HomeMy WebLinkAboutAPA124c.C PROJEC
.~LICATION
tIIO.7114-000
.8 .~ceDted ..y FERC.July.27.1983
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ARLIS
Alaska Resources
Library &Information Services
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BEFORE THE
FEDERAL ENERGY REGULATORY COMMISSION
APPLICATION FOR LICENSE FOR MAJOR PROJECT
SUSITNA HYDROELECTRIC PROJECT
VOLUME 2C
RED MODEL (1983 VERSION)
TECHNICAL DOCUMENTATION REPORT
ARLIS
Alaska Resources
Librarv &tnformation SerViceS
And..~.~..tska
JULY 1983
ALASKA POWER AUTHORITY
TK.
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110,\'2"\(..
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FINAL REPORT
RED MODEL (1983 VERSION)
DOCI.Jt~ENTATIO N REPORT
M.J.Scott,Project Manager
M.J.Ki n 9
B.L.Col es
B.J.Harre r
E.T.Marnell
R.J'.~~e
T.J.Secrest
L.A.Sk uma t z
June 1983
Prepared for Harza-Ebasco Susitna
Joint Venture,Anchorage,Alaska
under Contract 2311205912
BATTELLE
Pacific Northwest Laboratories
Richland,Washington 99352
SU~1~1ARY
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This report describes the 1983 version of the Railbelt Electricity Demand
(REn)model,a partial end-use/econometric model for forecasting electricity
consumption in Alaska's Railbelt region through the year 2010.It contains
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-
>~orthwest residential energy-use survey conducted in the Railbelt during i1arch
and April,1981.This survey was used to help calibrate the model.
RED has several unique capabilities:a t·10nte Carlo simulator for analysis
of uncertainty in key parameter values,a fuel price adjustment Inechanisrn that
incorporates the impacts of fuel prices on demand,and the capabil ity to
explicitly consider government.subsidized investments in conservation
measures.The 1933 version contains the following features:
~an aggregate business electricity consumption forecasting
methortology that is based on the model's own forecast of commercial,
light industrial,and government building stock
~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 r'bdule that transforms a forecast of the total nunber of
regional households into forecasts of the occupied and unoccupied
housing stock by four types of housing units
parameters updated to reflect 1980 Census infonnation and
construction and energy market activity between 1980 and 1982,as
well as additional energy research performed in several other parts
of the count ry
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two load centers,Anchorage-Cook Inlet and Fairbanks-Tanana Valley
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o a report-writing module that reports price elasticities ~nd 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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CONTENTS
THE HOlJS I NG r,10ntJLE ..
INTRO[)tJCTIor~.fl ••••"•••••I!I-~e
SUm1ARY •••••••••••
••••ill .
iii
1.1
2.1
2 .3
~.4
•••••.fl ••oil -iII .
•e ,.,oil •••••iII
OVERVIE\~
UNCERTAINTY ~10DIJlE •••
1.0
2.0
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MECHANISM
r1ECHAN I St1
INPUTS AND OUTPUTS .
nODULE STRUCTURE ••••••••••••••••••••••••••••••
2.4
?S
2.1)
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
Tren dS·••-•••••••••••••••Size and Demographic
Households.~1i 1ita ry
Household
Vacancies .•..••.•...•••..•.._.•...
Historic and Projected Trends in Demand for Housing •••••••.•
PARAMETER S.
r10DLILE STRUCTIJRE ••·~~..·e s s •••e
U~1 CERTAI NTY ~10 0ULE ill 111 ..
PEAK DE~1AND r··10DULEe "III."•••
THE HOUSING r·10DU'LE .
I~PUTS AND OUTPUTS ••••••c e.e ~
PARJ1.~1ETERS••.....................................
RESInENTIAl CONSlJt1PTION ~10DlJLE •••••••••••••••••••••••••••••••••••
RIJSp~ESS CON SU~1 PTI 0N ~10 0 LJ LE• • • • • • • •• • • • • • • • • • • • • • • • • • ••••• • • • •,••
PROGRM1-INDlJCED CONSERVATION ~10f)1Jlt ••••••••••••••••••••••••••••••
rn SCELlANEOlJS CONSU~1PTION MODULE.
4.0
3.0
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fJepreciation and Removal.•••••e ••e ••••••••••••••••e .4.17
5.0
Rase Year Housing Stock ••••••••••••.•.•..•...•....•••.•••
THE RESIDENTIAL CONSUMPTION MODIJLE •••••••••••••••••••••.••••
4.19
5.1
r1ECHAN 191 •••••••••••••••0.·••••••••••••0 ••••0.'0 05 •••'..5.1
INPUTS AND OUTPUTS •••••••••
MODULE STRUCTURE ••••
••••••••••C ••••0050.e ••C>••••••••••C>~Ill.,.5.2
5.2
PARAMETERS ••••••••••••••••••••••••••••••••••••••••fII 6 •••e.5.10
Ap pl i ance Saturations •.5.11
Fue 1 ~10 deS p 1 its II 'II ..ill 05 II Cl ..
Consumption of Electricity per Unit ••.••••••.•••••••••.•••••
5.26
5.28 -
El ectrical Capacity Growth.1).33
Survival •..••.•.....Appliance
Household Size Adjustments •••.. ............ill Cl Cl ,..
5.36
1).311 -
6.0
Pric e El as tic i t i-es Co II II Cl ..
THE RUSINESS CONSUMPTION MODULE
5.311
11.1
r1ECHAN ISM ....................................................................II·,II ..6.1
INPUTS AND OUTPUTS •••••••••••••••.. .. ............ .. ...... .. .. .. ........ .. ...... ...."..6.1
MODULE SnUCTURE ••••••........ .............. ....................'"6.2
PARAMETERS ••_•••••••••••••••••••••.••~••••.••.•e ••••••••••'•••••••
Fl 00 r Sp ace St 0 C k Eq uat ion s ••••••••••••••••••••••••••.•••••••
Business Electricity Usage Parameters •••••••••••••••••••••••
6.7
6.8
6.16
THE RED PRICE ADJUSTMENT MECHANISM •••••••••••••••••••••••••••••••
Business Price Adjustment Parameters ••••••••.••••••.••••••.•
7.0 PRICE ELASTICITY ••••••••••••••••ill ••••••••••••II ••••••••••••••••••
6.20
7.1
7 .1 -
LITERATURE SlJRVEY •••••••••••••••••••••.••••••••••••••••••.••,.......7.3
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SELECTION OF RED PRICE ADJIJSH1EIH MECHANIS~1 STRUCTURE
AND PARAt~ETERS •••••••••••••••••••••••••••••••••••••••••••••••••••
Sector Division •••••••
Vari abl e El asticity ..............•...........•.............".
Adjustment Over Time •••••
Cross Price Elasticities .
Parameter Estimates.
7 .10
7.10
7 .12
7.12
7 .13
7.14
DERIVATION OF RED PRICE ADJUSTMENT MECHANISM EQUATIONS.7 .15
8.0
GLOSSARY OF SyMBOLS ••••••.•••••••••••"e ••••III ••••••ill •••••••••••••011.
THE PROGRAM-INDUCED CONSERVATION MODULE ••••••••••••••••••••••••••
r·1E CHAN I S~1
INPUTS AND OUTPUTS .•..•••~••..••.....•...••..-.••...••...•.•.•...'II ••
7.22
8 .1
8.1
8.5
MODULE STRUCTURE.•••••••••••••••••••0 •••"•••••••••."••••.••••••••••8.5
Scenari 0 Preparation (CONSER Program)•....•.••....•••..•.•..
Business Conservation-Residential Conservation.••••••••••••••••••••••a •.iIl ••••••••••
2..7
H.1 [)
8.1 ?
-Peak Correction Factors ..•..•••..•.••e ••••••••••••••••••••••
PARAMETERS ••••
8.16
8 .16
9.0 THE MISCELLANEOUS MODULE.••••••••••••••e ••__9.1
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MECHANISM
INPUTS AND OUTPUTS._.__..e..__._.-Il __._·.ID.ID_._~__._.__.·
9 .1
9.1
-MODULE STRUCTURE •......••..••......•....•
PARAt1ETERS •••
9 .1
9.3
10.0 LARGE INDUSTRIAL DEMANf).__•__••~_••••••••••••••_•••••••••••_.10.1
t1ECHANIS~1,STRUCTURE,INPUTS AND OUTPUTS •••••••••••••••••••••••••
PARAMETERS ••••••••••••
If i i
10.1
10.2
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11.0 THE PEAK DEt1AND r1ODULE...........................................11.1
~1ECHANIS~1 ••••••••••••••••••••••••••••••••••••••••••••11 ••••••••••11.2
I NPUTS AND OUTPUTS •••••••••••••••••••••••••••••••e • • • • • • • • • • • • • • •11.1
MODULE STRUCTURE •••••••••s~••••••••••ee ••••••e •••••••••••••-••••••11.2
PARAMETERS.s ••••..•..••.•..•.•••.•..••.•••••••.••.•.•••..•.•.•...11.5
Quantitative Analysis of Trends in Load Factors
in the Railbelt ••••••••••••••••••••••••e •••••••e ••••••••••••ll.fi
Qualitative Analysis of Load Factors 11.10
12.0 r10DEl vALlnATION .•••••••.•••.•••••o ••••••e •••••••e •••••••••••••e ••12.1
ASSES9~ENT OF RED!S ACClIRAO.....................................12.1
SAr~PLE SIZE AND cm~pos ITIO N...•••••..•A.2
l1AILING PROCESS AND COLLECTION OF RESULTS........................A.5
REASONABLENESS OF THE FORECASTS ••••••••••••••••••••••••••••••••••12.3
-R.10
A.2
R.1
8.13
A.1
••••••••,••••••••••••••••••••••••e •••••••••.•••"•••••••••
BATTELLE-NORTHWEST RESIDENTIAL SURV~y ••••••••••••••••••••
DESIGN •••••••••.••••••••••flo •••••••••••••••G •••••••••••••••
WISCONSIN ELECTRIC POWER COMPANy •••••••••••••••••••••••••••••••••
ALA SKA N RAILBEL T• •• • •• •• • •• • •• •• • •• • •• •• ••• • •• ••• •• • •• ••• • •• •• •••
PACIFIC NORTHWEST POWER PLANNING COUNCIL........................8.3
BONNEVILLE POWER ADII.1INISTRATION.8.4
CALIFORNIA ENERGY COMMISSION.....................................B.6
OUTPUT ~•.•.•..•.•••..••...$••••••••••$.e •••G.................A.6
APPENDIX B:CONSERVATION RESEARCH...................8.1
APPENDIX A:
REFERENCES
SURVEY
13.0 tlISCELLANEOUS TABLES 13.1
APPENDIX C:RED 110DEL OUTPUT-........................................C.1.
LIST OF TABLES .C .3
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11.1 RED Peak -Demand"Module •••••••••_•••••••••••••••••••••••••••••~•••11.3-
1.1
2.1
3.1
4.1
5.1
6 .1
8.1
9 .1
FIGlJRES
The Railbelt Region of Alaska ••••••••••••••••.•••••••••••••••.••
Information Flows in the RED rbdel ••••••••••••••••••••••••••••••
RED Uncertainty Module ••••••••••••·•••••••••••••••~••~~••••••••••
RED Hous i ng r1:Jdul e ••••••••••••••••••••••••••••••••••••••••••••••
REO Residentia1 ConslJTlption Mod!J1e ••••••••••••••••••••••••••••••
RED Business Consumption MJdul e .
RED Program-Induced Conservation ~1odule•.•••••••••••••••••••••••
RED r~i see 11 an eo U5 t"'1o du1 e •••••_•••••••••••••••••••••••••••••••••••
1.2
2.2
3.3
4.3
5.4
6.3
8.2
9.2
11.2 Oaily Load Profile in the Pacific Northwest 11.12
A.1 Battelle-Northwest Survey Form..................................~.3
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A.2 Saturati on of Freezers in Anchorage-Cook Inl et Load Cente r .
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A.7
3.1
3.2
4.1
TABLES
Inputs and Outputs of the RED Uncertainty Module ••••••••••••••••
Parameters Generated by the Uncertainty tbdule .•••••••••••••••••
Inputs and Outputs of the RED Housing Module ••••••••••••••••••••
3.2
3.4
4.2
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4.2 Number of r~ilitary 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,Railbelt 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 ~1aximum Vacancy Rates by Type of House.......4.18
4.11 Assumed Five-Year Housing Removal Rates in Railbelt
Region,1980-2010 •••~•••••••.••.••••.•.•••.e...................4.18
4.12 Railbelt Housing Stock by load Center and Housing Type,1980 ....4.19
5.2 Percent of Households served by Electric Utilities in
Ra i 1 be It Load Centers,1980-2010................................5.11
5.1 Inputs and Outputs of the RED Residential Module ••••••••••••••••5.3 -
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 oil II -...5.14
5.5 r~arket Saturations of Large Appliances with Fuel Substitution
Possibilities in Mobile Homes,Railbelt load Centers,
1980-2010.........5.15
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5.6
5.7
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5.8
Market Saturations of Large Appliances with Fuel Substitution
Possibilities in Duplexes,Railbelt Load Centers,1980-2010 •••••5.16
Market Saturations of Large Appl iances with Fuel Substitution
Possibilities in Multifamily Homes,Railbelt Load Centers,
1980 -2010 •••••••••••••••••••••••••••••••••••••••,• • • • • • • • • • • • • • • •~•17
t1arket Saturations of Large Electric Appliances in
Single-Family Homes,Railbelt Load Centers,1980-2010.......••••5.21
5.9 t1arket Saturations of Large Electric Appliances in
Mobile Homes,Railbelt Load Centers,1980-2010...•••••••••••••••5.22
5.10 i1arket Saturations of Large Electric Appliances in
Duplexes,Railbelt Load Centers,1980-2010....••••••••••••••••••5.23
5.11 r1arket Saturations of Large Electric Appliances in
~1ultifamily 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 Appl iances...................5.29
5.14
5.15
5.16
5.17
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6.1
6 .2
6 .3
6.4
Comparison of Appliance Usage Estimates from Selected Studies •••5.30
Electric New Appliance Efficiency Improvements 1972-1980.•••••••5.34
Percent of Appliances Remaining in Service Years After
Purchase,Railbelt Re,gion ••••••••••••••••••••••••••••••_.~.,'......5.37
Equations to Determine Adjustments to Electricity
Consumption Resulting from Changes in Average
Househol d Si ze...................................................5.38
Inputs and Outputs of the Business Consumption ~1odul e...........6 •.2
Calculation of 1978 Anchorage Commercial-Industrial
Floor.Space ••••••.••••••••••••••••••.••••-•.•.••••••••.•••,.......6,.5
1978 Commercial-Industrial Floor Space Estimates................6.6
Comparisons of Square Feet,Employment,and Energy
Use in Commercial Buildings:Alaska and U.S.Averages..........6.10
6.5
6.6
Business Floor Space Forecasting Equation Parameters ••••••••••••
Original RED Floor Space Equation Parameters ••••·••••••••••••••••
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6.13
6.14
6.7
6.8
6.9
7.1
7 .2
7.3
7 .4
I I iU
Predicted Versus Actual Stock of Commercial-Light
Industrial-Government Floor Space,1975-1981 ••••••••••••••••••••
Business Consumption Equation Results •••••••••••••••••••••••••••
Electricity Consumption Per Employee and Square Foot and
Square Footage Per Employee for Greater Anchorage and
Fairbanks,1974-1981 .••••.••••••.•.••.•••.••..••..••..•.••.•...•.
Residential Electricity Demand Survey •••••••••••••••••••••••••••
Residential Survey Parameter Estimates •••••••••••••••.••••••••••
Commercial Electricity Demand Survey ••••••••••••••••••••••••••••
Commercial Survey Parameter Estimates •••••••••••••••••••••••••••
6.15
6.17
6.19
7.6
7 .8
7.11
7 .12
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7.5 Parameter Values in RED Price Adjustment Mechanism..............7.14
8.1 Inputs and Outputs of the Conservation MJdule •••••••••••••••.•••8.6
8.2 Payback Periods and Assumed Market Saturation Rates for
Residential Conservation Options................................8.17
9.1 Inputs and Outputs of the r1iscellaneous Module..................9.1
9 .2 Par ame t ers for the r~i s cell an eo us MJ du1 e.••••••••• ••••••••••. ••••9 .4
11.1 Inputs and Outputs of the Peak Demand Module ••••••••••••••••••••11.2
11.2 Assumed Load Factors for Rai"lbelt 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 ~"easures ~1ost Li ke ly to be Impl emented
in the Residential Sector of Alaska •••••••••••••••••••••••••••••11.14
12.1 Compari son of Actua 1 Base Case,and Backcast El ectri ci ty
Consunption (GWh)1982 ...••.•.••.•.•.•...•.............•.•••..•.12.2
12.2 1982 Values of Input Variables •••••••••••.•••.••••••••••••••••••12.3
12.3 Comparison of Recent Forecasts,1980-2000 •••••••••••••••••••••••12.5
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13.1 Number of Year-Round Housing Units by Type,Railbelt
Load Centers,Selected years....................................13.2
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13.2 Rai1belt Area Utility Total Energy and System Peak Demand .......13.3
13.3 .Anchorage-Cook Inlet Load Center Util ity Sales and Sales
Per Customer,1965-1981 .••......................................13.4
13.4 Fairbanks-Tanana Valley Load Center Utility Sales and Sales
r--Per Customer,1965-1981 •.•......-................................13.5
13.5 Adjustment for Industri al Load Anchorage-Cook
Inlet,1973-1981 .••..••.••••..•••••••.•...••...•....•.•.•.•..•.•13.6
A.1 Customers,NUllber Surveyed,and Respondents for the
Residential Survey Battelle-Northwest A.S
A.2 Weights Used in Battelle-Northwest Residential Survey...........A.6
8.1 PNPPC Likely Conservation Potential at 4.0 Cents/kWh by
the Yea r 2000...................................................8.5
8.2 BPA Budgeted Conservation Program Savings.......................8.7
B.3 CEC Conservation Programs Electricity Savings in
the Year 2002 •••.•..•.••e.......................................B.9
B.4 CEC Potential Energy Savings by End-Use Sector by
the Year 2002 ••~••••••••,••••••••'.............................•••R.10
8.5 WEPC Conservation Potential by the Year 2000....................8.12
B.6 Average Annual Electricity ConsUllption per Household
on the GVEA System,1972-1982...................................8.14
.,...8.7
B.8
Progerammatic Versus Market-Driven Energy Conservation
Projections in the At~L&P Service Area .
Programmatic Energy Conservation Projections for AML&P ••••••••••
B.15
8.16
.-Appendix C has a special list of tables .
xii i
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1.0 I NTR OOUC TI ON
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 North\'iest 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 Governor's Policy Review Committee bet\'ieen 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 REO 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 v.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 ~aska'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 mode1 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 nunber of energy-using devices and aggregating their
potential electricity demand into preliminary end-use forecasts.The model
then modifies these prel iminary 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
I I 'II
.h--
FAIRBANKS TVAL1.E¢NANA .~£
\(~I
)
r 0--~IV~\}J:
__....,-.......GLENNALLEN
'00 MILES
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FIGURE 1.1 The Railbelt Region ofAl aska
1.2
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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-
1 iminary 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 model parameters,a method for explicitly including
government programs designed to subsidize conservation and consumer-installed
dispersed energy options (i .e.microhydro and smalJ wind energy syste1ns),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 Penninsul~and Fairbanks-Tanana
Valley.The model produces annual energy and peak demand forecasts for every
fifth year from 1980 to 2010,and then 1 inearly 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 emplo~nent 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 Al aska 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 IS 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 rvbdule,which provides
the model with r10nte Carlo simulation capability,is described.Section 4.0
describes the Housing rbdule,which forecasts the stock of residential housing
1.3
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units by type.These forecasts are used in the electricity demand forecasts of
the Residential Consumption ~bdule,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 rnis-
cellaneous electricity demand (street 1 ighting,second homes,etc.).Large
industrial demand is covered in Section 10.0.The Peak Demand ~10dule,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 Rattelle-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 Rai1be1t Electricity Demand (REO)model is a simulation model designed
to forecast annual electricity consumption for the residential,commercial-
1 ight industrial-government,heavy industrial,and miscellaneous end-use
sectors of Alaska's Rai1be1t region.The model also takes into account
1J0vernment intervention in the energy markets in Al aska 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:
a 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 f'tlnte Carlo mode,the model
produces a sample probability distribution of forecasts of electricity
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 mode;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 1'1:>du1e to model the effects on electricHy
sales of subsidized conservation and dispersed generating options.The revised
2.1
ECONOMIC UNCERTAINTY
FORECAST MODULE
HOUSING
STOCK
,}
.RESIDENTIAL
'--)r .::BUSINESS JC:,...
~
{
~PROGRAM-INDUCED If"'"
CONSERVATION
~
LARGE
INDUSTRIAL MISC.
\)'J
ANNUAL SALES
...:>AND
PEAK DEMAND
'"
'"
FIGURE 2.1.Information Flows in the Red "1odel
2.2
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liiii.-(,
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 estimate of large
industrial load and trial system load factor forecast to estimate peak
demand.The model then returns to start the next r'onte Carlo trial.'vJhen the
model is run in certainty-equivalent mode,a specific "default"set of
par~neters is used,and only one trial is run.
The REO 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 marke~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 r-bnte Carlo routine,which uses
information on the distribution of each parameter (such as its expected value
and range)and the computer's random nlJl1ber 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 REO is run.The
parameter file contains "default"values of the parameters that may be used to
conserve computation time.
HOUSING MOOULE
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 popul ation,the
housing stock module first derives a forecast of the nunber 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,and multifamily
un its.
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 this 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 Modul e 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
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IIIOi'\
i
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~.
".....
-I
intervention to promote residential electric energy conservation or self-
sufficiency.Such intervention is covered in the Program-Induced Conservation
i~odule.The Residential Consumption t10dule employs an end-use approach that
recognizes nine major end uses of electricity,extra hot water for hJO of these
appl iances,and a "small appl iances"category that encompasses a large group of
other end uses.For a given forecast of occupied housing,the Residential
Consumption t10dule 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"values of parameters used to compute price elastic-
ities and cross-price elasticities of demand from the Uncertainty r"odule.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 t10dul es.
RIJSINESS CONSUMPTION MODlJLE
The Business ConsLlllption Module forecasts the consumption of electricity
by load center in commercial,small industrial,and government usesfbr each
forecast year (1980,1985,1990,1995,2000,2005,2010).Oirect promotion of
conservation in this sector is covered in the Program-Induced Conservati~n
rbdule.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 equi pnent to
forecast the derived demand for electricity.Using an exogenous forecast of.
regional employment,the module forecasts the regional stock of floor space.
2.5
I I ill
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 t~odule is applied to derive a forecast of business electricity
consumption (excluding large industrial demand,which must be exogenously
determined).The Business Consumption r~dule 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 forlns
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 utility-generated electric-
ity •Th i s mo du1e wa s des igned for ana1y zi ng po ten t ia1 fut ure con ser vat ion
programs for the'State of Al aska 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 option's
payback period for technologies considered "acceptable"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
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technical potentia1 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-
1ates tota1 electricity savings due to market intervention in new and retrofit
applications and adjusts residentia1 and business consumption for each load
center and forecast year.
r1I SCELLANEOUS CONSUMPTION MODULE
The r1iscellaneous Consumption ~10dule 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 1 ighti ng requi rements.Finally,all three are SUllmed
.-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 bu~iness consumption,miscellaneous consumption,industrial
demand and load center load factors generated by the Uncertainty ~dule are
first used to forecast preliminary peak demand.Next,displaced consumption
(electricity savings)calculated by the Program-Induced Conservation Module is
multipl ied by a peak correction factor suppl ied by the Uncertainty I~odul e to
allocate a portion of electricity savings from conservation to peak demand
periods.The a110cated 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
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ranges,and (i f requ i red)the
Table 3.1 provides a slJ1lmary of
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3.0 THE UNCERTAINTY MODULE
RED's Uncertainty Module allows the forecaster to incorporate uncertainty
in key parameters of the RED r'1odel 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 nunber 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.
"1ECHANI91
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
The Uncertainty Module requires three basic inputs:
•the nunber of values to be generated
• a selection of parameters to vary
•the parameter file.
The parameter file contains the default va1ues,
expected value and variance of each parameter.
the inputs and outputs of the module.
3.1
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TABLE 3.1.Inputs and Outputs of the RED Uncertainty ~~odule
i
(a)Inputs
-
Symbol
N
(see Table 3.2)
(b)Outputs
Symbol
(See Table 3.2)
N
MODULE STRUCTURE
Variable
Number of Values
to be Generated
Par ame t e r I s Ra nge,
Va ri ance,and
Expected Values
Variable
Random Parameter
Values
Number of Times
MJdel is to be Run
In put From
User Interface
Parameter Fil e
Output To
Other f"odules
Model Control Program
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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 val ue is to be
generated,then the user is queried as to which parameters will be allowed to
vary.
The next step is to choose the number of values to be generated for each
parameter.Thi s i sthe number of times the remainder of the model will be run,
each time with a different generated value for each parameter.Next,an
arbitrary seed for the random number generator is entered.
Next,the computer generates a random number for each value to be pro-
duced.This is accomplished by calling the computer1s "pseudo"random nUTlber
generator,which generates a random number between a and 1.From the parameter
file,the information on the range of the parameter,or (for parameters with a
normal distribDtion)the range,expected value,and variance is used to
3.2
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ASSUMED RANGE
EXPECTED VALUE
START
SELECT PARAMETERS
TO BE
GENERATED
RANDOMLY
SELECT NUMBER
OF VALUES TO.
BE GENERATED
IN]
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 cumul at ive probabil ity 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
II !II
TABLE 3.2.Par~neters Generated by the Uncertainty Module(a)
Symbo 1
SAT
A;B;A;OSRz ;
GSR z
BBETA
CONSAT
LF
Name
Housing Demand Coefficients
Sa t urat ion 0 f Re sid e n t ia 1 Ap P1 ian ces
Residential,Business Parameters for
Own-,Oil-Cross and Gas-Cross Price
adjus tment
Floor Space Consumption Parameter
Saturation of Conservation Technologies
Load Factor
Statistical
Distribution
No rma 1
Uni form
No rma 1
No rma 1
Uni form
Un i form
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(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.
-
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3.4
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4.0 THE HOUSING MODULE
The const.nning unit in the residential sector is the household,each of
which is assumed to occupy one housing unit.The Housing rbdule 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,
tllerefore,provides essential inputs for the Residential Const.nnption i~odule.
r~ECHANISM
The Housing Module accepts as input an exogenous forecast of the regional
population and nlJllber of households to forecast household size.The total
househol ds forecasti s adjusted for mi 1i tary househol ds and is then strati fi ed
by the age 0 f the he ado f h0 use hold and the n lJll ber 0 f h0 use hold memb er s•Th e
housing demand equations then use this distribution of households by size and
age of head to predict thp.initial demand for housing by type of dwelling.Tne
initial demand for each housing type is compared with the remaining 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 st~te-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 Module's 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 asst.nned number of households for each load center is
r-first adjusted for military housing demand and multiplied by a decimal fraction
4.1
I',III
TABLE 4.1.Inputs and Outputs of the RED Housing Module
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.-,
(a)Inputs
Symbol Variable Variable In put From ~l
THH Regi ona 1 Household Fo recas t Forecast Fil e
HH Ata State Households by Age Group Fa recast Fi 1e ~
b,c,d Housing Demand Coefficients Uncertainty t<'odul e
!OI'!!\
(b)Output s
Symbo 1 Variable Variable Output From
HD TY Occupi ed Housi ng Stock by Type Res i dent i al t<'odul e
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 is 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.
(4.1 )
where
CHH =total number of civilian households
BHH =military households residing on base (exogenous)
THH =total households (exogenous)
=region subscript
t =forecast period subscript.
On-base mil itary households are subtracted out because they do not signifi-
cantly affect off-base housing.In addition,since the military supplies
4.2
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REGIONAL
FORECAST
•POPULATION
•HOUSEHOLDS
-
STRATIFY
HOUSEHOLDS BY
AGE OF HEAD
SIZE OF HOUSEHOLD
•AGE DISTRIBUTION
OF HOUSEHOLD
HEADS
•SIZE DISTRIBUTION
OF HOUSEHOLDS
DEMAND
PARAMETERS
(UNCERTAINTY
MODULE)
INITIAL HOUSING
·STOCK TY
REINITIALIZE
HOUSING
STOCKS
L _
CALCULATE
DEMAND FOR
HOUSING UNITS
BY TYPE TY
FORECASTS OF OCCUPIED,
UNOCCUPIED HOUSING
BY TYPE
·NEW
CONSTRUCTION
OF TYPE TY
FILL VACANCIES
TYWITH
COMPLEMENTARY
DEMAND
FIGURE 4.1.RED Housing Module
4.3
I I III
electricity to them,on-base households have no impact on the residential
demand for utility-supplied electricity.(a)
Once the total number of civilian households in the load center has been
obtained,they are stratified by the size of the household and the age of the
household head.To obtai,n the distribution of households by size of household,
the total nunber of households is multiplied by the probabilities of four size
categories derived from information provided in the 19~O 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 remain constant.The user supplies 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
.inu 1tip 1yin g the two dis t rib uti 0 ns:
-
where
HH AtaHHitas=CHH it x THH x Pits x Ria
Ata
(4.2)
HH =number of households in an age/size class
THH ='total nunber 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.
(a)Military purchases of electricity from the utility system are handled as
industrial loads.
4.4
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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 198Gb).The remaining
category (duplex)is filled with the remaining households.
The demand for a particular type of housing is given by the following
equations:
'"""'
where
HD SFit =CHH it x b O + ba 1 x S1 i t
+ b a2 x S2it + b a4 x S4 it +
b2s x A2it +b3s x A3it + b4s x A4it
HD MFit =CHH it x Co +Cal x Sli t + c a 2 x S2ft + c a 4 x S4it +
c 2 s x A2i t +c3s x A3it +c4s x A4it
HO~1Hi t =CHH it x do +d a1 x Sl it + d a2 x S2it + d a4 x S4ft +
d 2s x A2ft + d3s x A3i t + d4s x A4it
HD OPit =CHH it -HO SFit -HO MFi t -HO~1Hi t
(4.3)
(4.4)
(4.5)
(4.6)
HO =housing demand
!"""SF :::index for single f ami 1y
Ss i t =a~l HH itas ; s =1,2,4
jrr-a
sf lAait=HH itas ; a =2,3,4
MF =index for mult ifamily
~~1H =index for mobile home
OP =index for duplex
4.5
I',III
The model then adjusts
housing market is cleared.
previous period1s stock net
the housing stock and housing demand so that the
Initially,the housing stock is calculated as the
of de mol Hion:
-
-
where
(4.7)
HS =housing stock
TY =index denoting the type of hou~ing (SF,MF,
r =period-specific removal rate (parameter).
r·1H,and DP)
Net demand for each type of dwel1ing is defined as the demand minus the housing
stock:
where
ND =net demand.
ND TYit =HD 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 req~ired td ensure normal functioning of the housing market:
4.6
"...,
where
NC TYit =NDTYit ~VTY x (H5TYit +ND Tyit )
NC =new construction
V =normal vacancy rate (parameter).
(4.9)
The equil ibrium vacant housing stock is the "normalll 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:
AV TYit =1 -
where
AV =actual vacancy rate.
(4.10)
-
-
If the actual vacancy rate is greater than its assumed maximum,then the excess
supply of that particular type of housing is assumed to drive down the price of
that type of dwell ing.Individuals residing in other dwelltngs could 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:
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.
4.7
I I III
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 assuned
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:_
where
VH it =E
TY
(4.11)
VH =total vacant dwelling units.
PARAMETER S
Military Households
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 Fairbanks
3,212 3,062
Sou rce:Suppl i ed by I SER.
4.8
-.
-
-
,-
,-
-
H00sehold Size and Demographic Trends
A key factor in the residential demand for electricity is the number and
type of residential customers.The nunber of customers approximately equals
the number of households served by electricity,with the difference being
caused by such factors as vacant housing with electrical service.Thus,it is
important in forecasting the demand for el ectricity to forecast the number of
households.The nunber 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 nunber of persons of household fonnation 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.
Table 4.3 shows how the size of households has changed in the United
Stntes and in the Railbelt since 1950.The table indicates that the average
nLmber of persons per housing unit has declined dramatically in both the II.S.
and the Rai]belt during the period.Since 1970,the size decline has been more
TABLE 4.3.Household Size v.estern U.S.and Railbelt 1950-1980
(~ersons per Occupied Unit)
1950
1960
1970
1980
Un ited
St ates
3.S(a)
3.3
3.1
2.7
Ancho rage-
Cook Inlet
3.4(a)
3.4
3.4
2.9
Fa i rbank s-
Tanana Valley
3.3(a)
3.6
3.4
2.9
(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 196.0.
4.9
I I III
rapid in the Railbelt than in the nation as a whole,resulting from increasing
ntmbers and proportions of young,singl e adul t 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 r~AP economic and population model to forecast the nunber of
households based on the household formation rates impl icit 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 scenario 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 household formation rates based on these variables.
The RED model formerly estimated the nunber of households in each Railbelt
load center from a MAP model estimate of statewide households and the
TARLE 4.4.Forecast Size of Households,Railbelt Load Centers
""'"
Year
1980
1985
1990
1995
2000
2005
2010
Anchorage-Cook Inlet
2.91
2.73
2 .69
2.67
2 .64
2.63
2 .62
Fairbanks-Tanana Va1ley
3.00
2.89
2.85
2.81
2.79
2.76
2.71
Source:University of Alaska Institute of Social and
Economic Research,case HE.6,FERC 0%Real
Growth in Oil Prices
4.10
"...
,....
relationship between the age distribution of the population in each load center
and the age distribution of Alaska's population.The 1983 version now simply
accepts a MAP model forecast of the number of households in each load center.
The nlETlber 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 nU1lber of households served by
utility-generated electricity is virtually 100%in Anchorage.Rural areas of
the Matanuska-Susitna Borough and Kenai Peninsul a Borough have a few residences
not served (mostly seasonal homes).but the Fa i rbanks 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 proxy 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 Al aska 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.~1uch 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 interes~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 quality of
housing purchased.New homes built during 1980-1981 were an average $25,000
more expensive than existing homes.The proportion of mul tifami ly construction
was not clearly affected one way orthe other by the loan ~rograms.In 1980
and 1981 new multifamily construction in Arichorage 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 1evel s 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,ISER's
findings suggest that continuation of these large-scale subsidies would result
in the fall owi ng:1)more fi rst-t ime home buyers and more expens ive unit s
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 peopl e l s tastes for singl e-fami ly detached units versus condomini ums and the
builder l s cost of providing units of each type,government programs could cause
single-family construction to increase.Q.C..decrease as a proportion 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
10"0\,
-
,....
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.Wi~h 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 year 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
forecast s.
TABLE 4.5.Impact of Householder Age and Household Size on Housing t1ix
and Total Utility Sales,Anchorage-Cook Inlet
.-
-
Single Family Proportion
of Served Households:
With Age and Size Effects
Without Age and Si ze Effects
Multifamily Proportion of
Served Households:
With Age and Size Effects
\~ithout Age and Si ze Effects
Mobile Home Proportion of
Served Households:
With Age and Size Effects
Without Age and Si ze 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 ~ze 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
-Source:RED Model Runs,Case HE.6,FERC 0%Real Price Increase.
4.13
III
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 Railbelt 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
197 6 t 0 13.5%ina une 198 0•Th e va can cy rat e s have fa 11 end r ama tic all y sin ce
(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
TABLE 4.6.Single-Family Housing as Proportion Year-Round Housing
Stock by Type,Railbelt Load Centers,1950-1982
-
-
-
1950 (a)
1960
1970
1980
1982(a)
Proportion Single-
Fami ly Housi ng
Built 1980-82
Anchorage -
Cook Inl et .
0.592
0.628
0.471
0.462
0.472
0.539
Fai rbanks -
Tanana Valley
0.713
0.518
0.389
0.450
0.472
0.781(b)
(a)Ur ban An ch 0 rag e and Fa i r ban k son 1y •
(b)Fairbanks-North Star Borough only.
Source:Table 13.1.
4.14
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 taki ng 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
-.
-
-
-
-
~,
TABLE 4.9..Housing Demand Equations:Parameters'Expected Value.
Range.and Variance
Pa rameter 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 a 2 0.086 0.133 0.001
c a4 -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
d a1 0.068 0.101 0.001
d a2 0.039 0.109 0.001
d a4 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
So u rce:Goldsmith and Hu s key 19 80b •Tabl e 8.6.
Depreciation and Removal
Housing demol ition rates (Table 4.11)are a function of the age of the
hous i ng stock and the demand fo r hous i ng.ISER found tha t approx imate ly 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)
Type
Single Family
MJbi 1 e Home
Oupl ex.
Multifamily
No rmgl )
Rate ~a
1.1
1.1
3.3
5.4
Ma xi ~~~Rate
3.3
3.3
10.0
16.0
(a)Imputed by ISER from Bureau of
the Census (1980a).
(b)Imputed by ISER from Anchorage
Real Est imate Research Committee
(1979)•
TABLE 4.11.Assumed Five-Year Housing Removal Rates in Railbelt
Region,1980-2010 (Percent of Housing Stock at
Beginning of Period Removed During Period)
Years
1980-1985
1985-1990
1990-1995
1995-2000
2000-2005
2005-2010
Source:
Remova 1
Rate (percent)
1.25
1.50
1.75
2.00
2.25
2.50
Author Assumption.
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
m6st estimates grouped around 1.0 to 1.5%.Leigh herself calculates about 1%
4.18
-
~,
-
for the period 1950 through 1970.
rate of removal for Anchorage and
estimated number of units in 1970
ISER calculated an approximate five-year 1%
Fairbanks housing units by comparing the
and 1979 with cumulative building permits
data.Because the housing stock ages and new houses provide more "services"
than old houses,the rate 0'\economic depreciation for a given area is assumed
to be larger than the rate of physical depreciation.Consequently,housing
units are physically repl aced 1ess 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.
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 ~aska.
TABLE 4.12.Railbelt Housing Stock by Load Center Qnd
Hous i ng Type.1980 (n lITlber of unit s)a)
Housing Type Anchorage Fairbanks
Single Family 40.562 10,873
Mobile Homes 10.211 2,175-duplexes 8,949 2.512
Mu 1 t ifamily 27.980 8,607
Tota 1 87 .702 24,167
(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
,....,
.....
--
.....
5.0 THE RESIDENTIAL CONSUMPTION MODULE
The Residential Consumption r10dule 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
Fl.O).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 ~10dul e estimates the amount of electri·city
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 v.ood)are handled in
the Program-Induced Conservation Module.
~~ECHAN I SH
The Residenti al Consumption ~~odul e ernploys an end-use approach.In an
end -use ana1y sis,the fir st st e pis t 0 ide nt ifY the maj 0r use s 0f e1ect ric-
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 nlD1lber of devices by their predicted annual average consumption of
electricity.Using the same procedure for miscellaneous residential uses and
sl11lmingover all end-uses yields an aggregate forecast of electricity
requi rements.
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.8yadjust-
ing the aggregate residential consumption figure 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-
tion induced by changes in fuel prices.
INPUTS AND OUTPUTS
Table 5.1 presents the inputs and outputs of the module.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
electricity.
MODULE STRUCTURE
The Residential Consumption Module identifies the following major uses of
electricity in the residential sector:
1.Water Heating
2.Cooking
3.Refrigeration
4.Freezi ng
5.Clothes Washing (and additional water heating)
6.Clothes Drying
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
-,
.....
,...,.
-
"""I
~,i
TABLE 5.1.Inputs and Outputs of the RED Residential Module
(a)Input s
Symbol
HD TY
A,B ,A ,
OSR,GSR
SAT
(b)Output s
Symbol
RESCON
Variable
Electrically Served Households
by Type of Dwelling
Price Adjustment Coefficients
Appliance Saturations
Variable
Residential Electricity
Requi rements
From
Housing Stock Module
uncertainty Module
Uncertai nty r"bdul e
To
Miscellaneous,Peak Demand
and Conservation Modules
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 appl iances recognized by the model.
The module then calculates the initial fuel mode split for multifuel appl i-
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 "trial"-specific own-price and cross-price
adjustments are used to adjust the preliminary forecast.The adjustments are
described in Section 7.0.
5.3
FORECAST OF
OCCUPIED HOUSING
STOCK BY TYPE
{HOUSING MODULE)
CALCULATE STOCK OF
LARGE APPLIANCES
8Y END USE.
DWELLING TYPE
APPLlANCE
SATURATIONS
8Y HOUSING TYPE
(UNCERTAINTY
MODULE)
CALCULATE INITIAL
SHARE OF EACH
APPLIANCE USING
ELECTRICITY
FUEL MODE
SPLIT
1980
CALCULATE AVERAGE
ELECTRICAL USE IN
LARGE APPLlANCES
BY APPLlANCE
~FF1CIENCY
STANDARDS
CALCULATE TOTAL
PRELlM1NARY LARGE
APPLlANCE USE
BY
APPLIANCE
CALCULATE
PRELlMINAY
SMALL APPLIANCE
USE OF
ELECTRICITY
SUM PRELIMINARY
CONSUMPTION FOR
ALL APPLlANCES
PRICE
.ADJ.PARAMETERS.
RESIDENTIAL SECTOR
(UNCERTAINTY
MODULE)
PRICE AND
CROSS-PRICE
ADJUSTMENTS
RESIDENTIAL
CONSUMPTION
PRIOR TO
CONSERVATION
ADJUSTMENT
PRICE FORECASTS
(EXOGENOUS)
FIGURE 5.1.REO Residential Consumption Module
5.4
I'-
I
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 ~1odule 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 Consumption t-bdule forecasts the nLlllber of primary occupied housing
units served:
where
HHS TYit =SE it x HD TYit
HHS =households served
TY =denotes the type of dwe 11 in 9
SE =proportion of households served by an electric utility
HD =stock of occupied dwellings from the Housing Module served by
electricity
i =region subscript
t =forecast period (t =1,2,3,•••,7).
(5 .1 )
Once the nunber of electrically served households by type of dwelling is
known,the appl icance 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 nLlllber 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:
(a)Excluding second or recreation homes.
5.5
(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 nlJ11ber of new appliances in period t is found by
calculating the stock of appliances surviving from all previous periods and
subtracting this surviving stock from appliance demand:
(5.3)
where
NA =number of new appliances
AS i ok =initial stock of appl i ances (1980)
m vi ntage spec ifi c rate in period for vintage mdtk=scrap t·,
(parameter)(m ;:1,2,3,oil ••,7).
Equation 5.3 can be rearranged so that the stock equals the demand:
-.
-
-
t
AD itk =AS iok x (1 -d~k)+m~1 NA imk x (1 -d~k)-.
The future appl i ance stock,therefore,can be strati fi ed by vi ntage.Next,the
model calculates the initial stock of electricity-consllTling appliances by mul-
tiplying the number of appliances in each vintage by the percentage using
electricity:
ENA imk =FMS ik x NA imk
5.6
(5.4)
(5.5)
~.
(5 .6)
where
EAS =initial stock of electric appl iances-Fr1S =fuel mode s pl it
ENA =additions to the electric appliance stock
EAO =total electric appliance stock.
The Residential Consumption nodule next calculates the average annual
electricity consumption of each major appliance.nifferent vintages of
appliances use different amounts of electricity,so the average consumption
:!lust reflect the vintage composition of the stock.Furthennore,industry
energy efficiency standards for appliances could change in future years.The
future vintage specific consumption rate can be derived by !nultiplyin~the
current (1980)consumption rate by a growth factor and adjusting for any
c han 9 e sin eff ici en cy s tan dar ds•By we i 9hti n 9 the s e fi 9u res by the pro po rt ion
of the stock they represent,the av~rage consumption of each appl iance type in
a forecast year is derived:
=AC iok x
EAS"ok x (l-d t
O
k )!( ""("I)Z
+I AC.k x (1 +9 k )m-x
"'0EADitkm=l
where
m
ENA imk (l-d tk))
x (l-c smk)x "-------
EA°itk
AC itk =average consumption of appliance k in period t (parameter)
AC iok =average consumption of appliance k in the beginning period
(pa ramete r)
Z =length of forecast periods t and m in years (parameter)set
equal to 5 for this study.
g =growth rate of appl iance k consumption (parameter)
5.7
(5.7)
III
cs =conservation standards target consumption reduction
(par arne t e r)•
Finally,the preliminary consumption for each major appliance can be
calculated by multiplying the stock of each appliance by its calculated average
consumpt ion:
where
CONS itk =EADitk x ACitk x AHS itk (5.8)
CONS =preliminary consumption of electricity prior to price
adjus tments
AHS =household size adjustment parameter for clothes washing,
clothes drying,water heaters only.·
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:
CONS itsa =~Y HHS TYit x [ACios a +(AfG itsa x t x Z)]
where
ACG =growth factor in small appliance consumption
sa =index denoting small appliances.
(5.9)
Total preliminary residential consumption is found by summing across end
use s:
9
RESPRE it =I CONS i tk +CONS itsak=l
where
RESPRE =total preliminary residential consumption.
5.8
(5.10)
-
.....
.....
-
.....
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 •
Simi1arly,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 ....ords,the cross-price adjust-
Inent 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 el ec-
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 thei r
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 sw.itching in the
long run.The Uncertainty f1Jdul e generates both the short-run values of the
5.9
I I III
price effect for specific trials and the coefficient of the speed of consumer
response.Chapter 7.0 di scusses both the economi c theory and 1iterature under-
lying 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.
The actual calculation of the price adjustment of residential consumption
i s as f 011 ow s:
-
-
....,
I
where
RESCON =
OPA =
PPA =
GPA =
RESCON it =RESPRE it x (1 +OPA it ) x (1 +PPA it )
x (1 +GPAit).
consumption of electricity in the residential sector
own-price adjustment for electricity
cross-price adjustment for fuel oil
cross-price adjustment for natural gas.
(5 .11)
RESCON is the predicted electricity consumption in the residential sector
before adjustments for program-induced conservation.Thi s figure is passed to
the Peak Demand and Program-Induced Conservation Modules.Note that RESCON is
a single number.The Residential Consumption r-tJdule doeS not report price-
adjusted consumption of electricity by end use.
PARAMETERS
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
198Gb).Due to the high emphasi s the Al aska state 1 egi sl ature 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.
5.10
-
-
-
....
-
TABLE 5.2.Percent of Households Served by
El ect ri c Ut il it ie sin Ra il be 1t
Load Centers,1980-2010
-Yea r
1980(a)
1985(b)
1990(b)
1995(b)
2000(b)
2005 (b)
2010(b)
Anchorage
100
100
100
100
100
100
100
Fairbanks
91
93
96
100
100
100
100
(a)
(b)
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.
.....
-
Ap P1 ian ce Sa t urat ion s
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 appl i ances,somewhat arbitrary maximum penetration rates have been est i-
mated.The estimates were made by comparing recent util ity saturation rate
studies by San Diego Gas &Electric (SDG&E)in 1982 and Southern Ca1ifornia
Edison (SCE)in 1981 (realizing their limited relevance in estimating Alaska
saturation rates),information from 1980 Census of Housing for Alaska,
informatlon 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.
t1arket 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
5.11
TABLE 5.3.Appliance Saturation Rate Survey (table values in percent of households)
SCE (1981)
SDG&E(1982)(a)
(range of values
observed in
Appl i ance (total market area)market area)(b)
Clothes Drier --71.1-81.2
Refrigerator 97.5 96.2-96.6
Freezer 26.2 9.1-33.5
Hot Tub/Jacuz z i/Saunas ..11-39 1.3-19.4
Water Heater --92.3-97.7
Cooking Range 96.2 98.3-99.5
lT1.Dishwasher 55.4 .41.2-58.0~
N
Clothes Washer 68.9 75.6-89.3
t~icrowave Ovens 34.5 17.9-38.9
Space Heating 94.6
Railbelt:Housing
Cen sus (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 ty~e)
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 1i st.
I J J J _I ]J I J I J ]J J J .1 I )I
....
.....
-
....
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 he 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 appl iance are given
be low.
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 incr~ase 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 questioninthe 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 (cooking)Sa una s-J acu zz is
Load Center Year Default Range Defa ul t Range Oefaul t Range Defa ul t RaQge
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
Ul 2005 99.0 98-100 95.0 92-98 100.0 100-100 25.7 20-30.
l--'
.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.
.......J .1 ...•~.J •J J )J ,...~J 1 il
•••J J
J 1 I 1 '.
')]J -1 --'1 I )1
TABLE 5.5.Market Saturations (percent)of Large Appliances with Fuel Substitution
Possibilities in Mbbile Homes,Railbelt Load Centers,1980-2010
Wa ter Heater Clothes Dryers Range (cooking)Saunas Jacuzzi s
Load Center Year Default Range Default Range [)efaul t Range [)efaul t Range--
98.2(a)95.7(a)
----
a.Anchorage 1980 --79.0 ----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 7-15I-'
U1
b.Fai rbanks 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 arid cooking,missing values in the Rattelle-Northwest end-use survey were not
counterl.
TABLE 5.6.Market Saturations (percent)of large Appliances with Fuel Substitution
Possibilities in Duplexes t Railbelt load Centers t 1980-2010
--
~
Water Heater Clothes Dryers Range (cooking)Saunas Jacuzzis
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
U1 2010 100.0 100-100 95.0 92-98 100.0 100-100 29.8 25-35.
.......
100.0(a)85.5(b)Q)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-lUO 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 expl an at ion t see text.
(b)1980 clothes dryer penetration in Fairbanks for 1980 adjusted downward by one to match the nurnber of
washers in duplexes.
j J J J I -J g J }J •J J J J J I
J -I -)}1 -)1 1 1
TABL E 5.7.Market Saturations (percent)of Large Appliances with Fuel Substitution
Possibilities in Multifamily Homes,Railbelt Load Centers,1980-2010
Water Heater Clothes Dryers Range (cooking)Sa una s J acu zz is
Load Center Year Default Range Default Range Defaul t ~~Defaul t Ri!nge---.-
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
Ul 2005 100.0 100-100 gO .0 85-95 100.0 100-100 19.9 15 -25.
l--'-.....2010 100.0 100-100 95.0 92-97 100.0 100-100 20.9 16-26
b.Fairbanks 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 fLO 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.
i I III
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 abo~t 61.5%of electrically served housing units have an electric or
gas dryer (up from 44.6%in 1970)(8ureau 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 Fai rbanks to 90%in Anchorage.Because Al aska
al ready 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 availabil ity 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.
Cook i ng Range s
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 h'ouseholds surveyed had a range available.SDG&E (1982)reported a
96.2%saturation rate while SeE (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
-
-
-i
having
~,market
(1981 )
ing to
one of
SDG&E.
,~,
.-
Saunas,Jacuzzis,Etc.
These units are a relatively new phenomenon in private homes,almost all
been installed since 1970.The Battelle-Northwest end-use survey found
saturations ran~ing from 2.5 to 17%,SOG&E (1982)11 to 39%,and SeE
1.3 to 19.4%,all depending upon market area and housing type.Accord-
the survey,14%of Anchorage single family households reported having
these units,compared to 10.4 and 11.0%,respectively,forSCE and
Among single-family homes built since 1975 in Anchorage,the saturation
I'las 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 slightly larger than both was assumed for newly constructed single-
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 bel ief that saturation growth rates woul d fall as the newness of the item
,'lore off.Thi s phenomenon may happen with any rel atively 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 assumed.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.Ry
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
i I III
mu 1t i fami ly homes by not aski ng res i dents of thi s type of hous i ng 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 thei r 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 c1 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
-
-
1 -l l "1 ~-1 ,-->I ,'I ,),}-1 ,)•1 f_'1
•
TARLE 5.S.Market Saturations (percent)of Large Electric Appliances in Single-Family Homes,
Railbelt Load Centers,1980-2010
Refrigerators Freezers Dishwashers Clothes Hashers
Load Center Year Default Range Defa ult Range Default Range Defaul t 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
tJ1.2005 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 Centers,1980-2010
Refrigerators Freezers Dishwashers Clothes Washers
Load Center Year Defa ul t Range Defaul t Range Default Range Defaul t ~~--
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
U1 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 9.0.0 85-95 90.0 85-95 95.0 92-98
b.Fai rbanks 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
.J -.l t •))~_..)~.~-,I •1 J ,]
-)1 I I ~l ']'}')-I '-)]1
TABLE 5.10.Market Saturations (percent)of Large Electric Appliances in Duplexes
Railbelt Load Centers,1980-2010
Re fri ge rators Freezers Dishwashers Clothes Washers
Load Center Year Default Range Default Range Defaul t Range Defaul t ~~~--
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
tTl 2000 99.0 98-100 85.0 80-90 90.0 85-95 95.0 92-98.
N
LoJ 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.Fairbanks 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 ~1ultifamily Homes t
~
Ra il be lt Load Centers t 1980-2010
Refrigerators Freezers Di sh\'Iashers Clothes Washers
Load Center Year Default Range Defaul t Range Default Range Defaul t 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
(Jl 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.Fai rbanks 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
I ,t J l J _.,))f .J ~~•J I J
.,.,.
,
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%above 1970
Census values for the Railbelt,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 proba~ly 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
,1""'"for a freezer to conserve scarce 1 iving space in duplexes and multifamily
unlts.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.
Di shwashers
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 whol e,the 1979
saturation was about 41%of homes served by electricity (Bureau of Census
1980b),but thi s percentage ranged from 50%in Fai rbanks to 75Yo 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
III
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 assumed to fall to zero.A wide range
of uncertai nty is assumed for dishwasher saturat ions because of the tenuous
nature of the required assumptions.
Clothes Washers
The Battelle-Northwest end-use survey found that area-wide 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 hi gher than that for dryers.Where thi s 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 Spl its
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
-
-
-
,-
1 l J )1 ~l 1 -)1 J 1 l -,
TABLE 5.12.Percentage of Appliances Using Electricity and Average Annual
Electricity Consumption,Railbelt Load Centers
Anchorage Fa i rbank s
Percentage Using Electricity(a)Annua 1 kWh Percentage Using Electricity Annua 1 kWh
Appl i ance ~MH OP MF Con s umpt i o~SF MH OP MF Cons Ullpt ion
Space Heat (Existing Stock)
Single Family 16.0 NA NA NA 32,850 9.7 NA NA NA 43,300
l-1ob i 1e /lome 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
Mult i Family NA NA NA 44.4 15,390 NA NA NA 14.8 19,080
Space Heat (New Stock:1985)
Single Family 10.0 NA NA NA 40 ,lOa 9.7 NA NA NA 53,000
ItJb 11 e /lome NA 0.7 NA NA 30,000 NA 0.0 NA NA 40,600
Duplex NA NA 15.0 NA 26,600 NA NA 11.7 NA 35,100
Muh I Family NA NA NA 25.0 18,800 NA NA NA 14.8 23,300
Water Heaters (Exi st ing)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
(Jl Clothes Dryers 84.3 B8.1 81.3 86.6 1,032 96.2 94.6 94.4 100.0 1,032
N
'-l Cooking Ranges 75.8 23.2 85.2 88.2 050 79.0 48.2 95.0 97.1 850
Sauna-Jacuzzi s 93.5 100.0 93.7 81.B 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
Oi shwashers 100.0 100.0 •100.0 100.0 250 100.0 100.0 100.0 100.0 250
Additional
Water Ileating (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 Wa shers 100.0 100.0 100.0 100.0 90 100.0 100.0 100.0 100.0 90
Addlt i ana 1
Water /leating (Existing)36.5 50.4 44.0 60.9 1,202 33.1 42.8 43.1 26.2 1,202
Water lIeating (New:1985)10.0 50.4 15.0 25.0 1,202 33 .1 42.B 43.1 26.2 1 ,202
111 scell aneous 100.(J 100.0 100.0 100.0 2,110 100.0 100.(J 100.0 100.0 2,466
(a)SF =sin!)le family;1111 =mohilehomes;OP =duplexes;f.1F =multifamily.
I I III
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 I;y
the RED price adjustment coefficients after 1980.Accordingly,the 1983
version of the model judgmentally imposes reduced incremental e1ectric 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 spl its are shown,el ec-
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 analysi s 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 strnmarized 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
-
-
-.
-.
-i
-
-
-
).~.~l --"l }.l 'I '-'1
TABLE 5.13.Growth Rates in Electric Appliance Capacity and Initial Annual
Average Consumption for New Appliances
Average Annual
kWh ConsLDllption for Grovlt h Rate in
New Appl iances {1985}Electric Capacity
Appl i ance Anchorage Fairbanks Post-19S5 (annual)
Space Heat
Single Family 40,100 53,000 0.005
t-bb i 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
U'1.Cooking Ranges 1,200 1,200N 0.0
\.0
Saunas-Jacuzzi s 1,750 1,750 0.0
Re fri gerators 1,560 1,560 0.00
Freezers 1,550 -1,550 0.00
Dishwashers 230 230
Ad d it ion a 1 Wa t er He a tin 9 740 740 0.005
Clothes Washers 70 70 0.0
Ad d it ion a1 Wa t er He a tin g 1,050 1,050 0.005
Small Appliances and Lighting 2,110 2,466 (a)
(a)Incremental growth of 50 'kWh per custolner in Anchorage per 5-year period;
70 UJh in Fairhanks.
i .1 1
I '.i ••
,,,1 )~I -J J j f •
Space Heat
For space heating in the existing housing stock,the average annual
consumption figures derived by ISER are used (Goldsmith and Huskey 198Gb).
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 ISERis study.
Water Heaters
The average consumption for water heaters is based on the California
Energy Commission's (CECls)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 U~RI).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
appl i ance.
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
!I In
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
residential survey.
Freezers
This figure showed little variation among ~1erchandising Week,~1RI,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.
Dishwasher and Clothes Washer Water
These values are from the CEe,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 rvbdel checked ISER's work by assuming:1)televisions
(rated at 400 kWh/year)are included in small appl iances;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
-
-
"'"'"i
are as fo110ws:Anchorage.459 kWh/year/customer;Fairbanks.1127 kWh/year/-
customer.Because the results were broadly consistent with I5ER 's figures.
I5ER's totals were used (Goldsmith and Huskey 1980b).
E1ect ric a1 Ca pac it y Gr owt h
Table 5.15 presents average annual kWh consumption for new appl iances 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 ahout 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 appl iance 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.5.Department of Energy (DOE)have
developed estimates of appliance efficiency for several types of new appliances
(see King et al.1982).The major source for the efficiency ratings on new
appliances was a DOE survey of appliance manufacturers (Form C5-179)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)model~and has found that estimated efficiencies have improved more than
was anticipated at the time of the C5-179 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).1eadi ng to a 5i g-
nificant net reduction in average kilowatt-hours used in the new models.(a)
Table 5.15 summarizes the findings of the C5-179 survey and appliance
manufacturers.
(a)Personal Communication.Jim Mct1ahon.Energy Analysis Program.lawrence
Berkeley laboratory.May 24.1983.
5.33
III
"""\
TABLE 5.15.El ectri c New Appliance Efficiency Improvements 1972-1980
(percent impact on energy use,1972 base)
CS-179 Findings(a)Appliance Manufacturers(b)
Appl i ance 1972-1978 1972-1980 1972-1980
1.Water Heat
Efficiency -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
Size Inc rease NA NA NA
Other Features riA NA NA
Net Energy Use NA NA NA
3.Clothes Dryers
Effici ency -0.0 -4.2 -3.1 -Si ze Increase NA NA 0.4
Other Features NA NA 0.4-
Net Energy Use NA NA -2.7 -4.Re fri gerators
Efficiency -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 -Efficiency -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.Dishwashers
-45.0(d)Effi ci ency NA NA
Size Increase NA NA ,114 .0 (d)
Other Features NA NA
Net Energy Use NA NA -31.0(d)
7.Clothes Washers·
-51.6(d)Efficiency NA NA
Si ze Increase NA NA "s 1 ight"(d)-Other Features NA NA (d)12.1(d)
Net Energy Use NA NA -39.5
NA =Not Available
(a)Source:Ki ng et ale 1982.
(b)Source:McMihon 1983 ••
(c)Net decrease in average size.More compact models sold.,-
(d)1972-1981.
5.34
-
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 si ze of these appl iances.For the
future.consumers are assumed to adopt more efficient available ~odels 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 al ready reflect a large proportion of frost-
free units.(Battell e-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.Mthough nationally freezers have become more efficient.additional
5.35
I I III
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 sl ightly
larger units than comprise the 1980 stock.
Agpliance Survival
Table 5.16 presents the percentage of appliances remaining in each five-
year period after thei r 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 particul ar 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 U1idwest 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 to compute the price
section of this chapter.
used in the Residential Module are the parameters
effects described briefly in the module structure
Because of the complexity of the algebra involved,
5.36
~~
TIl,BLE 5.115.Percent of Appliances Remaining in Service Years After
,....Purchase,Ra i 1 be 1t Region
a.Old Appliances 5 10 15 20 25 30----
-1"'''\Space Heat (All )0.90 0.80 0.6 0.3 0.1 0.0
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
Ranges-Cooking 0.6 0.3 0.1 0.0 0.0 0.0
Saunas-Jacuz zi s 0.8 0.6 0.3 0.1 0.0 0.0
Refrigerators 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
~""Dishwashers 0.6 0.3 0.1 0.0 0.0 0.0
Cl athes Washers 0.6 0.3 0.1 0.0 (1.0 0.0
--b.New Appliances
Space Heat(Al 1)0.89 0.73 0.56 0.42 0.3 0.1
r~\~ater Heaters 0.75 0.35 0.1 0.0 0.0 0.0
Clothes Oryers 1.00 0.75 0.35 0.1 0.0 0.0
Ranges-Cooki 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
Re fr i gerato rs 1.00 0.75 0.35 0.1 0.0 0.0
Freezers 1.00 1.00 0.75 0.35 0.1 0.0
Dishwashers 0.75 0.35 o .1 0.0 0.0 0.0
~Cl othes \~ashers 0.75 0.35 0.1 0.0 0.0 0.0
Sou rce:ISER (Goldsmith and Huskey 198Gb)except for saunas-jacuzzis,
which is author assumption.
--(
5.37
TABLE 5.17.Equations to Determine Adjustments to Electricity
Consumption Resulting from Changes in Average
Household Size
Appliance E9 uat i on
Clothes Hasher AHS(a)=1 x AHH(b)
Clothes Wa s her Water AHS =0.25 +0.75 AHH -.Clothes Dryer AHS =0.25 +0.75 AHH
Wa ter Heater AHS =0.51 +0.49 AHH
(a)AHS =Adju stment factor.
(b)AHH =Average househol d si ze (Based on 3.0).
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 frorn
~1ount,Chapman,and Tyrell (1973).
-
-
5.38
.~.
6.0 THE BUSINESS CONSUr1PTION r10nlJLE
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 rlodule.Heavy industrial use is forecasted exogenously,
as described in Section 10.0.
;,IECHANI sr1
The structure of the forecasting mechanism in the Business Consumption
~odule 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,1 ight industrial,and
government electricity consumption.No information exists forconsurnption of
electricity by end use in this settor,so RED produces an aggregate forecast of
business electricity consumption.The Business Consumption ~10dule 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 r.>quiprnent)to predict an initial level of business electricity
consumption.This initial prediction is then adjusted for price impacts to
yield d price-adjusted forecast of business electricity consumption.
INPUTS AND OUTPUTS
Table 6.1 presents the inputs and outputs of the Business Consumption
r'1odule.Load-center-specific forecasts of total employment are exogenous to
RED.Currently these come from forecasts of the ISER Man in the Arctic Program
(r1 AP)mod e 1.Th eel as tic ity 0f use per s qua ref00 t 0f bu il din g spa ce and pric e
adjustment parameters are assigned in the Uncertainty Module.The output of
the Business Consumption MJdule is the price-adjusted forecast of electricity
requi rements of the business sector before the impacts of program-induced
conservation are considered.
6.1
I I 'II
TABLE 6.1.Inputs and Outputs of the Business Consumption Module
a)Inputs
Symbo 1
TE~1P
GBETA
A.R,A .OSR ,GSR
b)Output s
Symbo 1
BlJSCON
t10nULE STRUCTURE
Name
Total Regional Employment
El ectricity Consumption Floor
Space Elasticity
Price Adjustment Coefficients
Name
Pri ce-Adjusted Bus i ness
Co nSLDTIpt ion
From
Forecast File (exogenous)
uncertainty Module
(paramete r)
uncertainty module
(pa rame t e r)
To
Miscellaneous,Peak Demand
and Conservat ion r10dul es
Figure 6.1 presents a fl ow chart of the modul e.The fi rst step is to use
employment forecasts to construct estimates for the regional stock of floor
space by five-year forec.ast 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,Batte 11 e-Northwes t researchers decided to use a ve ry s i mpl 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 LDTIed to
increase at a constant rate to levels about 10%and 15%,respectively,above
today's levels by the year 2010.This 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 asa whole.The assumption is still quite conservative,since Alaska l s
commercial floor space per employee is far below the national average.The
forecasting equation is shown as equation 6.1.
--6.2
FORECAST
EMPLOYMENT
CALCULATE
BUSINESSi
GOVERNMENTi
LIGHT INDUSTRIAL
FLOOR SPACE
PRiCE
FORECASTS
(EXOGENOUS)
CALCULATE
PRELIMINARY
BUSINESS
ELECTRICAL
CONSUMPTION
PRICE AND
CROSS·PRICE
ADJUSTMENTS
BUSINESS
CONSUMPTION PRIOR
TO
CONSERVATION
ADJUSTMENTS
PRELIMINARY
BUSINESS USE
COEFFCIENTS
(UNCERTAINTY
MODULEI
PRICE
ADJ PARAMETERS
BUSINESS SECTOR
(UNCERTAINTY
MODULE)
where
FIG URE 6.1•REO 13 u sin ess Con s lJn pt ion ~10 du1 e
STOCK =fl oar space in business sector
a =
initial (1980 )floor space per employee
b =annual g rowt h factor (l plus growth rate)in floor space per
employee
TH1P =total employment
=index for the region
t =time index,t-=1,2,3,•••,7
k =time index,k=1,2,3,•••,3L
6.3
(f).1)
I I III
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 whi~h 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:
-
-
where
PRECON it =exp(BETA i +BBETA i x 1n(STOCK it )](6.2)
PRECON =nonpri ce adju sted bus i ness consumpt ion
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 for the forecast year (1980,1985,•••,2010).
Finally,price adjustments are made with the price adju-stment mechanism
ide ntic a1 tot hat i nth e Re sid ent ia 1 Co nsump t ion MJ du1 e.
where -
BUSCON
OPA
PPA
GPA
p ri ce-adju sted business requi rements (MWh)
own-price adjustment factor
=cross-price adjustment factor for fuel oil
=cross-price adjustment factor for natural gas.
6.4 -
~
f
18,918
23,149
4,630
27,i79
M1ATS Survey (Anchorage Bm'Jl,1975)
t~inus Non-energy Using (parking lots,
c erne t erie s,etc.)
Energy Using Floor Space
20 Percent Adjustment for Underreporting
TABLE 6.2.Calculation of 1978 Anchorage Commercial-Industrial Floor Space
10 3ft2
42,067
Sectors
l.
2.
3.
4.
not Included in Survey:
Girdwood/Indian(a)
Eagle River/C~Ugiak(b)
Ho tel s I ~10 tel s C)
Assorted Cultural Buildings(d)
53
300
1,000
500
29,632
It em:(e)
.~'
Retail Trade
Warehousing
Education
\·/ho 1e sal e Tr ad e
Tran spo rt-Commu ni cat i on-
Public Utilitites
Government
Manufacturi ng
Other
6,148
3,722
3,528
3 ,131
2,663
1,405
706
7,331
Gr owt h Be t we en 19 75 -1 9 78 (f)(abo ut 25 %)
1978 Estimated Commercial-Industrial Floor Space(g)
7 ,400
37,000
General
Educat ion
Wa rehousi ng
Hotels
Manufacturi ng
25,120
5,000
4,520
1,500
860
1978 Non-Manufacturing Floor Space,Anchorage 36,140
Source:Adapted from Goldsmith and Huskey (l980b).
6.5
II I"
TABLE 6.2.(contd)
(a)Twenty-five businesses in 1975acording to telephone book.Assume 2,50fJ
square feet/business.
(b)Rased on the ratio of the housing stock in 1978 between Eagle River/Chugiak
and Anc ho rag e.
(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)Detail does not add to total in original.Total was asslJT1ed correct.
(f)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.
(g)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.
TABLE 6.3.1978 Commercial-Industrial Floor Space Estimates
Greater Anchorage Area
An cho rage
Kenai-Cook Inlet
Matanuska-Susitna
Seward
Greater Fairbanks Area
Fairbanks
Southeast Fairbanks
Source:Adapted from Goldsmith and Huskey (198Gb).
6.6
Mill ion
Square Feet
41.4
36.1
3.2
1.5
0.6
10.8
10.4
0.4
-"
"....
The price-adjusted business requirements are then passed to the Program-
Induced Conservation and Peak Demand Modules.
PARAlJIETERS
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.
o While an e~timate exists for the stock of business floor space in
the Railbelt in 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 impossibl e.However,it was unclear whether stock or
employment was a better predictor of business electricity consumption.
The approach used to r;esolve the issue consisted of three steps.First,
the historical relationships of electricity consumption per employee and per
6.7
II III
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 sl ightly 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 Rail belt.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 basi s for the method used for
forecasting floor space.
Data on lI ac tual"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
,,-.
Alaska).(a)Some Lower 48 multistate regional estimates,but no independent
state-wide estimates,were availabl e.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 l~est.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 Z;and office
employees use 305 ft2.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
kl~h 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 divi sion of McGraw-Hi 11,Inc.,markets local hi stori cal
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
II III
TABLE 6.4.Comparisons of Square Feet,Employment,and Energy Use
in Commercial Buildings:Alaska and U.S.Averages
22
(range 5-65)
EIA(a,b)
IJ •S• (19 79 )
NE
NC
S
W
Alaska(1978)(c)
Anchorage
Fairbanks
Cl imate Zone(a,b)
<2000 CDO(d)
<2000 COO
<2000 COO
<2000 COD
>2000 COD
PG&E (1981)(f)
7000+HOD(e)
5.5-7000 HOD
4-5,500 HDD
<4000 ·HOD
<4000 HOD
ft2 /Employee
531
562
751
476
364
375
336
kHh/Emp 1 oyee
7,303
7 ,310
9,997
7 ,358
4,468
7,851
7 ,550
13.75
13.02
13.31
15.45
12.27
20.9
22.5
10.21
13.02
11.16
15.15
16.80
,~
Power Counci 1 (1983)(g)
Warehouse
Offi ce
Hospi tal
BPA (1980)(h)
Trade
servi ces
Offi ce
RED Alaska (1980)(i)
Anchorage
Fai rbanks
891
1,194
305
429
360
Retail/Wholesale
Office
l4arehouse
real th
8,407
7 ,496
16
36
45
18.16
7.75
5.34
24.31
19.57
20.80
-
(a)EIA 1983.
(b)U.S.Bureau of the Census 1980b.
(c)Goldsmith and Huskey 1980b.
(d)COO =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 Prices (Employment
Alaska Department of Labor basi s from MAP model).
6.10
,-
and low cooling load)in the West,and the large heating load in the North
Central,these figures are reasonable.Maska's figures of 7851 and 7550 kWh
per employee are slightly higher than the national average,which follows,
given Maska'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.The definition of employees
differs between the two estimates for the Railbelt,but a figure 10 to L5%
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
buildings shown in the table is 13.75 kWh/ft 2 •The 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.This 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
cool i ng-degree-days (COD)]does not support thi s hypothesi s.t1Jvi ng 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
II III
18.25,and health facilities at 24.5 kWh/ft 2 •As shown in Table 6.3,non-
energy usi ng commerci al space has been el iminated to the extent possibl e 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 1 ike 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 1 imited 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 reasonable,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 noar 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 simpl ified 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)woul d 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
-
-
Anchorage
Fairbanks
429.5(I.0033)k x Emp~oyment
360.4(1.0046)k x Empl Dyment
\~here 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.
TARLE 6.5.Business Floor Space Forecasting
Equation Parameters
Load Center
Anchorage
Fa i rbanks
Parameter Values
a·b·1 1
429.5 1.0033
360.4 1.0046
Other i1=thods Tri ed
In previous versions of the REO model,the parameters used to forecast the
annual'change ir"l floor space stock were extracted from work at Battelle-
Northwest for SPA.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
!!!II
(6.4)
contd
-
where
Stock =floor space stock
61-13 9 =parameters
t.=symbol for the first difference (annual change)
GNPDEF =gross national product price deflator
POP =population
INC =income
=index for the region
£=index for the year
II =symbol for the annual percentage change
r =nominal interest.
The Anchorage Consumer Price Index (CPI)was used as a proxy for the GNP
price deflators.It is assumed (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.Or i gina 1 REO Floo~Space Equation Parameters
Parameter Coefficient Standard Error T-St at is tic
61 -0.1291 0 •.00345 -3.75
62 1.27 53 0.2566 -4.97
63 0.3553 0.0302 11.76
64--0.113 0.0037 -3.04
65 0.1929 0.0355 5.43
66 -0 .0947 0.0078 -12.09
67 -0.0078 0.0008 -9.92
13 8 ~0.0116 0.0253 -0 .46
69 -0.0412 0.0061 -6.68
6.14
-
-
Table 6.7 shows how well the stock-flow floor space relationship performed
in Anchorage and Fairbanks historically.Although the stock-fl o~"equation
performs fairly well on backcast and could be used to predict stock of COlnmer-
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 l 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 formul ation 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-Ll~~t
Industrial-Government Floor Space,1975-1981,
(million square feet)
,',.11.
Fo recast Error Fo recast Er ro r
Anchorage as Percent of Fai rbanks as Percen t 0 f
r Year Predicted Ac tua 1 (%)Predicted Ac t ua 1 (%)
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
I',III
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 used 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
o population,both lagged and first difference
•employment,both lagged and first difference
•income,both lagged and first difference.
The equations "fit"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.r''bre 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:
I"ffl,
-
-
-
In(CON it )=BETA i +BBETA i x In(STOCK it )+Sit (6.5)
(a)Copyright restrictions precluded the combining of "ac tual"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
.....,
\'Jhere
CON =historical business sector consumption U1I-Ih)
8ETA =intercept
BBETA =regression coefficient
STOCK =predicted stock of floor space,·hundreds of square feet
E =stochastic error term.
,...,..Ta b1e 6.8 presents the results of the regression analysis.(a)The
parameters BBETA are allowed to vary within a no rma 1 distribution,truncated at
the 95%confidence intervals in An chorage and 90%in Fairbanks ••
TARLE 6.8.Business Consumption Equation Results
BETA
standard error
t-statistic
BBETA
standa rd erro r
t-statistic
GAI~I"1A
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 rbank s
-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 cal ibrate 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 OT +Et
wi th CON t ,BETA,RBETA,and E defined as above and where
0 =Dummy vari abl e (1974 through 1981 =1)
V =Dummy variable (1975 through 1977 =1)
T ::::Time index for T =1,9.(1973 th ro ugh 1981 ).~.,
GAr~MA ,THETA =regression coefficients.
The dummy variables were held at zero in forecasting.
(6.6)
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
size of demand and there is no reliable way to disaggregate these two types of
consumers.This is fe1t 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 assJIning 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 this decrease in Fairbanks 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
-
-
,~'
-
-
TABLE 6.9.Electricity Consumption Per Employee and Square Foot and
Square Footage Per Employee for Greater Anchorage and
Fairbanks,1974-1981
kI.~h/ft2 k\~h/Emp 1oyee ft2/Emp 1 oyee
Year Anchorage Fairbanks Ancho rage Fa i rbank s Anchorage Fa i rbank s
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 (24.1
1978 21.9 29.6 7847 7550 359.1 255.1
Ig79 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 silnilar
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 unstabl~,and too dis~arate for long-term appl i-
cation,reflecting a period of extreme growth within the state.With more
disaggregated data,employment may prove to be a suitabl e argtlTlent for
industrial electricity consumption.However,with a rather 1 imited 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 el~ctricity consumption
(a)No data are available on consumption of electricity by SIC industry
code.~1ultiple regression techniques proved unsuccessful in determining
the separate effects of each subsector ' s employment on commercial demand,
due to high colinearity among explanatory variables.
6.19
I I In
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 1 imited data series prevented statistical
estimates of consumption ona 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 ~·1odule,the parameter default values and ranges were picked froln
t,1ount,Charman,and Tyrell (1973).Chapter 7.0 discusses these parameters and_
their use in the price adjustment mechanism.
-
-
6.20
·.....
.-
-
,.,.,.
....I
~,
-
-
7.0 PRICE ELASTICITY
This section describes the price adjustment mechanism employed in the RED
model.In both the Residential and Business r"odules,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 meChani sm can be considered price-induced
conservation of electricity.(a)Outputs from the price adjustment mechanism
are the final REO 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 REO
price adjustment mechanism are discussed.Implementation of the selected
structure and parameters is described in the final subsection.
THE REO 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
"scale"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 "scale"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-'pri.ce
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
con sum pt ion 0 r "neg at i ve con ser vat ion"0 f e1ect ric ity•Th e pric e
adjustments include fuel switching.
7.1
I I III
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 preliminary 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 Reriod 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 "lagged 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
-
~
I
"""'I
-
-
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 consumers 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 +co 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-'\
where ESR is the short-run elasticity and ,\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 consumption caused by
a price change once the consumer has reached a point of equilibrium with that
pri ce change.
LITERATURE SURVEY
Since the "energy crises"of the early 1970s,an extensive economic/
econometric literature on the demand for energy,and electricity in particular,
has been generated.A survey of this literature 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?
o 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 t\~O
types of elasticities be included in the mechanism separately?
o 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 fuctional 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 th-e prices,the natural logarithms of the other
independent variables,and the reciprocals of the prices:
-
log Q ~a +b log P +++cliP +++(7 .2)
where "l og "denotes natural logarithm,Q is consumption of electricity and P
its price,a,b,c are parameters to be estimated,and "+++"denotes the other
price a~d independer.t variables in the equation.In this specification,the
own-price elasticity is equal to b -c(p,which depends on P.
7.4
-
,',...
.-
Several studies include only natural gas as a substitute for electricity,
a smaller nllTlber include only oil,and some studies include both.The substi-
tute commodities included in an eqllation depend on the intentions of t~e
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.Pooledtime-
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
can sump t ion i sin c 1 ud ed a san ex p1 a nat ary va ria b1 e,the pric e cae ff ici en t s
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 el asticity est imated (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.Also shown are the substitutes'prices
and non-price factors considered in each study.The own-and cross-price
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.
Estimates of the short-run own-price elasticity vary considerably.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 datd would be expected to differ from estimates for
7.5
TAEiE 7.l.Residential Electricity Oemand Survey
Type of Clher Damnd
Author Elasticity Iirre Fr i:JT'e Type of Data Substitute Prices l1:!tenni nants(a)
Alderson,K.P.(1972)OJnstant Long run O'oss-section Average price
Residential [)emnd for 1969"states of Natural Gas -
~
Electricity:Econmetri c
Est irmtes For Cal Homi a
and the lhited 9::ates.
The ~nd OJqXlration,
Santa ~tJnica,CA
,lInderson,K.P.(1973)Qmstant 9l0rt run Cross-section Fuel oil,Y,HS,SHU,NU,
I€sidential Energy Use:long run 1969.states bottled gas,W,S
,lin Econmetric ,lInalysis R-coal
1297-NSF.lhe ~nd OJrp.,
Santa fvbnica,CA
--.J.Raughnan,M.L.,fnnstant 9l0rt run TifIE series Ener'gy'price Vi,N.MT,LT,c;n
Joskcw,P.L.,Dilip,K.P.long run 1968-1972 index Pi
1979 Electric POt.er in the 48 states
lhited 9::ates:~txlels
and Policy ktal'y?is.
MlT Press,Carbridge,MA
Blattenberg=r,G.R.,Constant 910rtrun TifIE series ttirginal price rrpe,fce,x,
Taylor,L.O.,long run 1960-1975 nat ura 1 gas,ddh,ddc
Rennhack,R.K.1983.states fi xed charg2
1If'·atural Gas Availability natural gas,
and the Residential [)emnd price of fuel
for Energl.The Energy oi 1
Journal.4(1):23-45
1-k11 vorsen,Robert.1976 Constant·Long run Cross-section Average price cr ' Pnn'Y*,J,
1I0ar6n d For Electric 1969 p2r thenn for 0,Z,R,H.E
Energy in the United states all types of
Statesll
•Southern Econ gas purchased
,Journa 1.42(4):610-625.by sector
J I ...•.J )J .J )J J J J J I j
1 1 ]1 )1 ]1 I ))1 J J 1
TABLE 7.2.Residential Survey Parameter Estimates
9-ort-lt.Jn Long-~n LCJJged G3s Oil
fu1 Price (1,n Price MjustJrent Cross-price Cross-price
Moor Elasticity Elasticity O:lefficient (>.)Elasticity E1ast icity
JIllderson (1972)---0.91 --O.ll..
J'fiderson (1973)-0.3 -1.12 0.732 O.3Q 0.27L
BalJ]h11an,et a1 (1979)..;0.19 -1.00 0.842 0.055,0.11L 0.015,0.009...
Blattenberger,et al (1983)-0.101 -1.052 0.904 0.0025,o.rna
Halvorsen (1976)---0.97 --O.Hi
-.,J Halvorsen (1978)---1.14 --O.o!i...
00 Hi rst,Carn~(1979)-0.16 -0.83 --0.025,0.2Q 0.(l05,0.04L
I-bJthakker,Taylor (1970)-0.13 -1.89 01373
M:Junt.O1apnan.Tyrrell -0.14 -1.21 0.884 0.025,0.21L
(1973)
.~I J j !.1 _I j J J ,)i J
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 endogenous variable.For the studies surveyed,the second
approach generally I eads to 1 arger (i n absolute val ues)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 conSLmers can 1'1anage only
a limited response to price changes in the short run,when thei r housing an1
appliance stocks are fixed,but r~spond more fully over time when these stocks
can be varied.
Estilnates 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 o.ther parameters of interest,but quite a
lot relative to their magnitudes and are considerably smaller than the own-
price elasticities.
Most of the 1 iterature 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.~st business concerns in the Railbelt,however,are commercial or
1 ight industrial.Therefore,the industrial electricity demand elasticities
were deemed in~ppropriate to the Railbelt~and only the commercial electricity
demand literature was surveyed.
7.9
Only two studies that deal expl icitly 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;~10unt,Chapnan,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.
sect 0r Di vis ion
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
I~'
-
-
1 1 -1 J J 1 j 1 1 -J J 1 I ]1 1 j
TAILE 7.3.Canrercia1 Electricity Demand Survey
-.....I........
.......
Author
feier1ei n,Jares G.,[\nn,
Jares W.,fvtConnon,
Jares C.1981.liThe
IBnand for Electricity
and N.ltl1'a1 rilS in the
f'brtheastem United
9::ates ll
•The Revi ew of
EconOTJi cs and Statistics •
AugJst 1981,pp.403-408 •
Type of
E1 asticity
lbnstant
TinE FraTE
9urt-run
long-run
Type of Data
cross-sect ion
tilre series
1967 -1977
regi anal NE
9.lbstitute Prices
Nltl1'a1 gas,
fue 1 oil
Cther Oemnd
rJetenni nants(a)
Yj ,PEj •
Qit-1j
tbunt,T.D.,O1apnan,
L.D.,and Tyrell,T.J.
1973.Electricity Demand
in the lhited (tates i kt
EConaretric Ala 1ysi s.
Cbntract No.t.J-7405-eng-
26.ORNL,Oak Rid~,
Tennessee
Variable 9l0rt-run
long-run
Cross-sect ion Gas
1947 -1970
States
Y,P,PI:.(\-1
(a)For synbo1s,see glossary at end of section.
I I III
-0,
TABLE 7.4.Commercial Survey Parameter Estimates -
9nrt-fW Long-Run La;Jged Gls
00 Price CWn Price A:Jju strrent Cross-price
A.rtllJr Elasticity Elasticity r.oefficient (;I.)Elasticity
Bierlein,et.al.(1981)-0.03 -0.37 0.9167 0.045,0.4a
M:lmt,et.al.(1973)-0.29 -1.36 02724 0.015,o.oa
Oil
Cross-price
Elasticity
-0.095,-1.0Sl
Variable Elasticity
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
est i mat in9 s am p1e Sip ric e 1eve 1s•
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 Railbelt were uniform,and if real electricity prices in the
Railbelt 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 Railbelt.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 Railbelt electri-
city prices in 1980 was significantly below U.S.average electricity price;
within the Railbelt,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%higher 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 -
employed.Thus,long-term elasticities appear explicitly in the mechanism via
the relationship given above.This choice was made for three reasons.First,
the explicit short-run elasticities are consistent with the impl icit 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
i mpac t.
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 orthe..-
other fuel was found to be a substitute for electricity,although due to data
1 imitations 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 relatinn to the effect of electricity prices,cannot be
,.--ignored.It is important to include these prices in the RED price adjustement
mechanism for th.e following reasons.~1uch of the own-price elasticity of
electricity demand can be attributed to "fuel switching."As real electricity
prices increase,some households and businesses will,the mechanism predicts,
"switch"from electricity to natural gas or oil for heating and other energy
I"""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
The parameter estimates for each of the two price adjustment rnechani SInS
were taken from the study by rvbunt,Chapnan,Tyrrell (1973).Oil cross-price
elasticities,which were not estimated in the MCT study,were based on profes-
sional judgment and values taken from the 1 iterature survey.The parameter
values used in RED are presented in Table 7.5.The MCT parameter values were
used in RED for two reasons.First,their models were most consistent ''lith the
structure sel ected for the RED price adjustment mechani sms;there are separate
equations for the residential and business ~ectors,variable own-price elasti-
cities are einployed,lagged adjustment coefficients are estimated,and a cross-
price elasticity (gas)is included.Second,the elasticities estimated by MCT,.
when evaluated at 1980 Anchorage and Fairbanks prices (in real 1970 dollars,as
in MCT),appear reasonable.In the residential sector,calculated short-run
elasticities were -.1462 in Anchorage and -.1507 in Fairbanks;calculated
TABLE 7.5.Parameter Values in RED Price Adjustment ~chani sm
-
-
-
Short-Run Elasticities
Own-P ri ce
Natural Gas
Oi 1
Lagged Adjustment
Residential
Secto r
-.1552 +.3304/p(a)
.0225
.01
.8837 .
Business
Secto r
-.2925 +2.4014/p(a)
.0082
.01
.8724
-
~,
,
j
(a)Measured in mills per KWH,1970 dollars.
7.14
-
....
.....
-
r
long-run elasticities were ~1.2571 and ~1.296,respectively.The short-run
elasticities are sl ightly 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 sl ightly 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 electricity comprises a large share of consumers l 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 t\nchorage 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 time 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:
(7 .3)
where
~1iI!
:::
K :::
t :::
OPA :::
PPA :::
GPA :::
~
region index
time period index
sector index (:::1 residential,:::2 business)
own-price adjustment factor
oil (petroleLnTl)-price adjustment factor
gas-price adjustment factor and denotes multiplication.
7.15
I I In
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
PE iKZ 'PO WZ 'and PG iKZ '(define the five-year percentage change in prices):
--
-
PEl·K-1 z)/PE i K-1 Z
" "
POi K-1 z)/PO i K-1 Z""
(7 .5)
(7 .6)
PCPG iKZ =(PG iKZ -PG i ,K-1,Z)/PG i ,K-1,L
Then calculate the average annual percentage change in price during the
five-year period:
PCPEA iKZ =(1 +PCPE iKZ )**.2 - 1
PCPOA iKZ =(1 +PCPO iKZ )**.2 - 1
PCPGAiKZ =(1 +PCPG iKZ )**.2 - 1
(7 .7)
(7 .8)
(7 .9)
(7.10)-
where "**"denotes exponentiation.Thus,during each of the years behJeen K-1
and K,prices increase'an average of 100 •PCPEA iKZ 'and 100 •PCPOA iKZ 'and
100 •PCPGA iKZ 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
(7.11)
7.16
where ~~8.denotes percentage change,0t is consumption in year t,sector t,
region i,Pitz is the price,and ESR itz 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.
Fir s t,1agged co ns urn pt ion has fa 11 en by %8.Qi U'sot his per i 0 dis con s um pt ion
falls by A%8.Qit£.Second,the price change which occurred 'in year t persists
(the price did not go back to its year t-1 level)so consL§llption in year t +1
fa 11s byE SRi t +1 z •%8.Pi U •Th us,the c han ge i n ye art + 1 con sump t ion 0 f, ,
electricity caused by a price change in year t is given by
.....,
%8.Qi t+1 t ::A%8.QiU +ESR i t+1 £•"IollP iU, ,, ,
(7.12)
(7.13)
Similarly,the change in year t + 2 consumption is equal to the sum of two
components:
....
,~
~~8.0i ,t+2 ,£::A%Q i ,t+1,£+ESRi ,t +2 ,£•%8.Pi U
This process can be carried out to year t,+4,the final year of the
five-year period:
2
+>.:ESR i t+2 £+A ESR i t+3 z
""
+ESRi ,t +4,£)
7.17
(7.14)
(7 .15)
(7.16)
-
which gives the percentage change in year t + 4 consumption resulting from the
price change %lIP iU in year t.Similar price changes occur in year
t + 1 (%1I Pi,t +1 ,R.)'t + 2 (%lI Pi,t +2 ,R.),t + 3 (%1I Pi,t +3 ,£.),and
t + 4 (%lIPi,t+4,£.)'with equal percentage price changes assumed during each of
the five years.That is:
.....
-
(7.18)
(7.17)
%lIQ,.t+4 R.,,
The impact of these individual price ch~nges on consumption in year t +4
can be derived in a manner similar to that used to obtain equation 7.1fi.The
sum of the impacts of the five annual price changes is given by equation 7.18:
=PCPEA i Ia •()4 ESR it.!
+2).3 ESR,".t+1 £.+3).
2
ESR i t+2 £.",,
+4).ESR.t+3"+5 ESR i t+4 £.),,,'""
Equation 7 .18 accounts for price changes which occur between period K-1
and K;price changes which occurred before K-1 also influence consumption in
period K,just as pricechanges in period t affect consumption in,for example,
period t +9:
%lIQ i ,t+9 ,R.(7.19)
+•••+A
5
ESR i ,t+4,R.+A4 ESR i ,t+5,R.
"""
++A ESR i t+8 £.+ESR i t+9 £.)
""
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
7.18
~0 -\5~,O
IO/).·i ,t+9,2 -1\'oLl i ,t+4,2 (7.20)
3
ESR i ,t+5,2 +2.\ESR i ,t+6,2
+3.\2 ESR i ,t+7,2 +4.\ESR i ,t+8,2
+5ESR i t+9 ~)., ,
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-1 and K,K-2 and K-1,etc:
(7.21)
(
K
+I
m=l
3
ESRi ,K1 ,2 +2.\E5Ri ,K2 ,2
2
+3)..ESR i ,K3,2 +4)..ESR i ,K4,2
+S ESR i ,KS,')
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-1 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.
Equation 7.17 defines OPA i k 2 as the percentage adjustment to electricity, ,
-consumption which must be made because of real electricity price changes.
Restated,
7.19
OPA iKl =.\5 OPAi,K_l,l
+Ct pePEA;",,)•(A 4 ESR;,k!,'
(7.22)
-
+.\3 ESR i ,K2,l +.\2 ESR i ,K3,.e.
+A ESR;,K4,'+ESR;,K5 ,.)
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,
"'"',
I
....
•(.\4 +2.\3 +3.\2 +4.\+5)
PPA iKl =.\
5
PPA i ,K-l ,.e.
+•OSR l
(7.23)
-
5
=.\GPA i ,K-l,l
•(.\4 +2A 3 +3\2 +4.\+5)
(7 .24)
where OSRl is the short-run oil cross-price elasticity in sector ~and GSR~is
the short-run gas cross-price elasticity in sector .e..
7.20
All t hat r em a ins i s t 0 a tt ach val uest 0 E5Ri •Kj .9..• Inth e r'1 CT stu dY,
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)
-.5 Bo/P.K'0~1,J ,'"
where Pi,Kj-1,.e is the price at the end of the year before Kj,and Pi ,Kj,9..is
.the price at the end of year Kj.
7.21
I
y
HS
SHU
~U
w
S
Yi
N
Pi
MT
LT
mpe
fce
x
ddh
ddc
Cr
Prm
y*
J
o
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 CPI (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
=cool i ng 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
....
-
....
-
-
-
'~
I"""
I
u
EU
9t-1
=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.
Xt =total personal consumption expenditure per capita in 1958 dollars
p =implicit deflator for electricity/implicit deflator for peE (1958=100)
Yj =value of retail sales
PE j =average deflated price per KWH of electricity
Qit-1j =lagged per capita fuel consumption
Y =income per capita
P =population
PE =price of electricity (mills per KWH)
Qt-1 =lagged demand in millions of KWH.
L =long run
....
-
.....,
7.23
-
-
8.0 THE PROGRAM-INDUCED CONSERVATION MODULE
The purpose of the Program-Induced Conservation 1'10dule is to account for
the electricity savings that can be obtained with a given set of conSL!Tler-
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 ~10dule.
The module forecasts only those portions of conservation that are not
rnarket-or price-induced.The module was developed as part of Rattelle-
Northwest's Alaska Railbelt Electric Power Alternatives Study in 1981 and was
designed as a tool to enahle the State of Alaska to analyze the impact of
potentia1 large-sca1e conservation programs.The future of such programs in
Alaska is in doubt (Tillman 1983)and the data on the savings and costs of
existin,g programs are uncertain.The Program-Induced Conservation i"odule was
not used in the 1983 updated forecasts,but a description of the module is
given be10w.
MECHANISt1
The fuel price adjustments in the Residential Consumption and Business
Cons umpt i on ~bdul es account fo r rna rket-i nduced techno 10 gy-re lated <;onservat ion
impacts,as well as reductions in appl iances use and changes in the way in
which they are used.The Program-Induced Conservation .t''Ddule analyzes
government attempts to intervene in the marketplace to induce conservation via
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
-
.,.,\
""'"
-
-
SALES
SUM OVER
USES
-SAVINGS,
•COS TS
A.DJUST ~
REQU1RE'.'E"lT_S I
FOR SUBSIOII~.?I
CONSERVATIO~
CALCULATE
•SAvrNGS
•COSTS
IN NEW ANO EXISTING
USES
SUM OVER
OPTIONS
•SAVINGS
•COSTS
OUTPUT'S
en fCTRICITY SAVfD
e(()'5T OF SAVI"lGS~VlAI((ORRI.C nON
FACTOR--._------
RESIOENTIAL
REQUIRE."ENTS
I RESIDENTIAL
"ODULE)
ADJUST
REQUl REI.~ENTS
FOR SUBSIDIZED
CONSERVATION
CALCULATE
TOTAL COST
OF CONSERVATION.
BY OPTION
GO TO NEXT
CONSERVATION
OPTION
TECHNICAL INPUT
•PEAK CORRECTION
·FACTOR IPCF)
TECHNICAL INPUTS
•MAXIMUM
SATURATION
.-PAYBACK RULE
TECHNICAL INPUT
•UNSUBSIDIZED
INSTALLED COST
TECHNICAL INPUTS
•SUBSIDIZEO
INSTALLED COS T
.O&M COST
TECHNICAL INPUTS
.ELECTRICITY SAVED
ellFETIME
.ELECTRICITY
PRICES
AlJSINfSS INPUTS
[NEW &EXISTING USES)
efolOTENTIAL SAVINGS
.....RO?O~nON SAVED
ePi:.AK CORRECTION
FACTOR
-COST uF SA.VINGS,
MWH
CONS~RVATION
DA TA FILE
SELECT
RESIDENTIAL
CONSERVATION
OPTION
WRITE
•SATURATION
.·PCF
TO CONSERVATION
FI LE
RED Program-Induced Conservation ModuleFIGURE8.1.
various consumer-
(up to ten options
-
thequeries
may be
CONSER
each option
of market acceptance of
the residential sector,
and the level
options.For
parameters of
costs,energy savings,
installed conservation
user for the technical
8.2
I~
-
,-
-
,-
-
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 thi s 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
sat urat ion rat e t 0 an 0 ut put dat a f·i 1 e.Th e use r i s the n que ried for the
market saturation of electricity in the use that the conservation option
offsets (e.g.,electric water heating).This market saturation is also written
to the output data file.
Government residential conservation programs primarily reduce the
effective purchase price of conservation options to the consumer.Therefore,
CONSER next requests the user l 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 fil e.
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 file for the Conservation r1:Jdule.
RED uses the residential conservation infonnation 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 nunber 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 period1s 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 level of savings 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 economic costs of the residential conservation technology package are
found by multiplying together the government SUbsldized 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.
Fin all y,the Co nse rv at ion r-.rb du1 e ti e 1 ps cal cu1 ate the e ff ect 0 f
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 val ue between zero and one if the option receives credi t 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 perlod.Next,the wei ghted correcti on 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
..-:
-
-
-
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
defined 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 nUTIber 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 maintenance)are interactively
specified within CONSER by the user.
The business segments of CONSER also query 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 ~1odul e are the fi nal
electricity sales to the business and residential sectors,and ~he electricity
savings of the conservation technology set considered in a given run of the RED
mode 1.
~MODULE STRUCTURE
The price adjustment mechanisms used in the Business and Residential
,-Consumption r-bdules employ price elasticities derived from studies that did not
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 REO was cautiously
developed to eliminate the possibility of double counting energy savings and
costs.
8.5
-
Inputs and Outputs of the Conservation Module
Peak Demand Mode 1
Residential Mc1ule
From
-
-
-.
,-
I
-
1nout
To
Report
Report
Residential Module
Miscellaneous and Peak
Demand Modules
Miscellaneous and Peak
Deman d Modu)es
Business Modul~
CONSER,Interactive Input
CONSER,Interactive Input
CONSER,Interactive Input
CONSER,Interactive
CONSER,Interactive Input
CONSER,Interactive Input
CONSER,Interactive Input
Uncertainty Module
CONSER,Interactive Input
CONSER,Interactive Input
CONSER,Interactive InputResidentialsaturationofthe
device (with and without govern-
ment intervention)
Total cost of conservation
(business plus residential)
Nam~
Aggregate peak correcti on factor
Name
Tota'households served
Adjusted residential consumption
Total electricity saved
(busin~ss plus residential]
Technical energy savings
Installation and purchase cost
of the residential conservation
device
Adjusted busi ness consumption
Residential electric use
saturati on
Ooeration and maintenance costs
of the residential conservation
dev i ce
Business conservation saturation
rate (with and without Qovern-
ment intervention)-
Peak correcti on factor
Cost per megawatt hour saved
in bu si ness
Business price-adjusted
consumption .
Exoected residential electri-
city pd ce
Price-adjusted residential
consumo t ion
Potential prooortion of elec-
tricity saved in buslness in
new and retrofit uses
TABLE 8.1-
a i ~
5YTTIb a 1
HHS
TECH
COSTI
COSTO
RCSAT
ESAT
PRES
RESCON
CF
PPES
BCSAT
CaST
BUSCON
b)Outputs
5YTTIb a 1
TCONSAV
TCONCOST
ADRESCON
ADBUSCON
ACF
8.6
-
-
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
programs could have a significant,direct impact upon the level of conservation
ado pte (j.and sin ce the inc r eme ntal i mp act s 0 f the sea ct ion s are not
incorporated in the price adjustment process of the Residential and 8usiness
Consumption ttodules,the Program-Induced Conservation r'odule 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
par arne te r s are the n writ ten t 0 a dat a fi 1e wher e they can be ace e sse d by the
remainder of the Conservation t'odule.Two steps are required:l)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 1iquid.
8.7
I I III
·3.They have trivial requirements as to the size of the initial deposit.
4•Tn ey are rea di 1y avail ab 1eta eve r yo n e•
Investments in conservation technologies,however,are characterized by
the foll O\·ti ng:
1.ri sky
2.difficult to liquidate
3.(sometimes)require a large initial payment.
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.
The IRR is calculated with the following formula:
....
I
(8.1)
where
T =lifetime of the device (maximum of 30 years)
p =internal rate-of-return
~=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
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:
where
(8.2 )
PRES i
TECH ik
=doll ars per kWh in load center 'i
=annual kWh savings in region i per installation of device k.
8.8
_.
The cost (Ci£k)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 individual s to install energy-conserving
equi~ent.Furthermore,the State of Alaska has also instituted several
programs aimed to promote installation of conservation equipnent.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 rate 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 nLlTlber 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 subsidi zed case usually will have lower installation
8.9
I I in
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 displaces
electricity cons~nption at the peak.This information,as well as the costs
and saturation of the conservation option (for the unsubsidized and subsidized
cases),is wri tten to a data fi 1e for 1 ater access by the rema i nder of the
Program-Induced Conservation Modul e.
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 CONSER's 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 l1)ultiplying the electric device saturation and the
incremental nunber of households served,the total nlJnber 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 al ready include the current level of conservation.
8.10
-
-
"""'"
-
-
-
-
By next multiplying the potential number of uses by the savings per
installation and the saturation of the conservation option,the amount of
e1ectricity saved is derived:
CONSAVit~=RCSATikj x TECH ik x
(ESAT itk x HHS it -ESATi(t_l)k x HHSi(t_l)
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 (l,2 ,3,•••,7)
j =denotes subsidized (j=l)or nonsubsidized (j=o).
The total electricity displaced through the residentia1 conservation set
considered is found by summing across the options (subscript k):
K
RCONSAV itl =k:l CONSAV itkl
where
RCONSAV =residential electricity conserved (kWh)
K =total number of residential opti'ons 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:
(8 .3)
(8 .4)
-
ADRESCON it =RESCON it -(RCONSAV itl -RCONSAV ito )
where
ADRESCON =final electricity requirements of residential consumers
RESCON =price-adjusted residential consumption.
8.11
(8.5)
I I III
The electrical device saturation and the incremental number of households
define the number 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.nuring 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:
=[COST!i kj x RCSAT it kj x (ESATit k x HHS it -
ESAT i (t_1)k x HHS 1(t_1));I'S +COSTO ik x
CONCOST it kj
t
E RCSAT·k · x
h=1 1 J
(ESAT;hkj x HHS;h -ESAT;hkj x THHSi(h-l))](8.6)
where
CONCOST =the option's total annual cost
COSTI unit cost in 1980 dol 1 ars for purchasing and installing the
conservation option
COSTO =unit cost in 1980 dollars of operating anq maintaining the
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
K
RCONCOST i tJ'=E CONCOST it.kJ'
k=1
(8.7)
RCONCOST =present value of the total costs of the set of
residential conservation options.
8.12
-
-.
-
The total costs of conservation are the unsubsidized total costs
(RCONCOST ito )'consumers pay the subsidized costs (RCONSAV it1 ),and government
pays the difference (RCONCOST ito -RCONCOST it1 ).
Business Conservation
For business conservation impacts,funding constraints prohibited
collection of detailed cost and performance data.Fortunately,a 1 imited
nunber 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 neVJ construction.As in the
residential segment of the Program-Induced Conservation i'bdule,the potential
pool of electricity that can be displaced must be identified for both new
construction and retrofits.This "pool"is determined by the state of
conservation technology and is supplied to the conservation module from the
CONSER output fil e.The actual amount of conservation that occurs depends upon
the price of electricity and competing fuels and upon the cost and perfor~ance
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 i'odule
is assumed to be the sales to new buildings:
,~
SALNB it =BUSCON it -BUSCONi(t_l)
where
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:
8.13
(8.8)
where
SALEX it =BUSCON i (t_1)(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
Conservation Module multiplies the disaggregated sales figures times the
potential percentage of electricity saved in new and retrofit buildings:
where
POTN8 it =SALNB it x PPESitN
POTEX it =SALEX it x PRES itE
(8.10a)
(8.1Gb)
These figures,however,only provide
electricity that could be displaced.
potential electricity savings will be
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.
POTEX =potential amount of displaced electricity in existing buildings
E =subscript for existing buildings
N =subscript for new buildings.
the technically feasible amount of
Market forces determine what level of the
achieved.
In the residential segment of the Program-Induced Conservation Module,REO
used an internal rate-of-return test and a payback period decision rule to
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
...-a.
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 =BCSAT itN x POTNBitj
BCONSAV itEj =BCSAT itE x POTEX itj
BCONSAV =electricity savings
BCSAT =saturation rate for conservation options in business.
(S.lla)
(8.llb)
As in the residential sector,the business requirements must be adjusted
for the incremental impact of government programs:
ADBUSCON it =BUSCON it (BCONSAV itN1 BCONSAV itNo )(8.12)
where
-(BCONSAV itE1 -RCONSAV itEo )
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:
,r--
where
BCONCOST itj -(BCONSAV itEj x COST i Ej +BCONSAV itN1)
BCONCOST =business conservation costs,future forecast year
COST =1980 doll ar costs per megawatt hour saved.
(8.13)
The total costs of the conservation in a future forecast year to "society"is
the nonsubsidized costs (BCONCOST ito )'whereas the value of the subsidy in that
yeaT is (BCONCOST ito -BCONCOST itl ).and businesses bear only the subsidized
costs (BCONCOST it1 ).
8.15
iii ill
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 option1s peak correction factor by
the option's proportion of incremental conservation:
K (CONSAV itkl -CONSAV itkO )x CF k
=k:1 (RCONSAV i t1 -RCOI~SAV ito)+ (RCONSAV i t1 -BCONSAV~(8.14)
(BCONSAV itEl -8CONSAV itEo )x CF E +(BCONSAV itNl -BCONSAV itNo )x CF N
+(RCONSAV itl -RCONSAV ito )+(BCONSAV itl -BCONSAV ito )
where
PARAI'1ETERS
ACF
CF
--.=aggregate peak correction factor
=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).-
One of the requi rements of the Al aska 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
option1s payback period is assumed to be greater than seven years.the options
market penetration will be very limited,effectively zero.However,if tfle
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
-
~.
8.17
-
9.0 THE MISCELLANEOUS MODULE
t1ECHAN I sr1
The Miscellaneous Module uses outputs from several other modules to
forecast electricity used but not accounted for in the other mod~es,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 Rus i nes s Requ i rements
Adjusted Residenti al Requi rements
Vacant Housi n9
Name
r~i scell aneous Requi rements
From
Program-Induced
Conservation Module
Program-Induced
Conservation r10dul e
Ho usin g tvb du1e
To
Peak Demand MJdul e
Figure 9.1 provides a flowchart of this module.For street lighting,the
requirements are assumed to be a constant proportion of conservation-adjusted
business and residential requirements:
SRit =sl x (ADBUSCON it +ADRESCON it )
9.1
(9.1)
(
RES I DENT I AL
PLUS
BUSINESS
\...CONSUMPTION
.J•,
CALCULATE CALCULATE CALCULATE
SECOND HOME STREET LIGHTING VACANT HOUSING
CONSUMPTION REQUIREMENTS CONSUMPTION
•
SUM FOR
MISCELLANEOUS
CONSUMPTION
•
(.
Ml SCE L LAN EO US
CONSUMPTION
FIGURE 9.1.RED Miscellaneous Module
./I!I!Ii,
-
-
-
--
!!"'!!,.
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
forecas t peri od (1,2,3 •••,7)
street lighting parameter.
For second-home consumption,RED calcul ates the number of second homes as
a fixed proportion of the total nll1lber of households.A fixed consumption
factor is then applied:
SHR it =sh x CHH it x shkWh
9.2
(9.2)
where
SHR =second hOlne requi rements
CHH =total nember of civilian households
sh =proportion of total households having a second home
shkWh =consumption factor.
Finally,the use of electricity by vacant housing ts a fixed consumption
factor times the nember of vacant houses:
where
VHR it =vh x VACHG it
VHR =vacant housing requir~nents
VACHG =n cmbe r of vacant houses
vh =assumed consumption per vacant dwelling unit.
(9.3)
Total miscellaneous requirements are found by scmming the three components
above:
(9.4)
where
MISCON =miscellaneous electricity consumption.
PARAMETERS
Table 9.2 gives the parameter values used for the Miscellaneous t~odule.
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
Symbol
Sl
sh
shkWh
Vh
Name
Street lighting(a)
Proportion of households having a seco~d home(b)
Per unit second-home consumption(b)
Consumption in vacant housing(c)
Value
0.01
0.025
500 k\4h
300 kt~h
(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.
this component of miscellaneous demand will continue to be about proportional
to residential and business use in the future.
9.4
-
_.
10.0 LARGE INDUSTRIAL DEMAND
Large industrial demand for electricity in the RED model is not provided
by the model itself;rather,the modeJ 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 ibnte 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 giveh model forecast.This
was done for the 1983 update.
The EXTRA OAT data set has other uses.Al though mi 1i tary 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 he 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
f i 1e wi 11 acce pt neg at i ve n urn bers show i ng net con ser vat ion.Ot her ty pes 0 f
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
I I III
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 re~ults.
Inputs and outputs are identical.Outputs are supplied to the Peak Module (to
calculate system peak demand)and to the report writing routines.
PARM1ETERS
There are no parameters in the RED model large industrial demand
calculations.
10.2
11.0 THE PEAK DEMAND MODULE
Up to this point,only the method to forecast the total amount of electri-
city demanded in a year has been considered.However,forcapacity 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.
i1ECHANISM
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 Railbelt 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 fi rst cal cul ates the peak demand without the peak savi ngs 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
~'1odul e.The load factors (LF)are generated by the Uncertainty MJdul e,~vhereas
the aggregate peak correction factor (ACF)comes from the Conservation
r'1odule.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 r1odule.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
TABLE 11.1.Inputs and Outputs of the Peak Demand t10dul e
MODULE STRUCTURE
a)Inputs
Symbo 1
LF
RESCON .
BUSCON
AORESCON
ADBUSCON
ACF
b)Output s
Symbo 1
FPO
PS
Name
Regional load factor
Residential requirements prior to
adjustment for subsidized conservation
Business requirements prior to adjustment
for subsidized conservation
Residential requ"irements adjusted for
subsidized conservation
Business requirements adjusted for sub-
sidized conservation
Aggregate peak correction factor
Name
Peak demand
Incremental peak savings
From
Uncerta i nty r·'odul e
Residential
Co nsump t ion 1.tJ du1e
Rusiness
Co nsump t ion rb du1 e
Conservation Module
Conservation Module
Conservation Module
To
Report
Re po rt
~I
-
Fi gure 11.1 provi des a flow chart of thi s modul e.Fi rst,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 ntJ1lber 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.
REO 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.
The first step is to calculate the total electricity requirements without
subsidized conservation by adding the residential,business,and miscellaneous
requi rements:
11.2
-
_ANNUAL SAVINGS
DUE TO SUBSIDY
•PEAK CORRECTION
FACTOR
[FROM CONSERVATION
MODULE]
CALCULATE
PEAK
SAVINGS
LOAD
FACTORS
[FROM UNCERTAINTY
MODULE)
CALCULATE
PRELIMINARY
PEAK DEMAND
ANNUAL ELECTRICITY
REQUI REMENTS
-RESIDENTIAL
-BUSINESS
•MISCELLANEOUS
LARGE
INDUSTRIAL
DEMAND
PEAK
DEMAND
FIGURE 11.1.RED Peak Demand Module
TOTREQB it =BUSCON it +RESCON it +MISCON it (11.1)
where
BUSCON =
RESCON =
~1I SCON =
=
TOTREQB =total electricity requirements before conservation adjustment
(I"1Wh)
business requirements before conservation adjustment U1Wh)
residential requirements before conservation adjustment (MWh)
miscellaneous requirements (MWh)
index for the load center
t =index for forecast period (t=1,2,•••,7).
Next,the Peak Demand Module calculates the peak demand without accounting
for the incremental conservation due to subsidized investments in conservation
by applying the load factor:
11.3
! I !II
TOTREQB i t
=,..-;:'---;;-=;'="LF it x 8760 (11.2)
where
PD =peak demand (r1~J),~,
LF ;:load factor
8760 =number of hours in a year
p =index denot ing 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 -ADRESCON it
where
(11.3)
TOTREQS =incremental megawatt hours saved by subsidized conservation
investments
AOBUSCON =business requirements after adjustment for the incremental
impact of subsidi zed co.nservation
ADRESCON =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:
where
TOTREQ\t
=ACF it x LF it x 8760
PS =peak savings (MW)
ACF =aggregate peak correction factor.
(11.4)-J'
Finally,by subtracting the peak savings from the preliminary peak demand,
the final peak demand for each region is derived:
PD "tpl
11.4
(11.5)
,-where
FPD =index denoting final peak demand.
PARM1ETERS
The only parameters in the Peak Demand Module are
assumed for the Anchorage and Fairbanks load centers.
shown in Table 11.2.
the system load factors
These load factors are
r--TABLE 11.2.As sumed Load Factors for Railbelt Load Centers
Load Factor (%)
Load Cente r Defaul t Range
Anchorage 55.73 49.2-63.4
Fa i rbank s 50 .00 41.6-59.1
In the RED 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 fut~re load factors and thus,peak ~lectrical energy deman~s,
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.
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
11.5
conducted of the impacts of conservation 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 0 f the st udy.(a)Thus.average load factors fo r the peri od 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 l·oad factors and/or peak
loads is a complex task.In addition.while Anchorage Municipal Light and
Power has conducted very 1 imited 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.At1lP economist.
to Mike King.June 11.1981).
11.6
-
"'""
-,
.~.
--
-,
area.Thus,in this study,quantitative analysis of Anchorage and Fairbanks
load factors wa s 1 imi ted to trend ana lys is.
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
pr1W =largest monthly peak regional energy demand for Anchorage or
Fairbanks areas in megawatts.
TABLE 11.3.Computed Load Factors and ~1onth of pe 9 k)Load Occurrence
for Anchorage and Fai rbanks 1970-1981 ~a
Load Facto r Peak Load t>nnt h
,.-
!
Year
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
0.524
0.575
0.562
0.585
0.589
0.495
0.583
-0.548
0.576
0.593
0.541
0.559
An chorage
December
Janua ry
December
January
December
December
December
December
December
December
December
Decembe r
Fa i rbank s
Load Factor Peak Load f1Jnth
0.445 December
0.443 December
0.486 January
0.505 January
0.446 December
0.474 Decembe r
0.555 January
0.466 Decembe r
0.553 January
0.574 January
0.488 December
0.511 December
-,
(a)Computed from data presented i ri DOE/APAdmi n (1982).
11.7
! I !II
All data for estimating the load factors were obtained from tables
developed by the Alaska Power Administration (APAdmin)(OOE-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 Fai r-
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 darkness.It is desi rabl e for forecasti ng purposes to stan-
dardi ze for weather-rel ated 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 multipl ied by
the nlJl1ber 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 expl anatory power of time in expl aining 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
~.
-I
.....,
-
r-.
-
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 ~vas 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 ARmA model (Autoregressive Integrated fvbving 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 specification of the
ARH1A 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
st~tionary 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 particular month should be correlated 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-
bank s.
Thus,the ARIMA model for load factors was identified as the following:
11.9
where
at ::
B -
cJ>1 :;:
8 1 =
B12 =
Yt =
ran dam err 0 r term (II Whit e n0 i se")
1 ag operator
sequential autoregressive parameter for the first difference
on the load factor of the previous month
sequential moving average parameter for the first difference
on the load factor of the previous month
seasonal moving average parameter for the first difference on
the load factor of the previous year
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 ARH1A model were then input into an ARH1A
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 attenti on,the rel ationshi p 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
-i
-
-
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 applicable 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 Mchorage
and Fairbanks load factors.11uch 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 analysis of power demands by customer is important.If it could 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.
c~stomer 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...............
f-'::[........
f-'
0
f-'
N
.............00 o..0.
00 o...·o.•0 ••0 ••
o.00..0··-o•..'"......."...''.
••••••........••.......~..........
•••••••••••••••
.
..",.,..----"."-...
/"
,/"",--.---..........-------------/"------'"-
~_..-.-.----~,----_...--_---
25 t-
20 I-
51-
12
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
J J .~01 )~..]..J .....!)"I I j J J J !J )
,-
,-
since sectoral load patterns in most utility service areas will reveal substan-
tially greater variation in residential loads overtime than for other sectors.
Data on load patterns by type of customer in Al aska were not avail~ble.
However,a 1 imited amount of data on total utility system loads was avail-
able.An analysi s of these data shows that highest power demands in Al aska
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.
TABLE 11.4.Time Period of Peak Oemgnds in
Anchorage and Fairbanks\a)
i---
Time Peri ad of Peak Demand
Servi ce Area December 29,1981 Janua ry 2,1982
An cha rag e(b)4 p.m.5 p.m.
Fairbanks(c)4 p.m.5 p.m.,-
(a)Source:r'1emorandum from r~yl es C.Yerkes of the
Alaska Power Authority to the Committee on Load
Forecasts and Generation,Alaska Systems Coordi-
nat i ng Co unci 1•
(b)Includes Anchorage ~1unicipal Power and Light and
Chugach Electric Association.
(c)Includes Fairbanks Municipal and Golden Valley
Electric Association.
The late afternoa~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
appea r that the load factor of the Al as ka power system waul d be part i cul arly
sensitive to changes in the relative mix of residential and commercial power.
The percentages of total Railbelt forecasted power consumption comprised
by individual sectors for various future time periods are presented in Table
11 •5•Th e i n forma t ion pre sen ted i n t hi s tab 1 e d em 0 nst rat esthat i nth e cas e
examined there is no clear trend in the share relationship between commercial
and residential demand.Thus,even if Rai"lbelt residential and commercial use
had different load patterns,it is not clear that this would result in any
11.13
'I ill
TABLE 11.5.Percentages of Total Forecasted Railbe1t
Electrical Consumption Compr~sed by
Indi vi dua1 Cus tome r Se c tor\a)
Anchorage Fairbanks
Year Residential Commerci a 1 Residential Commerc i a1
1980 52.8 47.2 44.8 55.0
1990 49.1 51.9 49.2 50.8
2000 47.9 52.1 51.8 48.2
2010 46.1 53.9 51.4 48.6
(a)Sectors add to 100%(excludes miscellaneous and
industrial demand).
Source:RED Model Run,Case HE6--FERC 0%Real
Growth in Price of Oil.
clear trend in system load factor.Industrial demand could change the load
factor,but industrial demand is handled separately in RED (see Section 10.0).
-
Impacts of Conservation on the Load Factor
Future conservation efforts in the Rai1belt have the potential to improve
the annual system load facto r by reducing wi nte r e 1ectri ca 1 demand s by a -
greater amount than average electrical demands.The residential energy conser-
vation measures that are most likely to be included in Alaska's long-term
energy conservati on program are presented in Table 11.6.
TABLE 11.6.Conservation t-'easures MJst U kely to be
Implement~d)in the Residential Sector
of Alaska\a
Measure
Ceiling Insulation
Wall Insulation
Gl ass
Weatherstripping
Water Heater Improvement
Level
R-38
R-ll
Storm Window Installation
Doors and windows
Bl anket s and Wraps -
(a)Source:1983 Alaska Long-Term Energy Plan
11.14
-
-
~.
The measures listed in Table 11~6 are generally related to the overall
goal 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 enti re year.
However,it should be noted that electricity is used for space heating in
only a small percentage of the Railbelt'sresictences 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
Ala~ka.Without a sustained conservation effort in lighting,it appears
unlikely that conservation will result in a significant change in the annual
load factor in the Railbelt •.
-.In summary,it appears that future conservati on efforts in the Ra i lbel t
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 t~o load centers are intertied in 1984.
(a)Note also (from Section 5.0)that the incremental electric fuel mode
in space and water heat for the Anchorage service areai s very low.
means that over time the measures shown in Table 11.6 will grow 1 ess
less effective in saving electricity,other things being equal~
11.15
spl it
Th is
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),reveal s that the diversity among util ities in the t imi ng of peak demands
is not great.The ratio of the highest peak demand for the Al aska 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 overall peak demand.A primary cause of peak power demands that
occurs in Alaska is high-pressure Arctic weather systems that generally tend to
i.ncrease 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
-
--
-,I
-
jf1lJii!l'f'.
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 experim~nts,where a syst~ncan be character-
ized,simuJated,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-
sibl e,therefore,to conduct the type of val idation 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 appl ies both of these tests to the RED model.
ASSEssr'1ENT OF RED 'S ACCIJRACY
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 (incl udi ng fuel mode spl i t
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 1 imited.
A partial validation of REDls accuracy,therefore,was performed hy taking
the linearly interpolated forecast values from the case.
The 1 inearly interpol ated 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-Compari son of Actual Base Case,and Backcast Electricity
Consumption (GWh)1982
Anchorage-Cook Inlet Fairbanks-Tanana Va 11 ey
Base{b)Base(b)
Actual Case Backcast Actual Case Backcast
Residential 1,146 1,060 1,097 178 205 208
Business{a)
""'I
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%3.5t
(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,anrl 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 rlrivers anrl
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.Maska
"actual"data periodically undergo substantial revision.Therefore,the per-
formance of individual sectors for a short-term forecast of thi s type shoul d
12.2
-
-
TABLE 12.2.1982 Values of Input Variables
Househol ds (a)
Employment(a)
Electricity Prices
Residential
Business
Natural Gas Prices l
Re sid ent i a1
Business
Fue 1 Oil Pri ces
Re sid en t i a1
Business
Anchorage
Cook-Inlet
83,677
120,533
($/k Wh)(b)
0.45
0.42
(S/mcf)(b)
1.84
1.61
(S/gall on)(b)
1.19
1.12
Fai rbanks-
Tanana Vall ey
22,922
33,500
.•1 on
.095
12.53(c)
11.08
1.21
1.17
(a)Forecasts by r1AP model for Sherman Cl ark NSo case.Consis-
tent estimates of households and total employ-
ment are not available for 1982 from official sources.
(b)All Pric es are inn om ina 1 dol 1ar s •
(c)Propane pri ceo
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 REO's long-term forecasts,we com-
pared the base case used in the 1983 update with three comparabl e 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
I~orthwest,prepared a twenty-year forecast of electricity demand in the North-
\'.Jest.PNPPC modelled four alternate load growth scenarios (low,medium low,
medium high,and high)for the purposes of generation pl anning.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 ~tropolitan Statistical Area,plus selected counties 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 residential electric
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 constJTIption 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 less than
in the PacificNorthwest or the Railbelt.In WEPCOls service area,it was
12.4
""'"i
assumed electricity would capture a high (40-65 percent)share of nev-I
testdential 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 Pacific Northwest forecasts,but lower than the rates·
in the WEPCO forecast.
TABLE 12.3.Comparison of Recent Forecasts,1980-2000
Pacific Northwest Power Council
Bonneville Power Administration
Wisconsin 8ectric Power Company(a)
RED
An cho rage
Fai rbanks
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
.,~
(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.Furthennore,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 \,'JEPCO area,the Railbelt is
expected to have a declininy 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
-
13.0 MISCELLANEOUS TABLES
~-~Abbrevi at ion s Used
APA =Al aska Power Authority
AP&T =Al as ka Power and Tel ephone (TOK)r"
AP Admi n =Alaska Power Administration
CEA =Ch ugach Electric Association
~,
GVEA =Go 1den Va 11 ey Electric Association
GWH =Gigawatt Hour
~
HEA Homer El ectric Association=
k \~h =Kil owatt Hour
pIII~KVa =Kilovolt
MEA =~1a tanuska Electric Association
~1\~=Megawatt
MWH =Megawatt Hour
Fr~US =Fairbanks Municipal Utility System
SES =Seward Electric System
SO FT =Square Foot
ril'~
13.1
-
TABLE 13.1.Number of Year-Round Housing Units by Type,
Rail belt Load Centers,Sel ected Years
Si ngl e
Family Duplex Multifamil,:t
r10bil e
Home Tota 1
-
1,815
12,598
13,729
24,167
25,889
5,1119
30,563
46,578
87 ,702
100,781
7,434
43,161
60,307
111,869
126,670
2
853
1,254
2,175
2,193
204
2,636
7,657
12 ,386
13,572
202
1,783
6,403
10,211
11,379
352
4,547
6,072
·8,607
8,927
1,480
12 ,580
20,331
36,587
40,820
1,128
8,033
14,259
27 ,980
31,893
166
671
1,068
2,512
2,551
1,130
2,223
5,049
11 ,461
12,450
4,620
25,722
27,270
51 ,435
59,828
1,295
6,527
5,335
10,873
12,218
Load Cente r:
3,325 964
19,195 1,552
21,935 3,981
40,562 8,949
47,610 9,899
Valley Load Center:
Anchorage-Cook Inlet
(Ur ban )19 50 (Aa )
1960{b)
1970(c)
1980(d)
1982(e)
Fairbanks-Tanana
(Urban)1950(a)
1960(b)
1970(c)
1980(d)
1982 Ce )
Rail belt:
1950{a)
1960(b)
1970(c)
.1980(d)
1982(e)
(e)
(c)
(A)
(a)
(b)
Excludes Kenai-Cook Inlet Census Division,Seward Census Division,
~1atanuska-Susitna Census Division.
U.S.Department of Commerce Census of Housing 1950;Al aska,General
Characteristics,Table 14.These are all dwelling units.
U.S.Department of Commerce Census of Housing 1960:Al aska,Table 28.
These are all housing units.
U.S.Department of Commerce Census of Housing 1970:Al aska,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.
1980 Census,plus estimated 1980-1982 construction from Mr.Al Robinson,
economist,U.S.Department of Housing and Urban Development,Anchorage.
-
.~,
13.2 -
TABLE 13.2.Railbelt Area Utility Total Energy and System Peak Demand
Anchorage-Cook Inlet Fairbanks-Tanana Valley
Annual Peak Load An nua 1 Peak Load
Energy (Gt-ih)Demand UH~)Factor Energy (GWh)Deman d (~1W)Facto r---
1965 369 82.1 0.51 98 24.6 0.45
1966 415 93.2 0.51 108*26.7 0.46
1967 461 100.8 0.52 NA NA NA
1968 519 118.0 0.50 141*42.7 0.38
1969 587 124.4 0.54 170*45.6 0.43
1970 684 152.5 0.51 213 57.1 0.43
1971 797 166.5 0.55 251*70.6 0.41
1972 906 195.4 0.53 262 71.2 0.42
~1973 1,010 211.5 0.55 290 71.5 0.46
1974 1,086 225.9 0.55 322 89.0 0.41
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
"'""1978 1,747 382.8 0.52 432 95.8 0.51
1979 1,821 409.6 0.51 418 100.7 0.47
1980 1,940 444.4 0.50 402 95.4 0.48
1981 2,005 444.7 0.51 422 93.1 0.52
1982 2,254 471.7 0.55 452 94.4 0.55
13.3
-
TABLE 13.3.Anchorage-Cook Inlet Load Center Utility Sales and ~
Sales Per Customer,1965-1981
Residential Commercial-Industrial-Government
Sa 1es Sal es Per sal es Sa 1es Per
(GI'JH)Customers Customer (kWh)(GWH)Customers Customer (kWh)
1965 174 27 ,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
I!~
1968 233 34,443 6,766 277 4,804 57,715
1969 262 37 ,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
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
739 846 9,860 85,753 -.1977 71 ,275 10,375
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
An nua 1 Growth
Rate 1965-81
10.9%7.0%3.7%11.2%•6.5%4.4%
(a)1979 data used for SESe ~
I
(b)Based on 1980 ~~EA,1979 SES data.
13.4
TABLE 13.4.Fairbanks-Tanana Valley Load Center Utility Sales
and Sales per Customer,1965-1981
Residential Commercial-Industrial-Government
Sales Sa 1es Pe r Sa 1es Sa 1 es Pe r
(GliJH)Customers Customer (kltJh)(G\lJh)Cu st orner s Customer (k'"Jh)
1965 39 8183 4,804 55.198 1,313 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
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 ,86 \:1
1973 133 11,825 11,233 150.823 2,054 73,429
1974 154 13 ,261 11 ,600 161.615 2,242 72 ,085
1975 190 13,877 13,719 210.759 2,342 89,991
1976 194 15,419 12 ,561 219.175 2,530 86,630
1977 198 17,197 11,500 240.463 2,834 84,849
1978 178 17,524 10,153 242.668 2,854 35,027
1979 169 18,070 9,344 219.335 2 795(a)7.'3,474,
1980 160 18,054 8,890 214.263 2,737 78,283
1981 159 19,379 8,219 224.354 2,942 76,259
Annua 1 Growth
Rate 1965-81
9.2%5.5 3.4 9.2%5.1 3.8
(a)Includes 1979 estimated 70 customers for AP&T.
13.5
TABLE 13.5.Adjustment for Industrial Load Anchorage-Cook Inlet,1973-1981
Tota 1 Achorage Homer Electric t(W~Anchorage
Comm-Ind-Govt MWH Demand Industrial Load a "Commercial"
1973 540,476 56,130 484,346
1974 579,068 58,298 520,770 29,660,900
1975 661,192 62,806 598,386 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 87,955 816,896 42,733,800
1980 988,957 99,103 889,854 44,042,700
1981 1,030,753130,318 900,435 44,817 ,400
MWH use/59 Ft.kWh/SO FT %6.From Previous Yr
1973 0.0179 17.9
1974 0.0176 17.6 -1.7
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
1981 0.0201 20.1 -0.5
Anchorage
59 Ft.(b)
-
!!!I!IIU"
-
-
(a)Commercial-Industrial Load over SO KVA (commercial users included)
(b)Predicted value.See Chapter 6.0.
13.6
~
I
REFERENCES.....
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\,Ii sconsi n•
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1979.Electric Power in the
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Bonneville Power Administration.1978.Draft Environmental Impact Statement,
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Powe r Admi ni st ra t ion,Po r~1~Dc9 hOregQQ."
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Bonneville Power Administration.1982c.Technical Documentation of Final SPA
Energy Forecasting f"bdels.Appendix II to Forecasts of Electricity
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Cronin,F.J.1982."Estimation of Dynamic Linear Expenditure Functions for
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R.2
-
El rick and Lavidge,Inc.1980.The Pacific Northwest Residential Energy
Survey.Prepared by El rick and Lavidge Inc.for the Bonnevil1e Power
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Energy Information Administration (EIA).1983.Nonresidential Buildings
Energy Consumption Survey:Part 1:Natural Gas and Electricity Consumption
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Gi 1bert/Commonwealth.1981.Feasibil ity Study of El ectrical Interconnection
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Goldsmith,S.,and L.Huskey.1980a.Electric Power Consumption for the
Rail belt:A Projection of Reguirements.Institute of Social and Economic
Research,Anchorage -Fairbanks -Juneau,Alaska.
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Social and Economic Research,Anchorage -Fairbanks -Juneau,Alaska.
Halvorsen,R.1978.Econometric Models of U.S.Energy Demand.Lexington
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Hunt,P.1.,Jr.,and J.L.Jurewitz.1981.An Econometric Analysis of
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Institute of Social and Economic Research.1982.A Study of Alaska1s Housing
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Consumption 1973-1980.
1~ashi ngton.
An Analysis of Changes in Residential Energy
PNL-4329,Pacific Northwest Laboratory,Richland,
R.3
King,i1.J.and M.J.Scott.1982.REO:The Ranbelt Electricity Demand
~1odel Specification Report.Volume VIII,Battelle,Pacific Northwest
Laboratories,Richland,Washington.
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the United States,1950-1970."The Review of Economics and Statistics
62(2):200-206.
~1addala,G.S.,W.S.Chern and G.S.Gill.1978.Econometric Studies in
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~1idwest Research Institute.1979.Patterns of Energy Use by El ectrical
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Ca 1 i f 0 rn i a.
Municipality of Anchorage.1982.1982 Population Estimation r1ethodology.
Pl anni ng Department,Munici pal ity of Mchorage,Anchorage,Al aska.
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Data.National Climatic Center,Asheville,North Carolina.
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Buffalo,New York.
~
I
-,
.....
Southern California Edison Company.1981.1981 Residential Electrical
Appl i ance Saturat i on Survey.Southern Cal i forni a Edi son 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.
R.4
-
-
-.
,...,
Tillman,D.A.1983.The Potential for Electricity Conservation in the
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Saving Energy at Home.11
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R.5
,-
.,....
I
APPENDIX A
BATTELLE-NORTHWEST RESIDENTIAL SURVEY
~J
i~
-
-
APPENDIX A
BATTELLE-NORTHWEST RESIDENTIAL SURVEY
To cal ibrate an end-use model of electricity demand,the initi al nunber of
appliances that use electricity must be known.At the time the RED model was
undergoing initial develo~ent (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.v/hile 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 infonnation 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
Railbelt community except Anchorage and because utility customers could 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 appl iances),
and questions requiring substantial respondent effort (estimates of annual
electrical bills)were dropped.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.1.
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
~\
()Battelle
Pacific Northwest Laboratories
P.O.Box 999
Richland.Washington U.S.A.99352
Telephone (509)
Telex 15-2874
Alaska Rai1be1t 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
Railbelt 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 d~this properly if
we know how people in the region use electricity.
That 1 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 lq,cal 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.l.Battelle-Northwest Survey Form
A.3
Please complete the following quest lonnalre and return It In the enclosed
en~elope.If you ha~e 41ready completed and returned a questionnaire.please
disregard this request.
1.What type of building do you res Ide Inl
()single family home ()duplex
()mobile home ()multifamily (]or more units)
8.~hat.proport ion of your heat ing needs are IlJet by:
0-1/4 !/4-112 11'0/4 lL~_:..i!..!.!
main fuel ()()()()
secofld fue 1 ()()II ()
other fuels ()()()()
2.Number pf persons In your household (ple4se respond In each category):9.Wllat type of heating distribution system do you use?
Adu Its 18t
o 1 "2-34"or morc
()()()()II
Children 5-18o-l-Z-----ror more
()()()()
eh Ildren Under 5o-1-2--j-4or morc
()()() ()()
()forced air ()radiant or convection ()hot water or steam.
6.What Is the main fuel used for heating your homel
].How many rooms are In your residencel Uow many bedroomsl _
4.Approximate square feet of Hvlng space (just your estim4te):
()no
(I 2 ()]or more
()just In the mornlngl
plugging them Inl _
()second or vacation home.()primary residence
1].The uses described abo~e are for my:
Do you ha~e an electric refrl~erator1 ()yes
If yes.is It frost freel ()yes ()no
12.If you use plug-Ips for vehicles:
!lOtI many vehicles do you usually plug-Inl ()1
Do you plug the vehlcle(s)In:()overnight
At approximately what temperature do you'start
11.
10.Please Indicate the fuel your appliances use:
>0 I-
OJ ...D
>.~
oS u ~OJ
.<:[~,0>.~c:
'"OJC:DOJ
.~~l-e'"L .....U ::t ",a."0 ~'"o:;fcOJ....""~Q D ...a0~IQ'U :J&..0 0 ::tOJ
"0 OJ c:'".l:l 0.~U .........><
water heater ()()()()()()()()
rangl!/stove ()()()()()
sauna/jaculzi/etc.()()()
clothes dryer ()()II ()
clothes washer ()()
freezer ()()
dishwasher ()()
(I electricity
(coal or coke
(wood
(district heating
(I electricity
(COol 1 or coke
woodIdistrict heating system
facing windows ()custom solar design)
11
1601-2000
2001-2400
greater than 2400
()1910-1974
()1975-1980
()ber ore 1950
()1950-1959
()1960-1969
II
less than 700
701-1000
1001-1300
1301-1600
In what year was your house (building)bulltl (just your estimate)
()natural gas
()propane-butane
()fuel oil.kerosene.or coal 011
(I solar collectors
()passive solar (check one:()south
In addition to your main fuel.what additional fuels do you use to heat
your homel
()none
II natural gas(propane-butane
(fuel oil.kerosene.or coal oil
(solar collectors
()passl~e solar (check one:()soulh facing windows ()custom solar design)
1.
.p.
5.
».
FIGURE A.1.(contd)
).1 J }J }1 1 ))J j J ,)j
rate of 44.1 percent.Table A.l shows the total number of residential
customers in each utility,the nunber and percent surveyed,the nunber and
percent responding.
RES IDENTIAL
TABLE A.l.Customers,Nunber Surveyed,and Respondents for
the Residential Survey Battelle-Northwest
1980 Yea rEnd
Customers(b)
Customers Surveyed Customers Responding
I~
_.
Ut il ity(a)
Chugach Electric (CEA)
Anchorage Municipal (AMPL)
Seward Electric (SES)
Homer Electric (HEA)
Matanus ka El ectri c (MEA)
Goblen Valley (GVEA)
Fairbanks Municipal (FMUS)
Copper Valley (CVEA)
Tota 1
Tota 1 Used
42,567
13,744
1,090
8,620
11,722
13,591
4,463
1,588
97 ,385
97,385
Number
530
522
424
518
520
524
504
458
4,000
4,000
Percent
1.2
3.8
38.9
6.0
4.4
3.9
11.3
28.8
""""4:T
4.1
Number
222
214
185
249
268
252
156
252
1,798
1,764
Pe rcent
41.9
41.0
43.6
48.1
51.5
55.0
31.0
55.0
44.9
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:Alaska 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 nunber 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 Ft1US 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 year1s count
available at the time the file was assembled.The weights are shown in
Table A.2.
-
-
TABLEA.2~Weights Used in Battelle-Northwest Residential Survey
OUTPUT
Util ity
Ch ugach
Anchorage Municipal
Seward El ectri c
Homer El ectri c
Matanuska Electric
Golden Valley
Fairbanks Municipal
Copper Valley
We i ght
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
1 -J -i -J J -1 )-)I -1 1 J I
,......,...-~...-..---..------''.~_~~""'\'h ••••
STATISTICAL PACKAGE FOR THE SOCIAL SCIENCES
FILE ENDUSE.D (CHEATION DAT~=Ob/17/91)
SUBflLE C~A AMLP SEA HEA MEA
07/28/lH
**********•****•**C R 0 SST ABU L A T I UNO F *..**l
Ff fH~EZER fUEL BY TYPE
**•*****•**•••**********•**••••**•*********~
COUNT
((OW pcr
COL PCT
'1'OT PCT
TYPE
I
1
I
I
SINGLE f MOBILE H DUPLEX
AM I LY OMf~
-1.1 1.1 2.1
MU (,1'I fA M
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3.1 4.1
HOW
TOTAL.
ff ---~----I------·-I-··--·--r·------·I--------{--------l
DO NOT HAVE
-I··-----·I~w-~----I------·-I--------I--------I] I 26 I
~.4 I 20.7 1
4.5 I 23.1 I
0.3 1 2.3 I
):;>........
MISSING
-1.
o.
1
1
I
I
1
I
I
I
o I
0.7 I
6.1 I
0.0 1
1 I
0.4 I
8.1 1
0.0 I
36 I
52.8 I
4.4 I
3.1 I
59 1
46.8 I
1.)I
5.2 I
o I
0.7 1
0.7 I
0.0 I
11 I
16.0 1
9.8 1
0.9 1
20 I
29.9 I
13.11
J •B I
31 1
29.7 I
24.4 I
3.3 I
67
5.9
126
11.0
-I·-----4~I-··---··I-~-.·-·-I-------~1--------I
1 •
1 b I 712 I 62 I 73 1 96 1 949
HAVE 1 0.6 I 15.1 1 6.5 1 7.7 I 10.1 1 IH .1
I 85.2 I e8.3 1 94.8 1 66.5 I 62.5 I
I 0.5 I 62.4 I 5.4 I 6.4 I 8.4 I
~I~-w-----I·----~~·I-~---w-~I-----~-·I--------I
COLUMN
TOTAL
7
0.6
807
70.6
05
5.7
110
9.6
153
13.4
1142
100.0
CHI SQUARE =91.30715 wITH a DEGR~ES OF FHEEDOH SIGNIFICANCE =u.OOOO
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 counted as lido not have ll ).The computer shows
this as 88.3 percent saturation of single family households.This 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 -
rr
r
r=
i
r
,-
-
APPENDIX B
CONSERVATION RESEARCH
-"
-
-
--,
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 Tillman (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
with 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 climate 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 fore~asts which made either direct or indirect
comparison difficult.
o 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 asslJlled
in a sensitivity case.
•Savings could be projected either by program,or appl iance,or end-
use sector.
In addition,each of the four Lower 48 entities quantifies the components
of conservation effect s differently.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
B.2
~·I
-
-
~.
-
-
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 following subsection discusses 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 Pacifi c Northwest El ectri c Power Pl anni ng 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 conSlJ1ler 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 asslJlled 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.l lists the likely conservation sav-
i ngs at a cost equal to or 1ess than 4.0 cents per kilowatt hours by the year
2000.r-tlst 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
B.4
-
-
-
TABLE B.1.PNPPC Likely Conservation Potential at 4.0
Cents/kWh by the Year 2000
,-
,
Residential (kWh/household)
Ex is tin g Sp ace He at
New Sp ace Heat
Wate r Heat i ng
Air Conditioning
Re fr i gera to rs
Freezers
Cooki ng
Lighting
Other
Commercial (kWh/em~oxee)(a)
Existing Structure
New St ructures
854
1404
1364
o
259
108
15
150
229
4383
1199
825
2024
Industri al (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 mining,manufacturing,and construction.
Source:Pacific Northwest Power Planning
Counc il,1983.
roughly equivalent to the area covered by the PNPPC power planning efforts
--:-(Oregon,Washington,Idaho,Western M:Jntana).Long-range electricity demand
forecasts are made by BPA 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 BPA covering the
region defined by the Pacific Northwest Electric Power Planning 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.
B.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 pri ce response.
The types of programs represented by the base,low,and high forecasts
inc 1ud e the f 011 owi ng:
•home energy efficiency improvement
•commercial energy efficiency improvement
•street and area lighting efficiency improvement
•institutional building efficiency improvement
o 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,
encouragi ng the di stribution 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.
BPAls 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 (Public Resources Code,section 25309)to lIidentify 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 •••II
•In
8.6
~,
-
TABLE B.2.BPA Budgeted Conservation Program Savings
(annual kWh savings by the year 2000)
Resi dent ia 1 (kWh/household)
~Region Wide Weatherization 4,933
Low Income Weatherization 4,933
Water Heater Wrap 435
/"'''',
Shower Flow Restrictor 400
Re sid ent ia 1 Flow Contro 1
Shower Heads 600
Faucet Heads 270
1""",So 1 ar/Heat Pump Water 2,200
13,771
Commerc ia 1 (kWh/e~pl oyee)(a)
Pub 1 i c
Heati ng
Cool i ng
Water Heating
Lighting
Other
Private
Heating
Cooling
Water Heating
Lighting
Other
537
o
o
36
o
916
o
o
43
o
1,532
)"....,
(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.
B.7
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 C£C has adopted the following definition of conservation.
uConservation 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."
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 est imate.Those in Catego ry 2 are cons idered to have
a moderate probability of occurring because of a higher uncertainty factor.
Category 3 incl udes 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 CECls 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 lists the savings by end-use sec-
tor.
The C£C 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 "sharing U structure is set up which includes effects of programs
and price fluctuations.Price-and program-induced conservation becomes "dis-
jointed.1I For example.in general the residential sector model does not have
price-induced savings from consumer choice of more efficient appliances.
B.8
-
-
TABLE B.3.CEC Conservation Program(Electricity
Savi ngs in the Yea r 2002 a)
391
Sector Oemand(GWH)
Residential
Existing Retrofit and
Programs
kWh/house ho 1d
34
1975 HCO Building Standards·
1978 CEC Building Standards
1982 CEC Building Standards
1978 CEC Appliance
OI I-42 Programs
Other Retrofit Programs
Load Management Cycl i ng
Commercial
1978 CEC Building Standards
1983 CEC Building Standards
1983 CEC Equipment Standards
School s and Hospital s
Load Management Audits
Other Commerci al
Indust ri a 1
1978 CEC Building Standards
2,292
644
5,108
6,069
o
301
1,160
15 ,965
6,011
1,083
1,057
234
1,683
1,846
11,914
323
201
57
449
533
o
26
102
1,403
kWh/employee
549
99
97
21
154
169
1,088
97
(a)Reasonably expected to occur.Street lighting and agriculture sectors
exc 1 uded.
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.CEe Potential Energy Savings by End-Use
Sector by the Year 2002
Sector
Residential
Commercial Bldg
Other Commercia 1
Street Lighting
Process Industry
Assembly Industry
Extract i on Indust ry
Tota 1
GWh
23,313
12,849
1,593
983
o
4,985
o
43,723
kWh/HH or employee
2,049
1,173
145
86
o
1,501
o
~
Source:California Energy Commission,Volume I Technical Report,1982,
Table 3-7.Agriculture not included.
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.
WISCONSIN ELECTRIC POWER COMPANY
The Wisconsin Electric Power Company (WEPC)is an investor-owned utility
servi ng the Mi 1waukee,Kenosha,and Raci ne Standard r-'etropo1 itan Areas,central
and Northern Wisconsin,and the Upper Peninsula of Michigan.Wisconsin's 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-
B.10
-
-
-
mission feels that WEPC 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 Wisconsin's 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 order to maximize
efficiency and maintain financial strength with the ultimate purpose
of insuring that reliable service can be del ivered at the lowest
reasonable cost.The encouragement of efficient end-use of electri-
city contributes to the achievement of planning goals to the extent
that peak use is constrained.It may be detrimental to the extent
that it results .in inefficient plant utilization."~b)
Two poi nts about thi s controversy are important to thi s study.Fi rst,
total state or regional energy planning will be less efficient unti.l 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 WE PC 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 prbviding adequate capacity and their modeling effort
reflects that concern;there is very little disaggregation 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 omme rc ia 1)•
Table 8.5 presents the energy savings by customer for the year 2000.
Energy savings per household or employee were not available.
TABLE B.S.WEPC Conservation Potential by the Year 2000 (Base Case)
secto r
Residential
General Secondary
(c omme rc ia 1)
Savings
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 Tabl e 2-1.4.
These conservation estimates represent only part of the total potential.
Although the a.ir 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 adjustments for baseload and water heating efficiencies.
B.12
-
-
._ldl,
ALASKAN RAILBELT
The State of Alaska,various utilities in the Railbelt region.and the
l"1unicipal ity of Anchorage have impl emented energy conservation programs that
include measures for conserving electricity that have al ready reduced electri-
city consumption.
Major conservation programs currently available in the Railbelt include
the State Division of Energy and Power Development 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 onDEPD
program electricity savings are available in the Railbelt load centers.
According to Tillman (1983).almost all of the Railbelt programs have been
aimed at the residential sector.with con~ervation 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 8.7)
with approximately a 40 and 60%share.respectively.For a breakdown by pro-
gram.see Tab 1e 8.8.
Tillman indicates that price-induced electricity conservation will be more
important in the future than programmatic conservation for the following
reasons:
B.13
TABLE B.6.Average Annual Electricity Consumption per
Household on the GVEA System,1972-1982
An nua 1 Monthly
Cons umpti on 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.
•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 1ighting
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 inSect ion 5.0
In attempting to determine the level of conservation potential,the ques-
ti on ari ses as to whether further i nves tment in energy-savi ngs prog rams
B.14
-
-
TABLE B.7.Programmatic Versus Market-Driven Energy Conservation
Projections in the M~L&P Service Area
Programmati~
Conservation\a)
Year
1981
1982
1983
1984
1985
1986
1987
Cumulative
(MWh)(%of
12,735
19,609
20,896
27,619
30 ,195
32,614
35,421
179,089
Tota 1)
39.5
34.9
37.1
41.1
40.4
40.6
41.0-
40.3
Market Driv~8
Conservation l )
(MWh)(%)
19,558 60.5
27,243 65.1
35,374 62.9
39,560 58.9
44,536 59.6
48,133 59.4
50,940 59.0
265,344 59.7
Total(a)
(MWh)(~Ic)
32,294 100
46,853 100
56,289 100
67,133 100
74,730 100
81,015 100
86,363 100
444,,677
(a)Detail does not add to total in the orginal.1981 programs
inc 1uded:
Residential
We at her i za t ion
State Programs
Wa t er Flow Re st rictor
Water Heat Injection
MWh/yr
586
879
200
3,921
5,586
kWh/Cu stomer
42
63
14
281
400
--
Industri a1
Boiler Feed Pumps 7,148 2298
Planned conservation programs include hot water
wraps ~n the residential sector and street light
conversion and utility transmission conversion in
the commercial sector.The number of customers was
provided by the 1982 Alaska Electric Power Statis-
tics of the Alaska Power Administration.
(b)1981 Price elasticity effects equaled 19,558 MWh/yr.
Source:AML&P 1982.
B.15
TABLE B.8.Programmatic Energy Conservation Projections for AML&P (MWh/yr)
Program
Weatherization
State Programs
Water Flow
Restrictions
Water Heat
Injection
Hot Water
Heater Wrap
1981
586
879
.'200',
3,922
NA
1982
762
1,759
464
3,922
NA
1983
938
2,199
464
3,922
249
1984
1,114
2,683
464
3,922
249
1985
1,290
3,078
464
3,922
249
1986
1,466
3,518
464
3,922
249
1987
1,641
3,737
464
3,922
249
Street Light
Conversion
Transmission
Conversion
o
o
555
o
1,859
4,119
3,307
8,732
4,788
9,256
6,306 7,861
9,811 10,399
--,
~I
Boiler Pump
Convers ion
TOTAL
%Change From
Previous Year
7,148 7,148 7,148 7,148 7,148 7,148'7,148
12,735 14,609 20,896 27,619 30,195 32,614 35,421
NA 14.7 43.0 32.2 9.3 9.8 8.6
Source:AML&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 Rai1belt 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.
!L16
-
~
I
APPENDIX C
RED MODEL OUTPUT
-~
-
r~,
-t
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 per employee,as well as
sLDllmaries of price effects and programmat-ic 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
suppl ied by Harza';'Ebascoof mil itary and industrial demand.They do not
include an adjustment for transmission losses.However,for the 1983 update of
the alternative generation plans these reported figures were adjusted for
transmission losses.
C.1
.-
-
LIST OF TABLES
H-12--SHERMAN CLARK NO SUPPLY DISRUPTION ••••••••••••••••••••••••••••••••••C.11
Households Served,Anchorage -Cook Inlet ••••••••••••••••••••••••••••C.13
Households Served,Greater Fai rbanks •••••••••••••••••••••••••••••••••C.14
Housi ng Vacanci es,Anchorage -Cook Inl et ••••••••••••••••••••••••••••C.15
Housing Vacancies,Greater Fairbanks •••••••••••••••••••••••••••••••••C.16
Fuel Price Forecasts Employed,Electricity ($/kWh)•••••••••••••••••••C.17
Fuel Price Forecasts Employed,Natural Gas ($/M~1Btu)•••••••••••••••••C.18
Fue 1 Pri ce Forecast s Employed,Fuel Oi 1 ($/Mf1Btu)••••••••••••••••••••C.19
Residential Use Per Household (kWh)(Without Adjustment
for Price),Anchorage -Cook Inlet •••••••••••••••••••••••••••••••••••C.20
Residential Use Per Household (k\~h)(Without Adjustment
for Price),Greater Fairbanks ••••••••••••••••••••••••••••••••••••••••C.21
Business Use Per Employee (kWh)(Without Large Industrial)
(Without Adjustment for Pri ce)•••••••••••••••••••••••••••••••••••••••C.22
Summary of Price Effects and Programmatic Conservation in
GWh,Anchorage -Cook In 1et ••••••••••••••••••••••••••••••••••••••••••C.2 3
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 El ectrical Requi rements (GWh)(Net of Conservation)
(Includes Large Industrial Consumption)r-'edium Range {PR =.5)•••••••C.27
Peak Electric Requirements (MW)(Net of Conservation)
(Includes Large Industrial Demand)r-'edium 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
Housi ng Vacancies,Greater Fai rbanks ••••••••••••••.•••••.••••••.••...C.34
Fuel Price Forecasts Employed,El ectricity ($/kWh)•••••••••••••••••••C.35
Fuel Price Forecasts Employed,Natural Gas ($/~11~Btu)•••••••••••••••••C.36
Fuel Price Forecasts Employed,Fuel Oil ($/Mf'IBtu)••••••••••••••••••••C.37
Residential Use Per Household (kWh)(Without Adjustment
for Pri ce),Anchorage -Cook Inl etc ••••••••••••••••••••••••••••••••••C.38
Residential Use Per Household (kWh)(Without Adjustment
for Pri ce),Greater Fa i rbanks ••••••••••••••••••••••••••••••••••••••••C.39
Business Use Per Employee (kWh)(Without Large Industrial)
(Without Adjustment for Price)•••••••••••••••••••••••••••••••••••••••C.40
SLmmary of Pri ce Effect sand Programmatic Conservati on in
GWh,'Anchorage -Cook Inlet ••••••.•••••.••.•••.••••••••••••••••••••••.C.41
Summary of Price Effects and Programmatic Conservation in
GWh,Greater Fairbanks ••••••••••••••.••.••.•••••••••••••••••••••••••.C.42
-J
Breakdown of Electricity Requirements (GWh)(Total Includes
Large Industrial Consumption),Anchorage -Cook Inlet ••••••••••••••••C.43
Breakdown of Electricity Requirements (GWh)(Total Includes
Large Industrial ConsLrnption),Greater Fairbanks •••••••••••••••••••••C.44
Total Electrical Requirements (GWh)(Net of Conservation)
(Includes Large Industrial Consumption)Medium Range (PR =.5)•••••••C.45
Peak Electric Requirelllents (MW)(Net of Conservation)
(Includes Large Industrial Demand)Medium Range (PR =.5).C.46
HE9--DOR 50%••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••C.47
Households Served,Anchorage -Cook Inlet •••••••••••••.••••••••••••••C.49
Households Served,Greater Fairbanks •••••••••••••••.•••••.••.••.•••••C.50
Housing Vacancies,Anchorage -Cook Inlet ••••••••••••••••••••••••••••C.51
Housing Vacancies,Greater Fairbanks .......•..•.•••••.••••••..•.••.•.C.52
Fuel Price Forecasts Employed,El ectricity ($/kWh)•••••••••••••••••••C.53
Fuel Price Forecasts Employed,Natural Gas ($/~1MBtu)•••••••••••••••••C.54
C.4
-
.-
1~
-
Fuel Price Forecasts Employed,Fuel Oil ($/I'1r~Btu)••••••••••••••••••••C.55
Residenti al Use Per Househol d (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)
(~!i tho ut Ad jus tm ent for Pric e)•••••••••••••••••••••••••••••••••••••••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 ••••••••.••••••.•..•••••••...••.••.•.••......•.•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 (M~n (Net of Conservation)
(Includes Large Industrial Demand)~~edium Range (PR =.5)••••••••••••C.64
H10--DOR 30%••••••••••••••••••••••••••••••••••••••••••,.,•••••••••••••••••••C.65
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 ($/kWh)•••••••••••••••••••C.7l
Fuel Price Forecasts Employed,Natural Gas ($/MMBtu)•••••••••••••••••C.72
Fuel Price Forecasts Employed,Fuel Oil ($/MMBtu)••••••••••••••••••••C.73
Residential Use Per Household (kWh)(Without Adjustment
for Price),Anchorage -Cook Inlet •••••••••••••••••••••••••••••••••••C.74
Residential Use Per Household (kWh)(Without Adjustment
for Price),Greater Fairbanks ••••••••••••••••••••••••••••••••••••••••C.75
c.s
Business Use Per Employee (kWh)(Without Large Industrial)
(\~ithout Adjustment for Price)•..••...••••..•..•.•.••..••••..••••••..C.76
Summary of Price Effects and Programmatic Conservation in
GWh,Anchorage -Cook In 1eta e _II C.7 7
Summary of Price Effects and Programmatic Conservation in
GWh,Greater Fairbanks •..••••••••••••••••••••••••••••••••••••••••••••C.l8
Breakdown of El ectricity Requi rements (GVJh)(Total Includes
Large Industrial Consumption),Anchorage -Cook Inlet ••••••••••••••••C.79
Rreakdown of Electricity Requirements (G\'Jh)(Total Includes
Large Industrial Consumption),Greater Fairbanks ••.•.••••.••.....•..•C.SO
Total Electrical Requirements (GWh)(Net of Conservation)
(Includes Large Industrial Consumption)~dium Range (PR =.5)C.8l
Peak Electric Requirements (I'~W)(Net of Conservation)
(Includes Large Industrial Demand)Medium Range (PR =.5)C.82
H13--DRI SCENARIO ••••••••••••••••••••••••••••••~••••••••••••••••••.••••••••C.83
Households Served,Anchorage -Cook Inlet ••••••.••••••.•.•••.••.•••••C.85
Households Served,Greater Fairbanks •....••..•.......•.•...•••••..•••C.86
Housing Vacancies,Anchorage -Cook Inlet ••••••••••••••••••••••••••••C.87
Housing Vacancies,Greater Fairbanks ••••••••~••••••••••••••••••••••••C.BB
Fuel Price Forecasts Employed,Electricity ($/U~h)•••••••••••••••••••C.89
Fuel Price Forecasts Employed,Natural Gas ($/MMBtu)•••••••••••••••••C.90
Fuel Price Forecasts Employed,Fuel Oi 1 ($/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 Fai rbanks ••••••••••••••••••••••••••••••••••••••••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
C.6
-
.....
-
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.97
Breakdown of El ectricity Requi rements (GWh)(Total Includes
Large Industrial Consumption),Greater Fairbanks •••••••••••••••••••••C.98
Total Electrical Requirements (GWh)(Net of Conservation)
(Includes Large Industrial Consumption)r~dium Range (PR =.5)•••••••C.99
Peak Electric Requirements (MW)(Net of Conservation)
(Includes Large Industrial Demand)rv'edium Range (PR =.5)••••••••••••C.100
HE4--FERC +2%.••••••••••••••••••••••••~••••••••••.••••~••••••••.••••••.•.•C.l01
Households Served,Anchorage -Cook Inlet ••••••••••••••••••••••••••••C.103
Househol ds Served,Greater Fa i rbanks •••••••••••••••••••••••••••••••••C.104
Housing Vacancies,Anchorage -Cook ·Inlet ••••••••••••••••••••••••••••C.105
Housing Vacancies,Greater Fairbanks •••••••••••••••••••••••••••••••••C.106
Fuel Price Forecasts Employed,Electricity ($/kWh)•••••••••••••••••••C.107
Fuel Price Forecasts Employed,Natural Gas ($/MMBtu)H'•••••••••••••••C.108
Fuel Price Forecasts Employed,Fuel Oil ($/MMBtu)••••••••••••••••••••C.109
Residential Use Per Household (kWh)(Without Adjustment
for Price),Anchorage -Cook Inlet •..••......•.••.•••••••••...••...••C.110
Residential Use Per Household (kWh)(Without Adjustment
for Price),Greater Fairbanks ••••••••••••••••••••••••••••••••••••••••C.111
Business Use Per Employee (kWh)(Without Large Industrial)
(Without Adjustment for Price)..••..••••.•••.••.•••.••.•••.••••••.•••C.112
Summary of Price Effects and Programmatic Conservation in
GWh,Anchorage -Cook Inlet ••••••••••••••••••••••••••••••••••••••••••C.113
Summary of Price Effects and Programmatic Conservation in
GWh,Greater Fa;rbanks •••••••••••••••••••••••••••••••••••••••.•••••••C .114
Breakdown of El ectricity Requi rements (GWh)(Total Includes
Large Industrial Consumption),Anchorage -Cook Inlet ••••••••••••••••C.115
Breakdown of El ectricity Requi rernents (GWh)(Total Includes
Large Industrial Consumption),Greater Fairbanks •............•...••••C.116
C.7
Total Electrical Requirements (GWh)(Net of Conservation)
(Includes Large Industrial Consumption)Medium Range (PR =.5)•••••••C.117
Peak El ectric Requirements (i~W)(I~et of Conservation)
(Includes Large Industrial Demand)~"1edium Range (PR =.5)••••••••••••C.118
HE6--FERC 0%.•.••.....•......•..•..•....•................•..•....•o ••~••••C.119
Households Served,Anchorage -Cook Inlet ••••••••••••••••••••••••••••C.121 ~
Households Served,Greater Fairbanks •••••••••••••••••••••••••••••••••C.122
Housing Vacancies,Anchorage -Cook Inlet ••••••••••••••••••••••••••••C.123
Housing Vacancies,Greater Fairbanks •••••••••••••••••••••••••••••••••C.124
Fuel Price Forecasts Employed,Electricity ($/kWh)•••••••••••••••••••C.125
Fuel Price Forecasts Employed,Natural Gas ($/t~MBtu)•••••••••••••••••C.126 ~
Fuel Price Forecasts Employed,Fuel Oil ($/MMBtu)••••••••••••••••••••C.127
Residential Use Per Household (kWh)(Without Adjustment
for Pri ce),Anchorage -Cook Inl et •••••••••••••••••••••••••••••••••••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 Effects and Programmatic Conservation in
GWh,Anchorage -Cook Inl et ••••••••••••••••••••••••••••••••••••••••••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 Inl et C.133
Breakdown of El ectricity Requi rements (GWh)(Total Incl udes
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 E1 ectric Requi rements (MW)(Net of Conservation)
(Includes Large Industrial Demand)Medium Range (PR =.5)••••••••••••C.136
C.8
-\
-
-
,fIl'l1'iIII
-
HE7--FERC -1%•••••••.•..•••..••..•..•..•..•.•..•....•..•.•..•.•.••........C.137
Households Served,Anchorage -Cook Inlet ••••••••••••••••••••••••••••C.139
H0 use hold s Se r ve d,Gre at er Fa i rban ks •••••••••••••••••••••••••••••••••C.140
Ho us i ng Va can cie s,An c h0 rag e -Co 0 kIn 1et ••••••••••••••••••••••••••••C.14 1
Hous i ng Vacancies,Greater Fa i rbanks •••••••••••••••••••••••••••••••••C.142
Fuel Price Forecasts Employed,Electricity ($/kWh)•••••••••••••••••••C.143
Fuel Price Forecasts Empl oyed,I~atural Gas ($/MMBtu)•••••••••••••••••C.144
Fuel Price Forecasts Employed,Fuel Oil ($/~1MBtu)••••••••••••••••••••C.145
Residential Use Per Household (kWh)(Without Adjustment
for Price),Anchorage -Cook Inlet C.146
Residential Use Per Household (kWh)(Without Adjustment
for Pric e),Gre at er Fa i rb an k s.•••••••••••••••••••••••••••••••••••••••C.14 7
Business Use Per Employee (kWh)(Without Large Industrial)
,(Wi tho ut Ad jus tme nt for Pric e)•••••••••••••••••••••••••••••••••••••••C.148
Summary of Price Effects and Programmatic Conservation in
GWh,Anchorage -Cook Inlet ••••••••••••••••••••••••••••••••••••••••••C .149
Summary of Price Effects and Programmatic Conservation in
GWh,Greater Fairbanks •••••••••••••••••••••••••••••••••••••••••••••••C.ISO
Br~akdown of El ectricity Requi rements (GWh)(Total Incl udes
Large Industrial Consumption),Anchorage -Cook Inlet ••••••••••••••••C.151
Breakdown of Electricity Requirements (GWh)(Total Includes
Large Industrial Consumption),Greater Fairbanks •••••••••••••••••••••C.152
Total Electrical Requirements (GWh)(Net of Conservation)
(Includes Large Industrial Consumption)Medium Range (PR =.5)•••••••C.153
Peak Electric Requirements (MW)(Net of Conservation)
(Includes Large Industrial Demand)Medium Range (PR =.5)••••••••••••C.154
HE8~~FERC -2%•...•.......•.....•....•.•................•.................C.ISS
Households Served,Anchorage -Cook Inlet ••••••••••••••••••••••••••••C.157
I-Iouseholds Served,Greater Fairbanks •••••••••••••••••••••••••••••••••C.158
Ho usin g Va can cie s,An ch0 rag e -Co 0 kIn 1et ••••••••••••••••••••••••••••C.15 9
C.9
Housing Vacancies,Greater Fairbanks •••••••••••••••••••••••••••••••••C.160
Fue 1 Price Forecasts Employed,El ectri c ity ($/k Wh)•••••••••••••••••••C.161
Fuel Price Forecasts Employed,Natural Gas ($/W1Btu)•••••••••••••••••C.162 """
Fuel Price Forecasts Employed,Fuel Oil ($/~1HBtu)••••••••••••••••••••C.163 -"Residential Use Per Household (kWh)(Without Adjustment
for Pri ce),Anchorage -Cook Inlet •••••••••••••••••••••••••••••••••••C.164
Residential Use Per Household (kWh)(Hithout Adjustment
for Price),Greater Fairbanks ••••••••••••••••••••••••••••••••••••••••C.165
Business Use Per Employee (kWh)(Without Large Industrial)
(Without Adjustment for Price)•••••••••••••••••••••••••••••••••••••••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 ••••••••••••••••••••••••••••••.••••••••••••••••C.168
Breakdown of El ectricity Requi rements (GWti)(Total Includes
Large Industrial Consumption),Mchorage -Cook Inlet ••••••••••••••••C.169
Breakdown of Electricity Requirements (GWh)(Total Includes
Large Industri al Consumption),Great.er Fa;rbanks •••••••••••••••••••••C.170
Total Electrical Requirements (GWh)(Net of Con"servation)
(Includes Large Industrial Consumption)Medium~ange(PR =.5)•••••••C.171
Peak Electric Requirements (MW)(Net of Conservation)
(Includes Large Industrial Demand)i<1edium Range (PR =.5)••••••••••••C.172
C.10
-
I~""
H12--SHERMAN CLARK NO SUPPLY DISRUPTION
C.11
C.13
SCEN6RIOt HEll t HI2·.5H£R~AN CLARK NO SUPPLY OISRUPTIO~·.6/2411q8J
HOuSEHoLns SERVED
GREATER FAIRAANKS
~w ••••••_••~••••w •••••
YEAR SINGLE FAMILY I4UL T!HMILV HUSILE HOliES OUPLEXES TOyAL.............~....................•......•.•....................•••.•........
1980 nao.5281.1\89.UP.15JI1.
0.00(1)(0.(00)(0.0(0)(0.0(0)«0.(00)
t98S 101111".S Abl.2130.lUSt 20 1107.
0.0(0)«0.0(10)(0.0(0)(0.0(0)«0.(00)
(J.t990 1112 11 •7 Q bO.2270.2315.2I1J32 •
I--'(0.000)«0.0(0)«1'1.000)«0.000)«0.(011)~
1995 t 117lb.711111.HU.nn.?'1I2 1l 1l.
0.0(0)(0.(011)«1).000)«0.000)«0.00(1)
2000 tb52 8 •770J.]811115.n98.30]74.
O.(lIIO)(0.000)«0.000)(0.000)«0.(100)
200S t7 q 51.8b"ll.IIUO.i!UI.32q73.
0.(00)(0.0(0)«0.0(0)(0.000)(0.000)
aoto 19 b75.9bU.IIfl7J.2)34.lb294 •.
(1.000)(0.0(0)(0.0(0)«0.000)«0.0(11)
J .1 J 1 )1 J _J J I J J
]~1 ))1 "'1 'J 1 ~J 1 .)]:I 1
SCENARIOI HED I HIZ ...SIiERHAN CURl<NO SI/PPLV OISRIJPTION ••6/1!qJI~81
HUUSING VACANCIES
ANCHORAGE •COOl<INLET..----...•.••...~.....
'fEAR SIIlGLE FAHlly HIJL TlFA I4 1LY '101.l1l E HOMES DUPLEXES TOTn................................~..--......................,...................._~
nAo 5089.1bbb.1991 •Illbl.IUO tl •
0.0110)(0.000)(0.001))(0.000)(0.(00)
1985 15011.IlIqb.Ii!I.~lJ2.ZIII7 •
C)«0.000)(0.000)«0.000)«0.(00)(0.0011)
t-'19lJO bllb.100!i.IIICI.2Sq.2(81).(Jl
0.000)(0.000)«0.000)(o./)on)(o.,oon)
19lJS 711.UU.Ibll.2811.2771.
o.oon)«0.000)«0.000)«0.000)(0.000)
2000 7b8.l19b.178 •11115.1181.
0.000)(0.000)«0.0(0)(0.000)(0.000)
2005 (1]0.Iqbll.195.288.3281.
0.(00)(0.000)(o.oon)«0.000)(o.oon)
2010 tiP.2182.Z17.11 9 •]b]II.
o.noo)«0.000)(/).000)(0.000)(o.oon)
SCHllRIOI MED I Hll-.RHERM4N CLARK NO SUPPLY OISRUP'ION-.b/~4/t98J
HUUSING V4CANCIES
GREATER FAIRBANKS•...••.•..•.•-........
YEAR SINGLE FAMILY MUI..TI F AHIL V MOBILE Hom::s OUPLElCES TOTAL......~.......................-...-..........."'•........lilt··......••·••.
1980 J~SJ.3320.98~.895.88511.
o.nOO)(o.oon)(0.000)(0.0(0)(11.11(0)
Iq85 liB.2b5 11 •24.722.351".
0.000)(0.000)(".000)(0.00(1)(0.000)
("")1990 Ii!q.IiSIl.25.81 •.f,8Q •.......(0.000)(O.OO{l)(0.0(0)(0.(00)(0.000)en
Ins Illi/.4411.:n.80.726.
0.000)(0.000)(0.(00)«0.0(0)(0.0(0)
2000 18i.1l1i0.42.78.71l~.
0.000)«0./1(0)(0.0(0)«0.000)«0.0(0)
lOOS 197.lIt.Q.lit..i!09.Q21.
0.000)(0.00/1)(0.0(0)(0.(00)(0.0(0)
2010 21t..51q.51.77.8bll.
0.(00)«0.0(0)(O.OO{l)(0.000)«0./100)
J ]J I )J .I .1 _J ).J )~I .~I
1 ~'j
I J I 1 1 1 ~J 1 ]J 1 ')1 j ])
(")
I-'
.......
SCENARIO,MfO I HI2 ••9HERMA~CLARK NO SUPPLY OISRUPTION·.tl/2Q/198l
FUEL PRICE FD~ECAST9 EMPLOY~O
ELECTRICITY (S I KWH)
ANCHOPAGE •COllK I NL E'GREATER FAIPRANKS.._•..•.•._•.•.••.•••••._.~_.•.•.•_.R ..~..~......_.~...••....-..._........
YEAR RESIDENTIAL BUSINESS RESIDENTIAL RIIS 1 NE S!I..........................................--.......
1980 0.0l1 0.0)11 0.0'15 0.090
1'185 0.0 1111 0.1)115 0.095 0.090
1990 0.OS2 0.0119 0.092 0.081
1995 0.05~0.055 0.091.1 0.089
&'000 O.Obl 0.OS9 O.Oqb 0.091
lo05 O.C'b5 n.Ob!0.098 0.09]
2010 O.Ob'o.nbll 0.100 0.0'15
SCENARIO'~EO,Hta-.SHERMAN CLARK NO SUPPLV OISRUPTION--b/2Q/198J
fUEL PRICE FORECASTS EMPLOYED
NATURAL GAS (S/MM8TU)
_..._.••............................~
ANCHORAGE •COOK INLET GREATER FAIR9ANKS..~...-.-..._.....~•••.....•.•.....••
n.
I-'
CO
YEAR RE81DENIUl BUSINF.SS AESIDENTI AL IHISINfSS......•.••...••..........-"1 •••.....~......................_-
Iq80 1.7]0 I.SOO U.711c)11.l90
Iq85 1.9 50 1.7ilo 10.bOO 9.150
1990 2.1180 l.bS/)11.2110 9.790
1995 ".050 ].820 1].0]0 11.580
lOOO 11.290 1I.0bO 15.110 1 ).&60
2005 (I.9bO II.Ho 11.521'1 !b.Olo
2,,10 5.)(10 5.150 20.310 18.11611
I ,)B J J --)).1 J _l J J J 1
1 )J j J 1 I J 1 ')1 -]I 1
SCENARIO,MEl),Hli!...IlHERM4N CLMU<NO SUPPLY £lISRLJPTlON ..·e.I2IlJl'l6]
FIiEl P~ICE FORf:CASTS EHPLOYF'D
FUEL OIL (SJHH8TUJ
_NCHORAGE ..COOk I~LET.._•.•....•..•.......•..•._~.~
GREATER 'AJRRANK~........•..•.....•..--.-.~_.
n
t-'
1.0
YEAR Rf.SIDENTIAL BUS'NESS RESIDENTt4L FlIlSI Nf!lS..........•..•.•.•.._-...................-.........
I'l80 7.750 7.200 7.830 7.'50l)
I'l85 e..uSO '5.'lOO e..510 fl.180
1990 e..8 u O b.l!9n fl.910 e..580
''l9;7.'l30 7.3811 11.010 l.fl80
2000 9.190 A.bliO 'l.290 19.'lu
2005 10.b!.0 10.100 '0.770 to ./f1i0
2010 Ii!.JsO 11.800 12.1180 12.150
SCENARIO.~ED I ~la.·8HEAMAN CLARK NO SUPPlV OISRUPTIOH·.6/2~/1~8J
RESIOENTIAL USE PEA HOUSEHOLD (KWH)
tWIT~OUT ADJUSTMENT 'OR PRICE)
ANCHQPAGE •COOK INLET.....~.......-......~~
SMALL LARGE SPACE
VEAR APPllANCES APPLIANCES HEAT TOTAL..........••..•..................................
19811 2110.1)0 b500.01 SOU.sa Ub'l9.15
0.1)00)(0.1)00)c 0.000)c O~OOO)
n 1985 ;11£10.00 h151.1.19 ~821."3 IJIH.3J.C 0.000)(0.000)(0.000)(0.000)N
0
1990 221 0 .00 bOI9".1b 451i~.35 U8111.1i
0.000)(0.000)(1).000)(0.000)
1995 221>0.00 5959~31 1.I'51~.'5b 12734.87
0.(00)(0.000)(1).1100)(0.000)
2000 2310.1)0 S'UI9.J8 1.1 11 53.811 Iii 7'.i3 •j!1
0.000)(Il.OOO)(o.lIlIn)(0.000)
2005 Huo.oO bO'59.12 41120.01.1 12839.17
0.000)(0.000)C 0.000)(0.000)
i!010 21110.00 U;n.98 Qllq].S5 1l971.52
0.000)(0.1)00)(0.000)(0.000)
J J I J j J -)•I .J I J .~J ]J ~J
I 1 )1 J I )1 J 1 1 -])1
SCUURIOI HED I Hla·.SHERMA~CLARK ~O SUPPLY DISRUPTION·.b/2/l/198J
RESIDENTIAL USE PER HOU~EHOLO (KWH)
(WITHOUT AOJlIST'lI::NT FOR PIUCE)
GREATER FAIR8ANKS
••••~•••_•••_~•••••aD.
SHALL LARGE SPACE
YEAR APPLIANCES APPLl'NCES HEAT TOTAL......................................................,.,.
191\0 lUb6.00 Hlq.52 HlJ.6b 1l51 Q.18
0.000)(0.000)(0.(100)(0.0(0)
n 1985 253'.i.Q 9 bl78.""lbOb.ll 12l'-l.lu
N (0.000)(0.0(0)(0.(100)(0.000)I--'
1990 lUb.OO bIl5J."a 1812.52 1293Z.07
11.(00)(0.000)(0.0(0)(0.0(0)
1995 2b76.00 Ubb.n 11050.111 I HU.OO
0.000)(0.0(0)(0.000)(0.0(0)
lOI)O l7llb.00 61 9 5.115 IIJlO.)O 1)651.75
1).000)(0.0001 (0.(00)(O~OOO)
20jl5 lIlU.OO 6818~86 /l';]5.80 1/l190.b.
0.(00)(0.(00)(0.(110)(0.000)
2010 281J6.00 ..887.85 1I&55.9b 1I1 1lZQ.81
0.000)(0.000)(0.0(0)(0.0(0)
"
N
N
SCENARIU,MEO,Hll ..SHERMAN CLUIK NO SUPPLY DlSRUPTIOII ....bI2I1/198J
BUSINESS USE PER EHPLOVfF (KWH)
(WITHOUT LA~r.E INOUSTRiAl)
(WITHOUT ADJUSTMENT Fqp PRICE)
VEAR ANCHORAGE ..COOK INLET GREATE~FAIRBANK!.........~..•..............•••••.......•....•..•
1980 8401.011 7~q5.70
0.000)(0.000)
1985 9560.18 Hli!.11
0.000)(1I.00U)
1990 10355.0&8327.:55
0.000)«0.000)
19l/iS 10 9 18.115 8bbt!.iT
0.000)(0.000)
lOOO 11 11 111.110 S957.9i!
0.000)(II./lOCI)
2005 IlO69.U CI:SOB.OJ
0.000)(0.0011)
2010 IlU?.U 9711.05
0.(00)(0.000)
J J)J -J )B J J I ))I _I J J J I
j 1 1 -~J 1 I 1 J I )j
SCENARIO.MEl'!I HI2··l!fiF.RMAN CLAIlK NO SUPPLY OISRUPTIOtl ••6/l11/19n
SU~MARY OF PRICE EFFECTS AND PRDGRAHATIC CONSERVATION
IN GWH
ANCHOqAGE •COIlK IULET
RESIDENTI -L Rlllll~IESS..............................
OWIl-PRICE PRUGRAI1-IN()IICFO CROSS-PRICE OWN.PRICI!PROORA"'·INDlIC~D (:ROSS-PRICf
YEAR P[DUCTION CONSER"!~IOrl qEO.UCTtO.N RE~q.e.r IO~,CO~4~~~~!!9~.__.AEl'IueTiON....................................................................................................................................................
1980 11.000 0.000 0.000 O.OO/)0.000 0.000
1981 6.169 0.000 ..0.'5b7 9.327 /).000 O.'H2
198i 12.JH 0.000 ·1.115 Ill.651 0.000 1.061
1981 18.'506 O.OOll ..1.702 21.qaO 0.000 1.'595
19811 lll.f,711 0.000 -2.271)31.307 0.000 2.12b
1985 30.8'll O.OO/)..l.ll]l 1I!>.6H 0.000 2.lo58
198b ]11.1116 11.000 ·10.645 58.180 0.000 .0.356
1981 IIb.t09 o.ono -18.11511 b9.U6 0.000 ·3.]10
1988 53.h2 0.000 -2b.i!!>i!111.213 0.000 -6.385
1989 61.]15 0.000 -]11.071 92.1119 0.000 -9.399
1990 "9.008 0.000 .111."7 9 1011.366 n.oOo -12.1111
n.19 9 1 115.01lb 0.000 -91.191 119."110 0.000 ·19.060Nw1992Ibl.0811 O.OOll .1110.'5\5 1)'1.'5111 0.000 ·is.l07
199)207.121 0.000 "I 89.lIlJ 151.088 0.000 -12.l53
19911 25).15 9 0.000 -23 9 .150 16b.6b3 0.000 -)9.00Cl
1995 299.197 0.000 -288.1I!>8 182.231 0.000 -115.6117
199b 211l.01 lJ 0.000 -?25.00a 19A.H8 0.000 -'52.5811
19'H 168.8112 0.000 -161.5111 21 1J .\20 0.000 -59.'530
1998 103.b!>5 o.nOO _lJll.08b nO.3bl 0.000 -6b.1I71
1999 lR.1I81l 0.000 -]11.626 2111>.1103 0.000 -73.1112
2000 -2b.b8lJ O.ClOO la.lllS 2b2.1J 1I 1I 0.000 -80.1'511
2001 -7.502 0.000 6.1170 21l2.1J 1l9 0.000 -90.2115
~oOi 11.68S 0.000 -15.89S 30~.H'i 0.000 -100.131
ZOO)10.87Z 0.000 -lA.lbO !22.sao 0.000 -ltO.OZll
20011 50.059 0.000 -bO.b2'i 311<'."25 0.000 -119.920
1005 b9.211l>0.000 ..82.~qO ~fJ2.fJ70 0.000 -129.lItl
200b "8.151 0.000 -9'5.9011 lfl A •tli!0.000 -1111.]]1'1
2007 1\7.055 (l.(l00 -1 /18./1 I 9 ull.'i Q 5 0.000 -ISb.llbll
2006 9'i.91>0 0.000 -121.733 1119.057 O.OOCl -170.391
2009 tOll.hll 11.000 -nll.1>1I1 IIbtl.520 0.000 -183.917
20.10 tll.lb lJ o.oon -,"7.Sbt'URlJ.9S2 0.000 -197.111111
SCENARIO'MEO I ~IZ--8HER~AN CLAR~~O SUPPLY OI8RUPTION ••6/24/1983
SUMMARV Of PRICE EfFECTS AND PROGRAMATIC CONSERVATiON
IN GWH
.............
GREATER FAIRBANkS
flESID[NTlAL
"
N.po
YEAR........
1980
1981
1982
1981
19811
1985
19h
1987
1988
1989
1990
1991
199a
19'H
19911
1995
1996
1997
1998
1999
lOOO
2001
2002
2003
20011
2005
20010
2007aoOB
2009
2010
OWN-PRICE
REOUC T1 ON
....
0.000
0.000
0.1'100
11.0011
0.1)00
0,000
-0.200
..0.1100
-/l.000
-0.800
-1.1)00
-'.008
-1.Olb
-1.0211
-1.1)])
-1.0111
-0.86"
..0.695
-11.522
-0.1110
-0.176
0.129
o.lIn
0.738
1.0112
'.:547
,.772
2.19~
2.6211
1.0119
'5.117'5
PAUGH ...1-1 NOUCEO
CONSERVA~IUlj _
.............................
(1.000
1).1)00
0.000
0.000
0.000
0.000
11.000
O.Olll)
1l.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
(1.000
0.000
0.000
0.000
°.00 0
O.lIOO
o.OtlO
0.000
0.000
(I.1I00
CROSS-PRICE
REDUCTION
......................
0.000
0.758
'.5U
2.n4).on
3.189
4.184
4.'378
4.9n
'.JI»7
5.HI
5.170
11.592
4.008
3.1li!1I
2.83 0
I.J50
-0.11l0
-1.b30
-).119
-11.609
.b.8a5
..9.0112
-1l.2S8
-U.1l75
-1l5.691
-18.bU
-21.~H
-all.bO Il
-a7.liTS
-.sO.SIIO
OWN-PAlel!
REI).~P .•ON_
..................
0.000
0.000
0.000
0.000
0.000
0.000
-0.]112
-0.685
-1.027
-1.309
_1.712
-1.61]
·1.0111
_1.595
-1.55b
..1.517
-1.2117
..0.078
.0.708
_fl.1I39
_0.109
0.297
I).H)
1.228
1.6911
2.160
2.819
1.417
11.1)0
11.7 0 5
l§.tlSII
BUSINESS..............
PROGRAM-INOUCED
CONHf.l~~npN __.,
....
0.000
0.000
0.000
0.000
D.ClOII
0.000
0.000
0.1\00
0.000n.ooo
0.0110
0.000
0.000
0.1100
0.000
0.000
0.1100
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
CROSS-PRICE
REDUCTION
..................
0.1100
0.5111
1.028
1.';42
2.1156
2.570
i.758
2.9qb
J.1]4
J.J21
).51t
J.08t1
2.b57
e.2J!
1.8011
1.378
0.556
-0.265
"1.080
-1.907
·2.72'9
-1.910
-5.091
-b.271
.7.1I!i2
-8.6)]
-10.215
-I t .836
-11.1118
-15.01'9
-Ib.blll
I !J J I ])1 I J I 1 )
J 1 J J 1 I 1 ]'1 J J ---f I 1 I
SCENARIOI HEO I HI2 ....SHEFlt1AN Cll~1(NO SUPPLY OI8RUPTlor~..-bI21l119R]
BREAKDOWN ot ELECTHICITY REQUIRfM~NTS CGWH)
(TOT~L INCLuDES LARGE INDUSTRIAL CONSUMPTION)
ANCHnRAn[..conK INLET
••••_•••N •••••••••••••
~EOIUH RAHGE (PR_.5)
••••••••••••4.4 •••••
RE510EIITIll 8USINESS M18CELUNEOUS Hon.INDUSTRIAL
YEAR RLQlJIREIIENTS REQUIREMENTS REQUUIEHENU LoAn TOTAL._..~-.-.-_..._....-....._--.......-.•.•.•..••..........._~.~_..-......-....••..•.........
..~110 '1H.'51 875.1b 211.:SI 8tJ.no 196].19
1981 1019.55 Q46.55 all.bll 92.011 208i/.82
1982 1059.57 10U.U ;!1l.'HI 100.16 2202.1Ie;
1983 1099.bO IOR8.92 25.)1 108.211 HU.07
191111 113'1.62 IlbO.1I !5.b!116.12 211111.70
1'J8S 1179.611 UJI.JO 2S.CJ8 \211.40 !'UI.12
198b 1212.b5 1280.79 26.83 137."9 2658.16
1'187 1,!1l'S.b!1J1O.28 n.b7 151.JB '1511.99
1988 1216.b6 1179.77 28.51 Ibll.88 l851.8i'
U89 nll.b7 111 29.21,29.36 17".17 29118.b6n
N \990 1l1l1l.6J 11118.75 ]0 •.20 191.86 ]0 11 5.119
U1
19'11 11711.10 1510.lIb )o.Be 195.IJ )IIO.5b
1'192 140).52 IS1I2.17 1l.56 19 8.110 ]175.611
1'19)11132.'111 1'7].87 U.2l!201.66 32 11 0.72
19911 IlIbi!.]6 1605.~8 H.9l'2011.")]]05.79
1995 11191.78 1637.29 H.bO 208.iO ]]70.87
I'I9b IS17.'70 16/)).011 311.16 2\11.111 )lIi9.0/J
19'17 I S/J).b&!1684.80 311.n 220.Il8 11187.22
1998 15b9.'5J \7111.'55 lS.29 U6.0i!]5115.110
1999 IS9'5.1l1ii 17110.11 15.81,21 I.9b H01.51
2000 Ib21.1t>I1bb.OtJ ..JtJ.1I2 237.90 JUI.7'5
2001 I!lSs.eli IAI2.b'J 31 .27 21111.9b J750.7t>
2002 ItJ90.H IIISCl.lI 18.I I 252.02 3819.78
200]11211.81 1905.9 Q ]8.91,259.08 1928.7Q
20011 1759.10 19'52.'57 )9.80 2(,6.111 11017.81
2005 179].71\ICl'lCl.21)110.65 273.20 1I10b.82
200b IflJ9.ii'i!Ob Cl •1l 2 111.117 281.58 II i!li!.1111
2001 llillll.bS il /JO .11';/J].O!l 2/19.qb 1I]58.IS
lOOI!1'110.09 2211.0ti II/J.]O 298.]11 0411].81
2009 I 'HS .5]~28t.71 /JS.52 JOb.H 11609.1111
2010 20ilO.'Jb i!1S<!.1 11 111,.711 115.10 11715.111
SCENARIO'MED I HI2 ••SHERMAN CLARK NO SUPPLV DISRUPTION ••b/24JI983
aREAKOIIWN OF ELFCTRIC ltV REQIJlAfMfNTS (GWH)
(TOTAL INCLUDES LAH~E INDUSTHIAl CONSUMPTION)
GREATER FAIRBA~KS._..._-..._....~-.....
MEDIUM RA~GE IPR ••5)..._.......~~.......
RESIDENTIAL BUSINF.SS MISCELLANEOUS nOG.INDUSTRIAL
YEAR IU.I)ll!RF.MENTS REQUIREMENTS REQUIREHENTS LOAD
..-"!"....~...-.~.~..........~.......~.~......~...~.•.•...••......_..••...••.-
IUO 17b.39 21?ICl b.78 0.00
1981 190.b4 229.114 b.15 o.no
lUi!204.90 242.S5 b.71 0.00
1983 i!19.1S 25'5.25 b.bl 0.00
1984 &!13.qo 2bl.9b b .113 0.00
19111S 247.bS 280.h b.59 0.00
198b 2bO.IO 289.ClS b.llS 10.00
1981 iP2.55 298.i!Cl b.l0 20.no
1988 2115.00 307.04 II .l!I JO.oo
1989 2 9 7.45 315.83 b.80 40.00
(").1990 109.90 32".bC!b.Bb S/).OO
N
())
19 9 1 lU.22 132.113 7.U8 50.00
1992 Ub.5,J HI.oS 7.11 50.00
1993 149.8S JCl9.C'1 1.54 50.00
19911 ]U.u 157.11 11 1.17 50.00
1995 31b.1l?3blj.'70 1.99 50.00
Iq9b 18b.28 111.1 9 8.lb 51).00
1991 Hb.OC!317.87 8.32 50.00
1998 1105.90 ]81.9b 8.49 50.00
1999 4IS.7t 19n.OIl 8.105 50.00
2000 1125.52 "")9b.li!8.8?50.no
2001 /13t1.8tl 405.bl 9.011 50.no
2002 ClIHI.il 1115.10 '1.27 50.no
ZOO]1I')9.5a 11211.59 9.50 50.00
200Cl II 10.9t 1I1a.08 '1.12 50.00
ZOOS 482.25 11111.51 9.q'S 50.00
200b IlllS.9/>457.05 111.22 50.00
2001 50'1.&1 1110.51 10.50 50.no
2n08 521.37 IIBII.OI 10.7A 50.00
2009 517.013 aIl1."Q II.U5 So.oo
2010 550.19 'HO.en 11.31 50.00
J )J I ··.1 I )I .1 J ]J )
TOTAL•..•..•••••....•..
400.31
421.21
11511.15
1l81.01
501.99
S3".ql
561>.20sen.so
1>28.79
bIlO.08
b91.38
7ll.14
UII.811
75b.b5
718.111
800.17
8U.2J
832.29
8118.]1l
8bll.1I0
81\0 '.lIb
901.52
1:122.58
91l3.aS
91:111.11
985.17
1013.23
10110.70
10l:l8.lb
IOQ5.62
IIB.OQ
.1 J
--]1 ]1 )-1 1 '}1 ]J 1 1 J I '1
n
N
'J
S((NARIO,~Eo,HIl ••SH~RHAN CLAR~NO SUPPLY OI8RUPTION ••bll~/198!
TOTAL ~LECTnlCITY PE~UIREHEN1B (GWH)
'NET OF CONSERVATION)
'INCLUDES LARGE INOUSTAIAl CONBU~PTIOH)
HfDIU"PANGf (PR ••S)
YEAR ANt~ORAGE •(OU~INLET OA~ATER ,AIRAANkS TnTAl....--....•..•••.•.•.•....~..~.•~-·R-·--·..__..."..~~-..~.~..~.--~
1980 19(oJ.19 /Ill".JI 2Jb)~51
19"1 lOIJ2.Bl /li7.U 2'5ln,0'5
IqBi!Zi!n2.115 050.15 "..'5b.bO
19AJ "2i.n1 061.07 "Blll.IO
1911Q 211 11 1.711 '§(l1.Qq "9I1 Q .b9
19115 25b1.U ~)1I.91 )09b~21
198b letS8.U 5bb.ZO ]"O'II,H
1987 17'511.99 5'H.50 ]]52,119
1988 lI851.112 blB.1"111110.111
\9A'iI i!9118.U blIO.IlB 1608.70
1990 ]0115.119 b'H.18 1111>.81
19 9 1 SlIO.'511 11).111 lAU.70
1'J9l JI75.tllI 7]0.B9 ]910.51
199)]2 11 0.71 15b.b5 )997.H
199q H05.7 9 nll.1I1 Q1I811~21J
\995 1'70.~7 "00.17 IIIl a".00
1996 Hi!9.0a lllb.2l 02U5~l7
19'H lQIl7.22 8U.29 0]19.51
1998 J5 0 5.110 81111.)q 019]~7~
1999 JbIl).57 IIbll.al)Ollll7.91
2000 Ubl.7'5 '"'O."b Q511i!~i!1
ZoOI ]1'50.HI 901.52 Ilb!l2.2A
2002 )/1]9.71\922.58 1l7b2.1b
200)]9~8.7'i1 90).b5 111172'."11
20011 aOI7.81 9b/J.11 119112'.5'
2005 III"".Ili!'il8'i.n "inQ2.S Q
lO06 oaU.Q9 1"".23 'ii1115~72
lOOl IIJ51\.15 111110.70 '5]QR.1\1l
2008 1111 11 ].111 IOb8.'t.'551;1.91
Z009 Ilb n 9.Il!l 109'5.b2 '5705'.10
lOIO 111H.III 1\2).(19 -;f\"iA.2J
-)
n
Nco
]]
SCENARIO'~ED.HI2 ••8HERHA~CL1R~NO SUPPLY OISRUPTION ••~/111/Iq8J
PEAK ELECTRIC REQUIREMENTS (MW)
(NET o~CONSERVATIO")
UNCLI.IDfS LARGE 11101l6TIU4L DEMAND)
HEDIUM RANGE (PR &.5)•..•.•..........•....~
YEAR ANCHORAGE •COOK INLET GREATER ,AIR8ANKB ;'OTH
•u ••••••••••••••••••••...~.._..••.•..•.••..~~•.....•.._.•.•.•.....
1980 3 9 b.51 91.40 41'17.9/1
1981 1120.b8 97.5/1 518.21
19l\2 11 11 11.81:1 103.b9 5111'1.5'5
198]/lb9.011 In.n 578~1j7
1984 119J.21 115.98 6 11 9.19
1985 511.3'1 U2.1J 619.52
198b 537.82 1i!9.2J bb7~08
1987 558.211 Uo .111 b9Q.b5
1ge8 578 ..b7 143.55 722~22
1989 599.111 150.b'J 7119.79
1l~90 b19.51 157.83 777~H
1991 Ul.15 I til.ItO 795~55
IlJ92 &45.97 1ft.,.77 813.111
1C19]b'iIl.11I I U.7/1 8J I ~92
19911 b1i!.'H 111.70 850.11
1995 b85.b3 162.07 '"US'.31l
19911 b97.11 tee..JIl 1l1J3~b5
1997 706.91i 191).00 898~99
1998 720.b7,1'H.b7 9111.]ll
1999 73Z.35 191.1'1 Cil29.b8
2000 7 1H1.01 >'01.0C)945~OJ
2001 hZ.OO 205.81 1Il/7.81
200i!779.lib 21/).02 990.51\
lOO]797.'n i!1S.1lJ lOtl.J6
20011 815.90 221).24 10Jb~1l
2005 831.8b 225.05 10~8~91
2000 859.29 2lt.3Z 1090~bO
2007 884.71 217.'59 1122.30
2008 9111.ttl 2/U.80 1153'.99
2009 915.50 ~511.13 11II5'.b 9
2010 9110.9B <'50.40 1217~3l\
J c )d I !]J ])).1 ...1 ]1 l
,..,.
-1
-
i~
HE3--DOR AVG SCENARIO
C.29
)1 ~.1 1 I I 1 1
SCEtlARIUI "'ED I HEJ--OOR _VG SCENARIO--b/2/1/1981
HOUSEHOLDS SF.RVED
~~CHOPAGE •COOK INLET-_.•.••...._-......-~.
YEAR SINGLE FAMILY HUL TlF AMILY t40fll LE HottE 5 OUPLEXES TOUL...........••.............-................---...............................-..
•'80 l5"H.2u]llI.8210.7Ul:lb •11501.
0.000)«0.000)(0.000)(0.000)«0.000)
1985 115b7'5.2bi.!01l.10851.8Sbl.9110].
0.(0 0 )(0 ..000)«0.01)0)«0.000)(0.000)
n
w •"0 ';I)Z9'.25871.12121.BilbO •102157.
I--'(11.000)«0.(00)(0.000)(o.noo)(0.0(0)
•995 6101:19.21629.I/lObft.8131 •Illlt7.
0.000)(0.000)(0.000)«0.000)(0.000)
2000 bb02 9 •1082'5.t5J1~.8161.1201bl).
0.(00)(0.0(0)(0.000)(0.000)«0.(00)
ZOOS 1I7qb.Jqllbl.1&822.821i].I3lJb9.
0.(00)«0.01)0)(0.000)(0.(00)(o.noO)
ZOIO '790bb.38J'iI.18115.91S q •.q5~91.
0.0(0)«0.000)(0.000)(0 ..000)(0.(00)
_.---""'"...e cc co II'C ...0 tIlC O'c
_0 Cl 0 O'CO ....0 ~e ,..0 ,oe
"'0 _c
'"C ,","0 ~C O'e -0
~III 0 0 0 ...·"I 0 II)·0 ·01'·..-0 "10 "Ie "Ie NO ...C>"'0~
C
~
..-..-..-..---""0 00 III 0 0'0 ClO NO CO
CD _0 IV 0 ,..0 1"t0 0'"0 It'IO 0'0
1&1 ~O "'0 "'0 "'0 ftl 0 no 0 -0x-·-·'"·IV •fU ·IV •'"·iii 0 0 0 co C 0 0
....I JfI'il1!l
11.
=:I
C
••0 •....CIt •CD _.-..-_.-.-_.-..-
>-x •I&l O'C co ,",0 ,..0 #0 ,00 -0a::z •~~o ..,c::00 0'0 00 "10 CO
W «<•co _C -0 -0 "0 ~o O'C ~c::
lD rr X -·IV ·'"·N -..,·...·:::-·-...a::0 c co Co co 0 0
CIl CD -W
0-C «<..,l-....I IL.-.....0 c
~'J:a::0
N 1&1 1&1 %~....CD ~
..0 ::l «<,0 ILl•X l%
0 e ..-_.-..-..-->-..e ~c cco -e ...0 lIlCo _c
l%.....I ell co 1Xl0 .oc::#0 co 0'0 l1"0 ,,,,,",,..-NO "I Co o-e ceo to-0 acro coz~II'•III ·,..·...·,..·...•0-•..«<0 0 C 0 0 0 0
u li-
CD -~
CI ~
"""'""::::l..%
a::
0...•>-.-..-..-.-0.-
I ..J ceo "Ie>NC Co C co AI 0 Co:>,.,-'"Co #0 If\0 lJ 0 ,"",0 .00 1\10
III :r NC',oe:Cle:~c::00 Cl 0 II'0:::«<...0 0 ·0 ·...·lI'O ·.0 ·Cl 0
II.co -0 _0 _c::_C -0 -c
1&1
..J ,,M>fll,
C e
1&.1
~-Q)
0-a::
"'"z a-•<:>lI'O 0 11\<:>II'0......•II)til 0'"0-0 0
U IIJ •0'"0-0-0'e 0 0
CD >-•'"AI AI
JfIIIillS\,
-
C.32
1 I )i 1 !1 '}'1 '1 1 -1 1 I 1
SCENARIOI MEn I HE3-.0UR AVO SCENARIO ••6/~q/lq81
HOUSING VACANCIES
ANCHORAGE -COOK INLET......-_.•............
YEAR SINGLE FAMILy MULTIfAMILY HORJU HOMES DUPL£)([S TOTll_......................................................................................
iQ80 S08q.?btlll.Iqql.IlIb].lbiO q •
0.000'«0.(10(1'(0.000)(0.000)(0.000)
Iq85 SOl.IQqb.II q.2 Q2.211iO.
n (0.(00)(0.1'100)(0.(00)(0.(00)C 0.000).
ww IqqO bOA.I !I 17 •1110.28Q.215111.
0.000'«0.0(0)(0.0(0)(0.0(0)(0.(00)
jQQ5 bH.IlIqP'.155.2611.2601.
0.0(0)(0.(00)(0.0(0)r 0.(00)(11.000)
2000 ?lb.'bb~.Ibll.2H.il\3q.
0.(00)(0.000)(0.(00)(0.000)(0.000'
2005 790.111&1.j8~.111.2l\50.
0.(00)«0.0(0)(0.000)(0.0(0)(0.0(0)
20iO 810.lOli.20&.102.lQIlQ.
0.(1)0)(0.0(0)(°.(00)(0.(00)(0.(00)
.-.-.-~o VI c Ire .(Ie -0 -0 O'C
1/'10 ::r c>,..0 00 1\1 0 0'0
_C'
II)<:>~Cl .(I C "'0 ,..0 "'0 lI)C
~<I}..,····..0 0 0 0 e:.C'0.-
0
~
......-,..........
VIC ...C'-=00 co ....C 0'0co0'0 40 COO COO ,...0 ,...0 ,...0
w II)0 ...0 e:-o c>0 0x· ··· ···IW c>0 c>c>c>c 0
~.....
lL.
:J
0
~.
II)
w Cf)CD .-...................-x w ..co ~o ....0 co ~=::r 0 til 0
U Z ~irQ 1\1 c>1\1 0 ",,=",,0 .,.0 .,.c>z ..0 0'0 0 0 0 0 Cl Ccrr::r ••··••·""'"'"'U Ill:e:-o 0 =0 c =co c -l&I
0':>c ~-~-"'c:I III
~Z IE 0
1\1 -IaI ::r ~,"'Cf)-.0 :J c
I ~IU•::r a:
0 0 ...........-->-00 ....=~c 11;.0 00 VIC ClltC
Ill:....I !\Ie ....0 '"<:>~o ~Cl <El Q <l}Cl -c -...Cl 11:'0 ~Cl ~e ."c 0 ~o....::r ...,•'"•·••·•w ..0 Q 0 0 0 0 c
U \0.
llQ --t:I -'I""'":>~..:r ''¥pi
IE
0
0•>-.-...............•....l ...=C 0 0'0 a 0 VI co U'\c ~o "'"''"-11'10 _0 _0 .,.0 4Q CIlO oe
~::r .(Ie:._c -=-=-c c="'=:r .....,····...0 Q C C 0 C 0
~
....I ~.
c:t:I
W Zx-en
c-1"""1>a:::..z IE •0 10 0 '"<:>VI 0
laO ...•co lID 0-0-0 0 -U IU •0'0'0'0'.Cl c><:>
II)>-•""1\1 1\1 -.
C.34
,~--~'>C-'J -1 ]'I 1 1 )1 1 ~-]1 1 I 1
8CE"ARIO,HED I ~E3·.00R AVG SCE"ARIO·-b/ZQ/IQ8J
'UEL PRICE FO~ECAST9 EHPLOYfO
ELECTRICITY ($I KWH)
ANCI-fORAGE ..COOK INLET GPEUER FAIRBANKS..~..•...•....••.~-...~...._~.__..-.-..-•.•...•..~.~.-.-.-~-.
n
w
t1l
YEAR RESIDENTIAL AU5 Itl!9!1 RESIDENTIAL RIJSIN!"!S•......•....•.......••....•.•.•.......·.411 ___
1980 0.11]1 o.o]a 0.095 O.OqO
n85 0.0118 0.0115 0.090 0.085
19 9 0 0.051 O.OQ8 0.090 0.08'
1995 0.051.1 o."'H 0.090 0.06"5
2000 0.057 o.ll5b 0.090 0.085
lO05 O.Obl 0.058 0.092 0.08J
2010 O.Ob]O.Obo 0.095 n.090
SCENARJO.MEO I HEJ ••OUP AVO 8CENARIO ••h/2~/lq8J
FUEL PRICE FORECASTS E~PlOY£D
NATURAL GAS (S/HM8TU)
ANCHQRAGE •COOK INLET..~.._--....•••.•...._-.-.--GRfATER FAIRRANKS__W·••.~."_M ••.••••_~~._._.__._._~
n
(.oJ
m
YEAR RESIOENTI Al RliSINf58 RESIDENTIAL BUSlNES~.....•...•..............--.._......--_.--.--..----_.
1980 1.730 I.SOO 12.740 II.l90
IQ8S I."bn t.nn 9.810 9.300
1990 l.lI 0 2.4RO 9.100 8.:uo
1995 3.no 3.020 1O.]1/)8.920
2000 3.1110 3.180 11.220 9.110
lOOS J.500 3~HO 11.UO 10.520
2010 J.lin 1.11$10 12.770 11.320
/
.J J j ,))..1 }J J il J )J !•J J
'J 1 1 i ')1 }-hI 1 1 J
SCENARIO.MEO.HEl·.oO~AVG SCEHARIO.·bllq/I981
ANCHORAGE •COOK INLET
FUEL PRICE 'ORECAST!EMPLOYED
FUEL OIL (S/MMBTU)
GREATER FAIRBANKS
R •••••••••~•••_•••••••••••••••••••_•••..••.•..•.................••.•...•.•
YI!:AR RESIOENT IAl HUS I HE sa RESIDENTIAL BUSINESS....................•....•••.....•••..•--............
&980 7.750 7.l00 7.810 1.~00
n 1985 ".970 15.4ll)b.Olll 15.100.
w.......1990 S.91f'l 5.190 11.000 5.lt10
1995 b.]IO 5.7b0 b.]10 b.1I40
i!ooo b.830 ~.i!80 1t.890 b.5bO
lOOS 1.290 b.1110 7.]bO 1.1110
21110 7.180 1.llo 7.1350 7.520
_J J J .~.~J )~I .~J J ]J ),J }J J
1 "1 1 --~l 1 1 I ..._)1 J
SCENARIO'/'lED I HE1·.OhR AVO SCE~ARIO ••6/2q/1981
RESIDENTIAL USE Pf.~HUUSEHOLO (KWH)
(WITHOUT ADJUSTMENT FUR PRICE)
nREA'!~FAIRBANKS
.~......-.•.••....•.••
SMlll lARGE SPACE
HAR APPll ANCFS APPLIANCES HEAT TOJ,\L..••..........................•-............
t980 j!/J6f-.00 l5739.S?H Il.bh 1I51Q.18
0.1I00)(0.0(0)(0.000)(0.000)
1985 2'5J&.00 UAI.3/J lSQJ.90 123It.2'3
n (O.lI OO)«0.000)(0.(00)(0.001))
ww 1990 2&0&.00 &1.140.61 3Il119.117 1281)5.29
0.(00)(0.(00)(0.0(0)(0.0(0)
1995 2bH.Ot &&5&.15 1I088.It 13420.27
1).0(\0)(0.0(0)(0.0(0)«0.000)
2000 2711&.00 &193.05 11320.70 1]859.15
0.0(0)(0.0(0)(0.01)0)(0.000)
2005 2(\16.(10 b8'H.Sb 11507.50 tUI77.Db
0.000)C 0.(00)r 0.000)(0.000)
20tO 21\06.00 &8 9 1.Jb IIbSb.'H 11I1I!/'.31
0.1'100)(0.0(0)(0.000)(0.000)
n
.p
a
SCENARIO,MED I HE3 ••00R AV~SC£NARIO.·b/2q/t~81
RUSINESS USE PER EHPLOVEE (KWH)
(WITHOUT LARGE INDUSTRIAL)
(WITHOUT ADJUSTMENT fOR PRICE)
YEAR ANCHORAGE •COOK INLET GREATEP FAtPBANKS..........••.•...•.•.~.......*......-•••~•••••••••-
1~80 l\IJ 07.0tl 7t1QS.711
0.0(0)(0.(00)
1985 9518.18 191J1.43
0.000)(0.0(0)
1990 10089.60 8Z/lQ.l/1
0.00(1)(0.1)00)
1995 IObO/l.9a 8558.6/1
0.(00)«0.0(0)
2000 Ilili .414 861/1.75
0.000)«O.OOU\
2005 11850.11 Q221 .q~
0.000)(0.0(0)
2010 Ii!b75.U qb28.1l
0.000)«(I.OOU)
,t •J ),l ,~J J l ),•,.J J )J I
1 '··-1 )1 )'1 1 '.'}1 )"a ~t ~'J .,
1 1
SCENAIHO.HEll •HE3--00R AVG SCE~ARI0--6/~q'I961
SUMMARY OF PRICE EFFECTS AND PROGRAMA'TC CONSERVATION
IN GWH
ANCHORAGE •COO~INLET
RU IIJENTI AL IHJSINf!lS..................._~......
OW/I-Pill CE PROGR AM ..I NDUCED CROSS-PRrt[O"IN-PIlI CE PROGR All-I NDUCED t::ROSS·PRTa
YEAR REOUCTlQN CoNSERVA TI or~REDUCTION REI'lVC TI ON eONH~VATI~N REOUcTION.................................................................................................................................................
1980 0.1100 0.000 0.000 0.000 0.000 0.000
1981 6.120 0.1)00 .0.115 9.III 0.000 1.002
1982 12.240 0.000 ..(1.]50 IlI.U7 0.000 ~.005
198]18.160 0.000 .0.52"27.]40 0.000 1.1)07
14811 211.1180 0.000 -0.h94 :\6.11 51 0.00/1 tl.009
1985 ]0.'i9 4 0.000 .0.R7 11 115."'66 0.000 Ii.0 II
196b 1h.711'i 0.000 -6.195 15/1./l139 1).000 1.581
1987 112.1190 0.1100 ·11.512 b].lIl1 0.000 2.I'i1l
19118 119.055 0.000 -Ih.'lll 72.33/1 0.000 0.720
1989 S'i.leo 11.000 -22.150 1'.1.257 1).000 -0.711
1990 bl.]25 0.000 -27.1111 9 9/1.179 0.000 -2.IUn
-Po 1991 118.1109 11.1100 -H.7t1 11 tltI.7811 0.000 -5.000
I--'1992 lh.292 11.000 -1I11.IltI 109.189 0.000 ·1.'158
1''19]8J.776 0.(1)0 --52.11411 1I8.Q911 0.000 -10.717
19911 .tll.21111 0.1100 -60.769 128.'599 (1.000 -11.')75
1995 98.711]0.000 -69.0911 1)11.20/1 0.000 -Ih."]"
199h loe.8 lH 0.1100 -1 9 .056 151.908 ..0.000 -ltI.no
19 9 7 11 8 •9 51 0.000 -1l9.1117 Ih'i.611 0.0011 -21.026
Itl9a 129.055 0.0(1)..98.978 119.11 11 0.000 -2f••122
19 9 9 1]9.151)0./11)0 -10".9J9 191.017 0.00/\-29.hlll
2000 1119.26]0.(100 -1113.901 106.720 0.000 -]2.91/1
2001 1111.975 0.000 -1]O.Olll.221.422 0.000 -]l..866
ZOOI!11lI.b~7 0.0'>0 -1111.111 1'16.125 tl.ooo -40.8IQ
200]11l7.H8 0.000 -152.211 250~~27 0.00"-IIII,77~
20011 200.11/1 o.oon -ll.\.l22 2hl).'529 0.000 -IIFl.721l
I!OO$ZIi',l\22 0.000 -17".1127 ~AO.211 o.oon -';?671
2tl06 2Z 9 .02 11 0.000 -189.201 298,900 0.000 -57./196
2001 2/15.226 0.000 -2 1)1.975 117.';6'1 O.OOQ -61.116
2008 hl.'~2e 0.1100 -218.71l9 ]31..'-3'1 0.000 -loll.H5
2009 U7.flll 0~1I00 -~]].r;21 ''iIl.QOIl o.oon -71.5511
2010 2 Q ].Fl]]1I.001l -21113.2Qh J71."77 0.0.00 -7'1.774
SCENUUOI MEn I HE]••nOR AVG 8CENARrO ••bI211/196]
SUMMARY OF PRrCE EFFECTS AND PROGHAMiTrc CON8ERVATTO~
iN GwH
GREATER fAIRBANKS
~E8JOEIITIAL A.UUN[SS..,........................
OWN.PRICE PROGRAM.INDUCED CROSS·PRICE IIWN~PAICf PROGRAM.PJOIICFD CROSS-PRIC:£
'fEAR REDUCTION CUNSFRV~Tlorl REDUCTION REDUCTION C:ON~~~v~!~nN ._.REOUCTION
••••.................................................................................................................
1980 0.000 (1.1)00 0.000 0.000 (1.000 o.oon
19111 ~0.20b 0.1100 1.')&1 .0.119]0.000 0.12~
1982 .0.531 0.000 2.121 .(I.~86 0.000 I.IIS7
198]·0.797 0.000 ].1 S2 .1.1179 0.000 2.18b
19811 ·1.Ob1 0.0l)0 lI.au ..1.~72 0.0011 2.9111
19B5 ·1.J29 11.000 5.]011 .~.1I1l11j 0.000 J.bll]
USf,·1.1500 0.000 b.21111 ..1.110'5 o.oon 1I.15.11
198T ..I.HI 1).000 .,•18'5 ..].III~0.000 II.Ub
1988 .2.1122 0.0011 8.125 ..1.1185 n.ooo 5.1711
\989 "2.253 0.000 9.0bb .'.82b 0.000 ~.b90
1990 "2.11811 0.000 iG.OOb .1I.lbh 11.000 b.~O?
n.
1991 .."."]5 b.18$.po ..2.fo85 0.000 10.11011 0.000
N 19'12 .~.88b o.noo IO.Claa ..1.1.7011 0.000 b.'in
1993 ..3.0&7 0.000 11.380 .1I.97i!0.000 b.750
IIl911 .J.(llIq 0.001)11.11]8 ..'i.ii!1I I o.oon b.IIU
\995 .].4'10 (1.000 lii!•'Hlb .5.510 0.000 ,.115
1990 ..3.b38 0.000 li!.llb ..11.9711 0.00(1 b.211';
1997 .].187 0.000 1l.9]7 ..1I.lIl1b 0.000 !i.l7S
\998 ..].9]&0.0011 II •'5 7 ..1.915 0.11(10 11.505
1999 ·1I.MIli 0.000 11.578 .J.J83 0.0011 !.6]5
2000 -1I.2B o.oon 11.198 ..2.851 (I.oon 2.7&5
2001 ..11.175 11.0011 10.8911 -].33'5 0.000 1.050
2002 ..11.111 0.(1)0 10.382 ..].'H9 o.oon 3.]]5
200)..11.°5 9 n.ooo 9.1175 _II.]O~0.0(10 1.b19
20011 ..11.000 n.oOIl 9.161 .1I.18b 0.000 J.9011
200S ..J.9112 O.(100 ~.A5q _5.270 0.000 lI.ta9
200b .1.&23 0.111111 S.Obll ..1I.8111 o.oon 3.799
2007 -).305 11.01)0 1.~6q ..1I.1l11 0.0011 3.110/1
2008 ..2.98b 0.000 b.1I7].3.911'-0.000 J.018
2009 ..2.lIb7 n.ooo 5.&78 -3.'iS:?0.000 i!.&28
2010 ·2.]IIR 1I.00 n 11.8111 ..].12J 0.000 2.2]8
)I J }J J J .,J J I ..J I ,I -J J )~
J 1 -,-j '1 )·l )l I 1 I 1 I 1•
SCENARIO'I-4EO I HE1··nOR Ava SCENARIO··b/~U/1901
BREAKDOWN O~ELECTRIC lTV REQUIREMENTS tG"fH)
(TOUL lllCLlIDES LARIlE ItHlUSTRUL CONSUMPTION)
ANCHORA~E •COOK I~LET....-.....~--~..-_....
MEDIUM RANGE (PR_.5)..•......•..•.••....
RESIDENTJ AL HilS HIE S B HIllCELLANEOUS [ltOG.INDUSTRIAL
YEAR REQUIREtlENTS REQUIREMENTS REDUIREMENTS LoAn TOTAL•..•••..•........•...•....•....~_...~_.._..•••......•.._....~..-.-.-.....-....--..........
1980 979.51 875.1b 211.31 811.110 19"3.1'1
1981 1017.711 9110.811 211.5b 92.08 <'1175.21
1982 1055.Q'!j 100b.B 211.112 11l0.lb 2187.C!/)
198]10911.17 1071.81 25.08 1011.211 2299.30
19811 1132.38 11]'7.29 25.311 Ilb.12 21111.H
1985 1170.59 U02.TO 25.bO 121l.UO <'523.37
1986 1192.97 1i!32.TC!2b.IS I J1 •89 2589.n
1987 ta15.3U 12U.b5 2b.71 151.38 USb.09
1988 1237.n 1292.59 27.21 Ibll.88 2U2.U5
1989 1260.09 1322.53 27.83 t111.37 27118.81
n 1990 1282.117 1152.U6 211.31'19 1.86 PA'i5.17.
-Pow 1991 1l02.97 I J79.'57 28.89 195.13 290b.55
1992 I Hl.1l7 IlI0b.be 2 ct •]Q 198.110 2951.'n
1993 11U3.q 7 111]].71\29.89 i!OI.bb 301)9.31
19911 Ilbll.U7 IlIbO.8Q 30.110 20U.H ~Obl).6q
1995 111411.911 IU87.99 30.90 208.20 :J112.07
ICJ9b IIlOB.59 1!518.20 31.1111 211l.11l 3I12.111
1997 t1lJa.21 151111.1lC!12.05 220.08 3212.16
1998 11155.1'\2 l!ile.bl 32.U 226.02 3293.10
1999 11I19.UU IbOIl.AII H.20 <,It.qb H'53.Il~
2000 150].01)16H.Ob H.711 2]7.qO 3UI3.7Q
2001 1'H2.\7 U81.35 lU.511 2 I1 U.Qb llllJS,Oi!
2002 ISbl.29 17i7.bq 35.31)252.02 351b.2 1•
2001 15~0.uO 1171.9]H.Ob 25lJ.08 '(lS7.1l7
20011 IbI9.Si'IBlb.22 1b.83 2bt..til HJ8.10
2005 Ibllij.6J 18bO.51 37.SlJ ?13.20 JIIllJ.Q1
200b Ib8b.lJO IQ2',.IS 38.bS i'81.56 J911.30
2007 1125.1'7 IQ81.7Q 39.17 ?1l9.qb uoa2.b~
21)08 11b3.1I]2051.113 lIo.ab jllJA.JIl uI511,06
2009 180t.71l 2115.0b 1I1.q5 J06.72 112bS.U3
2010 ,allJ.'H 217~.70 Ill.011 :H5.IO ll17b.81
SCENARIO,MEO,HE3 ••00R .VG &CENARIO ••b/2Q/14ilS3
BREA~DOWN UF ELECTRICITY REQUIR!MENTS (GWH)
(TOTAL INCLUDES LARGE INDUSTRIAL CONSUMPTION)
GREATER FAIRBANKS.•..•.•..••...••.~....
~EOIUM RANGE CPR_.S).........~.•.•..••..
RESIDENTIAL.RU6JNE8&MISCELLANEOUS UDa.INDUSTRIAL
YEAR REQIJIREMfNT&REQUIREMENTS REQUIREMENTS LOAD TOTAL••••••..•......•..••••a ••••a •••••••••••..•.•.........•._.-.~.......•••••_••••~•••••••••••w
1980 17b,39 217.\11 10.78 o.no 1100.31
1981 190,01 i!j!R.93 b.7iI 0,/1(1 1125.b8
1982 2(lJ,bi'2 If(l,71 b.70 0,00 IISI.OIJ
198]217,211 25l.S0 b.bb (1.00 il7b.Il(l
19811 iBO.8';2bll.2'1 b.b2 '1.00 501 •.,.,
1985 211/.1,117 27fl.Oll b.58 0.00 527.U
1911b 25l1,ll 281.U 6.56 10.00 552.]1
1987 2U.AO 287.21 e..53 20.00 sn.bll
1988 an.lI7 292.80 6.51 111.00 b02.81l
I 989 2113.111 298.115 6.Q9 40.00 b28.01
n \990 292.80 10ll.04 6.4fa 50.00 be;3.30
.j::o.
~lUI lOl.i!7 110.23 b.bll 50./10 b70.111
1992 313.74 3U.U 0.81 50.00 686.98
199)U/.l.21 liI.fal 11.99 50.00 70].82
I 991.1 ]34."8 121\.110 7.17 50.00 nO.6~
1995 1115.Ie;!U.OO 7.]0 50.00 737.119
19911 35].53 3ltl.7Il 7.50 50.00 752.81
1997 361.91 l/.1ll.So 7.ofa 50.00 768.12
1998 370.~O :JSS.B 7.82 50.00 783.110
,19 9 9 ]71j.b7 lbi!.tI 7.97 50.00 798.7t.
i!OOO 387.05 ]08.119 6.13 50.00 814.01
200l 39b.lt"377.71 6.)0 50.00 832.1,j9
2002 1I0S.92 18t1.S2 8,117 50.00 SOJO.91
2003 IU 5.35 395.311 8.bll 50.00 8~9.n
20011 11;»11.7"000.15 11.61 50.00 88"".71)
2005 11111.22 1112.97 8.98 '50./10 906.16
i!00~/f1l5.52 11211.75 9.20 50.00 929,51
iOO7 115b.83 1136.5\9.51 50.00 9'U,U
ilO08 Q66.1J 1148.31 9.71 50.00 970.i I
2009 117Q.1l/l 1160.08 10.01 50.00 999.56
20'0 q~O.7q !I11.8b 10.]0 50.00 1022.90
~l t 1 )J I ]
••
j J ):1 J ..~~..J .J )J J
-J ))))-..)1 1 1l..,•
SCENARIO,MEO.HE3-.nup Jvn SCENARIO·-bI211/1983
TOTAL ELECTRICITY REQUIREHENTS (GWH)
(NET OF CONSERVATION)
flNCLUOEB LAPGE IHOUSTRIAL CONSUMPTION)
~IE01lJM RANGE CPR ••5)
n
-Po
U1
YEAR ANCHORAGE.tOOK INLET GPEATER fAIRKANKS TOTAL
••••••••••••••••P •••••..P •...-•.__..•..~............-..-~....._.-.
1980 ''1fJ].I'I 1100.31 .?]f.3.51
1981 lO75.lJ 1125.b8 251)0~""
1982 ZU7.21:1 1J51.011 2b]R'.30
1983 22 9 ".30 /17&.110 ~715~70
19811 ZIIII.n 'i01.17 2'HJ.IO
1985 2Si!3.37 521.13 '\051)'.5 0
198b ~589.7J 552.37 31112~10
1987 2f/5b.0 9 !l71.ltO J2H.b9
1'188 2722.1I!;602.84 Hi!!i~29
1989 271lB.81 b28.01 )1I1b~8R
1'190 2855.17 b'i].10 ]50R~q8
19'11 290b.55 #)70.111 1,,7b'.b9
1992 2957.91 b8b.ge lbll l l,91
1993 30 0 9.31 701.82 3711.11
19 9 q ]ObO.bll 721).lt5 l1AI~311
!'I9S )112.01 731.1111 lRq9~5b
IC,9b ]172.111 752.81 H25~i!i!
1997 HH.7b 1&11.12 1I000.AB
1'198 3293.10 781.1111 IIOlb~5IJ
1999 ]]'5].1111 798.1b 1I1!12.20
2000 31113.79 AIII.07 11221~8b
2001 3119'5.02 1'32.119 4327.51
2002 15U.211 850.91 11/127.15
200]lb51.117 1If/9.U /lo;~b.an
20011 37]8.70 881.75 4b'.,,'.1111
2005 3619.93 'lOb.lb 1I72b.O'l
200b 3'111.30 n9.51 lI/1bO.1I1
2007 110'12.bl!9S2.8b IIQ95.511
2008 111511.0b q7b.21 'H30.2b
200'}112"5.111 Q9'l.5b 5?f,1I.9'l
2010 1.137b.81 1/122.90 o;lqq'.71
1
n
+::0
0"1
)
SCENARIOI ~EO I HE1--00R AVa SCEHARIO ••b/24/198)
PEAK ELECTRIC REQUIREMENTS (HW)
(NET UF CONSERVATION)
(INClIJOES LARrof.INDUSTRIAL DEMANO)
HEDIU~RANGE CPR ••51..-....._.....•..•..••
YEAR ANCHORAGE •COOK l~lET GREATFR ~AIRBANKS TOTAL•.••.•.....•............••.•...•..•.......••_.••.••••.....•.••..-.
1980 )9b.51 91./.10 1187~90
1981 419.U 'H .I'J 5U~J2
1982 4111.75 102.98 5 11 4,11
198)lIU.37 108.77 lin~11I
19811 116b.99 tlll.56 bOl.51J
1985 51)9.bl 120.35 629'.91
USb 521.80 12&.11 649~91
1987 537 .99 131.87 &69.85
1988 552.11 IH.U 6e9~80
1989 S6b.J6 1111.)8 109.711
1990 5 8 0.511 149.til 729~b8
1991 590.90 152.98 7113~911
1'~92 bO 1.37 156.8J 158,20
199)bll.19 lbO.U 172~4b
199/j b22.20 lillI.52 1116.72
1995 b32.bZ I bll.36 800~98
199b 0 4 /1.711 171.8b 81b~60
1997 &5&.87 IU.1b 1112.21
1998 bb8.99 178.85 8111~8lI
1999 b~1.It 182.15 86].lIb
2000 b Q1.211 185.85 an'.08
1001 109.&1 190.05 899~U
2002 725.98 1911.26 920.211
2003 1112.35 198.lIb 9/.10.81
20011 7lifl.13 202.U 9bl.39
2005 175.10 20&.81 981 ~'H
200b 191.00 212.20 101l9~80
2007 820.09 217.53 1031,&1
2008 B1J2.59 222.8b 1065.115
2009 111>5.09 ::!28.19 IOcU~2~
2010 881.59 2n.52 IUI'.lI
•,)J J •J j .~,t "J J ,J i J t ))
r
-
.....I
HE9--DOR 50%
C.47
I
,~
t ),---
J )"i 1 '1 1 )'1 -.))")1 ")t•"
SCENARIO,HEO ,~E9.-000R SOX.-~/lq/l'81
HOUSEHOLDS SERVED
ANC~ORAGE •COO~INLET-_.......-..--~...~-..
YEAR SINGLE IF AHllY HUl TI FUti LY HORllE HoHES OUPlElCU TOTAL....-..-.-..................--....................---_.-................
1980 )5471.lO)I".8i30.lU6.71503.
0.000)(0.000)(0.000)(0.000)(0.000)
Iq85 45b65.lbiO".1065'.1:15&1.cn:H';.
0./100)(0.000)(0.001l)(0.000)(0.000)
n 1990 550]'3.25817.Ilbbl.811&0.1020]6.
+:>(0.(00)(0.(00)(0.000)(0.000)(0.000)
I.D
IH5 SQQU1.lh890.1378Q.83B.to8 Q SQ.
0.000)(0.000)(0.000)(0.000)(0.000)
loOO &II'U I •lCHS5.IIIQIO.8181.111163.
0.000)(0.00(1)(0.000)(0.000)(O~OOO)
lOOS 69514.JHbl.IU95.ROlli.127255.
0.000)(0.000)(0.000)(0.0(0)(0.000)
iOIO 7&lf.lO.HOI?.180Ti.8845.'''028A.
n.OOO)(0.0(0)(0.000)(0.(00)(0./1011)
SCEN.RIO."'E.O I HE9-_DOnR 50X--6/24/1Qa]
HOUSEHOLOS SERVED
GREATER F.IRRANKS......••...........•..
YEAR SltlGLE FAMIlV MUlTlFAMIlV HOBllE HOMES DUPLEXES TOT.L...................•••••••••••••........................•...•...•..•....
1980 1220.5287.II~Q.lbP.151lJ.
0.00 0 )(0.000)(0.000)(0.000)(0.000)
1985 10646.SUfi.21l0.1721.lO1815.n (0.000)(0.000)(0.000)(0.000)(0.1)00)
U1a ICJ90 11)125.7 Q1l0.2101.2175.Bib]'.
0.000)(0.000)(0.000)(0.000)(0.000)
1995 12Q80.78111.2571.213CJ.251H.
0.000)(o.oon)(0.000)(0.000)(0.000)
2000 Pllall •7103.11911.2298.2!~20.
0.000)(0.000)(0.000)(0.000)C 0.000)
2005 t620b.7511Q.:1808.2252.iCJal5.
0.000)(0.000)(0.000)(0.000)(0.000)
2010 t7773.8b61.112i!].2109.32181.1.
0.000)(0.(00)(0.000)(0.000)(0.000)
.~)••.).,..~,ii J ••J )J )
--
J J ,I ),I
)--J I ))'}'1 1 -1 )''I J )~!1..
BCEN~RIOI MEO I HEq.·OllOR sox·.e.1l1l/1981
HOIJSINQ VACANCIES
ANCHORAGE ··COOI<INLET.._.-.....~...~.......
YEAR SINGLE '~HllY HUL TJf A'1ILY HOULE HOMES OlJPL[)(ES TOTAL......•...•••......................••.......•.............................
19110 50B9.1bf>b.Iqq1.IlIb].16209.
0.(100)«0.000)(0.(00)(0.000)(0.(00)
1985 50).,IIQb.120.20:,2,211111.
n (o.nOO)(0.00(1)«1'1.000)«0.000)«0 .•0(0)
U1.....IQqO b05.11171.119 •28Q.2'i10.
0.1100)(0.0(0)«0.000)(0.000)«0.001'1)
IQ95 t1Sq.'iQ.152.2811.IlllQ.
0.0(0)«0.(00)(0.000)«0.000)«0.000)
lOOO 101.1b01.1611.219,215~.
11.(01))(0.(00)«0.000)(0.000)(11.0011)-
200'5 1b'l.1802.179.2711.]020.
0.000)«0.000)«0.000)«0.000)(0.0011)
1OIO B41).1999.Iqq.aqtl.H2Q.
0.000)«0.0(0)(0.000)(0.000)«o.oon)
SCENARIO'MEO •HE9·.000R SOX ••6J24/198!
HOUSING VACANCIES
QREATER FAIRBANKS..~...........~.......
YfAR SINGLE FAMILY MULTIfAMILY HOULE HOHn OUPLElCES TOTAL
••••..............•••••••••••••...~...•....................................
1980 US3.Hao.986.89S.88SII.
o.noo)«0.(00)(0.0(0)(0.000)(0.1'100)
1985 li8.2A33.24.7bll.'741.
0.0(0)(0.000)«f).OOO)«0.000)«o.oon)
n 1990 11 R•454.2].81..677 •.
U1 (o.noo)(o.noo)«0.000)(0.000)(0.(00)
N
1995 143.1141\.28.80.bq9.
0.000)(0.(100)C 0.000)(0.000)(0.(00)
1000 158.1140.l!5.78.·Ht.
0.(00)(0.0(0)(0.(00)«0.000)(0.000)
2005 178.IIll.42.77.·728.
0.(00)(O.DOO)«n.oOO)«0.000)(0.000)
2010 19&.IIb 9 •116.Ib7.·B78.
0.000)(0.0(0)«0.000)(0.000)(o.noo)
})•J J cJ )of ,J J -~J ~.J ,J J ]I
',I )'"'l -'1 J "J )~I '('j )-1 ~-~
I ll
SCENARIO.HEO I HEq ••OOOR 50X.·bI2UJlq81
FUEL PRtCE FORECASTS EMPLOYED
ELECTRICITY ($I ~WH)
ANC~ORAOE •tOOK INLET GREATER fAIRBANKS•...•........•.•.••••.•........•~....~•.....•.•.._.....-...•..••...__.....
YE:AR RE810ENy UL BUS I NE 55 RE8IOENTlAl RIISINfSS
.~..•...•....•••••!W •••••••.............~.-......_-
Iq80 O.OH O.OH 0.095 O.Oqll
(J 1985 0.0118 0.0115 0.OCJ5 o.Oqf).
mw Iqqo o.ollq 0.Oll6 0.0"0 0.081j
Iqq5 0.050 0.I)Q1 0.090 0.085
2000 0.051 0.Oll8 0.090 0.08'5
2005 0.051 0.0/18 O.OqO 0.085
2010 ".1151 o.olle O.OqO 0.08111
SCENARIO,~EO,HF.9·.000R 50X.·~/lll/lq8J
ANCHORAGE •COUK INLET
FUEL PRtC!FORECASTS EMPLOYED
NATURAL DAS (S/MMBTU)
GREATER FAIRBANKS.............•...................•.••...•..•..•....•._...........•~.
YEAR REUOENTUL BUSINESS RESIDENTIAL BUSINESS......................................................
1980 1.730 1.500 12.140 1 t .i90
0 19A5 2.00ll 1.770 10.UO ".210.
Ul
~
"'.6110lQ90a.fllo 1.1100 9.090
tQ9S 2.1\10 l.~60 8.110 ".610
2000 2.710 1.lIe/)?flU 6.210
2005 2.UO i.UOO 1.210 5.820
1010 1.l5bO 2.no e..890 5.1140
))}),..J J ..1 I •J •J J .J I
,
\
1 1 '1 1 )"}i 1 )I 1
SCENARIO,~ED,HEq··DOOR 50'••~/211/Iq81
ANCHORAGE·tOOK INLET
FUEL PRICE FORECASTS EMPLOYED
'UEL OIL ('/MMBTUJ
GREATER FAIRRANKS..........~-~...•••.•..•.......•••-......•...__....
YEAR RESIOENTlAL BUSINESS RESIDENTIAL BUSINESS_........-•.......•.•....•..........-....--.......
1980 1.75(\7.200 1.830 7.501\
PUS fJ.lIql)5.QtI(\b.550 b.l20
n 19QO 5.510 lI.q80 5.5t10 5.2bO
U"1
1I.tltlO ".6bOU"1 IQQ5 lI.tlSO 11.1100
2000 lI.btlO 1I.ltO 11.710 1l.1AO
iOOS 11.030 1.880 O.lIbO Il.no
2010 11.200 :'.650 11.2110 ].qIO
SCENARIO'I'4EO ,HfQ··POOR 50~··6/2qJI'8]
RESIDENTIAL USE PER HQUS[HOLO (KWH)
(WITHOUT ADJUSTMENT ,OA PRICE)
ANCHORAGE •COO~INLET........~.......-.~...
SMALL LARGE,SPACE
VEAR APPLI ANtES APPLIANCES HEAr TOTAL_.-........•...................•.••...........
1980 2110,00 UOO'.tIl 5088.52 Ilb''''.15
0.000)(0;000)(0.000)(0.000)
t985 2IbO.OO 6154.fIIl 4"31,62 131116.27
0.(00)«0.'000 )(0.000)(0.000)
n.1990 i!21O,00 b026.77 4b27.fl2 128"11.60U1
O'l «0.000)(0.'000)f 0.000)(0.000)
1995 22bO.OO 59ge~41 4509.]9 12727.87
0.000)(0.000)(0.000)(0.000)
2000 2:\1.,.00 51)88.15 44]6.117 127]1.1.bt
0.00(1)«O.UOO)(0.000)(0.000)
20015 ilbO.OO 6060.911 4 11 21.117 128~2.40
o.oOtl)(0.000)(0.000)(0.000)
2010 2 11 10.00 6127.51 4113'.13 12'76.70
0.(00)(0.'(00)(0.000)(0.000)
).))J .J l -I ))}J
C.57
....•••••...~..-.ANCHURAGE •COOK INLET._..__.~.YEAR....
SCENARIO'MED I HEq--OOOR SOX.·6/ZQ/IQ83
BlISlNESS USE PER EMPLOYEE (KWH)
(WITHOUT LARGE INDUSTRIAL)
(WITHOUT ADJUST~ENT 'OR PRICE)
GREATER FAIRBANKS
n
Ulco
1980
1985
19QO
1995
2000
2005
2010
8t1UJ~04
0.000)
951 Q .9b
0.000)
10059.bCl
0.000)
lOCl8l.bO
0.000)
11021.1.92
f 0.000)
IlbllO.U
0.000)
12C183.97
1).000)
1C1 9 5.10
0.000)
791.17.93
0.000)
el37.21
0.000)
8515.05
0.000)
8822.88
0.000)
91b9.82
0.000)
95btl.4"
0.000)
}~J )J J J .,)J J .~J J •I
'I 1 ))~1 I !'l l )1 1 ~l 't I
SCENARIOI MED I HE9--~OOR 50X--bl?1I/1983
SUMMARV OF PRICE EFfECTS AND PROGRAMATIC CONS£RVATrON
IN GWH
ANCHORAGE -COOK INLET
RESlOEtHIAL RUSINEU............................,.
OIlN-PRtCE PRaGIUM-I NOUCED CROSS-PRICE OW~I_PR ICE PRQt;RAtI-INOUCED CROSS·PRlr.E
VEAR REDIICTIO"!CONSERVATION REDUCTiON PEnUCTlON .~9NSERVAUQ~..REOUCTION......................................................................................................................................
1980 0.000 0.000 0.000 0.000 0.000 0.000
U81 O.tQ'5 0.000 -0.9611 fl.I ]9 0.000 {I.326
IflU 12.290 0.000 -1.928 18.271 Q.OOO 0.6H
198]18.113S 0.000 -2.892 27.11 16 0.000 o .flJq
Ifl811 211.1560 0.000 -].856 J6.5S11 0.000 1.306
19~5 311.725 o.oon -1I.tl20 1I~.6f11 0.000 1.632
lf1Ab ]5.683 o.oon -8.771 51'.1180 0.000 1.288
Iflft7 11/1.6111 0.000 -12.721 SfI.2bb 0.000 n.fllI3
Ifl88 115.5n 0.000 -16.672 66.051 0.000 0.59f1
Ifl89 50.557 0.000 -i!0.6i!3 72.1111(1 0.000 0.255
1990 5'5.515 0.000 -i!II.S7J 7(j.1l2'0.000 -0.090
n.Iflfll 59.IIIS 0.000 -i!J.1I10 AIl.flbO 0.000 0.223U1
0.0 1992 01.31 11 0.000 -)0.i!1I6 (j0.2f111 0.000 0.516
19f1]07.213 0.000 -H.on 9'5.627 0.000 0.11119
19911 11.113 0./100 -J5.91 9 ton.9bl 0.000 1.162
1995 7t;.012 0.00/1 -]B.n5 lO6.2911 n.ooo 1.1175
lQ90 18./1112 0.000 -]9.5119 112.088 0.000 l.5Jq
Iflfl7 61.871 0.000 -1I0.3l1]117.883 0.000 3.683
19f18 1\5.)00 0.000 -111.131 123.671 0.000 1I.78b
19f1f1 88.72 9 0.000 -111.931 129."71 0.000 !i.890
2000 92.15'1 0.000 -/l2.72§1l1l.2bS 0.000 6.flfl3
lOOI 9~.081 0.000 -IIZ.5110 1110.0 85 0.000 8.6b]
2002 96.0011 0.000 -112.355 11l6.705 0.000 10.332
iOO]100.fl27 0.000 -/12.170 152.lll6 0.000 1l.002
i!OOIi 103.650 0.000 -111.9(15 15/1.llIb o.noo 11.fl71
2005 106.7711 0.(100 -"I.800 Ib3./lbf>0.000 15.31l1
iOOb IOlJ.7ol1 0.000 ~1I1.008 170.710 0.000 17.7b7
2007 112.755 0.000 _110.<'15 17'.67 Cl 0.000 20.1911
2008 115.7116 0.000 -]lJ.lI21 HIII.~7I'1 0.000 22.621
2009 118.'31 0.000 ·38.631 I'H .1I112 0.000 2'i.01l1l
2010 121.72/1 0.000 ·37.638 19 R .1Bb 0.000 2',1I71l
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C.61
SCENARIO'MED I HE9·.nOOR 501-.6/24/1963
BREAKDOWN O~ELECTRICITY REQUIREMENTS (GWH)
(TOTAL INCLUDES LARG~II~OUSTRIAL CONSUMPTION)
GREATfR fAIR8ANKS................•.....
MEDIUM RANGE (PR_.S)..•.....•••.•••.•...
RESlDEtlTlAL 8IJSINfSS MISCELLANEOUS nOG.INDUSTRIAL
YEAR REQUIREMENTS REQUIR~t4ENT!I REQUIREMENTS LOAD TOTAL......~.••..•.•......•••...•..•...~..••.......•.•.••.•.••....•..-......~...•.............~
1980 17b.)9 llJ.11I b.18 0.00 1I00.)t
19111 190.0 9 Ull.10 0.14 0.00 1125.51
1982 21)).79 2110.2t!fJ .10 0.00 "'°.7"U81 217.1111 251.82 b.bb 0.00 475.96
19811 231.111 2U.]8 b.bl 0.00 !I01.18
1985 i:!1I1I.87 2H.9S b.51 0.00 5U:1 lt
198b 2S3.79 219.11 tI.53 10.00 550.09
1987 262.10 2811.159 0.49 20.00 513.19
1988 2U.U 289.111 b.46 30.00 1)97.119
1989 280.5/1 2911.U 6.42 110.00 621.18
n 1990 21\9.4115 299.05 b.38 50.00 b l lll'.88
0"1 1991 ~97.i7 102.90 b.SO 50.00 050.68N
1992 ]05.09 30b.75 b.b]50'.00 b68.117
199]lt2.cU 110.60 b.75 50.00 b1l0.26
1994 HO.71 3111.115 b.88 50.00 692.06
1995 H8.511 JlII.10 7.00 50.no 703.85
199t.J n4.40 32l.55 7:12 50.00 715~U
U97 )110.25 HS.llo 7.al 50.00 7h,2 11
1998 111b.IO HIl.OS 1.35 50.(\0 TU.50
1999 ]'51.95 U9.30 1.4b 50.00 7"8.71
2000 ]In .80 3411.55 7.58 50.00 759.91
2001 U4l.119 351.Ab 7.13 50.00 771l.07
2002 ]11.111 )59.17 7.87 50.00 788.22
200)H7.8b 'Ibb."A 8.02 50.00 80Z.37
20011 ]"11.5-;Hl.79 8.17 50.00 aU.5t
2005 ]91.Z11 381.10 8.31 50.no 810.6t1
200t.J ]99.1>5 ]9t.~1 8.52 50.00 8119.41\
2007 1108.05 1101.52 8.72 50.00 868.30
2008 IItb.lIb III I .7 q s.qi'5(1.00 887.12
2009 424.87 1I21 •Q5 9.U 50.00 QOS.911
ZOIO 4B.i'1!IIP.It>9.H 50.00 9211.7t.
~.Al •})J )..~)J )J J »)'I .~
'J }
-1 1 -~i -1 ~-))J I "}J '}1
S[~NARIO,MED.HfQ ••OOOR 50X ••6Jlll'IQ~)
TOTAL ELECTRICITY REQUIREMENTS (OWH)
'~ET OF CONSERVATION)
(INCLUDES LARGE INDUSTRIAL CONSUMPTION)
IlEDrUM RANGE CPR ••5)...•...•.--..•...•...•
n
(J)
w
YEAR ANCHORAGE •COOk INLET GREATER FAIRBANk!TOTAL....••.•........•...••....•..••..•..•..-........-.....•.....•~.--.
IQ80 1'''·3.lca 1100.31 i'Jbl~51
1981 i!1l7b.Tea 1125.53 ~5Iljf.32
IQ8i!1I QO.38 IISO.TIl 2111l1~1l
1!l8]23OJ.91l 1175.96 i!779~Q/J
198q .?1I17.51 501.18 29IB~75
1985 1511.17 Si!b.39 10S1~56
lca8b 2591l.b1 550.09 H/JIl~7b
1981 2b'5B.lb 571.H ~2Jl,q5
I 'HIS 2121.bb 597.1l9 HU,15
1989 1.785.U bll.18 )qOb.311
1!l90 lalla.bO;6QU.1l8 3119].5/1
Iq91 18811.79 bSb.b8 15111~117
19 Q2 l!l20.93 b1l4.1I 7 3589,39
19Q3 29S1.0b b80.2b lbJ7,32
Ica91l j!99].20 "92.06 161\5.25
1995 3029.H 103.1\5 31H~18
19Qb 3081l.50 1I5.0b 31!1q~57
1997 Jl39.b8 7lb.28 1865.96
1998 31911.115 731.SO 3932,11l
1999 3250.02 711 8 .11 3998.73
i!000 BOS.19 159.93 1I0b5~Ii!
i!001 H R2.I q 1711 .01 III %,1i!1
20\12 )115C/.08 188.22 112 111 ,]0
2003 )SJb.OJ 802.31 11]]8,19
200Q ]til 2.91 8H.51 1l1l2'J.1I1l
2005 3t>AlJ.9Z 1ll0.bb 115i'0~51l
i!OOb lH5.bll 1\1I9.Qa /JbIl5~1i!
2001 190 I.H 8b8.3(1 11]b!l.6b
2008 11007.08 AB1.1i!118!l1l~i!0
200</IIlli.60 'J05.!l1l 'i0IA.71l
lola IlZIIl.5i!</ZII.1b IjIUl.2 A
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n
0)
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J
SCENARIO.MEO.HEq ••~UOR SO'••&/211/1 9 83
PEAK ELECTRIC REQUIREMENTS CMW)
(NEr Of CONSERVATION)
(INCLUDES LARGE JNOUSTRIAL DEMANO)
MEDIUM RANGE lPR ••5).__....•...._.~.
YEAR ANCHORAGE •COOK INLET GREATER fAIR8ANKS TOTAL.......~.._.•..•......•••.••..•...•.•.••..•.....~....._••.•...••......
1980 39b.51 91.tl O 1187.90
1981 1119.115 97.15 5lb.bO
1982 lI t1 i.39 IU.91 5115'.30
198)IIbS.]]108.61 S7II~OO
19811 lII\8.ii!7 1111.112 b02.711
1985 511.21 1211.18 b31~]9
198b 5211.81 125.59 bSO~1I0
1987 5]8.41 131.00 b09.1I1
1988 552.UI Ub.1I0 b8/i.1I1
1989 5&S.bl 1111.81 707.112
1990 579.c!l 1117.22 72b~1I3
1991 Sllb.511 149.91 "3b~1Il
1992 S93.H 152.&0 111b ..110
199]bOI.09 155.30 71\&.]8
\l~94 b08.31l 151.99 1tI1j.37
1995 b15.b7 IbO.&8 77"~n
lnb b2b.H Ib5.2/1 78 'f.98
1997 bJ7.80 Ib5.80 I\IIJ.bO
1998 bI.l8.f16 lb8.3b 817~13
1999 &li9.91 110.92 1\10.85
2000 II 70.99 IH.U 81111.118
2001 b Ab.1I9 IH.7I 861~20
ZOOC!70 1.98 179.9"881~93
2003 117.lIe 183.11 900.b5
20011 732.97 18&.110 ."9~3e
ZOOS 748.117 IS9.&3 91e'.11l
200b 7&9.81 19 1.9.5 9b1~711
2007 791.15 1911.23 9119 ~37
200S 812.1111 &'02.52 1015.01
2009 H3.8O'Z/)b.82 101l0~bl.l
20 I0 855.lb 211.12 IOOb".2@
):1 ).J .1 --)I -J J I
-
HIO--DOR 30%
C.65
~I
-
J )I '!i ~~1 )-J 1 -.~
I 'J "I
SCENARIO'HED ,HIO-·OUR JO~··6/24/1~RJ
HOUSEHOLDS BERVED
ANCHORAGE •COOK INLET....~..--.-...~.~.-_..
YEAR SINGLE 'AMILY HULTlFA l lILY OiORILE HOMES DUPLEXES TOTAL..-....•......•••..............-_...--.....~-..---............................
1~80 )S4H.i!1l11/l.8210.1486.7l'50~.
0.0(0)(1).000)(1).000)(0.000)(0.0(11)
1~85 IISH8.ltli'OIt.10803.8'5t1'1'.qOIlSJ.n (0.000)(0.000)(0.(00)«0.(00)(0.(00)
0\.......1990 SH3S.lSR77 •'2287.8460.1)9958.
0.000)(0.(00)(0.0(0)«0.000)(0.0(0)
1995 5832~.is,,qJ.13 4 07.UH,1059Sb.
0.(00)(0.0(0)(0.000)(0.000)(0.000)
iOOO 625b'5.26717.'L1'505.8181,11'97'5.
0.000)(0.000)(0.000)(0.000)(0.000)
2005 b1 R9 O.H5bR.15906.1833.U~I 1)7.
0.000)(0.000)(0.000)(0.000)(0.(00)
20 to 7Qn 9 •1b272.1770'5.86&7.,n4n.
O.OUO)(0.0(0)(0.(00)(0.000)«0.000)
-
~\.""'.-....0 .....-
'""C ""0 .00 _0 -0 OC ICC
-0 =:1'0 lit e CO =:I'C>0'0 NO
'"0 00 11'0 til 0 ..ce C1'C 00
-I lit.·C ·nt .~0 .0 •CD.·N ·.._0 1\1 CO Ate NO NO NO ,",,0
~
C
~
-.;.........._......-......
"'0 ,""e ...0 0'0 CD C Ne _c-..-C>0'0 Cl'O lOt 0 0'0 lit 0 '"0l&oI ..a 0 ..co _0 lOt co NO NO 00
:II!-·-..N ..N ..N ·N ·t\I •I'!"'\iii 0 C 0 0 c 0 0
-Ia.
::Jc
0
IIJ en eft ..-.....-.-.............
:>~l&.I O'C OC-....0 00 ..aC cDC ..a 0c:z ~COO ""'0 0=-0 C'O ""'0 NO
l&.I <C C -0 _0 _0 =:I'CO 00 .oc::_0
fD II::t:-•N ·N ..N •"'"•..,·=:I'•lJ:0 C>0 0 0 0 c
III)-IU
Q <C -I
-I ...-0 III:r:cr 0
l&.I W ~en ......
::J ..•"'"C ILl •II::r:a:•Cl'0 •.-..........-..............•>-...c ..,0 ..,0 -0 1"'10 e-c ....0.....•-I lIlO ...0 =:1'0 ~o 00 ::2'0 ClQ
~-NO 11'0 ...c:11:="'0 "'0 :::Ie
AI x It'I •U\•........•...•...·ce ·.....<C e-o c 0 0 0 0......•-•~
M -I
C>::l..,X
:r
0 ~,c•>-.........-.....-.....•-I 00 ..co ,",,0 NO ..,0 00 ..,0
C>-'"=::r 0 -0 0'0 '"'C>lit C lit ~
1 NO ..c~11'0 NC ..cc 1I'l0 ",g
X c ...•0 ·Q .t\I ·""•lit 0 ...·lL.0 -e _c _0 -0 _c -0
lU
-I
Q e
I&J :::
1 -en
!"I'l'I,
0-II::c
Z a:•co lit 0 II'l C>ill 0
I&l C •ID CD e-O'0 0
u ..,•0'0'0'0'0 Q 0e>-•N N N
If""!
C.6B
I J 1 1 '1 1 })1 1 1 }
SCENARIO,Ht.O •HIO·.OOR 10t--bI2Q/1983
HOUSI~G VACANCIES
ANCHURARl •conK INLET---•..•.-•.......•••..
YEAR SI14GL£FHIILY MULTIFAMILY HOR ILE HOME S DUPLEXES TOTAL..-.-_.................................-.•....--...-.....,............~~..-_..-..
1980 50S CJ •1&bfl.ICJq ••'lIb3.tbi!OQ.
0.000)(0.(100)«°.0 1'0)(1'1.000)«0.000)
1985 IIqCJ.IIICJft.I 1 q.2CJZ.ii!1I0b.
0.000)(0.000)(0.000)(0.000)(0.000)
n
m 1990 587.11111.135.289.ii!QRB.
1O (0.000)(0.000)(0.000)(0.000)(0.000)
19 CJ S fltll.1050.1111.2ell.21ii!1I.
0.000)(0.0(0)(0.000)(0.000)«0.0001
2000 688.1551.IbO.219.2ft18.
0.000)(0.000)(0.000)«0.000)«0.000)
100S 1111.175 q •175./Ifill.3111 11 •
0.0(0)(0.000)«0.000)«0.000)«0.000)
1010 821.1 9 59.195.28ft.12fll.
0.000)(0.000)«0.000)«0.000)(0.000)
........e ....-<:Ie #e co 0 O'e _0 Cl'C c.e
~c 10 0 ,..0 100 OC>-0
_c:>
a::c ClC cc .0 C ...C ...C 0'0
..J ec ~'"'·-·.....0 Cl C C C 0 c:
t-o...
~
..............-11'0 :;I'0 0'0 ec 100 ...Cl .0 c:>en f7'0 f7'0 ~0 cD 0 ...c ...0 -0..,01)0 "'0 IV 0 0 0 0 NO
]I(••· ·
.•e
IaI 0 0 0 co co co c
..J
0.
:J
C
-,•..CD CD e ........................-X I&.l .00 ~c ,",0 r-c:'"'0 co .,.c
u Z ::E:10 0 /\to All c:>NO ,",0 ::1'0 :;1'0
;Z -e C oc>C>C>c:0 co C>-e a:l X •·••..••u a:co co 0 c c 0 0 """'\c -ILl
>.....J
lL -e lID
Z a::C-..,::E:.,...
::l -e
'"'C !AIco::E:a:
0-e ..........e ........................
>-co e c,C
_C>
c>c 00
_0
ec.....-'NO :::ro ....Cl ~o ~Q ,",0 II'0
~""Q O'C .0 Co ~o :;I'c :::ro ~C>
N ::E:'"'·tV ..· ·
••......c 0 CI C>CI C 0 0
4 lL•-•...
~-'0 ~
""::E:
a:
0
0•>-...................................•..J ""Cl CI:ICl ...0 If'0 CQ _0 _0
0 -II'IQ -0 _0 ""Q ."Q ...e 0'0
%.oC'-c -c -c -0 _0 -Q
X -e ....···..·.
lL c co 0 C>C Co C
W
-'0 ~
lLI Z
::t -CD
C-II:-e
;z Ill:•co If\0 11'1 <:)In 0
Mol -e •10 ..,0"0'e:>0 -U Mol •0"a-go a-<:)0 e:>..,>-•N All '"
C.70
~I
,I ')1 1 1 i I ~J }'J '-1 "I
SCENARIO.~EO I HIO·.DOR 101 ••6/iQ/198)
fUEL PRICE 'OREC.STS EMPlOYF.O
ELECTRICITY"I KWH)
n.
........
ANCHORAGE ..COOK INLET GREATER FAIRBANKS•...•...•................~........-........~.....~•..........~......._..~.
YEAR RESlDENJI At BUS IIIESS RESlDENlI At RIISINEU..........•...••........................•........•
(!Jeo 0.0)7 0.0)11 0.091§0.090
U85 0.•1)119 0.01115 0.095 0.090
1990 0.11119 1).0111)0.090 0.085
Ins o.oso O.DIU 0.090 0.085
iOOO 0.050 11.0117 0.090 0.08!i
2005 0.050 0.047 0.090 0.08'5
iOIO 0.050 0.0117 0,090 0.085
SCENARIO'~ED,HIO--DOR ]OX--6/211/198}
FUEL PRICE FORECASTS EHPLOYEn
NATURAL GAS (t/HHBTU)
ANCHORAGE -CODK INLET GREATER FAIRBANKS......~............•..~.......•..•............•..............••.-~
n
"-.J
'"
YEAR RESIOENTIAL BlJSI NE as qESlDENTUL RIJSINF.SS.........••...•......................................
1980 I.no 1.500 12.1Cl0 11.290
1985 1.9]0 1.700 9.090 1.6110
1990 2.480 2.250 7.7b0 6.310
1995 2.530 2.)00 6.?lIO 5.290
l060 2.4S0 i.illO 6.290 4.8110
2005 2.hI)2.130 5.820 11.110
2010 2.2tlO l.030 5.190 3.CJII(l
I )}I J J ~..J ]_;1 I J _J _J J i ;)J
j ~l .~
I 1 1 I ~l 1 I ]1 1 1~l
SCENARIO'MEO'HIO·.OQR ]OX--bll4/198]
FUEL PRICE FORECASTS EHPLOYEO
'UEL OIL (~/MMRTU)
(J
~
W
ANCHORAGE -COOK INLET GRfATER FAIR8ANKS.......-...•.••.•.~.............~....-.••.•..•.-.....-_..-...-.-.~-..-....
YEAR REunEHTI At BUSINESS RESIDENTIAL RlISINfSS..-................-........,..-..........•....•.•.•
19M 1.150 7.200 7,R]0 7,500
1985 lS.tno 1I,geo 5,590 5,2&0
1IJ90 4.7)1)(1,180 11,770 "."40
1995 ".11 0 1.!lbO ".140 3,810
2000 3.81(1 J ,280 J,8bO 3.5Jo
2005 1.550 1.000 1.580 1~250
lOU 3.280 1.710 3,310 2.980
SCENARIO'Io\EO I HIO-.DOR 10 •••blzQ/ICJa)
RE810ENTtAL USE PEA HOUSfHOLD (KWH)
(WITHOUT AOJUSTHENT FOR PRICE)
~NCHURAQE •COO~INLET-...-.-.~...-......._.
SHALL LARGE SPACE
YEAR APPLlA"ICES APPL1A~ICES HEAT TOTAL
••••........-................--........................
1980 2111\.00 bSO(l.ttl 5088.52 13bQQ.1S
0.(00)(0.'000)(0.000)(O~OOO)
n 1985 Zl60.00 UStJ.5J 4831.U 13153.75.(0.(00)(0 ..000)(0.00(1)(0.000)......
.J;::o
lCJCJ 0 2210.00 tiQl0.91 Q651.a4 un4.34
0.000)(0.000)(0.000)(O~OOO)
1995 Z'.bO.OO 5QS8.55 4!07.71 121211.25
0.000)(0.'0 0 0)(0.000)(0.000)
2000 Zl19.00 5Q88.1]4I1U.bQ U130.82
0.000)(0 ..000)(0.000)((1.000)
2005 2J60.00 60&2.19 Q4 21.68 U8t14.811
0.000)«0 ..000)(0.000)(O~OOO)
iOlO j!Qll).OO 6l2Q~36 44]8.60 129n.96
0.000)(0.'000)(0.000)(0'.000)
J ."J j I J :I ~...J J J .J J I
1 ')j 1 'J -1 1 -1 "]1 J }
SCEtURIO,"'EO I HIO-.OOR ]~X ••o/2Q/I~8J
RESIDENTIAL USE PER HOUSEHOLD (KWH)
(W!THOYT ADJUSTMENT 'OR PRICE)
GRE_TER FAIRBANKS
--.-.-.~-....-...-....
S'1ALL L~RGE SPACE
YEAR '\APPLHNCES APPL "NeES HF.H TOTAL..-.-.......•..........-.....•...•.............
1980 2 11 00.00 ~7]q.Sl ]llJ.6b 11519.18o.ono)«0.000)«o.ono)«0.000)
(J 1985 25ll:i.Q9 bl A i.9]351\6.15 lHOS.Ol'.(0.(01))«0.0(0)(0.000)«0.0(0)........
tn
1990 21"01,,.00 61J]q.bO 3822.(1]12862.111
0.000)(o.oolj)(0.0(0)«0.'000 )
1995 2b16.00 bb1l1~01 11075.11 IHlJlI.12
1).000)«0.0(0)(n.(lOO)«0.000)
2000 27116.00 67139.50 11]29.67 1J86S.18
0.(00)«0.1)00)(0.000)«0'.000 )
2005 2 8 11".00 68159.08 IIS02.l1 1 11 1]7.]0
0.(00)«0:0 (I 0)(0.1100)«0.000)
2010 2~8b.0I 68 11 9.116 "65'S.9ft lllllll 1•'HI
1'1.1)00)(0 ..000)«0.000)«O~OOO)
SCENARIO,~ED I HIO ••OOR JOi ••b/24/t'Al
...•.............._...ANCHORAGE •COOk INLETYEAR.....
t980
(")1985
.......en 1'90
1995
lOOO
2005
2010
(
(
(
aQ07.Q4
0.000)
9482.69
".000)
99Je.1t
0.000)
IOJIH.91
D.OOO)
10908.41
n.ooo)
11~8].80
0.000)
12391.t2
0.000)
eUSIN!SS USE PER fMPLOYEE (kWH)
(WITHOUT LARGE INDUSTRIAL)
(WITHOUT AOJUSTMENT fOR pqlCE)
GREATER 'AIR8ANKS....••........•...•...
1495.10
0.000)
U32.lI
0.000)
81 Q 2.lb
G.OOO)
8467.59
O.Ollll)
~782.72
0.000)
'1131.18
0.(00)
9536.3]
0.(00)
]I oj .J J J I J .J ...J J J J
II•--1 '1 'J l I ')'}1 1 1 '1 1
SCE!-URIOI I4EO I HlO·.OUR 30'••b/211/'~A]
SUMMARV 0'PRICE EFfECTS ~ND PROGRAMATIC CONSfRVATION
IN GW'"
ANCHUR~GE •COOK INLET
RF.lIJOENTlAL BUSINfll9...................~.._..
OwN-PRICE PIlOGJUM-INDUCfD CROSS-PRICE OWN·PlllCE PROGRU1-I NOUn'0 CROSS·PRICE
YEAR REDUC TI ON CONSERVATION RE!l,U.C T1.Q~_._•.,REOUPI9t:!,.CONSERVATION REDUCTION...................................-.:..................................................................;~+;+:.....;:.........................
,"1l0 0.000 0.000 0.000 0.000 0.000 0.000
flUll 41.08/1 0.000 '0.IIS4!l 8.982 0.000 ,.ISH
I ~82 12.1/'7 0.000 0.978 17.qbll 0.000 1.'5''''81 18.n,0.000 I.II/,8 2/,.qll/,0.000 11.727
U811 211.U5 0.000 ,.957 15.928 0.000 b.l01
,"85 JO.418 0.000 iI."lIb "II.'H'0.000 7.8H
1~86 3q.98~0.000 0.02'5'.'15 0.000 8.5'~
1987 39.51.10 0.000 ..2.11011 57.319 0.000 9.UO
1988 U.Il'0.(11)1)"Q.82"61.5211 0.000 9.800,qll"118.702 0.000 ..7.255 69.728 /).000 '0.4111
'990 51.271 0.000 .9.fill 0 7S.HJ 0.000 '1.082n
'--J '991 56.6'b 0.(11)1)-10.1118 80.Q ,0 0.000 12.'59'
'--J '992 5 9 .9bO 0.000 ·11.19'3 115.1187 0.000 111.'01
'99]41].]0]0.000 -11.95]90.11b)0.000 15.611
1994 b6.6117 D.OOll -12.111 95.81lO 0./)00 11.Ii!0
Ins bQ.990 11.000 -ll.lIb~100.811 0.000 18.b]0
1996 HI.62 I 0.000 -12.9"9 10'i.1I00 0.000 20.786
1997 75,.251 n.ooo ..U.1I28 109.98J 0.000 22.9112
1998 n.8S&'0.000 ·11.908 114.565 O.OO/)25.098
19Q9 80.'512 0.000 -1I.J87 '19.148 0.000 21.25"
ilooo 83.'112 0.000 -10.867 12].7]'0.000 29.1110
2001 85.612 0.000 -9.J2 Q 121\.691 0.000 ]2.1111
2002 88.'22 0.000 -7.'191 I H.6bll 0.000 ]5.5]2
200]90.612 0.(100 -/'.2'511 118.6]0 0.000 38.591
20011 93.10&'0./)00 ..4.116 'IIl.S'?0.000 Ill./,511
2005 95.592 0.000 -].'78 'IIS.561 0.000 1111.7'5
2006 98.267 O.flO~..0.613 154.705 /).000 119.150
2007 1/)(1.911](1.000 1.'ISt'160.11116 0.000 53.585
2008 IOJ.61 8 0.000 11.517 lflb.qBl 0.000 58.021
2009 10b.&,q]0.000 1.0B2 11].1211 0.000 62.115"
2010 108.Q6Q 0.000 q.b1l1 ,7 Q .210 O.OO/)bb,lIq,
SCENARIO.MED •HIO-.DDR ]0~••b/2UI1981
SUMHAR~OF PRICE EFFECTS AND PROGRAMATIC CONSERVATION
IN GWH
GREATER fAIRBANKS
RESIDENTIAL BUSINEU....-.....~....••...••
OWN-PRICE PROGIUH-I NOUCED CROSS-PRICE OWN-PRICE PPOGRAM-INDUCED CROSS-PRTCE
VEAIl REDUCTlOtl CIWSFltllATrorl REDUCTION RE!,.U~Tl Q","~_CON~!~Y~!ION _.RE/)UcT ION....................:..f:~.:........-..-..,..".'--,.--..............................................................................................................
1980 11.0011 0.000 0.000 0.000 0.000 11.000
1981 0.000 0.090 1.338 0.000 0.000 0.899
IU2 0.000 0.000 2.b1b 11.000 0.000 1.798
1983 0.000 0.000 4.01"0.000 0.000 2.6''1
19811 0.000 0.11')0 5.152 0.000 0.000 3.59b
1985 0.000 0.000 b.b91 0.000 0.000 1I.1I9!l
net.-0.310 0.000 8.527 .O.'!II 11.000 '1.520
1987 -/).020 0.000 10.3b3 _1.022 0.000 b.557
19118 ·o.cno 0.000 12.199 -1.5]]0.000 7.'S88
IU9 -1.240 0.000 111.035 _2.01111 0.000 8.019
n 1990 -1.550 0.000 15.872 -2.555 0.000 9.b'50.
.........lnt .1.791 0.000 18.093 -2.878 0.000 t (I.763ex>1992 -2.0]1 0.000 20.115 _].200 0.000 11.910
199]-2.27 I 0.000 22.53b -3.522 0.000 n.OIl'
1994 -2.512 0.1100 211.758 .3.8411 0.000 111.18 :5
"~95 -2.75~0.000 26.979 _1I.lbb 0.000 15.31b
1996 -2.928 0.0011 29.0111 _11.1101 0.000 Ib.lI23
1997 -3.1011 0.000 11.11119 .11.648 0.000 11.530
1998 -3.2811 0.000 33.083 _1I.88 C1 0.000 18.037
1999 .3.1I5&0.000 H.1l8 -5.1111 0.000 19.11111
2000 -3.61L'0.000 37.153 -5.371 0.000 20.851
2001 .3.7811 0.000 39.3b I _5.'591 0.000 22.128
2002 -3.93'5 1).000 111.'\70 .5.811 0.000 2].1105
2003 .11.081 0.000 113.778 ..b.031 0.000 211.682
20011 -4.21q 0.000 IIS.98b -6.251 0.000 25.959
2005 .11.391 11.000 111'.'9'3 _b.1I71 0.000 21.2310
lOO~-1I.SII3 0.000 50.197 -~.712 0.000 28.8q8
2007 -1l./l9f,0.000 53.199 _b.952 0.000 30.1160
2008 e4.81l8 0.(\00 56.01)'_1.l l n 0.(100 32.012
2009 -5.1101 0.000 5A.bOIl _1.1I1l 0.(100 n.b811
2010 -'5.1511 0.01)11 i>l.?Ob _1."7 U 0.000 35.29"
J J J )cJ _J )J J }J J J
)]1 1 ~)11 1 ..-.-j 1 1 1 j 1 -·1 1
I
SCENARIO'MEO ,HIO·.OOR JOX·.6/2 u /198J
BREAI(DOWI!OF ELHTRICtTY REQUIR!'HENTS (aWH)
(TOTAL INCLUDES LARGE INDUSTRIAL CONSUHPTION)
ANCHORAGE •COO~INLET....~.~_.....•..•...•.
HEDIUH RANGE (PR_.5)•......•.•.••.......
RE$lDENTJAL BU51NESS MISCELLANEOUS ElIOG.INDUSTRIAL
nAR REGUIREHENTS REQUIREME'iTS REQU I RE HE NTS LnAD TOTAL.......~••••••••••R •••••..•...•............._.-.~.~......~..••••••••__••••~a ••....~...••.......•
1980 cI79.5!IUS.30 lll.JI 811.00 19U,I9
1981 1016.n U1.i!S ZIl.51 9i!.08 2010.11
1982 1053.12 999.14 211.12 100.U ZIHolII
1983 10119.92 1061.03 211.93 IOB.24 nell.11
1984 112b.1I 1122.92 n.n 116.1Z 2lQI.OB
1985 1163.51 11811.BI 2'.5 .14 124.110 211q8.0b
1986 1179.87 UOII.H 25.n U7'.89 25117.9Z
1987 II qb.i2 IUll.Ob 26.12 151.1B 2597.7q
1988 1212.511 1241.69 26.50 164.88 hIl7.b'5
198q 1228.911 12bl.12 Ib.1!I9 I1S.31 2bQ7.52
n 1990 12115.30 UU.95 11.28 191.Sb 27117.18.
"-.l
lD Inl IZ5Q.b2 1299.15 U.5!195.l]2710.11)
19 Qi Ilb3.911 IJl5.lb 21.18 198."0 2805.111
1991 1273.2b IUI.51 28.02 20 I.b6 2B]II.52
19911 121.'2.51\1141.18 2B.21 201l.9J i!8bl.5b
Ins 1291.90 llU.99 28.52 208.20 2B92.bl
199b 1109.27 13'15.110 29.0b i!11I.11I 2Qll1,87
19Q1 112b.6J IlIib.B2 29.bO 220.08 ]001,11
1998 1]111.99 11158.23 ]0.14 22b.Oi 10'!B.lQ
1999 IHI.]'"Illa9.b5 10.b8 21\.96 3111.b5
20110 IH8.72 1521.07 31,23 ?]1.CJO 1168,Ql
ZoOI 1110].55 l'ib'i.~5 H .97 21111.Q6 H1I5.n
i002 11128.311 1&09.63 ]2.72 252.02 H22,7S
2003 IIISJ.21 1651.91 H.llb 1159.08 H99.b7
20011 11178.011 IbQA.20 ]11.20 ~bb,I/I 3117b.59
2000;1502.88 11112.lIa H.Q~273.20 1551.50
2006 1515 ..21 18011.20 15.9]281.58 3656.98
2007 1567.b6 1865.93 lb.'ll 289.96 17bO.llb
2008 IbOO.06 \927.65 31 .89 298.3/1 18b3,"11
2009 1631.U5 1geCl.HI 18.81 10b.72 39&7.1111
2010 166'1.1\Q 21l51.IU 19.86 1\5.10 11070.90
SCE~ARIOI ~~O I Hl0 ••00R IOX ••b/211/1983
BREAKDOWN OF ELECTRICITY REQUIREMENTS rGWH)
(TOTAL INCLUDES LARGE INDUSTRIAL CONSUMPTION)
GREATER FAIA6AHKS........~.-.••.....•..
MEDIUM RANGE (PR ••S).•.•••••.•.••.•.....
RESIDENTIAL BUSINESS MlSCI!:LLANEOUS
YEAR REQUIREMEIHS REQUIREt4ENTS REQUIREMENTS................•••..•.•.•..••.•......•...•.....•.•....•.•
1980 11b.39 il1.14 b.18
lUt 189.10 227.53 fa.'73
198i lOI.80 231.93 tI.b1
1983 lll,l.51 2118.H &.bi
1981,1 2U.U 25/\.12 b.Sb
1985 219.92 20 9 .11 b.51
198&2111.10 272.22 b.1,I5
1981 2511.211 2115.33 b.39
IU8 2bl.lif.21/\.113 b.H
1989 2..,8.b]281.'51,1 b.21
n.IUD 215.81 281,1.64 b.ill
(»
0
lUI U2.l,Il 288.03 b.29
1992 289.11,1 291.1,11 b.31
199]2Q5.80 291,1.19 &.4b
1991,1 3112.1,11 29~.ll b.51,1
1995 309.U )01.55 o.b2
199b 311,1.48 30&.1)1 b.l11
1991 319.82 3U.Ob &.85
1998 325.lT 311.3i!b.9&
1999 )10.52 1;Hl.'i8 '1.07
iOOO 335.flb 321.811 1.18
2001 3112.lJ US.09 1.32
200l 3118.110 1112.311 1.11&
2001 1511.&&3119.59 1.bl
20011 lbo.en 15&.811 1.15
2005 l"'.iO ]611.08 '1.89
200b 375.05 373.911 8.09
iOOl 382.91 ]83.19 8.2'1
2008 190.71>393.61.1 8.1.18
2009 JQ~.b2 1103.50 8.b8
2010 11110.118 1Il!.3S 8.87
J J J I .J J J I
E~Or..INDUSTRIAL
LOAO TOTAL•..•.•.•.-........R •••••••••••••••••
0.00 1100.3\
0.00 4U.Jb
11.00 IIl1b.1I0
0.00 IIb9.1I'5
0.00 1192.50
0.00 515.511
10.00 535.77
2/).00 555.qq
30.00 51&.2t
110.00 !i9b.1I11
50.00 &Ib.b"
50.00 &2&.n
50.00 Ub.92
50.00 blll.G!
'iO.00 U7.U
50.00 6U.J1
50.00 U8.02
50.00 fa88.73
50.00 &99.115
50.00 1\O.U
50.00 nO.88
50'.00 1311.511
50.00 1118.20
50.00 'Ib!.8'S
50.00 175.5\
50.00 1119.17
50.00 807.08
50.00 Utl.98
50.00 8112.8e;
50.00 860.1Q
50.110 816.70
J ~.J J J
]1 )1 1 --1 -J ]J 1 1 1 1
n
00
--'
SCENARIO'HlO I HIO·.nOR 30X··bI2Q/19~3
TOTAL ELECTRICITY REQUIREM£NTS (GWH)
(NET OF CONSERVATION)
(INCLUDES LARGE I~DU~TRIAL CONSUHPTION)
~EDIUH RANGE (PR ••5)•...•.•..•.......•....
YEAR ANCHORAGE •COO~INLET GREATER FAIRBANKS TOTAL
••••••••••w ••••••••••_.....~...•..•••.•...••..~•••••••••••••R •••••
1980 I 9U.1 9 QOO.31 <'3f1]~51
1961 20 1 0.11 02J.U 1l"93 ~52
19112 2111.10 C1Q6.QO llb23~5C1
19U 2281.1.11 Qb9.115 ~15:J.5f1
1980 2)cH.OIl 1192.50 iHIA).58
19a5 j!1I9a.Ob !i15.5Q JO I:f.bO
1986 25'0.92 535.17 J083~b9
1987 2597.79 555.99 ]153,78
1988 hl.l7.bS 5H.21 ]223,111
1989 h97.51 59b.QO !2H.9b
1990 271.17.]8 bl b.bb J3bIl~O'i
1991 211b.IIJ blb.79 3lI0]~22
1992 2605.111 blb.9i!JqQi!~]9
1993 28311.52 blll.05 301l1.51
I99Q 18bJ.5b b51.18 3520.7Q
19 9 5 28 9 2.bl &b7.JI 3559°.92
199b 29 07.81 U8.0i!3b25~89
1997 1003.1 J .668.711 ]b91.81
1998 10~8.l9 699.1.15 J75l~61.1
1999 JIIJ.b5 1I0.lb JAil.8i1
2000 JlfIB.91 120.88 ]8119°.19
2001 H1I5.83 1311.511 39110·.n
2002 J122.7'5 7Q8.20 11070:95
ZOO]]]99.bl lbl.85 111&1.52
20011 3111b.59 175.51 1I2o;?~IO
2005 J55].50 789.11 IIH2.flll
200b U'5b.9A 807.08 IlIlflO~Ofl
2001 llbo.lI&82Q.98 11585.lIq
2008 JII&3.91.1 111.12.119 1170b,IIJ
2009 19b7.'l2 8&0.19 111128.21
2010 11010.90 818.70 11911q~bO
n.
OJ
N
SCENARIU,MED I H!O--OOR !0¥--bl24,IQa]
PEA~ELECTRIC RfQUIREMENTS (MW)
(NET OF CONSERVATION'
(INCLUDES LARGE I~DUSTRIAL DEMAND)
MEDIUH RANGE (PR ••5)..•..••.....•••••.••..
'tEAR ANCHURAGE •COOK INLET GREATER fAIRBANKS TOTAL.....~....~..••.....•..~~.._.~.......~~.....•...••.•..•.~.••......
IQAO lQb.SI Ql.4U 1181.90
IQBl 1118.0'1 9b.bb 514~15
1982 1.139.08 IOI.Qj!511I~bO
198]IIbl.2b 101.18 5&8.44
lQ811 IIRi!.8'!5 112.114 595'.29
1985 5011.113 111.70 bi!i!'.lJ
U811 515.24 122.la U7~S6
1'187 526.04 lill.U 652.98
1988 Sh.8!ill.55 U8~1IO
1989 5117.66 Ub.I b 683'.8l
1990 558.lIb 1110.17 Uq~211
19'11 5611.10 '"3.09 701.n
1992 570.1'~IltS.1I0 715.511
19C1]515.98 1117.71 7n~70
19911 5"1.82 150.03 HI'.U
1995 5~1.bb 152.]11 1110~00
I,Clb 59'!.7S 15 11 •T8 lS]~S3
1991 &0'1.83 IS1.n lU,Ob
19'18 O20.'iI 159.b8 180.5'1
199'1 Uil.OO 102.12 194'.12
2000 bIl3.011 1&11.51 801~bl5
lool 1>58.57 Ib7.b9 82b,i!5
2002 bU.Oo 170.81 MII.BlI
2001 b89.55 173.92 861'.111
20011 1(15.011 171.Oil 811i.(III
2005 720.53 1811.lb 900'.69
200b 1 "11.111 1811.25 925.65
2007 7b2.28 188.14 9r;0~b2
2008 183.16 192.1,12 q75~59
2009 8011.011 19b.SI IOOO~5b
2010 B211.9i!200.flO I 02'i~52
)~J J J J .JJ vJ ".J J J J --]J J I j
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-
~,
H13--DRI SCENARIO
C.S3
flIIIIlIi\.
~j
-
C]1 1 1 1 1 j ]J 1 --j 1 -1 1 ]1
SCW.RIO,Mf;.n ,HIJ·.ORI SCENARtO·.bll~/I~8]
HOUS£HOLOS SEPVEO
ANCHORA~E •COOK INLET..~.•......•-.......-.
YEAR SINGLE F.,,1IlV HUL TlFAHIL V MOR I LE HO'1E S DUPLEXES TOTAL•...._.....~..........•....-..-...•.............•.........................
1980 3S~n.20]l'l.8Bo.l~aet.11503.
1).000)(0.0')0)(0.000)(0.000)(0.(00)
1985 IIb221.2b2011.10957.85b7.Q1950.n (0.00/1)(0.000)(0.(00)(0.000)(0.000).
00
(J"l 1990 51890.25871.I UO I.allbO.105528.
0.(00)(0.0(0)(1).000)(0.000)(0.(00)
1995 b5'H7.30~21l.15120.8]]].lt9]SIl.
0.000)(0.(00)(0.000)(0.000)(O~OOO)
2000 l!9b Q •]'i~'5=!.IllIS.8532.1351&7.
0.001)(O.DOO)(0.0(0)(0.0(0)(0.(00)
2005 83357.1I02b 7 •19580.9blj~.15281lf'.
/1.000)(0.000)(/1.0(0)(0.000)(0.000)
lOIO 9Sli!7.~b1l55.U5aQ.11051.115327.
O.OIJO)(/).001)(1l.000)(0.000)(0.(00)
C.86
--
-
-
~,
.....0-
O'C ...C O'C o:I:C 0'0 It'C O'c
00 -0 0:1:0 -0 0'0 t\I 0 040
AI 0 ~C 11'\0 ClC _0 o4C -0
....l oD 0 tv.0 tv 0 tv 0 ...0 ...0 ~0..-0 c 0 C-O 0 0....
t-O....
~.-.-....0 n.C 0'0 ~O 1\10 CC '"0CO-DO 0'0 ClO CO Clo -0 -DC
lOt ~C'1\1 0 NC NO NO ...0 '""0X...0
W 0 C C 0 C CO C
...J
Q,.
=-Q
.......
on ...J
'LI Z CO .-.-0-0-0---'"'-0 -c -DC -DC O'C II'C 0'0
U ::r 0'0 t\I CI 0:1::-.00 ec -0 ~Q
Z :.I:0 O'C _0 _C C 0 1\10 I\IC::..0 z.-0 0 .0 0 0 .
U 0 CI 0 C 0 c::c C....U lOt
>...J•-....~~ec z ....0
0'-C·:z:en ......::l .It.
~0 0
""J:::r.....u 0- 0-0-
.0 Z >.oe -4C "'C ""'Co ~C OJO 0'0•0<....l .00 0'0 ...0 ~O -CI P-0 00•....oC ~O o:I:C ..oC O'e _c IIIC
0 ::.P-o -•-.t\I .N .-..C C C C Co C C
IX:~
<C -Z ...........J
U ::l
on ::r-a::c•>.-.-0-•...J O'C Cl:e "'0 CO ~C "'C l['C...-ec oC ...0 I\IC 0 -c ~O
::£C.C If'.C ..oC'"'c II)0 U Co CC
J:....."..0 0 0 0
IL C C 0 0 C 0 C
OIl
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C.Sl
SCENARIO.MED •HU-.DRt 5C£IIARIO ••bn~/lq,,]
HOU91~G VACANCIES
GREATER FAtRRANKS•.•••....•....•......•
YEAR SINGLE FAMIly rolUlTlF4t1JlV MOBILE HilMES OUPlE)(ES TOTAL.....................................,..............................••...•...•.•
1~80 3651.H2O.q6~.895,8854.
0.000)(0.0110)(0.1\00)(o.ono)(/).000)
1985 II A.2655.a~.H2.lSlq.n «0.1)00)(0.1\00)(0.000)(0.000)(0.000)
OJ
OJ 1990 Ub.1151.1.C!~.81,b86.
0.(00)(0.000)(0.000)(0.000)(1'1.1'100)
1995 Ib 4 •4'1fl.H.60..729.
0.0(0)(0.000)(0.000)(n.ooo)(0.000)
2000 1911.441.115.78.7oa.
0.000)(0.0(10)(0.000)(0.000)(0.000)
2005 21 A•520.5 ••1R.8bl.
0.000)(0.000)(O.lIIlO)(0.000)(0.001l)
2010 l~".I§qq.5 1f •8CJ.99'3.
0.000)(0.000)(0.0110)(0.000)(0.000)
I J J I J J J )J J .I J J il 'I )-].J
1 -1 ]]1 -]]I I I )1 1 ]1 j
SCEHARJU,HED I Ht3--DRI SCEHARIU.-b/2Q/I Q al
FIIEl PRICE FOREC~ST5 EHPLOYEn
ELECTRICrTY (S I KWH)
ANCHORAGE.COOK INLET GRf4TEA FAIR8ANKS.......-•.•.....•••.••.....•~....__...~~.--.-.._ _..........•••..•.•
YEAR RE8IOENTlAl BUst NE'sa RESIOENTrAL RlISINESS................................................•.•.•.
1980 0.031 O.OJQ 0.095 0.090
n 1985 0.Oa8 o.nlit;0.09t;0'.090
00
lD 19 9 0 0.0511 (1.051 0.092 0.087
1995 0.Ob3 0.0&0 O.09/i 0'.089
2000 0.Ob9 0.1)611 0.0 9 6 0.091
2005 o.on O.Ob'0.098 0.09]
2010 0.07'5 n.on 0.100 0.095
SCENARIO,MEn I HIJ-.DRI 8CENARIO.-~/l4/Iq81
ANCHORAGE -COOK INLET
fUEL PRICE FORECASTS EMPLOYfO
NATURAL GAS (I/MMRTU)
GREATER fAIRBANKS.....~•...........................~..•••••••..............•.~..•...••..•.•
YEAR RESlOENTlAL ~ustNESS RESIDENTIAL RUSlNES9.........,............................................
1980 t.HQ 1.500 12.no It.290
n 1985 2.030 1.800 Il.b'O 1I).2110.
\.0
0 1990 J.lISQ J.Zi!Q 1~.OlO Ill.~u
1995 1S.IOQ 1I.lJ10 19.8110 1lI.]'Q
2000 '5.750 lS.lilO 21.120 21 ~670
2005 b.Ol0 ••180 24.410 n~l)20
2010 b.1bO b.1l0 26.230 211.780
I I 1 ]t I 1 ;.I J J ]J ).1 J J
J I I 1 1 j I 1 ]1 1
SCENARIO,M[O,HIJ-.ORI SCENARJU··~/~q/l'63
FUEL PRICE FORECASTS EMPLOYEO
FUEL OIL ("I4MBTU)
ANCHORAGE.COOK JIll ET...._-..--._--~.._.-.GRE~TER FAJRBANKS.~._...••...•-.~--..-.
HAR RlSlOENTUL fHIS I tlESS RESIOENTJAL fHlSINESS........................................•••..........-.....
1980 1.150 1.?OO 1.830 1.1i00
1985 1.120 6.57n 1.180 1..850
n 1990 9.150 Q.200 'i.allo 9."'110.
I.D 1995 12.080 It.53n 12 .190 U'.8bO--'
2000 11l.0SO 11.530 111.210 U.B80
2005 IIl.qoO 14.350 15.0110 11.110
2010 15.971\11l;.1l20 tb.UO 15.190
SCEN.RIO.Io4ED I ~tl·.OPl SC£NAR10 q -6/iQ/IQ81
RESIDENTIAL USE PER HOUSEIlOLD (KWH)
fWIT~OUT ADJUSTMENT fOR PRICE)
ANCHORAGE •COOK INLET
~._....~•....•...•_...
SHALL LARGE SPACE
YEAR APPLllNCES APPLIANCES HEAT TOTAL..............................•...•...•...••...
1980 lil/J.OO 6S00.bJ SOSS.Sl UU9.U
0.00/)(0.000)(0.000)(O~OOO)
n 1985 61'51 ~lIq 1I8ZI.87 1l111.H.21bO.00
\.0 (11.000)(0.000)(0.000)(0.000)N
1991)&'21/).00 6020.51 1I'58f>.U USU.IlI
0.000)(0.'000)(0.000)(0.000)
1995 226/).00 5960.28 4518.86 12139.14
(l.001l)«0 ..000)(0.000)(0.000)
2000 HIO.OO 5 q H.1 q 4115J.51 12756.U
0.000)(0.11(0)(0.000)(0.000)
i!005 2]bO.OO 6062.'51 4I.1U.21 U8/HI.n
0.001»(0.000)(0.000)(0.000)
2010 lIlIO.OO bl27".20 lIlI'50.M 12987.81.1
0.000)(0.000)(0.000)(0.000)
.J I ...__J I I J .I I I .1 I I I J J ]-I I
I ]J 1 j ]I I '-1 1
SCENARIO'MED ,HI~··OHI StE~ARIO··b/24JlqA1
RESIOfNTI~l USE PER HOUSEHOLO (~~H)
'WITHOUT AI)J"STl1E~r '-OR PRTCE)
GRE'TF-R fAI~RANKS•.••.•........~...-...
SHAll L'~GE SPAtE
YHR APPLIANtES APPL I AIleES HrAl TOTAL...........~.........................................
1980 i!4bb.IIO '51 Jq'.52 HlJ.t>~IISlll.IS
0.000)(0.000)«0.001)(0.0001
1985 25Jb.OO e.t 7lf.qR 3606.28 UUI.25n(0.000)(0.000)«0.000)(0.000)
ill
<-v IqqO ab06.00 b1l1l8.a8 38&1.13 ll922.21
G.OOO)(0.'000)«0.0(0)«0.000)
IH5 2117b.1I0 bb&9.21 11051.13 13397.00
G.OOII)«0.000)(0.(00)(0.000)
2000 H1I6.01 b7q2~9!1 11]]&.15 13875.10
0.000)(0 ..000)(0.00(1)(0.0001
2005 i!BU.9q &Ene.SlI /iSLI].81j JIll 98.38
/).000),(0 ..000)(0.000)(0.0001
2010 2886.01 "881).16 11659.66 1l'4U.4b
0.(00)(0.0001 ,0.0(0)(0.0(0)
SCENARIO,MED I HI3 ••0RI SCENARIO··6/~q/1981
(}
\.D
~
YEAR....
1980
1985
1990
19Q5
2000
2005
2010
ANCHORAGE ~COOK INLET......~-...........•..
61l01.IlQ
0.000)
9SBO.13
/1.000)
IOibl.1I
0.1100)
110]1 ..(11
0.000)
I 185S.81.1
0.000)
12141\.53
0.000)
13"111.57
0.000)
BUSINESS USE PER EMPLOYEE (~WH)
(WITHOUT LARGE INDUSTRIAL)
(WITHOUT 'OJUST~ENT FOR PRICE)
GREATER fAIR9ANKS
•••••~•••••••••••~•••w
7Q95.70
0.0001
797a.0]
O.Ollcl)
8300.29
0.0001
1\1195.01
0.(00)
9068.00
0.000)
qSOO.al
0.000)
99118.16
0.000)
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SCEIURI0."ED.HI3--0Rl SCENARIO·.6/,Q/lq81
SUM~ARY OF PRICE E'fECTS AND PROGRAMATIC CONSERVATION
IN GWH
GREATER FAIRBANKS
RUIDfNTHL IIIUSINESS........................
(lI'HI.PP I Cf.PROGRAM.INDUCED CROSS ..PRICE OWN-PRICE PROGRAM"INOUCED CROSS ..PRICE
YEAR REDUCTION CotlSfRYATlON REDUCTlO_N _RE DUC TlO.,,!_CON~{'!Y~.!J.QN _••PEDUCTlON......................................................-................................................*"............................................
Iqeo 0.001)O.noo 1'.000 0.00/1 0.000 11.0011
U81 0.000 0.000 0.351 0.000 0.(100 (I.l!1l3
1982 0.000 0.01)0 0.113 n.ooo 0.000 0.1l8S
Iq8J 0.0011 0.000 l.n10 0.000 0.000 0.128
19811 0.1100 0.000 I.lla?0.00/)0.1100 o.qll
1985 0.000 0.001'1.1811 0.000 0.000 I.l!l]
1986 -0.191 0.000 0.1114 ..n.nl 0.000 O.J/lb
1987 ..0.39/1 0.000 .0.95b ..0.bb5 0.000 -0.521
1988 ..0.591)0.000 ..2.U5 ..0.99(1 0.0/)0 ..1.388
1989 ..11.161 /).01)0 -3.U!_I.UO 0.000 ..2.256
1990 -0.91111 0.000 -5.0n -1.b63 0.000 ..3.121
n.19 q 1 ..0.99'0.000 .1.6q1 _1.651 0.000 -4.584lD
(])19 9 2 -1.010 1'1.0111)-10.3)0 -1.651 0.000 -6.046
19'JJ ..1.021 0.000 -U.9b2 ..1.6115 0.000 .1.501
19911 .1.01&0.0110 -15.595 -1.619 0.000 -8.9b8
1995 ..1.0119 0.000 ..18.228 -1.&12 0.000 ..'0.4]0
1996 "0.871 0.000 ..21.578 ..1.31.13 0.000 "12.209
1997 -0.7011 0.000 -ill.Q29 ..1.05 4 0.000 -l].Q89
1998 -0.'532 0.000 -28.280 -0.hli 0.000 -15.1&8
1999 -0.3bO 0."011 ·31 ••31 _0.476 0.000 -11.5118
2000 ..0.181 0.1I00 -311.lf81 _0.181 0.000 -19.321
2001 11.1/111 0.000 -]B.2b8 0.3118 0.000 -21.0S0
2002 0.111111 0.000 .QI.S55 0.88)0.000 -22.113
200)o.!\ZO 0.000 ../Ill.811 I 1.lIlS 0.000 -24.1196
20011 1.151)/).(100 ../jA.US t.9511 0.000 -a6.211f
2005 1.'JQI 11.000 -'1\.1.1111 2.118Q o.no(l -27.9112
200la I.Qqz 0.0(1(1 ..5'5.168 1.100 0.000 -30.034
2001 Z.1I911 0.000 -S8.ge?II.U2 (1.1)00 -32.126
2008 2.991i 0./100 -b2.b16 /I.Q21l 0.1100 -)4.211
2009 3.119b 0.000 -bb./lln 5.7.3'3 0.(100 -36.)09
zolo ].99/\0.000 -1(\.183 6.'5117 0.000 -3/\.40\
I I ,I I I J I J ...J I J I I I )
I J 1 j I 1 1 1 J 1 I 1 I i
1 1 -j J
SCENARiO,MEO I HI3·.0RI SCENARIO··bIZIl/1983
BREAKDOWN O~ELECTRICITY REQUIREMENTS IGHH)
(TOTAL INCLUD~S LARGE INDUSTRIAL CONSUMPTION)
ANCHORAGE·COOK INLET.._.....-....._.......
HE 0 111'-1 RANGE H'R ••5)........-...........
RESIDf.NTIAL BlJ9IN~SS MrsCElLANEOUS [ICOR.PJOUSTRIAL
YEAR RE.QUIRH'ENTS REQUIREMENTS REQUIREMEIHS LOAD TOTAL.............•.•.•....~.._..•..••......•..•...•.........••.......••.•..•..••••............
1980 9H.5J 815.3&2Q.31 811.ll0 1963.19
1981 1020.70 ClIl7.IIZ 211.b6 92.lIe !O811.86
1982 1061.8&10n./18 25.02 100.lb 220b.52
1983 I 103.02 1091.55 25.37 1011.211 2328.18
1984 11"11.19 IlbJ.bI 25.7J Ilb.J2 211119.85
1985 1185.'35 llH.b7 2&.08 121.1.110 2571.51
1geb IZ18.115 1277.9&26.88 137.89 26&1.21
1981 USI.S5 1121).30 21.U 151.]8 2750.91
ICJ88 Ii!Aq.b5 l1U.bl 28.1l7 IU.U UIIO.l.1
1981J IJ17.75 IIIOIt.9i!29.27 178.37 2930.3n
n 1990 1350.85 111117.23 30.06 I~I.U 3Oll0.00.
<.0
-.....J 1991 1390.20 11198.51 'Sl .02 U5.1S 31111.86
1992 11129.55 19119.79 31.98 198.110 3209.71
1993 IlIb8.89 IbOI.07 H.93 201.66 33nll.57
19911 1508.ZI1 Ib52.'b 33.89 2011.'3 3399.11<'
1995 ISII7.S9 1103.b4 H.8S 201].20 31.1911.21'
199&1592.28 U61.89 55.911 2111.111 36011.25
1997 103&.97 IRZO.IS 37.03 HO.08 17111.2J
1998 10AI.bI.1878.110 :U.12 226.nz 38211.21
19'19 I7i'IJ.3/1 IUb.blol 39.22 231.96 3911l.1~
~OOO 1711.03 19Q1l.9;!110.31 237.90 1101111.U.
2001 1821.37 lObl.l!9 III.bl 2411.9b 11175.22
2002 1811.71)Zl3CJ.6b 1Ii!.90 252.0il 1130&.28
2003 1'122.03 21'12.03 411.i!0 259.1)8 4417.111
20011 I'H2.3"22811.111 IIS.S0 2hl..14 11568.111
2005 c.»022.b Q 2356.7 8 lIb.79 :?73.c.»0 11699.111
200b c.»01\7.81l i!l.Ibi!.19 48.59 281.58 118(110.21
2007 ~15].Ob C!S&7.S9 50.38 289.9&S061.00
2008 U18.2S 2671.no S2.18 2 911.]11 'S2111.7'F
lO09 U8i.1I1 2771\.111 S3.<H )0".72 'i1l22.'i3
·2n10 211.18.61 i'R81.l\2 55.77 11'S.In %03.10
SCENARIOe ~~O I HI1.·D~I SC~NARIO·.b/~Q/lq63
BREAKDowN O~ELECTRICITY ~EQUIRF.~ENTS (GWH)
(TOTAL INCLUDES LARGE INDUSTRIAL CONSUHPTIONJ
GAEATEH fAIRBAN~8......~...~.....~.-...
"EDIUM RANGE (PR ••~)._•..•.•...•..••~...
RfSllJEIHIAL BUSIIH!S8 MISCELLANEOIJS EXOC.INDUSTRIAL
YEAR REtltllREME/lTS RI:QUI AE ME NTS REQUIREMENTS LOAQ TOTAL.............•...•~... ...~.....~..............~.~.....••..............•..••._...••.•.•.•...•..
1980 17t••JII 1.17.III b.U 0.00 1100.]1
11181 191.01l 210.11 11.15 0.00 427.90
1982 20S.b Q 241.08 1I.7Z 0.00 /155.50
,98]2i!0.)1l 2911.05 11.119 0.00 1Ill3.0Q
1984 23l1.99 2b9.OJ b.66 0.00 510.68
1985 249.n 282.00 b.b]0.00 H8.27
198b 2b2.9'5 290.40 b.b8 10.00 510.03
1981 Z1b.~11 2'18.n 6.111 20.00 601.18
11188 2811.511 301.19 6.80 10.00 bB.53
1989 1112.84 US.59 4.8'3 40.00 6b5.28
(").1990 31b.14 123.98 b.91 50.00 691.01
lD
(Xl
'991 313.15 3h.U 1.22 50.00 721.00
199i!]'j(l.lh 349.29 1.53 50.00 75b.98
U93 3fl1.17 3111.94 7.811 50.00 786.9'5
1994 384.18 !711.511 8.15 50.00 8U.92
19115 /j01.18 3B7.25 8.4&50.00 8/1b.89
I9 Qb 1117.511 40 n .5/j &.11 50.00 81b.90
19111 4]11.00 41J.1I2 9.08 50.00 90b.90
1998 IISO.1l1 /j21.11 11.38 50.00 nb.91
1999 /jllb.III 4110."O 11.69 50.00 9bb.91
2000 lIS1.&'2 1151.69 lo.on 50.00 99b.9&'
2001 500.15 Cl&1l.65 10.34 50.00 1029.13
200i 517.01 48J.6O 10.b1 50.00 10bl.311
2003 533.9Q 1I9A.55 11.01 50.00 In93.56
20011 S50.Q2 513.'51 II.lII sn.oo 1125.'11
2005 Sb7 .811 528."6 11.68 50.00 IliJ7.98
200b 51l7.911 54A.H 12.10 50.00 1198.82
2001 b08.01 SbQ.05 12.53 50.00 12H.b'i
2008 b28.IQ 589.35 12.115 511.00 1280.49
20011 bIlA.1I b09.b4 13.l8 50.00 1321.H
lOIO &b8.Qi!b29.94 13.8n 50.00 Ilb2.17
J )_J )I I I J )1 )I I j -.1 -I J J
1
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\.0
J 1 J J 1 -~_.J -I .~-]J '}1 -_..•.]J 1
SCENARIo.~EO.HI)••nRI SCE~ARIO·.~/~q/198)
TOTAL HHTRICITY REQUIRfMEllTS COlo/H)
(NET OF CONSERVATIOH)
(INCLUDES LARG~INDUSTRIAL CONSUMPTION)
MEDIUM PAtlGE CPR ••5)
~.~~.-_.
YEAR ANCHORAGE •CUOK INLET GREATER FAIRB'~KS ToTAL.......•.....•..•••.•••••.•...._-...-....-~.-._-_.._.~..._••.•...•..•....
1'80 I U).19 1J0O.31 2nf.'S1
1'81 20811.86 1Ji1.90 20;1l~7b
'982 UU.52 IJ'iS.SO 2&62.02
198)U21J.'9 lJa3.09 UIl~27
198q 211I1Q.8'i 'iIO.66 29bO.51
1985 2571~51 538.27 110Q~79
1986 21161.21 570.03 3211 ~211
1987 2UO.91 601.78 H'52.69
1988 28/10.61 6H.!53 ]471J,11
19119 2930.30 665.28 ]595.58
1990 30i!0.00 1>97.03 3711~OJ
1'91 UI 1l .8"721.00 l81J1~86
1992 )209.11 156.98 3CJ6b.69
1993 nOIJ.51 186.95 11091.'52
19911 ))99.02 816.9 2 Oi!lb.)lI
1995 JqQll.2"8IJb.89 1J]1I1~11
IH6 16011.25 816.90 lIIl"I~IS
1991 HllI.2]906.90 lIUI,1J
1998 16211.21 9H.91 /1761,11
199Q 19JIJ.I"9U."11901.09
200l)IJOIJIJ.U "96.9i!501J1~01
2001 ClI7'S.U 1029.13 5201J.35
200i!/1306.-28 IObl.311 5JU~b2
200)ll1U1.31J 1093.56 55~9~90
20011 1J568.1I1 II2~.n '!i691J~11
2005 11699.1J7 1157.Q8 5A'51~IJCj
2006 lIeIlO.i!l 1'98.82 bOH~05
201)7 '50111.00 UH.IIS 6]00.60;
2008 52 11 1.77 1280.a9 65~i!.2"
2009 'P122.51 1l21.]]"'lJl.B"
2010 'j60l.Jo 1l6i!.1/:I "Q6'S'.lI/)
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C C .•.....•...·.... ....•.....·...•U 4 _U'C.a-<7'00 ....411'=:I',.......N_...............N "'lI'IN«o II'-COIII\'N II)•.,.",or"'CP'...1rI ....0 ............0-lOt 11'1 ....0 no ;;r ..ofti '"<7'N..QC ...
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HE4--FERC +2%
C.101
-
C.103
SCENARJO,I1EO ,HE4 ••FERC ti!X·.&/24/1983
HOUSEHOLDS SERVED
GRE~TER FAIRRAHKS
•••••••4 ••••••••••••••
YEAR SINGLE FAMILY HllLTlFHIJLY "'OAILE Hm1ES DUPLEXES TOTAL..-............................................-.._......~.................-..
lQ80 7;»20.5287.lln.1 &17.15113.
(0.(00)«0.0001 l 0.000)(0.0(0)I 0.0(0)
1985 10b Ub.5 Af.llI.illo.lJb~.201l0A.
0.(00)(0.0(0),0.000)«0.(00)«0.000)
n 19QO 111111.79&0.U08.2375.240lJ..«0.000)(0.0(0)(0.(00)(o.noO)«0.(00)-'
0
-Po 1995 lI1HU.l8 /H.33 9 1.2139.28'505.
0.(00)(0.000)(0.(00)(0.(00)(°.(00)
lOOO 171\59.81132.4113.2298.321bf!.
.0.000)«0.0(0)(0.000)(0.000)(0.(00)
Z005 l'HIR.«JlS7.4119b.225~.35129.
0.0(0)«0.000)(0.000)«0.0(0)I 0.(00)
2010 20455.9911>.4852.Z1l22.31705.
0.(00)((1.0/0)«0.(00)(0.(00)«0.0(0)
}••I J .1 J j J I '._J J .)I I J J )I _.J
1 I J )1 J ]1 I j
SCENARIO,MEl)I HEII~~FEqC t2X--bI11l/198]
HOUStrlG VACANCIES
ANCHO~AGE •COOK INLET........~...~.••......
YEAR SINGLE FAMILY l1UlTlfAHtLY MO~JLE tlOMES DUPLEXES TOTAL..........."'....~........~.........•..••..•..•.~._~....~........................
1980 50 119 •7666.1 99 1.IlIb].Ib209.
1).0llO)(0.000)(0.000)(0.001l)(0.000)
1985 SilO.11'96.lib.292.21155.
0.000)«0.000)(0.000)(0.000)(l'I.OOO)
n ..190 6b~.200.152.289.1303.
0
-J (0.1'1(0)(0.000)«0.000)(0.001l)(0.000)
0m 1995 71l~.1751.17)•780.]"5~.
0.001l)(0.000)(0.000)(1).000)(0.000)
2000 ~Sll.2020.200.len.H75.
o.oon)(0.000)(0.000)(0.000)C 0.000)
2005 921.2173.21b.119.]1,27.
0.1100)(0.000)(O.OI)OJ (0.000)«0.00(1)
2010 q~8.2J1l6.2B.]l/2.)909.
O.l'IOO)(0.000)(0.000)(0.000)C 0.000)
SCENARIO'IoIEO ,HEq ••FERC t2X ••6Ilq/lQ8]
HOU51HQ VACA~C'ES
GREATER FAIR8ANKS....._--_..-..~.-~-.--
YEAR SINGLE FAMILY HUL TlFAMtLY HOIJILE HOMES DUPLEXES TOTAL..-...........-......-_...........---...........................................
1980 3b')3.H2O.966.89,.8 A5O.
0.1'100)(1).000)(0.(00)(/).000)(0.1'100)
1985 tl8.2b 'H.ll&.?li.3'§l1.
0.000)(0.(00)(0.(00)(0.0(0)(0.0(0)
n Ino 12fl.115°.21&.81.U6..(0.000)«0.000)(0.(00)(0.000)(0.000)---J
0
0'1 19~5 IbO.tltlA.37.80.72".
0.(00)(0.000)(0.000)(1'1.000)(0.000)
2000 196.1I5!1i.lib.78..7711.
(1.000)(0.000)(0.000)((1.000)(0.00/1)
laOS 210.~oo.So.70.1130.
0.(00)(0.000)«0.000)(0.000)(0.000)
lola liS.'53 9 •S3.80.IJQ7.
0.09 0 )(0.000)(0.000)((l.000)(0.000)
cJ J I J I j !I J ]I J I .1 I J I
I 1 1 1 --1 1 1 j t J j ]J I
SCEN.RJO.t-1ED t ,jEI.I·..FERC tU ...bl2lt'IQa]
FUEL PRICE FORECASTS EHPlOYF.O
ELECTRICITY ("KWH)
ANCHQR.GE •COOK INlET GRE.TER FAIRRANKS
•••••••~••••••••M.~•••••~•••__~•••••~...._..-__---~-.~~~.
YEAR III SI DE NT UL 811S II~ESS RESIDENTIAL RllSINfSS.............-...............•••..................
1980 0.031 (I.Olll 0.0915 1).(191
(J 1985 (1.0118 o.nltS 0.0915 0.090.
-'
a 1990--.,J (1.055 0.0'50 O.oqZ 0.081
1995 0.OS8 0.0'55 0.119"0.089
2000 0.0(.2 0.1159 0.096 0.091
2005 0.065 O.lIbl 0.IIQ8 0.091
lOIO ".(lb1 0.06/1 0.100 0.OQ5
SCENARIO'~EO I HfU ••FERC tlX ••b/211/198]
FUEL PRICE FORECASTS EHPLOYfD
ANCHORAGE.COOK INLET....................~~
NATURAL GAS (J/HMBTUJ
GREATER 'AJRBANKS....~•..•...••~.•.....•..•.••••.~...~
n.
--l
o
CO
YEAR RESIDENTIAL BUS INF.SS RESIDENTIAL IUlSINESS
••••...................................................~.
198D !.130 1.500 12.1110 11.290
1985 2.030 1.800 13.0110 II.bIlO
ttl 9 0 3.190 i!.QbO 111.390 12.BSO
1995 lI.ibO 1.1.030 15.890 11.1.190
2000 11.590 1.1.1011 17.511(1 15.670
2005 11.950 11.120 19.370 17.300
2010 5.3110 5.11 0 21.390 19.100
J J J J J J J .1 J J ~~)J )l J I •}
I -,1 ]1 -1 J 1 1 ..--1 ]
SCENARIO'MEO I ~E4 ••FERC t2X--bl?/f/198J
ANC~ORAGE •COOk INLET
FUEL PRICE FORECASTS E"PLOYED
'UEl OIL ($/MMBTU)
GREATER FAIRRANKS
••~W •••__•••_•••••_•••••_.~•••••••__•.__.-..-..-._~--.-----_-----
n
o
'-.0
YEAR RESIIlENTI Al flUSINESS RESIOENTUL RIISINESS_._.....-....",._-..........-....................--.......
1980 7.750 7.200 7.111"7.500
1985 7.q UO 7.420 8.010 7.no
U90 1I.7bO 9.1 9 0 8.8110 8.510
U95 9.b81l Q.OtlO q.UO Q.U20
2000 10.flAO Q.9AO 10.7811 10.tlOO
200S II.HI'II.OZI'11.900 II.tleo
2010 13.020 U.17o I).IlfO l2.b80
SCfNAR 10 I MEO I Hfq·~FERC .z~••~/aq/lq8J
RESIOENrlAL USE PER HOUSEHOLD (KWH)
(~ITHOUT ADJUSTMENT FOR PRICE)
ANCHOR ARE •COOK INLET
-.~.....-........_....
SI'ULL l.ARGE SPACE
YEAR APPLIAr-ICf.S APPLlll~CES HUT TOTAL.....-..................................•...•..•
1980 2110.00 f.l500.~]SOR8.'U 13bq9.15
1).000)(0.1)00)(0.000)(0.000)
1985 21&0.00 6092.53 11l1l.bl U0211.1U
0.000)(0.000)(0.000)(0.(00)
"lno 2210.00 5915.911 41519.116 U165.1l(l.
--'«0.0(0)(0.(00)(0.000)(0.(00)
-'
0 5q21~JO19952allO.OO 4!SH.1I7 12l1U.77
o.oon)(0.0(0)(0.000)(0.0(0)
2000 HIO.OO ~qsl.az 4111l7.6li Ullll.8tJ
0.000)(0.000)(0.0(0)(0.0(0)
200':5 albG.GO 6020.37 111109.15 1218ti.53
0.(00)(0.1)00)(0.0(0)(0.000)
lOIO 2 UIO.OO fl0 8 2.00 11436.52 U928.S2
0.000)(0.(00)t 0.0(0)(0.(00)
J }J I J J .-1 l J )J .J I I c1 J ]J !
-)-~
JI 1 "I -I ]1 1 ]J 1 ]1 ----]1
8CHI~RIOI "IEIl I HEQ-.FERC +2~••6/2q/lqaJ
RESlnENTI~L USE PER HOUSEHOLO (~WH)
«O'/ITHOIlT AOJUSTMENT FOR PRICE)
GREATER ,AIRBANKS-....-.-....~...-._...
S'-UlL LARGE SP4CE
't'E AR APPlI~NCfS APPLIANCES HEAT TOTAL.....••.••.••.•.........-........--.........
1980 lllbf).OO 57)9.52 HIJ.Mt 115I Q .II'
0.001)«0.0(0)(0.000)«O~OOO)
PI 85 2Sl5.99 121 78.92 }b012.J7 123?1.i!8
0.000)(0.0(0)(0.000)(0.000)
n 1990 2ftOb.OO bll a 9.0J 3 8 b7.SlJ 12922.b2.
0.(01))(0.000)(0.000)(0.0(0)
1995 2b1b.OI bh 9 .21 11051.72 U19b.qS
0.(00)«0.000)(0.000)(0.000)
2000 2 JQ S.QQ 6192.90 1I111J.1I8 lJaa2.31
o.noe'l)«0.0(0)(0.0(0)(0.000)
2005 281 b .01 12834.89 11510.1211 IUIIJl.53
0.000)«0.0(0)«O.OOtl)«0.(00)
2010 288~.00 68 8 2.91 lIb1l9.81 11I1I18.18
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SCEN4RJOI MfO I Hfq·MFERC +~X ••b/2q/1983
n.............
N
YEAR-.-.
nAn
IUS
1990
Ins
2000
200S
2010
ANCHORAGE •COOK I~LET......~.•.....•.-.....
8U01.0q.
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9580.&1
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BUSINESS USE PER EMPLOYEE (KW~)
(wITHOUT LARGE INOUSTRIAL)
(WITHOUT AOJUSTMENT FOR PRICE)
GHEATER 'AIRBANKS
~.•••.•.•....•.•.•....
11195.70
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SCENo\RIUI MEO I HEU ••FERC t2X-.~/~q/IQ6]ITf'UTln~IS ~
SUMMARY Of PRICE EFFECTS AND PROQR4 M4TIC CONSERVATION
IN GwH
QREATER fAIRAANKS
RESJDFrHIAL RUSINESS...........................
OWlj.PIUCE PROGRA M.P4DUCEIl CROSS·PRICE O"'N·PRIC~PROGR AM·I NOUCf 0 CROSS-PRIer
VEAR REDt/CTION CONURVATIOlj REOUCTlON REOlle nON CONSERVATIIlN PEDUCrr(l~J....................................................................................................................................................
1980 o.noo 0.000 0.01)0 0.000 0.000 0.000
1981 0.000 0.000 _0.091 .0.091 0.000 -0.080
198i 0.(100 0.000 .0.195 .0.194 0.000 -0.159
1983 0.01)0 0,000 -0.292 -0.292 0.001l -0,239
19811 0.000 n,Ol)o -0,390 ..0.]89 0.000 -0.319
IUS 0.000 0,000 -0.U87 .0.1186 0,000 -O,]Q8
198b -0.197 0.000 -1.0115 ..0.886 0,000 ..0.750
1987 ·O.HU 0./100 ..1.702 _1.,-86 0.000 ·1.102
1988 -0.591 0,000 -2,110 .t.686 0.(100 ·1.1153
1989 ..0,T811 0.000 ..2.9t8 .~.086 o.nOo ·1.805
("")1990 .O.lIRIi o,noo -3.525 .2.1186 0.000 -&'.157.......1991 -0.9Cil7 9.000 ..4.7l].i!.54J 0.000 ·2.786.......1'=>1992 ·1,010 0,00(1 ..'5.921 .2.599 0.000 ·1.111£1
1993 -1.023 0,000 .1,tl9 _2."55 0.000 -".0"]
19911 ·1.030 0,000 .8.li7 ..2.11 t (I.oon -11,672
1995 .1,049 O.(H)O .9 .'H 5 _i!.7b1 0.000 .~.]ot
1996 .0,877 0.000 -11.311 -il.541 0.000 .".2UO
I 9en -0.105 0,000 ..1l,1I0 -2.llS 0.(100 .1.17 9
1998 .0.5311 0.001)..11I.Q08 .2,089 o.oon .8.tl7
1999 -O.lbt'0.000 ·16.705 _1.8b2 0.000 .9.056
2000 ·0.1911 0.001l ..18.50]-1.63&0,000 _9.lJ 911
ZOOI 0.135 0.001l ·20.54]_1.lbO 0.000 ·10.9tcJ
2002 O.UbO 0.000 ·Zl.~8i!.0.68/1 0.000 -11,8411
200]O.76Q a.noo -Ztl.bZZ .0.207 0."00 -12.769
20011 1.II)Cil 0.000 .H.bbi!1).269 0.000 ·13.&911
Z005 I.U3U 0,0011 ·28.702 0.14~0.(100 ·I/I.U"
200b I.fib"0.000 -3'.U2 1.1b6 0.01l0 ·1'i.78]
2007 2.]011 0.0')0 -B.5b2 1.981 0.000 ·16.9117
2008 2.131\0.000 -JS.9'U 2.607 Il.000 ..til.I U
20011 ].11 ]0.0 1)0 .18.lIa2 3.~28 0.000 ·ICil.176
2010 3.608 0.000 -1.1/).1\52 1.8tl'l 0.000 "ZO.1I110
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SCENARIO,HEO,HEII-_FlRC +1X--1>1211'1983
TOTAL EUCTRICITY PEQUIREHENT9 (GwH)
!NET O~CONSERVA'ION)
(INCl'lnES LARGE IllOUSTFllAl COI'j5U H PTlON)
MEDIUM R'NGf CPR ••5)
n.
I-'
I-'......,
YEAR ANCHORAGE -COOK INLET GREATER F41PAANKS TOTAL•.....••...•••...._..~_.~.~......•..•....~......._.........._-....
1980 1901.19 11011.11 236]~SI
1981 20 9 3 •.,112/1."0 2S21·.9S
\"IIi!22<13.1\1157.~9 h~/I~qO
198)235).01 /.1613,17 <11\lA.BII
\l~Bq 248).01 'illl.i6 19 Q 7.Z q
1985 L'b12.99 5112.75 31~S.711
\96b 2102.91 Sn.37 H7b.211
ICl87 27 Q 2.61 601l.nO uqO~II]
191H\i!9~2.7!i "311.62 ~517.]1
1989 2972.67 605.25 ~bl7.92
1990 3062.59 09'."7 1758~1I6
19 9 1 1IS9.t>9 7 H'-liS 388),13
1992 32Sb.76 751.113 111107,7 9
199)]]SJ.85 716.61 II13Z~lIo
19 9 11 )1151'.91 1106.1"11257.13
1995 ]5118.0?eU.77 IIJltI.79
199t1 3079.I L'81111.75 115 11 1,87
1997 11110.2\"913.111 11105.9'5
1998 ]9/11.11 Qa6.fa 111168·.01
1999 11072./11 957.10 '50]0~11
2000 /.I2/l].50 QS'3.69 5I Q Z'.19
2001 112bll.02 100".28 526'''.)1)
2002 1I1111.5J 1019.87 1J31I11~1.10
200)IIj1l5.011 1035.117 ~1I20.51
20011 1111 11 5.5/'1051.01)'5119b.0?
2005 11506.07 I06b.lIS 5512·.H
200tl 115 9 6.17 1086.78 56£12.95
2007 /.Ib!lb.2~1106.90 5791.1 11
200e 11771>.]1\1\27.113 ';903-.41
ZOO'}1I"l!>b.II"\\111.15 6011.61
lnlo 11951>,""Ilh7.28 I>liB.Rb
n.............
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SCENARIO'HEO,HE'I ••FERC +iX--bI2Q/1983
PEAK fLECTRIC REQUIREMENTS (MW)
(NET UF CO~5fRVATIONJ
«JlICLIJDE S LARGE I NDUSTR I AL DEMAND)
MEDIUM AANGF CPR ••5J..~~.
YEAR ANCHORAGE -COO~INLET GREATER FAIRRANKS TOTAL............_..._......._...-.~_••.•••..•.••.......••...•..•..•••..•...•
1980 :sq".51 91.'10 11111:90
1981 IIZl.811 97.90 '3t'0~70
1982 lI119.n 104.110 S!if.50
1983 11"5.39 110.91 58b ·.29
19811 'jOI.be 111.41 bl9:oq
198'5 527.97 12].'*1 &"I.n
198b 511".98 110.90 b77~89
1987 5&b.00 137.119 703 ..89
1988 5RS.OI 111 11 .88 729.89
1989 b04.02 151.87 1SS:89
1990 bU.OJ 158.8b 7111.89
19111 b ll Z.1I1 IMi.lb Ml~9b
1992 "H.58 111.115 IHII.OII
1993 baZ.!b 177.75 81-C)'.II
19911 702.1 11 1l~1I.0S llh:IO
1995 HI.92 190.H 912:2"
pnb 7118.53 197.112 CilIlS:95
19'17 775.l'5 ?OIl.1I9 9H~"4
1998 eo 1.77 211.5"1013:])
1999 818.38 2111.611 1047:02
2000 855.0(1 22S.1t 101l1).71
ZOOI 8U.1l lZ9.27 109b.40
2002 819.2b 231.83 1112.09
2003 891.39 23&.39 lin .1f!.
20011 9(13.52 B9.QS 1111]'.111
2005 9IS.&S ;'113.51 II!iCil~lb
200b °35.7"2118.11 11@I~flCil
2001 951.93 252.70 1t'01l.b!
2008 970.0&2S1.~11 12n:!b.
2009 Q1l1l.20 2bl.89 1t!50~IO
2010 100b.30 2bb.1I9 1212'.fl~
J .J J J J )J 1 J I )1 )J
HE6--FERC 0%
C.119
-
....
-)--1 1 1 1 ]~--l J 1 ----~I 1 1 1
SCENARIO.MEO •HEb.~FEAC 01~.bJl"/lq8]
HOUSEtiOLOS S£RVED
~NCHORAGE ~tOOK INLET.-.....~--..--..-....-
YEAR SINGLE FAMllV MULTIFAMILY "'ORILE HOMES OUPLEXES TOTAL_.-...,.~.......-......~.........~...........,......••..•..•..-........-........
IqSO lSIlH,2031/1,82}0.7 1l Sb.11'503.
0.00 0 )(0,000)«0.000)(0.01)1))(0.000)
n.Iq85 llb227.2b201l.10958.85b1.q 1 qSb •.--'
N ,/).oon)(0,000)«0.000)(0.000)(O.nOo)
Iq90 SHOb.25 8 77.1)')05.SllbO,1055/18.
0.000)((1.01)0)(0.(100)«0.000)«0.1'(00)
lenS bb"9".]11"11).15lb1,813J,120'50Q.
0.(101))(0,000)(1).000)«0.000)«0.0(10)
2000 b9bb8.HI qo.IblSI,7 q9 b,li!blJSS.
0.000)«(1.01)0)(0.0011)«0.000)«o.oon)
20015 7I1liOJ.J5&69.17412,8579.Ilb2 0 1.
1).000)(0.000)(0.0 0 1)«0.000)«o.non)
2010 801l1lJ.HISA.191H.qUO.11I8591l.
0.000)(0.1100)«11.000)(o.oo/)«/).oon)
SCENARIO'MED ,HEb--FERC QX.-bl2Qil94]
HOUSEHoLn8 SERVED
GRE'TER FAIR6lNKS........-.-...._.~....
YEAR SINGLE FAMILY MUL lIFA/illY MOAILE HOliES DUPLEXES TOTAL.................................................•..••....•.....................
19So 7220.518'.1189.Ibl7 •1531'5.
0.(01)(0./)(10)(0.000)«0.(00)(0.000)
triSS 10bllb.S"h7.lila.alb5.20110".
0./)/)0)(0.0(0)(0.000)(0.001)(0.(00)
(I.1990 IIUbl.1 9 t1o •UOI:I.2375.2/1001.-I
N (0.1)01))(0.1)00)«G.OOO)(0.000)«0.000)
N
1995 lSUR.7Rlll.l1l1l8.2339.28lbb.
0.(00)(o.oon)(0.(00)(0.0(0)(0.000)
lOOO lbl84.7101.leOl.2298.1~I91..
0.(100)(0.0(0)(0.000)«0.0(0)«tI.1l00)
200'5 17555.829].4123.22S2.3~?ll.
1'1.000)(0.1)00)«0.0(0)(0.(00)«0./100)
lOIO 18 9 1b.925~.4501.22149.]11981.
0.0011)(o.oon)(0.(00)(0.000)«0.000)
~J J )J 1 ._J I .1 ]J I I I !I
1 .--]-]_I J 1 I 1 I i ]
SCENARIO'14[0 I HEb·.rERC 0~••bI2q/t981
HOUSI~G V~CANCIES
ANCHOR~nE •COO~INLET
_•••_•••W_M_~•••••••~.
YE~R SINGLE F"A~ILY fo1UL TlF"AM Il V MOBILE HOMES OUPLEllfS T.,YAl-.-....................."'........................-.........._-.........__.....
Iq80 508 9 •76&b.1q9 I,IIlb3.1&i'Oq.
0.(00)(0.001l)(0.001l)(o.oon)(o.non)
n lq85 1i0A.1 4 Q&.U!I.2Q2.i'1I11.
0.(100)(/1.(00)(D.OOB)(o.o/)n)(o.oon)
Nw t990 bH.1411.Illb.289.2!iI.~•
0.000)(o.nOIl)(O,()OO)(0.000)(0.000)
IH5 72 7 •1664.108.2811.2841.
O.{I(0)(0.01)11)(0.(00)(0.000)(O.l)on)
2000 7bb.•''l0.178.111'•~(I01l •
0.1100)(o.oon)(0.00.0 )(n.ooo)(n.OOO)
2005 82°.1 9 21.192.i!83,3222.
0.000)(0.000)(0.000)(0.000)(o.oon)
20 10 elfO.2115.211.)09,.~524 •n.l)on)(0.000)(0.1100)(0.000)(0.0(0)
SCENARIO'MEO ,HEo--fERt OI.-blla/t98]
HOUSING VACANCltS
GREATER f.IRRANKS.•.•.•.•.....•.•.....•
YEAR SINGLE fAMILY MULTIFAMILV MOBILE tlOMES DUPLEXES TOUL....................."......""......•••.•..•........•........•..•...•...•
1980 3&51.112n.98&.895,88sa.
o.noo)(o.oon)«0.000)«0.0(0)«".000)
".1985 118.Zb5(1.2/1.722,3'i11l.-'
N (0.(01))(".0(0)«0.000)(0.0(0)«0.(100).p.,
1990 t2&.as(I.24.8',68b.
o.noo)(0.000)«o.non)«O,O()O)«n.ooo)
1995 1&7.4411.18.8(1.Bi.
o.noo)«0.000)«0.000)(0.0(0)«0.0(0)
2000 180.'I4n.42.78.740.
o.oon)«0.1100)«0.00(1)«0.000)«Il.oon)
200S 191./ilill.lIIIi.77.763.
n.ooo)«n.I)OO)«/).000)«0.0(0)«0.00(1)
ZolO il'OQ.500.SO.28.. 1 fib.
0.000)«O.OOn)«0.000)(0.0(0)«0.(00)
J J )I I"J )J l -_J J -)I ))J ~t J )
n
N
Ul
~1 1 1 1 -~-1 -~-1 J 1 i ••1.
SCENARIO,~ED I HEb ••tERC O~··blaq/l~81
FUEL PRICE FORECASTS EMPLOYEO
ELECTRICITY lS I KWH)
ANCHORAGE ..COUK INlE T GRf.ATER HIRBANK8..~..--...~.......-....-...-•......•••.••.•_._.~M_...._...•_•.___~.~.__.__
HAR RESIOENTI AL BlJSl tiE SS RESIDENTIAL RUSINESS_.-......••.....__..-._~--...._...-.....-..............
Iq80 0.037 0.0111 0.n9S 0.090
1985'(1.0111'0.01l~0.091)0.090
19QO O.OS~(1.01113 0.090 0.011'5
,qqs 0.057 n.OSll 0.090 0.08S
2000 o.nsq /l.OSf!0.090 O.Ofl'i
lOOS O.Obi 0.058 0.090 0.(1l15
2010 0.06]O.ObO 0.090 0.(185
SCENARJU.MEO I HEb--FERC OX--6/24/1985
fUEL PRICE FORECASTS EMPLOVED
Hi TlIRAl GAS (S/MMBTUJ
n.
--'
N
0'\
ANCHORAGE •COOK INLET GREATER FAIRBANKS.........-•....••..••.•..._-......~..-...-.••.•..•...-..-......~.......•..
YEAR RESIDENT Ul BUSINESS RES!OENTI AL BIISINUS...................--............-..................
1980 '.730 I.SOO t2.7QO 11.290
1985 2.010 1.780 U.'!I30 '1.190
1990 2.91>0 z.no 12.5]0 1 1.190
1995 3.bOO J.170 U.!iJO 11.190
2000 1.bon J.HO U.!ilO 11.190
ZOOS 1.1100 1.170 U.SlO II.tel 0
2010 1.600 J.HO 12.15]0 II.t 90
.1 .~I ~.I I !J ~.)_cJ I I 1 I .J I I I
J J I )J CJ -1•]j ]J )1 )J
~-..~.._---~..----..
SCENARIO.HEO.HEb ••FERC OX ••bll"/198l
ANCHOPARE •COOK INLET
FUEL PRIC!FORECASTS EHPLOYfn
FUEL OIL <t/HH8TU)
GREATER FAIRBANKS
_.~._.w._.__~.
n
N
........
YEAR RESIDENTIAL BUSINESS RESIOENTJ AL AUSHIESS
.~.-...........-......_--.................111 ......••••...-
1980 7.750 7.200 7.830 7.IliOO
1985 1."30 7.1]0 1.100 7."]n
1990 1.1130 '.t .~o 1.700 7.G]0
1995 '.bJo 7.13n 7 "'00 7.G~n
2000 7.b]0 '.130 7.100 7.G]0
lOOS 7.630 7.130 7.709 7.4]0
i!010 7.1,3(1 '.130 7.700 '.4]0
SCENARIO.IoIED •HEb ••FERC OX ••~/24/,q8]
RfSIOfNTIAL USE PER HOUSEHOLD (KWH)
(WIT~OUT AOJUSTMENT FOR PRICE)
AHCHORAqE •COOk INLET..~.....-.-....~~.~...
SMALL LARGE SPACE
YEAR 4PPll ANtES 4PPLIA~ICES HEAT TOTAL...............................~........~............
n 1980 21l0.no bsoo.ol 50811.'52 llbqll.15
N (0.(01))(0.0(0)(0.000)(0.0001
00
IQ8S 2IbO.OO U51~qb 482\.18 11113.ill
0.0(0)(0.;11110 )(0.(00)(0.000)
IQqo 2210.00 bOi!'O·.l.Ifl 458&.110 1281b.88
(l.OOD)(0.'(00)(0.000)(0.000)
Iq9S 22bO.OO 59110.98 451lJ.96 12740.94
0.000)(0.000)r n.ooo)(0.000)
2000 2111).00 OJ!JA8.0f,Q4 4B.08 127~b.Uo.noo)«0.0(0)(0.000)«O.t)OO)
2005 2"&0.00 b058.34 4418.39 12836.7-J·
0.000)C 0.1000 )(0.000)(0.000)
2010 2 U IO.OO f.ll:!3.o0 4 4/1(1.09 1297'S.OQ
0.(00)(0.000)f 0.(00)r 0.000)
)-~J J J I J I })J I I J J I j J I
-1 1 1 ]1 1 1 .-----·1 1 -.'-J I J 1 I I 1
SCENARIO.MED I HEb--FlRC Ql.-bI2~/I9~]
RESIDENTIAL USE PER HOUSEHOLD (KWH)
(WITHQUT ADJUSTH~NT FOR PRICE)
GAEATfR fAIR8AN~S
._~---..-...-.-~.-_.-.
SHAll LARGE SPACE
yEAR APPLI ANCES APPLI ANCES HF.AT TOUL.......~.."'...--.....tII"'..........-....._-...-........
n 1980 211bfJ.OO 1§719.S:!Htl.6b '1519.18.
-'(0.000)(0.(00)(0.000)«0.000)
N
to
'985 251';.qq b171'\.96 3606,11 12lZ'~2b
0.000)(-0.'000)C 0.00(1)(0.006)
1990 2M6.00 64t18~8q 38&7,a2 129?2.31
0.001))(0.'000 )«0.000)(0.000)
1995 267&.01 bb11~S{l £1053,13 13u~O.8J
0.(00)(1).'000)(0.000)(1i.000)
2000 j!74b.OO &7 q 3."'4301i.7?138114.90
0.(00)(0;000)«(1.000)«0.000)
2005 2 8 1b.OO b8/pr.7 (\t1517.iO I t1 P8.90
0.0 00)(0.'0001 «(1.00/1)«0.000)
ZOIO 2"8~.1l0 6 8R 7'.91l £1656.67 Itl4]O,tll
0.000)(0.'000)t 0.0 0 0)(0.000)
SCENARIO.MED.HE~·-'ERC Ol--bI24/IQ83
8USINESS USE PER EMPLoYEE (M~H)
(WITHOUT LARGE INOUSTRI1L)
(WITHOUT 'DJUSTME~T FOR PRICE)
n
.......
wa
YE~R..-.
1980
l'US
,1990
1995
2000
2005
20tO
~NCHOR~GE -COOK INLET
•••••••~~•••••_••w ••••
8 L1 01.0ll
°.(00)
91580.53
lI.oon)
IO'-bl.I'C!
0.(00)
1 I OR5 .'J2
0.(00)
1135(1.10
0.00'l)
11'~2q.1l5
11.(01))
12707.1~
0.0(0)
GREATER 'AJR8~NKS_.a_•••_•._._••••.•__~
1495.7n
0.000)
HU.111
D.otH»
a3GO.55
0.0(0)
U()1.76
0.(00)
aen3.11
0.000)
9Z1§2.oQ
0.000)
q~1b.1J
0.000)
I I J J J J I I I I J I I I 1 J J J
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...u 4
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11.4
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SCENAlUOa IolEO I HEI>••FE~C OX._~/24/19RJ
SU~MARY of PRICE EFfECTS AND PROGRAMATIC CONSERVATION
IN QWH
GREATER FAIRBANKS
RESIOfNTlAl IlU!lINE8S...........................
OWN-PRICE PROGR AII.J NUllCEII CROSS.PRICE OWN.PRlCE PAOGRAM·INDIJCfD eROS,S·PRTa
YEAR PEDUCTION CONSfJ~.vA !.I0N REDUCTION REOU(:TJON.CON~fRYAIJQN •_..AEDUrT tON....................................................................................................................................................
1980 0.000 0.000 0.000 0.000 0.000 0.0011
1981 "1l.i'b7 0./\00 11.010 0.001l 0.000 0.024
1982 .0.11)13 0.11011 0.11111 0.001l 0.000 0./\48
1983 .II.AOO 0.000 0.20IJ 1I.0ClO 0./\00 0.072
1984 .I.O~I>0.000 0.21 11 0.000 0.000 0.11911
1985 ·t.U)0.1100 0.H9 o.noo 0.000 0.1211
198b .1.'i1i!0.000 0.4 \2 ..11.552 11.000 O.\]I>
1981 ·1.fllZ n.OOIl 0./114 .t.IOS 0.000 0.153
1968 ·2.1151 o.llon 0.531 .t.1>51 0.001l 0.t70
t 989 .1.291 11.0011 0.599 .2.210 0.000 °.lAb
n t990 .2.'HII 11.1100 O.flEll .~.71>2 0.0110 (1.203
--'w t991 ·2.712 o.noo 0.12'5 .1.201 o.lInll O.-?I'I
N 1992 .3.011 0.000 0.788 ..J.bllll 0.0011 O.BII
19QJ ..1.25 /1 0.01)0 0.1151 ..4.079 0.000 0.i's6
19911 ..11.49/1 0.000 0.9111 .4.511 11.000 0.2/1"
1995 ·3.'1'31 1l.1I00 O.Cln .4.05fl n.ooo (I.2Si?
1911b .J.Ab9 0.000 I.OU .5.141 0.11011 0.2Al
1'~97 ..14.001 0.000 1.04b .S.UR 11./1011 0.292
11198 ..4.1])(1.1100 1.1)81 .5 ...211 0.000 O.l91
t999 .4.1."1:>1I.00n I .1I S .'!I.1iO 0.000 0.~0]
20(10 -4.3911 (I.nOIl i.ISO ....'111 0.000 0.3011
lOOt .11."(1)o.lIon 1.1112 .6.109 11.000 0.'11Ij
2001...1A.,,41 0.000 1.2111 .6.30b n.ooo 11.323
2003 .1I.1b6 0.1100 I.lllb .b.t;04 0.0011 O.HI
2004 .11.1188 0.000 I.ne ..".701 11.000 o.HIl
2005 ..S.oll n.ooo 1.110 -b.1I911 0.000 O.Jllb
20Gb ·'5.1110 o.ono 1.311 4 .7.t31 (I.lIOO 0.3'51>
2007 ·"I.2b9 n.ooo 1.171 ..7.3bll 0.000 11.31:>7
200A -S.19 Q 0./\00 i •II II .1.0;91>11.1\00 n.311
lO09 ·S.t;c!A 0.001l 1.11115 .J.A29 0.1100 n.3Al
i!010 .CO.l-liJ o./\on 1./17 0 ..R.Ob~O.OOll 0.'97
.1 J )J .J i J •J _31 J }J -_.•J .J J _.J
")-1 »>-1 J 1 1 1 "]I 1 1 -;1 1 1
seEN_,UOs MEl)•HEt>·.FERC OX.·~/20/IqR3
HPE.KDOw~OF ELfCTRlelTY REQUIREHENTS (GWH)
(TOTAL lNCLIIDES URr.E I'lOIJSTRI Al CONSUHPTlON)
A~eHI1R.r,E •cou~INLET
.W.W •••~••••~.~__~N ••P
HEDIUH RANGE (PR ••~)......-._...~~......
RE'SIOfrITIAl nUSINESS MISCELLANEOUS BOG.INOUSTR'_L
YEAR RE~lIIRI':~If.NTS REIWIRE-HENTS REQlJlR[HENTS LoAO TOTAL.....~..•...••.•.-..•...•.•..•••..•••••w •••••••••_••~.••••••••••N •••••••.-.-.....--.-.--..
1980 IIJq.S3 ~11j.1b 211~1I 84.00 1116].111
1981 10?,Q.9Q 9117.110 211.b1 92.08 ;'085.fllI
1982 10*,2.4'5 1020.ll5 25.03 10(l.lb noe.OIl
1983 110.1.90 1091.00 25.1111 loe.i?a ?330.50
191\11 ttlls.n 11~5.55 25.1f,ttl>.!2 lIa"i2.Q9
11185 II'lb.lli!12311.IlQ 21>.12 1211.110 lI575.1I1
191.'6 121b.fl1 t27 Q •]1)211.81\137 ./t9 ;'b~O.ll1
19A1 120b.51 1120.'51 21.!>J 151.38 <?141>.OIl
1988 127f1.3'"nbl.72 2/1.38 Ull.1I8 illlJl.lll
1ge9 130b.21 1402.91 l!9.U 118.H ;tqlb.1>4
n "'90 l33b.Ob 14ao.lll 29.89 191.111>'00".1111
W 19 11 1 1]1].11 1500.82 ~30.811 195.13 lOIlIl.llaw1992IllIO.1b 1551.'H ]1.87 1911.110 11111.911
IIl!)3 1/141.21 1&1 11 .19 H.8f,201.bb ~2QS.9J
IIlIlII HII14.27 Iuo.1l1\H.81>i'01l.9]n Q].9J
1'1'15 15il.li'1127.'~34.85 <t08.?0 ]IIQI.II'
IqQ~15]b.QI\172 Q.9S 35.01 ill ".III ]51b.11I
1'117 15r;2.~"17H.15 3'i.21l 220.OS lsaO.J6
19 Qe ISbtl.]1 17]/1.111 '5.51)i!2b.02 '5611.57
IIIH ISIU.'I7 1117 .11 15.12 i!JI.Cl&'5I1A.1Q
2000 ISQ'l.&/A I nll.on 35.<3Q 237.'10 ltd J.OO
(1001 lo<tJ.bl I1n.72 36.55 ;?1l1l.1I1>:!I&HI~84
2002 1 bllL60 11107.'11 J7.15 '5i'.02 HOII.bll
200S 1&71.5 q 11.\42.09 H.'"?5Q.08 lAIO.5?
20011 Ib9S.57 11111,.28 38.3b ''''1>.111 l87b.3ft
2005 1719.5'5 1910.111 16.97 '73.20 'Q 0 2.20
loOI>17"Z.II]196A.jlll H.9?281."i8 110112.17
2001 17A5.Jo 2(12b.01 00.811 ;lIIQ.Qb 01 11 2.15
2u08 I Rill.III lOB.78 Ill.84 2911.311 /12112.1'
?001l IR'5I.0~2 I III •~II 42.1'1 lOlt.72 IIJII2.11
2010 IflllLq2 21911.11 11].10;llS.11I 1I1111?08
SCENARIO'HED.HEb-.FERt 0~_.~/la/19Al
BREAKOOWN OF ELECTRICITY REQUIREHENTS (GWH)
(TOTAL INCLUDES LARG~INDUSTRIAL tONSUHPTION)
QREATF.R FAIR8ANKS......~....."._..-..~.
MEDIUM RANGf (PR ••S)•....•.•..•..•...~..
R£SJDEN'I-'L BUSINESS HISCELlHIEOUS
YEAR REQIJIRfMEIlTB .REQUJREHEIlTS REQUIRE~£NTS•...........•...•~...._......-....~.•..•...•..•-......
1980 I1b.3°l!17.111 b.1II
1981 I °I.1:>0 no.n b.U
1982 20b.81 !1U.53 6.711
198]222.01 251:>.n 6.71
10811 217.2i!2*,4.0]6.bO
1985 2CS2.41 281.12 6.U
198b 2104.3'5 190.8&b.1O
1<187 21b.27 298.bO b.711
198/1 21111.1 0 JO~.Ja b.77
1989 JOO.U 114.08 b.81
1990 312.'0 11 121.112 ~.84
19QI 327 .28 n U.19 7.1Q
19Q2 3t1~.52 )1I~.55 1.43
1043 357.11;)SA.9 I 1.U
190/1 )71.01 171.21 8.ot
1995 3M.i!5 381.611 8.30
19QI:>3°11.85 l8li.Z3 8.38
1907 1I01.t1"1 J8~.a2 8.111
1941\11011.05 188.''\8.51:>
1999 1111I.tl5 190.1/0 8.~1I
2000 4ZI.2'S 191.'5~8.73
2001 1129.1"UR.a]8.88
2002 Ino.09 40S.Jb 9.04
2001 4alLllIj 412.25 Q.19
2004 115l.H 1110.13 4.]11
2005 /lbO.S9 4i!f,.0i!9.50
200~470.27 457.10 9.11
2007 1179.9U 111.111.17 9.9]
2008 J~A9 .'b'-115Q.25 10.15
i1110Q 4qQ.]n 1119.\]10.3b
2010 SRIl.Q't 1181.II I 10.58
..~•••••••w ••~••••
E lCOG.J NDllSTR fAl
LOAO
-,I
n
---..
w
+:>
J I J J J I I J )J _J
0.110
0.00
0.00
0.00
0.00
0./10
10.110
iO.oO
311.00
110.00
50.00
50.00
50.00
50.0 0
50.00
50.no
511.00
511.00
$0.00
50.00
50.00
50.00
50.00
50.00
50.00
'iO.oo
so.nn
50.00
so.no
50.00
50.00
l
TOTAL•........•-.......
400.31
U28'.b9
11151.01
IIAS.IIS
!51].8~
5112.21
511.91
MII.t"
611.:11
UI.(l1
b90.7t
71S.bO
74b.SO
711.1.39
1102.211
1130.111
1I111.4b
eilh.1 4
855.01
8t.3.20
UI.';'
AAb.1I1
901.3 11
CJlb.29
IUI.211
Qllb.lt
QU.OIl
98A.05
IOOCJ.O:-
t029.90
Inl)0.9f,
j )J .~
'1 J 1 1 J 1 1 ]I 'J )']1 i
n
w
(Jl
SCE~ARIO.MEO.HEb-.fERC Ol·-b/~~/1985
TOTAL ELECTRICITY REQIIIREME"TS (GWH)
(NET OF C04SERVATION)
CINClllOES LARGE INDUSTRIAL CONSUMPTION}
~EDIU~RANGE CPR ••5)
YEAR ANCHORAGE ..[IIDK INLET GREATER F'IR8'N~S TOTAL.~....-...••....•..•.•.•.••..R.__.......•__.•••••M ••••••••••••••••
Iqao 19b3.lq 1100.II 2163'.51
U81 211flS.b~1l21J.69 (!5'~,JJ
19112 2208.0q 051.01 ~bb5.lb
1981 i'HO.SQ 1185.115 ~81"i~9q
'9R~ii!~"2.99 5tJ.8]H6b.82
1985 2515.111 '511'-.21 :HI1~o'5
1980 '-obO.1a 511.91 12J2~o5
1987 21/1b.oa 1,,01.61 1)111.05
1988 (!1l11.311 631.31 1l1fJ2~b!i
19119 291b.b~bol.nl 3511.b5
1990 1001.91l b90.11 16q2~b'5
199'10 9 9 ..911 118.U 1Il11l~511
19 9 2 11 9 1.911 111b.50 19/1~.1I1
199J 12 9 5.9)17/1.19 11010'.11
19911 11 9 3.91 1102.29 1119&~22
1995 11191.9 J II 30.18 113i'i!~11
19 9 0 Hlb.11I IIlll,lIb /lHII~bO
Iq97 'SIl0.3b 1111".111 11]1\1.09
1998 J5"1I.51 85S.(l1 IIl1lq~59
1999 ]Ij A8.79 1'01.29 /l1I52~OA
2000 UI3.00 1111.111 11/111 1".51
2001 1/;11 A•8/1 Mlb.1I1 11565~H
2002 HO~.61i 901.311 ~6I1o~0"
2003 1810.S~91&.79 lI1?b.81
200/1 J81b.1&931.i'0 IIAnl~511
2005 19 0 2.20 9I1b.I'/lflAII.]O
200b 001li!.11 907.08 '5009.25
2007 lIloa.I IIi qSII.I"i ~"0.20
2008 1Ii!~i!.13 101)9.02 ~2"i1~15
2009 11]02.11 11l2 9 .99 1;]12.09
2010 lI~a2.0tl 1050.96 "i~q].o~
n
wen
SCENAPIUI MEO I HE&-_FE~C OX--o/2qI19113
PEAK ELECTRIC ~EQUIREHENTS (HW)
(NET OF CONSERVATIOH)
(INCLIlIIES LARGE INOUSTRUL DEMAND)
H~OIUM RANGE (PR ••5)
~.••••~.•.••.._··W·.~.
YEAR ANCHORAGE •COOK INLET GREATER FAIRBANKS TOTAl..,....•••••••••~••••~••w ••••.~.-..•.•....••.•._...••••.•..•.•..•.•.....•
1980 19&.51 'H .qO 487'.90
Iq8t IIZt .lb 9?87 51 9 '.lq
1982 4110.0Z 1011.15 550.3?
-1983 Q?0.77 1111.83 liillt:.bl
198Q 1195.51 1t1.:U b12.811
IQIlS 520.28 IH.U bllll~07
198t>518.15 130.1'1 b68~9~
IClII7 5'10.11 1 1]1.15 bU.7b
1988 5?4.48 UII.13 118."0
19~9 592.511 150.90 741.QIl
1990 bIO.&1 I S't.1,8 ?b8~i!9
1991 UO.5?Ibll.05 nll~u
1992 b50.53 1711.4t!UO~9S
1991 6?0.SO 176.79 847.2 9
1994 b9(1.I1&181.15 8?f.bl
1995 710.113 16 9 .52 1\99~9'i
1990 7\5.15 191.111 90&~56
1997 719.81 191.10 91l.111
19ge 72Q.bO 195.l9 91 9 .7 9
1999 P9.3i!19?08 92b~40
2000 1111.011 191\.97 9H~02
2001 7 11 1.2"202.311 9119~bll
2002 7bO.QIl 20'5.113 9&6.2b
1.00]71].1(1 ~Oq.18 1',l1l2~lIq
zoo II 7 11 &.92 2li!.S9 q99~51
2005 1100.111 11'5.9 Q 101b~11
200b 8i10.11 i!20.78 101l1~08
(1007 840.11 1 li!';.51 In6b~01
2008 AbO.U ;>]0.15 IOql»~8
2009 680.7 9 21li.1 II IIICi.9'}
2010 QOO.9b '.H.91 IIIIO'.8Il
J ...1 j J ;J,•1 J I J •,)J J I I
-,,)
--
-
-
..-
HE7--FERC -1%
C.137
-
-
1 -'}1 ~l "}~-~l .-.1 J 1 ~-}1 "J 1 'J ]I
SC[NAHIOI ~EO I HE7·.'ERC .IX··&ll/lJIQ81
HQUSFHOLOS SERVED
n.
W
lD
~NC~ORAGE •COOK INLET...........•..........
YEAR SINGLE faMILY MULTIFAMILY MUBILE HOHfS nUPLEXES TOTAL
.~.-.....-.........................................................~..-._......
IQ80 J5473.2031 11 •82l0.JlIStI.71503.
0.000)(O.tlOO)(0.000)«0.000)«n~noo)
IQ85 IIQ138.2&2011.11502.~Sb7.Q:illl~•
0.000)(0.000)(0.000)«0.000)(0.01)0)
1990 bOH7.21257.138b5.811bO.IOQQ2Q.
0.0 00)(0.000)(0.(00)(0.000)r n".oon)
I9CJ5 6&718.310011.1';372.8133.1211126.
0.01)0)(0.000)(o.ono)«0.000)«0.000)
2000 701118.HbOA.16393.81115.1288b].
0.000)r o.oon)(0.000)«0.001)"«n.noo)
2005 7573'l.3&2b1.11119.87iH.1381132.
0.000)(0.000)(0.000)(0.000)«0.000)
aOlo 8H1I7.3118111).lI1Q&9.952b.151181.
n.oOO)(0.001)(0.0 0 0)(0.000)(n.ooo)
SCENARio,MED I HE7·.FERC .1~••&/24/1981
HOUSEHOLDS SERVED
GREATER FAIRBANKS........~-~..-.........
YEAR SINGLE FAMILy foIULTlFAMILY "1091lE HOliES DUPlEXES TOTAL....................."'.....................•...............--...•....••.•"'..
1980 7220.5lAl'.1189.IbU.15311.
0.000)(.Q.OOO)«0.000)«0.000)«0.(100)
1985 10&46.5880.2130.llba.20/.124.
0.000)(0.000)«0.000)«0.000)«0.000)
n
1990 IISH.19bO.Ul2.2U5.24090.
.p.(0.0(10)(0.1100)«0.000)«0.000)«0.000)0
1995 14/.101.7841.3236.2339.27823.
0.000)«0.000)«O.OOn)«0.000)(0.(100)
;1000 15712.710l.3~1".2298.29348.
0.000)«0.(100)«0.000)«0.000)(0.000)
200S 17104.8020.qOt?•2252.Jl3 cn.
0.000)«0.000)(0.000)(0.000)C 0.000).
2010 1852".9031.4191.2t9b.]IH5Q.
0.000)(0.000)((l.000)«0.000)(o.OOQ)
J J I .~)•cJ I .J ]J 1 B }J J I I
)1 ..1 l )1 J 1 1 l I J '11.J 1 1
SCENARIO,HEO I ~El ••FERC .1~••bliI.lJI981
~OU8ING VACANCIES
ANCHORAGE •COOK INLET....~...-.............
YEAR SINGLE ""'IlV HUL TlFAMll V HORILE HOMES DUPLEXES TOTAL•....,.......................~...-...................................--..........-.....
lq80 5069.7~b~.1991.11.101.t&i10 lJ •
(0.000)(0.001)(0.000)(0.000)(0.000)n.
-'1985 51.11.11.19ft.121.292.245'5.-Po
-'I 0.000)(0.0(0)(0.000)«0.000),0.000)
1990 ~bU.91.151.28q.120?
1).000)(0.000)(0.000)(0.000)«0.000)
1995 711'.Ift7 tJ •1&9.21Hl.26bl.
o.noo)(0.001)(0.000)(O.flOO)(0.000)
2000 718.18111.100.152.312b.
0.000)(0.000)(0.000)(0.0(0)«0.000)
ZOOS 8B.1 9 58.195.2B~.3274.
0.(0 0)(0.000.1 (0.000)(0.000)«0.000)
ZOIO 90&.2151.211.1.]14.356ft.o.oon)(O.OQO)(0.000)(0.000)((1.000)
-.....................
::re NO ...e I\IC -c l\iC -e
inC 0=«l0 1\10 ""Q ::r 0 I\IQ
IIlC If'C ..cC ...c "'c "'C CO
-'a:t ....···....C 0 0 0 0 C C
~
C...
....
.-.............-....
IIIC O'C -0 CO 0 10 e ....C _0
Ir)0-0 _C 100 100 ...0 ...0 IOC...ClC ....0 0 CO C 0 0 -X · ·
••·••....0 C 0 CO 0 0 0
-'!L
:::I
C -....
en
11.1 en Ir).-.-.-.-.-.-.......X ....040 ::rO II'C 04=OC #C ..,0
U z r «le 1\1 0·NC ..,0 ::r=::r c .,.:-
Z ...0 O'C C 0 Cl Cl C =C lEi ::I:•·0 0 ••0
u a co 0 co CO =Cl CO
<C ...IU>...-'......-C III
Z er 0......:r
4D ~•........:::l ...co 0 lIoI
0-r Ill:
C ........0-.-..................»co -e ~c II:tC 0.:....0 II:tO::z -'(lIO ::z co "'0 .,c ::r 0 '"'c «to
(lI -....co -00 .,c ::ZC '"'C "0 ::rc....:r ..,·1\1 • ••·••..0 ...0 .0 c 0 C>C =•lL•...
~~--'•:::I:r
u
DC !'!liiIwJ
It.•>-........•-'....0 aDC ...0 O'Cl ....c II)C ::z .:......inC _0 l\I =>11'0 "'0 ceo 00....r .QC _0 -c _e -0 -c I\:C
::I:..""'··•···It.0 0 c 0 c c C
IU
-'
Q CI
III Z:r -CD
0...
a:...z a:•CO In 0 11\C 11\0
IaJ ...•II)ell 0"0"Cl 0
U ....•0"0"0-0'0 0 0
«t >-•-N IV N
-
C.,42
•0 0 WI If'!WI Itt Ifl•a-l'"Cl Cl Cl III III
If]•c:.0 Cl 0 0 C 0
en •· · ·'"....•0 0 Cl Cl C 0 Q
:z:.Z ..
:z -•C G:l f
lZ:~•0 a:III •I.s 0-•><II[
0 ..
~
Il.~a:
%:~ILl
11./~0-~•.,.III C 0 0 Cl 0....•a-a-a-a-a-l'"a-
~....'"'-•0 0 Cl 0 0 Cl c:>
0-a:0-f ··.·~Il!I C Z •Cl e-O 0 C Cl Cl
<I/[ILl •U 0 •W •a:>c •0 0-11./I
L -a:•u
11./-U a:-0-
~-II:U
i Il.W
~
~III
W
::l...
...•#."~IV a .0
III •...~a-lii III 11\11\
l'"0-lD •C C co C 0 0 0
i'fl!iit'liJ4."11./CD r ·•·•••....-J 1&0 •C C 0 0 co 0 c
a-z z •AI --•....G:l •.0 X •....•I 0 cr •.1"""r c •M U
I
y Wa:~-J •,..Cl N #If\po.a-..,-e ••....a-11\."11\III III...a:-•C co co 0 c 0 Cl•C ...•·•·•·.••:r.z •0 C c:-o c 0 e...y ILl •IIJ Z 0 •:r.•-•
F~
..,•W •a:•
0
W
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0
a:
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Lot IIJ •a-0-0-a-0 0 0
ID >•-AI tV AI
C.143
SCENARIO,HEO I HE7 ••FERC .1 ••·.114/1981
ANCHORAGE •COOK INLET
fUEL PRICE FORECASTS EMPLOYFD
NATURAL GAS (S/HM8TU)
GREATER FAIRRANKS..............~....•..................--__.-~-~.~.-.-.-.
n
.j::>
.j::>
YEAR RESIDENT tAL 8uSINESS RESIDENT!AL BUSINESS....••....•........••.•.............................
1980 1.730 1.500 12.740 11.i90
1985 2.000 1.770 12.280 tO~~80
1990 i.fl70 iI.&40 H.UO 10.430
1995 3.120 ].OqO It.UO 9.920
2000 3.nol)2.830 10.5110 9,4]0
2005 2.4hll)2,130 10.040 a,970
2010 i.BoO 2.030 9.550 R,5l0
J ]J J J J '.J )1 ),J j J 1Io,J J .1
)1 "1 I 1 )l 1 )I J i 1 1 ")1 'I 1
/'
8CEtURIO./04[1)•HE1··FERC .tX••~/24/t~8)
FUEL PRICE FORECASTS EHPLOYfO
'UEL OIL (S/H~RTU)
\).
+:-
U1
ANCHORAGE •COOl<INLET GREATER FATRBANKS.....~--•......••-..•.....•-.-........•..........•..--_••...•...•.•.....-.
YEAR RES WENT!AL BUSINESS RESIPENTIAL BUSINESS_._..............................-...-..."'".•..•....••
1~80 7.150 7.200 ?elO 7.1300
1985 1.1180 fl.HO 7.550 7.280
IqqO 7.11 0 &.650 7.180 &.q]n
P~95 &.101)~.1C!0 b.1\20 b.~59 0
2000 0.1I3n b.OlO 6.Q90 6.1&0
2005 b.un c;.uo 6.110 ~~9bO
lolo 5.1120 ~.QqO S.810 l§.bbO
n
--'
./::00en
SCENARIO.MED.HE7·.FERC .1~~.6/~Q/lq83
PESIOENTIAL USE PER HOUSEHOLD (KWH)
PUTHOlJT AOJUSTMENT I:QR PRICf)
ANCHORAGE •COOK INLET........-•.•.•.......•
6liALL LUGE SPACE
YEAR APPLIANCES APPLI lNCES HEAT TOTAL....................••...........................
IUO 2110.00 6500.0]5088.52 tl699.15
O.(l(iO)(0.,000)(0.000)«0.000)
1985 2lbO.OO "O92~311 4770.11 1;S0?3.0';
0.000)(0.000)(O.O!)O)(0.000)
Ino 2210.00 597S~flO 4579.19 12764.7"
0.000)(O~I)09)(0.000)(o.ono)
1995 2'.bl).00 59,q~57 4S0.35 12692.91
0.001))(0.0(10)(0.000)(0.000)
2000 2310.00 59149.22 ij1l46.9q 1210b.lb
o.ono.)(0'.000)(0.000)(0.0001
2005 2JbO.OO l:lO19~13 11 11 16.)8 U1IJS.SI
0.000)(0.000)f 0.000)(0.000)
2010 2/110.00 601\11.01 qll/lO.b8 llI.9H.7S
0.000)(0.(100)«0.000)«O!.'OOO)
J •I •J ;1 J J J i I ,J J ))j J JJI
~1 1 1 I I i -)1
StEN&lHOI HEO I HE7--FE~C -IX--6/~q/1983
RESIOENTIAl USE PER HOUSEHOLD (KWH)
(WITHOUT AOJUSTME~T FO~PRICE)
GREAT!R FAIRBANKS..~----.----~..-~.~--.
SMALL LARGE SPACE
YEAR APPLIHICES APPlI ANtES HEAT TOTAL......-------....-..--...-...........-...---_.----
1980 ZQbb.nO 5739.52 3Jl 3.66 11519.11~
(0.009)(0.000)(o.oon)(0.0(0)n.
--'1985 215lS.qll 6178.78 3&07.U U32l.00-Po (o.non)(0.0(0)«o.noo)(o~OOO)'-J
1990 2Mb.1I0 bQClt,I.'H 38tl8.80 129ZQ.11
0.1100)(0.0(0)(0.000)(0.000]
It,lt,lS Zf,7b.Ol b66q~6B ljOlj8.13 11389.02
«0.11 00)(0.0(10)(0.(00)(0.0(1))
2000 27Q6.01 b7 Q Z.01 Q]08.98 ne1.l7.06
0.(00)(0.000)t 0.000)(0.000)
2005 21.116.00 "Mer.00 11510.10 11.1115.10
(1.000)(0.000)t 0.000)(0.000)
2010 2886.00 b8f1Q~7(1 Qb5&.]Q 1111132.0Q
0.(00)((1.'000)t 0.000)(0.000]
8tENlRtOI ~EO I HE7-.FERC .IX ••b/2Q'198J
".
-'
,J::.
OJ
VE4R..-.
1980
IUS
UlJO
ntis
zooo
200S
2010
4NCHOR4GE •COOK INLET...•.•..........••....
81l0'7.011
,0.(00)
QSfllJ.1l3
0.000)
I olB.~b
O.OOD)
10"23.18
0.(00)
11223.18
0.000)
It8ZQ.e.9
0.000)
12blJ.lJS
0.(00)
BUSINESS lISF PER EMPLOYEE (KWH)
(WITHOUT l4~GE INOUSTRI4L'
(WITHOUT 40JUSTMENT FOR PRICE'
GR~ATER '4YR8ANKS.•.............-.
'711lJ5.70
0.000'
un.7S
0.000)
SlOtt.lb
0.000)
8Ub.oe
0.000)
8889.85
0.1)00)
qalQ.Oill
0.000)
QbOS.15
0.000)
)--J )»-,J J I 1 "J I 11 I J )J J,J
,.-......•-'.a:..
Q...
I •en2 •e .-.tf"Io f'\t CP"..0 lDC"f\I~oD -.4)-..0 .,«)1\1 II'0'"0'"0'.,tnlt'..ooD ...
!"""....0 •0 c_fI!ljf\l ...0'.;;oruC).,0".....CJ n:...~-~..o ...11\..0 Cl 0'=:1 ....0,."..0
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GREATER F41RBAUKS
A[8JI)£NTI ~L BUSINESS..........................
IIWN ..PRICE PROGIH ,••1FlootED -..CROSS ..PRICE ..pWN.PRICE PPOI;RlH ..INOIICED CROSS-PRlCf
YElR IlEOUCTIOI~C:ONS£RYl TI!Jrl _IlEOUrtTOr-J D~nu;:H(hi -.-."enHS~"lJUlaf>l.-.__IlEDIJr:T TO~J.........._...---_._0.__-..-..,.......,-----.._..~-.•.~;;..........~t:.........................................-.....................................................................
1980 0./100 0.000 0.000 0.001)0.000 ll,DOO
1981 o.nOI)o.ouo 0.1511 0.000 0.000 1).01111
1982 0.000 o.oon 0.J01 0.000 0.000 n.1S 1
1981 0.000 o.oon 0.1161 0.000 0.000 0.226
19811 0.000 11.000 0.615 0.000 0.000 0.1t12
1985 o.nOIl 0.000 0.U8 1).000 o.noo 0.111
1986 ..0.J35 0.000 I • I 711 .0.550 0.000 o.!I7'5
1987 ..0.6711 0.000 1.579 ..1.099 0.000 0.771
1988 ..1.1)0'5 0.000 1.9811 .1.6119 0.000 0.971
1989 ..1.3111 0.01)0 2.389 ..2.199 0.000 1.169
1990 ·1.676 o.non 2.19'5 ..2.1 lHI 0.000 I.]66
n 1991 ..1.9bO 0.000 3.1.159 ..3.109 0.000 I.Ul
tTl 1992 ·i?211~0.000 1I.llli ..1.1169 0.001)1.9111
a 1993 ..2.521:1 0.000 11.188 .1.829 0.000 2.231
19911 .2.81/1 0.000 5.1153 .11.190 0.000 i.!527
1995 .3.0911 0.000 6.It 8 .11.550 0.000 l.8U
1996 ..1.282 0.000 6.896 .11.111 0.000 1.117
1991 ..1.116b 0.000 1.67U .11.1172 0.000 1.1111
I 9 1:1 a ..1.650 0.000 8.1152 ..5.03J 0.000 1.711
199".3.813 0.000 9.230 .5.11:111 0.000 1I.01f>
2000 .11.011 o.noo 10.008 ..5.356 0.000 11.116
2001 ..11.168 0.000 10.9611 .6.e96 0.000 6.I "
2002 .11.311:1 0.000 1I.9aO .8.1131 0.000 1.907
200J .11.1171 0.000 12.87b ..9.978 0.000 9.702
20011 .11.622 0.000 U.~:n ·11.5111 0.000 11.1191
ZOOS .11.711 0.000 1 11 .189 ·1].(159 0.000 11.292
Z006 ..11.920 0.000 15.971 "12.161 0.000 12.776
2007 .5.0b7 0.000 17.152 ·11.2b2 0.000 12.259
2008 ..5.2tll o.noo 18.1111 ·10.363 0.000 ll.1IIJ
2009 ..S.lbl 0.000 1 9 .5lb .9.lIbS 0.000 1I.2i!6
2010 .5.'508 0.000 20.698 MR.'H'b 0.000 10.110
)I J )J I I )il i •_I ~j _J j·11
"j 1 "}~.
1 1 1 C]'1 '1 1 l cl f »
SCENARIO'HEn I HE1-_FERC .1¥--6/~II/IQAl
BREAKDOWN 0'ElfCTRICITV REQUIREMENTS CGWIl)
'TOTAL INCLUDES LAPGE INDUSTRIAL CONSUHPTION)
ANCHOPA~E -coo~INLET
----.-~.-..--._-.---.-
MEDIU~RANGE (PR ••~)._....-.--..-~...-_.
RESIOENTJ AL BUSINESS HIIC!LUNEOUS [.OG.INOUSTRTAL
YEAR Rf:QlJIREMENT6 RE llU I RE HE.NIS AEQUIREMEIHS LOAD TaTAL..........•.........••.•.••.•..•...-...-._...--.-----~....---.-~--.----~..._...-...-------
1980 919.5)8H.Jb 211.)1 BLI.OO 1961.19
1981 IOn .65 9118.'7 211.15 92.08 2092.65
1982 1015."111 IOlo.99 25.1/1 100.16 22<'2 ..10
198)1123.8"1093.80 25.~11 108.211 2)~1.~5
19811 1171.99 111Ib.U h.08 II b .12 2/181.01
19115 IUO.I'(1)9.11)26.52 124.lIO 2610./16
198b 12'J2.1J 1280.bll 21.22 131.1)9 "697.8/1
19117 12811.15 1121.1\5 i!1.91 151.18 n85.29
1988 11t~.11 11b).06 28.bO 16l1.88 2812.11
1989 IJLl8.19 111011.27 29.)0 178.31 291>0.11
()1990 1180.21 1I'1I5."9 29.99 IIH.86 I "01.l1.~11
--'
U1 1991 11I08.]?llla.lIb )0.12 195.11 1111.08--'1992 11ll6.5/l 1508.211 )1.1.111 198.lIO 11111.61
199]1111111.71 1519.1.11 Ja.lb 201.1.16 1218.1"1
19911 1"92.88 I!HO.Cl9 12.89 i!04.9)])01.68
1995 1521.05 U02.1b H.61 208.20 131>'i.22
199&1518.05 U19.111 ]].98 2111.14 1405.51
1997 1555.0'i 1~3lo.11 )1I.H 220.08 l1111S.80
1998 1572.0"1653.29 )11.11 22b~02 11l8b.09
1999 1589.0'5 Ul0.h 15.I I 231.'16 )l;2b.38
2000 IbOb.'05 11>81.2/1 15.11"231.90 1566.67
2001 1~28.7b 17211~21\lb.11 21111.96 3b31.1.1t
2002 Ib51.117 t7bl.3J U.711 252.02 )101.56
200l IlIlll.I"1798.n 11.31 25'1.08 3169.00
20011 Ib'Jb.88 111 1'5."2 18.00 2bb.14 ]83b.411
2005 '119.59 11I1'..lIb lA.b3 27].20 1903.8/1
200b 1150.'61 19 29.0;1 3Q.S6 281.~8 1.1001.32
2001 1781.bl t98b.b1 110.50 28q.9b L1098.7lo
200A tRIl.6b 20111.11 Ilt.ll]298.111 L1t'l6.20
2009 IHI.Il.bll 2100.88 112.3b 10b.J2 IlZ91.IlLl
2010 18111.10 ,,!IS1.'l1!Ill.aq 115.10 /1]91.011
SCENARIO.HEO.Hf7 ••rERC .1~·-6111l/Iq8l
BREAKOOWN OF ELECTRICITY REQUIREMENTS (GWH)
(TOTAL INtLllDES LAROE INDUSTRIAL CONSUMPTION)
GREATER ,AIRBANKS
~..~..•....•.......•_.
MEDIUM RANGE (PR_.S).•.•••.....•...•••-~
IlESIDEInlAL 811S ll~f sa MISCELLANEOUS ElIOG.INDUSTRIAL
'fAR Rf.DUIf:lPtfNU REQIIIREMENTS REAUIREMENTS LoAn TOTAL...-.•..•..•.....•.••.•.................~..••.••.•...•...............-...........................
1980 17b.JQ 211.14 6.78 0.00 IIno.]1
19S1 19 1.2 9 2]O.lb 6.76 0.00 IjC!8.II I
1982 20b.19 143.58 fI.7J 0.00 45b~51
U81 221.09 i!H.81 6.70 0.00 484.bO
19811 2l5.9IJ 210.0J 6.b7 0.00 5U.7n
1985 250.89 281.ib ,,~n 0.00 SIIO.SO
198b 2b2.h 290.9 ]6,&8 10.00 570'.31
1987 j!71.l.bl 298.59 11.12 20.00 599.94
1986 28b.50 101l.,h 6.75 30 .•00 U9.52
1969 298.11 111.93 6.19 40.00 b~9.09
n 61\S'.U.1990 310.ill ]21.60 6.81 Sn.oo
-I
U1
N 1991 322.09 328.611 7.0]50.00 7n7.U
1992 311.911 US.73 7.21 50.00 ,726.90
1991 )115.80 3112.80 7.43 50.00 711&.02
1994 551.&5 149./111 7.bl 50.00 765.111
1995 3b9.50 156.9]7.8]50.00 1SII.26
199b US.flR ]&(\.18 7~9]50.00 H J.99
I9Cl7 1FJ1.86 lU.U 8.03 5n.oo 80].12
1998 388.011 H7.1.8 8.tl 50.00 813.115
1999 194.21 170.73 8.23 50.00 8i.'J.18
i!000 1100.39 1711.18 e.n 50.00 8H.lll
2001 1107.11 ]81.13 8.118 SO.flO 8116.Cl2
2002 1I111.2l 1811.0S 8.02 50.00 860.en
200]1121.Pi 395.03 8.77 50.00 8711.q~
20011 1128.(lb 1101.0 8 8.'l1 50.00 fle8.96
lOOS 11311.98 1108.93 Cl.OS 50.1)0 90i.91
200b IlII1.52 1l19.112 9.2&5/1.00 9i!i!.20
2007 IlS?.OIl 1Ii!'1.Clt 9.116 50.tlO 9111.112
2008 1IIl I.l.SCl IIl1fl.3 Q 9.6b 50.00 9faO.bll
2009 1169.13 1150.118 9.B6 '50.00 Cl19.67
iflll)1171.67 /lbt.l!>10.Ob 50.00 QQll.Oq
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SCENARIo.MEh I Hf7 ••FERt .1~·.b/2qJI1I81
TOTAL El!CTRltITV REQUIREMENTS CGWH)
UIET OF CflNSERVATIOtl)
CI~ClUOE8 LARGE INDUSTRIAL CONSUMPTION)
~EOIIIM R.4NGE CPR ••'!i)......__._._4~.
YEAR ANCHURAGE •COOK INLET GREATER FAlRBANKS TIlT AL.....•......•...•.•.....•..._...._._.......~.......--.•...•...•.•..•.•
11180 11110].111 1I00.31 iHbl.SI
1981 201J2.b'J IIl8.1I1 i!S21~Ob
1982 2Ul.IO 115b.51 ~b16.bl
1118]2351.55 11811.bO 26J6~lb
19811 21181.01 '512.10 2QH .11
1985 2bI0./lb ~ql).80 1151'.2b
198b 2b'H .88 970.11 Hb8~25
1987 i!1A5.a9 599.911 H85.211
11188 2872.11 bZIJ.52 lSOil.el
1989 29bO .1)bS9.09 1blll.21
11190 10117.511 IlU.bit 171b'.21
1991 1111.08 101.78 1818~8b
111112 lIll1.bl Uf!.qo ]qOI~S2
un 1231J.15 111b.02 H811.11
19911 UOI.be lb'5.lq 1I0U·.82
IUS 31b5.22 1811.2b 111119'.118
199b 1Il0S.51 191.99 111119~50
1997 1/1115.80 801.12 42119.52
19q8 lI~Rb.OIl 811.115 112qll~511
1999 352b.H 821.18 111119'.lib
2000 ]'ibb.b1 8H.II1 tl1qq~Sll
20 0 1 U3II.11 8I1b.9Z 111181~0]
2002 370'.Sb 8bo.93 IlSb2.Qq
200]l1b9.01)111Q.95 IIb/Jf.911
201)11 181b.llt1 868.lIb tlH'f.IIO
2005 1901.81\902.q l 1180b'.8b
200b 111101.12 Q22.i!O 119B.S2
201)7 1I0Iltl.1'.>qlll.tlo!'501l0~1I\
a008 /J1 9 b.20 IIbO.tllI 'i1'io.611
200Q 112 9 ].1.111 97 9 .A1 'liP :J'.51
iolO 1I191.011 99 Q .OQ '131111'.1 J
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ICENARIO.MED •HE8 ••FERC .l,·.&/2U/19A]
HOUS(HOlOS SERVED
ANCHORAGE •COOk INLET........-.............
YEAR SINGLE FAllILy Io1ULTJFAMILY ~OBILE HOMES DU'LEXES TOTAL_._.
.._.,.....~fIII_••..........................-......,..~........~.-.................
1980 351111.20311.1.8HO.71186.11503.
0.000)(0.000)«0.000)(0.000)l 0.000)
n 1985 1.1908&.l&201.l.I1Q92.85&7.9531.19.
«0.000)«0.00('1)«0.000)(0.000)(o.oon)U1
........
1990 b01.l6 9 •27)117.1]897 •811bO.110171.
0.000)(0.000)«0.000)«0.000)«0.000)
lq95 b521.15.]00&3.15018.83]].II 96'H.
0.000)«0.000)«0.000)(0.000)«0.000)
2000 6929b.3i!901.1&055.7 Q Q8.12b201.
0.000)«0.000)«0.000)(0.000)(11.000)
lO05 71.126&.35573.17384.8557.I1seoo.
0.000)«0.000)«0.000)«0.1)00)(0.000)
aOIO 80Q 12.19156.UI3I1.91&).11.18'5&'5.
0.000)(0.000)(0.000)(0.000)(0.(00)
SCEN4RJOI HEO I HE8 ••FERC .2~••bI2q/198]
HOUSEHOLDS SERVED
GREATER fAIRBANkS................-_.....
n4R SINGLE FAHILY IotULTlfAMILY HOBILE HOMES DUPLEXES TOTAL......................................................................-........
1980 nao.5287.!l89.1617 •15113.
"«0.000)«o.POO)«1).000)«0.(00)«0.(00).........IUS IOb46.5~67.2UO.1765.20Q07.U1
ex>(0.000)«0.(00)«0.000)«0,(00)«0.(00)
1990 11575.7~bO.2!H.2175.241111..
0.(00)(0.(00)«0.000)(0,000)(0.000)
UQ5 U1l86.78111,:S083.23H.271IlQ.
0.(00)«0.0(0)«n.ooo)(0.000)(0.(00)
i!000 15152.7703.3481.2298,aSbllO.
0.000)«0.000)«0.000)(0.0(0)«0.(00)
2005 16717.?7qU.3 q 2"'.2252.]0702,
«0.(100)«0.(00)«0.000)«0.000)«1'.(00)
2010 18155.flRS'S.11]10.215!.1]1I7l'.
0.(00)«0.000)«0.000)(0.(00)«0.0(0)
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SCENARIOI /olEO I HE8-.FERC -ZX--6/24/198J
HOUSING VACANCIES
ANCHUR4GE •COOK INLET•....•.......•........
YEAR SINGLE FAHll Y HUll If AM Jl V HOBllE HOMES DUPLf.XES.TOTAL.......-.•...............................................--..................
n IUD SI)6.lJ.1&U.19q1.14b3.Ib~OlJ..«0.000)«0.000)«0.000)«0.000)(0.000)--'
U1
1.0 Ilf8!)540.Illqb.li!b.2Q 2.2115'5.
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I If If 0 btl'S.'.153.289.II 1/1.
0.000)«0.000)«0.000)«0.000)«0.(00)
1995 118.1621.1f/5.26t1.2190.
0.000)«0.(00)«°.0(0)(0.(00)(0.(00)
2000 Jbl.U77 •111.Sill.323'5.
0.(00)«0.(00)(0.(00)(0.000)«0.000)
2005 811.IlJl'.Iql.2eil.321 ~.
0.000)(0.(00)«0.(00)«0.00(1)(0.(00)
2010 890.2115.211.JOq.3'i21l.
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SCENARIO,MEO I HE8 ••rERC -2X.-bllQ/IQ8),
fUEL PRICE FORECASTS EMPLOYEO
!LECTRICITY (5 I KWH)
ANCHORAGE·COOK WI ET r.RfATER FAIR9ANKS•.••.......•~~..~-~~_..~...••••.•.......•..~~...••.•........_--
n
---'en
---'
YEAR RESIDENTIAL BUSHIESS RESIOENTI AL 8USINESS
~...............................•.•...••...•...•.....
1980 0.037 n.03/J 0.095 0.090
1985 o.oqa O.O/JS 0.09'5 O.OQO
19 9 0 0.051 0.0/J8 0,090 /).085
1995 0.053 0.050 0.090 0.085
2000 0.115'5 0.052 0.090 1'1.095
2005 0.056 0.053 0.090 O.0811J
2010 0.051 O.OS/J 0.090 0.0"5
SCENARIO.MEn.HE8-.FERC -l¥--6114/19S3
FUEL PRICE FORECASTS [HPLOYEO
NATURAL GAS (S/MMBTU)
ANCHORAGE •coo~INLET GREATER FATRAANKS......- -..~.~.-_~..-..~...-..•...•...-.•.•.......•~....-
n.
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N
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YEAR RESIDENTIAL BUS ItJES$RE st DENT!Al IHJ91NFSS..-......~-............---.............................
1980 1.730 '."i00 12.510 11.290
1985 t.Q 811 1.750 12.010 10.750
1990 2.170 1.540 10.880 9.710
Ins 1.070 2.8/10 9.830 8.780
2000 2.A80 ~.bSO 8.890 7.IH10
lOOS 2.720 2."90 S.OlO 7.110
2010 ~.~tlO II.HO 1.2bO b.U80
.J .oel I ,.)~.J I I .J ....1 .1 I .,..J J I
.,j 1 ],]1 j ])1 .~
8tEN4RIOI ~ED.HES--FERC -lX--b/24/196J
ANCHORAGE -COOK INLET
FUEL PRICE FORECASTS EMPlOY[O
FUEL OIL (I/HMBTU)
GRF.ATER F.tR~ANK9.__._.~...-.•..••...•.....•.••.•.....••••••••••••••w •••••••••••__••••_••••
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YEAR RES IDENT IAl BUSINESS RESIDENTIAL AUS'NfSS.....••...•............................................
1980 1.150 7.l00 1.830 J.50n
1985 1.UO 1:1.850 7.390 1.110
IHO I:I.UO 1:1.190 f:t.bAO b.IlS0
1995 'S.Q90 5.tlOO 6.0110 -;.11]0
2000 S.lllo 5.0bO l!5.lIbO I§.no
zoos 4."90 4.570 ~.9110 4.160
2010 4.1120 11.110 If.ll bO 11.310
SCENARIO'MED I HES·.FfRC .i~·.~/24/1g81
RESlnENTUL liSE PER HOUSEHOLD (KWH)
(WITHOUT AnJUSTHENT FOR PRICE)
ANCHORAGE •COOK INLET-.......~....•..•..•.•
8MALL LARGE SPACE
YEAR APPLIANCES APPLIANCES HHT TOTAL........_.....~...................•..............
n 1980 ii!lto.1I0 f»SOO'.bl 5088.52 13b~9.1 S.
----'(o.noo)«0.000)t ".0(0)(o.ono)
0'\+:-
1985 iho.oO bO QZ.51 477I.U 130~4.11
(0.0(0)(0.'001)(o.oo/)(0.000)
1990 2210.0U 597b~2l 4579.27 111~5.4Q
(0.000)(0.000)(0.000)(0.000)
19q5 22bO.(l0 15q IIf.5 9 4510.05 12688.b4
0.000)(0.0(0)(0.000)(0.000)
1000 2110.00 59 11 9.30 qaSI.1]11110.11]
(0.00/)(0.000)(1).000)(0.000)
2005 2}bO.OO 6019.52 4411.0]U1Q6.S'
0.(00)(0.0(0)(0.(00)«0.000)
2010 2 11 10.00 6085~O2 q/~1I0"21 12935.22
0.(100)(0.1)00)(0.000)(0.000)
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J
SCENARIO.Io1EO I HE8-.FERC -?¥--b/24/1qaJ
~E9IOENTIAt liSE PER I-IOUSnlOLD (KWH)
(WITHOUT ADJUSTMENT FOR PRICE)
GAEAT£R FAIR8A~K8........-...••.•••...•
SMALL LaRGE SPAtE
YEAR API'L1 ViCES APPLl ANeES I1EU TOTAL.......................".......•......•......•
n
1980 i!Qbb.OO 51H~5?HtJ.bb lIS19.18
m (o.noo)(0 ..000)(0.000)(0.000)
1TI
1985 2535.99 b178.9"HOb.)j!li!311.2fl
o.noo)(0,.(00)(0.000)«0~01'l0)
1990 2Mb.OO flIlSO.9Q lEIb9.'59 129~6.S3
n.ooo)(0.000)(0.000)«0.000)
1995 261f».01 bflbO.1S 110115.01 lU81.H
0.1100)(0,1)(10)(0.000)(0.000)
lOOO Hllb.OO b7 9 1.29 4)11.'59 138 11 8.88
n .1I0ll)«0 ..000)(o.OOn)«0.000)
lOO'5 2 8 16.00 b852.Sb 115011.)9 11I11i!.911
11.000)«0.01'11)(n.OOO)(0.000)
lOla 288".00 MI91~15 IIb5b.1J9 14lI~1I.35
/).000)«0.000)(lI.non)(0.000)
n
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SCENARIO,MEO'~E8·.FfRC .l~··b/24/19Ul
BUSINESS USE PER EMPLOYEE (K~~)
(WITHOUT LARGE INDUSTRIAL)
(~ITHOUT ADJUSTMENT FOR PRICE)
YEAR ANCHORAGE •COOK INLET GREATER FAIRBANKS_._....~...-.-_...-........••••....•.....•.•....
1980 8l107.04 1495.70
1'1.000)«0.000)
1985 9'580.48 7972 .14
0.000)(0.1'11'10)
U90 1010 /1.51 8313.01
0.00/)(0.1'100)
n9S IOMO.lIb 8585.26
0.1)00)«0.1'100)
2000 IIIH.bS 88139.70
O.()OI)(0.000)
2005 Il1Sl.91 cUH.17
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lolO 1253q.2]9S81.lb
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SCEN~llJO.HEO I HEe·.rEPC .lX·.&/211/198]
SUMMA~Y OF PRICE EFFECTS AND PROGRAMATIC CONSERVATION
IN GI'4H
GREATER fAIRBANKS
flESIUfNTlAL RUSlNfllS.........................
OI'jN.PRICE PROGR AI~.INDUCED CROSS.PRICE OWN.PRICE PROGRAM.INDUCED CROS9 M PRJCE
YEAR IlEOUCTION CutlSEHVATIOU REDUCTION REDI!PION CONSERV~TlON REDUCTION..................................................................................................;.;;...;;.;;....;;;..................;..
1980 0.000 11.000 0.000 0.1100 11.000 0,000
lUI 0.000 0.000 0.192 0.000 0.000 0,130
1982 0.000 0.000 0,385 0.000 0.000 0.259
IUS 0.000 0.000 0,577 0.000 0,000 0.389
lUll 0.000 0.000 0.lb9 0.000 n.OOO 0.1319
IUS 0.001}0.000 0,9b2 0.1100 0.000 0.61l8
US..0.3311 0.000 l.bU .0."95 0.000 o .U9
1987 ·O ••b9 (1.000 2.3&2 .0.990 0.000 1,309
198&.1.00]o.noo 1.llbi!.1.118'5 0.000 1.6 ]q
19S9 -1.337 «>.oon J,7&2 .1.981 0.000 1.970
(J 1990 ..1.6U 0.000 1I.lIb].2."76 0,000 2.]00
en 1991 .1.'1139 0.00(\S.UI ·2.95b 1'.000 2,997
CO 1992 ·2.20b 0.000 6.799 ·3.1136 0.000 1.691
1993 ·2.47)0,000 7.9b7 .3.9tb 0.000 4.390
19911 .2.7]9 0.000 9,135 .11.39&o,oon 5.086
1995 .l.006 /J.OOO 10.'503 .1I.llh 0.000 1i.783
199b ·3.18b 0.000 lI.7119 ..5.0b6 0.000 b.1I110
1991 ·1.3b6 0.000 13.195 ..5.256 0.000 1.097
1998 ·].5116 0.000 111.&111 .S.lI ln 0.000 7.753
1999 -3.12b 0.000 16.087 ..'5.637 0.000 8.1110
2000 .3.906 0.000 -11.513 .!i.527 o.oon 9.067
2001 ..11.056 0.000 19.HII ..b.027 0.000 9.9bO
2002 ·1I.20b 0.000 21.1311 .6.221 0.000 to.853
200]·1I.3'3~11.00/1 H.915 .6.112&0.000 1I.11l!i
20011 .11.505 0.000 211.73S .to.626 n.ool'l 12.bl8
2005 -11.6511 o.noo 26.!iJ6 ..b.82&0.000 1l.530
200b ·1I.t!Ol 0.000 28.1811 .7.057 0.000 111.717
2007 .1l.~1l1l 0.000 31.032 .7.2fl~0.000 15.901l
2008 ·'i.0911 0.000 13,279 .7.51 Q 0.000 17,091
2009 ·!i.llll 0.000 35.521 .7.750 0.000 111.278
2010 ·5.3611 O.lIOO 37.775 .7.11 82 0.000 19.1165
,I I J .J J I I ).J J ,I ]]!1
1 )1 1 1 1 1 )))J -1 1
ICENAfllOI MEO •HE8--FERC .2~.-~/2~/lq8]
aREAKOOWN OF ELECTRICITY REQUIREMENTS (GWH)
«rUTAL INCLUDES LARGE INOUBTRIAL tONSUHPTION)
ANCHORAGE •COOK INLET-.....---.•.•.•.•.-...
MEOIUM RANGE (PR_.'5)•........•.•.--.....
HE S I DHITI AL 8IJSJI~ESS MISCELLANEOUS flIOG.INDUSTRIAL
YEAR REQUIRE!'1ENT8 REQUIREMENTS REQUIREMENTS UlAn TnTAL..-----~•.•.•.•...-_...._~-.~-..-.--.•..•.__......N._._._-----.-.-._----.-~-...-.---.-----
1980 q7'1.53 1115.31.1 Zli.]1 811.flO '9h].19
1981 10V.?]1l1l1.5&ali.111 9z.n8 20ql.&0
11182 10711.92 1011.10 25.11 tOO.I&n20.fl~
198]1122.&~1091.911 25,111 108.2L1 n1l8.Ll3
198/1 117U.]1 IIIILI.I&2f1.04 UlI.U 2117&.1I1I
Iqll5 1218.01 12311.11 ill.li7 12L1.LlO l'605.2'5
19ab 1250.]9 1281.28 21.20 Il1.S9 2f.,1l6,17
1981 1282.71 13Zb.20 21.9]151.]8 2788.29
1988 IllS.15 1311.12 28.U 1&11.8,8 i'6H.1!1
n 1989 1J1H.53 111111.011 29.3 9 178.37 ?(nl~B.
--'19'10 1379.91 1 0 1111.95 30.12 l'n.8&3062.85(J)
'-0
1991 1199.07 10 1'5.95 30.511 195.13 3100.111
1992 IIIIR.2J 11191).95 H.05 198.1I0 11l8.61
199]1'07.']9 ISO'5.95 31.51 201.&11 31111.52
199/1 1/~'5b.5"IS2fl.Q 5 ]1.91 20Ll.9]]2Il1.111
1995 11175.71 153S.9 5 32./111 2011.20 3252.]0
19911 IlIql.lli!15S"i.n 32.8]2111.11I '1293.87
1997 1507.52 1S111.IIU H.l]"20.0~B'5,II'
1998 1521.111 1591.92 31.&]<'26.02 ])71.00
1999 1539.]11 Iflll.t'S H.o]2H .9&]LlI8.57
2000 1555.211 1612.57 3li.lI],,17,90 ]llflO.ILI
2001 15111.1,]11,11 9 .115 ]S.OIJ 2111.1.911 ]526.Ll7
2002 15Q8.01 1707.13 35.611 252.02 ]592.81
2003 lbI9.'lO 171111.LlI '11.25 259.08 ]&59.1 0
2001l ItI/HI.78 171l1.6Q ]11.86 2116.111 3725.LlII
2005 Ib1l2.11 1111 11 •9 7 n.Ll1 ~7].lO 3791.81
200&Itl91.80 187'5.10 38.n 2/11.0;8 ]8117.Ll7
2007 1121.1111 1912.111 3'1.30 Z~9.911 Hel.1l
2008 17Sl.0R 1ge9.1"110.22 "QIl.]1.1 Ll01R.7 9
2009 I7RO.H 20115.8'1 II 1.1l 306.72 01711.115
2010 1810.]1>2102.(,1 112.011 315.10 lIa70,It
SCENARIO'MEo,HE8--FERC .2*-.6/~q/1981
BAEAKOOWN UF ELECTRICITV REQUIREHENTS (GWHI
(TOTAL INCLI'OES LARGE INDUSTRIAL CONSUMPTION)
GREATER FAJRRANKS......_-~.-..-._-_.---
MEDIUH RANGE (PO_.5)......~....-...~....
RESiDENTIAL BUSINESII MISCELL4NEOUS
YEAR REQUIREMENTS RfQIJ1 REHENTS REQUIREMENTS..................~........-._._.-.-----.__.....-~----....
1980 Pb.]9 211.14 6.78
1981 191.21 230.U b.7S
1981 20b.OJ 243.3a 6.15
U81 220.1J4 25&.111 b.7~
1984 US.6b "69.50 6.b7
1985 lSO.48 162.59 b.bll
.198b 262.211 i!911.bl b.b8
1987 2H.OO 298.65 b.72
("")1988 285.16 J06.b8 b.7S
1989 2 1H.52 Jill.71 b.79
--'
........1990 J09.28 J22.75 b.8]0
1991 118.bl 32b.78 6.91
1992 327.97 UIl.Sl 1.11
1993 U7.Jt nll.eq 1.26
1994 lq6.65 J]ll.87 'I'.QO
1995 155.99 111".90 7.54
1996 lbl.39 J46.58 1.&11
1991 366.60 350.20 7.7J
1998 51Z.211 151.9 3 1.83
1999 377 .60 J~j7.61 7.92
2000 UJ.ol ll;aI.?9 8.02
2001 389.0b U7.91;a 8.111
200l ]9'5.11 HIl.b!8.25
2003 '101.16 181.30 8.37
201)11 1107.21 181.°7 8.49
2005 II I3.Z6 1911.bll 8.&1
2006 lIilO.7b 1104.51 8.81
2007 11i!8.lEt 11111.38 9.01
2008 415.7b 1.1211.~5 9.22
2009 II Ill.26 II liA.12 9.42
2010 il!'iO.1b /I/l1\."q 9.h~
J ]j ~J ~J I J )cJ I .1
E~oG.INDUSTRIAL
lClAO TOTAL.•.•.....•..•.••..~._••w ••••••w.·••_
0.00 400.31
0.00 11211.14
1).00 IISb~01
n.GO 483.95
0.00 511.61
0.00 '5]9.11
10.00 56Q.511
20.00 599.]7
3(1.00 U9.20
40.00 659.0:1
50.00 688.86
50.00 702'.!?
50.00 7I5.eq
50.00 729~llO
50.00 7/Ai.92
50.00 156.41
50.00 ·16lJ.61
50.00 7711~78
50.00 1B.q6
'511.1)0 79].IIJ
50.00 802.31
50.00 815.15
50.00 827.99
50.00 81.10·.81
5(1.00 8'U.61
'So.oo 8bb.51
50.00 884.08
50.1)0 901.e.!
50.no 919.21
50.00 9u.ao
50.00 Qlijll.J7
J I )J .........1
")1 ')~1 1 1 1 -]1 -1 J
n
"-I
--'
SCENARIO'HEO,HE8 ••FER~.2X ••IJ/24/19A]
TOTAL ELECTRI~tTV REQUIPEHEHT9 (G~H)
(NET OF CO~SeAVATIO")
(INLLIJOES L~RGE INnUSTRIAL CONSUMPTION)
Hrolu~RANGE CPR ••5)•.............•.......
'fE ~R ANCHORAGE •COO~INLET GREAT!R FAIRBANkS TOTAL•.......•.••..•••••..........~..•..•.........••.....••.....•.....~
1980 19b1.19 1100.31 .iIlb 3'.51
19S1 Z091.bO 1128.19 2519~80
1982 2220.02 IISb.01 2bH,09
19U 2]118.113 118].95 21\32,18
19811 211 7e..811 '511.83 ~988.67
19115 2bO'5.25 539.71 HIIII .9b
198f!2b9b.77 '5tJ 9 .!i1l 3i!bb~31
1987 2188.29 599.37 un ,bb
1988 21U9.81 6Z9.20 3S09.01
1989 Z911.33 t>59.03 h10~3b
19 9 0 10bZ.8!5 b88.86 11')\'.11
I qq I 1100.711 702.37 HO],II
1992 1136.b]11'5.89 If:I'5Q,SI
1993 Jl7b.Si!729.110 HOS,9i'
Iq911 J2111.1I1 7112.9Z 39'51.H
1995 J,?Si!.]O lSb.1I3 1I0l'l8~7!
1996 HH.81 1tJ5.tJI 1I0'5q~1I1
1997 lHS.lIJ 7711.18 1I110.U
1998 BH.OO 163.9b IIlbO~96
Iq9q ]1118.57 193.111 0211.11
zOOo HbO.11I 'Joi!.]z 112b2.11'5
2001 15i'b.1I1 8111j.15 113111~bl
2002 15 9 2.111 1321.99 114i!0~80
2003 )1..59.1/1 8110.83 /11199.97
20011 3125.118 853.U 4'57q~I'5
2005 1191.81 8bb.lil IIb58.]Z
ZOOb ]8/17.111 884.(18 11711:55
2007 ]Q81.IJ 901.65 1188Q~rq
201)8 (1078.79 919.2J 119 9 8".02
2009 /lUII.1I5 93".80 'il II '.2'5
2010 11210.11 95 11 .11 '52211'.119
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SCENARIOI HfD.HE8 ••FERC .2X ••ll/i4/198J
PEAk £LECTRJC REQUIREMENTS tHW)
fHET 0'CONSERVATION)
(INCLUDES l &RGE INDllSTR UL DEMAND)
HfDIUH RANGE (P~••5)..~......•..--.-_...~.
YEAR ANCWOR&GE •COO~I~LET QREATER FAIRBANKS TOTAL....•••••••••••••••••••w ••.......~.~.........--.•••••__•••••••••••v_~•
UBO ]9".5t 91.40 487~90
1981 "ii!.ll8 91.7&920.2 11
1981 "IIB."&1011.11 552.S9
1983 ,,7lI.(l(I 1111./19 S811~'n
198"500.1l1 lU.80 &17.27
1985 520.19 1i!3.2i!b1l9.111
19B&5115.7J 130.03 675~7&
lq&7 5&5.07 1311.811 701.90
191U 5811.40 1113.&11 728~OS
1989 &0).711 lSo.as 1511.19
1990 &23.OA.151.2&7(\0~11l
19'n b30.7J 1110.H 7'H ~08
1992 b38.1'il IU.1I1 801.82
UH &11&.011 166.'H 'ili!.55
19q1l b5].69 169.bO 821.i!9
1995 &&1.)11 172.b9 8:411'.OJ
t99b bb9.U 1111.78 811(.110 .
1991 071.90 176.88 81)11.77
1998 o(\o.le lU.IH "65~lS
1999 b 9 4.45 181.111 1J75~S2
2000 10Z.13 1131.1&1185.139
ZOOl 7111.05 186.09 q(l2~t5
i002 729.]7 189.02 9111.110
20n 711z.70 t 9 1.95 9111.&5
20011 150.02 1911.89 950.91)
200S 7&9.311 tn.8i!'9bl~11>
ZOO&7 A 8.b2 201.111 9qO~45
2001 8(17.90 201S.811 1013.1/1
a008 an.t7 209.85 10H~0l
2009 846.11'5 2LJ.87 lObO.H
2010 8toS.7J 211.88 1081.bl
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