEMP it
The exponential growth path in July 1983 version of RED assumed that the
increments by which business space per employee grew increased with time.The
current version of the model results in constant increments,which implies a
slowly decreasing growth rate.(a)Because of differences in the respective
economies and the labor and materials cost differential between Anchorage and
Fairbanks,it is assumed that floorspace (STOCK)per employee in Fairbanks
grows at the same rate as in Anchorage,but from a lower base so that it does
not reach the 1979 national level by 2010.Equation 4.1,thefefore,was
replaced with the following form in the RED85A version of the model:
where
4.4
the RED model relies on a forecast of this measure of total employment,as
generated by ISERls Man in the Arctic Program (~1AP)regional economic model,
consistency in the final forecast requires consistency between the two models·
definitions of employment.
In the RED85A version of RED,the ISER definition of total employ~ent was
used to adjust 1979 BEA national total nonagricultural employment figures to
more closely represent the employment distribution in the Railbelt Region.
This was done by adjusting the national square feet per employee:netting out
industrial (heavy manufacturing)and mining employment,and adding in military
employment to the national figure.Industrial employment is netted out because
there is little industrial employment or floorspace in the Railbelt region,and
because industrial electricity demand is exogenously predicted in RED.
National mining employment was deleted from the national total because the
lower 48 figures primarily represent individuals working in mines,while in the
Railbelt,mining workers are mostly headquarters staff working in offices.
~1ilitary employment is included because the ~1AP definition includes military
employees.Table 4.1 documents the 1979 national stock per employee calcula-
tion,as modified.The 613.1 square feet per employee matches fairly well with
the 623 square feet per employee derived by EIA for their entire 1979 sample of
nonresidential buildings (which includes industrial buildings and some build-
ings that have both residential and nonresidential uses).
The value of b in Equation 4.1 1 is econometrically derived from historical
employment and estimated building stock data described in Appendix B.The
"intercept"coefficient a was derived by solving the equation for the 1980
Anchorage and Fairbanks estimated values of square feet per employee.In 1980,
the variable t had the value of 8,since 1973 =1 in Equation 4.1 1
•Table 4.2
shows the values for these parameters for Anchorage and Fairbanks.When
utilized to predict a value of square feet per employee in the year 2010,
Equation 4.1 1 produces values of 603.8 in Anchorage and 538.4 in Fairbanks,
both less than the 1979 national values of 613.1 (as calculated)or 623 (as
reported for the sample buildings).
4.5
4.6
TABLE 4.1.Calculation of 1979 National Square Footage Per Employee
TABLE 4.2.Parameter Values for Business Floorspace Equation,RED85A
(a)The coefficient value was adjusted in 1980 so that the
estimated 1980 values of 429.5 square feet per employee in
Anchorage-Cook Inlet and 364 in Fairbanks-Tanana Valley were
reached in year t =8 (the years 1973-1982 were used in the
regression).The original value in the Anchorage equation
was 316.22,with a standard error of 10.661.
(b)The Fairbanks equation is not econometric.See text.
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72 ,698
44,570,000
613.1
Fairbanks-
Tanana valley(b)
317.562(a,b)
5.811
93,600
22,137
865
2,100
54,825,000
3,115,000
7,140,000
Anchorage
Cook Inlet
383.023(a)
5.811
1.718
Coefficient
a.Value
Standard Error
b.Value
Standard Error
Average Square Feet per Employee
Employment (Thousands):()
1979 ~gQfarm Employment a
Less:')
Industrial
rli ni ng
Plus:
r1il it a ry (a)
Total Adjusted National Employment
Square Feet (Thousands):( )
Total Nonresidential c
Less:
Residential
Indust ri al
Total National Business Square Feet
(a)Source:1981 Statistical Abstract of the United States,Table 634.
(b)Ibid.,Table 658.
(c)"Nonresidential"buildings are buildings used for some purpose other than
residential.Those buildings used primarily as residences (residential)
were removed from the business total,as well as industrial buildings
(which are much more heavily represented in national than in Railbelt
floorspace).
PREDICTING BUSINESS CONSUMPTION
In the July,1983 version of RED,business preliminary electricity con-
sumption (that is,in the absence of price effects)was estimated by regressing
historical commercial-industrial-government electricity consumption in the two
load centers on the corresponding estimated historical stock of business floor-
space and other selected parameters.In his March,1984 review of the RED
model,Dr.Tyrrell suggested that we attempt to introduce fuel prices directly
into our regression equations to hold price effects constant.At the same
time,we felt the historical commercial-industrial-governmentelectricity con-
sumption data series could be refined.First,heavy industrial electricity
consumption was removed from the Anchorage-Cook Inlet series so that only com-
mercial-light industrial-government consumption was estimated by the equation
for that load center,consistent with the RED definition of the business sec-
tor.Next the Fairbanks city government consumption of electricity data were
edited to attain consistent reporting of this category within the business
sector.
The second adjustment made in the data for the RED85A version of RED was
in the historical building stock for Anchorage and Fairbanks.During the
summer of 1984 the FW Dodge Construction Potentials data series for Alaska was
acquired,giving us access to the most complete data set on commercial building
starts.Documentation of the analysis of the F.W.Dodge data is shown in
Appendix B.The resulting estimates of total commercial building stock for
1973-1983 are shown in Table 4.3.(a)
Following Dr.Tyrrell's suggestion,historical data series on fuel prices
in the Railbelt were used to estimate consumption equations for electricity in
the business sector.No price series were available for natural gas or fuel
oil and the best series for electricity contained some gaps.In the resulting
regression equations the electricity price did not contribute to the explana-
tion of electricity consumption.Following standard econometric procedures,
(a)It was assumed that the average Railbelt commercial building took from
1 to 2 years to complete,once begun,to estimate construction
completions~
4.7
TABLE 4.3.Estimated Commercial Floorspace,Anchorage-Cook Inlet and
Fairbanks-Tanana Valley Load Centers,1973-1983
(million square feet)
Year
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
Anchorage
Cook Inlet(a)
26.2
29.0
33.1
36.8
39.6
41.4
42.8
44.1
44.9
47.6
49.8
Fairbanks-
Tanana Valley(b)
3.8
4.4
5.4
7.5
8.7
9.8
10.1
10.4
10.6
10.8
11.2
(a)1978 estimated stock (Goldsmith and
Huskey 1980),plus or Minus F.W.Dodge
gross additions,lagged one year.
(b)1983 estimated stock (Scott and Imhoff
1984.See Appendix A),minus F.W.
Dodge gross additions to stock,lagged
one year.See Appendix B.
price was dropped from the final equation in both load centers.There was
virtually no effect in either region on the coefficient BBETA (the effect of
business floorspace)from dropping electricity price.
The effect of reestimating the business electricity consumption equation
was to reduce the business forecast to some degree relative to the July 1983
version.In combination with the higher business space estimate and larger
(negative)price effect,the net effect on the forecast is an increase in the
forecast.
Table 4.4 reports the differences between the RED85A version of the busi-
ness electricity consumption equation for Anchorage-Cook Inlet load center,
while Table 4.5 reports the differences in Fairbanks.
4.8
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-TABLE 4.4.Differences in Equations for Business Electri~ity Consumption in
Anchorage-Cook Inlet,RED85A Versus July 1983~a)
RED85A July 1983
BETA -6.320(b)-4.7963
standard error .0622 0.6280
t-statistic 101.60 -7.6368
BBETA 1.224 -1.4288
standard error .062 0.0491
t-statistic 19.74 29.1159
~.98 .9906
(a)Both equations take the form
ln (CON t )=BETA +RBETA *ln (STOCK t )+~t
where:
CON =business consumption (but exclud-
ing large industrial in the 1985
version)
STOCK =predicted stock of floorspace
(b)To calibrate to 1980,the intercept BETA is reset
to -2.2118 in the model.
The July,1983 version of RED used a predicted stock series (the best
available at the time)that showed less rapid and less variable growth in
building stock than that indicated by more recent actual construction data.
For example,actual 1975 commercial construction starts in Fai'rbanks repre-
sented almost 48%as much space as the entire commercial building stock
available in 1974.At the same time,rapid electricity price increases after
1973 caused many building owners and managers to drop electric space heat.The
consequence was that historically,Fairbanks showed a building stock elasticity
of demand of only 0.4 to 0.5,whether or not the Fairbanks electricity price
series was included in the equation.This is not representative of current
(post-1980)conditions,since most of the electric space heat conversion has
been accomplished.Therefore,an elasticity of 1.0 for the period after 1980
was assumed.The intercept of the Fairbanks consumption equation was cali-
brated to actual 1980 consumption per square foot.This implies Fairbanks-
Tanana Valley business consumption would grow in proportion to commercial
building space in the absence of price effects,a somewhat slower rate of
growth than that projected in Anchorage.
4.9
4.10
Differences in Equations for Business Electricity Consumption
Equations(i Q Fairbanks-Tanana Valley,RED85A Versus
July 1983 a)
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July 1983
-0.9611
3.6314
-0.2647
1.1703
.0.3293
3.5538
0.1629
0.0535
3.0444
-0.0028
0.0024
-1.1547
0.9121
RED85A
-.7980
1.0
The July 1983 version of the equation is
ln (CON t )=BETA +BBETA *ln (STOCK t )
+GAr1MA *V +THETA *
Dt Tt +€t
BETA
standard error
t-statistic
BBETA
standard error
t-statistic
(a)
GAMMA
standard error
t-statistic
THETA
standard error
t-statistic
where
CON,BETA,BBETA,and STOCK are defined
in Table 4.4 and where
o =Dummy variable (1974-1981 =1)
V =Pipeline period dummy variable
(1975-1977 =1)
T =Time trend for T =1-9 (1973-81)
(b)The RED85A version of the Fairbanks-
Tanana Valley consumption equation is not
econometric and contains only two vari-
ables.See Appendix 8,Section 8.5.0.
Source:Appendix 8.
TABLE 4.5.
REFERENCES
Imhoff,C.H.and M.J.Scott.1984.Railbelt Commercial Building Stock and
Energy Use Data.Rattelle,Pacific Northwest Laboratory,Richland,Washing-
ton.
U.S.Department of Energy.Energy Information Administration.1983.Nonresi-
dential Buildings Energy Consumption Survey:1979 Consumption and Expendi-
tures.Part 1:Natural Gas and Electricity.DOE/EIA-0318/1 Superintendent
of Documents,U.S.Government Printing Office.
4.11
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5.0 PEAK DEMAND
Review of recent annual load factors for the Fairbanks-Tanana Valley load
center showed that the load factor assumed for the Fairbanks-Tanana Valley load
center in the July 1983 version of the model was too low by about 10%.The
July 1983 assumed value was 0.50.Recent historical load factors are shown in
Table 5.1.
The average of the 1980-1983 estimated load factors is 0.553.We used
0.55 as the value for the RED85A version of the RED.
TABLE 5.1.Historical Annual Load Factors,Fairbanks-
Tanana Valley Load Center
Annual
Sales to F1n~1 Final cust(m)r
Custome'rs a Peak Load b Load
Year (GWh)(MW)Factor
1980 412 90 0.522
1981 421 87 0.552
1982 446 88 0.579
1983 461 94 0.560
(a)From Alaska Power AdMinistration,Alaska
Electric Power Statistics,[Annual].
(b)Reported Peak from Alaska Electric Power
Statistics,September 1984,less 7%for
line loss.
5.1
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6.0 EFFECT OF THE f100EL CHANGES ON THE FORECASTS
This section of the paper details how the RED model forecasts are affected
by the changes made to the model in the RED85A version.In order to compare
the RED85A and July,1983 versions we ran simulations of the RE085A version for
three cases contained in the July 1983 Susitna License Application.These
cases were:
•H12 -Sherman Clark No Supply Disruption Reference Case
o H13 -ORI Case
•HE8 -FERC -2%Case
The H12 reference case was chosen to see how the base case utilized in July
1983 to plan the Susitna project would be affected.ORI and FERC -2%cases
were chosen because they previously represented the highest and lowest fossil
fuel price (and Alaskan economic growth)paths reported in the license.applica-
tion.There was enough price variation among these cases to determine how
responsive the new model was to very different price scenarios.In the refer-
ence case,real residential fuel oil prices grow by 59%from their 1980 base to
the year 2010 (1.6%per year,averaged over 30 years);in the ORI·case,the
growth is 106%(2.4%per year average);in the FERC -2%case,prices fall 43%
(about -1.9%per year).
Table 6.1 summarizes the overall effect on the forec~sts.In general,
compared to the 1983 forecasts,the new forecasts are more price-responsive,
but grow more quickly in the absence of price effects.The overall effect is
to narrow the range of the forecasts to some extent.Long run price respon-
siveness of demand dominates at high prices (ORr case),dampening the forecast
of 1983.At low prices,the non-price factors (e.g.economic drivers)dominate
and the forecasts are increased over 1983.The RE085A version of the model
shows about 0.1%lower consumption in 2010 than the July,1983 version.
Table 6.2 shows a more detailed comparison of the RED85A reference case
forecast with the July,1983 forecast for the year 2010 by sector and load
center.Generally speaking,the price-adjusted Anchorage load forecast in the
6.1
TABLE 6.1.Comparison of Railbelt Total Electricity
Consumption Forecasts,RED85A Versus
July 1983 RED Model
RED85A July 1983
Case and Year GWh r1W GWh MW
Reference Case (HI2):
1980 2409 489 2364 488
1985 3063 622 3096 640
1990 3650 746 3737 777
1995 4087 836 4171 868
2000 4486 916 4542 945
2005 5063 1034 5093 1059 )
2010 5854 1195 5858 1217
DRI Case (HI3):I
1980 2409 489 2364 488
1985 3074 624 3110 642 )
1990 3609 738 3717 773
1995 4176 854 4341 904
)2000 4829 987 5041 1050
2005 5626 1149 5857 1220
j -2010 6709 1370 6965 1450
FERC -2%Case (HE8):
I198024094892364488
1985 3108 631 3145 650
I199036777293752780
1995 4000 795 4009 834
I200043398634262886
2005 4829 963 4658 967
2010 5499 1099 5224 1084 I
RED85A version has been reduced due to increased long run price responsiveness )
of the model.The Fairbanks-Tanana Valley load center experiences very little
electricity price change,so the non-price effects increase the forecast over
)
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TABLE 6.2.Detailed Comparison of Reference Case.Forecasts,
Year 2010,RED85A Versus July 1983
Anchorage Falrbanks
ResIdentIal RED85A July 1983 %DIH.RED85A July 1983 %01 H.
Total ConsumptIon (GWh)1,863 2,021 -7.8 540 551 -2.0
ConsumptIon,Before PrIce
Effects (GWh)2,135 1,987 7.4 526 524 0.4
Total ConsumptIon per House-
hold (KWh)12,167 13,198 -7.8·14,888 15,176 -1.9
Total ConsumptIon per House-
hold,Before PrIce Effects
(kWh)13,941 12,978 7.4 14,501 14,429 0.5
BusIness
Total ConsumptIon (GWh)2,410 2,352 2.5 618 511 20.9
ConsumptIon,Before PrJce
Effects (GWh)3,130 2,645 18.4 615 500 23.1
Total ConsumptIon per
Employee (kWh)11,785 11,502 2.5 12,001 9,929 20.9
ConsumptJon per Employee,
Before Price Effects (kWh)15,307 12,932 18.4 11,958 9,712 23.1
Total ConsumptIon per Square
Foot (kWh)19.52 27.38 -28.7 22.29 23.64 -5.7
Consumpt.lon per Squa re Foot,
Before PrIce Effects (kWh)25.35 30.79 -17.7 22.20 23.12 -4.0
Tota I Forecast
Total ConsumptIon (GWh)4,634 4,735 -2.1 1,220 1,123 8.6
Peak Demand
(MW)940 960 -2.1 254 257 -1.2
Average Growth Rate In Con-
sumptIon from 1980 (%)2.9 3.0 -0.1 3.6 3.5 0.1
Growth Rate In per CapIta
ConsumptIon (%)0.6 0.7 -0.1 1.6 1.5 0.1
the 1983 forecast.Additionally,in Fairbanks rising fuel oil and gas (pro-
pane)prices cause electricity to become increasingly attractive,so the price
effects on electricity consumption are more positive than in 1983.
The detailed comparison in the Anchorage residential sector in Table 6.2
shows a decrease in consumption per household in the year 2010 compared to the
July,1983 forecast even though the preliminary forecast of consumption per
household is higher.This reflects the increased long run price sensitivity of
the model.Fairbanks also shows increased conservation due to price effects.
6.3
The difference between the price-adjusted forecasts is a decrease in consump-
tion of about 2.0%as opposed to a 0.4%increase before price adjustments.
In business,RED85A forecasts of total consumption and total consumption
per e~p10yee are increased slightly (2.5%)in Anchorage compared to the July
1983 forecast.The forecast in Fairbanks is 20.9%higher.However,electric-
ity consumption per square foot of business f100rspace,both before and after
price adjustments,is significantly below that in the 1983 forecast in both
load centers.This is due to the increased long-term price responsiveness of
the model and to the adjustments made in the historical business consumption
and building stock data series (and,hence,the business consumption equation).
The increase in consumption per employee prior to price effects shown in
Table 6.2 is due mostly to the increased floorspace per employee projected in
the RED85A version.
Overall,the reference case forecast is reduced by 2.1%in Anchorage and
increased by 8.6%in Fairbanks.The net effect is an increase of less than
0.1%in Rai1be1t consumption and a decrease in peak demand of 1.9%or about
23 r1W.The 3D-year average growth rate in electricity consumption per capita
is reduced from 0.7%to 0.6%in Anchorage,and increases from 1.5 to 1.6%in
Fairbanks •
.Table 6.3 compares price-impacted electricity consumption in the
Anchorage-Cook Inlet load center for the reference case,DRI case,and FERC-2%
case to demonstrate how price scenarios now affect the details of the forecast
compared to 1983.In the reference and DRI cases,electric,gas,and oil
prices on a conversion-efficiency-adjusted basis all rise significantly;in the
FERC-2%case,electricity and gas experience much more modest increases while
oil falls in price.The key price is electricity,because electricity is not
cost-competitive on an efficiency adjusted Btu basis with gas in the Anchorage-
Cook Inlet load center in any of the cases.The availability of cheaper oil
and gas in the FERC-2%case makes some ~ifference in the RED85A residential
forecast (the price-adjusted forecast is lower than in the reference case even
though the electricity price is lower).However,it is clear that increased
electricity prices are having a bigger influence than in the 1983 1 s forecast.
Price responsiveness in the residential sector is sufficient to cause
6.4
TABLE 6.3.Comparison of Price-Impacted Consumption,RE085A
Versus July 1983 Forecasts,Anchorage-Cook Inlet
1980
2010
1980
2010
3.FERC-2%
Consumption Per Household (kWh)
RED85A July 1983
(a)Prices are in 1980 constant dollars for fuel delivered to the customer,
adjusted for average conversion efficiency of end-use appliance using the
fuels.
Consumption Per Employee (kWh)
RED85A July 1983
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Residential
1.Reference Case:
Cost of Energy ($/10 6 Btu)(a)
E1 ect ri c Oil Gas
1980 $10.84 $11.92 $2.66
2010 19.64 19.00 8.28
2.DRI (High Price)Case:
Cost of Energy ($/10 6 Btu)(a)
E1 ect ri c Oi 1 Gas
$10.84 $11.92 $2.66
21.98 24.57 9.78
(Low Price)Case:
Cost of Energy (S/10 6 BtU)(a)
Electric Oil Gas
$10.84 $11.92 $2.66
16.71 6.80 3.94
Business:
1.Reference Case:
Cost of Energy ($/10 6 Btu)(a)
E1 ect ri c Oi 1 Gas
1980 $9.96 $11.08 $2.31
2010 18.76 18.15 7.92
2.DRI (High Price)Case:
Cost of Energy ($/10 6 BtU)(a)
Electric Oil Gas
1980 $9.96 $11.08 $2.31
2010 21.10 23.72 9.43
3.FERC (Low Price)Case:
Cost of Energy ($/10 6 Btu)(a)
E1 ect ri c Oil Gas
1980 $9.96 $11.08 $2.31
2010 15.83 6.35 3.59
13 ,699
12,167
13,699
11 ,943
13,699
11 ,895
8,672
11 ,785
8,672
11,716
8,672
11 ,956
13,699
13,198
13,699
13,396
13,699
12,186
8,407
11 ,502
8,407
12,035
8,479
11,049
6.5
consumption per household in the DRI case to drop below that in the reference
case.Previously,non-price effects on consumption caused by higher growth in
the housing stock dominated,so that high consumption was associated with high
prices and vice versa.
Rusiness consumption per employee is also shown in Table 6.3.The price
elasticities of demand are somewhat larger in business now than in the July
1983 forecast.This is reflected in the fact that the order of 2010 consump-
tion per employee aMong the three cases is reversed from the 1983 forecasts.
In the July,1983 version of RED,non-price effects on consumption (growth in
floorspace per employee and growth in consumption per square foot)more than
offset the conservation effects of higher prices in the DRI case.Then,lower
electricity prices did not result in enough extra consumption to offset the
effect of lower growth in the FERC-2%case.Now,however,higher (lower)
prices more than offset the relatively higher (lower)growth in the DRI (FERC)
case,so that consumption per employee is highest when the prices are lowest,
and vi ce versa.
Tabl e 6.4 shows the detai 1s of the"three forecasts in the Fai rbanks-Tanana
Valley load center.In this case,the price of all three fuels rises in the
reference and DRI cases and declines in the FERC-2%case.Howe~er,note that
electricity prices are virtually constant in all three cases.By contrast,oil
increases in cost to nearly the level of electricity in the first two cases,
but falls by to about a quarter of the cost in the third.Thus,the effect of
the level of electricity cost and changes in electricity cost are minimal,
while cross-price effects of changes in oil prices are relatively important.
Comparing residential consumption per household forecasts with the July,
1983 forecasts,one may note that one out of three forecasts has increased,
rwhiletheothertwodecreased.This primarily is due to larger impacts from
price effects in the higher price cases.The (positive)impact of rapidly
rising oil prices on consumption of electricity (substitution of electricity
for oil)is especially evident in the DRI case,boosting consumption per house-
hold in the former from 14,510 kWh/yr without price adjustments to 15,376 KWh
with price adjustments.In contrast,although the effect is not directly shown
in the table,cross-price effects reduce electricity consumption per household
6.6
TABLE 6.4.Comparison of Price-Impacted Consumption,RED85A
Versus July 1983 Forecasts,Fairbanks-Tanana Valley
(a)Prices are in constant 1980 dollars for fuel delivered to the customer,
adjusted for average conversion efficiency of end-use appliances using the
fuels.
1980
2010
1980
2010
3.FERC-2%
Consumption Per Household (kWh)
RED85A July 1983
7,496
9,340
7,496
9,929
11 ,519
15,176
11,519
16,019
7,496
10,500
July 1983
11 ,519
13 ,467
Per Employee (kWh)
11 ,519
14,888
RED85A
11 ,519
15,376
8,009
12,001
8,009
12,226
8,009
11 ,931
11,519
14,032
Consumption
Cost of Energy ($/10 6 Btu)(a)
El ect ri c Oil Gas
1980 $27.84 $12.05 $19.60
2010 29.31 19.20 31.25
2.DRI (High Price)Case:
Cost of Energy ($/10 6 Btu)(a)
Electric Oil Gas
$27.84 $12.05 $19.60
29.30 24.80 40.35
(Low Price)Case:
Cost of Energy ($/10 6 Btu)(a)
Electric Oil Gas
$27.84 12.05 19.60
26.38 6.86 11.16
Residential
1.Reference Case:
Business:
1.Reference Case:
Cost of Energy ($/10 6 Btu)(a)
El ect ri c Oi 1 Gas
1980 $26.38 $11.54 $17.37
2010 27.84 18.69 29.02
2.DRI (High Price)Case:
Cost of Energy ($/10 6 Btu)(a)
E1 ectri c Oi 1 Gas
1980 $26.38 $11.54 $17.37
2010 27.84 24.29 38.12
3.FERC (Low Price)Case:
Cost of Energy ($/10 6 Btu)(a)
E1 ect ri c Oi 1 Gas
1980 $26.38 $11.53 $17.37
2010 24.91 6.63 9.97
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6.7
in the FERC-2%case,from 14,502 (without price effects)to 14,032 kWh.This
is because oil's price advantage increases.over electricity in this case,
reducing electrical consumption.The overall price effect is to increase the
dispersion of the residential forecasts in Fairbanks.
In the Fairbanks business sector,per-employee electricity consumption in
all three forecasts has increased due to non-price effects.Because Fairbanks
business electricity consumption in the absence of price effects now increases
proportionately with increases in business floorspace in the RED85A model,
which is in turn proportional to employment,the consumption per employee
before price effects is identical in all three cases (11,958 kWh).Next,
although the own-price effects of rising electricity prices are ~inimal and
offset by the cross-price effects in both the ORI and reference cases,in the
FERC-2%case falling electricity prices are also ~ore than offset by the cross-
price effect of falling gas and oil prices.The (positive)cross-price effect
of rapidly rising oil and gas prices outstrips the (negative)own-price effect
in the DRI case and to a lesser extent in the reference case,with its smaller
oil and gas price increase.The signs of the effects are reversed in the FERC
case.Consequently,in the RED85A per-employee forecast.the FERC case is the
lowest of the three and DRI,the highest.In July,1983,the ranking of the
cases were ordered the same way.The ranking of total business consu~ption is
also unaffected between RED85A and July,1983.Highest business consumption
still occurs in the DRI case due to higher economic growth and cross-price
effects while lowest consumption still occurs in the FERC-2%case.Dispersion
among the forecasts is reduced.
6.8
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APPENDIX A
RAILBEL T COMMERCIAL BUILDING
STOCK AND ENERGY USE DATA
RESULTS •••••••••••••••••••••••••••••••••••••••••••••••••••••••A.l.3
APPROACH ••••••••••••••••••••••••••••••••••••••••••••0 •••••••••
A.l.O INTRODUCTION
CONTENTS
•••••••••••••••••••••••••••••••••••••0 ••••••••••••A.1.l
A.1.2
A.2.0 COMMERCIAL BUILDING STOCK AND ENERGY USE DATA •••••••••••••••••A.2.1
NATURE OF THE INITIAL DATA ••••••••••••••••••••••••••••••••••••A.2.l
Rationale for Additional Data Collection ••••••••••••••••A.2.4
.\
Usefulness of the New Data Search •••••••••••••••••••••••A.2.4
RAILBELT COMMERCIAL BUILDING STOCK ••••••••••••••••••••••••••••A.2.6
RAILBELT ENERGY USE DATA BY BUILDING TYPE •••••••••••••••••••••A.2.l0
EVALUATION OF COMMERCIAL BUILDING STOCK AND ELECTRICITY
CONSUMPTION DATA •..•.•.••.•••..•••.•••.•..••••••••..•.•••••.•.A.2.13
A.3.0 INTERVIEW SUMMARIES •••••••••••••••••••••••••••••••••••••••••••A.3.l
FAIRBANKS AREA INTERVIEWS
Fairbanks Municipal
.............'.
Utilities System ••••••••••••••••••••
A.3.l
A.3.l
Golden Valley Electric Association ••••••••••••••••••••••A.3.2
Fairbanks North Star Borough Assessor's Office ••••••••••A.3.2
North Start Borough Engineering •••••••••••••••••••••••••A.3.3
Realty,Inc .............................................A.3.3
Fairbanks Development Authority.........................A.3.4
Market Basket Food Stores •••••••••••••••••••••••••••••••A.3.5
Department of Transportation and Public Facilities ••••••A.3.5
Chugach Electric Association ••••••••••••••••••••••••••••
ANCHORAGE AREA INTERVIEWS
Anchorage Municipal Light and Power •••••••••••••••••••••
A.3.5
A.3.6
A.3.7
A.iii
EXHIBIT A.I -INTERVIEW GUIDE FOR UTILITY MANAGERS •••••••••••••••••••
EXHIBIT A.2 -INTERVIEW GUIDE FOR BUILDING MANAGERS ••••••••••••••••••
•••••••••••••••••••••••••••••••••"•••••"••••••••••••" • " 0 •
A.i v
U.s.General Services Administration -
A.3.8
A.3.8
A.3.8
A.3.8
A.3.9
A.3.9
A.3.9
A.3.10
A.3.10
A.3.10
A.R.l
A.A .1
A.B.I
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Anchorage Telephone Utility •••••••••••••••••••••••••••••
Anchorage School District
Municipality of Anchorage Energy Coordinator ••••••••••••
Municipality of Anchorage Community Planning
De part me nt .""e III " D • 0 • 0 •tl •0 •••••••••••••
Department of Administration -The State of Alaska
Sears -Anchorage ••••..••••••••••••••••••••.•.••••••••••
Department of Transportation and Public Facilities -The
State of Alaska •••••••••••••••••••••••••••••••••••••.•••
Federal Buildings •••••••••••••••••••••••••••••••••••••••
State of Alaska Bui 1di ngs •••••••••••••••••••••••••••••••
Realtors/Developers •••••••••••••••••••••••••••••••••••••
REFERENCES
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TABLES
A.2.1 Anchorage-Cook Inlet and Fairbanks-Tanana Valley Benchmark
1978 Commercial Building Stock,July 1983 Version of RED ••••••A.2.2
A.2.2 Estimated Commercial Building Stock Series for 1973-1981,
July 1983 Version of RED ••••••••••••••••••••••••••••••••••••••A.2.3
A.2.3 Collected Building Stock Data in the Fairbanks Area •••••••••••A.2.7
A.2.4 Fairbanks 1983 Building Stock Estimate ••••••••••••••••••••••••A.2.8
A.2.5 Available Commercial Building Stock Data for Anchorage ••••••••A.2.10
A.2.6 Building Energy Use Data ••••••••••••••••••••••••••••••••••••••A.2.12
A.3.1 Building Type Designations on Fairbanks Assessor Forms ••••••••A.3.3
A.3.2 Building Characteristics on Fairbanks Assessor Forms ••••••••••A.3.3
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A.I.O INTRODUCTION
This document presents a review of recent patterns of commercial building
energy use and building stock in the Alaska's Railbelt region.The information
was collected to address questions concerning the availabillty of additional
information about the Railbelt Electricity Demand (RED)model business sector
structure.The questions were received at a workshop conducted in Anchorage in
September 1983 to explain the RED model to Federal Energy Regulatory Commission
(FERC)staff.FERC staff wondered whether local utility customers,utilities,
and government units 1)had counts or estimates of the current commercial
building stock and trends in the stock;2)had done studies of historical or
current electrical consumption that would relate building stock to electricity
demand.In response to these questions,Battelle-Northwest and Harza-Ebasco
technical staff set up a short data collection project designed to determine
the availability and usefulness of information from Railbelt sources on:
•the current total stock of buildings in the commercial sector and the
composition of the stock;
o recent changes in the type of buildings being constructed that might
affect average electrical consumption per square foot of floorspace,
per employee,or per customer;
•actual electricity use per business customer,per customer,per
square foot of business space,and per employee;
•the types and intensities of electrical end uses and known recent
trends in these end uses.
The purpose of this project was to either provide additional data that could be
used to improve the electricity demand model in the Railbelt business sector
or,alternatively,to demonstrate what critical data items were still missing
and would have to be assumed in order to forecast business electrical
consumption.
A.I.I
APPROACH
In most regions of the U.S.,the primary sources of commercial building
stock and energy use information are usually utilities.During the last 10
years,many pUblic and private utilities have initiated energy auditing pro-
grams for commercial customers to support conservation programs.Typical types
of information collected include:
•number of commercial customers by 4-digit Standard Industrial Classi-
fication (SIC)code
•annual energy use by customer type
•building characteristics of representative customers,including
square footage,construction characteristics,type of HVAC system,
and occupancy trends.
Therefore,our initial target for collecting commercial building information in
the Railbelt region was the utilities.Interviews were conducted with managers
of the four main urban Railbelt electricity utilities by representatives of the
Harza-Ebasco Susitna Joint Venture and Battelle Northwest Laboratories during
late February-early March,1984.The discussions addressed:1)commercial
building stock data,2)the types of energy use in the commercial sector,and
3)data describing energy use by building type.Our questions were submitted
to the utilities in advance~The interview guideline used in our interviews of
the utilities is displayed in Exhibit A.l.
To insure that all data sources were identified,similar interviews were
conducted with government officials,commercial realtors,and with the building
managers of representative commercial buildings in Fairbanks and Anchorage.
Additional information sources identified during the interviews were also
reviewed to insure that all available data describing commercial building stock
and energy use in the Railbelt region were identified and characterized.The
interview guide for the building managers is displayed in Exhibit A.2.
Once the interviews were completed,the information collected from the
region was analyzed to determine 1)the quality of the data collected,and
2)whether the original RED business sector model parameters should be revised
in light of the new data.
A.l.2
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RESULTS
The results of the data collection effort are reported in greater detail
in the next two chapters.This section summarizes those results,however,as
they relate to FERC staff questions.
•The original RED model relied on benchmark estimates of the commer-
cial building stock prepared in 1980 by the University of Alaska
Institute of Social and Economic Research (ISER)for the year 1978 to
test our historical a time series on commercial building stock.A
sufficient body of information was available on the Fairbanks-Tanana
Valley load center to allow production of a new benchmark estimate of
11.2 million square feet for the 1983 building stock in that load
center.There was insufficient coverage of the Anchorage-Cook Inlet
load center or agreement among data sources to develop a new bench-
mark estimate of commercial space in that load center.However,the
available partial stock counts and estimates were consistent with the
sum of commercial construction between 1978 and 1983,plus ISER's
benchmark stock for 1978.We therefore retained that benchmark esti-
mate as the basis for the Anchorage stock series.
•Only anecdotal information was available on trends in the commercial
building stock by type of building.Anecdotal information included
estimates that high-rise class IIA II office space was becoming more
common in Anchorage,while strip-type suburban developments featuring
office and retail space were becoming more common in Fairbanks.This
information was not necessarily considered indicative of significant
changes in the types of building being constructed.F.W.Dodge Con-
struction Potentials data set published by McGraw-Hill were acquired
in order to answer FERC's questions in a more precise manner.
•There was no comprehensive data source in the Railbelt on current
electricity use per square foot of business space or per employee in
either load center,although all the utilities contacted could esti-
mate use per customer.Portions of the commercial stock were covered
by various data sets,however.It proved possible to collect con-
sumption data on several individual customers which could be matched
A.1.3
"lith floor space data from a variety of sources.In general,it
appears from the buildings examined that the RED model's average use
per square foot is approximately correct.However,use per square
foot and per employee is at least as variable within classes and
types of buildings as it is between classes.Thus,even if it were
determined that there was a significant trend in certain types of
buildings being built,this would not imply that average electricity
consumption per square must change as a result.
o Likewise,only anecdotal information was available on trends in elec-
tricity consumption in the business sector.Very few businesses were
tak i ng extraordi nary measures to conserve.f10st were cutting down
lighting or changing over to fluorescent fixtures,insulating,sett-
ing back temperatures,and reducing window area.In some "special
opportunity"situations such as supermarkets,heat was being con-
served or recovered off process loads.Little or no information was
available on the success of these measures in reducing electrical
loads,however.
In summary,although a considerable fragmented body of information exists,
there is no comprehensive body of data in the Railbelt on commercial buildings
and their electricity use.The evidence that does exist for portions of the
stock suggests that the estimates of total commercial stock and electricity
consumption per square foot used in the Railbelt Electricity Demand Model are
approximately correct.
The remainder of this report is organized as follows.The second chapter
describes the study's findings on business electricity consumption and commer-
cial building stock data.It begins with the previous estimates from the RED
model which the FERC staff had reviewed,including data sources and limita-
tions.The chapter then describes the results of the current data collection
effort concerning building stock counts and energy use in the business sec-
tor.The final section of the chapter evaluates the current results in light
of FERC staff questions and describes how the new data have been used to refine
the RED model.The last chapter provides summaries of the individual inter-
views conducted as part of the data collection effort.
A.1.4
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A.2.0 COMMERCIAL BUILDING STOCK AND ENERGY USE DATA
NATURE OF THE INITIAL DATA
In the July 1983 version of the RED model,commercial-light industrial-
government (business)electricity consumption was forecasted using a four-step
process:1)A "predicted"historical building stock series was constructed for
the two Railbelt load centers,using an econometric equation derived from
pooled national data.2)Historical commercial-industrial-government electric-
ity consumption in Railbelt load'center.s was regressed on this historical "pre-
dicted"stock to estimate a consumption equation.3)Building stock was next
forecasted into the future in each load center and business electricity con-
sumption was then derived using the equation in Step 2.4)Finally,this pre-
liminary consumption estimate was adjusted for price effects.Because of the
importance of the II pre dicted ll stock series to the analysis,it was necessary to
check its accuracy.This was done in the July 1983 version of the model by
constructing an independent benchmark estimate of the 1978 commercial building
stock from locally available data (shown in Table A.2.1)and then using the
F.W.Dodge Construction Potentials data series on total square fe~t of
construction starts to derive an lI ac tual ll (really,estimated)building stock
series.This actual series was then compared to the predicted historical
series.(a)This comparison is shown in Table A.2.2.In some years,particu-
larly in Fairbanks-Tanana Valley,the II pre dicted ll series appears to be a signi-
ficant underestimate.However,the Fairbanks "actual"series was based on the
assumption made by ISER that square footage per employee was identical in
Anchorage and Fairbanks in 1978.In fact,because of higher building costs and
energy prices it is likely that Fairbanks-Tanana Valley building stock per
employee was less than in Anchorage in 1978.Thus the II pre dicted"building
(a)The predicted series was used for forecasting because of certain restric-
tions contained in the Battelle-Northwest agreement with McGraw-Hill for
access to the Construction Potentials Data.The testing was actually done
as part of the project that developed a stock prediction equation for the
U.S.Department of Energy.
A.2.1
A.2.2
TABLE A.2.1.Anchorage-Cook Inlet and Fairbanks-Tanana Valley Bench~ark
1978 Commercial B~i1ding Stock,July 1983 Version of RED
(10 Square Feet)
(a)Twenty-five businesses from telephone book;2,500 square feet assumed per
business.
(b)Ratio of Eagle River/Chugiak housing stock to that of Anchorage,times
Anchorage commercial stock.
(c)Assumes 2000 rooms at 500 square feet/room.
(d)Forty-six establishments at 10,000 square feet per establishment.
(e)Twenty-five percent growth,based on 1975-78 growth in civilian emp1oy~ent
(10%)and assessed value (48%),plus assumed crowding during 1975 because
of rapid 1974-75 employment growth.
(f)Allocated to manufacturing in original source,probably by relative
employment in the sector.
(g)Based on Anchorage square feet per nonagricultural civilian employee.
Source:Goldsmith and Huskey 1980.
Anchorage-Cook Inlet:
Anchorage Metropolitan Area Transportation Study Survey
Less:Non-Energy Using (Parking lots,etc.)
Plus:Undercount (20%)
Plus:Excluded Sectors in(A~chOrage
1.Gridwood/Indian a)
2.Eagle River/Chugiak(b)
3.Hotels/Mote1s(~)
4.Assorted Cultural BUi1dir)~~(d)
Plus:Growth Between 1975 and 1978\)
1978 Anchorage Commerci a1-Industri a1 F100rspace
Less:f1anufacturi ng (f)
Plus:Other Areas in Load CeQter
1.Kenai-Cook In1et{g)
2.r1atanu $k~-Sus it na (g)
3.Seward~g)
1978 Anchorage-Cook Inlet Total
Fairbanks-Tanana Valley
Fairbanks-North Star Borough(g)
Southeast Fairbanks Census Division(g)
1978 Fairbanks-Tanana Valley Total
42.1
18.9
4.6
0.05
0.3
1.0
0.5
7.4
37.0
0.9
3.2
1.5
0.6
41.4
10.4
0.4
10.8
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where:
A.2.3
(a)Based on stock prediction equation:
Pet.Change (STOCK)=f(GNPD,POP,INC,r)
Fairbanks-Tanana Valley
Predicted(a)Actual (b)
5.4 5.4
6.0 6.0
6.6 6.9
7.2 8.8
7.8 10.1
8.2 10.8
9.4 11.1
9.9 11.4
10.4 11.5
27.1
29.7
33.6
37.2
39.7
41.4
42.7
44.1
44.9
27.1
29.7
31.2
33.8
37.0
40.5
42.3
43.8
44.7
Anchorate-cook Inlet
GNPD =Gross national product deflator
POP =Regional population
INC =Income
r =nominal interest rate
For the exact formulation,see Susitna Hydroelectric
Project FERC License Application,Volume 2C:RED
Model (1983 Version)Technical Documentation Report,
pp.6.13 to 6.16.
(b)Based on the 1978 estimate in Table A.2.1,plus F.W.
Dodge construction starts,not edited for completions
of construction projects that were started and later
abandoned or modified.
Year
1973
1974
1975
1976
1977
1978
·1979
1980
1981
TABLE A.2.2.Estimated Commercial Building Stock Series for 1973-1981,
July 1983 Version of RED
stock series may provide an adequate basis for forecasting in Fairbanks-Tanana
Valley.There was little difference·between the "pre dicted"and "ac tual"
series in Anchorage-Cook Inlet.
The initial data utilized to benchmark building stock in the RED model
came from a number of sources.The basic source for the Anchorage area was the
Anchorage Metropolitan Area Transportation Study (AMATS)conducted in 1975.
This study,conducted by the Municipality of Anchorage,counted the commercial,
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industrial,and govern~ent building stock in the Municipality.The University
of Alaska .Institute of Social and Economic Research (ISER)updated this 1975
estimate to 1978 by taking account of 1)non-energy using building stock
2)building stock in areas outside of Anchorage and 3)growth in employment,
1975 to 1978.The Fairbanks 1978 benchmark stock was estimated by ISER by
assuming square footage per employee was the same in Fairbanks as in Anchorage.
Rationale for Additional Data Collection
The model was tested in the Railbelt using F.W.Dodge Construction Poten-
tials data on annual commercial construction in Railbelt locations.The II pre -
dicted ll stock series obtained by using the model had been considered close to
the lI ac tual ll stock series obtained by combining F.W.Dodge construction with
the 1978 benchmark stock estimate from ISER;how~ver,no independent verifica-
tion had ever been obtained of the 1978 benchmark estimated building stock or
the implied average business electricity consumption rates,which appeared high
in comparison to national figures (~20 kWh per square foot per year,versus
13.75 kWh per square foot in the U.S.).In addition,since all commercial,
light industrial,and government building space had been combined in the 1978
stock estimate and the predicted stock series,FERC staff were interested in
possible recent historical changes to the mix of building stock that could have
resulted in changes to energy use characteristics.The Railbelt interviews
were directed both toward verifying or revising the earlier stock estimates and
toward verifying or revising estimates of commercial-light industrial-
government electricity consumption at the most detailed end-use level possible.
Usefulness of the New Data Search
The search for better bui 1di ng stock and energy use data focused upon the
electric utilities,government agencies,planning agencies,and commercial
building owners in the Railbelt region.Interviews were conducted primarily in
the Fairbanks and Anchorage areas,with other contacts being made by telephone
and/or mail.The primary goal was to identify the availability and quality of
data which described building stock and/or energy use for the commercial
sector.Pertinent data was collected and analyzed;the objective was to deter-
mine if the original forecasted stock and energy use data could be improved
with utility and government data.
A.2.4
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The search for better stock and energy use data produced some useful,
aggregate figures for specialized subsets of the commercial sector.In gen-
eral,the Railbelt utilities maintain information of electricity sales by cus-
tomer name only;no existing data bases could provide summaries of building
stock and energy use for each customer type and area.The utilities also had
no formal auditing or conservation programs for their commercial customers,
thus,minimal information describing the building stock (square footage,con-
struction type,heating data,etc.)was available.
Additional data describing commercial building stock and business elec-
tricity consumption data was recovered from a variety of public and private
sources other than the utilities.However,no comprehensive data set on build-
ing stock and business electricity consumption could be found.The main reason
is that the various data sources we evaluated necessarily have specific mis-
sions and objectives that caused them to collect the data in the first place.
These missions and objectives did not include forecasting electricity demand
for the Railbelt using building stock as an independent variable,however,so
the data contained several gaps.For example,a survey of 1983 Fairbanks com-
mercial building stock was described as "comprehensive."Further"examination
revealed that the survey was designed to focus only on the supply of office,
retail,and business park (warehousing and distribution)space in the immediate
Fairbanks vicinity.The survey excluded more of what we defined as commercial
space in the Fairbanks-Tanana Valley load center than it included.Missing
were all public buildings,lodging facilities,assembly halls,hospitals,and
transportation-related facilities such as repair shops.The outlying towns of
North Pole,Nenana,and the Delta Junction area were also excluded.Finally,
there was a significant undercount in the categories of stock that were
addressed.Thus,although the survey was "comprehensive,"given its purpose,
it was necessary to bridge several data gaps with other sources and by assump-
tion to arrive at a more comprehensive stock estimate.
In Anchorage-Cook Inlet,the only counts of commercial stock were even
more limited.One count by the Municipality of Anchorage Community Planning
Department covered only offi ce and some retail space in the city core C'Down-
town"and "Midtown"areas),excluding suburban Anchorage,Eagle River,Chugiak,
A.2.5
Kenai,and Matanuska-Susitna areas.There was no comprehensive source for
electricity used per square foot.Federal,state,and local governments cov-
ered this for their own facilities,but these records were not always complete.
A handful of private buildings had been surveyed by one Anchorage utility for
energy use and was available.Where possible,we also combined utility-
reported consumption data in both load centers for some individual larger cus-
tomers with building square footage identified from the 1983 Fairbanks and
Anchorage surveys.In sUMmary,while the data collected were suggestive,they
were by no means complete.
RAILREL T Cor1t1ERCIAL RIJILDI NG STOCK
The data search in Fairbanks began with interviews with utility staff at
two local utilities (see interview results with FMUS and GVEA in Chapter"
A.3.0).Since neither utility maintained information on floorspace or energy
use for the basic types of commercial buildings,additional sources were
sought.The Fairbanks Development Authority made available a survey of several
commercial building types in the core area of Fairbanks.This survey,per-
formed by Mundy-Jarvis and Associates,Inc.,included about 2.0 million square
feet of office,retail,and business park space in downtown Fairbanks (see
Table A.2.3).The survey,however,excluded 1)other types of commercial
buildings such as public,lodging,health,and assembly buildings and 2)out-
lying areas such as North Pole,Nenana,Delta Junction,and recent development
towards the airport.These shortcomings led to additional interviews to obtain
stock data for the areas and building types not included in the Mundy-Jarvis
survey.Descriptions of floorspace for North Star Borough buildings were
obtained along with estimates for the major state and federal buildings in
Fairbanks.These data are also shown in Table A.2.3.The cumulative floor-
space described in these three sources was approximately 5 million square feet,
which was still far lower than the original REO estimates and did not include
many commercial building types in the Fairbanks area.
Other sources were sought to describe the remaining areas and building
types that were not included in the above data bases.The remaining building
stock was not covered by any other comprehensive data source.Therefore,to
A.2.6
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TABLE A.2.3.Collected Building Stock Data in the Fai rbanks Area(a)
obtain an approximation of the balance of building stock,Battelle Northwest
conducted a count of commercial businesses in the phone book by type of busi-
ness.These counts were combined with conservative U.S.median values for
square footage (about one half the mean values)by business type (U.S.Energy
Information Administration,1983)to estimate the building stock of these
(a)Not complete.Excludes lodging,health care,laundry,churches,
auto supplies/sales,and all types outside of the downtown area.
Source:Mundy-Jarvis Associates (1983).
(b)Not complete.Retail core is 5 buildings,Retail suburb is
5 mall s.
(c)Not complete.Excludes buildings under 7,500 square feet.
(d)Estimate of percent missed supplied by Mundy-Jarvis Associates.
(e)Source:North Star Borough Engineering Department.
(f)Source:U.S.General Services Administration.
(g)Source:Alaska Department of Administration.
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Offi ce
Mixed Use
Retai 1 (Core)
Ret ail (S ub)
Office/Warehouse (owner)
Office/Warehouse (renter)
North Star Borough Buildings(e)
(1984)
Schools
Others
State and Federal Buildings
(1984 )
Federal(f)
State(g)
Total
Counted
Square Feet(a)
419,458
108,324
146 073(b),
445,000(b)
285,016(C)
599 060(c),
2,002,931
1,395,753
258,853
157,000
1,186,420
5,000,957
Total,Incl udi ng
Estimate of Missed(d)
557,879
140,821
189,984
600,750
688,768
688,919
2,867,121
1,395,753
258,853
157,900
1,186,420
5,865,147
A.2.7
businesses.The results,displayed in Table A.2.4,totaled about 5.7 million
square feet.A total figure was developed by combining the counted stock ln
Table A.2.3 with the estimated stock in Table A.2.4 and subtracting one-third
of the construction between 1984 and 1983 to allow for stock changes between
the 1983 average,the Mundy-Jarvis counts,and our early 1984 estimates.The
total estimated square footage for 1983 from all the identified sources was
11.2 million square feet;this compares favorably with the predicted stock of
10.4 million square feet for 1981 shown in Table 6.7,Volume 2C of the July
1983 Susitna license application.(a)
The data collection situation in the Anchorage area was similar to that in
Fairbanks.The utilities offered no auditing or commercial conservation
TABLE A.2.4.Fairbanks 1983 Building Stock Estimate
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A.2.8
F.W.Dodge Construction Potentials data collected for this project showed
about 554 thousand square feet of commercial construction from 1981 to
1983.Our estimated stock for 1981 thus would have been 10.6 million
square feet (11.2 million,less 1981-83 construction).
(a)Calculated from business counts (phone book)and
national median (approximately 0.5 mean)space per
business by type.
(b)Count incomplete.No figures are available on City Hall
or Alaskaland buildings.Source:City of Fairbanks
Fire Marshall.
(c)Source:Fairbanks Memorial Hospital.
(d)Adjusted downward by 390,000 to account for dates of
estimates (early 1984),Mundy-Jarvis count (late 1983).
(a)
Total,Table A.2.3
Lodgings
Churches/Social Grange
Transportation/Mobile Home
City of Fairbanks Buildings
North Pole
Hospital
Subtotal
Total(d)
870 450(a),
774 OOO(a),
1,188,000(a)
260 463(b),
2,432,000(a)
201,000(c)
5,865,147
5,725,913
11,201,060
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programs,and little building stock data was maintained.The main sources of
data were 1)the Anchorage Community Planning Department,2)the Anchorage
School District,and 3)the State Department of Administration.These three
sources provided limited coverage of the Anchorage bowl,city schools,and
State buildings.Significant commercial development in retail and warehouse
buildings,especially outside of the downtown Anchorage area,are not covered
in any data base,leaving a large portion of the total commercial space unac-
counted for in any accessible data base.(a)Overall,the available building
stock data for Anchorage did not adequately cover many areas of the Anchorage-·
Cook Inlet load center,particularly areas that have recently experienced rapid
d~velopment.This allows only general comparisons to the Anchorage-Cook Inlet
stock data used in the RED model.The first conclusion·is that the commercial
building stock has grown since 1978 -almost 9.7 million square feet added
between 1978 and 1983,according to F.W.Dodge.Several areas have experi-
enced significant growth during the last few years.Second,the 1983 and 1984
stock data that were obtained are broadly consistent with the 1978 estimates
for the covered areas (primarily the core area,including State and public
school buildings).(b)More specific comparisons are not possible.The
information in these three sources is summarized in Table A.2.5.
(a)A new private firm in Anchorage estimates about 10 million square feet of
retail,office,and warehouse space in Anchorage for a data base of 350
buildings.The Community Planning Department's data base was 455
buildings for the core area alone.
(b)ISERls 1978 estimate of the Anchorage Municipality commercial was 36.1
million square feet.F.W.Dodge commercial construction statistics show
9.7 million square feet added between 1978 and 1983,for a 1983 estimated
total of 45.8 million.F.W.Dodge also shows about 27 percent on average
of all space added in Anchorage-Cook Inlet is office space.This implies
12.4 million square feet of office buildings in Anchorage.Twelve point
four million is slightly less than we got by multiplying the 9.96 million
estimate of 1983 office building stock from the Municipality times the
ratio of business telephone main stations covered by the survey area to
adjust for unsurveyed buildings in the municipality.This total was 13.0
million.
A.2.9
TABLE A.2.5.Available Commercial Building Stock Data for Anchorage
Municipality of Anchorage Planning Department--
1983 Commercial Office Inventory
Square
Footage
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IICore Area ll Offices (455 buildings)9,960,232(a)
Anchorage School Board of Education
A.2.10
RAILBELT ENERGY USE DATA BY BUILDING TYPE
The utility interviews revealed that the four urban utilities had col-
lected minimal information describing the energy use of their commercial cus-
tomers.The Fairbanks utilities (FMUS and GVEA)maintained monthly energy use
·(a)Tim Lowe (Lakeland Corp.)estimate was 6.5 million
office for the whole Municipality;Jack White
Company estimate was 7.4 million.This is probably
net office space rather than the building total.
Offices comprised 7.1 million square feet of the
space in the buildings shown.The II core area ll is
the area south of Elmendorf AFB,north of Dowling,
east of Minnesota,and west of Bragaw.
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2,353,930
4,246,252
1983 Stock
1984 Square
Footage
Elementary,Secondary
and Special Services
State of Alaska Building Index--
Anchorage Load Center
The existence of energy use data for the main types of commercial build-
ings was also reviewed.The main goal was to examine the accuracy of the
original RED model estimates for electricity consumption.This accuracy check,
however,depends upon the availability of recent energy use per square foot
data for the commercial buildings.The initial sources interviewed about the
existence of such data were again the utilities and government planning groups.
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records only according to the customer name;there was no classification by
customer type such as SIC code that would enahle analysis by commercial build-
ing type.The records also contained no information on building size,thus
eliminating the possibility of estimating energy use per square foot.The
Anchorage utilities (AML&P and Chugach)also had minimal information on energy
use and building size for their commercial customers.AML&P had developed
energy use data for their customers with connected loads greater than
300 KvA.Unfortunately,not all of these customers were included in available
building surveys;those customers for which building space data were available
are reported in the Anchorage results shown in Table A.2.6.
The overall value of the utility data is as a check on aggregate energy
use per unit area for major types of commercial buildings.The values were
generally consistent with the previous RED 1980 average values of about 20 kWh
per square foot in both load centers.However,the small number of actual
buildings for which energy use and building space information was available
resulted in significant variance in energy use per square foot between indivi-
dual buildings of the same type.This small sample size limits the value of
the data to simply serving as an aggregate check of the original RED data.
The only governmental groups that maintained similar data were the North
Star Borough Engineering Department in Fairbanks and the U.S.General Services
Administration (GSA)in Anchorage.The Borough Engineering group provided
energy use and building size data for a small set of Borough buildings.The
GSA office maintained data for the Federal Building in Fairbanks and the Fed-
eral Building in Anchorage.The other government groups only maintained stock
data which was discussed earlier.
In order to estimate the energy use per square foot of building space
and/or per employee for major types of commercial buildings,annual energy
consumption of commercial buildings was also collected when available.Energy
use information was collected in both Anchorage and Fairbanks;the results are
displayed in Table A.2.6.The utilities maintained monthly and (usually)
annual energy consumption information yet the value of this information was
minimal unless the building floorspace data for each customer was available.
A.2.11
TABLE A.2.6.Building Energy Use Data (kWh/square foot)
A.2.12
U.S.Energy Information Administration,Non Residential Buildings Energy
Consumpt i onSu rvey,1979 Consumption and Expenditures.
Anchorage Muni ci pa 1 Light and Power,IiEmergency Power Report.II These are
averages for several Anchorage buildings in the given category.
Building Type
Assembly
Auto Sales and Service
Education
School 1
Library 1
School 2
Li brary 2
School 3
Food Sales
Food Store 1
Bakery 1
Health Care
Medical-Dental Prof.
Buil din g Part.
Lodging
Motel 1
Office
Federal Building
Borough Building
Bank 1
Courthouse Square
Bank 2
Retai 1/Servi ces
Retail 1
Retail 2
Retai 1 3
Retail 4
Retail 5
Retail 6
Warehouse/Storage
Other
Vacant
(a)
(b)
Anchorage
26.19
NA
11.72
19.17
NA
NA
12.67
16.73
17.59
NA
NA
NA
Fairbanks
18.48
NA.
16.68
18.49
13.83
NA
19.66
26.02
NA
14.93
NA
26.5
9.70
45.75
56.01
NA
15.48
16.04
12.21
29.88
35.54
NA
NA
U.s.AverQge
(1979)~a)
7.03
10.26
8.21
29.31
20.22
16.71
17.29
11.14
12.9
18.46
9.67
AML&P
Average (b)
NA
NA
NA
NA
30.66
15.8
20.4
20.4
24.35
NA
NA
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The Fairbanks energy use data were provided by the North Star Borough Engineer-
ing Department FMUS and GVEA (only for customers for which building area infor-
mation had been collected from other sources),the GSA,and the State of Alaska
Building Inventory.The Anchorage data were obtained from the Chugach power
requirements study,the AML&P internal review of municipal buildings,and phone
calls to building owners.
Table A.2.6 indicates the limited availability of energy use data for the
Railbelt which could be readily combined with building space information.This
limited availability resulted in small samples for each major commercial build-
ing type (sometimes only one example was obtained for a building type in each
major load center);thus,there was variability in the energy use values among
each building type.The primary benefit of these results is limited to pro-
viding a rough check of the original estimates.The new data did appear con-
sistent with the ·RED estimates for 1980,and the limited nature of the new data
permitted the original RED estimates to still be used as the best estimates of
energy use per square foot in Railbelt commercial buildings.
The interviews verified that data in existing data bases describing com-
mercial building stock and energy use in the Railbelt region is limited.
Unlike many larger utilities in the Lower 48 states,the Alaskan utilities
have little useful information on building size and energy use according to
customer type and maintain no auditing programs.Only scattered data collected
by planning and government organizations was available,and this data covered
only certain customer types.The information was,however,generally consis-
tent with the data used in the initial RED model forecasts.No trends were
identified in the interviews in either building stocks or energy consumption
.that were significantly different from previously forecasted data.
EVALUATION OF COMMERCIAL BUILDING STOCK AND ELECTRICITY CONSU~1PTION DATA
The renewed business sector data collection effort was successful in
addressing many of the FERC staff questions regarding the RED model.As a
result of this effort we can say with reasonable confidence that no comprehen-
sive data base exists in the Railbelt on commercial building stock,changes in
the stock,or energy use characteristics of that stock.Neither the utilities
A.2.13
themselves nor other public and private agencies collect the necessary data in
usable form.Limited data do exist on portions of the commercial building
stock which we were able to use.In the Fairbanks-Tanana Valley load center
enough buildings were counted that,~making some assumptions about missing
data,we were able to construct an improved benchmark estimate of the commer-
cial building stock for 1983.The data coverage from available sources would
not support a new benchmark estimate in Anchorage-Cook Inlet;however,the pre-
vious benchmark was found consistent with new data collected on portions of the
stock.No quantitative information was available from either load center on
trends in the building stock.Electricity consumption data likewise were
limited.Utilities kept consumption data only by customer name;no quantita-
tive information was available from this source for end uses of the electricity
or trends in consumption per employee or per square foot of building stock.
Through building owner interviews and through matching customer consumption
records and square footage information from a number of sources,it was possi-
ble to estimate total electricity consumption per square foot for a few dozen
commercial buildings for one recent year.These limited data on consumption
suggest that electricity consumption per square foot is likely well above the
U.S.average,as had been previously estimated in Volume 2C of the July 1983
Susitna License Application.The detailed data are consistent with the previ-
ous estimates of about 20 kWh of electricity consumption per square foot per
year average for the business sector.No information is available on histori-
cal changes in the intensity of use per square foot from Railbelt sources.The
F.W.Dodge construction potentials data set was acquired to help answer that
question.Analysis of the Dodge data appears in Appendix B,The Effect of
F.W.Dodge Construction Data on Railbelt Electricity Demand Forecasts.
A.2.14
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A.3.0 INTERVIEW SUMMARIES
A major element of the RED model is the estimates of both existing and
future square footage of commercial buildings.One element of the data collec-
tion effort in the Railbelt was to gauge the accuracy of the initial square
footage or "buil di ng stock II est i mates used in the RED model.The i ntervi ews
were used to identify available data bases describing building stock by cus-
tomer type.The strategy was to begin by determining what information the
utilities had acquired,and then interview other sources such as city,state,
and federal government officials,and developers/realtors.The interviews tar-
geted contacts primarily in the Fairbanks and Anchorage areas;brief descrip-
tions of the interview results are presented below for each area.
FAIRBANKS AREA INTERVIEWS
Power supply in the Fairbanks area is provided by the Fairbanks Municipal
Utilities System (FMUS)and the Golden Valley Electric Association (GVEA).The
interviews indicated that neither utility had any useful commercial building
stock data;thus numerous other government and private groups were contacted.
We found that several segments of the commercial building inventory were
covered in various studies,yet significant portions of the commercial building
categories were not covered.The specific results are described below.
Fairbanks Municipal Utilities System
FMUS categorizes commercial customers in two groups.Small commercial
customers have less than 15 Kw connected load while large customers have
greater than or equal to 15 Kw.There are about 300 large commercial customers
in their service area,and about 66 percent of these have connected loads
greater than 50 Kw.FMUS customer accounts are identified by customer name;no
information such as the SIC code or building size is maintained on their
records.The customer data that FMUS could provide to us included the customer
name,demand for one month,kWh usage for one year,load factors,and monthly
power costs.FMUS has no auditing program for the commercial customers;thus
they have no data on building stock,building size,building construction char-
acteristics,etc.They mentioned that several large retail buildings had been
A.3.1
audited by the customer (e.g.,the J.C.Penney department store at the request
of corporate headquarters),but FMUS did not have copies of the audits.
FMUS offers no formal commercial conservation program and,since no audit-
ing program is provided,no studies of trends in energy use were available.
The staff mentioned that refrigerated cooling of office space is increasing,
and that one local food market chain is experimenting with conservation and
energy management systems (see discussion with the building manager of Market
Basket Stores).
Golden Valley Electric Association
GVEA categorizes commercial customers in two groups;the dividing point is
a connected load of 50 Kw.The utility identified the customer by name in the
billing records,but no information on the customer type (SIC code)or on the
size of the building was available.The commercial customer data that GVEA
could provide included monthly electricity demand and consumption for 1980
through 1982.This information was keyed by customer name only;no assessment
of consumption by customer type could be conducted.
The GVEA staff indicated that GVEA offered no formal commercial conserva-
tion programs;they felt that conservation in newer buildings was simply the
result of owner interest in reduced energy costs.Several trends they identi-
fied include 1)a reduction in electric heating and 2)an increase in air
conditioning.The reduction in electric space heating was due to rules
restricting the installation of electric heat in buildings built after the mid
1970s.
Fairbanks North Star Borough Assessor's Office
The Assessor's Office maintains standard property records in manual phys-
ical files.No compilations of property by type and by square footage have
been completed.The forms contain information on the type of building (see
Table A.3.1)and on building characteristics (see Table A.3.2).
A.3.2
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TABLE A.3.1.Building Type Designations on Fairbanks Assessor Forms
The total number of Borough buildings is about 35,and 15 to 20 buildings
have had conservation retrofits.Typical activities include the following:
The property records are scheduled to be transferred to a computerized
filing system within the next several years.Once these records are placed
into such a system,extraction of the building stock data might be possible.
INCANDESCENT LAMP REPLACEMENT WITH FLUORESCENT LAMPS
REDUCED WINDOW AREA
NIGHT SETBACK OF THERMOSTATS
INSULATION RETROFITS.
APARTMENT
STORE
GAS STATION
GREENHOUSE
HEATING SYSTEM TYPE
ELEVATORS
NUMBER OF STORIES
FLOORING TYPE
Building Characteristics on Fairbanks Assessor Forms
HOSPITAL
CHURCH
BANK
INDUSTRIAL
TABLE A.3.2.
LODGING
WAREHOUSE
THEATER
GARAGE
RESTAURANT
FOUNDATION TYPE
EXTERIOR TYPE
ROOFING TYPE
FRAME TYPE
North Star Borough Engineering
This organization maintains square footage data for all Borough buildings,
including schools.The data includes monthly energy usage and costs and
includes consumption per square foot normalized to correct for the degree days.
Realty,Inc.
We interviewed a realtor recommended by the utilities as being the fore-
most local authority on commercial development trends,both past and future,in
the Fairbanks area.The first discussion topic was the availability of data
describing building space by type of establishment.We learned that no such
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A.3.3
data is m~intained in a central form.Realtors simply follow recent sales and
construction trends to estimate near-term growth patterns.The growth has
apparently slowed down since the boom in the 1970s,with a steady trend to
shopping centers and small shopping malls.
Information on energy use trends was also based solely upon the personal
experience of the realtor;no data base of energy information is maintained or
used by the realtors/developers in Fairbanks.Conservation options such as
added insulation and efficient lighting are being used in new buildings;the
impetus for these actions are simply owner interest in lower energy costs.A
common feature of new buildings are 6"-10"walls with vapor barriers.New
buildings are also smaller than in the past,with higher density development
becoming more common.If potential buyers wish to know past energy use perfor-
mance of a building,the relator reviews past utility bills from the current
owner;again,the relators have no central source of information to use.
Fairbanks Development Authority
Al DeKrey of the Fairbanks Development Authority (FDA)discussed his orga-
nization's activities,including a recent survey of office space in the "core
area"(downtown or more developed area)of the Fairbanks North Star Borough.
This survey,titled "A Comprehensive Space Inventory for the Fairbanks Develop-
ment Authority"was prepared by Mundy,Jarvi s and Associ ates Inc.in August
1982 and was updated in November,1983.It updates a simi 1ar survey performed
in 1980.The building types covered in the survey are listed below:
OFFICE SPACE
RETAIL
MIXED USE
WAREHOUSE.
Several limitations of the data are 1)a twenty-to-thirty percent under-
count of some building types (e.g.,C and 0 class office space)2)the exclu-
sion of commercial building space outside the "core area"and 3)the exclusion
of several building types important to the RED modeling effort.The building
types not covered in the FDA survey include public buildings,hotels/motels,
churches,automotive/service stations,and aircraft-related businesses.
A.3.4
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The FDA representative also discussed the planned development activities
in downtown Fairbanks.The main project is the possible construction of a
large motel/convention center.This proposed project will add significantly to
lodging capacity and to the retail and office space needed to provide the ser-
vices such as food,small retail stores,etc.The planned size of the conven-
tion center is "about the same size"as the Sheraton hotel in Anchorage:
8 stories tall,recommended for 250 rooms plus first class facilities (see
Laventhol and Horwath 1983).The added load of this center merits considera-
tion in the RED business sector if the project is actually built.
Market Basket Food Stores
The building engineer for Market Basket Food Stores discussed conservation
activities that his company is pursuing in the Fairbanks area.Because no
utility sponsored conservation programs are available,Market Basket has initi-
ated several programs of their own.All stores are experimenting with red~ced
lighting loads.New stores are having heat recovery installed on their refrig-
eration equipment.This has been very successful;apparently most of the
stores'heating loads have been met by the reclaimed heat system.All new
buildings have 10"ceilings and 6"walls with installed vapor barriers.No
submetering of electrical loads has been performed;thus,they could provide no
information on energy consumption for individual end-uses in their stores.
Department of Transportation and Public Facilities (DOTPF)
A listing of Fairbanks area buildings operated by the Alaska DOTPF was
obtained from the maintenance and operations staff at the Peger Road DOTPF
offices in Fairbanks.Included in the information base were the building name,
total square feet,and electrical consumption for the first eight months of
FY-1984.Twenty-eight buildings,totaling 371 thousand square feet,were
listed.About 4.3 million kWh were used during the eight months.Annual elec-
trical consumption was not available.
ANCHORAGE AREA INTERVIEWS
The Anchorage area receives electricity primarily from the Anchorage
Municipal Light and Power (AML&P)and from the Chugach Electric Association.
A.3.5
As in Fairbanks,neither of the Anchorage urban utilities categorized their
commercial customers by either customer type (SIC codes)or by building size.
Several small studies of their major customers had been performed,however,and
the t1unicipality of Anchorage performed a survey of commercial building stock
for the downtown area.Not included in any studies were the buildings in the
new growth areas outside the city center.The Federal buildings were not cov-
ered in a central database either;each agency is responsible for its property.
State building stock data is maintained in a central data base;we have
recei ved summari es of thi s data.The results of the i ntervi ews are out 1;ned
below.
Anchorage Municipal Light and Power
Anchorage Municipal Light and Power (AML&P)has no comprehensive data base
describing energy use and building characteristics by customer type for their
commercial customers.Their commercial customers are divided into two
classes;the dividing point is a connected load of 25 Kw.The only available
data on building stock was from a survey of their top 250 customers (down to
100 Kw),and included information on the number of occupants,the square foot-
age of the building,billing demand,and projected power requirements during
severe service disruption.About 60 to 70 usable responses were available from
this survey for inclusion in the floorspace estimates.The latest (1983)AtlL&P
Power Requirements Study was obtained;however,no information was available
relating consumption to building stock or employment.
Several general trends in energy use were identified in the interview.
First,cooling requirements are creating peak load problems in the summer.
Second,At1L&P and Chugach are exchanging several small service areas,thus some
system power requi rements changes are foreseen.At1L&P al so has a formal con-
servation plan.This plan addresses the following conservation activities:
CONSUMER I NFORt1A TI ON PROGRM1
MUNICIPAL WEATHERIZATION PROGRAM
SUPPORTED STATE PROGRAMS
WATER FLOW RESTRICTORS
WASTE HEAT RECOVERY IN CITY WATER
HOT WATER HEATER WRAP PROGRAM
A.3.6
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STREET LIGHT CONVERSION
TRANSMISSION VOLTAGE CONVERSION
STEAM DRIVEN BOILER FEED/CIRCULATING PUMPS.
The emphasis of these programs is on residential home owners and on city
facilities such as street lighting.
Chugach Electric Association
The Chugach Electric Association (CEA)has no commercial customer informa-
tion that could provide either energy use trends by customer type or informa-
tion on customer building stock by customer type.The utility provides no
auditing program for the commercial customers,either.
Chugach has collected limited load data from a survey of customers that
had greater than 350 KVA loads.They found that commercial energy use was
extremely diverse,even within the same customer group.The survey was
designed to identify where voluntary load reduction actions might be possible.
The survey included information on the following topics:
OPERATION HOURS
FUEL TYPE
HVAC SYSTEM TYPE
8ACKUP ELECTRICITY AVAILABILITY
BUILDING MINIMUM POWER.REQUIREMENTS
POSSIBLE LOAD REDUCTIONS (TYPE AND MAGNITUDE).
Better information may become available when Chugach switches to a new computer
system later in 1984,yet no customer classification by SIC code is planned.
The Chugach personnel indicated that several locally important energy use
trends merit attention.As noted by the other utilities,there is a signifi-
cant increase in cooling load.Most of this increase on the Chugach system is
due to the large new buildings being built in the Anchorage area.There is a
steady increase in construction of office buildings and shopping centers,yet
some of this commercial load will be lost in the planned service area switches
with AML&P.
A.3.7
Anchorage Telephone Utility
The Anchorage Telephone Utility (ATU)maintains a count of commercial cus-
tomers (business main stations)by geographic area (i .e.,wire center).They
could provide no classification of these customers by type of building/cus-
tomer,and their records did not distinguish between single and multiple cus-
tomers in a building.Therefore,this data was useful only as an approximate
indication of commercial activity in different areas of the city.
Municipality of Anchorage Community Planning Department
The Anchorage Community Planning Department maintains a computerized list
of commercial office space in.the downtown area of Anchorage (south of Elmen-
dorf Air Force Base,north of Dowling Avenue,east of Minnesota Street,and
west of Bragaw Street).The survey contains the property parcel number,build-
ing location,manager name,manager location,building 5quare footage,and
office square footage.The survey was subjected to no cross checking to verify
accuracy,yet the results are viewed as being reasonable.
Anchorage School District
The Anchorage School District provided their annual report which contained
current square footage estimates for all the city schools.The representative
indicated that energy conservation is considered when new schools are built,
yet could provide no indication of energy conservation programs in the existing
schools.
Municipality of Anchorage Energy Coordinator
Peter Poray,the Anchorage Energy Coordinator indicated that the only cen-
tral data base on Anchorage building stock was the survey done by the Anchorage
Community Planning Department (see discussion above).He mentioned that there
are about 200 municipal buildings,yet only 50 to 60 of these buildings are
significant in size and energy use.He also indicated that the State of Alaska
maintains a central data base of state buildings.
A.3.8
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Department of Administration -The State of Alaska
The Department of Administration offi ce maintains a summary of building
stock in local municipalities that is leased by the State of Alaska.The data
for the major Railbe1t communities is listed below:
Anchorage 892,610 sq ft
Delta Junction 8,518 II
Eag1 e Ri ver 1,287 II
Fairbanks 100,588 II
Homer 14,247 II
Kenai 139,584 II
Pal mer 31,109 II
Seward 109,897 II
Was i 11 a 11 ,354 II
Data are also available for state-owned buildings in the smaller communities,
e.g.,Moose Pass,Talkeetna,etc ••
Department of Transportation and Public Facilities -The State of Alaska
Harry Du11inger of the Department of Transportation and Public Facilities
(DOTPF)was able to provide square footage and annual energy use data for the
buildings under his control.This was only a subset of state buildings in the
region;he indicated that a complete survey of state buildings was published
until 1977.The current survey is maintained by the General Services and Sup-
ply office of DOTPF in Juneau (see following discussions).
t1r.Dullinger indicated that funding for investment in conservation is
scarce,yet he has experimented with the buildings under his control.Actions
implemented include 1)incandescent lamp replacement with fluorescent lamps,
2)flue dampers,3)efficient burners for furnaces,and 4)ceiling fans in
maintenance shops.
Sears -Anchorage
Roger Wallis,the building manager of Sears,discussed the energy use pat-
terns of this large retail building and his energy conservation activities.
The building has 120,000 square feet of retail floorspace with 30,000 square
feet of office and cafeteria space on the second floor.The walls are insu-
lated with standard batt-type fiberglass insulation.The HVAC systems operate
A.3.9
24 hours a day,yet the lighting i~reduced to 5 percent of normal load during
the evening (from 9:30 P.M.to 8:00 A.M).Space heat is provided by natural
gas,and cooling is provided by a 320 ton air-cooled chiller.This chiller
operates only 50 hours per year on the average.
The Sears maintenance staff has implemented lighting conservation by
1)reducing lighting levels at night,2)replacing Some incandescent lamps with
fluorescent lamps,and 3)removing some of the high-intensity display lamps on
the retail floor.The energy use data for the Sears store is in the Chugach
sales data and is shown in Table A.2.6 in the previous chapter.
State of Alaska Buildings
The General Services and Supply office of the Department of Administration
in Juneau maintains a computerized listing of all state buildings.The infor-
mation includes building number,facility name,age,cost,and a description
that includes the square footage.A listing of the survey was obtained.
u.S.General Services Administration -Federal Buildings
The U.S.General Services Administration was contacted to determine the
square footage of Federal buildings in the Railbelt Region.The GSA represen-
tative indicated that each Federal agency is responsible for maintaining
records of their own buildings;GSA only maintains information on their own
buildings.Several calls were made to representative Federal agencies to
obtain information yet most never provided the requested data.Agencies that
were called are listed below:
U.S.Department of Interior
U.S.Fish and Wildlife Service
Corps of Engineers
The National Park Service
The U.S.National Forest Service.
Realtors/Developers
Two realtors/developers were contacted to determine their opinion of fut-
ure trends in the commercial sector of Anchorage.Tim Lowe of the Lakeland
Corporation estimated that current office vacancy in Anchorage is 700,000 to
A.3.10
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1,000,000 square feet.He estimated that current base office space is 6.5
million square feet,and that retail space is between 3 and 4 million square
feet.
Norm Rokburg of Jack White and Associates estimated the base inventory of
office and all commercial at 7.4 million square feet.He indicated that aver-
age annual addition of new space is 350,000 square feet.About 1.3 million
square feet was built in 1983,and about 500,000 square feet will be added in
1984.
Note that these estimates covered pffice and large retail space only;
pUblic buildings such as schools and small businesses were excluded from the
estimates.
A.3.11
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2.Alaska Power Authority.1983.Susitna H droelectric Project FERC License
A lication.Volume 2C.RED Model 1983 Version Technical Documentation
Report.Project No.711 -000.As accepted by FERC,July 29,9
3.Goldsmith,S.and L.Huskey.,1980.Electric Power Reguirements for the
Railbelt:A Projection of Reguirements,Technical Appendices.Institute
of Social and Economic Research,University of Alaska.
4.Mundy-Jarvis and Associates,Inc.1983.Comprehensive Space Inventory of
Fairbanks,Alaska.Prepared for the Fairbanks Development Authority.
Mundy-Jarvis and Associates,Seattle,Washington.
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REFERENCES
u.S.Energy Information Administration.1983.Nonresidential
Energy Consumption Survey.1979 Consumption and Expenditures.
Buildings
Part 1.
Printing
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5.
6.
7.
Burns and McDonnell.1983a.Re ort on the Power Re uir~ments Stud
Chu ach Electric Association,Inc.,Anchora e Alaska.Alaska 8 Chu
82-18 -4-0001.Burns and McDonnell,Inc.,Kansas City,Missouri.
Burns and McDonnell.1983b.Report on the Load Forecast Study for
Muni~ipal Light and Power,Municipality of Anchorage.83-027-4-0001.
Burns and McDonnell,Inc.,Kansas City,Missouri.
Laventhol and Horwath.1983.Proposed Hotel,Fairbanks Alaska:Updated
Market Study and Financial Projections,February 1983.Laventhol and
Horwath,Certified Public Accountants,Seattle,Washington.
A.R.l
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EXHIBIT A.1
.I INTERVIEW GUIDE FOR UTILITY MANAGERS
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EXHIBIT A.l
INTERVIEW GUIDE FOR UTILITY MANAGERS
1.Boundaries of service area/population/households.
2.Commercial customer identity:
a.who are the small customers (50kVA),medium (50 to 350 kVA),and
large customers (over 350kVA)
b.mix of customers by type of business,size
c.growth/change in the mix of .customers by type,size.
3.Electricity use by commercial customers:
a.use of electricity by customer class -how much used for heating,
ventilation systems,lighting,process loads.
b.trends in electrical use by type of commercial customer--recent
changes,if any
c.trends and recent changes in use by large customers
d.conservation,trends and programs
4.Forecasting electricity use in the commercial sector:
a.techniques used by the utility in forecasting commercial load
b.what relationships (e.g.use/square foot of commercial space;
use/employee)seem most appropriate
c.annual load factors,especially in comparison to residential
customers
d.trends in building space/employee in the commercial sector.
5.Billing data -commercial sector:
a.uses to which this data has been put for electric load forecasting in
the commercial sector
A.A.l
b.aggregations of billing data (have they attempted to estimate loads
by type of bus i ness or type of load)
c.release of actual billing data for selected customers or types of
customers (e.g.office building,strip development,etc.)
6.Related matters -commercial sector:
a.conservation incorporated in (commercial)building codes,compliance
procedures
b.key contacts in the commercial sector to discuss "typical"energy use
c.local authorities to contact on energy use in the commercial sector
7.Residential sector:
a.recent data on appliance saturations
b.recent data on fuel mode splits
c.recent data on energy use/appliance
d.recent data on amount of conservation due to fuel costs/conservation
programs
A.A.2
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EXHIBIT A.2
INTERVIEW GUIDE FOR BUILDING MANAGERS
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EXHIBIT A.2
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INTERVIEW GUIDE FOR BUILDING MANAGERS
1.Building characteristics
a.size (square feet)
b.insulation
~c.heating plant and HVAC system size and type.
)
2.Energy usage,especially electric.
3.Energy audit results if one has been conducted.
4.Conservation actions taken/planned.
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5.Building occupancy characteristics.
a.number of people working in building
b.hours of building operation
c.off-hours operations--are lights left on--heating plant turned back?
6.Trends noticed in construction/operating practices of commercial
buildings.
A.B.1
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APPENDIX B
THE EFFECT OF
F.W.DODGE CONSTRUCTION DATA
ON RAILBELT ELECTRICITY
DEMAND FORECASTS
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B.3.0 F.W.DODGE CONSTRUCTION POTENTIALS DATA.......................B.3.1
B.4.0 EFFECT OF DODGE CONSTRUCTION DATA ON CDrU1ERCIAL
BUILDING STOCK ESTIMATES.......................................B.4.1
CONTENTS
B.1.0 INTRODUCTION •.••••••••••••.••••••••••.••••••••••••••••..••..•••
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B.2.0 CONCLUSIONS •••••••••••••••••••••••••••••••••••••••••••ooe •••••
B.1.1
B.2.1
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THE RAILBELT COr1MERCIAL BUILDING STOCK...........................B.4.1
Anchorage-Cook Inlet........................................8.4.1
Fairbanks Tanana Valley.....................................8.4.3
DISTRIBUTION OF BUILDING STOCK BY TyPE...........................B.4.4
B.5.0 EFFECT OF DODGE CONSTRUCTION DATA ON ELECTRICITY
CONSlJr1PTION FORECASTS..........................................B.5.1
FLOORSPACE VERSUS EMPLOyMENT...................................B.5.1
BUILDING STOCK •••••••••••••••••••••••••••••••••••••••••••••••••8.5.3
KWH PER SQUARE FOOT............................................B.5 .7
REFERENCES ••••0·.......................................................B.R.I
B.iii
TABLES
B.4.1 Calculation of 1978 Anchorage Commercial Floorspace............B.4.2
B.4.2 Anchorage-Cook Inlet Estimated Building Stock,1973-1984........B.4.3
B.4.3 Fairbanks-Tanana Valley 1983 Building Stock....................B.4.5
B.4.4 Fairbanks-Tanana Valley Estimated Building Stock,1973-1984....B.4.6
B.4.5 Anchorage-Cook Inlet Commercial Building Construction by Type
as a Percentage of Total Commercial Construction 1973-1983.....B.4.7
B.4.6 Fairbanks-Tanana Valley Commercial Building Construction by Type
as a Percentage of Total Commercial Construction 1973-1983 ••~..B.4.8
B.5.1 Commercial Consumption Trends 1973-1983........................B.5.2
B.5.2 Time Trends in Commercial Building Stock Per Employee,
Railbelt Load Centers •••••••••••••••••••••••..••••o............B.5.4
8.5.3 Forecasted Commercial Floorspace per Employee and
Average Growth Rates 1980 to 2010..............................B.5.7
8.5.4 Econometric Results for IIBest ll Business Electricity
Consumption Equations,Railbelt Load Centers ••••••••••••••~....B.5.10
B.5~5 Business Electricity Consumption Equation Historical Test......B.5.11
B.5.6 Forecasted Business Sector Electrical Use Per
Square Foot,July 1983 Reference Case..........................B.5.12
B.iv
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B.1.0 INTRODUCTION
The Railbelt Electricity Demand (RED)t1odel is a computer simulation model
designed to forecast electricity consumption for the residential,commercial-
light industrial-government (business),heavy industrial,and miscellaneous
sectors of Alaska's Railbelt region.A key feature of this model is that it
employs the stock of commercial,light industrial,and government floorspace in
order to forecast the future demand for electricity in the business sector.
Documentation of the original approach for forecasting business ofloorspace and
'electricity consumption was provided in the RED t10del (1983 Version)Technical
Documentation Report,Volume 2C of the Susitna Hydroelectric Project Federal
Energy Regulatory Commission License Application,Project No.7144-000,July
1983.
During September,1983 a workshop on the model was held by Harza-Ebasco
Susitna Joint Venture,and Battelle,Pacific Northwest Laboratories (Battelle-
Northwest)in Anchorage,Alaska to brief staff members of the Federal Energy
Regulatory Commission (FERC),on the structure and assumptions of the RED
model.The workshop was also attended by the Alaska Power Autho~'ty and the
Power Authority's attorneys (Pillsbury,t1adison,and Sutro of Washington,
D.C.).
In the course of the workshop,the FERC staff asked whether additional
information existed concerning past changes in the mix of Railbelt commercial
building stock and effects this may have had on the RED model's estimated
relationship between commercial building stock·and electricity demand.In
addition,it became clear that the approach employed in the July 1983 version
of RED could be simplified.
As a result,the Battelle-Northwest and Harza-Ebasco technical staff laid
out a short research plan to
o interview utility managers,building managers,and other sources of
data to estimate the current types and rates of uses of electricity
in the Railbelt commercial sector;the current stock of commercial
buildings and past changes in the stock of buildings;and electrical
use;
B.1.1
•acquire and analyze the F.W.Dodge Construction Potentials data set
published by McGraw Hill,Inc.to determine the rate of change in the
level and composition of Railbelt commercial building stock during
the 1970s;and
•utilize the Dodge data to reestimate business electricity consumption
equations,as appropriate,to simplify the approach.
The first of these items was documented in a previous report to Harza-Ebasco
from Battelle-Northwest entitled Railbelt Commercial Building Stock and Energy
Use Data by C.H.Imhoff and M.J.Scott (Appendix A).The use of the Dodge
data is covered by the current report.
The remainder of this report is organized as follows.Chapter 8.2.0 dis-
cusses the principal findings of the study.Chapter 8.3.0 is a brief introduc-
tion to the F.W.Dodge Construction Potentials data set.Chapter 8.4.0
discusses the effect the analysis of this data set has had on the estimates of
commercial building space in the RED model.Chapter 8.5.0 discusses the effect
of the revised commercial stock estimates on the business electricity
consumption equations.
B.1.2
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B.2.0 CONCLUSIONS
The brief study of available data sources on Railbelt commercial building
stock resulted in five major conclusions.The study results are stated briefly
below.
Based on the analysis of other available data sets,we concluded that the
Construction Potential data set published by the F.W.Dodge Division of
McGraw-Hill,Inc.was the best data series available for estimating the Rail-
"belt c~mmercial building stock.While local governments in the Railbelt do
keep records of building permit activity,these were not available in a form
that would enable us to estimate building completions,cancellations,and size
or type of building.In many cases,building stock data locally available in
the Railbelt would have required extensive collection and compilation.
The analysis of the Dodge data showed that there was very little year-to-
year consistency in the type of buildings constructed during the period 1973 to
1983,although Anchorage-Cook Inlet load center showed a more consistent pat-
tern of additions than did Fairbanks-Tanana Valley.The Fairbanks data did not
indicate a consistent historical pattern in construction activity.
The analysis of the Dodge data showed no obvious trends in types of build-
ings being built in either load center,although information received from
utilities staff in the Railbelt suggested that there was a trend toward strip-
type development in Fairbanks and large office buildings in Anchorage Csee
Appendix A.)The Dodge categories we analyzed did not reflect such trends.In
Anchorage-Cook Inlet for example,office space was a fairly constant 26 to 27
percent of total construction during the period 1973 to 1983.
Based on our findings in the two load centers,we concluded that the Dodge
data should be utilized to estimate historical commercial building stock in the
Anchorage-Cook Inlet and Fairbanks-Tanana Valley load centers but that no
attempt should be made to differentiate between types of commercial buildings.
Finally,we-concluded that the resulting estimated building stock should
be used to derive new business electricity consumption equations in Anchorage-
Cook Inlet.We concluded that this should not be done in Fairbanks-Tanana
B.2.1
•
Valley because the historical record does not appear to be applicable to prob-
able future conditions in this load center •.These conclusions were taken into
account in the RED85A version of RED.
8.2.2
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B.3.0 F.W.DODGE CONSTRUCTION POTENTIALS DATA
The F.W.Dodge Division of McGraw-Hill Information Systems Company com-
piles and publishes a proprietary data set known as Dodge Construction Poten-
tial Service.This service provides a month-by-month listing of individual
construction projects by county,type of intended use (e.g.,shipping center,
refrigerated warehouse,primary school),framing code,number of floors,floor
area,and value.The data set also indicates whether a project is new con-
struction,an addi~ion,or an alteration of an existing structure.For
residential construction,Dodge also reports the number of dwelling units
constructed.
The Dodge Construction Potential Service is available by subscription or
.purchase in machine-readable form for all counties in the United States.A
continuous data series is available for Railbelt census divisions from January
1973 forward through 1983.The covered projects are edited periodically to
account for errors and omissions and to account for later information on a
given project's being abandoned,deferred,or put into abeyance.Dodge pub-
lishes a standard procedure for accomplishing this editing.This procedure was
followed in processing the data tapes for this project.
The Dodge construction statistics that result from aggregating these indi-
vidual projects represent information on construction starts.For energy plan-
ning,a more useful data set would be one of project completion or building
occupancy,since a building begins significant energy use when it is completed
and especially after it is occupied.No such completion data series was avail-
able.The F.W.Dodge Division technical staff indicated that the lag period
from project start to building completion depends on the size and type of
building.For their own purposes in providing complete tape processing ser-
vices on a subscription basis,Dodge uses the date at which the last construc-
tion step is begun (usually,wall coverings and exterior paints),plus one to
two months as the date of completion.Standard lags from project initiation to
B.3.1
initiation of this last construction step are available.(a)Because standard
construction time lags do not necessarily apply in the Alaska construction
environment,we adopted a simplified assumption that buildings on average took
between one and two years to complete and were available for occupancy on
average in the year following their start date.This may result in some upward
bias in construction completions for some categories of buildings such as large
office buildings ~nd hotels in the early years of the period (when the projects
were started)and a downward bias in construction completions later on (when
they were actually completed).Overall,the expected impact of the simplifying
assumption is small.
The Dodge construction statistics represent the best available construc-
tion statistics for the Railbelt region.Imhoff and Scott (1984)found that
only fragmentary data exist on building stock in either Anchorage or Fairbanks;
no comprehensive data base is available.Imhoff and Scott note,for example,
that the Fairbanks North Star Borough Assessor's Office maintains its standard
property records in manual physical files with no compilation of the type
needed for this project.Neither Anchorage nor Fairbanks authorities have a
complete count of the current building stock;nor do they have accessible
records for changes in the stock over time.
Battelle-Northwest compiled information on completed commercial buildings
by type and year for the Anchorage-Cook Inlet load center (Anchorage,Kenai-
Cook Inlet,Matanuska-Susitna,and Seward 1970 Census Divisions)and for
Fairbanks-Tanana Valley (Fairbanks and Southeast Fairbanks 1970 Census Divi-
sions)from the Dodge data.Minor geographical mismatches exist in these data
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(a)These standard lags indicate that buildings under $250 thousand in value,
the least costly group,take an average of about 5 months to complete '\
while buildings costing over $25 million (the most expensive class)take
an average of 27 months to complete.Buildings from $250 thousand to
$1.25 million take 9 months;buildings $1.25 million to 8 million take 15
months;and buildings from $8 million to $25 million take 22 months on
average to complete.
B.3.2
in the Fa~rbanks-Tanana Valley load center because the combined census divi-
sions do not match the boundaries of the combined Fairbanks Municipal Utilities
System and Golden Valley Electric Association service areas;however,the
expected discrepancies are insignificant.(a)
(a)The difference between the 1980 populations of the areas served by the
Fairbanks-Tanana Valley utilities and the 1980 census areas used as a
proxy was only 150 people.Personal communication,Scott Goldsmith,ISER,
November 6,1984.
B.3.3
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8.4.0 EFFECT OF DODGE CONSTRUCTION DATA ON CO~lMERCIAL BUILDING STOCK ESTIMATES
The Dodge Construction Potentials data were used by Battelle-Northwest to
provide additional information concerning the Railbelt commercial (including
government and light industrial)building stock.The first item of information
to be collected was changes in the stock of commercial buildings in the
Railbelt load centers in years past.The second item was the changes in the
mix of buildings in the load centers.
THE RAILBELT COMMERCIAL BUILDING STOCK
The commercial building stock in a given area can be described as an
inventory of building space to which new construction and additions are adding
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space,and from which demolitions are removing space.No information is avail-
able on building removals in the Railbelt,although some removal is taking
place.For the most part,however,Railbelt commercial building space i~of
very recent vintage with few demolitions so that the chief difference in the
stock from year to year is new construction and additions to existing build-
ings.Thus,the building stock in any year can be approxi~ated by'taking the
stock in a year when it is known and then subtracting or adding construction
completions to account for the changes in stock between the given year and the
year for which a count is available.The process is described below for the
Railbelt load centers.
Anchorage-Cook Inlet
In the Anchorage-Cook Inlet load center,the best comprehensive estimate
of the building stock is for 1978.The estimate was made ~the University of
Alaska Institute of Social and Economic Research (ISER).This estimate was
based on the 1975 Anchorage Metropolitan Area Transportation Study (AMATS),
with several adjustments for outlying areas,non-energy-using buildings and
other miscellaneous items.This estimate is shown in Table B.4.1.
The 1978 building stock estimate for the Anchorage-Cook Inlet load center
was converted into a time series on building stock by adding commercial con-
struction from the Dodge Construction Potentials data for the years 1978 and
B.4.1
TABLE B.4.1.Calculation of 1978 Anchorage Commercial Floorspace
AMATS Survey (Anchorage Bowl 1975)
Less:
Plus:
Plus:
Plus:
Non-Energy Using (parking lots,
cemeteries,etc.
20 Percent for Underreporting
Sectors not included tn AMATS
1.Girdwood/lndian(a)
2.Eagle River/Chugiak(b)
3.Hotels/Motels(C)
4.Assorted Cultural Buildings(d)
Growth between 1975 and 1978
(about 25%)(e)
42,067
18,918
~
27,779
53
300
1,000
500
29,632
1978 Commercial-Industrial
General
Education
Warehousing
Hotels
Manufacturing
Less:Manufacturing
Floorspace(f)
25,120
5,000
4,520
1,500
860
37,000
860
1978 Commercial Total,Anchor(age
Plus:Kenia-Cook Inlet g)
Matanuska-Susitna(g)
Seward(g)
Total,1978,Anchorage-Cook Inlet
36,140
3,200
1,500
600
41,440
Source:Goldsmith and Huskey 1980.
(a)Twenty-five businesses in 1975 according to telephone book.
Assume 2,500 square feet/business.
(b)Based on the ratio of the housing stock in 1978 between Eagle
River/Chugiak and Anchorage.
(c)Assumes 2,000 rooms at 500 square feet/room.Based on Jackson
and Johnson 1978,p.40.
(d)Forty-six establishments identified in 1975 telephone book.
Average size assumed to be 10,000 square feet.
(e)This is based upon two indicators.The first is the growth in
employment between 1974-75 and 1978.Civilian employment was as
follows:1974 -58,700,1975 -69,650,and 1978 -76,900.
Employment growth was 31%in the period 1974 to 1978 and 10%in
the period 1975 to 1978 (State of Alaska,Department of Labor,
Alaska Labor Force Estimates by Industry and Area,various
issues).The second is the growth in the appraised value of
buildings over the period 1975 to 1978.After adjusting for
inflation,the increase was 48%.Based on the assumption that
the rapid employment increase in 1975 resulted in under.supply of
floorspace in that year,Goldsmith and Huskey assume a 25%
growth in floorspace between the summer of 1975 and 1978.
(f)Independent estimates of floorspace in 1978 in the educational
category and the hotel/motel category were available from the
Anchorage School District and Anchorage Chamber of Commerce,
respectively.The remaining growth was allocated proportion-
ately among the other categories.
(g)Based on the Anchorage value of 480 square feet/non-agricultural
civilian employee.
B.4.2
after;commercial construction was subtracted to determine building stock
before 1978.In adding or subtracting construction,it was assumed that,as
stated in Chapter B.3.0,on average a building begun in one year would be
finished and available for occupancy in the following year.Thus,1978 con-
struction starts are added to 1978 building stock to obtain 1979 building
stock.The resurting Anchorage-Cook Inlet building stock series is shown in
Table B.4.2.
TABLE B.4.2.
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
Anchorage-Cook Inlet Estimated
Building Stock,1973-1984
(10 3 Square Feet)
26,236.0
28,970.5
33,086.6
36,848.1
39,563.5
41,440.0
42,761.9
44,110.5
44,964.2
47,553.8
49,795.5
54,331.5
Source:Table B.4.1 and F.W.
Dodge Construction Potentials.
Fairbanks-Tanana Valley
ISER also produced an estimate of the 1978 Fairbanks-Tanana Valley load
center commercial floorspace based on an assumption that Fairbanks-Tanana
Valley square footage per employee equaled Anchorage square footage per
employee.(a)A preferred approach to estimating the amount of commercial
(a)Goldsmith and Huskey,1980,Tables 0.39 and 0.40.
B.4.3
building space in Fairbanks-Tanana Valley load center for 19B3 is to combine a
partial central Fairbanks building stock count by Mundy-Jarvis Associates pre-
pared in November 1983 with other counts of certain public buildings and our
own estimates for some of the remaining categories.
This preferred process is documented in Imhoff and Scott (1984)(Appen-
dix A)and is summarized in Table B.4.3.
The estimate of Fairbanks-Tanana Valley 1983 building stock count was then
converted into a time series by subtracting commercial construction in each
year in the same way as in Anchorage-Cook Inlet.The results are shown in
Table B.4.4.
DISTRIBUTION OF BUILDING STOCK BY TYPE
There are no obvious trends to be noted in the types of commercial build-
ings being constructed in the Raflbelt load centers.In both load centers,
commercial construction showed considerable year-to-year variability in all
types,both in total square feet constructed and in the percentages of total
construction accounted for by each building type.The annual percentages for
selected major categories comprising the bulk of Railbelt commercial construc-
tion activity are reported in Table B.4.5 and B.4.6.As can be seen from the
table,there is no obvious trend in types of buildings being constructed in
either load center.This answers directly a question asked by the FERC staff
in their review of the July 1983 version of the RED model;namely,are there
trends in the type of commercial space being constructed?FERC staff were
interested in the differing degree to which electricity-using capital equipment
is used in various subsectors of the commercial building stock (e.g.,computers
are being added in offices,but more efficient heat recovery and cooling sys-
tems are being added in supermarkets).If the intensity or mix of energy uses
were changing dramatically in certain building types and their proportion of
the stock is also changing,then it might not be appropriate to estimate energy
consumption on the basis of aggregate commercial building stock.Tables B.4.5
and B.4.6 show,however,that the mix of the stock is not obviously changing,
so intensity of electrical consumption can be estimated on an aggregate basis.
B.4.4
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TABLE B.4.3.Fai rbanks-Tanana Valley 1983 Building Stock
(10 3 Square Feet)
Less:Adjustment for 1984/l983 difference
(Approximately 3.3%)(h)
1983 Estimated Stock,Fairbanks-Tanana Valley
Mundy Jarvis Associates count,
Adjusted for missed buildings(a)
Plus:North Star Borough Buildings(b)
Federal Buildings(c)
State Buildings{d)
City of Fairbanks Buildings(e)
Subtotal:Count
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Plus:Estimated Stock (ear1y)1984)
Lodgings/Hotel/Motel fChurches/socia1L~range(f)
Transportation f
North Pole)Area,Nenana,Delta(f)
Hospital g
870
774
1,188
2,432
201
2,867
1,655
157
1,186
260
6,125
5,465
11,590
390
11 ,200
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(a)Source:Mundy-Jarvis Associates,Comprehensive Space
Inventory of Fairbanks,Alaska,November,1983.Estimates
of missing data supplied by Mundy-Jarvis Associates.
Personal Communication,Jeff Wollen to Michael Scott,
March 20,1984.
(b)Source:Fairbanks North Start Borough Engineering Depart-
ment,March 1984.
(c)Source:U.S.General Services Administration,Anchorage,
Alaska,March 1984.
(d)Source:Alaska Department of Administration.
(e)Source:City of Fairbanks Fire Marshall,March 1984.
(f)Calculated by counts of businesses from Fairbanks telephone
directory and national median space per building by type,
March 1984.
(g)Source:Fairbanks Memorial Hospital,March 1984.
(h)F.W.Dodge Construction started in 1983 would have
increased stock by about 10%by 1984.One third of that
amount adjusted for differences between the November 1983
stock and the estimated stock in approximately March 1984,
about one-third of a year later.The final figure was
rounded to the nearest hundred thousand square feet.
B.4.5
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
TABLE B.4.4.Fai rbanks-Tanana Valley Estimated
Building Stock,1973-1984
(10 3 Square Feet)
(10 3 Square Feet)
3,764.8
4,417.2
5,407.1
7,468.3
8,691.5
9,806.3
10,145.3
10,385.6
10,634.8
10,777.0
11,200.0
12,288.9
Sources:Imhoff and Scott and
F.W.Dodge Construction Potential.
This is also true whether we examine the year-by-year figures or group
them into economic subperiods.Both tables show average percentages for the
Trans-Alaska oil pipeline period (1973-1977),the post-pipeline period (1978-
1983),and the period since the 1979 Iranian oil price shock (1980-1983).
These subperiod averages also show no obvious trend when compared to the 1973-
1983 period as a whole.In Anchorage-Cook Inlet the subperiod and year-to-year
percentages vary less than in Fairbanks-Tanana Valley;however,even in
Anchorage the subperiod averages tend to be dominated by very large scale
construction in one or two years.For example,in Anchorage warehouses there
appears to be declining trend in warehouse construction;however,in the two
most recent years construction was at or above the ten-year average for this
category of construction in both absolute and percentage terms.Similarly,
there is an apparent recent decline in public construction in Fai rbanks as a
percentage of the total.However,public construction was among the more
important categories in both 1979 and 1982.It is only the large amount of
B.4.6
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TABLE B.4.5.Anchorage-Cook Inlet Commercial Building Construction by Type as a Percentage
of Total Commercial Construction 1973-1983 (Percent of Total)
Retail /All Other
Year Office Wholesale Warehouse Education Public Miscellaneous Subtotal Types
1973 14.0 9.6 8.9 33.9 4.3 16.3 87.0 13.0
1974 31.2 4.0 18.5 3.7 9.3 27.7 94.4 5.6
1975 15.8 8.6 23.6 15.0 6.4 24.2 93.6 6•.4
1976 36.5 16.8 4.7 8.6 0.6 17.3 84.5 15.5
1977 46.8 6.0 10.2 1.0 6.8 12.0 82.8 17.2
1978 33.0 10.4 16.0 23.3 1.8 11.7 96.2 3.8
.1979 13.7 34.1 15.0 15.1 0.5 12.1 90.5 9.5
1980 2.7 11.4 1.8 15.5 8.9 39.2 79.5 20.5
OJ 1981 52.0 15.7 4.9 2.8 5.6 18.2 90.2 0.8.
+::-.1982 20.1 13.5 14.4 20.1 8.3 17.0 93.4 6.6--.J
1983 21.3 20.7 12.9 20.5 0.9 9.8 86.1 13.9
Averages
73-77 27.2 8.7 14.6 12.5 5.8 21.0 89.8 10.2
78-83 26.5 18.2 11.4 16.3 3.7 15.1 91.2 8.8
80-83 27.3 17.1 10.3 15.5 4.4 15.9 .90.5 9.5
73-83 26.8 13.1 13.1 14.2 4.8 18.3 90.3 9.7
Range,
1973-2.7-4.0-1.8-1.0-0.5-9.8-79.5-0.8-
1983 52.0 34.1 23.6 33.9 8.9 39.2 99.2 20.5
Source:Dodge Construction Potentials.
TABLE B.4.6.Fairbanks-Tanana Valley Commercial Building Construction by Type as a Percentage
of Total Commercial Construction 1973-1983 (Percent of Total)
Retail/All Other
Year Office Wholesale Warehouse Education Public Miscellaneous Subtota 1 Types
1973 18.7 19.2 14.2 14.9 7.2 14.1 88.3 11.7
1974 4.3 12.2 25.3 11.1 7.9 17.7 78.5 21.5
1975 20.3 3.4 22.1 21.9 1.2 14.0 82.9 17.1
1976 1.2 37.8 0.0 8.4 16.0 22.6 86.0 14.0
1977 1.0 21.2 8.3 12.1 42.0 13.1 97.7 2.3
1978 5.9 28.5 5.9 21.0 0.0 36.9 98.2 1.8
1979 22.6 36.2 0.0 4.5 24.9 1.8 90.0 10.0
1980 0.0 2.3 10.0 10.3 1.4 75.4 99.4 0.6
c:c 1981 0.8 0.0 31.6 47.2 4.2 9.6 93.4 6.6.
.p..1982 1.5 6.6 8.8 47.7 14.1 20.0 98.7 1.3co
1983 24.6 7.4 4.5 24.4 1.9 12.4 75.2 24.8
Averages
73-77 10.1 16.8 14.8 14.8 13.5 16.2 86.2 13.8
78-83 14.1 12.0 7.1 25.9 6.0 22.1 87.2 12.8
80-83 14.4 6.0 8.2 29.4 4.7 22.0 84.7 15.3
73-83 11.2 15.4 12.5 18.1 11.3 17.9 86.4 13.6
Range,
1973-0.0-0.0-0.0-4.5-0.0-L8-75.2-0.6-
1983 24.6 37.8 31.6 47.7 42.0 75.4 98.7 24.8
Source:Dodge Construction Potentials.
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such const ruct ion in 1976 and 1977 that creates the apparent lit rend.II The
burst of pipeline-related activity in 1976-1979 (especially 1976-1977)is also
the sole cause of the apparent trend in Fairbanks-Tanana Valley retail-
wholesale construction.The percentages are lower both before and after this
period.Office and education space is apparently added in large blocks at
irregular intervals in Fairbanks-Tanana;thus the subperiod averages mean very
little.
In general,the construction patterns in the two Railbelt load centers
appear to be quite irregular.There is no single set of percentages in
Tables B.4.5 and 8.4.6 that could be used to characterize trends in construc-
tion by type.Nor are there any apparent trends in construction by type that
on closer examination appear to be real or significant.As a consequence,the
simplifying assumption used in the RED morlel that all types of commercial stock
are growing at about the same rate is supported by the data.
8.4.9
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-R.5.0 EFFECT OF DODGE CONSTRUCTION DATA ON ELECTRICITY CONSUMPTION FORECASTS
The comMercial building stock estimates developed in the previous chapter
were utilized to estimate electricity demand equations for the two Railbelt
load centers.The relative stability of floorspace and eMployment as predic-
tors of electricity consumption in the business sector were examined.As a
consequence of this analysis,floorspace was identified as the preferable pre-
dictor of electricity consumption.Next,we econometrically estimated consump-
tion equations.In Anchorage-Cook Inlet,the econometric approach worked well
and produced estimates compatible with economic theory and the historical
record of consumption for the load center.In Fairbanks-Tanana Valley,this
approach produced a reasonably close fit to historical data;however,the
derived forecasting equation did not produce plausible forecasts of electricity
consumption.The forecast was one of a rapidly declining rate of electricity
use.Historically,this declining use actually occurred;however,it was
caused by a combination of events unlikely to be repeated.Consequently,a
simplified non-econometric equation was used to predict future business con-
sumption of electricity for Fairbanks.
FLOORS PACE VERSUS EMPLOYMENT
The first step in estimating business electrical consumption was to test
the historical data to see which of the available time series was the better
predictor of electrical consumption,square feet of business space or employ-
ment.Table B.5.1 shows historical trends in business electrical use per
square foot of commercial floorspace,floorspace per employee and electrical
use per square foot in the two load centers.Both Anchorage-Cook Inlet and
Fairbanks-Tanana Valley show increasing consumption per employee over the
period as a whole,although there is short term variation around the trend.In
the case of Anchorage,this trend appears to be composed of a slowly increasing
trends in use per square foot and a varying growth rate in square feet of space
B.5.1
TABLE B.5.1.Commercial Consumption Trends 1973-1983
Anchorage-Cook Inlet Fairbanks-Tanana Valley
Year kWh(a)/Employee(b)Ft 2 (c)/Employee(b)kWh/Ft 2 kWh(a)/Employee(b)Ft 2(C)/Employee kWh/Ft 2
1973 5941 321.8 18.46 6631 150.5 40.06
1974 5788 322.1 17.98 5399 147.6 36.59
1975 5758 318.4 18.09 5368 167.6 38.98
1976 6403 337.5 18.97 .5641 192.2 29.35
1977 6714 348.2 19.28 6922 250.2 27.67
1978 7218 367.9 19.62 7550 305.1 24.75
1979 7176 375.6 19.10 7577 318.6 23.78
co 22.73 7510 332.9 22.56.1980 7772 379.4U1.
N 1981 7285 363.7 20.03 7807 321.4 .24.29
1982 7388 347.2 21.28 7209 295.6 24.40
1983 NA NA NA NA NA NA
Sources:
(a)Commercial-government industrial use from FERC form 12s and Alaska Power Administration.
(b)U.S.Bureau of Economic Analysis (BEA),Regional Economic Information System,provided employment
data.
(c)See Chapter B.4,Tables B.4.2 and B.4.4.
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per employee.(a)In the case of Fairbanks,the trend masks a rapid increase in
the stock of building space per employee (which shows recent signs of slowing
down),combined with a rapid decrease and then stabilization in electrical
consumption per square foot of commercial floorspace.Since the decline in
electrical use per square foot appears to have halted in Fairbanks and
electrical use is starting once again to increase in response to demand for
more business services,then using either the trend of business use per
employee or an econometric equation with employment as the independent variable
is likely to give misleading results.Consequently,a two-step approach of
forecasting square feet per employee and kWh per square foot was employed.
BUILDING STOCK
Roth simple trend equations and regression equations based on historical
Alaska experience were considered for forecasting commercial building stock in
the Railbelt load centers.Neither linear,exponential,nor logarithmic equa-
tion forms produced acceptable forecasts for both load centers of commercial
building stock per employee.For example,consider the time trends in building
space per employee calculated from Railbelt data in Table B.5.2.
Table B.5.2 shows predicted values for three possible time trend lines
fitted to historical data on commercial floorspace per employee by regression
analysis.The three trend.lines are:
o Linear:Ft 2/Employee =a +b •(time)
o Exponential:Ft 2/Employee =e(a +b •time)
Q Logarithmic:Ft 2/Employee =a +b •ln (time)
In the fourth column of the table shows the estimated actual commercial floor-
space per employee for the period 1973 to 1982.In comparing each of the
trends to the actual values,one notices several things.
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(a)
All three time trends appear to perform about equally well during the
historical period.
In particular,the growth rate even becomes negative during periods of ~
rapid employment growth such as 1975 and 1981-1982.BEA employment figu~~S 'J
were not available for 1983 but a considerable amount of commercial ~
building occurred in that year.
B.5.3
TABLE B.5.2.Time Trends in Commercial Building Stock Per Employee,
Railbelt Load Centers (Ft 2/Employee)(a)
Anchorage-Cook Inlet:
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Year
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1990
1995
2000
2005
2010
Linear
Trend
322.8
327.8
333.6
339.5
345.3
351.1
356.9
362.7
368.5
374.3
380.1
386.0
391.8
420.8
449.9
478.9
502.2
537.0
Exponenti al
Trend
322.0
327.6
333.1
338.8
344.6
350.4
356.4
362.5
368.7
374.9
381.3
387.8
394.4
429.1
467.0
508.1
543.6
601.6
Logari thmi c
Trend
310.4
327.9
338.0
345.1
350.6
355.2
359.0
362.3
365.3
367.8
370.2
372.3
374.3
382.4
388.5
393.3
396.7
400.9
Actual(b)
321.8
322.1
318.4
337.5
348.2
367.9
375.6
379.4
363.7
347.2
Fairbanks-Tanana Valley:
Li near
Year Trend
1973 145.0
1974 167.9
1975 190.9
1976 213.8
1977 236.7
1978 259.6
1979 282.6
1980 305.5
1981 328.4
1982 351.3
1983 374.3
1984 397.2
1985 420.1
1990 534.8
1995 649.4
2000 764.0
2005 855.7
2010 993.3
Exponent;al
Trend
150.2
166.2
183.8
203.4
225.0
248.9
275.3
304.6
337.0
372.8
412.4
456.4
504.8
836.5
1386.2
2297.3
3441.2
6308.7
Logar;thm;c
Trend
106.7
171.6
209.6
236.5
257.4
274.5
288.9
301.4
312.5
322.3
331.3
339.4
346.9
377 .4
400.3
418.8
431.3
447.3
Actual(b)
150.5
147.6
167.6
192.2
250.2
305.1
318.6
332.9
321.4
295.6
(a)Employees are estimated by the U.S.Department of Commerce,
Bureau of Economic Analysis,Regional Economic Information
System.There are differences in definition between this
source I s defi ni ti on and that used by ISER.See text.
(b)Estimate from building stock in Chapter B.4.See also
Table B.5.1.
8.5.4
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2.Ti~e trends based on historical Railbelt data all predict higher
future floorspace per employee in Fairbanks-Tanana Valley than in
Anchorage-Cook Inlet.This is contrary to historic evidence and
contrary to economic incentives,since higher construction and
heating costs should keep space per e~ployee in Fairbanks-Tanana
Valley relatively low compared to Anchorage-Cook Inlet.
3.In view of the 1979 national average of commercial floorspace per
employee of about 613.1,(a)the unadjusted linear and exponential
trends appear to be forecasting unreasonably low in Anchorage and
unreasonably high in Fairbanks-Tanana Valley.Once adjusted for
differences between total employment as reported by the U.S.
Department of Commerce,Bureau of Economic Analysis and total
employment as estimated by ISER,the linear trend in Anchorage
produces a year 2010 value of 603.8,close to the 1979 national
value.
4.On the other hand,the logarithmic growth rates based on Railbelt
load center data appear unreasonably low in both load centers when
extrapolated for 30 years.Historical commercial construction
figures indicate that even in the post-pipeline period of 1978-1980,
the commercial stock per employee continued to increase at a rate of
1.6%per year in Anchorage-Cook Inlet and 4.5%in Fairbanks-Tanana
valley.(b)In contrast,the logarithmic trend yields 30-year annual
average growth rates of 0.18 and 0.99%respectively.
Because of the results obtained by trending Railbelt historical data,
Anchorage-Cook Inlet commercial building stock per employee was assumed to
(a)See U.S.Energy Information Administration,1983 for square footage
data.U.S.Statistical Abstract figures for total nonfarm civilian
employment for 1979 were modified to be consistent with Railbelt total
employment estimates as follows:industrial and mining (field)employees
were subtracted and military added.Unadjusted average commercial
building occupancy in 1979 was one employee per 623 square feet.
(b)Downturns are shown in 1981 and 1982 in both Anchorage and Fairbanks.
However,this is due primarily to rapid employment growth rather than a
construction slowdown.1983 construction continued under IIboom ll
conditions.
B.5.5
\
increase linearly at its historical rate,to yield 603.8 square feet per
employee by the year 2010.This is still fairly conservative,since it implies
an average growth rate of 1.1%,less than the 1973-1980 rate of 1.7%measured
at the endpoints.In order to have the growth rate decline as the national
average was approached and building stock needs were satisfied,it was assumed
that stock per employee follows a linear path beginning from a 1980 adjusted
base value.(a}
Historical data were not used to extrapolate the Fairbanks-Tanana Valley
growth rate in stock per employee.For Fairbanks Tanana Valley,it was assumed
that the very rapid past growth rate in square feet per employee would not
apply after the Anchorage-Cook Inlet value was approached.f1oreover,this
slowing-down process already may have begun (see Table 8.5.2).Because of the
higher relative cost of building and heating the commercial building stock in
Fairbanks,the space per employee in Fairbanks-Tanana Valley is not expected to
catch up to the Anchorage-Cook Inlet value.Instead,it is assumed that the
future growth rate in Fairbanks-Tanana Valley building stock per employee
parallels Anchorage-Cook Inlet growth,reaching about 538.4 square feet in the
year 2010.This is about 10.8%than Anchorage-Cook Inlet and implies a 30-year
average annual growth rate of 1.3%.The forecasted square feet of commercial
floorspace per employee and the average growth rates by forecast peri od are
shown in Table B.5.3 for the two load centers.(b}Because a different
employment base was used in Table B.5.3 than in Table B.5.2,floorspace per
employee in the early forecast years will not equal the values in Table B.5.2.
(a)The path in Anchorage is defined as y =383.023 +5.811.t,where t takes
on values 1,2,3,•••,7 corresponding to 1973,1974,•••,2010.In
Fairbanks,the intercept term is 314.562.Square feet per employee for
1980 were calculated using the best estimate of total employment available
in the July 1983 version of RED.The coefficient was not updated in
RED85A.The July 1983 estimate of square feet per employee was based on
the ISER definition of total employment,which is lower than the BEA
definition.This is due to different treatment of military reservists and
some categories of service workers.
-(b)The end year value of 603.8 is quite conservative,compared to the nation
as a whole.Based on the forecasted commercial square footage from the
U.S.Energy Information Administration's 1983 Annual Energy Outlook and
Data Resources,Incorporated U.S.Long Term Rev;ew (~I;nter 1983-84)
forecast values for employment,the national value for 1995 would be 723.6
square feet per employee,about 120 square feet more than the Anchorage
value for 15 years later.
B.5.6
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TABLE B.5.3.Forecasted Commercial Floorspace per Employee and Average
Growth Rates 1980 to 2010
1980
1981
1982
1983
1984
1985
1990
1995
2000
2005
2010
Anchorage-Cook Inlet Fairbanks-Tanana Valley
Ft 2j
Annual
Ft 2j
Annua 1
Growth Growth
Employee Rate(%)Employee Rate(%)
429.5(a)364.0(a)
435.3 1.4 369.8 1.6
441.1 1.3 375.6 1.6
446.9 1.3 381.4 1.5
452.7 1.3 387.2 1.5
458.5 1.3 393.0 1.4
487.6 1.2 422.1 1.3
516.6 1.2 451.1 1.3
545.7 1.1 480.2 1.2
574.7 1.0 509.2 1.2
603.8 1.0 538.4 1.1
(a)Using the ISER definition of total employment rather
than the Bureau of Economic Analysis definition in
Table B.5.2,Anchorage-Cook Inlet Ft 2jEmployee equals
429.5,Fairbanks-Tanana Valley equals 364.0.These
figures were used for projection purposes in the model
so that RED will forecast commercial floorspace consis-
tent with the ISER definition of employment used as RED
model input in the FERC license application.
KWH PER SQUARE FOOT
The intensity of electrical consumption per square foot was investigated
in some depth.Many econometric specifications were tried on historical data
for the Railbelt load centers in order to select an equation having both theo-
retical consistency and close statistical fit.Among tests attempted were:
o including and excluding the price of electricity as an explanatory
variable;
o utilizing dummy variables to account for left-out variables and
structural shifts in the econometric relationships;
B.5.7
•utilizing heating degree-days and cooling degree-days to adjust for
weather conditions;
•linear,log-linear,and exponential equation forms.
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A number of criteria were used for judging whether an equation was appro-
priate.Examined were the sign,size,and statistical significance of the
coefficients and the implied elasticity of demand with respect to the size of
the commercial building stock.For example,since economic theory predicts
that the marginal effect of electricity price on electrical consumption should
be negative,equations were rejected where electricity price came in with a
positive sign or a value not statistically different from zero,as measured by
the Student t ratio.The ~2 statistic was examined to determine goodness of
fit to the data and the Durbin-Watson statistic for evidence of autocorrelation
and misspecification.The equations were also tested by excluding a given
variable such as electricity price to determine if such exclusion had any sig-
nificant effect on the remaining coefficients in the equation.Finally,use
was made of supplementary information on the historical relationship between
commercial building stock and commercial-light industrial-government electrical
consumption.In Anchorage-Cook In1et,for example,the level of use per square
foot has increased slowly for several years.In the absence of further
increases in energy prices,there is no reason to anticipate either a major
reversal of this recent trend or a major acceleration.Equations showing a
stock elasticity dramatically less than 1.0 were rejected because such a value
implies either declining demand for electricity in the absence of price changes
in new construction or significant additional conservation retrofits of the
existing stock,or both.While modest amounts of this type of activity have
occurred historically in Anchorage (Imhoff and Scott 1984),large amounts of
such conservation activity are not expected at prevailing prices.Increased
energy consciousness in the commercial sector,on the other hand,probably
precludes rapid increases in energy use per square foot and stock elasticities
significantly greater than 1.0.
Available supplementary
center was less conclusive.
cant increases in the prices
i nformati on on
The hi stori ca 1
of elect ri ci ty
B.5.8
the Fairbanks-Tanana Valley load
peri od was marked by very si gni fi-
and fuel oi 1,as we 11 as by a
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moratorium in the installation of electric space heat.As a consequence,dur-
ing the historical period there apparently was a significant decline in elec-
trical use per square foot.Since much of today's commercial stock was built
during the period of rising or high electrical prices and electric space heat
moratorium,we expect that electrical use per square foot is near a minimum
value given current electricity prices.Additions to stock would probably have
use rates near current levels or slightly higher or lower.Econometric elec-
tricity consumption equations having implied stock elasticities much higher or
lower than 1.0 were thus rejected.
Table B.5.4 shows the results of the two econometric estimates having best
theoretical consistency and statistical fit.It was found that including
either electricity price or heating and cooling degree-days generally had
little effect on the stock coefficients in the two load centers.Moreover,in
most cases these auxiliary explanatory variables came in with the theoretically
IIwrongll sign (e.g.,positive effect of electricity price)or were not statis-
tically significant,or both.Table B.5.4 results show that as commercial
stock increases by 1%in Anchorage-Cook Inlet,electricity use increases by
about 1.22%in the absence of future changes in the prices of gas,oil,and
electricity.This is consistent with a slowly accelerating use per square
foot,which in turn is consistent with the lower 48-style building stock being
constructed in Anchorage at this time.When the equation was used to forecast
electricity consumption for the historical period,the difference between the
forecast and actual consumption was small,as shown in Table B.5.5.In
Fai rbanks-Tanana Vall ey,even the IIbest II equati on had to be rejected because of
the very low reported elasticity with respect to commercial stock,even though
historical forecast errors were reasonable.The low elasticity appears to be
due to the rapid historical decline in use per square foot in Fairbanks,a
trend that could not be forecasted for 30 years into the future,given the
supplementary information discussed above.Instead,a stock elasticity of 1.0
was assumed for Fairbanks,which implies constant electrical use per square
foot in the absence of future changes in electricity and fuel oil prices.For
both equations,the intercept value was adjusted to calibrate the forecasting
B.5.9
TABLE B.5.4.Econometric Results for "Best"Business Electricity
Consumption Equations,Railbelt Load Centers(a)
(standard error in parenthesis)
Anchorage-Fai rbanks-
Cook Inlet Tanana Vall ey
BETA -6.320 1.512
(0.656 )(0.469)
BBETA 1.224 0.435
R2 (0.062)(0.053)
0.980 00906
D.W.1.692 1.988
F 387.7 67.6
Degrees of 8 7
freedom
(a)The estimated equation was:
ln (PRECONit)=BETAi +BBETAi *ln (STOCK)it)
where:
(PRECON it )~estimated commercial
light industrial-
government electricity
consumption in load
center i and year t
BETA,BBETA =estimated coefficients
STOCK it =commercial building stock
ln =logarithmic operator
~2 =multiple correlation
coefficient,corrected
for degrees of freedom
DoW.=Durbin-Watson statistic
F =Snedecor "F"statistic
B.5.10
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TABLE R.S.S.Business Electricity Consumption Equation Historical Test
(GWh)
Anchoraqe-Cook Inlet Fairbanks-Tanana Valley
Percent Error Percent Error
Actual Forecast of Forecast Actua 1 Forecast of Forecast
1973 483 463 -4.1 151 159 5.3
1974 519 523 0.8 162 170 4.9
1975 596 616 3.4 211 186 -11.8-
1976 697 702 0.7 219 215 -1.8
1977 762 766 0.5 240 230 -4.2
1978 801 811 1.2 243 244 0.4
1979 790 843 6.7 241 248 2.9
1980 903 875 -3.1 234 251 7.3
1981 900 896 -0.4 258 253 -1.9
1982 1012 960 -5.1
Mean Absolute Percent Error 2.9 4.5
Root Mean Square Error 3.3 5.6
equation for 1980 consumption.The required values for the intercepts were
-2.2118 in Anchorage-Cook Inlet and 0.7980 in Fairbanks-Tanana Valley.
Table B.5.6 shows the forecasted values of electrical use per square foot for
the July 1983 reference case.
TABLE 8.5.6.Forecasted Business Sector Electrical Use Per
Square Foot,July 1983 Reference Case
(kWh/square foot)
Anchorage-
Cook Inlet
Before Price With Price
Effects Effects
Fai rbanks-
Tanana Va 11 ey
Before Price With Price
Effects Effects
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1980 20.19 20.19 22.20 22.20
1985 22.04 20.20 22.21 22.05
1990 23.06 19.82 22.21 22.24
1995 23.68 19.50 22.21 22.35
2000 24.14 19.22 22.21 22.35
2005 24.73 19.27 22.21 22.32
2010 25.44 19.59 22.21 22.30
B.5.11
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REFERENCES
1.Imhoff,C.H.,and M.J.Scott.1984.Railbelt Commercial Building Stock
and Energy Use Data.Prepared for Harza-Ebasco Susitna Joint Venture
under Contract 2311205912.Batte 11 e Pac ifi c Northwest Laboratori es,
Richland,Washington..
2.Dodge Construction Potentials.F.W.Dodge Division,McGraw-Hill
Information Systems Company,New York,New York.
3.U.S.Energy Information Administration.1983.Non Residential Buildings
Energy Consumption Survey 1979 Consumption and Expenditures.Part 1.
Natural Gas and Electricity.DOE/EIA-0318/1 Superintendent of Documents,
U.S.Government Printing Office,Washington,D.C.
4.Goldsmith,S.and L.Huskey.1980.Electric Power Consumption for the
Railbelt:A Projection of Requirements.Technical Appendices.Institute
of Social and Economic Research,University of Alaska,Anchorage,Alaska.
5.Jackson,J.R.and W.S.Johnson.1978."Commercial Energy Use:A
Disaggregation by Fuel,Building Type,and End Use.1I Oak Ridge National
Laboratory,Oak Ridge,Tennessee.
6.~1undy,Jarvis and Associates.1983.Comprehensive Space Inventory of
Fairbanks,Alaska.Prepared for the Fairbanks Development Authority.
Mundy,Jarvis,and Associates,Inc.,Seattle,Washington.
B.R.1
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