HomeMy WebLinkAboutAPA1794BEFORE THE
FEDERAL ENERGY REGULATORY COMMISSION
APPLICATION FOR LICENSE FOR MAJOR PROJECT
SUSITNA HYDROELECTRIC PROJECT
VOLUME 2A
:'OVERNMFt',\1 PUBLICATIONS
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405
A,IIG (\R 2001
U.OF WASH,LIBRARIES
EXHIBIT B
CHAPTER 5 &6
JULY 1983
ALASKA POWER AUTHORITY -
SUSITNA HYDROELECTRIC PROJECT
VOLUME 2A
EXHIBIT B
STATEMENT OF PROJECT OPERATION AND RESOURCE UTILIZATION
TABLE OF CONTENTS
5 -STATEMENT OF POWER NEEDS AND UTILIZATION ..•..................B-5-1
5.1 -Introduction B-5-1
5.2 -Description of the Railbelt Electric Systems .•...............B-5-1
(a)The Interconnected Railbelt Market B-5-2
(i)The Electric Utilities and Other Suppliers B-5-2
-Anchorage -Cook Inlet Area B-5-2
- Fairbanks -Tanana Valley Area B-5-4
-Other Suppliers oo •••••••••••••••••B-5-6
(ii)The Existing Electric Supply Situation ....•........B-5-6
- Total Energy Consumption and Supply B-5-6
-Electric Energy Supply B-5-7
(b)Railbelt Electric Utilities B-5-8
(i)Utility Load Characteristics B-5-8
-Month 1y Peak and Energy Demand...................B-5-8
- Daily Load Profiles B-5-8
-Railbelt Load Diversity B-5-9
(ii)Electricity Rates ..................•...............B-5-10
-Anchorage Municipal Light and Power (AMLP)B-5-10
-Chugach Electric Association,Inc.(CEA)B-5-1O
- Fairbanks Municipal Utilities System (FMUS)B-5-11
-Golden Valley Electric Association,Inc.(GVEA)..B-5-11
- Other Electric Utilities B-5-11
(iii)Conservation and Rate Structure Program B-5-11
-The Anchorage Municipal Light and Power
(AMLP)Program B-5-12
-The Golden Valley Electric Association,Inc.
(GVEA)Program...............................B-5-13
- Other Ut il ity Programs B-5-14
-.Other Conservation Programs B-5-14
(c)Historical Data for the Market Area B-5-15
5.3 -Forecasting Methodology B-5-16
(a)The Effect of World Oil Prices on the Need for Power B-5-16
(b)Forecasting Models B-5-17
(i)Model Overview .....................•...............B-5-17
(ii)Petroleum Revenue Forecasting (P"ETREV)ModeL .
(iii)Man-in-the-Arctic Program (MAP)Economic Model .
-Scenario Generator .
-Statewide Economic Sub-Model .
-Regionalization Sub-Model .
- Input Variables and Parameters .
-Map Model Output ..e ••••••• • • • • • • • • • • • • • • • • • •""•••
(iv)Railbelt Electric Demand (RED)Model .
-Uncertainty Module ••••••••••••••o.,,~••••••••••••••
-Ho usin9 Mod u1e ................•..e "•••••••••9 ••••
-Residential Consumption Module .
- Business Consumption Module .
-Program Induced Conservation Module .
- Miscellaneous Conservation Module .
-Peak Demand Module .
(c)
- Input Data .
-Output Data .
(v)Optimized Generation Planning (OGP)ModeL ..
-Reliability Evaluation ..
-Production Stimulation .
- Purchases and Sales .
- Conventional Hydro Scheduling .
-Thermal Unit Maintenance .
-Thermal Unit Commitment .
-Thermal Unit Di spatch ..
- Investment Costi ng .
-OGP Optimization Procedure .
..Input Data eo • .,e •••••••••••••
-Output Data .
Model Validation ..o •••••••••••••••••e e .
(i)MAP Model Validation ..
-Stochastic Parameter Tests .
-Simulation of Historical Economic Conditions .
(ii)RED Model Validation ..
B-5-19
B-5-22
B-5-23
B-5-24
B-5-25
B-5-26
B-5-28
B-5-29
B-5-30
B-5-31
B-5-32
B-5-33
B-5-33
B-5-34
B-5-34
B-5-34
B-5-35
B-5-35
B-5-36
B-5-37
B-5-38
B-5-38
B-5-38
B-5-39
B-5-39
B-5-40
B-5-40
8-5-40
B-5-41
8-5-42
B-5-42
8-5-42
B-5-43
B-5-43
5.4 -Forecast of Electric Power Demand B-5-44
(a) Oil Price Forecasts B-5-44
(i)Alaska Department of Revenue (DOR)B-5-44
(ii)Data Resources Incorporated (DRI)B-5-46
(iii)Sherman Clark Associates (SHCA)B-5-48
-Base Case B-5-49
-No Supply Disruption Case (NSD)B-5-51
-Zero Economic Growth (ZEG)8-5-52
(iv)Other Projections B-5-53
(b)Selection of Reference and Other Cases B-5-53
(c)Variables and Assumptions Other Than Oil Prices 8-5-54
(i)PETREV Model B-5-55
(i i)MAP Model B-5-55
(iii)RED Model e ••••••••8-5-55
(iv)OGP Model o."••••••••B-5-57
i i
(d) Reference Case Forecast •.....•••.......•.•..•..•.•.••...B-5-57
(i)State Petroleum Revenues .........•..................B-5-58
(ii)Fiscal and Economic Conditions ....•••.•.............B-5-58
(iii)Electric Energy Demand B-5-59
(e) Other Forecasts .•••.••••••..••.••.•••..•...•.....•.....•B-5-61
(f)Sensitivity Analysis B-5-61
(i)MAP Model Sensitivity Tests .••••.•...~..•.........••B-5-62
(ii)RED Model Sensitivity Tests ...•.•..•........•.•..•••B-5-62
(iii)OGP Model Sensitivity Tests ••••...••.••.............B-5-63
(g)Reasonableness of the RED Forecasts .....•.•...•.........B-5-63
(h)Comparison with Previous Forecasts •.•...•••.•...........B-5-66
(i)Impact of Oil Prices on Forecasts ..............••••.•...B-5-67
5.5 -Project Utilization B-5-68
6 -FUTURE SUSITNA BASIN DEVELOPMENT .•••••.••.•.........•......•.B-6-1
REFERENCES
LIST OF TABLES c v e C1"e"e e e"e CI e"41"0 CI."••o.II ••eo ••••••ID.(I 0 It e ••II.e (I e ••••B.69
through
B.132
LIST OF FIGURES •.eeoeo ••oooooeoeooo •••"••ee •••eClo ••ooeooee •••e"00 ••B.77
through
B.104
iii
LIST OF TABLES
Number Tit 1e
B.69 Total 1981 Alaska Energy Consumption
B.70 Railbelt 1981 Energy Consumption By Fuel Type For
Each Sector
B.71 Installed Capacity of the Anchorage-Cook Inlet
Area
B.72 Installed Capacity of the Fairbanks-Tanana Valley
Area
B.73 Generating Plants of the Railbelt Region
B.74 Monthly Distribution of Peak and Energy Demand
B.75 Projected Monthly Distribution of Peak and Energy Demand
B.76 Typical Daily Load Duration
B.77 Load Diversity in the Railbelt
B.78 Residential and Commercial Electric Rates -Anchorage-Cook
Inlet Area,March 1983
B.79 Residential and Commerical Electric Rates -Fairbanks-Tanana
Area,March 1983
8.80 Anchorage Municipal Light and Power,Cumulative Energy
Conservation Projections
B.81 Programmatic Versus Market Driven Energy Conservation
Projections in AMLP's Service Area
B.82 Average Annual Electricity Consumption Per Household
On the GVEA System 1972-1982
B.83 Historic Economic and Electric Power Data 1960-1982
B.84 Monthly Load Data from Electric Utilities of the
Anchorage-Cook Inlet Area 1976-1982
B.85 Monthly Load Data from Electric Utilities of the
Fairbanks-Tanana Valley Area 1976-1982
i v
LIST OF TABLES (Continued)
Number Title
B.85 Monthly Load Data from Electric Utilities of the
Fairbanks-Tanana Valley Area 1976-1982
B.86 Net Electric Power Generation By Electric Utilities
1976-1982
B.87 Simulation of Historical Economic Conditions
B.88 Comparison of Actual and Predicted Electricity
Consumption
B.89 Alternative Petroleum Price Projections 1983-2010
B.90 Level of Analysis Employed with World Oil Forecasts
B.91 Variables and Assumptions (PETREV Model)
B.92 Variabless and Assumptions -MAP Model
B.93 Summary of Exogenous Economic Assumptions
B.94 Variables and Assumptions -RED Model
B.95 Fuel Price Forecasts Used by RED
B.96 Housing Demand Coefficients
B.97 Example of Market Saturations of Appliances in Single
Family Homes for Anchorage-Cook Inlet Area
B.98 Parameter Values in RED Price Adjustment Mechanism
B.99 Percentage of Appliances Using Electricity and Averaged
Annual Electricity Consumption,Railbelt Load Centers
B.100 Growth Rates in Electric Appliance Capacity and Initial
Annual Average Consumption for New Appliances
B.101 Percent of Appliances Remaining in Service Years after
Purchase
B.102 Variables and Assumptions -OGP Model
v
LIST OF TABLES (Continued)
Number Title
B.103 Reference Case Forecast -Summary of Input ~nd Output Data
B.104 Reference Case Forecast -State Petroleum Revenues
B.105 Reference Case Forecast -State Government Fiscal
Conditions
B.106 Reference Case Forecast -Population
B.107 Reference Case Forecast -Employment
B.108 Reference Case Forecast -Households
B.109 Reference Case Forecast -Number of Households
B.110 Reference Case Forecast -Number of Vacant Households
B.111 Reference Case Forecast -Residential Use Per Household
B.112 Reference Case Forecast - Business Use Per Employee
B.113 Reference Case Forecast -Summary of Price Effects and
Programmatic Conservation -Anchorage-Cook Inlet Area
B.114 Breakdown of Electricity Requirements -Anchorage-Cook
Inlet Area.
B.115 Reference Case Forecast -Summary of Price Effects and
Programmatic Conservation -Fairbanks-Tanana Valley Area
B.116 Reference Case Forecast -Breakdown of Electricity
Requirements -Fairbanks-Tanana Valley Area
B.117 Reference Case Forecast -Projected Peak and Energy Demand
B.118
B.119
B.120
Department of Revenue,Mean -Summary of Input and
Output Data
Department of Revenue,50%-Summary of Input and
Output Data
Department of Revenue,30%-Summary of Input and
Output Data
vi
LIST OF TABLES (Continued)
Number Title
B.121 Data Resources Inc.-Summar y of Input and
Output Data
B.122 FERC +2%-Summary of Input and
Output Data
B.123 FERC 0%-Summary of Input and
Output Data
8.124 FERC -1%-Summary of Input and
Output Data
B.125 FERC -2%-Summary of Input and
Output Data
B.126 Results of MAP Model Sens it iv ity Tests
B.127 Results of RED Model Sensitivity Tests
B.128 Results of RED Mode 1 Sens it iv ity Tests
B.129 Results of RED Model Sensitivity Tests
B.130 Results of RED Model Sens it iv ity Tests
B.131 Results of RED Model Sensitivity Tests
B.132 List 0 f Previous Forecasts
vii
LIST OF FIGURES
Number Title
B.77 Railbelt Area of Alaska Showing Electrical Load Centers
B.78 Location Map Showing Transmission Systems
B.79 Monthly Load Variation for Railbelt Area
B,80 Daily Load Curves -1982
B.81 Historical Population Growth 1960-1980
B.82 Historical Growth in Net Generation 1960-1980
B.83 Relationship of Planning Models and Input Data
B.84 MAP Model System Flow Chart
B.85 MAP Economic Sub-Model Flow Chart
B,86 MAP Regiona1ization Sub-Model Flowchart
B.87 RED Information Flow
B088 RED Uncertainty Module
B.89 RED Housing Module
B090 RED Residential Consumption Module
8.91 RED Business Consumption Module
B.92 RED Program Induced Conservaton Module
B.93 RED Miscellaneous Consumption Module
B.94 RED Peak Demand Module
B.95 Optimization Generation Planning (OGP)Model Information
Flows
B.96 OGP -Example of Conventional Hydro Operations
B.97 Data Resources Inc. -U.S.Oil Outlook,Crude Oil Prices
and Production
vii i
LIST OF FIGURES (Continued)
Number
B.98
B.99
8.100
8.101
8.102
8.103
8.104
Title
Free World Petroleum -and 8road Sources of Supply -SHCA
Alternative Oil Price Projections
Alternative State General Fund Expenditure Forecasts
Alternative Rai1be1t Population Forecasts
Alternative Rai1be1t Households Forecasts
Alternative Electric Energy Demand Forecasts
Alternative Electric Peak Demand Forecasts
ix
5 -STATEMENT OF POWER NEEDS AND UTILIZATION
5.1 -Introduction
Electric power demand forecasts have been developed for the Railbelt
market that will be served by the Susitna Project.The forecasts begin
from the year 1983 and extend to 2010, a period during which the
resources of the Susitna Project will be developed.
The magnitude of the future power demand depends on a number of
factors,the primary one being the future price of oil which affects
the revenue to the state and the state's economic activity.To account
for a range of world oil price projections,varying demand forecasts
are developed.
In addition to world oil price,the influence of energy conservation
and the relative costs of alternative forms of energy are also
important and have been factored into the forecast.Other factors
affecting the forecast demand have also been included in the analysis.
The following sections present the existing electric power demand and
supply situation,the basic approach used to develop the forecasts,the
variables and assumptions in the forecasts,and finally the results of
the forecasts and their significance.
Section 5.2 describes the electric power system in the Railbelt,
including utility load characteristics,conservation programs and
electricity rates.Section 5.3 presents the methodology for making the
forecasts.The section describes the four computer-based models that
were utilized in preparing the economic and electric energy forecasts
and the generation expansion plan for meeting the loads.Section 5.4
presents the oil price scenarios forming bases for the forecasts,the
other key variables involved in producing the forecasts,the results of
the forecasts,and the impact of world oil prices on the forecasts.
Section 5.5 summarizes the planned utilization of the power from the
Susitna Hydroelectric Project.
Two new reference reports have been prepared to provide technical
documentat ion of two of the three computer model s that were developed
and utilized in the derivation of the forecasts.The Man-in-the-Arctic
Program (MAP)Model Technical Documentation Report provides a complete
expl anation of the economic forecasting model.The Rail belt
Electricity Demand (RED)Model Documentation Report provides similar
information for the load forecasting model.
5.2 Description of the Railbelt Electric Systems
In this section,a description of the Railbelt electric systems is
presented.First,a general description is given about the
B-5-1
interconnected Railbelt market and the electric utilities serving the
market.Next,the characteristics of the loads,electricity rates and
the conservation programs are discussed.Finally,historical data
covering Railbelt electricity demands and regional economic factors are
presented.
(a)The Interconnected Railbelt Market
The Railbelt region,shown in Figure B.77,contains two
important electrical load centers:the Anchorage-Cook Inlet .ar ea
and the Fairbanks-Tanana Vall ey area.These two load centers wi 11
comprise the interconnected Railbelt market when the intertie
currently under construction by the Alaska Power Authority is
completed.The Glennallen-Valdez load center is part of the
Railbelt region but is not planned to be interconnected nor to be
served by the Sus itna Proj ect.It is therefore exc 1uded from
discussions in this report.
The existing transmission system of the Anchorage-Cook Inlet area
extends north to Wi 11 ow and consi sts of a network of 115-kV and
138-kV lines with interconnection to Palmer.The Fairbanks-Tanana
system extends south to Healy over a 138-kV line.The intertie is
bei ng bui It by the Al aska Power Authority to connect Wi 11 ow and
Healy and will operate initially at 138-kV.The existing
transmission system in the Railbelt region is illustrated in
Figure B.78.
(i)The Electric Utilities and Other Suppliers
- Anchorage-Cook Inlet Area
The Anchorage-Cook Inlet area has two municipal
utilities,three rural electric cooperative associations
(REAs), a Federal Power Administration,and two military
installations,as follows:
Municipality of Anchorage-Municipal Light &Power
Department (AMLP)
·Chugach Electric Association,Inc.(CEA)
Homer Electric Association,Inc.(HEA)
· Matanuska Electric Association,Inc.(MEA)
· Alaska Power Administration (APAd)
Elmendorf AFB -Military
Fort Richardson -Military
All of these organizations,with the exception of MEA,
have electrical generating facilities.MEA buys its
power from CEA.HEA and SES have rel atively small
generating facilities that are used for standby
operation.They also purchase power from CEA.
B-5-2
AMLP and CEA are the two principal utilities serv ic mq
the Anchorage-Cook In 1et area.AMLP serves most areas
within the City of Anchorage except for some sections
served by CEA.AMLP al so serves the Anchorage
International Airport,and provides electricial energy
to Elmendorf AFB and Fort Richardson on a non-firm
basis.The customers and associated sales in 1982 are
listed below.Residential sales represented slightly
over one fourth of total commerci al -sal es .Its most
important load is the downtown business and commercial
district.
Customer Class Number En er ty Sales
MWh)
Residenti al 14,745 129,010
Commerci al 3,229 474,344
Street Lighting 7,663
Total 17,974 611,017
CEA serves certain urban and most suburban sections of
Anchorage.In addition,CEA serves customers at Kenai
Lake,Moose Pass,Whittier,Beluga and Hope.CEA also
provides bulk power to AMLP,CEA1s residential load is
greater than its commercial and industrial loads.
Furthermore,CEA's average commercial customer is
consistently smaller than that of AMLP.Its 1982 sales
are presented below:
Customer Cl ass Number Energy Sales
(MWh)
Residential 46,560 546,736
Commercial &Industrial
(50 kVA or less)4,519 161,290
Commercial &Industrial 359 214,679
(over 50 kVA)
Public St.&Hwy.Lighting 26 5,216
Sales for Resale 3 702,357
Total 51,467 1,630,278
B-5-3
HEA,MEA and SES provi de e 1ectri c ity servi ce to thei r
customers by purchases from CEA.In 1982,HEA,MEA,and
SES purchased about 347, 326, and 30 GWh of e 1ectri ca 1
energy respecti ve ly.HEA serves the City of Homer and
other customers on the Kenai peninsula.MEA has a ser-
vice area encompassing the Matanuska Valley and related
areas;SES serves the City of Sewar-d,These areas are
depicted in Figure B.78.
The Alaska Power Administration provides wholesale power
(fi rm and secondary)to MEA,CEA,and AMLP.These
utilities are interconnected with the Alaska Power
Administration on 115-kV lines owned by the
Administration.Fort Richardson and Elmendorf AFB
supply thei r own needs.Their e 1ectri ca 1 requi rements
in 1982 were approximately 70 and 87 GWh respectively.
Both bases have non -fi rm power agreements with AMLP.
Fort Richardson has recently entered into a new contract
with AMLP to purchase about 30 GWh on an i nterruptab 1e
basis.
-Fairbanks - Tanana Valley Area
The Fai rbanks-Tanana Valley area is current ly served
by a REA cooperative and a municipal uti lity.In addi-
tion,a university and three military installations have
their own electric systems,as follows:
•Fairbanks Municipal utilities System (FMUS)
• Golden Valley Electric Association,Inc.(GVEA)
•University of Alaska,Fairbanks
•Eielson AFB -Military
•Fort Greeley -Military
•Fort Wainwright -Military
Golden Valley Electric Association,Inc.and Fairbanks
Municipal Utilities System own and operate generation,
transmission,and distribution facilities.The
University and military bases maintain their own genera-
tion and distribution facilities.Fort Wainwright is
interconnected with GVEA and FMUS and is providing both
utilities with economy energy.
FMUS serves an area bounded by the city limits of
Fairbanks,except for several residential subdivisions
recently annexed by the city.The Chena River flows
through the northern part of the service area with Fort
Wainwright Military Reservation providing a border on
the east.The downtown business district lies in the
northeast corner of the FMUS service area along the
B-5-4
south bank of the Chena River.There is an industrial
area which is contained in part within the City of
Fairbanks.The north bank of the Chena River provides
the southern boundary of this industrial area.In
addition to serving its own customers,FMUS provides
economy energy to Golden Valley Electric Association.
The 1982 sales of FMUS are set forth below:
Customer Cl ass Number Energy Sales
(MWh)
Residential 4663 27,758
Commerc ia1 1050 68,695
Government 144 27,923
Street Lighting 4,911
GVEA 1 33,479
Total 5858 162,766
The commercial customers are significant in number but
more importantly al so in terms of total energy sal es .
The res i dent i a1 and government sectors had about the
same level of energy sales in 1982.
GVEA serves Fairbanks North Star Borough including
portions of the City of Fairbanks not served by FMUS,
the City of North Pole,the communities of Fox and
Ester,and the two military bases -Eielson Air Force
Base and Fort Wainwright.Other major communities
within its service area include the Cities of Nenana,
Healy,Cl ear,Anderson and Rex. In 1982,GVEA sal es
were as follows:
Customer Cl ass Number Energy Sales
(MWh)
Residential 16,176 150,487
Commerci al &Industri al
(50 kVA or less)1,859 43,195
Commerci al &Industrial
(over 50 kVA)233 129,394
Public St.&Hwy.Lighting 9 328
Sales for Resale 1 9,534
Total 18,278 332,939
The University of Al aska at Fairbanks,Fort Wainwright
and Eielson AFB generate their own electrical
requirements.At the present time,Fort Wainwright
supplies all of Fort Greeley's electricity needs by
GVEA wheeling the power on their transmission lines.
Fort Wainwright provides economy energy to FMUS and GVEA
from coal-fired units.In 1982,Fort Wainwright had net
B-5-5
generation of about 80 GWh and"Eielson AFB generated
about 59 GWh of electricity.
Other Suppliers
Several major industrial companies in the Railbelt
provide their own electric power supply.During 1981,
in the Anchorage-Cook Inlet area,such generation
accounted for nearly 130 GWh.The major industrial self
suppliers are located in HEAls service area.The main
industrial firms with operations in Kenai include Union
Oil of California,Phillips Petroleum Company,Chevron
U.S.A.,Inc.,and Tesoro-Alaskan Petroleum Corp.
In 1981,the most recent year for which data are
available,industrial sources of self generation in the
Fairbanks-Tanana Valley area did not produce any
electricity.
(ii)The Existing Electric Supply Situation
Because electricity must compete with alternative fuels
in the market place,a brief discussion of the consumption
and supply of energy in total is provided for an overall
setting.
-Total Energy Consumption and Supply
The State of Alaska is a major consumer of energy
resources.In 1981,Al aska I s total energy input was
about 543 trillion Btu.Of that total,273 trillion
Btu were consumed; about 184 tri 11 ion Btus were
exported;and the remainder was lost in refining,
electric generating,and processing activities.The
1 argest share of the input was accounted for by crude
oil input to refineries (44%)followed by natural gas
(37%)and imported petroleum products (15%).Coal,
hydro,and wood resource inputs accounted for the
residual 4 percent of total energy input.
The 1981 energy consumption for Alaska and the Railbelt
are summarized in Table B.69.The total energy
consumption for the Rai lbelt area was 236 trill ion Btu
in 1981. In 1981,Railbelt per capita consumption was
about 752 million Btu,which is approximately 5 percent
greater than the average Al askan per capita consumption.
B-5-6
The Rail beIt reg ion account s for almost 78 percent of
the total energy consumption in the State of Alaska.
Table B.70 provides a breakdown of energy consumption by
fuel type for various sectors of the state economy.The
transportation sector which relies almost entirely on
fuel oil is the most energy intensive sector.Besides
transportation,the industrial and utility sectors are
major energy consumers.
Fuel oi 1 represents the most important energy source
followed by natural gas.In the industrial,utility,
and commercial public sectors,natural gas consumption
accounts for over 50 percent of each sector's total
consumption.Natural gas consumption in the residential
sector is sightly less than that of fuel oil.
Other fuels are coal and wood which are of lesser
importance.Coal is used by electric utilities and
military bases,whereas wood is used in the residential
sector.
Electric Energy Supply
The Anchorage-Cook Inlet area is almost entirely
dependent on natural gas to generate electricity.About
92 percent of the total capacity is provided by
gas-fired units.The remaining are hydroelectric units
(5 percent)and oil-fired diesel units (3 percent).
Table B.71 presents the total generating capacity of the
Anchorage-Cook Inlet utilities,the two military
installations and the industrial sector.
For the Fairbanks-Tanana Valley area,the total
generating capacity of the utilities,the three military
installations and industrial self suppliers by type of
units are presented in Table B.72. A large portion of
the total installed capacity consists of oil-fired
combustion turbines (57 percent)and coal steam turbine
(30 percent).The remaining capacity is provided by
diesel units.The proposed transmission intertie
between Anchorage and Fairbanks wi 11 allow Fairbanks
utilities to purchase relatively inexpensive power
fuel ed by natural gas from Anchorage.It will also
allow both load centers to take advantage of the
additional peaking capacity available in the Fairbanks
area to provide greater reliability.
Table B.73 provides a complete list of generating plants
of the Railbelt area.
B-5-7
(b)Railbelt Electric Utilities
(i)Utility Load Characteristics
This section presents monthly peak and energy demand,
hourly load data for a typical week in April,August,and
December,and an analysis of load diversity between the two
load centers.
- Monthly Peak and Energy Demand
Table B.74 presents monthly distributions of peak and
energy demand for the period 1976-1982 for the two load
centers in the total Railbelt area.Figure B.79 shows a
graph of the 1982 monthly load for each load center.
Both regions have winter peaks,occurring normally in
December,and sometimes in January or February.The
peak demand is lowest duri ng the months of May through
August,and the ratio of summer to winter peaks varies
between 0.55 and 0.65.Although monthly peak demand
varies from year to year mainly due to weather
conditions,Table B.74 shows that the pattern has
remained relatively constant during the period
1976-1982.
As denoted by the data in Table B.74,the monthly
distribution of energy demand has also remained about
the same for the period 1976-1982 and both regions have
a similar distribution.The winter months,November
through February,had an average monthly demand of about
10 percent of the total annual energy.The summer
months,June through August,had an average monthly
demand of about 6.7 percent of the total annual energy.
These results were compared with an earlier study
(Woodward Clyde,1980) based on data through 1978,and
found to be consistent.As part of that study,a
forecast of the monthly distribution of peak demand was
done.Table B.75 summarizes those results,which have
been used in the generation expansion studies described
in Exhibit D.
Daily Load Profiles
Figure B.80 presents graphs of the hourly load data
for a typical week in April,August,and December 1982.
The data from individual utilities were combined to
produce representative load curves for each load center
and the total Railbelt area.The following three
paragraphs describe the weekly load profiles.
B-5-8
In April,there is usually a morning peak between 7 and
9 a.m.,and an evening peak between 6 and 8 p.m.The
evening peak is usually greater than the morning peak.
The night load is about 70 percent of the daily load.
The average daily load factor is about 85 percent.
In August,the load begins to rise from about 7 a.m.,
it continues to increase until 11-12 a.m.,when it
reaches a peak and decreases slowly'to about midnight
and then drops off sharply.The night load is about
55-60 percent of the daily peak load.The average daily
load factor is about 82 percent.
In December,there is usually a morning peak between 6
and 9 a.m.,and an evening peak between 4 and 7 p.m.
The evening peak is usually about 10 percent greater
than the morning peak.The night load is about 65
percent of the daily peak load.The average daily load
f actori s about 85 percent.
Table B.76 presents typical average weekday and weekend
daily load duration for the months of April,August,and
December. These data were taken from the Woodward-Clyde
study (Woodward-Clyde,1980),and found to be consistent
with the 1982 data.Similar load duration data were
computed for the remaining months. These data have been
used in the generation expansion studies described in
Exhibit D.
Railbelt Load Diversity
A system load diversity analysis was done for the peak
day in Fairbanks which was December 29, 1981 and the
peak day in Anchorage of January 6, 1982.The peak
coincident and non-coincident loads were collected from
all generating sources and the load diversity was
calculated based on the data.Table B.77 shows the
hourly load demand for these two peak days.The
diversity measure in the total Railbelt was about 0.98.
The basic conclusion of the analysis is that the total
coincident peak load for the Railbelt would probably be
within three percent of the total non-coincident peak
demand. For the expansion plans analysis,the Railbelt
peak demand is considered to be the sum of the projected
peak demand of the two load centers.
B-5-9
(ii)Electricity Rates
Electric utility companies in the Railbelt have their
tariffs approved by the Alaska Public Utilities Commission
or another regulatory body with jurisdiction over electric
rates.Tables B.78 and B.79 present the current residen-
tial and commercial rates for the main utilities of the
Anchorage-Cook Inlet area and Fairbanks-Tanana Valley area.
Electric rates are considerably less in the Anchorage-Cook
Inlet area than in the Fairbanks-Tanana Valley area.The
average residential cost per kWh is approximately 5J/kWh in
the Anchorage-Cook In1et area,and 81/kWh and 101 kWh for
FMUS and GVEA respectively in the Fairbanks-Tanana Valley
area.The lower rates in Anchorage-Cook Inlet can be
explained by the relatively low cost natural gas supply
used for electric generation.The relatively high rates in
Fairbanks-Tanana are a result of considerable oil-fired
generation.A description of these rates is presented in
the following paragraphs.
Anchorage Municipal Light and Power (AMLP)
AMLP tariff for residential service and general
service-small customers comprises a fixed monthly
customer charge and a flat energy charge per kWh.The
general service-l arge customers schedule has a monthly
demand charge in addition to a fixed customer charge and
a flat energy charge rate.In addition,AMLP has an
experimental program for time-of-day rates for customers
dependent on electric space heating.
-Chugach Electric Association,Inc.,(CEA)
CEA has tariffs for retail customers that reflect a
declining block rate structure.The residential and
small commercial customers schedules provide for a
monthly rate in cents per kWh which declines with
increasing blocks of electricity consumption.CEA's
schedule for large commercial and industrial customers
contains a demand charge as well as an energy charge
which decl ines in rel ation to increasing electric
consumption per kW of billing demand.
CEA has other tariff schedules for retail customer
classes such as churches and schools.CEA has a
wholesale electric power and energy contract with HEA,
MEA,and SESe In addition,CEA has a rate schedule for
intertie with AMLP which contains a flat energy charge
and certain commitment and start/stop charges.
B-5-1O
Fairbanks Municipal Utilities System (FMUS)
In the Fairbanks-Tanana Valley area,FMUS has
residential,all electric,and general service rate
schedules which reflect declining rates as energy
consumption increases in blocks.For general service
customers with demand bloc ks of 15 kW or greater,there
is (in addition to an energy charge)a monthly minimum
charge per meter based on a fixed do l l ar amount times
the highest demand reading of the preceding 11 months or
times the estimated maximum demand of the first year,
whichever is greater.
Golden Valley Electric Association,Inc.(GVEA)
GVEA has a residential schedule with an energy charge
for the first 500 kWh and a lower charge for each kWh
over 500 kWh of consumption.There is a separate
schedule for general service customers depending on
their kW demand. For GVEA I S general service customers
with electrical demand not exceeding 50 kW,there is
only a decreasing energy charge associ ated with three
increasing blocks of consumption.General service
customers with loads exceeding 25 kW have a schedule
which provides for a fixed demand charge per kW plus
dec 1 ining energy charges in correspondence wi th four
increasing consumption blocks.
Other Electric Utilities
The remaining electric utilities have tariff schedules
which differ in specific details but are similar in
structure to those of the larger Railbelt electric
utilities.The average residential cost per kWh for the
1 arger util ities in the Anchorage-Cook Inlet area would
tend to be less than that charged by the other smaller
util ities in the area.
(iii)Conservation and Rate Structure Programs
This section presents conservation and rate structure
programs initiated by the electric utilities and government
agencies.The effects of these existing programs have been
incorporated in the forecasting methodology which is
described in Section 5.3.
The utilities have various programs aimed at supplying
information to the public concerning the dollar savings
associated with electricity conservation.In general,the
utilities rely on market forces;however,they promote
6-5-11
of
by
Examples
introduced
forces.
programs
consumer recognition of those
conservation and rate structure
AMLP and GVEA,are described.
The Anchorage Municipal Light and Power (AMLP)Program
The AMLP program addresses electricity conservation in
both residential and institutional settings.It is a
formal conservation program mandated by the Powerplant
and Industrial Fuel Use Act of 1978 (FUA).The AMLP
program is designed to achieve a 10%reduction in
electricity consumption.To achieve this level of
conservation,AMLP provides information on available
state and city programs to its consumers.Additionally,
it has programs to:
Distribute hot water flow restrictors;
Insulate 1000 electric hot water heaters;
Heat the city water supply,i ncreas ing the temperature
by 15°F (decreasing the thermal needs of hot water
heaters);and
Convert two of its boi ler feedwater pumps from
electricity to steam.
Convert city street lights from mercury vapor lamps
to high pressure sodium lamps;and
Convert the transmission system from 34.5 kV to
115 kV.
AMLP also supplies educational materials to its
customers along with "Forget-me-not"stickers for light
switches.The uti 1ity has a fu 11 ti me energy engineer
devoted to energy conservation program development.
The projected impacts of specific energy conservation
programs are detailed in Table B.80 for the period
1981-1987.The greatest impact will occur as a result of
street light conversion,transmission line conversion,
and power plant boiler feed pump conversion.By 1987,
these programs are expected to provide 25,000 MWh of
electricity conservation,or 72%of the total program-
matic energy conservation.In the case of conversion to
new sodium lights,the record shows that AMLP installed
96 kw by the end of 1980,an additional 8 kW in 1981,
16.6 kW in 1982,and 14.3 kW of additional sodium lights
in 1983 to date.
In addition to these conservation programs,AMLP has
also projected conservation due to price-induced
effects.Table B.81 presents the projections.About
60 percent comes from price-induced conservation.
After 1983, the rate of increase in conservation is
expected to decline sharply,and price-induced
conservation will be the principal contributor.
B-5-12
The Golden Valley Electric Association,Inc.(GVEA)
Program
GVEA has an energy conservation program based on a
plan established pursuant to REA regulations.The
utility employs an Energy Use Advisor who:
Performs advisory (non-quantitative)audits;
counsels customers on an individual basis on means
to conserve electricity;
Provides group presentations and panel discussions;and
Provides printed material,including press releases
and publications.
GVEA also eliminated its special incentive rate for all
electric homes,and placed a moratorium on electric home
hook-ups in 1977.It has given out flow restrictors.
It has prepared displays and presentations for the
Fairbanks Home Show and the Tanana Valley State Fair.
It coordinates its programs with the state and other
programs.
The efforts of GVEA,combined with price increases and
other socioeconomic phenomena,produced a conservation
effect as shown in Table 8.82.Although much of the
decl ine in average consumption can be attributed to
conversions from electric heat to some other fuels,part
of the reduction is the direct result of conservation.
The data show a reduction from 17,332 kWh/house/yr in
1975 to a level of 9,080 kWh/house/yr in 1981.Table
8.82 also shows a moderate upturn in electricity
consumption per household in 1982,indicating that the
practical limit of conservation may have been reached in
the GVEA system.
Currently,GVEA1s load management program is directed
toward commercial consumers.A significant lower rate
schedule is available to commercial customers whose
demand is maintained at less than 50 kW.Larger power
customers are advised on ways to manage their electrical
load to minimize demands. In addition,seasonal rates
are avail able to those large consumers who significantly
reduce their demand during the winter peak season.A
program is underway to identify customers who operate
1 arge interruptible loads during periods of system peak
demand.Various methods of residential load management
are under study,but none appears cost effect ive at thi s
time other than voluntary consumer response to education
programs.
8-5-13
Other Utility Programs
Other utilities have progrillns similar to the ones
described above. For example,FMUS has two main
programs aimed at electric conservation and reducing the
consumers'electric bill.FMUS placed an advertisement
in a local newspaper about energy conservation and
offered to provide a free booklet on the topic.Also,
FMUS plans to advertise the av at l abi l i ty of a "Energy
Teller"device to allow the customer to determine the
direct cost of using a given appliance.These
instruments are expected to be avail able for free loan
for a period of up to two weeks.
Other Conservation Programs
There are sever a1 effort s, both pub 1ic and priv ate,
under way throughout the State of Al aska.The two main
programs that affect the Railbelt area are described in
the following paragraphs.
The State Program.The Conservation Section of the
Division of Energy and Power Development (DEPD)is
responsible for the administration of the United States
Department of Energy·s low-income weatherization
program. This program has involved the following
activities:
Training of energy auditors;
Performance of residential energy audits,which are
physical inspections including measurements of heat
loss;
Providing grants of up to $300/household,or loans,for
energy conservation improvements based upon the
audit ;
Providing retrofit (e.g.insulation,weatherization)for
low income homes.
The key to the program is the audit,which is performed
by private contractors.The forms employed are designed
to show savings that can be achieved in the first year,
the seventh year,and the tenth year after energy
conserv at ion measures have been impl emented.The
savings demonstrated provide the basis for qualifying
for a grant or loan.The audits focus on major
conservation opportunities such as insulation and
8-5-14
reduction of infiltration (e.g.",by weather stripping,
caulking,and storm window application).
The DEPD program achieved a significant level of
penetration into the conservation marketplace.
Penetration in the state as a whole achieved 24%;and in
the combined load centers of Anchorage and Fairbanks it
also achieved 24%.Market penetration is computed by
taking the ratio of audits relative to the total number
of homes in various regions:Kenai Peninsula,Anchorage,
Matanuska-Susitn a,Fairbanks,Southeast Fairbanks,and
regional total.It is useful to note that the audit
program was more effective in high cost energy areas
(e.g.,Fairbanks)indicating that public participation
was based upon market forces to some extent.
The DEPD program is currently being phased out,
for low income family assistance,particularly
Bush Communities where it is estimated that 13%
homes wi 11 be treated in the next three
Educational programs will continue.
except
in the
of the
years.
The City of Anchorage Program.The City of Anchorage
Program is operated by the Energy Coordinator for the
City of Anchorage. This program also involves audits,
weatherization,and educational efforts.Based on
walk-through audits performed on city buildings and
schools,detailed audits have been performed.
The cl t yvs weatherization program is available to low
income families and provides grants of up to $1600 for
materials and incidental repairs.Labor is supplied
from the comprehensive Employment Training Act (CETA)
program.However,thi s program is being phased out.
The educational program has involved working with
realtors,bankers,contractors and businessmen.It al so
has involved informal contacts with commercial building
maintenance personnel.Finally,it has involved
contacts with the general public.
(c)Historical Data for the Market Area
Available economic and electric power data for the State of
Alaska and the Railbelt are summarized in Table B.83.The table
shows the rapid growth that has occurred in the st at e' s and the
Ra i lbel t t s population,economy,and use of electric power.The
growth has been especially rapid during the last decade.
B-5-15
Between 1960 and 1982,employment in the Railbelt grew from 94,300
to 231,984,an increase of 146 percent,or an average of 4.2
percent per year.The number of households in the Railbelt grew
at a faster rate during this period,an average of 4.9 percent per
year,reflecting the nationwide trend toward fewer persons per
household.Much of the population and economic growth that
occurred during this period is attributable to the tremendous
increase in state petroleum revenues and general fund
expenditures.State petroleum revenues grew from only $4.2
million in 1960 to $3.57 billion in 1982,mainly due to the
discovery and development of petroleum on Al ask as North Slope.
Between 1960 and 1982 state general fund expenditures rose from
less than $100 million per year to $4.6 billion.Figure B.81
illustrates the historical growth in population,showing the
growth rate for each five year period from 1960 to 1980.
Consumption of electric energy in the Railbelt has risen
si gnifi cant 1y faster than the rate of economi c growth.Between
1965 and 1982 total energy generation rose from 467 Gwh to 2,934
GWh,a five-fold increase,or an average of 11.4 percent per
year.Figure B.82 illustrates the historical growth in net
generation,showing the growth rate for each five year period from
1965 to 1980.
Tables B.84 and B.85 present monthly electric power use and peak
demand during the period 1976 to 1982 for the Anchorage and
Fairbanks load centers.These tables show that while there has
been a steady rise in the use of electric energy and in peak
demand,there has been considerable variation in monthly energy
use and peak demand from one year to the next,most1y due to
different weather conditions in the Railbelt.Table B.86 gives
the net annual generation of each Railbelt utility between 1976
and 1982.
5.3 -Forecasting Methodology
This section presents the methodological framework used for the
forecasts of economic conditions and electricity demand in the
Railbelt.The first subsection discusses the effect of world oil
prices on power market forecasts.Next,the models used for
forecasting purposes are identified and fully explained.Finally,
model validation is discussed for the economic model (MAP)and
electricity demand model (RED).
(a) The Effect of World Oil Prices on the Need for Power
World oil prices affect the need for electric power in the
Railbelt in four basic ways, each of which is explicitly taken
into account in forecasting energy demands.
First,higher world oil prices produce higher levels of petroleum
revenues to the State of Alaska,mainly through production taxes
and royalty payments that are tied directly to the market price of
petroleum.Because of the importance of state revenues and
B-5-16
spending to the Alaskan economy,changes fn the world price of oil
have a significant effect on general economic conditions and the
growth in electricity demand.
Second,world oil prices impact the cost of power generation.
Since much of the electricity used in the Railbelt is generated
using fossil fuels,the price of electricity to the consumer will
be affected by the world price of oil.As long as fossil fuels
fire a substantial portion of the Ra i lbe l t t s t qener at lon facili-
ties,higher world oil prices will lead to higher electricity
prices,decreasing the overall demand for electricity.This
factor has been considered in the forecasts of electric demands.
The same factor has ~so been integrated in the economic analyses
associated with determining the most cost effective generation
expansion program for meeting the Railbelt's future electric power
demand, which in turn determines the future cost of electricity.
Thi rd,world oil pri ces affect the degree to wh ich oil and other
fossil fuels may be substituted for electricity in certain
applications.Inter-fuel substitution and its effect on the
demand for electricity was explicitly considered in the load
forecasting analysis for the Susitna Hydroelectric Project.
The fourth effect that world oi1 prices has on the need for power
occurs through the infl uence that petro 1eum pr ices have on the
profitability of exploration and development of petroleum reserves
as well as other energy resources in Alaska.Higher world oil
prices provide an incentive for higher levels of oil exploration
and development,which in turn leads to higher levels of
employment and gross output in the petroleum sector as well as
support sectors such as transportation,construction,and
services.The economic development and population growth
associated with such activity increases electric power demands in
the Railbelt as well as other parts of Alaska.
The following sections describe in some detail the ways in which
world oil prices and other factors were considered in the economic
and load forecasting analyses and generation expansion planning.
(b)Forecasting Models
(i)Model Overview
Four computer-based and functionally interrelated models
were used in projecting the market for electric power in
the Railbelt and evaluating alternative generation plans
for meeting electric power demands.First,a model
entitled PETREV,operated by the Alaska Department of
Revenue,was utilized to project state revenues from
petroleum production based on alternative future petroleum
B-5-17
prices.The revenue projections from PETREV and numerous
other economic and demographic data were then used by the
Man-in-the-Arctic Program (MAP)Model to project economic
conditions,including population,employment,and
households,for the Railbelt.The economic projections,
along with electric power and use information,electricity
demand elasticity functions,and other electric power data
then served as input to the RED Model to predict electric
energy and peak loads in the Railbelt by load center.
Finally,the Optimized Generation Planning (OGP)model was
used to develop the most cost effective generation plans
for meeting projected power requirements.The study on
alternative generation expansion plans is described in
detail in Exhibit D.The OGP Model is discussed in this
chapter in order to describe the total conceptual approach
utilized in analyzing the need for power in the Railbelt.
The relationship between the models and their principal
input and output data are shown on Figure B.83 which al so
shows the role of financial analysis in the selection of
the final generation expansion plan,also covered in
Exhibit D.
Figure B.83 illustrates the parameters and variables that
are common to different model s and the interdependency of
the models.While the planning process moves generally
from the PETREV model through the MAP,RED,and OGP models,
in one instance output from one model is fed back into a
previous model.Electricity prices are estimated and used
in the RED model to compute electric energy projections.
These projections are then used by the OGP model to
develop a generation expansion plan to meet projected
demand and the associated cost of electricity.If there is
a significant difference between the estimated and computed
data,the models are rerun until the cost of supplying
power is approximately equal to the price assumptions
utilized in the demand model.
The following sections describe each of the four principal
models,including their respective submodels and modules,
key input variables and parameters,and primary output
variables.Additional information on the PETREV Model is
available in the quarterly issues of Petroleum Production
Revenue Forecast (Alaska Department of Revenue,March
B-5-18
1983).Additional information on <the MAP model may be
found in a technical documentation report (Institute of
Social and Economic Research,June 1983) which presents a
detailed description of the model including a complete
listing of its equations and input variables and
parameters.Another technical documentation report
(Battelle,June 1983)presents similarly detailed
documentation of the RED model.The OGP model is a
proprietary program of General Electric Company.The
version used in the current study is presented in the
Descriptive Handbook,Optimized Generation Planning
Program,Financial Simulation Program by General Electric,
March,1983.
(ii)Petroleum Revenue Forecasting (PETREV)Model
Petroleum revenues currently constitute approximately 85
percent of total state revenues.For thi s reason,and
because state revenues and expend itures have consider ab 1e
potential variability and are important determinants of
future state economic conditions,projections of the most
important sources of petroleum revenues,production tax and
royalties,are generated by a specialized model,PETREV,
operated by the Alaska Department of Revenue (DOR).PETREV
is structured to take into account the uncertainties of
future oi1 prices and other factors associ ated with
forecasting petroleum revenues.Using PETREV,the DOR
issues updated petroleum revenue projections on a quarterly
basis covering a 17 year period,using current data
available on petroleum production,a range of world oil
prices,tax rates,regulatory events,natural gas prices,
and inflation rates.
PETREV is an economic accounting model that utilizes a
probability distribution of possible values for each of the
factors that affect state petroleum revenues to produce a
range of possible state royalties and production taxes.
The principal factors influencing the level of petroleum
revenues are petroleum production rates,mainly on the
North Slope,the market price of petroleum,and tax and
royalty rates applicable to the wellhead value of
petroleum.
Wellhead value is estimated by a netback approach whereby
the costs of gathering and transporting crude oil and a
qual ity differenti a1 val ue are subtracted from the market
value at its destination on the West Coast or Gulf Coast of
the United States.For petroleum produced on the North
Slope,the source of most of the oil produced in Alaska
subject to state royalties and production taxes,future
wellhead value is estimated as follows.The projected
B-5-19
world price of Saudi Arabia medium grade petroleum is
adjusted by subtracting (1)the projected cost of pumping
oil through the Trans Alaska Pipeline System from Prudhoe
Bay to Valdez,including the pipeline tariff,(2)the
proj ected cost of shi ppi ng the oil to refi neri es on the
West Coast and the Gulf Coast of the United States,and (3)
a proj ected quality differential factor representing the
difference in qual ity between North Slope petroleum and
Saudi Arabia medium grade.The result'is the estimated
value of petroleum at pump station #1 at Prudhoe Bay,
Al aska.
Future royalties collected by the state are estimated by
multiplying total projected production in barrels from
state lands by the estimated per barrel price at pump
station #1,subtracting field costs of production,
currently approximately $.68 per barrel,and multiplying
the result by .125.This amounts to a 1/8 royalty payment
on oil produced after all gathering and transportation
costs are met, which the State of Alaska may receive either
in kind or in dollars.Future severance,or production,
taxes are estimated by multiplying forecasted production,
net of the 12.5 percent taken by the state as royalties,by
the estimated pump station #1 price and the tax rate
adjusted by an economic limit factor (ELF).The tax rate
varies between 12.25 and 15 percent of net production
value,depending upon the age of production wells.The
economic limit factor (ELF)adjustment takes into the
account declining well productivity and increased
production costs.On the North Slope most production will
be subj ect to a 15 percent sever ance tax rate.The average
ELF for North Slope petroleum production is expected to
decline from its current level of 1.0 to close to 0.6 by
the year 1999.The decl ine in the ELF in effect lowers the
tax rate to which Alaskan petroleum is subject.
A change in the market price of petroleum of a given
percentage has a greater percentage impact on state
petroleum revenues.This occurs because the costs of
petroleum transportation and gathering and the quality
differential value are relatively stable,so the wellhead
price,on which state petroleum revenues are based,rises
and falls almost dollar for dollar with world oil prices,
producing a larger percentage effect on the wellhead value.
Due to the many uncertainties involved in forecasting
revenues,the forecasting model projects a range,or
frequency distribution,of state petroleum revenues by
year,so that for each year a forecasted petroleum revenue
B-5-20
figure may be selected based on a given cumulative
frequency of occurrence.The mode 1 accomp 1ishes thi s by
iteratively selecting a set of input variable values from
among alternative values and computing a petroleum revenue
figure for each time period.Each projection is computed
using a set of accounting equations that estimate royalties
and production taxes from each state oi 1-and gas lease for
each time period.By selecting the average value of all
input data the model produces an average petroleum revenue
forecast.
Because of the uncertainties in projecting petroleum prices
and their importance in developing alternative generation
plans and load forecasts,it is necessary to examine the
implications of several different world oil price projec-
tions in addition to the price projections developed by the
DOR.This need is accommodated by DOR through a petroleum
revenue sensitivity accounting model. This sensitivity
accounti ng mode 1,whi ch is in effect a submode 1 of the
PETREV model,utilizes the accounting equations and average
values for all input variables other than world oil prices
from PETREV,to compute an adjustment to PETREV I S average
petroleum revenue forecasts based on different assumed
world oil price forecasts.By executing the sensitivity
model with the alternative petroleum price projections,
alternative petroleum revenue projections are developed for
use in projecting state economic activity in the MAP model.
Most of the petroleum revenues are available for state
expenditures for operat ions and capita 1 cons truct ion.
Twenty-fi ve percent of state roya lti es are,by constitu-
tional provision,deposited directly to Alaska's permanent
fund.
The process of projecting state petroleum revenues and the
functions of the PETREV model are presented in some detail
in the quarterly report entitled "Petroleum Production
Revenue Forecast.II (Alaska Department of Revenue,March
1983).The petroleum revenue projections used in preparing
the electric power market and economic forecasts are based
on the March 1983 average expected va1ues of all factors,
including petroleum production,other than petroleum
prices.
While production rates can be estimated with reasonable
accuracy for the next decade because of the long lead time
required to put a field into production in Alaska,higher
world petroleum prices could be expected to result in
higher levels of exploration and development and, by the
B-5-21
1990 1s,higher levels of product i on.Production rates from
the North Slope,the source of most state product ion taxes
and royalties,are projected to be approximately 1.6
million barrels per day (MMBD)in 1983, to peak at nearly
1.8 MMB/d in 1987,and to steadily decline to .7 MMBD in
1999 (Al aska Department of Revenue March 1983).The
petroleum production projections assume continued
production from operating fields,production from fields
now being developed,and modest levels of production in the
1990 ls from new fields (Alaska Department of Revenue,March
1983) .
(iii)Man-in-the-Arctic Program (MAP)Economic Model
The MAP model is a computer-based economic model ing
system that simulates the behavior of the economy and
population of the state of Alaska and each of twenty
regions of the state corresponding closely to Bureau of the
Census divisions.The Railbelt consists of six of those
regions:Anchorage,Fairbanks,Kenai-Cook Inlet,
Matanuska-Susitna,Seward,and S.E.Fairbanks.The model
was originally developed in the 1970's by the Institute of
Social and Economic Research of the University of Alaska,
under a grant from the National Science Foundation.The
model has been continually improved and updated since it
was origially developed,and has been used in numerous
economic analyses such as evaluations of the economic
effects of alternative state fiscal policies and
assessments of the economic effects of development of outer
continental shelf petroleum leases.An important
appl ication of the MAP model has been in providing economic
projections for developing electric demand projections.It
has been used since 1980 in preparing economic projections
in support of planning and design for the Susitna
Hydroelectric Project.
The MAP model functions as three separate but linked
sub-model s,the scenario generator submodel,the
economic sub-model,and the regionalization sub-model,
as illustrated in Figure 84.The scenario generator
sub-model enables the user to quantitatively define
scenarios of development in exogenous industrial
sectors;i.e.,sectors whose development is basic to the
economy rather than supportive.Examples of such
sectors are petroleum production and other mining,the
federal government,and touri sm.The scenari 0 generator
sub-model also enables the user to implement assumptions
concerning state revenues from petroleum production.
The economic sub-model produces statewide projections of
numerous economic and demographic factors based on
B-5-22
quantitative relationships between elements of the
Alaskan economy such as employment in basic industries,
employment in non-basic industries,state revenues and
spending,wages and salaries,gross product,the con-
sumer price index,and population.The regionalization
sub-model enables the user to disaggregate the statewide
projections of population and employment to each of the
20 separate regions of the state,using data on histor-
ical and current economic conditions and assumptions
concerning basic industrial development.
Each of the three MAP sub-mode 1s exi sts as a computer
program,and each program is supported by a set of input
vari ab les and parameters.Each of these programs and
the supporting input variables and parameters are dis-
cussed briefly in the following sections.Detailed
information on each sub-model,including a complete
model listing and the input variables and parameters
used in executing the model,is provided in the MAP
Model Technical Documentation Report.
Scenario Generator Sub-Model
In order to operate the MAP mode 1, the user must make
a number of assumptions concerning the future develop-
ment of basic industries in the State.Such assumptions
are needed because the state economy is driven by inter-
related systems of endogenous and exogenous demands for
goods and services.Endogenous demands are generated by
the resident population and industries that serve that
population.
Exogeneous demands originate outside Alaska due to the
favorable position of the state to export its minerals
and other resources to other states or countri es. In
Alaska,exogenous demands stem from the state's natural
resource base,especially petroleum,non-energy
minerals,federal property,and tourist attractions.
Exogenous demands lead directly to employment in basic
sectors such as mining,and indirectly to employment and
output in industries such as oil field services that
support basic industry and industries such as housing
and restaurants that support workers in basic industries
and their families.
The scenario generator model permits the user to build,
from among a large number of alternative basic indus-
trial cases,economic scenarios that can be used to pro-
ject economic conditions in the state of Alaska and,
B-5-23
for purposes of the Susitna Hydroelectric Project,the
Railbelt.Input data for each of the scenarios are in
the form of employment projections by sector and region
of the state on an annual basis over the forecast
period.
The scenario generator model is also used to select the
level of state petroleum revenues that should be assumed
available to the state's general fund 'for expenditure on
state government operations and capital investment.As
indicated above,petroleum revenues constitute a large
proportion of total state revenues which provide the
basis for state expenditures,an important driving force
of the Alaskan economy.
Key input and output variables and assumptions for the
scenario generator are summarized in Section 5.4 of this
Exhibit •
Statewide Economic Sub-Model
The statewide economic model is a system of more than
1,000 simultaneous equations that individually and
collectively define the quantitative relationships
between economic and demographic factors in Alaska.
Values for input variables come from the scenario
generator,whose values can be expected to vary from one
execution of the model to the next,as well as from
files of other necessary exogenous data,whose values do
not change across runs.Parameters,whose val ues are
generally fixed from one model execution to the next,
are provided from another input file.The equations are
solved algebraically each time the model is executed
to produce a un ique set of val ues for the dependent
variables,some of which are computed only incidentally
as part of the mathematical process and others of which
constitute projections of statewide economic conditions.
While the equations in the statewide economic model are
solved as a unit each time the model is executed,they
are grouped for organizational and conceptual purposes
into four modules: economic module,fiscal module,
population module,and household formation module, as
illustrated in Figures B.84 and B.85.
in the economic module express
between economic factors such as
basic industri al sectors and output and
support sectors.Important products from
The equations
rel ationships
emp 1oyment in
emp 1oyment in
B-5-24
the economic module include projections of employment
and payroll by industry and personal income.
The fiscal module computes state government revenues and
the mi x of government expendi tures,whi ch is used as
input to the economic module. A separate module was
created for this purpose because of the significance of
state expenditures to the state's economy and the
model's periodic application in estimating the economic
effects of implementing alternative state fiscal
policies and assuming various alternative future state
revenue levels.This module plays a key role in examin-
ing the fiscal and economic effects of different future
world petroleum prices and state petroleum revenue
levels.Specific assumptions concerning state spend-
ing are implemented in the fiscal module as state fiscal
policy parameters,which are discussed below.
The population module expresses the relationships be-
tween population and economic factors recognized as key
determinants of population.Such factors include
employment,labor participation rates,fertility and
mortality rates,and unemployment and wage rate differ-
entials between Alaska and the rest of the United
States.
The economic,fiscal and population modules are operated
simultaneously to arrive at the solution.The fourth
module, household formation,is operated after the pop-
ulation module yields its results.
Equations in the household formation module express the
relationship between the formation of households in
Alaska and population by age group,sex,and race.Each
age-sex cohort has its own propensity to form households
which, over the last few years has generally increased.
This increase is expected to continue.
-Regionalization Sub-Model
Statewide employment,population,and household pro-
jections are disaggregated by the regionalization model,
the third sub-model of the MAP economic modeling system.
Disaggregation is accomplished by combining statewide
projections with regional industrial development data
from the scenari 0 generator mode 1 and regi ona1 para-
meters based on historical economic and demographic
relationships between each region and the state.This
process,illustrated in Figure 8.86,
8-5-25
produces projections by region or region group such as
the Anchorage and Fairbanks greater metropol it an areas.
Input Variables and Parameters
As indicated above,some input variables are factors
whose values are provided by the user to the model and
whose val ues can be expected to change from one
execution of the model to the next.Parameter values
are generally fixed both over time within each
simulation and during the course of successive model
executions.
The scenario generator model produces sixteen input
variables to define the exogenous economic assumptions
for each model execution:
·Agriculture Employment
·Mining Employment
·High Wage Exogenous Construction Employment
•Low Wage Exogenous Construction Employment
·High Wage Exogenous Manufacturing Employment
·Low Wage Exogenous Manufacturing Employment
·Exogenous Transportation Employment
Fish Harvesting Employment
Active Duty Military Employment
·Civilian Federal Employment
·State Production Tax Revenue
State Royalty Income
State Petroleum Lease Bonus Payment Revenue
State Petroleum Property Tax Revenue
State Corporate Petroleum Tax Revenue
·Tourists Entering Alaska
Of these sixteen variables,eleven are used to define
discrete industrial development scenarios and are
therefore region specific.The remaining five input
variables are elements of state revenue forecasts.
Estimates of future state petroleum revenue from state
petroleum production taxes and royalties are obtained
from projections generated by the Alaska Department of
Revenue based,for purposes of the Susitna Hydroelectric
Project,on alternative projections of world petroleum
prices.
To produce economic projections in years after 1999,
the last year for which petroleum revenue projections
are available from the Alaska Department of Revenue,
petroleum revenue forecasts were extrapolated to the
B-5-26
year 2010 using the average ~nnual rate of change
between 1996 and 1999.
The Institute of Social and Economic Research provides
corresponding estimates of future state lease bonus
payments,state petroleum property taxes,and state
petroleum corporate taxes.Other variables necessary to
execute the MAP Model incl ude less important exogenous
factors,such as natural population 'growth rates,and
startup val ues.
The regionalization model is executed using a data
series for 40 exogenous variables,based on 20 state
regions,and for each region,the basic sector
emp 1oyment and the government sector employment from the
scenario generator.Total state population,households,
and the ratio of support to total employment are
provided by the state economic sub-model.
The MAP model utilizes three
variable state fiscal policy
parameters,and calculated,
parameters.
types of parameters:
parameters,stochastic
or non-stochastic,
Variable state fiscal policy parameters are used
primarily in the fiscal module to represent policy
options for the collection of revenues and the timing
and composition of state expenditures.In general,these
parameters,which may be varied to reflect alternative
state fiscal policies or events were left unchanged in
preparing the el ectric power market forecasts for the
Susitna Hydroelectric Project.The most important
function of these parameters is to quantitatively define
state expenditure and revenue pol icies.In projecting
economic conditions for the Susitna Hydroelectric
Project,the following assumptions were made:
o state expenditures for operations and capital
improvements in 1983 dollars will rise in proportion
to state population as long as revenues can support
this level of expenditure;this assumption is in
accordance with a 1982 amendment to the Alaska State
Constitution setting a ceiling on state expenditures;
o when revenues from exi st ing sources cannot support
expenditures at the constant real per capita level,
earnings from the permanent fund wi 11 be made
available for operating and capital expenditures at
the expense of the Permanent Fund dividend program; as
B-5-27
revenues decline state spending priorities shift from
subsidies to capital improvements;
o when revenues from permanent fund earnings and other
sources are not sufficient to maintain expenditures at
the constant real per capita level,a state personal
income tax will be reimposed at its previous rate;
o when all of these revenue sources plus accrued general
fund bal ances are unable to support expend itures at
the constant real per capita level,expenditures will
be curtailed so that they will not exceed revenues.
Stochastic parameters are coefficients computed using
regression analysis.They are used primarily in the
economic module of the statewide economic model to
express the functional relationships between economic
factors such as employment,wages and salaries,wage
rates,gross product,and other national and regional
economi c factors such as unemployment and consumer pr ice
indices.Stochastic parameters are also used in the
popul ation module to express the rel ationship between
population migration into and out of Alaska and wage
rate and unemployment level differentials.
Calculated or non-stochastic parameters are generally
calculated rates or other quotients,and are used
primarily in the population and household formation
modules and the regionalization model.Calculated
parameters include factors such as survival rates for
the population by race,age group,and sex.Calculated
parameters used in the regionalization model include
factors such as ratio of population to residence and
adjusted employment by region.
-MAP Model Output
Economic forecasts through the year 2010 were
generated based on alternative petroleum price and state
petro 1eum revenue cases and other input vari ab1es and
parameters descri bed above.
Specific MAP Model output used directly as input to the
Railbelt Electricity Demand (RED)Model are the
following:
o population by load center,Greater Anchorage and
Greater Fairbanks,by year 1981 through 2010;
o total employment by load center by year;
6-5-28
o total households in the state by age group of head of
household - 24 and under years of age,25-29,
30-54,and over 55 - by year;
o total households by load center by year;
(iv)Rai1be1t Electricity Demand (RED)Model
The Railbelt Electricity Demand (RED)Model is a partial
end use -econometric model that projects both electric
energy and peak load demand in the Anchorage-Cook Inlet and
Fairbanks-Tanana Valley load centers of the Railbelt for
the period 1980-2010.The model was originally written by
the Institute of Economic and Social Research (ISER) of the
University of Alaska (ISER,May 1980).It was later
modified and expanded by Battelle Pacific Northwest
Laboratories (Battelle,December 1982,Volume VIII).The
present (1983)version is a further modification and
improvement,including a validation of the model
performance.The results of these efforts are fully
documented in the RED Documentation Report (Battelle,June
1983).A summary description of the methodology used by
the RED model,and an explanation of each module of the RED
model are presented in the following paragraphs.It is
followed by a description of the input and output data.
The RED model is a simulation model designed to forecast
annual electricity consumption for the residential;
commercial,small industrial,government;large industrial;
and misce11aneous end-use sectors of the two load centers
of the Railbelt region.The model is made up of seven
separate but interrelated modules,each of which has a
discrete computing function within the model.They are the
uncertainty,housing,residential consumption,business
consumption,program-induced conservation,miscellaneous
consumption,and peak demand modules.Figure B.8? shows
the basic relationship among the seven modules.
The model may be operated probabi1istically,whereby the
model produces a frequency distribution of projections
where each proj ect ion is based on a di fferent,randomly
selected set of input parameters.The model may also be
operated on a deterministic basis whereby only one set of
forecasts is produced based on a single set of input
variables.When operated probabilistically,the RED model
begins with the Uncertainty Module, which selects a trial
set of model parameters to be used by other modules.
These parameters include price elasticities,appliance
saturations,end-use consumption and regional load factors.
Exogenous forecasts of population,economic activity,and
retail prices for fuel oil,gas and electricity are used
B-5-29
with the trial parameters by the Residential Consumption
and Business Consumption Modules to produce forecasts of
electricity consumption.These forecasts,along with
additional trial parameters,are used in the
Program-Induced Conservation Module to simulate the
effects of government programs that subsidize or mand ate
the market penetration of certain technologies that reduce
the need for power. This program-induced component of
conservation is in addition to those savinqs that would be
achieved through normal consumer reaction to energy prices.
The consumption forecasts of residential and business
(commercial,small industrial,and government)sectors are
then adjusted to reflect these additional savings.The
revised forecasts are used to estimate future miscellaneous
consumption and total sales of electricity.These
forecasts and separate assumptions regarding future major
industrial loads are used along with a trial system load
factor to estimate peak demand.
After a complete set of projections is prepared,the model
begins preparing another set by returning to the
Uncertainty Module to select a new set of trial parameters.
After several sets of projections have been prepared,they
are formed into a frequency di str ibut ion to allow the user
to determine the probabi 1 ity of occurrence of any given
load forecast.
When only a single set of projections is needed,the model
is run in certainty-equivalent mode whereby a specific
default set of parameters is used and only one trial is
run.
The RED model produces projections of electricity
consumption by load centers and sectors at 5-year
intervals.A linear interpolation is performed to obtain
yearly data.
The outputs from the RED model runs are used by the
Optimized Generation Planning (OGP)model to plan and
dispatch el ectri c generating capac ity for each year.The
remainder of this section presents a description of each
module in the RED model.
Uncertainty Module
The purpose of the Uncertainty Module is to randomly
select values for individual model parameters that are
considered most subject to forecasting uncertainty.
These parameters include the market saturations for
major appliances in the residential sector;the price
B-5-30
e1ast ic ity and subst itute energy forms and cross-pr ice
elasticities of demand for electricity in the
residential and business sectors;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 Monte Carlo routine,
which uses information on the distribution of each
parameter (such as its expected val ue and range)and the
computer I s random number generator to produce sets of
parameter values.An overview of information flows
within the Uncertainty Module is given in Figure B.88.
Each set of generated parameters represents a "tri al".
By running each successive trial set of generated
parameters through the rest of the modules,the model
builds distributions of annual electricity consumption
and peak demand.The end points of each distribution
reflect the probable range of annual electric
consumption and peak demand,given the level of
uncertainty.
The Uncertainty Module need not be run every time RED is
run.The parameter file contains "default"values of
the parameters that may be used to conserve computation
time.
In the current study,the RED model was used in
certainty-equivalent mode for all forecasts.
Sensitivity runs were performed for the reference case,
using the probabilistic mode.The results are presented
in Section 5.4.
The Housing Module
The Housing Module calculates the number of households
and the stock of housing by dwelling type in each load
center.The Housing Module's structure is shown in
Figure B.89. Using regional forecasts of households and
total population,the housing module first derives a
forecast of the number of households served by
electricity in each load center.Next, using exogenous
statewide forecasts of households headship rates and age
distribution of Alaska's population,it estimates the
distribution of households by age of head and size of
household in each load center.Finally,it forecasts
the demand for four types of housing stock:single
family,mobile homes,duplexes,and multifamily units.
8-5-31
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 Module forecasts the
annual consumption of electricity in the residential
sector.The Residential Consumption Module employs an
end-use approach that recognizes nine major end uses of
electricity,and a "small appliances"category that
encompasses a 1arge group of other end uses.They are
water heaters,cooki ng,clothes dryers,refri gerators,
freezers,dishwashers,clothes washers,and sauna-
jacuzzis.Figure 8.90 shows the calculations that take
place in this module.
For a given forecast of occupied housing,the
Residential Consumption Module first adjusts the housing
stock to net out housing units not served by an electric
utility.It then for ec ast s 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 app 1ied to that port ion of the
stock of each appliance using electricity and the
corresponding consumption rate to derive a preliminary
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 "tr i al "values
of price elasticities and cross-price elasticities of
demand from the Uncertainty Module.It adjusts the
preliminary consumption forecast for both short-and
long-run price effects on appliance use and fuel
switchi ng.The adj usted forecast is passed to the
Program-Induced Conservation Module.
8-5-32
-Business Consumption Module
The Business Consumption Module forecasts the
consumption of electricity by load center for each
forecast year.Because the end uses of electricity in
the commerci al , small industri al ,and government sectors
are more diverse and less known than in the residenti al
sector,the Business Consumption Module forecasts
electrical use on an aggregate basis 'rather than by end
use.Figure B.91 presents a flowchart of the module.
RED uses a proxy (the stoc k of commerc i a1 and i nd ustr i a1
floor space)for the stock of capital equipment to
forecast the derived demand for electricity.Using an
exogenous forecast of regional employment,the module
forecasts the regional stock of floor space.Next,
econometric equations are used to predict the intensity
of electricity use for a given level of floor space in
the absence of any rel ative price changes.Finally,a
price adjustment simi 1 ar to that in the Residenti al
Consumption Module is applied to derive a forecast of
business electricity consumption,excluding large
industrial demand,which is exogenously determined.The
Business Consumption Module forecasts are passed to the
Program-Induced Conservation Module.
Program-Induced Conservation Module
Battelle developed this module for the State of
Alaska,Office of the Governor (Battelle,December 1980,
Volume VIII)to analyze potential large scale
conservation programs that would be subsidized by the
State of Alaska.This module permits explicit treatment
of such government programs to foster additional market
penetration of technologies and programs that reduce the
demand for uti 1ity-generated electricity.The module
structure is desi gned to incorporate assumptions on the
technical performance,costs,and market penetration of
electricity-saving innovations in each end use,load
center,and forecast year.Figure B.92 provides a
flowchart of the process employed.
The module forecasts the additional electricity savings
by end use that would be produced by government programs
beyond that which would be induced by market forces
alone,the costs associated with these savings,and
adjusted consumption in the residential and business
sectors.
B-5-33
In the current st udy, th is mod uTe was not used. There
were several reasons:existing conservation programs are
being phased out;there are many uncertainties in long
term government conservation programs;and reliable data
to estimate additional electricity savings beyond that
which would be induced by market forces alone,is
limited for the Railbelt region.
Miscellaneous Consumption Module
The Miscellaneous Consumption Module forecasts total
miscellaneous consumption for second (recreation)homes,
vacant houses,and street lighting.The module uses the
forecast of residential consumption to predict
electricity demand in second homes and vacant housing
units.The sum of residential and business consumption
is used to forecast street lighting requirements.
Figure B.93 provides a flowchart of this module.
Peak Demand Module
The Peak Demand Modul e forecasts the annual peak
demand for electricity.The annual peak load factors
were based on an analysis of historical Railbelt load
patterns.A two-stage approach using load factors is
used.The unadjusted residential and business
consumption,miscellaneous consumption,and load factors
generated by the Uncertainty Module are used to forecast
preliminary peak demand.Separate estimates of peak
demand for major industrial loads are then added to
compute annual peak demand for each load center.Figure
B.94 provides a flowchart of this module.
Input Data
There are fi ve input data fil es to the RED model.The
RDDATA file contains output data of the MAP model,
including load center population,households,and
emp 1oyment and state househo1d by age group,and the
real prices of fuel oil and natural gas,by load center
and end-use sector.
The RATE DAT fi le contains the real prices of
electricity by load center and end-use sector.These
prices are derived from present costs of electricity
adjusted to future conditions based on the OGP results.
The PARAMETER file contains the numerical values that
describe the distributions of the parameters varied in
B-5-34
the Uncertainty module. These variables are:housing
demand coefficients;saturation rate of electrical
applicances,floor space elasticities;short-term and
long-term own-price and cross-price elasticities for
electricity,fuel oi 1,and natural gas;and annual load
factors.
The EXTRA DAT fi le contains information on the annual
electrical consumption and peak demand of large indus-
trial projects.
Output Data
The RED output report contains various tables generat-
ed by the program.The main tables are the following:
o Number of househo lds for each load center,forecast
year (1980,1985,and at five year intervals to
2010),and type of housing (single family,multi-
family,duplex,and mobile homes);
o Residential appliance saturations for each load
center,forecast year,and type of housing;
o Residential use per household without price elastic-
ity adjustments for each load center,forecast year,
and app 1iance category (sma 11 app 1i ance, 1arge
appliance,and space heat);
o Business use per employee with price elasticity
adjustments for each load center,and forecast year;
o Electri c energy requi rements for each load center,
year,and category of consumption (residential,
business,miscellaneous,incremental conservation
savings,large industrial,and total;
o Peak e1ectri c requi rements for each load center and
year.
Output from the RED mode 1 is used as input in the OGP
computer model for the purposes of analyzing alternative
expansion programs.
(v) Optimized Generation Planning (OGP)MODEL
The OGP program was developed over ten years ago by
General Electric Company (GE)to combine the three main
B-5-35
elements of generation expansion planning (system
rel i eb i l ity,operating and investment costs)and automate
generation addition decision analysis.The following
description of the model was extracted from GE 1 iterature
and the Descriptive Handbook (GE,March 1983).
The first calculation in selecting the generating capacity
to install in a future year is the rel i abil ity eval uation
using either percent installed reserves or loss-of-load
probability (LOLP). This answers the questions of "how
much"capacity to add and "when"it should be installed.A
production costing simulation is also done to determine the
operating costs for the generating system with the given
unit additions.Ftnal l y,an investment cost analysis of
the capital costs of the unit additions is performed.The
operating and investment costs help to answer the question
of "what kind" of generation to add to the system.
Figure 8.95 outlines the procedure used by OGP to determine
an optimum generation expansion plan.
The next three sections (reliability evaluation~production
simulation,and investment costing)review the elements of
these computations.Then,the OGP optimization procedure
is descr ibed ,followed by a list of the input and output
files.
Reliability Evaluation
H'i st or ic al l y,electric utility system planners
measured generation system reliability with a percent
reserves index.This planning design criterion compared
the total installed generating capacity to the annual
peak load demand.However ~thi s approach proved to be a
relatively insensitive indicator of system reliability,
particularly when comparing alternative units whose size
and forced outage rate varied.
Since its introduction in 1946,the measure that has
gradually gained widest acceptance in the industry is
the "loss-of-load probabi 1 ity"(LOLP).The LOLP method
is a probabilistic determination of the expected number
of days per year on which the demand exceeds the
available capacity.It factors into the reliability
calculation the forced and planned outage rates of the
units on the system as well as their sizes.A LOLP of 1
day in 10 years is a usual industry standard.
8-5-36
Computing LOLP requires an identification of all outage
exents possible (in a system with n units,this means
2 events)and then a determination of the
probability of each outage event.However,since LOLP
is concerned with system capacity outages and not so
much with particular unit outages,the probability of a
given total amount of capacity on outage is calculated.
Utilizing a highly efficient recursive computer
technique,capacity outage tables are calculated
directly from a list of unit ratings and forced outage
rates.
The LOLP for a particular hour is calculated based on
the demand and installed capacity for the hour.The
reserves are given by capac ity mi nus demand.On thi s
basis,a deficiency in available capacity (i.e.,loss of
load)occurs if the capacity on forced outage exceeds
the reserves.The probability of this happening is read
directly from the cumulative outage table and is the
LOLP for a single hour.
In addition to calculating the percent installed
reserves,OGP can also calculate a daily LOLP
(days/year).The dai 1y LOLP is determi ned by summi ng
the probabilities of not meeting the peak demand for
each weekday in the year.The hourl y LOLP is cal cul ated
by summing the probabilities of not meeting the load for
all the hours in the year.
Production Simulation
Once a system with sufficient generating capacity has
been determined by the reliability evaluation,the fuel
and rel ated operating and maintenance (O&M)costs of the
system must be calculated.OGP does this by an hourly
simulation of system operation.
The program commits and di spatches generation based on
economics so as to minimize costs.However,the user
has the option of biasing or overriding the normal
economic operation of the system.This can be
accomplished in two ways.The user may specify
weighting factors for various environmentally rel ated
quantities such that the program will operate those
units to minimize their impact.The user may al so
limit,on a monthly basis,the number of hours that
units may run or the amounts of different fuels that may
be consumed.
B-5-37
The production simulation in OGP is performed in six
steps:load modification based on recognition of
contractual purchases and sales;conventional hydro
scheduling and its associated load modification;monthly
thermal unit maintenance scheduling based on planned
outage rates;pumped storage hydro or other energy
storage scheduling;thermal unit commitment for the
rema ini ng loads based on economi cs and/or env ironmenta1
factors,spinning reserve rules,and unit cycling
capabilities;and unit dispatch based on incremental
production costs and environmental emissions.The pro-
duction simulation is for a single utility system or
pool.Unrestrained power transfer capability is assumed
between areas or compani es i nterna 1 to the pool repre-
sented.
Purchases and Sales.The OGP production cost load model
is an hour-by-hour model of a typical weekday and week-
end day for each month,arranged in monotonically
decreasing order.These hourly loads are modified to
refl ect the fi rm purchases and sales between the area
being studied and entities outside that area.Each
contract has associ ated with it a demand charge
($/kW/yr)and an energy charge ($/kWh).
Convent iona1 Hydro Schedu 1ing.The power and energy
avail able from any conventional hydroelectric project
used in a simulation is divided into two types:base
load and peak load.The base load energy that must be
produced is accounted for by subtracting a constant
capacity from every hourly load in the month as shown on
Figure B.96. This capacity value is referred to as the
plant minimum rating.After this baseload energy is
used,any remaining energy available is used for peak
shaving.In such situations,the program uses the
remaining capacity and energy of the hydro unit to'
reduce the peak loads as much as possible.If any
excess energy exi sts at the end of a month, a user-
specified maximum storage amount can be carried forward
into the next month.
Thermal Unit Maintenance.On a utility system, the
planned maintenance of individual units is usually
performed on a monthly basis.During these periods,the
units are unavailable for energy production.Main-
tenance scheduling is normally done so as to minimize
the effect of both system reliability and system
operating costs.A common strategy for scheduling
ma i ntenance,and the method used in OGP,is the
levelized reserves approach.Basically,the monthly
B-5-38
peak loads are examined throughout the year,and
incremental amounts of generating capacity maintenance
are scheduled to try to levelize the peak load plus
capacity on maintenance throughout the year.
Increased maintenance levels which might be required
during the first few years of a un it 's operation are
modeled using an immaturity multiplier.OGP also allows
the user to annually input a pr edet.erm lned maintenance
schedule for units for which this information is
avail ab1e.
Thermal Unit Commitment.After modifications for
contracts,hydro,unit maintenance,and energy storage,
the remaining loads must be served by the thermal units
on the system.In OGP,the units can be committed to
minimize either the operating costs,as is usually done,
or some combination of user specified environmental
factors and operating costs.The operating costs are
calculated from the fuel and variable O&M costs and
input-output curve for each unit.Fixed O&M costs do
not effect the order in which units are committed,but
are included in the total production cost.
The unit commitment logic determines how many units
will be on-line each hour and also attempts to provide
an adequate level of operating reliability while
minimizing the system operating costs and/or
environmental emissions.The operating reliability
requirement is met by committing sufficient generation
to meet the load plus a user specified spinning reserve
margin.Units are committed in order of their full load
energy costs or emissions,starting with the least
expensive.
Thermal Unit Dispatch.If a unit is committed,the
unit's minimum loading level requires that its output be
at that level or higher.When the final commitment has
been established,each unit will be loaded to at least
its minimum.Typically the sum of the minimums does not
equal the load.Additional load will be served by the
un i t s 'incremental loading sections.The dispatching
function in the OGP production simulation loads the
incremental sections of the units committed in a manner
which serves the demand at minimum system fuel cost or
emissions.This dispatch technique is the equal
incremental cost approach.
8-5-39
Investment Costing
The investment cost analysis in OGP calculates the
annual carrying charges for each generating unit added
to the system.This is computed based on a $/kW
installed cost,a kW nameplate rating,and an annual
levelized fixed charge rate.
OGP Optimization Procedure
For the year under study,a reliability evaluation is
performed.This determines the need for additional
generating capacity.If the capacity is sufficient,the
program calculates the annual production and investment
costs,prints these values,and proceeds to the next
year.
If additional capacity is needed,the program will add
units from a list of available additions until the
rel i abil ity index is met. For each combination of units
added to the system,OGP does a production simul ation
and investment cost calculation for the year under
study.The program uses the information gained from the
cost calculations to logically step through the
different combinations of units to add,eliminating from
consideration combinations that would produce higher
annual costs than previously found.This process
continues until the expansion giving the lowest annual
costs is found.The selected units are added to the
system,and the program proceeds to the next year of the
study.
In cases where operating cost inflation and/or time
variation in unit outage rates are present,the OGP
optimization logic utilizes a "Iook-ahe ad"feature.The
look-ahead feature develops levelized fuel and O&M costs
and mature outage rates for use in the economic
evaluation.As part of the output information
available,the user obtains documentation of the
relative costs of all the alternatives examined.After
the generating unit selection,the reliability and
costing calcul ations are repeated for the chosen
alternative so that the expansion report available for
the user contains the correct annual values.
Input Data
There are two major input files to OGP:the Generation
file and the Load file.The Generation file model is
created for use as a data base representing the
8-5-40
in-service and on-order generating units.For each
unit,the following characteristics are described:
0 Type of generator
0 Unit sizes and earliest service year all owab 1e
0 Unit costs
0 Fuel types and costs
0 Operation and maintenance costs
0 Heat rates
0 Commitment minimum uptime rule
0 Forced outage rates
0 Planned outage rates
The Load file is specified by the user to represent peak
and shape characteristics which are projected to occur
for the years included in the OGP study.The user
supplies the following load shape data:
o Annual peak and energy demand
o Month/annual ratios
o The 0%,20%,40%,and 100%points on the peak load
duration curve,by month
o Typical reference weekday and weekend-day hourly
ratios by month
In addition to these two input files,the user uses the
Data Preparation (DP)program and the Generation
Planning (GP)program to run the OGP model.The DP
program is used in setting up standard tables which
descri be the thermal and hydro opt ions.Included are
tables for plant capital,O&M,and fuel costs,inflation
patterns,planned and forced outages rates,minimum
loading points,and environmental data.The GP program
includes input data on loss of load probability
criteria,hydro firm energy,economic parameters,and
output options.
Output Data
Output options have been designed and included in OGP
to provide the user with flexibility in the level of
detail and volume of documentation received.Complete
output reports as well as summary outputs are available.
The output available from the OGP program includes the
following information:
o Listing of the input data.
o Standard tables,as defined by the user,for
various unit characteristics.
B-5-41
o Listing of the unit types and "sizes available for
optimization and their characteristics.
o Listing of the Load file for the study period.
o Listing of the generating units on the system and
their characteristics.
o Year-by-year summary of the firm contracts input by
the user.
o Production simulat ion summari es,1i st i ng all of
the generating units of the system"with their
energy output,fuel and O&M costs,fuel
consumption,and environmental emissions.These
summaries can be obtained on a monthly or annual
basis,for all the decision passes or just the
optimum system.
o Summary of all the expansion ~ternatives,with their
associated costs and reliability measures,evaluated
during the optimization.
o Summaries of the final system expansion through time
and the associ ated costs.
(c)Model Validation
Both the MAP and RED models are used to simulate future
conditions based on alternative assumptions concerning world and
state economic conditions and electricity demand in the Railbelt.
Measures that have been taken to ensure that both model s simul ate
economic and electricity utilization conditions and relationships
as accurately as possible are summarized below.
(i)MAP Model Validation
Val i dat i on . of the MAP Model has been accompl ished using
two separate but interrelated techniques.First,a
standard set of statistics was computed for each of the
stochastic parameters used in the MAP Model equations.
These statistics provide information on the expected
accuracy of each coefficient and the probability that each
coefficient expresses the correct relationship between
variables.Second,the MAP Model was tested to determine
the accuracy with which it could simulate observed
historical conditions.
Stochastic Parameter Tests
Stochastic parameters are,as indicated above,
coefficients computed using regression analysis,a
statistical procedure whereby the quantitative
relationship between variables is estimated by one or
more computed coefficients.Most of the equations in
B-5-42
the economi c modu 1e of the statewi de economi c mode 1 are
computed using regression analysis.
In estimating coefficients using regression analysis a
number of statistics are computed that indicate the
accuracy of the coefficient and the overall efficiency
of the equation in estimating the 'true value of the
dependent variable.Among these statistics are t-values
and correlation coefficients.They are used both in
selecting the best independent variables for estimating
a given dependent variable and in determining the
expected accuracy of the final equation.
Correlation coefficients,t-values,and several other
statistics have been computed for each stochastic equa-
tion used in the MAP Model. In each equation efforts
have been made to obtain the highest possible values for
these statistics in order to ensure that the model
reflects actual economic relationships as accurately as
possible.As a result of this effort all the coeffi-
cients used in the MAP Model have a relatively high
level of statistical significance.
Simulation of Historical Economic Conditions
Although the MAP Model has been in use since 1975,
analyses conducted for the Susitna Hydroelectric Project
were the fi rst app1i cat ions of the mode 1 in long range
projection of economic conditions.Previous applica-
tions of the model had been in analysis of economic
effects of alternative state policies.It is not poss-
ible,therefore,to test the model's projection accuracy
using old forecasts.However,the model's accuracy was
tested by simulating historical economic conditions by
executing the model utilizing historical data and input
variables.Table B.87 summarizes the results of simu-
lation of selected historical conditions.The table
shows that the MAP Model reproduces hi stori ca1 cond i-
ti ons with reasonable accuracy,ina peri od when sign i-
ficant growth and structural change occurred.
(ii)Red Model Validation
The accuracy of the RED Model was assessed by sub-
stituting historical values for the "inputs"or "drivers"
of the model,and then the predicted val ues were compared
with actual values.The historical period used in the
analysis was brief because of the lack of available
B-5-43
data for the end-use forecasting model. Complete
historical data on end-use (fuel mode split,appliance
saturation,end-use energy consumption,etc.)are only
available for 1980.Therefore,the accuracy tests which
can be performed on the model are limited.The tests were
performed for the period 1980-1982.
Table B.88 summari zes the results.The 1982 results
obta ined from the RED Reference Case are compared to actual
data from utilities.In addition,the model was run using
the best estimates of 1982 economic drivers and fuel
prices.These results are ~so shown on Table B.88, as the
Backcast Case.
Even though the RED model is a long term forecasting model
which uses 5-year interval inputs,it produces a forecast
error of only 0.6 percent in Fairbanks and 1.7 percent in
Anchorage when compared to actual data.The remaining
discrepancies for the individual sectors appear related to
the qual ity of the input data.There might also be some
differences in the definition of each sector between the
RED model and the utilities.However,the overall results
show that the forecasts agree closely with the actual
values.
5.4 Forecast of Electric Power Demand
(a)Oi 1 Price Forecasts
Forecasting the future world price of oil is a complex task and
most previous forecasts have been lacking in accuracy particularly
over the last ten years when oil markets received radical upward
price shocks.
Numerous forec ast s of fut ure oi 1 pr ices are av ail ab1e and these
vary in methodology used,their purpose and underlying reasoning,
and the experience of the forecaster.In providing a complete
review of current oil price forecasts,several forecasts are
discussed below.
(i)Alaska Department of Revenue (DOR):
The DOR is the State agency responsible for forecasting
State petroleum revenues for the purpose of Al askan state
budgeting and economic planning.As State revenue from·
petroleum production accounts for almost 90%of the State's
annual budget,the forecast prepared by DOR is used to
provide information to the Governor and Legisl ature in
establ ishing the level of the State government I s
B-5-44
expenditures and monitoring the revenue flow during the
fiscal year.To assist in this process,DORis forecast
estimates the future petroleum revenues on a monthly basis
for two years,by quarters for the third year,and annually
for the fo 11 owi ng fourteen years.The forecasts are up-
dated quarterly.
In developing the revenue forecast,a number of State
emp Ioyees of the Offi ce of Management and Budget,
Department of Natural Resources,and DOR each develop one
to ten scenarios of future world oil prices,and assign a
subjective probabi 1ity to each scenario.Using the Delphi
method,DOR aggregates these individuals'forecasts and
develops a probabi 1ity density function using a computer
model.The individual probability density functions are
then aggregated by the mode 1 to produce a compos ite prob-
ability distribution of future world oil prices.
The mean or average oil price for each period is determined
from the compos ite frequency di stri buti on.The mean oi 1
prices for the March 1983 quarter are summarized below and
year-by-year values are presented in Table B.89.
Year(s)
1983
1984
1985
1986
1987
1988-1999*
Percent
Change -%/yr.
-17.2
-5.4
-1.4
-1.8
1.3
1.3
Price in Final Year
Period - 1983$/bb1
28.95
23.96
22.67
22.35
21.95
25.60
In addit ion to oi 1 pri ces,the DOR also enters into the
PETREV the probability distribution of many other vari-
ables,including North Slope production rates,which is 'an
extreme 1y important factor in future revenues.The mode 1
is then run to arrive at the probability distribution of
future revenues.The 30%Revenue Case is used for budget,
and the 50%Case is used for economic planning.
The two revenue cases mean that there is a probabi 1ity of
70%000%-30%)and 50%000%-50%)respective 1y that reve-
nues wi 11 be equal to or greater than the estimated reve-
nues calculated for the cases.
*If the 1.3%DOR annual escalation is assumed to continue to 2040, a
price of $42.48/bb1.would occur.
B-5-45
With each of the altern at i ve revenue cases,(30%&50%)
there is an implicit oil price forecast which can be
estimated using the PETREV model system in a reverse
fashion,beginning with revenues and running the models
until the associated oil prices are determined using the
mean values of other variables.The implicit oil price
forecasts for the 30%and 50%Revenue Cases are presented
below..
Percent Pr ice in Fin a 1 Year
Change -%/yr.of period 1983$/bbl
Year (s)30%50%30%50%
1983 -21.5 -17.a 28.95 28.95
1984 -7.7 2.5 22.74 24.04
1985 -3.2 -10.5 21.00 24.63
1986 -3.9 -2.5 20.32 22.05
1987 -1.2 -0.7 19.52 21.49
1988 1.0 -0.4 19.29 21.34
1989 -6.1 -1.1 19.10 21.25
1990 -3.7 -3.4 17.93 21.01
1991 -2.0 -1.1 17.26 20.29
1992 -4.1 -4.1 16.92 20.07
1993 -1.7 -2.0 16.22 19.25
1994 -2.3 -0.5 15.94 18.86
1995 -1.5 -4.1 15.58 18.77
1996 -2.5 -0.3 15.34 18.00
1997 -0.5 -0.9 14.95 17.94
1998 -0.8 -0.6 14.88 17.78
1999*-1.5 -1.1 14.76 17.78
(i i )Data Resources Incorporated (DRI)
DRI is a well-known forecasting organization which
provides forecasts of GNP,economic indicators,and
commodity prices incl uding prices for oil,gas and coal.
Extensive use is made of econometric and other computer
models including special energy forecasting models such as
the DRI Drilling Model,DRI Coal Model and the DRI Energy
Model.Supply and demand for oil are estimated to arrive
at a forecast price for oil.An example of the forecast
oil production and price data that DRI develops is shown in
Figure B.97.
*If the average DOR rate of change from 1994 to 1999 is extrapolated
through 2040,the forecasted prices for the 30%and 50%Revenue
Cases would be $7.90/bbl.and $11.40/bbl.,respectively.
B-5-46
DRI prepares long term forecasts of oi 1,natural gas,and
coal prices quarterly.Their Spring 1983 forecast provides
estimated future prices through 2005.**The key macro-
economic assumptions behind their oil prices are that the
U.S.economy will grow at an approximate 2%real rate in
1983,accelerating to a high 5.2%rate for 1984 and 4.5%
for 1985.From 1985 to 1990 the growth rate will stabilize
at an approximate 2.8%/yr.rate decreasing to 2.3%/yr.over
the longer term,t .e ,after 1990.Inflation,as measured
by the Implicit Price Deflator,is assumed to be 4.7% in
1983,5.2% in 1984,and about 6%/yr.from 1985-2000.
DRI IS Base-case estimate of future oil prices (average
crude acquisition price for U.S.refineries)shows prices
dropping to about $25/bbl (1983$)in 1984 and then increas-
ing at a real rate of about 6.6%/yr.from 1984-1990 to give
a price of about $37/bbl in 1990.The decrease in real
prices during 1983 and 1984 reflects a weak economy which
strengthens rapidly during 1984 and 1985 allowing OPEC to
exercise greater influence over the world oi 1 market such
that an average real rate of price increase of 6.6% can be
maintained from 1985-1990.After 1990,DRI has assumed
that the real rate of increase in oil prices will taper off
to 4.4%/yr.for 1990 to 1995,approximately 3%from
1995-2000 and around 1.0% from 2000-2005.DRI's Base-case
estimates are summarized below and presented year-by-year
in Table 8-89.
Year(s)
1983
1984
1985-1990
1991-1995
1996-2000
2001-2005*
Real Rate of Price
Change -%/yr.
-13.1
7.4
6.5
4.4
3.1
1.1
Price in Final
Year of Period-1983$/bbl
28.95
25.17
36.99
45.85
53.43
56.54
The 1983 prices listed above were determined by adjusting
the 1982 pri ces in the fo 11 owi ng manner:(1)the 1982
pri ces for the years of 1984,1990 and 2000 are increased
* DRIls forecast extends to 2005. Assuming the same DRI rate of change
(1.1%) from 2001-2005 app 1ies for 2006-2040,the 2040 pri ce becomes
$84.15/bbl in 1983 dollars.
** Data Resources,Inc.U.S.Long Term Review,Spring 1983.
B-5-47
by the 1983 vs GNP deflator value (4.7%)to provide prices
in 1983 dollars;(2)1983 prices for intervening years were
interpolated;and (3)prices from 2000 to 2010 are extrapo-
lated using Base Case escalation rate of 1.14%.
DRl also developed a lOWOll and HIGHOll price scenario
stat i ng that uncertai nty over oi 1 pri ci ng makes it usefu 1
to examine alternative scenarios.No specific discussion
was given by DRl of the economic or political forces which
would underlie the lOWOll HlGHOll scenarios.The lOWOll
HlGHOll forecast is:
lOWOll HlGHOll
Rea 1 Rate of Price in Final Real Rate of Price in Final
Price Change Year of Period Price Change Year of Period
Years(s)%/yr. 1983$/bbl %/yr. 1983$/bbl
1983 -20.4 28.95 1.3 28.95
1984 3.5 23.04 1.3 29.32
1985-1990 3.5 28.27 7.8 46.07
1991-2000 3.8 40.84 3.8 67.01
2001-2005 1.1 43.22 1.1 70.92
(iii)Sherman H.Clark Associates (SHCA)
Sherman H.Clark Associates specializes in all phases of
energy and resources economics.Clients include major oi 1
companies,independent oil producers,independent refin-
eries and tanker companies,state,federal and foreign
government, coal companies,electric utilities and others.
SHCAls experience in evaluating and projecting world econ-
omics and energy developments has resulted in the develop-
ment of an extensive and detailed energy data base which is
continuously updated.'
SHCA prepares a detailed annual twenty-five to thirty year
forecast of the wor 1dwi de supply and demand for all types
of energy and estimated prices entitled Evaluation of World
Energy Developments and Their Economic Significance.
Figure B.98 contains an excerpt from SHCAls May 1983 fore-
cast showing petroleum supply and consumption in the free
world for 1982-2010. This illustrates the supply/demand
analysis that SCHA performs to arrive at its estimates of
future world oil prices.
The May 1983 SHCA forecast of world oi 1 prices contains
three scenarios to which SHCA has assigned estimated
B-5-48
*
probabilities of occurrence.*These are the base case
(BC), the no supply disruption (NSD),and the zero economic
growth (ZEG).These scenarios are discussed in more detail
below.
Base Case. In light of precedent during the 1970's,SHCA's
base case envisions that a severe supply disruption will
occur in the world oil market in the late 1980's,followed
by production-limiting decisions of several key producing
countries.
Until the supply disruption occurs,SHCA is projecting
rea 1 Uni ted States economi c growth at an annua 1 rate of
3.0%and free world economic growth at 3.3%.After the
disruption,growth in the U.S.will slow to 2%annually and
to 2.7%annually in the free world.Prices,as measured by
the Producer Price I ndex are projected to remai n at a 2%
annual rate of growth through 1983 and then increase to
5%/yr through 1988.The di srupti on and its resu Hi ng oi 1
price increase will increase United States inflation to 10%
annually for the period 1989-90.After 1990, the annual
rate of inflation wi 11 decrease to 8%and remain at that
level for the remainder of the projected period.
SHCA forecasts prices for marker crude oi 1 FOB to remain
at the existing OPEC benchmark level for marker crude of
$29.00/bbl through 1985 but prices in 1983 dollars wi 11
decrease to $26.30/bbl in 1985 due to the effects of
inflation.OPEC will not be able to increase the bench-
mark price above $29.00/bbl before 1985 because of the low
average OPEC production of 18 MMBD or less which is expect-
ed from 1983-1985 versus OPEC's full production capability
of around 30-32 MMBD.On the other hand,increasing world
economic growth will prevent the benchmark price from
dropping below $29.00/bbl.
From 1985 until the assumed disruption occurs in about
1988, the annual rate of world economic growth of 3.3%will
increase the demand for OPEC oil to 20-25 MMBD which should
allow OPEC to increase the benchmark pri ce at a rate to
offset the inflation rate.The real price of oi 1 wi 11
remain at $26.30/bbl from 1985 to late 1988 when the supply
disruption is assumed to occur.
Evaluation of World Energy Developments and Their Economic
Significance,Sherman H.Clark Associates,Volume II,May 1983.
B-5-49
The effect of the supply disruption",stated in SHCA's own
words is:*
IIIn our base case,we have a supply interruption
in late 1988.(Sentence omitted to improve clarity of
description of supply disruption effects.)But
whether in the late 1980 ls or after 1990,the
necessary conditions include a large disruption such
as total loss of Saudi capacity for"a year,and either
a permanent loss or a change in OPEC policy that would
1 imit capac ity avail ab1e to about 20 MMBD.With 3%to
4%per year economic growth through 1988,the marker
price could increase to about $40 per barrel (1983
dollars)due to the disruption,slowing economic
growth thereafter to 2%per year and a ri sing real
price would hold OPEC production about const ant ."
SHCA's estimate of prices from 1988 to 2040 and the reasons
for those prices are summarized by the following
quote:**
IIIn the base case,the supply disruption
in the late 1980s results in a sharp price
increase and the limitation in capacity made
available by OPEC causes a steady real escalation
in prices that extends through 2010.Supplemental
oil (and gas)supplies become partially economic
by 2000 and generally economic by 2010.From 2010
to 2020 the price escalation slows to 1%per year
and after 2020 there is a price plateau that could
last for 20 years or perhaps indefinitely;i.e.,
prices are high enough to encourage all the
necesssary substitution for conventional oil
production.1I
Estimated prices for the Base-case in 1983$/bbl are
summarized below and presented year-by-year in Table B.89.
*
B-5-50
and Their Economic
SHCA has assigned a probability of occurrence of 40%to its
Base Case scenario.
Year
1983
1984
1985-88
1988-89
1989-90
1991-2000
2001-2010
2011-2020
2021-2040
Real Rate of Price
Change -%/yr.
-4.6
-4.7
0.0
52.1
0.0
3.0
3.5
1.5
0.0
Price in Fin~Year
Period -1983$/bbl
28.95
'27.61
26.30
40.00
40.00
53.76
75.75
87.80
87.80
No Supply Disruption Case (NSD)
This case is the same as the base case but it is assumed
that the supply disruption in the late 1980s does not
occur.Economic growth after 1988 is therefore assumed
to be at an annual rate of 3%in the United States slowing
gradually to an annual rate of 2.5%.Economic growth in
the free world will be 3.6%annually.The rate of
inflation does not increase after 1988 but remains at an
annual rate of 5%until after 2000.An additional
assumption for this scenario is that the finding and
production rate for non-OPEC crude increases above the rate
assumed for the base case.
For the years 1983-1988,forecasted oi 1 prices for the NSD
scenario are the same as the base case.From 1988-2010
prices increase at a 3.0%annual rate due to the relatively
high rate of worl d economi c growth.The rate of price
escalation is then assumed to taper off as the oil price
approaches the price that will bring forth supplies of
alternative fuels.This price occurs around 2035 to 2040.
SHCA has assigned a probability of occurrence of 35%to the
NSD scenario.SHCA's estimated prices in 1983$/bbl are
summari zed below and presented ye arvby-ye ar in Tab 1e B.89.
B-5-51
Price in Final Year
of Period-1983$/bbl
28.95
27.61
26.30
50.39
64.48
'74.84
82.66
-4.6
-4.7
0.0
3.0
2.5
1.5
1.0
Real Rate of Price
Change -%/yr.Year (s)
1983
1984
1985-88
1989-2010
2011-2020
2021-2030
2031-2040
Zero Economic Growth (ZEG).SHCA has al so developed a
scenario where world economic growth is zero in the United
States and 0.4% in the free world through 1990.The rate
of inflation would also be zero.After 1990,economic
growth woul d increase at a vi gorous rate of 4%s 1owi ng
gradually to 3.2%for the United States and 4.3%slowing to
3.8%for the free world.The assumed low economic growth
from 1983-1990 is based on the fact that economic growth
for the years 1979-1982 was zero and on the assumption that
the zero growth will continue until 1990.
Real oil prices under the scenario would decrease from the
existing $29.00/bbl to $27.00/bbl toward the end of 1983
and to $21.00/bbl in 1984. A further decrease to
$17.00/bbl would occur in 1985 and prices,both real and
nominal (since the rate of inflation would be zero)would
remain at that level through 1990 where the vigorous
resumption in economic growth would allow the real price to
increase slowly through 2010.The drop to $17.00/bbl
through 1990 reflects a severe reduction,if not a loss,in
control by OPEC over the world price of oil.SHCA has
assigned a point probability of occurrence of 25%to the
ZEG scen ari o.
SHCAl s estimated prices in 1983$/bbl are summarized below.
SHCA has not projected prices beyond 2010 for this
scen ari o.
29.00
27.00
21.00
17.00
17.00
45.11
Pr ice in Fina1
Year of Period
-1983$/bbl
-6. 9
-22.2
-19.0o
5.0
Real Rate of Price
Change -%/yrYear(s)
1983
1983 (4th quar.)
1984
1985
1986-1990
1991-2010
B-5-52
(iv)Other Projections
To provide a more complete range of possible future oil
price scenarios and the resulting effect on the Railbelt
Area demand for electrical energy,the Federal Energy
Regul atory Commi ss ion has suggested that several constant
price change scenarios be developed.The scenarios
presented for sensitivity analysis are 2.0%/yr.,O%/yr.,
-1.0%/yr.and -2.0%/yr.There is no supply/demand or other
type of analysis supporting these price change scenarios
presented below:
Prices in 1983$/bbl
Year +2.0%0%-1.0% -2.0%
1983 28.95 28.95 28.95 28.95
1990 33.25 28.95 26.98 25.13
2000 40.54 28.95 24.40 20.54
2010 49.42 28.95 22.07 16.78
2020 60.24 28.95 19.96 13.71
2030 73.43 28.95 18.05 11. 20
2040 89.51 29.95 16.33 9.15
(b)Selection of Reference and Other Cases.
The estimates of future world oil prices presented above
illustrate the different views and outlooks on the world economy
by various forecasters.The range of forecasts are graphically
displayed in Figure 8.99.
To assess the impact of future oil prices on the demand for
electric energy in the Railbelt,the broad range of forecasts has
been analyzed and evaluated.Although it is possible that anyone
of the scenarios could prove to be true in the future,some would
present 1y seem to be more probable than others.OPEC seems to be
holding the line on their new benchmark price of $29.00/bbl and
the United States economy is recovering from the 1981-82 recession
at a stronger real rate of growth than recently predicted by many
economists.The rest of the free world will probably follow the
United States lead in economic growth which will increase the
worldwide demand for petroleum.
In light of the foregoing,the SHCA NSD Case has been selected as
the Reference Case.The SHCA NSD case presumes that OPEC wi 11
continue operating as a viable entity and will not limit
production during the forecasted period.Recent trends in
economic growth in the U.S.and the free world will continue at
reasonab 1e rates . Although events may affect thi s forecast,the
Reference Case falls in the middle range of the forecasts
8-5-53
evaluated and appears at this time to be a reasonable forecast for
the purposes of this analysis.
Table B.90 identifies those forecasts which have been selected for
analysis and the level of analysis to which each forecast has been
carried.Ten world oil price forecasts have been used to estimate
Railbelt electrical energy demand,while four- of the forecasts,
DOR Mean,DRI,Reference Case,and the -2%/yr.constant price
change are carried through the Optimum Generation Planning (OGP)
mode 1.
(c)Variables and Assumptions Other than Oil Prices
Many variables and assumptions other than world oil prices are
used in the PETREV,MAP,RED,and OGP models described in Section
5.3(b).Most of these other variables and assumptions,and repre-
sentative values for the Reference Case, are listed in Tables B.91
through B.102. Input variables for each of these models are dis-
cussed in the following paragraphs.
(i)PETRE V Model
State petroleum revenues from North Slope oi 1 product i on
are expected to account annually for between 93 and 99 per-
cent of state petroleum royalties and production taxes dur-
ing the period 1983 to 1999.Remaining royalties and pro-
duction taxes will be generated by petroleum production on
state lands other than on the North Slope and from produc-
tion of natural gas.
Of the factors listed on Table B.91,North Slope petroleum
production has the largest potential impact on state petro-
leum revenues,and is therefore a key variable in project-
ing economic conditions.Projected North Slope petroleum
product ion is the sum of projected product i on from seven
fields:Prudhoe Bay-Sadlerochit,Kuparuk,Milne Point,
Canning River,Flaxman Island,Point Thompson,and Beaufort
Sea.Currently only Prudhoe Bay-Sadlerochit and Kuparuk
are producing fields.The other five fields are projected
to begin production between 1987 and 1989.Production from
the currently producing fields are projected to remain the
main producers,accounting for an excess of 75 percent of
total North Slope production in 1999 (Department of Re-
venue,March 1983).While production rate~during the next
eight to ten years can be forecasted wi th some degree of
certainty,production rates after this period will depend
on the rate of exploration and development of oil fields.
Exploration rates will depend largely on the level of world
petroleum prices and the demand for petroleum,but develop-
ment of oil fields will depend on oil discoveries and
production as well as petroleum prices and demand.
B-5-54
(ii)MAP Model
Table B.92 lists 10 categories of exogenous or basic
employment, one measure of tourism,five categories of
petro 1eum revenues,and fi ve nat iona 1 economi c parameters
that are used as input to the MAP Model. These factors are
the principal input variables and parameters to the MAP
Mode 1.
For purposes of projecting electric energy demand,the
values of all the variables listed in Table B.92 other than
petro 1eum revenues were 1eft unchanged duri ng each of the
MAP Model executions.While sensitivity tests indicated
that varying the value of several of these factors produc-
ed demonstrable effects on economi c projections,none of
these factors affected economi c projections nearly to the
extent that petro 1eum pri ces did,through its impact on
state petroleum revenues.Based on results of the sensi-
tivity tests discussed in Section 5.4 (f),the key input
factors to the MAP Model other than petroleum revenues are:
state mining employment, which includes petroleum produc-
tion;state active duty military employment;tourists
visiting Alaska;U.S.real wage growth rate;and price
level growth rate.Employment relating to construction of
the Susitna Hydroelectric Project was not tested for sensi-
tivity.Employment in construction of electric power
generating stations is considered in the larger category of
construction employment.
Table B.93 surrrnarizes the basis for selecting the values
for the ten exogenous employment variables.The values for
many of the variables listed in Table B.92 are taken from
the MAP Model Data Base, a volume of economic and demo-
graphic data compiled and maintained by the Institute of
Soc ia 1 and Economi c Research.These data are deri ved from
information collected by various state and federal govern-
mental agencies,published reports,and other sources.The
data are organized,adjusted,and in the case of some vari-
ables,projected to the year 2010 to meet the input
requirements of the MAP Model.
(iii)RED Mode 1
Table B.94 lists the main variables that are used in each
module of the RED Model. In the Uncertainty module,the
fuel price forecasts,the housing demand coefficients,the
B-5-55
saturation of residential appliances,and the price
adjustment coefficients are the main variables.
Table B.95 shows the projected customer real prices of
heating fuel oil,natural gas,and electricity for the
Reference Case.The heating fuel oil price forecast was
derived from 1983 actual price,esca1 ated at the same
growth rate as the world oil price.The.natural gas price
forecast for the Anchorage-Cook Inlet area was derived from
1983 actual prices and an estimate of the wei1hted average
price (old and new contracts)of natural gas I.The new
contracts were esca1 ated at the same growth rate as the
world oil price.In the Fairbanks-Tanana Valley area,a
continuation of present practices of using propane for
heating was assumed.The price would also escalate with
world oil prices.The electricity prices were first
estimated using weighted average price of natural gas and
the addition of coal-fired generation in the mid 1990's.
In addition,allowances to cover administrative and
distribution costs were included to reflect retail prices.
The prices were later adjusted to reflect the OGP results.
The revised numbers are shown on Table B.95 and were used
ina11 an a1yses .
Table B.96 presents the housing demand coefficients which
were used in the housing demand equations for single
family,multi-family,and mobile homes.Table B.97 gives
an example of market saturations of appliances in single
family homes for the Anchorage-Cook Inlet area,and Table
B.98 presents the parameter values of the price adjustment
mechanism.
For the Housing module,the two main variables are the
regional household forecast,and the state households by
age group.These variables are directly obtained from the
MAP output file.Tables B.99, B.100,and B.101 provide
detailed information on the annual consumption and growth
rate of residential appliances,as well as the survival
rate of the existing and new appliances.
The main variables of the Business Consumption module are
the regional employment, which is an output of the MAP
model,and the floor space consumption parameters.Vacant
housing,second homes,and street lighting,and their
expected annual consumption are the variables of the
Miscellaneous module.The annual load factor for the two
load centers are the main vari ab1es of the Peak Demand
module.
B-5-56
Because the RED model is an end-use model,the appliance
saturation rate based on the existing stock of appliances
is a key variable.Also,the energy usage per appliance
has a major effect on electricity demand.Further,the
growth rate of consumption per appliance type has a
significant impact on residential electricity consumption
in future years.In the business sector,the projections
of the demand for "f100r space"and the consumption per
unit of floor space are key variables.Own-and
cross-price elasticities of demand have a significant
impact on electricity consumption by influencing
consumption behavior in both the short and long term.The
own-price elasticity values that are assumed in the model
determine the extent and time path of electricity price
impacts on residential and commercial consumption.The
cross-price elasticities show the impact on electricity
consumption due to changes in the price of substitute
energy resources for electricity.The own-and cross-price
e1ast ic it ies of demand are used to adj ust e1 ectr ic ity
consumption for price induced conservation of electrical
energy.The last key factor is the regional peak load
factor,which is applied to the energy demand forecast to
forecast peak loads.The impact of these key parameters is
analyzed in Section 5.4 (f)on Sensitivity Analysis.
(iv)OGP Model
Table B.I02 presents the main variables of the OGP model.
The variables are:fuel costs and escalation rates,
thermal and hydro plant construction costs,and the
discount rate.A detailed presentation of these variables
is presented in Exhibit 0 and Appendix 0-1.
(d)Reference Case Forecast
The Reference Case forecast is based on the SHCA NSD world
petroleum price forecast discussed in Sections 5.4 (a) and 5.4 (b)
above.These petroleum prices served as the basis for the
Reference Case state petroleum revenue forecasts,which in turn
were used by the MAP Model to produce the Reference Case economic
projections,which were then used by the RED Model to forecast
electric energy demands.The Reference Case world petroleum price
forecasts were also used to estimate future fuel prices for use in
the RED and OGP models.
Table B.I03 summarizes the data for the Reference Case,showing
the oil price scenario and the corresponding set of 15 input and
output variables over the forecast period from 1983-2010,
including prices of other forms of energy,revenues,population,
and employment.Table B.I03 shows that in the Reference Case,
B-5-57
Railbelt population will grow approximately 67 percent between
1983 and 2010,reaching 533,218 by the year 2010. During this
same period the Railbelt's electric energy demand is forecasted to
rise from 2,784 to 5,709 gigawatt-hours,a 105 percent increase.
Peak demand is projected to rise from 576 to 1,187 megawatts,a
106 percent increase during the 27 year period,an average
increase of 2.7 percent per year.The following sections
summari ze the Reference Case forecasts of state petroleum
revenues,fiscal and economic conditions,and electric energy
demand.
(i)State Petroleum Revenues
Table B.104 presents Reference Case projections of state
petroleum revenues from each of the primary revenue sources
through the year 2010.The first two columns of this table
contain projected royalties and severance,or production
taxes,respectively.These projections are in nominal
dollars,reflecting an annual change in the consumer price
index of 6.5 percent.The projections of royalties and
severance taxes through the year 1999 were produced by the
Department of Revenue's PETREV petroleum revenue
forecasting model system,adjusted for minor differences in
the future assumed rate of inflation.Projections for the
years 2000 through 2010 were extrapol ated using the average
annual rate of change between the years 1996 through 1999.
Table B.104 also presents projections of state petroleum
revenues derived from corporate income taxes,property
taxes,lease bonuses,and federal shared royalties.Future
revenues from these sources,estimated by the Institute of
Social and Economic Research,were used along with the
projections of royalties and severance taxes as input to
the MAP economic model.
(ii)Fiscal and Economic Conditions
State petroleum revenues constitute a major proportion of
the total funds available to the State of Alaska for
expenditure on operations and capital investment,which in
turn greatly affects the general level of economic activity
in the state.Table B.105 presents projections of several
important components of the state's fiscal structure for
the Reference Case. These components incl ude unrestricted
general fund expenditures,the balance in the general fund,
permanent fund dividends,state personal income tax
revenues,level of outlays for subsidies,and the
percentage of Permanent Fund earnings that are reinvested.
The table shows that,based on the fiscal rules summarized
in Section 5.3 above,dividends from the Permanent Fund
B-5-58
continue to be disbursed through the year 1992,at which
time the program is halted.A state personal income tax is
reinstituted in the year 1994 in order to augment revenues.
State subsidy programs are terminated after the year 1988,
and rei nvestment of Permanent Fund dividend send s after
1994.The subsidy programs that may be affected include,
for example,mortgage subsidies,student loans and AIDA
industrial development loans.Each of these measures is
assumed to occur in order to permit state expenditures to
grow as closely as possible in proportion to the rate of
population growth,taking into account the effects of
inflation.However,while these fiscal measures are
assumed to be implemented,petroleum revenues are projected
to continue to provide the largest share of state
expenditures,accounting in the year 2010 for approximately
two-thirds of total unrestricted general fund expenditures,
those expenditures not funded by revenues dedicated to
specific functions.
Table 8.106 presents Reference Case population projections
for the state,Railbelt,Anchorage-Cook Inlet area,and
Fairbanks-Tanana Valley area.Railbelt population is
projected to grow by approximately 67 percent between 1983
and 2010,from 320,000 to 533,000.In the Rail belt ,the
Anchorage area is projected to grow by 69 percent,compared
to the projected growth in Fairbanks of 57 percent.
The growth of employment,shown on Table B.107,is
uniformly lower than that of population.While statewide
non-agricultural wage and salary employment is projected to
grow by 61 percent during the next 27 years,total state
emp 1oyment is forecasted to increase by only 51 percent.
Again the Railbelt is projected to experience a higher
employment increase,rising by 61 percent,with the
Anchorage area growing by 63 percent compared to 52 percent
growth in the Fairbanks area.
Table B.108 presents projections of households according to
state total,the Railbelt,the Anchorage area,Fairbanks
area,and statewi de by age of head of househo1d. In
contr ast to proj ected employment,households are proj ected
to increase faster than popul ation.Statewide households
are projected to increase by 72 percent by the year 2010,
compared to a 75 percent increase in the Rail belt,a 78
percent rise in the Anchorage area,and a 67 percent
increase in the Fairbanks area.
(iii)Electric Power Demand
The regional households projections obtained from the MAP
B-5-59
model are used in the RED housing module to derive the
number of households served by electric utilities and the
number of vacant households.Tables B.I09 and B.110 pre-
sent the output results for the period 1980-2010.The
residential module then computes the annual consumption per
type of household based on the market saturation of appli-
ances and the annual consumption per ap~liance.
Table B.111 summarizes the average consumption per house-
hold before and after conservation adjustment and fuel sub-
st ituti on. In the Anchorage area,the average consumption
per household is expected to decrease from about 13,700 kWh
in 1980 to 12,560 kWh in 1990,mainly due to the real
increase of electricity price which will continue to cause
some conversion from electric space heating to substitute
fuels.After 1990,the consumption is expected to slowly
increase to about 13,200 kWh in 2010,at an average annual
growth rate of 0.25 percent.In the Fairbanks area,the
average household consumption is expected to increase from
11,500 kWh in 1980 to 15,200 kWh in 2010,at about an aver-
age annual growth rate of 0.9 percent.This increase is
due to the stabilization of electricity prices,while the
price of substitute fuels are increasing.The projected
consumption in year 2000 is similar to the 1975 average
consumption.
The employment forecasts obtained from MAP are used in the
RED Business Consumption module to derive the electric
demand in the commercial-government-small industrial sec-
tor.Table B.112 summarizes the "bus i ness" use per
employee projections.The consumption projections were
obtained from a forecast of predicted floor space per
employee,and an econometrically derived electricity con-
sumption per square feet,wh ich is then adjusted for pri ce
impacts.The floor space per employee is expected to
increase by 10 percent in Anchorage and 15 percent in
Fairbanks to approach current national average by the year
2010.As a result,in the Anchorage area,the average
consumption per employee is expected to increase from about
8,400 kWh in 1980 to 11,500 kWh in 2010,at an average
annual rate of 1.0 percent.In the Fairbanks area,the
consumption per employee is expected to increase from 7,500
kWh in 1980 to 9,900 kWh in 2010, at an average annual
growth rate of 0.9 percent.
Tables B.113 and B.115 provide a year by year projection
of price-induced conservation and fuel switching for the
two load centers.Tables B.114 and B.116 give a year by
year breakdown of energy consumption projections for the
8-5-60
residential,commercial-government-small industrial,mis-
cellaneous,and large industrial sectors for the two load
centers.The industrial sector includes projections of
large industrial and military loads.Industrial loads were
derived from estimates of industrial growth in the Kenai
Peninsula.Military loads were derived from discussions
with representatives at each military in~tallation.
Finally,Table B.1l7 summarizes the annual peak and energy
demand projections for each load center and for the total
system.The annual load factor is also presented.The
average annual growth rate of electricty demand is expected
to slowly decrease from about 5.6 percent during the period
1980-1985 to 1.7 percent during the period 1995-2000.
After 2000,the demand is expected to increase at an aver-
age annual rate of 2.3 percent until 2005, and 2.8 percent
for the period 2005-2010.
(e)Other Forecasts
A broad range of world oil price forecasts has been analyzed in
Section (a)and (b).The forecasts are summarized in Table B.89,
and displayed in Figure B.99. In addition to the Reference Case,
eight scenarios were carried through the MAP and RED models.
These scenarios are the OOR-Mean,OOR-50%,OOR-30%,ORI,+2%,0%,
-1%,and -2%.The results are presented on Tables B.1l8 through
B.125.Historical data and projections of general fund expendi-
tures,population,households,energy demand, and peak demand are
displayed in Figures B.100 through B.104 for four scenarios:ORI,
Reference Case,OOR Mean,and OOR 30%.The OOR 30%and ORI fore-
casts are the lowest and highest scenarios,respectively.The
Reference Case and OOR Mean are shown for comparison purposes.
The State General Fund Expenditures are expected to vary between
6.9 billion dollars and 26.1 billion dollars in year 2010.The
Railbelt population is expected to increase from 320,000 in 1983
to 481,000 under OOR 30%and 609,000 under ORI,for the year 2010.
The corresponding number of households would increase from 111,500
in 1983 to 175,000 and 223,000.The employment is expected to
increase from 159,000 in 1983 to 231,500 under OOR 30%,and
300,000 under ORI,for the year 2010.
As shown on Figure B.103,the 2010 energy consumption would vary
between 4,950 GWh and 6,965 GWh.The corresponding average annual
growth rate over the period 1983-2010 would vary between 2.2 per-
cent and 3.4 percent.The peak demand is expected to increase
from 570 MW in 1983 to 1,026 MW under OOR 30%,and 1,450 MW under
ORI,for the year 2010.
(f)Sensitivity Analyses
Sensitivity analyses for variables other than oil prices were
B-5-61
conducted using the MAP,RED and OGP mcde ls in order to determine
the extent to which forecasts are affected by varying the values
of selected input variables and parameters,other than world oil
prices.Some of these tests were conducted initially prior to
execution of the forecasts and others were conducted during the
course of the forecasts.These analyses indicated that while
other factors do affect electric energy demand in the Railbelt,
the effect of anyone or two factors does not approach the effect
that world petroleum prices has on economic conditions and
electric energy demand.It was largely this finding that led to
the definition of alternative energy planning scenarios based
solely on alternative petroleum prices.
(i)MAP Model Sensitivity Tests
For the MAP Model,input variables subjected to
sensitivity testing included ten industrial development
factors,tourism in Alaska,and four national economic
variable parameters.The results of the sensitivity
analyses are summarized in Table B.126.The table shows
that of the variables tested,projections of households are
most sensitive to mining employment, which includes
petroleum production,military employment,tourism,growth
in real wages,and growth in the consumer price index.
Sensitivity tests were also conducted using selected
economic model parameters,including those relating to
labor force participation rates,Federal tax rates,and
population migration.Details of these tests are in the
MAP Model Technical Documentation Report.
(ii)RED Model Sensitivity Tests
Sensitivity analyses were conducted for key variables,
using the Uncertainly Module. These variables are (1)
appliance saturations,energy consumption by appliance,
growth rate of appliance consumption;(2)business
consumption;(3)own price elasticity;(4)cross price
elasticity;and (5) load factors.The sensitivity analyses
were carri ed out for the Reference Case.The results are
shown on Table B.127 through B.131.
Table B.127 summarizes the results obtained when parameters
of the Residential Module were allowed to vary.Table B.97
presents a typical example of market saturation ranges
which were used as input into the Uncertainty Module. In
addition,the annual consumption per appliance and the
expected growth rate of energy consumption were allowed to
vary by ±20 percent.As shown on Table B.127,the results
on the overall energy demand are within 3 percent of the
Reference Case values.
B-5-62
The sensitivity analysis of the Business Sector was done by
allowing the consumption rate parameter to vary while
maintaining a 95 percent confidence level.This resulted
in a range of values within +10 percent of the mean value
for the Anchorage-Cook In 1et area.As shown on Table
B.128,the effects on the overall energy demand are within
5 percent of the Reference Case val ues.Because of the
lack of detailed historical data for the Fairbanks area,
the range of the consumption parameter value is very large,
and the results are not reliable.
Table B.129 and B.130 present the results of the own-price
and cross-price elasticities variations.The values of the
parameters were allowed to vary while maintaning a 95
percent confidence level.The effects on the overall
energy demand are within 6 percent of the Reference Case
val ues.
Finally,a sensitivity analysis was done for the peak
demand,using the range of the annual load factors of the
two load centers for the period 1970-1982.The results are
presented in table B.132. For the year 2010,the peak
demand would vary between 1,008 and 1,308 MW,with a
Reference Case value of 1,217 MW.
(iii)OGP Model Sensitivity Tests
Sensitivity tests were al so conducted for the OGP Model.
The key variables other than petroleum price dependent
variables which were tested are discount rate,Watana
capital cost,base fuel price,and real fuel escalation.
The sensitivity analyses are described in Exhibit D.
(g)Reasonableness of the RED Forecasts
In order to test the reasonableness of RED's long-term
forecasts,the Reference Case was compared to three comparable
long-term forecasts.The three forecasts are: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
Michigan,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 the low electricity
prices the region shares with the Anchorage load center,while the
B-5-63
Wisconsin area closely corresponds to the climate and fuel mode
split exhibited in the Railbelt.
The Pacific Northwest Power Planning Council,created by an act of
Congress to coordinate and direct acquisition of generation
resources in the Pacific Northwest,prepared a twenty-year fore-
cast of electricity demand in the Northwest.PNPPC modelled four
alternate load growth scenarios (low,medium low,medium high,and
high)for the purposes of generation planning.We chose the
medi um high scenari 0 for comparison because it corresponds more
closely to the economic conditions expected to occur in the Rail-
belt.
The Bonneville Power administration (BPA)markets all federal
power in the Pacific Northwest.BPA recently completed construc-
tion of their own forecasting tools.We chose to examine BPA's
medium scenario as it represents their assessment of the most pro-
bable situation.
The Wisconsin Electric Power Company markets power to Mi lwaukee-
Kenosha-Racine Standard Metropolitan Statistical Area,plus
selected counties in central and northern Wisconsin and upper
Michigan.Unlike the two Pacific Northwest 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
tota 1 energy used.Consequent ly,there are fewer opportuni ti es
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 following table presents a decomposition of two commonly used
consumption rates 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.
B-5-64
Comparison of Recent Forecasts,1980-2000
Average Percent
Growth Rate
Use Per Household
Average Percent
Growth Rate
Use Per Employee
Pacific Northwest Power
Council -.64 .14
Bonneville Power Admini-
stration -.64 -.31
Wisconsin Electric Power
Company 1.41 3.97
RED:
Anchorage -.36 1.04
Fai rbanks 0.98 0.93
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 pene-
tration of electric space heat and water heat in both the residen-
tial and commercial sector.Furthermore,because Pacific North-
west 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.It is also assumed that,in WEPCO's service area,
electricity would capture a high (40-60 percent)share of new
residential heating appliances due to its projected cost advantage
over oil and gas.
The RED forecasts occupy a middle ground,both in terms of base
year consumption and in terms of the rate of increase in consump-
ti on.With moderate rates of e 1ectri city pri ce increases and
fewer inexpensive conservation opportunities,RED shows lower
rates of conservati on than the Pacifi c Northwest.In comparison
with the WEPCO area,the Railbelt is expected to have a declining
electric share in space heat and water heat,so the rate of in-
crease 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
pri ce increases,the forecasted rate of increase in the rate of
electricity consumption should be lower.Based on this compari-
son,the resu lts of the RED forecast seem to be consi stent with
what other forecasters are predicting.
B-5-65
(h) Comparison With Previous Forecasts
Two sets of previous forecasts have been used in the early
stages of Susitna Hydroelectric Project studies in addition to the
power market forecasts presented in detail in this section.In
1980,the Institute for Social and Economic Research (ISER)
prepared economic and accompanying end-use electric energy demand
projections for the Railbelt.These forecasts were used in
several portions of the feasibility study,including the
development selection study.
In 1981 and 1982,Battelle Pacific Northwest Laboratories produced
a series of load forecasts for the Railbelt,as shown on Table
B.132. These forecasts were developed as a part of the Railbelt
Alternatives Study completed by Battelle under contract to the
State of Al aska.Battelle's forecasts were based on updated
economic projections prepared by ISER and some revi sed end-use
models developed by Battelle which took into account price
sensitivity and several other factors not included in the 1980
projections.The December 1981 Battelle forecasts were used in
the optimization studies for the Watana and Devil Canyon
developments which were completed early in 1982.The 1981
forecast reflected a projection of world oil prices of $27.45/bbl.
in July 1981 to $31.45/bbl.in July 1982, with first quarter
prices increasing from $36.35/bbl.to $44.65/bbl.over the next
three fiscal years,and then from $53.22/bbl.in the sixth fiscal
year to $157.60/bbl.in the subsequent seventeenth fiscal year.
These previous forecasts were made for three electric load
centers:the Anchorage-Cook Inl et area;the Fairbanks-Tanana
Valley area;and the Glennallen-Valdez area.When these studies
were undertaken,it was not decided whether the Glennallen-Valdez
area would be included in the intertied Railbelt electrical
system.The decision was subsequently made, based on economics,
that the Glennallen-Valdez area would not be initially included in
the interconnected area.Therefore,the updated electric load
forecasts presented herein do not consider the power requirements
of this load center.
Both ISER and Battelle produced high,medium and low forecasts for
use in Susitna planning studies.The medium forecast was used for
determining base generation plans,with the high and low forecasts
used in sensitivity analyses.
In addition to the ISER and Battelle forecasts performed for the
purpose of planning the Susitna Hydroelectric Project,the
Railbelt utilities annually produce forecasts for their own
respective markets.The bases for these forecasts are not readily
avail ab1e.
B-5-66
Table B.132 provides a summary comparison of these previous power
market forecasts under the medium scenario.While these forecasts
are not precisely consistent in the definitions of the market area
or in the assumptions relating to the current reference case,the
comparison does provide an insight in the change in perception of
future growth rates during the time that the various sets of fore-
casts were developed.
(i)Impact of Oil Prices on Forecasts
The world price of oil is a significant factor in the Alaskan
economy.As a consequence,world oil prices influence the demand
for electric energy and other forms of energy.Although oi 1
prices are important,there are many other economic,social,and
political factors which affect future Alaskan economic trends and
energy requirements.For example, the anticipated higher price of
gas and its limited avai labi lity in the Anchorage-Cook Inlet area
wi 11 have an impact on future electricity demands and costs of
power purchases.
The impact of world oil prices in conjunction with other economic
causa 1 factors on future economi c conditi ons and e lectri c energy
and peak demands has been evaluated.A number of world oil price
scenarios were used in the PETREV Model to generate various petro-
leum revenues projections.Because royalties and severance taxes
are sensitive to changes in world oil prices,different petroleum
revenue projections were obtained.The projected petroleum reven-
ues along with specified economic development assumptions and
other variables were employed in the MAP Model to project economic
factors such as households,state government expenditures,and
employment.These economic factors were influenced by the various
oil price growth rate assumptions.Finally,electric demand fore-
casts were produced usi ng the RED Mode 1.The RED Mode 1 emp loyed
the output of the MAP Model as well as other assumptions and input
data.Fuel data on electricity,natural gas,and oil prices were
needed for the planning period.These data,for example, are
affected by the growth rates assumed for world oi 1 prices.An
electric demand forecast was made for each world oil price
scenario.This procedure resulted in the production of an elec-
tric demand forecast which incorporated all direct and indirect
effects of a given timepath of world oil prices on electric demand
in the Railbelt in a comprehensive and consistent manner.The
range of electric demand forecast results reflects the overall
impact of world oil prices as well as other key variables included
in the separate models.These electric demand forecasts are pre-
sented in Section 5.4(e)above.
B-5-67
5.5 Project Utilization
The purpose of this section is to describe how the power generated by
the Sus itna Project wi 11 be ut i 1ized in the interconnected rai 1be It
system.The discussion that follows is based on the Project's opera-
tion under the Reference Case power market forecast.
The characteristics of the combined railbelt load are discussed in
Section 5.2.Dai ly load curves and monthly load variation are also
presented in that section as Figures B.80 and B.79,respectively.
The operation of the Susitna Project as stated in Section 3.7 of this
Exhi bit wi 11 be as fo 11 ows:the Watana development wi 11 operate as a
base load project unti 1 the Devi 1 Canyon development enters operati on
at which time the Devi 1 Canyon development wi 11 operate on peak and
reserve.The dependable capacity and energy production from Watana
operating alone and with Devi 1 Canyon are presented in Section 4.3 of
this Exhibit.The firm and average annual energy production,and maxi-
mum dependab le capacity in 2020 for the Susitna Project under the
Reference Case flow regime,Regime C,are as follows:
Average Annual Energy,GWh
Firm Annual Energy,GWh
Maximum Dependable Capacity
in 2020,MW
Watana On ly
3499
2618
893
Watana Plus
Dev i 1 Canyon
6934
5451
1272 .
On-site use of the power and energy from the Project will be negligible
in comparison to the Pro.iect t s capability and therefore
assumed that all the above capacity and energy wou ld be
railbelt system after deduction of transmission losses.
shows the dependab le capaci ty of the project year under
regimes.
it has been
used in the
Figure B.76
vari ous flow
Although no firm sales contracts or commitments have been made by Rail-
belt utilities,it is anticipated that each utility's share of the pro-
ject would be similar to their proportionate share of the Railbelt
power market.Based on energy sales in 1982,each utility covers the
B-5-68
following approximate percentage of the total Railbelt market:
Uti1ity
Chugach Electric Association
Anchorage Municipal Light &Power
Golden Valley Electric Association
Matanuska Electric Association
Fairbanks Municipal Uti 1ities System
Homer Electric Association
Seward Light Department
TOTAL
8-5-69
Percentage of
Rail belt Energy
Sales (1982)
40
20
10
10
5
15
100
6 -FUTURE SUSITNA BASIN DEVELOPMENT
6 -FUTURE SUSITNA BASIN DEVELOPMENT
The Alaska Power Authority has no current plans for further development
of the Watana/Devi 1 Canyon system and no plans for further water power
projects in the Susitna River basin at this time.
Development of the proposed projects would preclude further major
hydroelectric development in the Susitna basin,with the exception of
major storage projects in the Susitna basin headwaters.Although these
types of plans have been considered in the past,they are neither
active nor anticipated to be so in the foreseeable future.
B-6-1
REFERENCES
Acres American Inc.December 1981.
Development Selection Report.
Authority.
Susitna Hydroelectric Project,
Prepared for the Alaska Power
February 1982a.
Geotechnical Report.
Susitna Hydroelectric Project,1980-81
Prepared for the Alaska Power Authority.
March 1982b.
Selection Report.
Susitna Hydroelectric Project,Access Route
Prepared for the Alaska Power Authority.
.March 1982c.Susitna Hydroelectric Project,Feasibility------~Report (7 Volumes).Prepared for the Alaska Power Authority.
April 1982d.Susitna Hydroelectric Project Reference Report,
Economic,Marketing and Financial Evaluation.Prepared for the
Alaska Power Authority.
.December 1982e.Susitna Hydroelectric Project,1982
-----=Supplement to the 1980-81 Geotechnical Report.Prepared for the
Alaska Power Authority.
Acres American Inc.and Terrestrial Environmental Specialists,Inc.
March 1982.Transmission Line Selected Route. Prepared for the
Alaska Power Authority.
Alaska Department of Fish and Game.1978a.Alaska's Fisheries Atlas
(Volumes I and II).Anchorage,Alaska.
Alaska Department of Fish and Game.1978b.Habitat Essential for Fish
and Wildlife on State Lands.Anchorage,Alaska.
Alaska Department of Revenue,Petroleum Revenue Division March 198
Petroleum Production Revenue Forecast.
Alaska Economics,Inc.March 1983. Alaska1s Economic Potential:
Background.
Battelle Pacific Northwest Laboratories.December 1982.Railbelt
Electric Power Alternatives Study (17 Volumes).Prepared for the
Office of the Governor,State of Alaska.
Volume I:Railbelt Electric Power Alternatives Study:Evaluation
of Railbelt Electric Energy Plans.
Volume VIII:Railbelt Electricity Demand (RED)Model
Specifications.
Volume IX:Alaska Economic Projections for Estimating Electricity
Requirements for the Railbelt.
June 1983.RED Model (1983 Version)Documentation Report.
CIRI/Holmes and Narver. 1980.
2.04.Land status maps.
Susitna Hydroelectric Project,Subtask
Prepared for Acres American Inc.
Commonwealth Associates Inc.November 1980. Anchorage-Fairbanks
Transmission Intertie-Transmission System Data. Prepared for the
Alaska Power Authority.
Data Resources,Inc.,Spring 1983. U.S.Long-Term Review.
Energy Probe.July 1980.An Evaluation of the ISER Electricity Demand
Forecast.
Friese,N.V.1975.Pre-Authorization Assessment of Anadromous Fish
Populations of the Upper Susitna River Watershed in the Vicinity
of the Proposed Devil Canyon Hydroelectric Project.Alaska
Department of Fish and Game,Division of Commercial Fisheries.
General Electric Company.May 1979.OGP5 User's Manual.
Institute of Social and Economic Research,University of Alaska.May
1980.Electric Power Consumption for the Railbelt:A Projection
of Requirements,Technical Appendices. Prepared jointly for State
of Alaska House Power Alternatives Study Committee and Alaska
Power Authority.
October 1981. Alaska Economic Projections for Estimating
Requirements for the Railbelt.Prepared for Battelle Pacific
Northwest Laboratories.
June 1983.MAP Model Technical Documentation Report.
Morrow,J.E.1980.The Freshwater Fishes of Alaska. Alaska Northwest
Publishing Co.,Anchorage,Alaska.
R&M Consultants.December 1981b.Susitna Hydroelectric Project,
Regional Flood Studies.Prepared for Acres American Inc.
November 1981a.Terrain Analysis of the North and South
Intertie Power Transmission Corridors.Prepared for Acres
American Inc.
Sherman H.Clark Associates,May 1983.Evaluation of World Energy
Developments and Their Economic si3nificance,Volume II.Als
Lonr-Term Outlook For Crude oil an Fuel Oil Prices,special
ana ysis prepared for Harza/Ebasco,May 18,1983 and price ta
of market crude in 1983 dollars provided Harza/Ebasco on May
1983.
Trihey,E.W.1981.Susitna Hydroelectric Project,Instream Flow
Assessment:Issue Identification and Baseline Data Analysis,1981
Study Plan.Prepared for Acres American,Incorporated.
u.S.Department of Agriculture,Soil Conservation Service.1979.
Exploratory Soil Survey of Alaska. Washington,D.C.
Van Ballenberghe,Francis.Alaska Department of Fish and Game.
January 1981. Personal communication on habitat data covering an
area from Fairbanks/Healy to Ester.
Woodward-Clyde Consultants.February 1982. Final Report on Seismic
Studies for Susitna Hydroelectric Project.Prepared for Acres
American Inc.
December 1980.
Alaska's Railbelt.
Forecasting Peak Electrical Demand for
Prepared for Acres American Inc.
TABLES AND FIGURES
TABLE B.69
TOTAL 1981 ALASKA ENERGY CONSUMPTION
Al aska Railbelt
Sector Bi 11 ion Btu (%)Bi 11 i on Btu J.!L.
Transportat i on 114,672 38 88,715 38
Industri al 64,823 21 44,699 19
Utility 46,344 15 40,115 17
Mil itary 25,847 9 25,847 11
Resident;al 26,571 9 19,434 8
Commercial/Public 11,913 4 10,658 5
Off-highway 13,069 4 6,430 3
Total 303,239 100 235,929 100
Note:The total electricity consumption is only reported in the
utility sector.
Source:1983 Long Term Energy Plan (Working Draft),Department
of Commerce and Economlc Development,Division of Energy and
Power Development,State of Alaska.1983 Figure 11-9 p. 11-14.
TABLE B.70
RAILBELT 1981 ENERGY CONSUMPTION BY
FUEL TYPE FOR EACH SECTOR
Energy Consumption
Sector/Fuel Type Billion Btu Percent
I ransportab on
Fuel Oil 88,649 99.9
Coal 66 0.1
Total 88,715 lcm:1i
Industri al
Fuel Oil 13,264 28.3
Natural Gas 31,435 67.1
El ectri city 2,130 4.6
Total 46,829 100.0
Util ity
Fuel Oil 2,152 5.9
Natural Gas 29,652 73.9
Coal 5,407 13.5
Hydro 2,904 7.2
Total 46,115 100.0
Mil itary
Fue 1 Oil 15,364 55.8
Natural Gas 4,590 16.7
Coal 5,893 21.4
El ectri city 1,690 6.1
Total 27,537 100.0
Residential
Fue 1 Oil 9,647 41.6
Natural Gas 8,109 35.0
Coal 140 ,0.6
Wood 1,561 6.7
El ectri city 3,745 16.1
Total 23,202 100.0
Commercial/Public
Fuel Oil 2,256 15.6
Natural Gas 7,333 50.5
Coal 1,069 7.4
Electricity 3,842 26.5
14,500 100.0
Note:Electricity consumption is reported in the utility sector,
and also in the other sectors.
Source:1983 ~ong Term Energy Plan (Working Draft),Department
of Commerce and Economlc Development,Dlvlsion of Energy
and Power Development,State of Alaska.Appendix S,
Tab 1e S-2.
TABLE B.71
INSTALLED CAPACITY OF ANCHORAGE-COOK INLET AREA-1982
HYDRO OIL NATURAL GAS
Combustion Steam
Hydro Diesel Turbine Turbine Total
Ut il it ies
Alaska Power
Administration 30.0 0 0 0 30.0
Anchorage Municipal
Light and Power 0 0 311.6 0 311.6
Chugach Electric
Associ aton 15.0 0 448.5 0 463.5
Homer Electric
Association 0 2.6 0 0 2.6
Matanuska Electric
Associ ation 0 0.9 0 0 0.9
Seward Electric
As sociat i on 0 5.5 0 0 5.5
Total 45.0 9.0 760.1 0 814.1
Mi 1itary Install ations
Elmendorf AFB 0 2.1 0 31.5 33.6
Fort Richardson 0 7.2 0 18.0 25.2
Subtotal 0 9.3 0 49.5 58.8
Industri al Installations
Subtotal 0 9.6 16.0 0 25.6*
Total 45.0 27.9 776.1 49.5 898.5
*Figure is for 1981,latest year that data was available.
Source:Battelle Pacific Northwest Laboratories.Existing
Generating Facil ities and Planned Add-itions for the
Ralfbelt Reglonof Alaska,volume vI,September,1982;
Alaska Power Admlnlstratlon 1983; updated by Harza-Ebasco
Susitna Joint Venture,1983.
TABLE B.72
INSTALLED CAPACITY OF THE FAIRBANKS-TANANA VALLEY AREA-1982
OIL HYDRO COAL
Combustion Steam
Diesel Hydro Turbine Turbine Total
Ut il it ies
Fairbanks Municipal
Ut i 1ity System 8.4 0 30.1 30.0 68.5
Golden Valley Electric
Associ ation 23.8 0 172.8 25.0 221.6
Un ivers ity of
Al aska 5.6 0 0 13.0 18.6
Subtotal 37.8 0 202.9 68.0 308.7
Mi 1itary Install ations
Eielson AFB 0 0 a 15.0 15.0
Fort Greeley 5.5 0 0 0 5.5
Fort Wainwright 0 0 0 22.0 22.0
Subtotal 5.5 0 0 37.0 42.5
Industri al Inst allat ions
Subtotal 2.8 0 0 0 2.8*
Total 46.1 0 202.9 105.0 354.0
* Figure is for 1981,latest year that data was available.
Source:Battelle Pacific Northwest Laboratories.Existing
Generatin~Facilities And Planned Additions for the
Rallbelt eglOn of Alaska,volume VI,September 1982;
Alaska Power Admlnlstratlon 1983; updated by Harza-Ebasco
Susitna Joint Venture, 1983.
TABLE B.73 (Sheet 1 of 5)
EXISTING GENERATING PLANTS IN THE RAILBELT REGION
Namep 1ate Generati ng
Prime Fuel Capacity Capac i ty Heat Rate
Pl ant/Unit Mover Type Date (MW)@ 00 F (MW)(Btu/kWh)
)
Alaska Power Administration
Eklutna(a)H 1955 30.0
Anchorage Municipal Light and Power--
Station #l(b)
Unit #l SCCT NG/O 1962 14.0 16.3 14,000
Un it #2 SCCT NG/O 1964 14.0 16.3 14,000
Un it #3 SCCT NG/O 1968 18.0 18.0 14,000
Unit #4 SCCT NG/O 1972 28.5 32.0 12,500
Di ese 1 1(c)D 0 1962 1.1 1.1 10,500
Diesel 2(c)D 0 1962 1.1 1.1 10,500
Station #2(d)
Unit #5 SCCT 0 1974 32.3 40.0 12,500
Unit #6 CCST 1979 33.0 33.0
Unit #7 SCCT 0 1980 73.6 90.0 11 ,000
Unit #8 SCCT NG/O 1982 73.6 90.0 12,500
Chugach Electric Association
Bel uga
Unit #1 SCCT NG 1968 15.25 16.1 15,000
Unit #2 SCCT NG 1968 15.25 16.1 15,000
Un it #3(e)RCCT NG 1973 53.3 53.0 10,000
Unit #4 SCCT NG 1976 10.0 10.7 15,000
Unit #5 RCCT NG 1975 58.5 58.0 10,000
Unit #6 CCCT NG 1976 72.9 68.0 15,000
Unit #7(f)CCCT NG 1977 72.9 68.0 15,000
Unit #8 CCST NG 1982 55.0 42.0
TABLE B.73 (Sheet 2 of 5)
EXISTING GENERATING PLANTS IN THE RAILBELT REGION
Namepl ate Generating
Prime Fuel Capacity Capacity Heat Rate
Pl ant/Unit Mover Type Date (MW).@ OaF (~W)(Btu/kWh)
)-
Chugach Electric Association (Continued)
Cooper Lake(g)
Unit #1,2 H 1961 15.0 16.0
International
Unit #1 SCCT NG 1964 14.0 14.0 15,000
Unit #2 SCCT NG 1965 14.0 14.0 15,000
Unit #3 SCCT NG 1970 18.5 18.0 15,000
Bernice Lake
Unit #1 SCCT NG 1963 7.5 8.6
23,400
Unit #2 SCCT NG 1972 16.5 18.9 23,400
Unit #3 SCCT NG 1978 23.0 26.4
23,400
Unit #4 SCCT NG 1982 23.0 26.4 12,000
Knik Arm(h)
Unit #1 ST NG 1952 0.5 0.5
Unit #2 ST NG 1952 3.0 3.0
Unit #3 ST NG 1957 3.0 3.0
Unit #4 ST NG 1957 3.0 3.0
Unit #5 ST NG 1957 5.0 5.0
Homer Electric Association,
Kenai
Unit #1 0 0 1979 0.9 0.9 15,000
Pt.Graham
Unit #1 0 0 1971 0.2 0.2 15,000
Sel dov i al
Unit #1 0 0 1952 0.3 0.3 15,000
Unit #2 0 0 1964 0.6 0.6 15,000
Unit #3 0 0 1970 0.6 0.6
15,000
TABLE B.73 (Sheet 3 of 5)
EXISTING GENERATING PLANTS IN THE RAILBELT REGION
Pl ant/Unit
Prime Fuel
Mover Type Date
Namepl ate
Capacity
(MW),
Generating
Capac i ty
@ 0°F (MW)
Heat Rate
(Btu/kWh)
Talkeetna
Matanuska Electric Associ ation
Unit #1 D °1967 0.9 0.9
15,000
Seward Electric System
Unit #1
Unit #2
Unit #3
D
D
D
°°°
1965
1965
1965
1.5
1.5
2.5
1.5
1.5
2.5
15,000
15,000
15,000
Elmendorf AFB
Mil itary Install ations -Anchorage Area
Total Diesel
Total ST
Fort Richardson
D
ST °NG
1952
1952
2.1
31.5
10,500
12,000
Total Di~s~l(c)D
Total STt 1)ST °NG
1952
1952
7.2
18.0
10,500
20,000
Golden Valley Electric Association
Healy Coal
Healy Diesel(c)
North Pole
ST
D
Coal
°
1967
1967
64.7
64.7
65.0
65.0
13,200
10,500
Combined Diesel D
Unit #1
Unit #2
Zendher
GTl
GT2
GT3
GT4
SCCT °
SCCT °
SCCT °
SCCT °
SCCT °
SCCT °
°
1976
1977
1971
1972
1975
1975
1960-70
64.7
64.7
18.4
17 .4
2.8
2.8
21.0
65.0
65.0
18.4
17.4
3.5
3.5
21.0
14,000
14,000
15,000
15,000
15,000
15,000
10,500
TABLE B.73 (Sheet 4 of 5)
EXISTING GENERATING PLANTS IN THE RAILBELT REGION
Namepl ate Generating
Prime Fuel Capacity Capacity Heat Rate
Pl ant/Unit Mover Type Date (MW)@ O°F (MW)(Btu/kWh)
University of Alaska - Fairbanks
Sl ST Coal 1.50 1.50 12,000
S2 ST Coal 1980 1.50 1.50 12,000
S3 ST Coal 10.0 10.0 12,000
D1 D 0 2.8 2.8 10~500
D2 D 0 2.8 2.8 20,500
Fairbanks Municipal Utilities System
Chena
Uni t #1 ST Coal 1954 5.0 5.0 18,000
Un it #2 ST Coal 1952 2.5 2.5 22,000
Unit #3 ST Coal 1952 1.5 1.5
22,000
Unit #4 SCCT 0 1963 5.3 7.0 15,000
Unit #5 ST Coal 1970 21.0 21.0 13,320
Unit #6 SCCT 0 1976 23.1 28.8 15,000
Diesel #1 D 0 1967 2.8 2.8 12,150
Diesel #2 D 0 1968 2.8 2.8 12,150
Diesel #3 D 0 1968 2.8 2.8
12,150
Military Install~tions - Fairbanks
Eielson AFB
si,S2 ST 0 1953 2.50
S3,S4 ST 0 1953 6.25
Fort Greeley
D1,D2 .~3(i)D 0 3.0 10,500
D4,D5 \1 D 0 2.5 10,500
Ft.Wainwright(j)
si,S2, S3,S4 ST Coal 1953 20 20,000
S5(i)ST Coal 1953 2
Legend H
D
SCCT
RCCT
ST
CCCT
NG
o
Notes
TABLE B.73 (Sheet 5 of 5)
EXISTING GENERATING PLANTS IN THE RAILBELT REGION
Hydro
Diesel
Simple cycle combustion turbine
-Regenerstive cycle combustion turbine
-Steam turbine
-Combined cycle combustion turbine
Natural gas
Distillate fuel oil
(a)Average annual energy production for Eklutna is approximately 148 GWh.
(b)All AMLP SCCTs are equipped to burn natural gas or oil.In normal
operation they are supplied with natural gas. All units have reserve
oil storage for operation in the event gas is not available.
(c)These are black-start units only.They are not included in total capacity.
(d)Units #5, 6,and 7 are designed to operate as a combined-cycle at plant.
When operated in this mode,they have a generating capacity at OaF of
approximately 139 MW with a heat rate of 8500 Btu/kWh.
(e)Jet engine,not included in total capacity.
(f)Beluga Units #6, 7,and 8 operate as a combined-cycle plant.When operated
in this mode,they have a generating capacity of about 178 MW with a heat
rate of 8500 Btu/kWh.Thus, Units #6 and 7 are retired from "gas turbine
operation"and added to "combined-cycle operations."
(g)Average annual energy production for Cooper Lake is approximately 42 GWh.
(h)Knik Arm units are old and have higher heat rates;they are not included in
in total.
(i)Standby units.
(j)Cogeneration used for steam heating.
Source:Battelle Pacific Northwest Laboratories.Existing Generating
Facilities and Planned Addition for the Rallbelf Reglonof Alaska,
Volume VI,September,1982; updated by Harza-E6asco Susltna Joint
Venture, 1983.
TABLE B.74 (Sheet 1 of 2)
MONTHLY DISTRIBUTION OF PEAK DEMAND
Anchorage -Cook Inlet Area
1976
Average
1977 1978 1979 1980 1981 1982 1976-1982
--r%T -ro \%T -ro \%T -ro "l%T (%)
January 94.2 76.8 89.2 90.5 89.9 79.1 100.0 88.5
February 91.2 91.8 85.8 100.0 84.8 84.8 93.3 87.4
March 81.7 75.4 77 .5 85.9 72.4 73.1 83.0 78.4
April 70.9 69.7 70.6 67.8 60.1 69.1 77 .4 69.4
May 63.9 59.8 62.6 58.9 55.7 61.3 64.3 60.9
June 59.9 55.6 59.7 58.5 52.7 61.5 61.8 58.5
July 62.3 54.2 59.4 54.9 54.2 63.0 61.6 58.5
August 63.6 57.6 61.8 55.5 50.4 62.0 63.4 59.2
September 70.1 67.5 66.1 61.9 58.3 69.7 73.8 66.8
October 89.2 78.1 81.5 72.7 69.9 78.7 90.9 80.1
November 88.8 91.7 92.3 80.0 78.7 90.2 94.4 88.0
December 100.0 100.0 100.0 99.0
100.0 100.0 95.6 99.2
Fairbanks -Tanana Valley Area
Average
1976 1977 1978 1979 1980 1981 1982 1976-1982--ro --ro --ro --ro --ro -ro -ro (%)
January 100.0 74.8 100.0 88.6 99.8 85.7 100.0
92.7
February 98.6 74.3 98.8 100.0 79.0 94.6 97.0 91.8
March 81.0 73.2 85.4 80.7 73.7 73.1 86.8 79.1
April 64.2 61.9 74.0 65.1 63.3 70.2 77 .1 68.0
May 54.3 51.2 60.6 56.1 58.5 69.4 71.0 60.2
June 49.2 47.9 60.4 53.5 56.8 63.9 66.6 56.9
July 53.6 46.4 57.7 55.4 58.5 62.9 65.4 57.1
August 52.4 47.3 57.7 56.5 62.3 65.5 68.5 58.6
September 59.4 55.7 65.5 59.6 63.9 70.8 73.9 64.1
October 81.3 67.4 75.5 66.3 74.2 77 .4 85.8 75.4
November 83.6 87.1 89.9 71.7 79.2 83.3 94.7 84.2
December 96.3 100.0 87.2 87.0 100.0 100.0 94.4 95.0
Total Railbelt Area
Average
1976 1977 1978 1979 1980 1981 1982 1976-1982--ro ""1%J -ro "l"%T --ro -m -ro (%)
January 96.5 76.3 93.7 90.2 91.6 80.2 100.0 89.8
February 93.9 72.4 90.8 100.0 76.4 86.5 94.0 87.7
March 82.2 74.9 81.1 84.9 72.6 73.1 83.6 78.9
April 69.9 67.8 71.9 67.3 60.6 69.3 77 .4 69.2
May 62.1 57.8 63.9 58.3 56.2 62.7 65.6 60.9
June 57.8 53.8 61.4 57.5 53.4 61.9 62.6 58.3
July 60.7 52.3 60.6 55.0 51.9 63.0 62.2 57.9
August 61.4 55.2 62.6 55.7 55.6 62.6 65.3 59.8
September 68.1 64.6 67.7 61.5 59.3 69.8 73.9 66.4
October 88.1 75.5 82.4 71.4 70.6 78.5 90.3 79.5
November 88.3 90.6 94.2 78.3 78.8 89.0 94.4 87.7
December 100.0 100.0 100.0 96.6 100.0 100.0 95.4 98.9
Source:TABLES B.84 and B.85
TABLE B.74 (Sheet 2 of 2)
MONTHLY DISTRIBUTION OF ENERGY DEMAND
Anchorage -Cook Inlet Area
Average
1976 1977 1978 1979 1980 1981 1982 1976-1982-m -m '"llT -m -m -m -m (~)
January 10.0 9.1 10.2 10.2 10.5 9.3 10.8 10.0
February 9.4 8.0 8.7 10.3 8.6 8.6 9.0 8.9
March 9.1 9.1 9.0 9.0 8.8 8.6 8.9 8.9
Apri 1 7.8 7.9 7.7 7.9 7.5 7.8 7.9 7.8
May 7.2 7.3 7.3 7.1 6.9 7.1 7.2 7.2
June 6.4 6.7 6.7 6.4 6.5 6.8 6.5 6.6
July 6.7 6.5 6.8 6.6 6.7 7.2 6.8 6.7
August 6.8 6.8 6.8 6.8 6.8 7.2 6.9 -6.9
September 7.5 7.1 7.2 7.0 7.2 7.5 7.2 7.2
October 8.9 8.8 8.8 8.2 8.4 9.1 9.0 8.7
November 9.5 10.7 10.0 8.8 9.6 10.0 9.6 9.8
December 10.6 12.0 10.8 11.6 12.3 10.8 10.2 11.2
Fairbanks -Tanana Valley Area
Average
1976 1977 1978 1979 1980 1981 1982 1976-1982-m -m -m -m -m -m -m (%)
January 11.9 9.9 11.2 11.0 11.3 9.5 11.0 10.8
February 11.4 8.5 9.7 11.3 8.6 9.1 9.2 9.7
March 9.4 9.7 9.6 9.5
8.6 8.6 8.9 9.2
April 7.4 7.8 7.8 7.9
7.4 8.0 7.9 7.7
May 6.4 6.7 6.9
6.7 7.0 7.3 7.2 6.9
June 5.8 6.0 6.4 6.3 6.3 6.8 6.6 6.3
July 6.0 6.0 6.5 6.6 6.8 6.8 7.0 6.5
August 6.1 6.4 6.6 6.5 6.9 6.8 7.0 6.6
September 6.7 6.5 7.0 7.0 7.3 7.6 7.3
7.1
October 8.6 8.6 8.6 8.1 8.1 8.9 8.7 8.5
November 9.1 11.1 9.5 8.4 9.2 9.4 9.3
9.4
December 11.4 12.7 10.2
10.8 12.5 11.0 10.2 11.3
Total Railbelt Area
Average
1976 1977 1978 1979 1980 1981 1982 1976-1982-m -m -m -m -m -m """T%T (%)
January 10.4 9.3 10.4 10.4 10.6
9.3 10.8 10.2
February 9.8 8.1 8.9 10.5 8.6 8.7 9.0 9.1
March 9.1 9.3
9.1 9.1 8.8 8.6 8.9 9.0
April 7.7 7.9 7.8 7.9 7.5 7.8 7.9 7.8
May 7.1 7.2
7.2 7.1 7.0 7.1 7.2 7.1
June 6.2 6.4 6.7 6.4 6.5 6.8 6.5 6.5
July 6.6 6.4 6.8 6.6 6.7 7.1 6.9 6.7
August 6.7 6.7 6.8 6.7 6.8 7.2 6.9 6.8
September 7.3 7.0 7.1 7.0 7.2 7.5 7.2 7.2
October 8.9 8.8 8.7 8.2 8.4 9.0 9.0 8.7
November 9.5 10.8
9.8 8.7 9.5 9.9 9.6 9.7
December 10.8 12.2 10.7 11.5 12.3 10.8 10.2 11.2
Source:TABLES B.84 and B.85
TABLE B.75
PROJECTED MONTHLY DISTRIBUTION OF PEAK AND ENERGY
DEMAND PERCENTAGE OF ANNUAL DEMAND
Total Railbelt Area
1990 2000 2010 2020
I
Pe akY Energ#PeakY Energ#PeakY Energ#PeakY Energy.!!
(%1 (%) (%) (%) (%) (%) (%) (%)
January 91.5 10.3 91.4 10.2 91.3 10.2 91.3 10.2
February 86.6 8.9 86.5 9.0 86.4 8.8 86.4 9.0
March 78.5 9.0 78.4 8.9 78.3 8.9 78.3
'8.9
April 69.5 7.7 69.6 7.6 69.6 7.7 69.6 7.7
May 63.0 7.1 63.6 7.1 63.7 7.1 63.7 7.1
June 60.3 6.5 61.7 6.6 61.9 6.6 61.9 6.6
July 59.5 6.5 60.5 6.5 60.5 6.6 60.5 6.6
August 63.2 6.9 64.4 6.9 64.3 6.9 64.3 6.9
September 68.5 7.2 69.4 7.2 69.4 7.3 69.4 7.3
October 79.0 8.7 79.4 8.7 79.3 8.7 79.3 8.7
November 92.2 9.9 92.2 9.9 92.1 9.9 92.1 9.9
December 100.0 11.2 100.0 11.1 100.0 11.2 100.0 11.2
l/Source:Woodward-Clyde,December 1980 Report, Table 3.2.11
2/Source:Results from the OGP Load Model,Reference Case Scenario
TABLE B.76
TYPICAL DAILY LOAD DURATION
SELECTED MONTHS
APRIL AUGUST DECEMBER APRIL AUGUST DECEMBER
1.000 1.000 1.000 .942
.871 .945
.990 .990 .997 .917 .868 .944
.983 .988 .979 .897 .858 .927
.981 .977 .968 .882 .846 .911
.978 .970 .948 .882 .845 .893
.966 .965 .918 .880 .842 .868
.963 .959 .915 .870 .837 .862
.957 .951 .914 .867 .835 .856
.953 .948 .913 .859 .832 .854
.947 .923 .909 .851 .830 .853
.939 .890 .905 .851 .820 .843
.936 .882 .897 .838 .816 .826
.936 .873 .896 .837 .797 .818
.931 .868 .879 .827 .786 .782
.888 .834 .873 .805 .724 .775
.853 .776 .812 .753 .703 .732
.750 .747 .804 .729 .667 .724
.769 .666 .747 .724 .623 .723
.712 .657 .710 .689 .616 .680
.698 .612 .702 .673 .595 .672
.683 .590 .675 .668 .580 .661
.672 .581 .668 .667 .564 .655
.670 .581
.664 .661 .555 .648
.670 .560 .661 .650 .545 .648
Source:Woodward-Clyde,1980.
TABLE B.77
LOAD DIVERSITY IN THE RAILBELT~).
Railbelt Loads -December 29,1871
Non-
Coincident
UTILITY 2PM 3PM 4PM 5PM 6PM 7PM 8PM Peak
CEA 168.55 170.7 178.7 179.4 182.1 180.8 173.2 182.1
AMLP 107 111 110 106 104 100 96 111.0
MEA 52.3 51.4 49.5 49.0 52.2 50.1 47.0 52.3
HEA 48.1 48.3 49.7 50.4 49.7 49.0 46.7 50.4
GVEA 71.8 71.8 75.4 69.1 72 .9 72.2 73.2 75.4
Ft.WR.9.5 11.0 11.7 10.2 9.5 8.8 9.5 '11.7
EIELSON 10.3 10.3 10.0 10.0 10.0 10.0 10.0 10.3
U.of A.5.8 5.8 5.6 6.0 4.9 5.3 4.4 6.0
FMUS 27.4 26.7 26.7 25.7 24.0 21.1 18.5 27.4
TOTAL 500.7 507.0 517.3 505.8 509.3 497.3 478.5 526.6
Diversity =Coincidnet Peak =517.3 =.982
Non-colncldent Peak 526.6
Railbelt Loads - January 6,1982
Non-
Coincident
UTILITY 2PM 3PM 4PM 5PM 6PM 7PM 8PM Peak
CEA 175 178 194 202 214 210 203 214
AMLP 109 109 117 115 116 112 107 117
MEA 66 71 71 71 73 74 74 74
HEA 57 56 60 62 62 63 61 63
GVEA 66.5 67.8 69.0 74.6 71.9 74.1 74.2 74.6
Ft.WR.11.0 11.7 11.7 9.5 9.5 9.5 8.8 11.7
EIELSON 11.0 11.0 11.2 10.9 10.7 10.4 10.4 11.2
U.of A.6.0 6.2 6.2 6.5 5.7 4.3 5.0 6.5
FMUS 27.4 27.2 29.7 26.2 24.0 23.5 20.4 29.7
TOTAL 528.9 538.3 569.8 577.7 586.8 580.8 563.8 601.7
Diversity =Coincident Pe~k =586.8 =.975
Non-colncldent Peak 601.7
Source: Alaska Systems Coordinating Council,April 16, 1982.
TABLE B.78
RESIDENTIAL AND COMMERCIAL ELECTRIC RATES
Anchorage-Cook Inlet Area
March 1983
Electric Rate
Ut il ity
Residential Rates
(monthly)
Energy Used Fixed Rate
Rate With Cost
of Power
Adjustment
Anchorage Municipal
Light &Power
Chugach El ectri c
Association,Inc.
Commercial Rates
(monthly)
Anchorage Municipal
Light &Power
Chugach Electric
Association,Inc.
Customer Charge $4.50 ---
Energy Charge 4.638~/kWh 5.199~/kWh
Cost of 1,000 kWh $46.38---$51.99---
First -50 kWh
13.6r
kWh 13.916rkWhNext200kWh6.7 /kWh 7.016 /kWh
Next 500 kWh 3.9 /kWh 4.216 /kWh
Next 750 kWh 3.5 /kWh 3.816 /kWh
Over 1,500 kWh 3.0 /kWh 3.316 /kWh
Cost of 1,000 kWh $48.45 $51.61
Customer Charge $8.24 ---
Energy Charge 5.62~/kWh 6.181~/kWh
Cost of 5,000 kWh $281.00---$309.05---
First -100 kWh 9.1r kWh 9.416rkWhNext150kWh6.1 /kWh 6.416 /kWh
Next 500 kWh 5.3 /kWh 5.616 /kWh
Over 750 kWh 4.8 /kWh 5.116 /kWh
Cost of 5,000 kWh $248.75 $264.55
Sources:
~/AMLP,Schedule II Residential Service,effective September 29,1982.
-/AMLP,Gas Cost Rate Adjustment,Tariff Sheet Number 101,effective
March 1,1983.
liCEA,Schedule No.1,General Residential Service,(Urban Areas),
effective October 26, 1982.
4/CEA,Fuel and Purchased Power Cost Adjustment Factor,Tariff Sheets
No.91-95,effective March 7, 1983.
i/AMLP,Schedule 21 General Service-Small,effective September 29, 1982.
2/CEA,Schedule No.3,Commercial Light and Power (Not exceeding 10 kw),
effective October 26, 1982.
*
**
***
TABLE B.79
RESIDENTIAL AND COMMERCIAL ELECTRIC RATES
Fairbanks-Tanana Valley Area
March 1983
Electric Rate
Rate Wi th Cost
of Power
Ut il ity Energy Used Fixed Rate Adjustment*
Residential Rates KWH KWH
Fairbanks Municipal 0-100 kWh**12000rkWh**---Ut il it ies System 100-400 kWh 8.20 /kWh ---
Over 400 kWh 5.90 /kWh ---Cost of 1,000 kWh $ 2.00 ---
Golden Valley Customer Charge***$10.00***$10.00***
Electric Assn.0-500 kWh 11.25rkWh 9.73rkWhOver500kWh9.50 /kWh 7.98 /kWh
Cost of 1,000 kWh $I 3.75 $8.58
Commercial Rates
Fairbanks Municipal 0-100 kWh**12000rkWh**---Utilities System 100-400 kWh 11.30/kWh ---
400-1,000 kWh 9.50 /kWh ---Over 1,000 kWh 7.80/kWh ---
Cost of 5,000 kWh $4 4.90 ---
Golden Valley Customer Charge***$20.00***$20.00***
Electric Assn.0-500 kWh lSo00r kWh 13048rkWh500-5,000 kWh 11.10 /kWh 9.58 /kWh
Over 5,000 kWh 9.50 /kWh 7.98/kWh
Cost of 5,000 kWh $5 4.50 $5 8.65
Golden Valley Electric Association electric rates include a Cost of
Power Adjustment Clause (CPAC)that raises or lowers the fixed electric
rate quarterly to reflect changes in the cost of fuel and the cost of
electricity purchased from other utilities.The CPAC for the quarter
that begins with the March billing cycle lowers the price of each kWh
sold by 1.5171.
Fairbanks Munlcipal Utilities System electric rates include a minimum
monthly charge of $9.00 per residential customer and $12.00 per
commercial customer.
Golden Valley Electric Association (GVEA)electric rates also include a
fixed customer charge of $10.00 per residential customer and $20.00 per
commercial customer.The total GVEA monthly bill is,therefore,the
sum of the customer charge and the kWh usage charge.
Source:Fairbanks North Star Borough.The Energy Report,March,1983.
TABLE B.80
ANCHORAGE MUNICIPAL LIGHT AND POWER
CUMULATIVE ENERGY CONSERVATION PROJECTIONS
Energy Conservation in MWh
Program 1981 19S2 1983 19S4 1985 1986 1987--..--
Weatherizat i on 586 762 938 1,114 1,290 1,466 1,641
State Programs 879 1,759 2,199 2,683 3,078 3,518 3,737
Water Flow 200 464 464 464 464 464 464 <
Restrictions
Water Heat 3,922 3,922 3,922 3,922 3,922 3,922 3,922
Injection
Hot Water NA NA 249 249 249 249 249
Heater Wrap
Street Light 0 555 1,859 3,307 4,788 6,306 7,861
Conversion
Transmission 0 0 4,119 8,732 9,256 9,811 10,399
Conversion
Boiler Pump 7,148 7,148 7,148 7,148 7,148 7,148 7,148
Conversion
TOTAL 12,735 14,609 20,896 27,619 30,195 32,614 35,421
Increase NA 14.7 43.0 32.2 9.3 9.8 8.6
From Previous
Year %
Source:AMLP,1983
TA8LE 8.81
PROGRAMMATIC VS MARKET DRIVEN ENERGY CONSERVATION
PROJECTIONS IN THE AMLP SERVICE AREA
Year Programmatic Price-Induced Increase From
Conservation Con serv ati on Total Previous Year
(MWh)(%)(MWh)(%)(MW)(%)(%)
1981 12,735 39.5 19,558 60.5 32,294 100 NA
1982 191,609 34.9 27,243 65.1 41,853 100 29.6
1983 20,896 37.1 35,374 62.9 56,289 100 34.4
1984 27,619 41.1 39,560 58.9 67,133 100 19.3
1985 30,195 40.4 44,536 59.6 74,730 100 11.3
1986 32,614 40.6 48,133 59.4 81,015 100 8.4
1987 35,421 41.0 50,940 59.0 86,363 100 6.6
Source:AMLP,1983
TABLE B.82
AVERAGE ANNUAL ELECTRICITY CONSUMPTION PER HOUSEHOLD
ON THE GVEA SYSTEM,1972-1982
Annual
Consumption Percent
Year (kWh)Change
1972 13,919 +5.6
1973 14,479 +4.0
1974 15,822 +9.3
1975 17,332 +9.5
1976 15,203 -12.3
1977 14,255 -6.2
1978 11,574 -18.8
1979 10,519 -9.1
1980 9,767 -7.1
1981 9,080 -7.0
1982 9,303 +2.5
Source:GVEA,1983
TJl.BLE B.83 HISTffiIC ECCNJvIIC JV\ID ELECTRIC IU.JER ffiTA
rr f«
(1)
1965 1970 1975 1900 Igg2ITEMLhit1960
State Oi 1 CJ1d Gas
Revenues to
106x $4.2(2)933.6(3)General Fund 16.3 88.3 2,262.3 3,567.3
State General Fund
Expenditures n.a.82.7 188.6 453.3 1,172.8 4,601.9
State Population 226,Lm 265,Lm 3)4,700 lX),000 402,000 437,175
State Einploym:nt 94,lX)110,000 133,400 197,500 211,Lm 231,984
Railbelt Population 140,486 n.a.199,670 n.a.275,818 Il7,107
Railbelt (4)
74,100 88,500 13),400 132,000 154,033finploym:nt n.a.
Railbelt Households 37,()s2 n.a.54,057 n.a.94,210 1()S,599
Railbelt Electric(5
Energy Generation Gt.tt
Jlnchorage n.a.526 885 1,451 2,~5 2,709
Fairbanks n.a.231 433 617 647 691
Total n.a.757 1,318 2,QS8 3,012 3,400
Railbelt(S,ak
M..J 171 296 420 634 655[Bnand n.a.
Railbelt Generation
Capocity M..J n.a.n.a.n.a.n.a.1,143 1,272
Sources :f1ll.P Mxlel Data Base;Federal Energy Regulatory Onmisstcn,PoV€r Systen Staterent;Alaska PoV€r Amtntstrattcn,
LhpLb 1i shed Printouts,1983.
IlJlnnUal data is not available on a consistent basis for all itens listed.
2 Figure is for 1961.
3 This figure results fron the collection of a large petroleun lease bonus.
4 Excludes agricultural workers and self-enployed.
5)Includes electric utilities,military generation and self-supplied industrial.
TABLE B.84 MONTHLY LOAD DATA FROM ELECTRIC UTILITIES OF THE ANCHORAGE-COOK INLET AREA
1916-1982 --------------
1~16 1977 1919 1980 19811978 1982
NET ENERGY (MWh)l!
January 161,141.5 163,477.1 197,195.3 209,274.5 221,099.0 202,340.0 264,648.0
February 151,168.2 143,889.6 167,616.7 210,332.0 181,893.5 187,783.4 220,393.7
March 146,509.1 164,983.4 173,181.4 185,059.4 185,943.1 186,765.9 216,461.3
April 126,761.1 143,022.2 149,674.5 161,606.5 156,987.2 170,237.0 192,249.0
May 117,125.5 131,440.5 141,333.2 145,917.9 146,260.9 154,246.8 176,556.1
June 103,078.8 118,039.1 129,703.3 131,699.7 136,742.5 148,192.0 158,777.1
July 108,553.9 117,770.2 132,305.2 135,651.7 141,134.1 155,776.0 167,278.6
August 110,786.5 123,445.4 132,216.7 138,170.5 143,856.5 157,135.7 168,890.9
September 121,003.0 128,232.2 138,889.5 142,352.1 152,210.2 163,671.3 175,186.4
October 144,716.2 158,886.4 169,395.0 168,032.0 177,254.6 196,922.6 220,848.4
November 154,417.2 193,630.9 191,146.6 179,280.7 202,484.4 218,191.4 234,428.6
December 172,100.4 216,793.6 209,149.0 237,780.1 259,118.5 234,472.2 250,034.5
---------------------- ----------- --------------------------------- -----------
ANNUAL 1,617,361.6 1,803,610.6 1,931,806.2 2,045,157.1 2,104,984.5 2,175,734.4 2,445,752.6
PEAK DEMAND (MW)Y
January 293.1 288.4 341.3 357.8 399.4 351.8 471.7
February 283.7 269.5 328.6 395.1 337.2 377 .0 440.4
March 254.0 283.0 296.6 339.5 321.9 324.9 391.5
April 220.4 261.7 270.3 268.1 266.9 307.3 365.2
May 198.8 224.6 239.8 232.7 247.7 272.5 303.6
June 186.4 208.7 228.6 231.1 234.3 273.4 291.4
July 193.9 203.3 227.4 217.1 224.2 280.1 290.6
August 197.7 216.3 236.6 219.5 240.8 275.9 298.9
September 218.0 253.3 253.1 244.8 259.2 309.7 348.4
October 277.7 293.0 312.1 287.4 310.6 349.9 429.1
November 276.2 344.1 353.2 316.2 349.7 401.3 445.2
December 311.0 375.4 382.8 391.1 444.4 444.7 450.9
----- -----
----- ---------- ----------
ANNUAL 311.0 375.4 382.8 395.1 444.4 444.7 471.7
~Includes total net generation by CEA,AMLP and APAD and sales to other utilities.
_ Note:includes AMLP &CEA (This equals total area except MEA purchase from APAD -
5 MW by contract.)
C:"'lrce·I\la~l-"Powl'~·'\dmi"'';-"':rat';r..~,urn,·h,lisf'H>rl prin+·~·!ts,1(l~3.
TABLE B.85 MONTHLY LOAD DATA FROM ELE£TRIC UTILITIES OF THE FAIRBANKS-TANANA VALLEY AREA
1976-1982
E176 1977 (1978 1979 1980 1981 .1982
NET ENERGY (MWh)(l)
January 55,675.0 47,753.3 52,380.1 49,177 .2 50,037.5 42,057.2 53,931.0
February 53,313.3 41,115.2 45,326.6 50,532.3 38,093.0 40,303.0 45,022.0
March 43,844.4 46,759.5 45,014.9 42,322.0 38,220.1 37,927.8 43,698.0
April 34,468.6 37,698.3 36,384.6 35,415.1 32,784.8 35,262.8 38,743.0
May 29,811.4 32,446.1 32,195.9 29,781.9 30,943.3 32,286.2 35,379.0
June 27,063.7 28,787.6 29,783.1 28,091.9 28,015.3 30,163.7 32,428.0
July 28,328.5 28,921.0 30,184.2 29,743.5 30,405.5 30,264.8 34,449.0
August 28,754.2 30,765.5 30,793.2 29,058.6 30,378.0 30,301.7 34,308.0
September 31,311.0 31,474.5 32,455.1 31,404.4 32,232.7 33,661.8 35,637.0
October 40,298.2 41,307.6 40,106.7 36,280.0 36,084.3 39,271.0 42,846.1
November 42,801.7 53,609.9 44,186.7 37,400.1 40,606.1 41,647.1 45,771.0
December 53,334.5 61,015.7 47,394.9 48,370.1 55,500.7 48,820.3 49,885.0
------------------------------------------------------- ----------- -----------
ANNUAL 468,004.3 481,654.2 466,206.0 447,577.1 443,301.3 442,967.3 491,097.0
PEAK DEMAND (MW)(l)
January 101.0 87.9 95.8 89.2 95.2 79.8 94.4
February 99.6 87.3 94.7 100.7 75.4 88.1 91.6
March 81.8 86.0 81.8 81.3 70.3 68.1 82.0
Apri 1 64.9 72.7 70.9 65.6 60.4 65.4 72.8
May 54.8 60.2 58.1 56.5 55.8 64.6 67.0
June 49.7 56.3 57.9 53.9 54.2 59.5 62.9
July 54.1 54.5 55.3 55.8 55.8 58.6 61.7
August 52.9 55.6 55.3 56.9 59.4 61.0 70.7
September 60.0 65.4 62.8 60.0 61.0 65.9 69.8
October 82.1 79.2 72.3 66.8 70.8 72.1 82.1
November 84.5 102.3 86.1 72.2 75.6 77.6 89.4
December 97.3 117.5 83.5 87.6 95.4 93.1 89.1
-------------------------------
ANNUAL 101.0 117.5 95.8 100.7 95.4 93.1 94.4
(l)Data for FMUS and GVEA including purchases.
Source:Alaska Power Administration,unpublished printout,1983.
TABLE B.86 NET GENERATION BY ELECTRIC UTILITY
1916-1982
nmnl
YEAR
UTILITY 1976 -1977 1978 1.97g-1980 1Y~1 lY~t
I
Anchorage
Municipal 444.9 420.3 443.1 473.1 486.6 485.3 579.5
Light &Power
Chugach
Electric Asso.1,054.5 1,179.7 1,308.6 1,401.0 1,434.1 1,467.7 1,718.4
Al aska Power
Administration 118.0 203.6 180.1 171.1 184.3 222.7 147.9
Anchorage Cook
Inlet Subtotal 1,617.4 1,803.6 1,931.8 2,045.2 2,105.0 2,175.7 2,445.8
Fairbanks
Municipal 123.3 128.5 124.7 124.7 125.6 126.1 140.7
Ut il ity System
Golden Valley
Electric 344.7 353.5 341.5 322.9 317.7 316.9 350.3
Associ ation
Fairbanks Area
Sub-total 468.0 481.7 466.2 447.6 443.3 443.0 491.1
Railbelt Total 2,085.4 2,285.3 2,398.0 2,492.8 2,548.3 2,618.7 2,936.9
Note:Subtotals and total shown may differ from column totals due to rounding.
Source:Alaska Power Administration,Unpublished Printouts,1983.
TABLE B.87
MAP MODEL VALIDATION
SIMULATION OF HISTORICAL ECONOMIC CONDITIONS
Observed Estimated Percent
Factor Year Value .Value Difference Difference--•!
Non-Agriculatural 1965 70,529 70,406 -123 -.174
Wage and Salary 1970 92,465 88,837 -3,628 -3.924
Employment 1975 161,315 154,893 -6,422 -3.981
1980 169,609 166,281 -3,328 -1.962
Wages and Salaries 1965 721 757 36 4.9
In Al aska -1970 1,203 1,134 -69 -5.7
$million -nominal 1975 3,413 3,408 -5 -0.1
1980 4,220 4,083 -182 -4.3
Personal Income 1965 827 861 34 4.1
In Al aska -1970 1,388 1,309 -79 -5.7
$million -nominal 1975 3,455 3,372 -83 -2.4
1980 5,030 4,972 -58 -1.2
TABLE B.88
COMPARISON OF ACTUAL AND PREDICTED
ELECTRICITY CONSUMPTION FOR 1982 (GWh)
Anchorage-Cook Inlet Area
RED Reference RED Ut il it ies
Case·Output Adjusted Data
Residenti al 1059 1097 1146
Business 1018 1070 972
Others 125 123 123
Total "'2'2U2'~724T
Fairbanks-Tanana Valley Area
RED Reference RED Util ities
Case Output Adjusted Data
Residential 205 208 178
Business 242 254 269
Others 7 6 5
Total 4'52f 'm;E'457
Table B.89 (Sheet 1 of 2)
AL TERNATI VE PETROLEUM PRICE PROJECTIONS 1983-2010
1983 DOLLARS
Department Reference Case
of Revenu{l)DR I Sherman Cl ark Sherman Clark
Mean-4/83 Spring 1983(2)Base Case -4/83 NSD Case -4/83
$/bb 1 %Chg $/bb 1 %Chg $/bbl %Chg.$/bbl %Chg
1983 28.95 28.95 28.95 28.95
4 23.96 -17.2 25.17 -13.1 27.61 -4.6 27.61 -4.6
5 22.67 -5.4 27.02
7.4 26.30 -4.7 26.30 -4.7
6 22.35 -1.4 28.77
6.5 26.30 0.0 26.30 0.0
7 21.95 -1.8 30.64 6.5 26.30 0.0 26.30 0.0
8 22.15 1.3 32.62 6.5 26.30 0.0 26.30 '0.0
9 22.34 1.3 34.74 6.5 40.00 52.1 27.09 3.0
1990 22.55 1.3 36.99 6.5 40.00 0.0 27.90 3.0
1 22.79 1.3 38.61 4.4 41.20 3.0 28.74 3.0
2 23.04 1.3 40.31 4.4 42.44 3.0 29.60 3.0
3 23.32 1.3 42.08 4.4 43.71 3.0 30.49 3.0
4 23.63 1.3 43.92 4.4 45.02 3.0 31.40 3.0
5 23.96 1.3 45.85 4.4 46.38 3.0 32.34 3.0
6 24.31 1.3 47.27 4.4 47.77 3.0 33.31 3.0
7 24.71 1.3 48.74 3.1 49.20 3.0 34.31 3.0
8 25.14 1.3 50.26 3.1 50.68 3.0 35.34 3.0
9 25.60 1.3 51.82 3.1 52.20 3.0 36.40 3.0
2000 25.93 1.3 53.43 3.1 53.76 3.0 37.50 3.0
1 26.27 1.3 54.04 1.1 55.64 3.5 38.63 3.0
2 26.61 1.3 54.65 1.1 57.58 3.5 39.78 3.0
3 29.96 1.3 55.27 1.1 59.58 3.5 40.98 3.0
4 27.31 1.3 55.90 1.1 61.66 3.5 42.21 3.0
5 27.66 1.3 56.54 1.1 63.81 3.5 43.47 3.0
6 28.02 1.3 57.33 1.1 66.04 3.5 44.78 3.0
7 28.39 1.3 58.13 1.1 68.34 3.5 46.12 3.0
8 28.76 1.3 58.95 1.1 70.73 3.5 47.50 3.0
9 29.13 1.3 59.77 1.1 73.20 3.5 48.93 3.0
2010 29.51 1.3 60.61 1.1 75.75 3.5 50.39 3.0
?~jDOR extrapolated after 1999 at last DOR rate of 1.3%/yr.
DRI extrapolated after 2005 at 1ast DRI rate of 1.1%/yr.
TABLE B.89 (Sheet 2 of 2)
ALTERNATIVE OIL PRICE PROJECTIONS 2010-2040
1983 DOLLARS
Department Reference Case
of Revenue DRI Sherman Cl ark Sherman Cl ark
Mean-4/83 Spring 1983 Base Case-4/83 NSD Case-4/83
$/bbl %Chg $/bbl %Chg $/bbl %Chg $/bbl %Chg
2010 29.51 60.61 75.75 50.39
1 29.89 1.3 61.28 1.1 76.89 1.5 51.65 2.5
2 30.28 1.3 61.95 1.1 78.04 1.5 52.94 2.5
3 30.68 1.3 62.63 1.1 79.21 1.5 54.26 2.5
4 31.07 1.3 63.32 1.1 80.40 1.5 55.61 2.5
2015 31.48 1.3 64.02 1.1 81.60 1.5 57.00 2.5
6 31.89 1.3 64.72 1.1 82.83 1.5 58.42 2.5
7 32.30 1.3 65.43 1.1 84.07 1.5 59.88 2.5
8 32.72 1.3 66.15 1.1 85.33 1.5 61.38 2.5
9 33.15 1.3 66.88 1.1 86.61 1.5 62.91 2.5
2020 33.58 1.3 67.62 1.1 87.80 1.5 64.48 2.5
1 34.02 1.3 68.36 1.1 87.80 0.0 65.45 1.5
2 34.46 1.3 69.11 1.1 87.80 0.0 66.43 1.5
3 34.91 1.3 69.87 1.1 87.80 0.0 67.43 1.5
4 35.36 1.3 70.64 1.1 87.80 0.0 68.44 1.5
2025 35.82 1.3 71.42 1.1 87.80 0.0 69.47 1.5
6 36.76 1.3 72.20 1.1 87.80 0.0 70.51 1.5
7 36.23 1.3 73.00 1.1 87.80 0.0 71.57 1.5
8 37.72 1.3 73.80 1.1 87.80 0.0 72 .64 1.5
9 38.21 1.3 74.61 1.1 87.80 0.0 73.73 1.5
2030 38.71 1.3 75.43 1.1 87.80 0.0 74.84 1.5
1 39.21 1.3 76.26 1.1 87.80 0.0 75.59 1.0
2 39.72 1.3 77 .10 1.1 87.80 0.0 76.34 1.0
3 40.23 1.3 77.95 1.1 87.80 0.0 77 .10 1.0
4 40.76 1.3 78.81 1.1 87.80 0.0 77 .88 1.0
2035 41.29 1.3 79.68 1.1 87.80 0.0 78.65 1.0
6 41.82 1.3 80.55 1.1 87.80 0.0 79.44 1.0
7 42.36 1.3 81.44 1.1 87.80 0.0 80.23 1.0
8 42.37 1.3 82.33 1.1 87.80 0.0 81.03 1.0
9 42.92 1.3 83.24 1.1 87.80 0.0 81.84 1.0
2040 42.48 1.3 84.15 1.1 87.80 0.0 82.66 1.0
Tab le B.90
lEVEL OF ANALYSIS EMPLOYED WITH WORLD OIL PR ICE FORECASTS
Oil Price Forecast Model or level of Anal ys is
Battelle General Electric
Rail belt Optimum
DOR Electric Generat i on
Petroleum Revenue ISER Demand Planning
(PETREV)(MAP)(RED)(OGP)
DOR Mean,Spring 83 Yes Yes Yes Yes
DOR 50%From PETREV Yes Yes No
DOR 30%From PETREV Yes Yes No
DR I Spr i ng 83 Yes Yes Yes Yes
DRI lOWOIl No No No No
DRI HIGHOIl No No No No
SHCA BASE CASE No No No No
Reference Case Yes Yes Yes Yes
SHCA ZEG No No No No
+2%Yes Yes Yes No
0%Yes Yes Yes No
-1%Yes Yes Yes No
2%Yes Yes Yes Yes
TABLE B.91
VARIABLES AND ASSUMPTIONS OTHER THAN OIL PRICES
PETREV MODEL
Name Year Reference Case Value Source----
North Slope Petroleum Production 1983 1.611 x 10
6 bbl/day Department of Revenue
1999 .699 x 10
6 bbl/day Department of Revenue
State Royalty 1981 12.5%Department of Revenue
1999 12.5%Department of Revenue
North Slope Production Tax Rate 1983 15%Department of Revenue
1999 15%Department of Revenue
Economic Limit Factor 1983 99 Department of Revenue
1999 585 Department of Revenue
Transportaton and Quality Differential 1983 $9.93 nominal/bbl Department of Revenue
1999 $13.86 nominal/bbl Department of Revenue
TABLE B.92
TOURIST Tourists Visiting Alaska
Comm.
Comm.
Comm.
Comm.
Source
AlasYa Department of Labor
MAP Model Data Base
Alaska Department of Labor
Institute of Social and Economic Research
Institute of Social and Economic Research
Institute of Soci~and Economic Research
Inst itute of Social and Economic Research
Institute of Soci~and Economic Research
Institute of Social and Economic Research
Institute of Soci~and Economic Research
Institute of Social and Economic Research
Institute of Social and Eocnomic Research
Institute of Social and Economic Research
Institute of Soci~and Economic Research
Institute of Social and Economic Research
Institute of Soci~and Economic Research
Bureau of Economic Analysis,U.S.Dept.of Comm.
Al aska Mil itary Command
Alaska Department of Labor
Institute of Soci~and Economic Research
Alaska Dept. of Commerce &Economic Develop.,
Division of Tourism
PETREV Model Output
Institute of Soci~and Economic Research
PETREV Model Output
Institute of Soci~and Economic Research
Inst itute of Social and Economic Research
Institute of Social and Economic Research
Institute of Social and Economic Research
Institute of Social and Economic Research
Institute of Social and Economic Research
Institute of Social and Economic Research
Bureau of Economic Analysis,U.S.,Dept.of
Bureau of Economic Analysis,U.S.,Dept.of
Bureau of Economic Analysis,U.S.,Dept.of
Bureau of Economic Analysis,U.S.,Dept.of
Alaska Department of Labor
Year
1983
2010
1983
2010
1983
2010
1983
2010
1983
2010
1983
2010
1983
2010
1983
2010
1983
2010
1983
2010
1983
2010
1983
2010
1983
2010
1983
2010
1983
2010
1983
2010
VARIABLES AND ASSUMPTIONS OTHER THAN OIL PRICES
MAP MODEL
Reference Case Value
203 Employees
704 Employees
9,387 Employees
16,282 Employees
3,261 Employees
1,056 Employees
290 EmployeesoEmployees
1,552 Emp 1oyees
3,279 EmployeesoEmployees
o Employees
10,433 Employees
11,617 Employees
6,421 Employees
7,096 Employees
23,323 Employees
23,323 Employees
17,989 Employees
20,583 Employees
730,000 Visitors
2,080,000 Visitors
1,480 MM Current $
699 MM Current $
1,430 MM Current $
1,592 MM Current $
26 MM Current $
o MM Current $
149 Mt~Current $
564 MM Current $
235 MM Current $
1,601 MM Current $
.01
.06
.015
.065
.9338
Real Wage Growth/Year
Unemployment Rate
Real Income Growth/Year
Level Growth/Year
Force Participation Rate
U.S.
U.S.
U.S.
Price
Labor
State Civilian Federal Emp.
State Active Duty Military Emp.
State Bonus Payment Revenue
State Low Wage Manuf.Emp.
State High Wage Manuf.Emp.
State Petroleum Property Tax
Revenue
State Petroleum Corporate Tax
State Petroleum Production Tax
Revenue
State Petroleum Royalty Revenue
State Mining Employment
State Low Wage Exog.Const.Emp.
State High Wage Exog.Const.Emp.
State Fish Harveting Emp.
State Exog.Transportation Emp.
Name
State Agricultural Employment
EMGM
RTCSPX
EMGC
EMMX2
EMT9X
EMCNX2
EMMXl
EMF ISH
EMCNXl
MBP9
RPPS
RPBS
Symbol
EMAGRI
RPRY
GGRWEVS
UUS
GRDIRPU
GRUSCPI
LFPART
RPTS
TABLE B.93 (Sheet 1 of 2)
SUMMARY OF EXOGENOUS ECONOMIC ASSUMPTIONS
Exogenous Employment Assumptions
Trans-Alaska Oil Pipeline System
Prudhoe Bay Field Employment
Upper Cook Inlet Petroleum
Production
Tertiary Recovery of North
Slope Oil
OCS Exploration and Development
Anchorage Oi 1 Headquarters
Beluga Chuitna Coal Production
Hydroelectric Projects
U.S.80rax Min e
Greene Creek Mine
Red Dog Mine
Operating employment remains constant
at 1,500 through 2010.
Construction employment developing
Prudhoe Bay and Kuparuk fields peaks
at 2,400 in 1983 and 1986. Operating
employment remains at 2,502 through 2010
for overall North Slope production.
Employment declines gradually
beginning in 1983 so as to reach 50
percent of the 1982 level (778)by 2010.
Tert iary oil recovery proj ect util izi ng
North Slope natural gas occurs in early 1990s
with a peak annual employment of 2,000.
The current OCS five-year leasing schedule
calls for 16 OCS lease sales subsequent to
October 1982,including the Beaufort,Norton,
and St.George Sales,which have already
taken place (Sales 71,57,and 70).
Development is assumed to occur only in the
Navarin Basin (1.4 billion barrels of oil)
and the Beaufort Sea (6.1 billion barrels of
oil).All other sal es are assumed to result
in exploration employment only.
Several oil companies establish regional
headquarters in Alaska in mid-1980s.
Development of 4.4 million ton/year mine
for export beginning in 1994 provides total
total employment of 524.
Employment peaks at 725 in 1990 for
construction of several state-funded
hydroelectric projects around the state.
The U.S.Borax mine near Ketchikan is brought
into production with operating employment of
790 by 1988.
Production from the Greens Creek Mine on
Admiralty Island results in employment of 315
people from 1986 through 1996.
The Red Dog Mine in the Western Brooks Range
reaches full production with operating
employment of 448 by 1988.
TABLE B.93 (Sheet 2 of 2)
SUMMARY OF EXOGENOUS ECONOMIC ASSUMPTIONS
Exogenous Employment Assumptions (continued)
Other Mining Activity
Agriculture
Forest and Lumber Products
Pu1P Mi 11 s
Commercial Fishing-Nonbottomfish
Commercial Fishing-Bottomfish
Federal Mil itary Employment
Federal Civilian Employment
Tourism Assumptions
Emp 1oyment increases from a 1982 1eve1 of
5,267 at 1 percent annually.
Moderate state support results in expansion
of agriculture to employment of 508 in 2000.
Employment expands to over 3,200 by 1990
before beginning to decline gradually after
2000 to about 2,800 by 2010.
Employment declines at a rate of 1 percent
per year after 1983.
Employment levels in fishing and fish
processing remain constant at 6,323 and 7,123
respectively.
The total U.S.bottomfish catch expands at a
constant rate to allowable catch in 2000,
with Alaska resident harvesting employment
rising to 733.Onshore processing capacity
expands in the Aleutians and Kodiak census
divisions to provide total resident
employment of 971 by 2000.
Employment remains constant at 23,323.
Rises at 0.5 percent annual rate from 17,900
in 1982 to 20,583 by 2010.
Number of visitors to Alaska increases by
50,000 per year from 680,000 in 1982 to over
2 million by 2010.
TABLE B.94 (Sheet 1 of 2)
VARIABLES AND ASSUMPTIONS OTHER THAN OIL PRICES
RED MODEL
d Vintage Specific Survival Rate
g Growth Stock of Appliances
Symbol Name...,----.-.--..,-----Uncertainty Module
Fuel Price Forecast
Housing Module
THH Regional Household Forecast
HH State Households by Age Group
Residential Module
HI Households by Type of Dwellings
AC Average Consumption of Appliances
AS Initial Stock of Appliances
Source
Housing Module Output
Battelle-Northwest End Use Survey;
Residential Energy Surveys by San Diego
Gas and Electric Company and Southern
California Edisosn Company;
King,et.al 1982;
McMahon,1983;
Goldsmith and Huskey,1980b.
Reference
Case Value
Table B.95
Tab le B.96
Tab le B.97
Tab le B.98
1983 Actual Data Combined
with Escalation Rates
Battelle,1983, based on
Goldsmith and Huskey 1980b
Battelle Northwest End Use
Survey,1981
Battelle,1983, based on
Mount,Chapman &Tyrrell
(1973),and other literature
101,346 Households MAP Output
189,418 Households MAP Output
Table B.108 MAP Output
Table B.101
Table B.100
Table B.109
Table B.99
Table B.97 &99
Year
1983
2010
Housing Demand Coefficients
Saturation of Residential Appliances -
Price Adjustment Coefficients
b,c,d,
A,B,
SAT
Business Consumption Module
TEMP Total Regional Employment 1983
2010
152,502 Employees MAP Output
255,974 Employees MAP Output
TABLE B.94 (Sheet 2 of 2)
VARIABLES AND ASSUMPTIONS OTHER THAN OIL PRICES
RED MODEL
Symbol Name".--..-----;~---Program-Induced Conservation Module
Not Used
Miscellaneous Module
VACHG Vacant Housing
vh Consumption per Vacant Housing
Sl Street Lighting Consumption
sh Proportion of Households
Having a Second Home
Year
Reference
Case Value
Tab 1e B.110
300 kWh
1.0%
2.5%
Source
RED Housing Module Output
Batte 11 e,1983
Battelle,1983
O.S. Goldsmith,ISER,
personal communication
Peak Demand Module
shkWh
LF
Per Unit Second Home Consumption
Annual Load Factor
Anchorage
Fairbanks
500 kWh
55.7%
50.0%
O.S.Goldsmith,ISER,
personal communication
Battelle,1983
TABLE B.95
FUEL PRICE FORECASTS USED BY RED
(1980 doll ars)
Anchorage -Cook Inlet Area Fairbanks - Tanana Valley Area
Year Residential Business Residential Business
Heating Fuel Oil ($/MMBtu)
1980 7.750 7.200 7.830 7.500
1985 6.450 5.900 6.510 6.180
1990 6.840 6.290 6.910 6.580
1995 7.930 7.380 8.010 7.680
2000 9.190 8.640 9.290 8.960
2005 10.650 10.100 10.770 10.440
2010 12.350 11.800 12.480 12.150
Natural Gas ($/MMBtu)
1980 1.730 1.500 12.740 1 11.290 1/
1985 1.950 1.720 10.600 9.150-
1990 2.880 2.650 11.240 9.790
1995 4.050 3.820 13.030 11.580
2000 4.290 4.060 15.110 13.660
2005 4.960 4.730 17.520 16.070
2010 5.380 5.150 20.310 18.860
Eiectricity ($/kWh)
1980 0.037 0.034 0.095 0.090
1985 0.048 0.045 0.095 0.090
1990 0.052 0.049 0.092 0.087
1995 0.058 0.055 0.094 0.089
2000 0.062 0.059 0.096 0.091
2005 0.065 0.062 0.098 0.093
2010 0.067 0.064 0.100 0.095
1Propane
TABLE B.96
HOUSING DEMAND COEFFICIENTS
Sing1e Fam il y Mu It i Fam il y Mobile Homes
Vari ab 1e Value Vari ab 1e Val ue Var i ab 1e Val ue
BA1 -0.303 CAl 0.225 DA1 0.068
BA2 -0.175 CA2 0.086 DA2 0.039
BA4 0.080 CA4 -0.090 DA4 0.014
B2S 0.182 C2S -0.203 D2S 0.008
B3S 0.317 C3S -0.280 D3S -0.020
B4S 0.380 C4S -3.352 D4S -0.016
Note: These coefficients were used in the housing demand equations.
A detailed explanation of these equations is presented in the
RED Documentation Report.
Source:Battelle,1983, based on Goldsmith and Huskey,1980b.
TABLE B.97
EXAMPLE OF MARKET SATURATIONS OF APPLIANCES IN
SINGLE-FAMILY HOMES FOR ANCHORAGE-COOK INLET AREA
Refrigerators Freezers Dishwashers Clothes Washers
Year Default Range Default Range Default Range Default Range--
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
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
Water Heater Clothes Dryers Range (cooking)Saunas Jacuzzi s
Year Default Range Default Range Default Range Default Range--
1980 98.6 --90.2 --99.9 --14.1
1985 98.8 95-100 91.2 88-94 100.0 99-100 16.3 13-19
1990 99.0 98-100 92.5 89-95 100.0 99-100 18.7 14-22
1995 99.0 98-100 93.7 90-96 100.0 99-100 21.0 16-26
2000 99.0 98-100 95.0 92-98 100.0 99-100 23.4 18-28
2005 99.0 98-100 95.0 92-98 100.0 99-100 25.7 20-30
2010 99.0 98-100 95.0 92-98 100.0 99-100 28.1 23-33
Note: Acomplete listing of market saturation data for single-family,multi-family,mobile-
homes,and duplexes in Anchorage and Fairbanks is presented in the RED Documentation
Report.
Source:Battelle-Northwest End Use Survey, 1981.
1980 Census of Housing
San Diego Gas and Electric Company,1982.
Southern California Edison Company,1981.
Saturation Survey.
1981 Residential Energy Survey.
1981 Residential Electrical Appliance
TABLE B.98
PARAMETER VALUES IN RED PRICE ADJUSTMENT MECHANISM
Short-Run Elasticities
Own-Price
Cross-Price
Natural Gas
Oil
Lagged Adjustment
Residential
Sector
-.1552 +.3304/p*
.0225
.01
.8837
Business
Sector
-.2925 +2.4014/p*
.0082
.01
.8724
*Electricity prices measured in mills per kWh,1970 dollars
Source:Battelle 1983, based on Mount,Chapman,Tyrrell (1973)and
other literature surveys.
TABLE B.99
PERCENT OF APPLIANCES USING ELECTRICITY AND AVERAGE
ANNUAL ELECTRICITY CONSUMPTION,RAILBELT LOAD CENTERS,1980
Anchorage Fairbanks
Percentage OSlng Electrlclty Annual kwh Perentage OSlng Electrlclty Annua I kwh
Appliance SF MH DP MF Consumption SF MH DP MF Consumption------------ -- --
Space Heat (Existing Stock)
16.0Sing1e Fam il y NA NA NA 32,850 9.7 NA NA NA 43,380
Moblle Home NA 0.7 NA NA 24,570 NA 0.0 NA NA 33,210
Duplex NA NA 22.8 NA 21,780 NA NA 11.7 NA 28,710
Multi Family NA NA NA 44.4 15,390 NA NA NA 14.8 19,080
Space Heat (New Stock
Single Family 10.9 NA NA NA 32,850 9.7 NA NA NA 43,380
Moblle Home NA 0.7 NA NA 24,570 NA 0.0 NA NA 33,210
DU)lex NA NA 15.0 NA 21,780 NA NA 11.7 NA 28,710
Mu ti Fami ly NA NA NA 25.0 15,390 NA NA NA 14.8 19,080
Water Heaters tExisting)36.5 50.4 44.0 60.9 3,300 33.1 42.8 43.1 26.2 3,300
Water Heaters New)10.0 0.7 15.0 25.0 3,300 33.1 42.8 43.1 26.2 3.300
Clothes Dryers 84.3 88.1 81.3 86.6 1,032 96.2 94.6 94.4 100.0 1,032
Cooking Ranges 75.8 23.2 85.2 88.2 850 79.0 48.2 95.0 97.1 850
Sauna-Jacuzzis 93.5 100.0 93.7 81.8 2,000 61.8 100.0 60.8 100.0 2,000
Refri gerators 100.0 100.0 100.0 100.0 1,800 100.0 100.0 100.0 100.0 1,800
Freezers 100.0 100.0 100.0 100.0 1,342 100.0 100.0 100.0 100.0 1,342
Di shwashers 100.0 100.0 100.0 100.0 250 100.0 100.0 100.0 100.0 250
Additional
Water Heating (Existing)36.5 50.4 44.0 60.9 799 33.1 42.8 43.1 26.2 799
Water Heating New)10.0 0.7 15.0 25.0 799 33.1 42.8 43.1 26.2 799
Clothese Washers 100.0 100.0 100.0 100.0 90 100.0 100.0 100.0 100.0 90
Additional
Water Heating fExisting)36.5 50.4 44.0 60.9 1,202 33.1 42.8 43.1 26.2 1,202
Water Heating New)10.0 0.7 15.0 25.0 1,202 33.1 42.8 43.1 26.2 1,202
Mi sce 11 aneous 100.0 100.0 100.0 100.0 2,110 100.0 100.0 100.0 100.0 2,466
Source:Battelle Northwest End Use Survey,1981
Kina,et ale 1982r'._.._.lon,__83
TABLE B.100
GROWTH RATES IN ELECTRIC APPLIANCE CAPACITY AND INITIAL
ANNUAL AVERAGE CONSUMPTION FOR NEW APPLIANCES
Average Annual
kWh Consumption for Growth Rate in
New Appliances (1985)Electric Capacity
Appliance Anchorage Fairbanks Post 1985 (annual)
Space Heat
Sing1e Fam il y 40,000 53,000 0.005
Mobile Home 30,000 40,600 0.005
Duplex 26,600 35,100 0.005
Multi Family 18,800 23,300 0.005
Water Heaters 3,475 3,475 0.005
Clothes Dryers 1,032 1,032 0.0
Cooking Ranges 1,250 1,250 0.0
Sauna-Jacuzzi s 1,750 1,750 0.0
Refri gerators 1,560 1,560 0.00
Freezers 1,550 1,550 0.00
Dishwashers 230 230
Add it iona1 Water Heati ng 740 740 0.005
Clothese Washers 70 70 0.0
Additional Water Heati ng 1,050 1,050 0.005
Miscellaneous Appliances 2,160 2,536 (a)
(a) Incremental growth of 50 kWh per customer in Anchorage 5-year
period;70 kWh in Fairbanks.
Source:King et al.,1982
McMahon,1983.
TABLE B.101
PERCENT OF APPLIANCES REMAINING IN SERV rCE YEARS AFTER PURCHASE
Years
5 10 15 20 25 30
a.Old Appliances
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-Jacuzzi s 0.5 0.3 0.1 0.0 0.0 0.0
Refri gerators 0.8 0.6 0.3 0.1 0.0 0.0
Freezers 0.9 0.8 0.6 0.3 0.1 0.0
Di shwashers 0.6 0.3 0.1 0.0 0.0 0.0
Clothes Washers 0.6 0.3 0.1 0.0 0.0 0.0
b.New App 1i ances
Space Heat (A 11 )0.89 0.73 0.56 0.42 0.3 0.1
Water Heaters 0.75 0.35 0.1 0.0 0.0 0.0
Clothes Dryers 1.00 0.75 0.35 0.1 0.0 0.0
Ranges-Cooking 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
Refrigerators 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
Dishwashing 0.75 0.35 0.1 0.0 0.0 0.0
Clothes Washers 0.75 0.35 0.1 0.0 0.0 0.0
Source:Battelle,1983 based on ISER,Goldsmith and Huskey 1980b
TABLE B.102
VARIABLES AND ASSUMPTIONS OTHER THAN OIL PRICES
OGP MODEL
Name
Fuel Costs -Nenana Coal
- Beluga Coal
- Natural Gas
Fuel Escalation Rates -Nenana Coal
- Beluga Coal
- Natural Gas
Thermal Construction Cost
Coal Steam -Nenana
Coal Steam - Beluga
Combustion Turbine
Combined Cycle
Hydro Construction Cost -Watana
-Oev il Canyon
Oi scount Rate
Reference
Value
Year Case Reference
1983 1.72 $/MMBtu Appendix 0-1
1983 1.86 $/MMBtu Appendix 0-1
1983 2.47 $/MMBtu Appendix 0-1
1984-2051 2.3 %/yr.
1/Appendix 0-1
1984-2051 1.6 %/yr .1/Appendix 0-1
1984-1988 Vari ab 1e Appendix 0-1
1989-2010 3.0 %/yr.Appendix 0-1
2011-2020 2.5 %/yr.Appendix 0-1
2021-2030 1.5 %/yr.Appendix 0-1
2031-2051 1.0 %/yr.Appendix 0-1
1982 2107 $/kW Exhibit 0
1982 2061 $/kW Exh i bit 0
1982 627 $/kW Exhi bit 0
1982 1075 $/kW Exhi bit 0
1982 596 $x10 6 Exhibit 0
1982 1554 $x10 6 Exhi b it 0
1982 3.0%Al aska Power
Authority
.!J Coal price escalation assumed only to initial operating date of a over the
coal-fired unit at which time there would be no real price escalatfon Beluga life of
the unit.Average real escalation of coal prices (Nenana and combined)for period
1993-2051 is about 1%/yr.
TABLE B.103
REFERENCE CASE FCRECAST
SlJ1vV.\RY (F INPUT AND WPUT ffiTA
Iten Description 1983 1985 19~199)am 2005 2010
World Oil Price (1ggJ$Jbbl)28.95 26.30 27.~32.34 37.50 43.47 50.39
Energy Price Use:!by RED (198QJ;)
H2ating Fuel Oil -Anchorage ($JMvlBtu)7.75 6.45 6.84 7.93 9.19 10.65 12.35
Natural Gas -And-orage ($/MvlBtu)1.73 1.95 2.88 4.05
4.29 4.%5.38
State Petroleun Revenues 1J(rtxn.$x106)
Production Taxes 1,474 1,561 2,032 1,868 1,910 2,150 2,421
Royalty Fees 1,457 1,555 2,43)2,651 3,078 3,799 4,689
State Gen.Fund Expenditures (rtxn.$x106)3,288 3,700 5,577 7,729 9,714 13,035 17,975
State Popu 1at ion 457,836 4~,146 554,634 608,810 644,111 686,663 744,418
State Ernploynent 243,067 258,3%293,689 313,954 325,186 345,701 376,169
Railbelt Population 319,767 341,613 389,026 423,460 451,561 486,851 533,218
Railbelt Bnploynent 159,147 169,197 1~,883 204,668 214,542 231,584 255,974
Railbelt Total Number of Households 111,549 120,140 138,640 152,463 163,913 177,849 195,652
Railbelt Electricity Consunption (GWh)
Anchorage 2,322 2,561 3,045 3,371 3,662 4,107 4,735
Fairbanks 481 535 691 8))800 986 1,123
Total 2,003 3,096 3,737 4,171 4,542 5,093 5,858
Railbelt Peak Demand (MW)579 639 777 868 915 1,059 1,217
1Petroleun revenues also inclule corporate incane taxes, oil and gas p-operty taxes, lease bonuses,and federal share:!
royalties.
TABLE B.I04
REFERENCE CASE
STATE PETROLEUM REVENUES
(MILLION $)
Total to
Total General
Including Fund (Net
Severance Corporate Property Bonuses of
Year Royalties Taxes Income Taxes and Permanent
Taxes Federal Fund
Shared Contri-
Royalties but ion)
1982 1530.000 1590.000 668.899 142.700 3960.199 3570.549
1983 1456.661 1473.507 233.969 148.600 3361.836 2985.396
1984 1450.305 1474.080 328.647 153.200 3441.298 3069.956
1985 1555.117 1560.529 365.362 158.000 3668.700 3272.498
1986 1724.811 1705.298 398.724 163.456 4020.278 3582.078
1987 1896.215 1857.760 438.776 169.101 4389.691 3908.677
1988 1997.731 1647.607 396.949 174.940 4245.582 3739.060
1989 2251.456 18S5.795 520.004 180.981 4837.387 4267.234
1990 2480.380 2031.695 591.983 187.231 5321.348 4693.734
1991 2352.500 1857.126 668.435 193.697 5102.781 4506.898
1992 2530.291 192 9.692 794.871 200.385 5487.250 4846.672
1993 2657.006 1986.190 906.959 207.305 5790.461 5117.957
1994 2742.898 2006.949 ·998.581 214.464 5996.891 5302.664
1995 2651.116 1868.193 1084.124 221.870 5860.301 5188.770
1996 2599.817 1737.659 1185.670 229.532 5788.676 5U9.719
1997 2755.836 1856.672 1326.406 237.458 6213.367 5515.156
1998 2865.556 1887.844 1474.798 245.658 6511.852 5785.961
1999 2950.992 1865.044 1549.613 254.141 6758.785 6011.285
2000 3077.885 1909.805 1841.891 252.917 7132.496 6353.023
2001 3210.235 1955.641 2056.580 271.996 7535.449 6722.641
2002 3348.276 2002.576 2296.294 281.389 7970.531 7122.961
2003 3492.252 2050.638 2563.949 291.106 8440.941 7557.125
2004 3642.420 2099.854 2862.802 301.158 8950.230 8028.625
2005 3799.044 2150.251 3196.489 311.558 9502.340 8541.328
2006 3962.404 2201.857 3569.072 322.317 10101.640 9099.540
2007 4132.781 2254.702 3985.082 333.447 10753.010 9708.060
2008 4310.492 2308.815 4449.578 344.962 11461.840 10372.220
2009 4495.844 2364.227 4968.219 356.874 12234.160 11097.950
2010 4689.164 2420.969 5547.316 369.198 13076.640 11891.850
SOURCE:MAP MODEL OUTPUT
TABLE B.10S
REFERENCE CASE
STATE GOVERNMENT FISCAL CONDITIONS
(MILLION $)
Unre-
stricted Percent of
General General Permanent State State Permanent
Fund Fund Fund Personal Subsidy Fund
Year Expendi-Balance Dividends Income Tax Programs Earnings
tures Reinvested
1982 4601.891 399.200 425.000 0.000 634.000 0.000
1983 3287.977 478.004 152.608 0.000 500.000 0.500
1984 3389.729 616.992 196.738 0.000 350.000 0.500
1985 3699.507 700.539 223.721 0.000 350.000 0.500
1986 4031.094 821.113 253.168 0.000 350.000 0.500
1987 4375.941 987.922 286.008 0.000 350.000 0.500
1988 4731.574 699.973 322.441 0.000 695.501 0.500
1989 5118.008 588.465 361.817 0.000 0.000 0.500
1990 5576.836 506.125 406.085 0.000 0.000 0.500
1991 5386.480 506.141 455.185 0.000 0.000 0.500
1992 5786.504 506.152 505.111 0.000 0.000 0.500
1993 6528.020 139.531 0.000 0.000 0.000 0.500
1994 672 9.594 139.543 0.000 338.049 0.000 0.500
1995 7729.250 139.563 0.000 680.847 0.000 0.000
1996 7822.879 139.586 0.000 748.723 0.000 0.000
1997 8361.188 139.609 0.000 809.145 0.000 0.000
1998 8794.711 139.633 0.000 .873.359 0.000 0.000
1999 9190.000 139.652 0.000 941.928 0.000 0.000
2000 9713.740 139.668 0.000 1017.188 0.000 0.000
2001 10278.270 139.691 0.000 1098.944 0.000 0.000
2002 10886.180 139.711 0.000 1188.241 0.000 0.000
2003 11545.180 139.734 0.000 1287.516 0.000 0.000
2004 12261.640 139.766 0.000 1396.169 0.000 0.000
2005 13034.660 139.789 0.000 1513.479 0.000 0.000
2006 13871.350 139.820 0.000 1640.603 0.000 0.000
2007 14777.160 139.852 0.000 1778.121 0.000 0.000
2008 15758.890 139.891 0.000 1926.802 0.000 0.000
2009 16822.770 139.934 0.000 2085.652 0.000 0.000
2010 17975.270 139.980 0.000 2257.400 0.000 0.000
SOURCE:MAP MODEL OUTPUT
TABLE B.I06
REFERENCE CASE
POPULATION
(THOUSANDS)
Greater Greater
Year State Railbe1t Anchorage Fairbanks
1982 437.175 307.105 239.830 67.277
1983 457.836 319.767 251.057 68.711
1984 473.752 330.202 259.679 70.523
1985 490.146 341.613 269.300 72.313
1986 505.884 352.187 278.082 74.105
1987 517.431 359.054 283.333 75.723
1988 526.823 364.583 287.969 76.615
1989 538.532 375.007 296.794 78.213
1990 554.634 389.026 308.196 80.831
1991 560.786 393.296 311.585 81.712
1992 581.846 405.991 322.865 83.127
1993 594.848 413.788 328.521 85.268
1994 602.027 420.130 332.694 87.436
1995 608.810 423.460 335.464 87.997
1996 616.422 428.574 339.629 88.945
1997 623.782 434.617 344.561 90.057
1998 630.352 440.001 348.981 91.021
1999 636.928 445.519 353.531 91.988
2000 644.111 451.561 358.441 93.120
2001 651.362 457.835 363.501 94.335
2002 658.994 464.362 368.801 95.561
2003 667.660 471.437 374.626 96.811
2004 676.878 478.925 380.769 98.156
2005 686.663 486.851 387.267 99.584
2006 697.022 495.287 394.168 101.119
2007 707.990 504.091 401.364 102.727
2008 719.644 513.431 408.995 104.436
2009 731.592 522.970 416.755 106.216
2010 744.418 533.218 425.115 108.104
SOURCE:MAP MODEL OUTPUT
TABLE B.107
REFERENCE CASE
EMPLOYMENT
(THOUSANDS)
State
No rr-Ag State Rai1be1t Greater Greater
Year Wage and Total Total Anchorage Fairbanks
Salary Total Total
1982 192.903 231.984 154.033 UO.533 33.500
1983 202.237 243.067 159.147 125.221 33.927
1984 205.903 246.984 162.259 U 7 .853 34.406
1985 216.612 258.396 169.197 133.668 35.528
1986 225.515 267.895 174.818 138.324 36.494
1987 230.833 273.581 177 .412 140.345 37.067
1988 234.657 277.669 179.422 142.065 37.357
1989 240.213 283.619 184.211 146.124 38.088
1990 249.654 293.689 190.883 151.685 39.198
1991 247.908 291.844 191.360 151.958 39.402
1992 264.012 309.031 199.404 158.995 40.409
1993 266.941 312.180 202.842 161.351 41.492
1994 267.220 312.511 203.630 161.669 41.961
1995 268.534 313.954 204.668 162.466 42.202
1996 270.783 316.404 206.258 163.772 42.486
1997 272.935 318.765 208.212 165.401 42.811
1998 274.346 320.353 210.041 166.916 43.125
1999 276.144 322.374 212.025 168.580 43.445
2000 278.729 325.186 214.541 170.645 43.897
2001 281.498 328.141 217.283 172.875 44.408
2002 284.643 331.499 220.293 175.333 44.960
2003 288.727 335.859 223.703 178.156 45.546
2004 293.137 340.569 227.487 181.265 46.222
2005 297.941 345.701 231.584 184.625 46.959
2006 303.062 351.172 235.985 188.226 47.759
2007 308.504 356.989 240.639 192.025 48.614
2008 314.317 363.203 245.561 196.044 49.517
2009 ·320.082 369.368 250.621 200.146 50.475
2010 326.440 376.169 255.974 204.512 51.462
SOUP~E:MAP MODEL OUTPUT
TABLE B .108
REFERENCE CASE
HOUSEHOLDS
(THOUSANDS)
Greater Greater
Year State Rai1be1t Anchorage Fairbanks
1982 145.453 106.572 83.678 22.894
1983 153.141 111.549 88.038 23.511
1984 159.154 115.671 91.425 24.246
1985 165.299 120.140 95.165 24.974
1986 171.192 124.275 98.580 25.695
1987 175.620 U 7.053 100.709 26.344
1988 179.287 129.415 102.669 26.746
1989 183.738 133.365 105.994 27.371
1990 189.696 138.640 110.267 28.373
1991 192.234 140.401 111.662 28.739
1992 199.886 145.348 116.024 29.324
1993 204.788 148.405 118.253 30.152
1994 207.695 150.964 119.963 31.002
1995 210.461 152.463 121.197 31.267
1996 213.508 154.590 122.921 31.669
1997 216.470 157.052 U4.921 32.131
1998 219.161 159.242 126.710 32.532
1999 221.854 161.483 U8.549 32.934
2000 224.751 163.913 130.515 33.398
2001 227.670 166.423 132.532 33.891
2002 230.716 169.023 134.636 34.388
2003 234.112 171.820 136.928 34.892
2004 ·231.695 174.758 139.329 35.429
2005 241.468 177 .849 141.853 35.996
2006 245.436 181.121 144.520 36.601
2007 249.609 184.516 141.285 37.231
2008 254.014 188.100 150.203 31.896
2009 258.519 191.748 153.162 38.586
2010 263.323 195.652 156.336 39.316
SOURCE:MAP MODEL OUTPUT
TABLE B.10S (CONTINUED)
REFERENCE CASE
STATE HOUSEHOLDS BY AGE OF HEAD
(THOUSANDS)
Head
Year Total Younger Head Head Head Older
Than 25 25-29 30-54 Than 54
1982 145.453 17.141 23.938 81.706 22.667
1983 153.141 18.110 25.128 86.087 23.816
1984 159.154 18.624 25.919 89.726 24.884
1985 165.299 19.085 26:763 93.487 25.964
1986 171.192 19.447 27.532 97.157 27.056
1987 175.620 19.526 27.905 .100.067 28.123
1988 179.287 19.488 28.085 102.516 29.199
1989 183.738 19.617 28.486 105.290 30.345
1990 189.696 20.014 29.285 108.807 31.591
1991 192.234 19.816 29.171 110.503 32.744
1992 199.886 20.529 30.434 114.787 34.137
1993 204.788 20.725 30.930 117.672 35.462
1994 207.695 20.603 30.909 119.437 36.746
1995 210.461 20.508 30.893 121.002 38.058
1996 213.508 20.500 30.996 122.606 39.407
1997 216.470 20.504 31.114 124.079 40.772
1998 219.161 20.485 31.199 125.334 42.143
1999 221.854 20.485 31.321 126.523 43.523
2000 224.751 20.530 31.532 127.771 44.917
2001 227.670 20.583 31.773 129 .000 46.313
2002 230.716 20.656 32.069 130.279 47.712
2003 234.112 20.780 32.472 131.742 49.119
2004 237.695 20.920 32.929 133.319 50.526
2005 241.468 21.077 33.435 135.024 51.932
2006 245.436 21.247 33.987 136.866 53.336
2007 249.609 21.432 34.583 138.856 54.738
2008 254.014 21.634 35.226 141.014 56.139
2009 258.519 21.833 35.878 143.272 57.536
2010 263.323 22.058 36.592 145.736 58.937
SOURCE:MAP MODEL OUTPUT
TABLE B.109
REFERENCE CASE FORECAST
NUMBER OF HOUSEHOLDS SERVED
Year Single Family Multifamily Mobile Homes Duplexes Total
Anchorage-Cook In1et Area
1980 35473 20314 8230 7486 71503
1985 46224 26204 10958 8567 91953
1990 58740 26349 13505 8460 107054
1995 64779 29931 14941 8333 117984
2000 69822 33259 16200 8022 127302
2005 75777 36378 17749 8738 138641.
2010 83343 40411 19721 9649 153124
Fairbanks-Tanana Valley Area
1980 7220 5287 1189 1617 15313
1985 10646 5867 2130 1765 20407
1990 11728 7960 2270 2375 24332
1995 14735 7841 3330 2339 28244
2000 16528 7703 3845 2298 30374
2005 17951 8681 4220 2121 32973
2010 19675 9612 4673 2334 36284
TABLE B.110
REFERENCE CASE FORECAST
NUMBER OF VACANT HOUSEHOLDS
Year Single Family Multifamily Mobile Homes Duplexes Total
Anchorage-Cook In 1et Area
1980 5089 7666 1991 1463 16209
1985 509 1496 121 292 2417
1990 646 1005 149 289 2089
1995 713 1616 164 284 2777
2000 768 1796 178 445 3187
2005 834 1964 195 288 3281
2010 917 2182 217 319 3634
Fairbanks-Tanana Valley Area
1980 3653 3320 986 895 8854
1985 118 2654 24 722 3518
1990 129 454 25 81 689
1995 162 448 37 80 726
2000 182 440 42 78 742
2005 197 469 46 209 921
2010 216 519 51 77 864
TABLE B.111
REFERENCE CASE FORECAST
RESIDENTIAL USE PER HOUSEHOLD
After
Before Conservation Adjustment and Fuel Substitution Adj ustment
Year Small Appliances Large App 1i ances Space Heat Total Total
(kWh)(kWh)(kWh)(kWh) (kWh)
1980 2110 6500 5089 13699 13699
1985 2160 6151 4812 13133 12829
1990 2210 6020 4584 12814 12561
1995 2260 5959 4516 12735 12644
2000 2310 5989 4454 12753 12736
2005 2360 6059 4420 12839 12938
2010 2410 6124 4444 12977 13198
Fairbanks-Tanana Valley Area
1980 2466 5740 3314 11519 11519
1985 2536 6179 3606 12321 12136
1990 2606 6453 3873 12932 12736
1995 2676 6667 4050 13393 13329
2000 2746 6795 4310 13852 14009
2005 2816 6839 4536 14191 14626
2010 2886 6888 4656 14430 15180
TABLE 8.112
REFERENCE CASE FORECAST
BUSINESS USE PER EMPLOYEE
Before Conservation Adjustment and Fuel Substitution After Adjustments
Anchorage-Fai rbanks-Anchorage- Fairbanks-
Year Cook Inlet Area Tanana Valley Area Cook Inlet Area Tanana Valley Area
(kWh)(kWh)(kWh)(kWh )
1980 8,407 7,496 8,407 7,496
1985 9,580 7,972 9,212 7,900
1990 10,355 8,327 9,749 8,281
1995 10,918 8,662 10,078 8,665
2000 11,416 8,958 10,349 9,024
2005 12,090 9,308 10,828 9,446
2010 12,933 9,711 11,502 9,929
TABLE B.113
REFEREf\CE CASE FffiECAST
StJ1;AAY (F ffiICE EFFECTS
ANCHffiAGE-eoa<INLET MEA
Residenti al Sector Business Sector
Ovvn-Price Cross-Price Ovvn-Price Cross-Price
Year Reduction Reduction Reduction Ra:luction
(Gi'Jh)(GtJh)(GtJh)(GNh)
1983 18.5 -1.7 28.0 1.6
1984 24.7 -2.3 37.3 2.1
1985 30.8 -2.8 46.6 2.7
1986 38.5 -10.6 58.2 -0.4
1987 46.1 -18.5 69.7 -3.4
1988 53.7 -26.3 89.3 -6.4
1989 61.4 -34.1 92.8 -9.4
199)69.0 -41.9 104.4 -12.4
1991 115.0 -91.2 119.9 -19.1
1992 161.1 -140.5 135.5 -25.7
1993 207.1 -189.8 151.1 -32.4
1994 253.2 -239.2 166.7 -39.0
1995 299.2 -288.5 182.2 -45.7
1996 234.0 -225.0 198.3 -52.6
1997 168.8 -161.5 214.3 -59.5
1998 103.7 -98.1 230.4 -66.5
1999 38.5 -34.6 246.4 -73.4
2CXXl -26.7 28.8 262.4 -80.4
2001 -7.5 6.5 282.5 -g).2
2002 11.7 -15.9 302.5 -100.1
2003 30.9 -38.3 322.6 -110.0
2004 SO.1 -60.6 342.6 -119.9
2005 69.2 -83.0 362.7 -129.8
2006 78.2 -95.9 388.1 -143.3
2007 87.1 -108.8 413.6 -156.9ars96.0 -121.7 439.1 -170.4zo»104.9 -134.6 464.5 -183.9
2010 113.8 -147.6 490.0 -197.4
TABLE B.114
REFERENCE CASE FCRECAST
BREAI1)()WN CF B..ECTRICITY REQJIREMENTS
Ilrldur age-O:>ok In let Prea
Res i dent i al Business Mi see 11 aneous Indust./Mil itary Total
Year Requirenents Requirenents Requirenents Requirenent s Requirenents
(GWh)(GWh)(GWh)(GI-kl )(GI-kl )
1983 1100 1009 25 108 2322
1984 1140 1160 26 116 2442
1985 1100 1231 26 124 2561
1986 1213 12131 27 133 2658
1987 1246 1330 28 151 2755
1988 1279 1330 28 165 2852
1989 1312 1429 29 178 2949
199)1345 1479 30 192 3045
1991 1374 1510 31 195 3111
1992 1404 1542 31 198 3176
1993 1433 1574 32 202 3241
1994 1462 1606 33 205 33())
1995 1492 1637 34 208 3371
1996 1518 1663 34 214 3429
1997 1544 1689 35 220 3487
1998 1570 1714 35 226 3545
1999 1595 1740 36 232 3604
2000 1621 1766 36 238 J562
2001 1656 1813 37 245 3751
2002 16g)1859 38 252 3310
2003 1725 19)6 39 259 3929
2004 1759 1953 40 266 4018
2005 1794 1999 41 273 4107
2006 1839 2070 42 282 4232
2007 1885 2140 43 2g)4358
2008 1930 2211 44 298 4484
2009 1976 2281 45 307 46aJ
2010 2021 2352 47 315 4735
TABLE B.115
REFEREflCE CASE FffiECAST
SlJvM1,RY rr ffiICE EFFECTS
FAIRBMKS-TANl\f'¥\VJllLEY MEA
Residential Sector Bostness Sector
ONn-Price Cross-Price ONn-Price Cross-Price
Year Reduction Reduction Reduction Re::Juction
(G'lh)(Qtjh)(GWh)(GWl)
1983 0.0 2.3 0.0 1.5
1984 0.0 3.0 0.0 2.1
1985 -0.2 3.8 0.0 2.6
1986 -0.4 4.2 -0.3 2.8
1987 -0.6 4.6 -0.7 2.9
1988 -0.8 5.0 -1.0 3.1
1989 -1.0 5.4 -1.4 3.3
199)-1.0 5.8 -1.7 3.5
1991 -1.0 5.2 -1.7 3.1
1992 -1.0 4.6 -1.6 2.7
1993 -1.0 4.0 -1.6 2.2
1994 -1.0 3.4 -1.6 1.8
1995 -1.0 2.8 -1.5 1.4
1996 -0.9 1.4 -1.2 0.6
1997 -0.7 -0.1 -1.0 -0.3
1998 -0.5 -1.6 -0.7 -1.1
1999 -0.3 -3.1 -0.4 -1.9
200J -0.2 -4.6 -0.2 -2.7
2001 0.1 -6.8 0.2 -3.9
2002 0.4 -9.0 0.8 -5.1
2003 0.7 -11.3 1.2 -6.3
2004 1.0 -13.5 1.7 -7.5
2005 1.3 -15.7 2.2 -8.6
2006 1.8 -18.7 2.8 -10.2
2007 2.2 -21.6 3.5 -11.8
2ffi3 2.6 -24.6 4.1 -13.4
2009 3.0 -27.6 4.8 -15.0
2010 3.5 -30.5 5.5 -16.6
TABLE B.116
REFEREt\CE CASE FffifCAST
BRffilWWN CF 8..ECTRICITY REQJIREMENTS
Fairbanks-Tanana Valley tcee
Resident i al Business Mi see 11 aneous Indust ./Mil itary Total
Year Requir81lents Pequirerents Requirerents Requirerents Requirerent s
(GWh)(GWh)(GWh)(GWh)(GWh)
1983 219 255 7 0 481
19&4 233 268 7 0 5C8
1985 248 281 7 0 535
1986 260 289 7 10 566
1987 273 298 7 20 597
1988 285 307 7 30 629
1989 297 316 7 40 660
1990 310 325 7 50 691
1991 323 333 7 50 713
1992 336 341 7 50 735
1993 350 349 7 50 757
1994 363 357 8 50 778
1995 376 366 8 50 8)0
1996 386 372 8 50 816
1997 390 378 8 50 832
1998 4C6 384 8 50 848
1999 416 390 9 50 864
2CXX)426 3%9 50 880
2001 437 4C6 9 50 902
2002 448 415 9 50 ~3
2003 460 425 9 50 944
2004 471 434 10 50 965
2005 4~444 10 50 986
2006 496 457 10 50 1013
2007 510 471 10 50 1041
zrs 523 484 11 50 1068
2009 537 497 11 50 1096
2010 551 511 11 50 1123
TABLE B.117
REFERENCE CASE FffiECAST
ffiOJECTED PEAK AND ENERGY CfMt\ND
Anchor age-Cook Inlet Prea Fairbanks-Tanana Valley Prea Total Systen kea
Year ,~)Peak ,erg)Peak ,erg)Peak loed Fector
~GlJh ~GlJh ~(%)
1983 2322 469 481 110 2003 579 55.3
1984 2442 493 508 116 2950 609 55.3
1985 2561 517 535 122 3036 639 55.3
1986 2658 538 566 129 2334 667 55.2
1987 2755 558 597 135 3352 695 55.0
1938 2852 579 629 144 3481 722 55.0
1989 2949 599 660 151 3609 750 54.9
199)3045 619 691 158 3737 777 54.9
1991 3111 633 713 163 3824 796 54.8
1992 3176 646 735 168 3911 814 54.8
1993 3240 659 757 173 3997 832 54.8
1994 3306 672 778 178 4084 850 54.8
1995 3371 686 an 183 4171 868 54.8
1996 3429 697 816 186 4245 884 54.8
1997 3487 709 832 190 4319 899 54.8
1998 3545 721 848 194 4394 914 54.8
1999 3604 732 864 197 4468 930 54.8
2000 3662 744 800 201 4542 945 54.8
2001 3751 762 g)2 206 4652 968 54.8
20CQ 3840 700 923 211 4762 991 54.8
2003 3929 7f£944 215 4872 1013 54.9
2004 4018 816 965 220 4983 1036 54.9
2005 4107 834 986 225 5UB 1059 54.9
2006 4232 859 1013 231 5246 1091 54.9
2007 4358 885 1041 213 5399 1122 54.9
2008 4484 910 1068 244 5552 1154 54.9
2009 4609 936 1096 250 5705 1186 54.9
2010 4735 961 1123 256 5858 1217 54.9
TABLE B.118
[XR+'1EAN SCENl\RIO
Sl..fvI'mY (f INPUT JlND OUTPUT DATA
Item Description 1983 1985 199J 1995 2CXXl 2005 2010
World Oil Price (1983$/bb1)28.95 22.67 22.55 23.96
25.93 27.66 29.51
Energy Price Used by RED (1900$)
Heating Fuel Oil -Anchorage ($/t-'MBtu)7.75 5.97 5.94
6.31 6.83 7.29 7.78
Natural Gas -Ancoorage ($/t-'MBtu)1.73 1.96 2.71 3.25 3.41 3.56 3.71
State Petro1eun Revenues 1/(Nan .$x106)
1,518 1,313 1,283 1,382ProducttonTaxes1,474 1,241 1,488
Royalty Fees 1,457 1,233 1,8t4 1,863 2,079 2,473 2,9U
State C£nera1 Fund Expenditures (Nan.$x106)3,288 3,100 5,080 5,834 7,182 9,424 12,677
State Population 457,836 486,247 535,300 574,869 609,9'l4 652,063 708,243
State fmploynert 243,lIS 7 254,316 279,744 294,410 300,491 330,150 359,155
Rai1be1t Population 319,767 339,161
372,777 399,548 427,836 462,5~ffJ7,558
Rai1be1t Emp10ynent 159,147 166,559 179,872 191,122 203,818 220,8t0 244,Oce
Rai1be1t Total NUTber of f-buseho1ds 111,549 119,247 132,857 143,731
155,042 168,580 185,697
Rai1be1t Electricity Qmsunption (GWh)
Anchorage 2,299 2,523 2,855 3,112 3,414 3,820 4,377
Fatrbaiks 476 527 653 737 814 g)6 1,CQ3
Total 2,776 3,050 3,SCB 3,849 4,228 4,726 5,399
Rai1be1t Peak Demand (MIJ)573 630 730 801 879 9~1,121
1petro1eun revenues also include corporate incane taxes, oil and gas p"operty taxes, lease bonuses,and federal shara:l
royalties.
TABlE B.119
om 50%SCENL\RIO
SlWARY (f INPur JlND ourpur DATA
Iten lescript.ton 1983 1985 1990 1995 2000 2005 2010
World Oil Price (1983$/bbl)28.95 24.63 21.01 18.77 17.70 16.79 15.93
Energy Price Used by RED (1900$)
Heating Fuel Oil -Anchorage ($/Mv1Btu)7.75 6.49 5.53 4.95 4.66 4.43 4.20
Natural Gas -Jlnchorage ($/MvlBtu)1.73 2.00 2.63 2.81 2.71 2.63 2.56
State Petroleun Revenues 1/(Nan.$x106)
Production Taxes 1,474 1,251 1,385 969 818 744 677
Royalty Fees 1,457 1,231 1,667 1,366 1,328 1,431 1,543
State Gen.Fund Expenditures(Nan.$x106)3,288 3,111 4,770 4,849 5,552 6,783 8,513
State Popu 1ation 457,836 486,327 533,lSl 563,529 593,612 631,699 684,180
State Emp 1oynert 243,Cfj7 254,400 277,633 286,643 300,109 319,313 346,691
Railbelt Population 319,767 339,204 371,539 391,838 416,~2 448,422 490,~0
Railbelt Employnent 159,147 166,610 178,556 185,903 197,460 213,403 235,394
Railbelt Total Number of Households 111,549 119,262 132,405 140,932 150,se3 163,310 179,313
Railbelt Electricity Gonsunption (GWh)
Anchorage 2,304 2,531 2,849 3,029 3,305 3,690 4,218
Fairbznks 476 526 645 704 7fJJ 831 se5
Total 2,78)3,057 3,494 3,733 4,065 4,521 5,143
Railbelt Peak Demand (MW)574 631 726 776 Sl4 938 1,Cfj6
1petroleun revenues also include corporate incane taxes,oil am gas p-cperty taxes,lease bonuses,ard federal sharEd
royalties.
TABLE B.l20
em 30%SCENARIO
SLM'1'lRY CF INPur ,fiND ourpur DATA
Item Description 1983 1985 199)1995 2CXXl 2005 2010-- -----
World Oil Price (1983$/bbl)28.95 21.00 17.93 15.58 14.53 13.46 12.46
Energy Price Used by RED (1900$)
Heating Fuel Oil -Mchorage ($/Mvbtu)7.75 5.53 4.73 4.11 3.83 3.55 3.28
Natural Gas -Mcoorage ($/M"IBtu)1.73 1.93 2.48 2.53 2.45 2.36 2.26
State Petroleun Revenues l /(Ibn.$xl06)
Prodoction Taxes 1,474 1,102 1,034 640 488 457 428
Royalty Fees 1,457 1,092 1,287 950 891 891 891
State General Fund Expenditures (rtrn.$106)3,288 2,7%3,%1 3,89)4,400 5,426 6,89)
State Population 457,836 483,812 522,041 548,379 578,103 617,487 671,471
State Employnent 243,067 251,771 269,932 278,384 292,900 313,327 341,269
Railbelt Population 319,767 337,814 364,097 381,365 405,002 438,370 481,497
Railbelt 6mploynent 159,147 165,005 173,452 100,284 192,563 209,228 231,546
Railbelt Total NuIDer of Households 111,549 118,748 129,695 137,079 146,858 159,429 175,691
Railbelt Electricity Qmsunption (GWh)
Anchorage 2,284 2,498 2,747 2,893 3,169 3,554 4,071
Fairbaiks 469 516 617 667 721 789 879
Total 2,753 3,014 3,364 3,560 3,89)4,343 4,950
Railbelt Peak D3nand (MiJ)568 622 699 740 as 926 I,CQ6
IPetroleun revenues also include corporate incane taxes, oil ard gas p-operty taxes, lease bonuses,am federal shara:l
royalties.
TABLE B.121
ffiI SCEflV\RIO
Sl.fv1\AAY (f INPlIT JlND OlITPlIT DATA
Iten cescription 1983 1985 199)1995 2CXXl 2005 2010
World Oil Price (1983$/bb1)28.95 27.02 36.99 45.85 53.43 56.54 60.61
Energy Price Used by RED (1980$)
Heating Fuel Oil -Jlnchorage ($/Mvbtu)7.75 7.12 9.75 12.08 14.08
14.9)15.97
Natural Gas -Jlncmrage ($fMv1Btu)1.73 2.03 3.45 5.10 5.75 6.01 6.36
State Petroleun Revenues 1/(Nan .$x106)
1,474ProductionTaxes 1,624 2,9)3 2,752 2,764 3,067 3,403
Royalty Fees 1,457 1,623 3,568 3,916 4,447 5,~6,519
State General Fund Expendttures (Nan.$106)3,288 3,697 5,547 8,217 12,061 17,554 26,110
State Population 457,836 490,133 550,045 614,876 680,962 751,2tQ 812,794
State Einploynent 243,067 258,382 289,578 320,974 352,300 386,560 433,793
Railbe1t Population 319,767 341,600 383,595 428,092 478,817 535,855 609,094
Railbelt Einploynent 159,147 169,186 186,951 200,761 243,133 261,894 299,610
Railbe1t Total Nunber of Households 111,549 120,136 136,764 154,096 173,69)195,554 223,283
Railbelt Electricity Consunption (GWh)
Anchorage 2,328 2,571 3,020 3,494 4,044 4,699 5,603
Fairbalks 483 538 697 817 997 1,158 1,362
Total 2,811 3,100 3,717 4,341 5,041 5,857 6,965
Railbelt Peak C6nand (MtJ)500 642 773 9J4 1,050 1,220 1,450
1Petroleun revenues also include corporate incane taxes,oil an:J gas p'operty taxes,lease bonuses,and federal shara:l
royalties.
TABlE B.122
+2%SCEflAAIO
SLMvY\Ry CF INPlJT JlND OlJTPlJT DATA
Iten cescri ption 1983 1985 1990 1995 2000 2005 2010
World Oil Price (1983$/bbl)28.95 30.12 33.25 36.72 40.54 44.76 49.42
Energy Price lsed by RED (1gg)$)
Heating Fuel Oil -.Anchorage ($/MvlBtu)7.75 7.94 8.76 9.68 10.68 11.79 13.02
Natural Gas -.Ancmrage ($/Mv1Btu)1.73 2.03 3.19 4.26
4.59 4.95 5.34
State Petroleun Revenues 1/(Nan.$x106)
Prcduction Taxes 1,474 1,897 2,515 2,120 2,024 2,127 2,235
Royalty Fees 1,457 1,894 3,079 3,000 3,261 3,762 4,340
State General Fund Expenditures (Nan.$x106)3,288 3,701 5,556 8,184 12,178 14,269 18,384
State Population 457,836 4~,157 550,359 614,826 687,750 726,125 769,233
State Inploynert 243,067 258,407 289,000 320,001 357,377 364,115 381,154
Railbelt Population 319,767 341,622 383,836 428,017 486,242 517,048 551,279
Railbelt Emplo,)ffient 159,147 169,205 187,116 209,620 213,937 245,595 259,656
Railbelt Total Number of Households 111,549 120,143 136,851 154,072 176,267 188,800 202,640
Railbelt Electricity tonsurption (GWh)
Anchorage 2,353 2,613 3,062 3,548 4,203 4,506 4,957
FairbCflks 486 543 6%834 989 1,066 1,167
Total 2,839 3,156 3,758 4,382 5,192 5,573 6,124
Railbelt Peak Demand (MW)586 652 782 912 1,081 1,159 1,273
1PetroleLffi revenues also include corporate incane taxes, oil cn:I gas p-cperty taxes, lease bonuses,erd federal sharEd
royalties.
TABLE B.123
0%SCENl\RIO
Sl..fvTvW<Y CF INPUT JlND OUTPUT DATA
Item Description 1983 1985 199)1995 20CD 2005 2010
World Oil Price (1983$/bbbl)28.95 28.95 28.95 28.95 28.95 28.95 28.95
Energy Price lsed by RED (1908$)
Heating Fuel Oil -Anchorage ($/MvlBtu)7.75 7.63 7.63 7.63 7.63 7.63 7.63
Natural Gas -Anchorage ($/MvlBtu)1.73 2.01 2.96 3.60 3.60 3.60 3.60
State Petroleun Revenues 1/(Nan.$x106)
1,474ProdicttonTaxes 1,8)0 2,130 1,642 1,437 1,387 1,339
Royalty Fees 1,457 1,797 2,6CQ 2,330 2,325 2,474 2,632
State General Fund Experdttures (Nan.$x106)3,288 3,701 5,539 7,542 8,367 10,140 12,632
State Popu 1ation 457,836 4~,154 550,151 617,971 641,432 673,537 721,159
State Ernp loyrent 243,rfJ7 258,404 289,626 322,653 320,751 334,939 360,89)
Railbelt Population 319,767 341,619 383,665 432,178 450,rfJ9 478,003 517,133
Railbelt Ernplo)fTIent 159,147 169,203 186,982 211,840 211,686 224,292 245,456
Railbelt Total Nunber of Households 111,549 120,142 136,790 155,506 163,382 174,668 189,812
Railbelt Electricity Gonsunption (GWh)
Anchorage 2,331 2,575 3,002 3,492 3,613 3,942 4,442
Fatrbaiks 485 542 691 830 872 946 1,051
Total 2,816 3,118 3,693 4,322 4,485 4,888 5,493
Railbelt Peak D:mand (MtJ)582 644 768 9JO 933 1,016 1,141
1Petroleun revenues also tnclule corporate incane taxes, oil am gas p-coerty taxes, lease bonuses,ard federal shared
royalties.
TABLE B.124
-1%SCENA.RIO
SLMvAAY (f INPlJf AND OlJfPlJf DATA
Item Description 1983 1985 1990 1995 2000 2005 2010
World Oil Price (1983$/bbbl)28.95 28.37 26.98 25.66 24.40 23.21 22.07
Energy Price Used by RED (1900$)
Heating Fuel Oil -JIl1chorage ($/fvMBtu)7.75 7.48 7.11
6.76 6.43 6.12 5.82
Natural Gas -JIl1chorage ($fMv1Btu)1.73 2.00 2.87 3.32 3.06 2.96 2.86
State Petroleun Revenues 1/(tbn.$x1(6)
Production Taxes 1,474 1,753 1,953 1,438 1,202 1,109 1,023
Royalty Fees 1,457 1,749 2,383 2,040 1,951 1,990 2,030
State Ceneral Fund Expeditures (tbn.$x1(6)3,288 3,702 5,559 6,561 7,324 8,732 10,714
State Popu 1at ion 457,836 4g),387 551,881 601,879 626,068 658,790 706,745
State Employnent 243,067 258,648 290,318 307,313 312,417 32£,554 354,812
Railbelt Population 319,767 341,852 384,89'\.419,075 439,370 467,659 506,906
Railbelt Employnent 159,147 169,404 187,470 200,363 205,960 219,881 241,205
Railbelt Total Nunber of Households 111,549 120,223 137,238 150,881 159,490 170,816 185,906
Railbelt Electricity Consunption (GWh)
Anchorage 2,351 2,610 3,047 3,365 3,567
3,<1)4 4,391
Fairbanks 485 541 689 781 833 903 999
Total 2,836 3,151 3,736 4,149 4,400 4,007 5,3g)
Railbelt Peak l:Bnand (MtJ)586 651 777 864 915 998 1,119
1Petroleun revenues also include corporate incane taxes, oil and gas p-operty taxes, lease bonuses,and federal shara::l
royalties.
TABLE B.125
-2%SCEN'lRIO
SlJ'IM1RY (F INPlIT ftND OlITPUT DATA
Item Description 1983 1985
199)1995 2(0)2005 2010
World Oil Price (1983$/bbl)28.95 27.00 25.13 22.72 20.54 18.56 16.78
Energy Price Used by RED (1~$)
Heating Fuel Oil -Anchorage ($/fvMBtu)7.75 7.32 6.62 5.99 5.41 4.89 4.42
Natural Gas -Jlnchorage ($/fvMBtu)1.73 1.98 2.77 3.07 2.88 2.72 2.56
State Petroleun Revenues 1/(Nm.$x1(6)
1,474 1,705 1,786 1,253 1,001 882 477ProductionTaxes
Royalty Fees 1,457 1,701 2,176 1,778 1,630 1,598 1,566
State teneral Fund Experditures (Nm,$x1(6)3,288 3,700 5,536 5,953 6,521 7,660 9,285
State Popu 1ation 457,836 490,151 551,818 589,214 613,39)646,700 695,204
State Ernp 1oynert 243,067 258,401 291,431 299,458 3CX5,835 323,689 350,023
Railbelt Population 319,767 341,616 385,935 409,758 430,535 459,156 498,676
Railbelt Ernplo)ffient 159,147 169,200 188,768 194,711 202,130 216,510 237,835
Railbelt Total Nunber of Households 111,549 120,141 137,567 147,521 156,215 167,584 182,700
Railbelt Electricity Consunption (GWh)
Anchorage 2,348 2,605 3,063 3,252 3,460 3,7fJ2.4,270
Fairbanks 484 540 689 756 'iJ)2 866 954
Total 2,832 3,145 3,752 4,008 4,262 4,658 5,224
Railbelt Peak Demand (MW)585 650 78)834 886 %7 1,084
1Petroleun revenues also include corporate incane taxes,oil and gas p-cperty taxes,lease bonuses,and federal sharei
royalties.
TABLE B.126
RESULTS OF MAP MODEL SENSITIVITY TESTS
Factor
Value in Year 2000
Low ~
Projected Statewide
Households in Year 2000
Low High--%DTfTerence
1,066,000 2,566,000
State Agri cult.
Employment 1 160
State Mining Emp.-/3,990
State Hi gh Wage
Exog.Constr.Emp.0
State Low Wage
Exog.Constr.Emp.0
State Exog.Trans.Emp.1,100
State Hi gh Wage
Manu.Emp.0
State Low Wage
Manu.Emp.8,205
State Fish Harvesting
Emp.4,536
State Active DU1~
Military Emp._7 16,892
State Civil Fed.Emp.1/17,800
Tourists Visiting
Alaska
2,000
19,107
2,000
1,000
2,968
486
16,000
9,192
33,000
21,719
215,436
200,458
212,523
215,119
214,306
215,824
210,106
213,557
209,936
212,372
209,936
217,352
229,782
217,971
217,579
217,223
216,610
220,833
217,744
224,575
217,962
224,575
.9
14.6
2.6
1.1
1.4
.4
5.1
2.0
7.0
2.6
7.0
U.S.Real Wag!;
Growth/Year-
U.S.Unemp.Rate
U.S.Real Income
Growth/Year
U.S.Price LeY7l
Growth/Year-
1Key Variable.
.005
.05
.005
.09
.015
.075
.025
.05
211.335
211,161
215,493
205,924
223,723
222,178
216,272
222,305
5.9
5.2
.4
8.7
TABLE B.127
RESULTS OF RED MODEL SENSITIVITY TESTS
ON RESIDENTIAL SECTOR
TOTAL ELECTRICITY REQUIREMENTS WITHOUT LARGE INDUSTRIAL
Anchorage-Cook Inlet Area
Maximum
25%GE
Mean
50%GE
75%GE
Minimum
Std Dev
Reference Case
1990
TGWfi)
2901
2872
2856
2855
2838
2801
23.4
2854
2000 2010
(GWh)(GWh)
3510 4496
3446 4461
3428 4420
3427 4421
3411 4388
3382 4294
24.3 46.9
3424 4420
Fairbanks-Tanana Valley Area
Maximum
25%GE
Mean
50%GE
75%GE
Minimum
Std Dev
Reference Case
655
648
642
643
637
626
6.9
641
849 1099
835 1082
829 1074
830 1073
823 1068
812 1052
8.2 10.3
830 1073
TABLE B.128
RESULTS OF RED MODEL SENSITIVITY TESTS
ON BUSINESS SECTOR
TOTAL ELECTRICITY REQUIREMENTS WITHOUT LARGE INDUSTRIAL
1990 2000 2010
(GWh)(GWh)(GWh)
Anchorage-Cook Inlet Area
Maximum 2989 3588 4642
25%GE 2920 3504 4528
Mean 2867 3440 4443
50%GE 2862 3434 4434
75%GE 2826 3391 4375
Minimum 2702 3241 4173
Std Dev 65.9 79.5 107.6
Reference Case 2854 3424 4420
Fairbanks-Tanana Valley Area
NOT APPLICABLE
TABLE B.129
RESULTS OF RED MODEL SENSITIVITY TESTS
ON OWN PRICE ELASTICITIES
TOTAL ELECTRICITY REQUIREMENTS WITHOUT LARGE INDUSTRIAL
1990 2000 2010
TGWh)TGW"h)(GWh)
Anchorage-Cook Inlet Area
Max imum 2900 3533 4614
25%GE 2877 3477 4516
Mean 2846 3406 4389
50%GE 2849 3412 4400
75%GE 2817 3337 4262
Minimum 2798 3292 4187
Std Dev 31.8 74.3 130.7
Reference Case 2854 3424 4420
Fairbanks-Tanana Valley Area
Max imum 642 830 1075
25%GE 642 830 1074
Mean 641 830 1073
50%GE 641 830 1073
75%GE 641 830 1071
Mi nimum 641 830 1070
Std Dev 0.4 0.150 1.5
Reference Case 641 830 1073
TABLE B.130
RESULTS OF RED MODEL SENSITIVITY TESTS
ON CROSS PRICE ELASTICITIES
TOTAL ELECTR IC ITY REQUIREMENTS WITHOUT LARGE INDUSTRIAL
1990 2000 2010
(GWh)(GWh)(GWh)
A.Oil Cross-Price Elasticities
Anchorage-Cook In 1et Area
Max imum 2870 3435 4498
25%GE 2859 3428 4446
Mean 2854 3423 4417
50%GE 2855 3423 4415
75%GE 2848 3420 4393
Minimum 2837 3412 4342
Std Dey 7.5 5.6 36.1
Reference Case 2854 3424 4420
Fairbanks-Tanana Valley Area
Maximum 645 833 1092
25%GE 643 831 1079
Mean 642 830 1072
50%GE 642 830 1072
75%GE 640 829 1067
Mi nimum 639 827 1054
Std Dey 1.7 1.3 8.7
Reference Case 641 830 1073
B.Gas Cross-Price Elasticities
Anchorage-Cook Inlet Area
Maximum 2904 3576 4688
25%GE 2872 3479 4521
Mean 2851 3418 4408
50%GE 2850 3414 4401
75%GE 2832 3359 4301
Minimum 2805 3278 4162
Std Dey 24.0 72.2 127.8
Reference Case 2854 3424 4420
Fairbanks-Tanana Valley Area
Maximum 645 834 1094
25%GE 643 832 1080
Mean 641 830 1072
50%GE 642 830 1072
75%GE 640 829 1064
Mi nimum 637 827 1053
Std Dey 2.0 1.6 9.9
Reference Case 641 830 1073
TABLE B.131
RESULTS OF RED MODEL SENSITIVITY TESTS
ON ANNUAL LOAD
TOTAL ELECTRICITY REQUIREMENTS WITH LARGE INDUSTRIAL
1990 2000 2010
TMWT TMWT TMWT
Anchorage-Cook Inlet Area
Max imum 661 793 1020
25%GE 641 749 965
Mean 598 698 903
50%GE 596 702 900
75%GE 566 650 846
Minimum 522 618 800
Std Dev 42.7 52.9 69.8
Reference Case 584 701 905
Fairbanks-Tanana Valley Area
Maximum 175 227 288
25%GE 164 211 273
Mean 151 194 245
50%GE 152 194 243
75%GE 138 177 223
Mi nimum 126 162 208
Std Dev 13.7 19.2 26.1
Reference Case 146 190 245
TABLE B.132
LIST OF PREV IOUS
RAILBELT PEAK AND ENERGY DEMAND FORECASTS
(MEDIUM SCENARIO)
Battelle 1982 Forecast Battelle Revised
ISER Battelle Pl an lA Pl an IB 1982 Forecast Utility Utility
1980 Fcrec ast L'1981 Forecast 2j (wjo Susitna)3j (wj Susitna 3j Plan lA4 j 1982 Forecast 5j 1983 Forecast 5j
PEAK ENERGY PEAK ENERGY PEAK ENERGY PEAK ENERGY PEAK ENERGY PEAK ENERGY PEAK ENERGY
YEAR DEMAND DEMAND DEMAND DEMAND DEMAND DEMAND DEMAND DEMAND DEMAND DEMAND DEMAND DEMAND DEMAND DEMAND
[MW)(GWh)(MW)(GWh)(MW)(GWh)(MW)(GWh)(MW)(GWh)(MW)(GWh)(MW)(GWh)
1980 510 2790 --------521 2551 521 2551 521 2551
1981 --------574 2893
1982 650 3570 687 3431 643 3136 647 3160 615 3000 769 3697 716 3531
1990 735 4030 892 4456 880 4256 924 4482 701 3391 1126 5305 940 4678
1995 934 5170 983 4922 993 4875 996 4894 791 3884 1626 7098 1167 5884
2000 1175 6430 1084 5469 1017 5033 995 4728 810 4010 2375 9067 1420 7335
2005 1380 7530 1270 6428 1092 5421 1073 5327 870 4319 NA NA NA NA
2010 1635 8940 1537 7791 1259 6258 1347 6686 1003 4986 NA NA NA NA
IjTable 5.6 - Acres Feasibility Report -Volume 1.Includes 30%of military loads,and excludes industrial self-
-supplied electricity.
~Table5.7 - Acres Feasibility Report -Volume 1.Excludes military and industrial self-supplied electricity.
~jTables B.12 and B.13 of Battelle Volume 1.Excludes military and industrial self-supplied electricity.
~jPage xv of Battelle Volume 1. Excludes military and industrial self-supplied electricity.
~At plant net generation.
Note:The ISER and Battelle forecasts are for end-use demand,and should be increased by approximately 8 percent
for actual at plant net generation.
FAIRBANKS-TANANA
VALLEY r::-r
..............................".....
::::::.;;;~~~~~:~::::::::::::::::::::............................-........................::::::::::::::::::"........".
.....................".... .. .
RAILBELT AREA OF ALASKA
SHOWING ELECTRICAL LOAD CENTERS
FIGURE 6.77
FIGURE B.78
L.OCATION MAP
LEGEND
\I PROPOSED
DAM SITES
PROPOSED 138 KV LINE
-EXISTING LINES
IN MILES
OCATION MAP SHOWING
'.;.
TRANSMISSION SYSTEMS
20 60
!
600..,....600
TOT AL RAIL BEL T AREA
ANCHORAGE-COOK INLET AREA
1-----/:::--~o;;;;;;;;::~=::;::::;;;;;-"L--------tJOO
+.+-200
400;-~---_\_---------I---I-------......400
SOO'T-------'oc----------------_I_-----......SOO
3:
~
Q
<:300
:;
(5
:.::<
it
200
l00-t--:/FAIRBANKS-TANANA VALLEY AREA'-=======-i1OO
~------~
o ~....,...-~--r---r---,...---r----,r---T'"""--.--.........---r--.....-..J..0
JAN. FEB. MAR. APR. MAY JUNE JJlY AUG. SEPT.OCT.NOV. DEC.
1982
MONTHL Y LOAD VARIATION FOR RAILBEL T AREA
FIGURE 8.79
500
400
~
~300
0z
<l::
~w
0
0-c 2000
....J
100
o
---~---------------------r--------------_..--~--------_._._---f---
TOTAL SYSTEM
----
.
~
r'-_
o 1/''1 r"I
r'",,
·...,1 J 1 _J
--
ANCHORAGE-COOK INLET AREA
FAIRBANKS-TANANA VALLEY AREA
~-~~"~--.!_-~1 .-11'1 ,-6 ...._.1 1...0-f -~1_..•_--•8 - -~'j,,,-•r r ..-1-..---.-.g -.~~",r ...-'l _~
500
400
300
200
100
o
SUNDAY MONDAY TUESDAY WEDNESDAY THURSDAY FRIDAY SATURDAY
DAILY LOAD CURVES-APRIL 1982
SHEET 1 FIGURE B.80
I -_..,
500 1-'-----T------------------r
l
--------r-------------,500
-,III
400
~300:.?:
oz«
:.?:wo
o«200o
-l
....,
'J
,J
L ,I
TOTAL SYSTEM
L.f
--------+----
"':r', r'.,.
400
300
" 200
100
ANCHORAGE-COOK INLET AREA
I I I ---t-----------l------+I t 100
".--
•""1)_~!
,-.,
--,,
.--~.
-.-~
..--~1.
-.
ANCHORAGE FAIRBANKS-TANANA VALLEY AREA
• I -.- _ •
""
o o
SUNDAY MONDAY TUESDAY WEDNESDAY THURSDAY FRIDAY SATURDAY
DAILY LOAD CURVES-AUGUST 1982
I SHEET 2 FIGURE B.80
500 T "I "nT-AlTOTA~(~~:M rf\[)J\I-~\500
400
,_-'1 I L,'1 ,I,I !
I .'1'I r'-..,·1,r'L,'I I'I -'1'.J t,r"",,.1 L'•r'-.-J", ,'L.I _,-I ' ,•L ,
'1 'U 'I I"1 I l,
•I.....f I I I I I
$\),~tt-'I ~I l,V·'"1 I '1rI.",I~300 '1 I -f-I II 300
o I I r ,I "J I
Z''I'~r',:r ,~j 'J
illo
o
C3 I -I"~ANCHORAGE-COOK INLET AREA 1 I J
-'200 I ---1----~-----200
FAIRBANKS·TANANA VALLEY AREA
100 J I .---4 L-~-J .-J 100
_--'_I _r~~~_I-_I,!~--_.--_II"--_~'''-~/-"_0--·_ •'-"'-'-..~I e-...-·-
I I I _I ~I
I _I I!!I I
o =''-',,'-'~0
SUNDAY MONDAY TUESDAY WEDNESDAY THURSDAY FRIDAY SATURDAY
DAILY LOAD CURVES-DECEMBER 1982
SHEET 3 FIGURE 8.80
300
'"'"'en 2500
0
0
,-.
'-'
z
0
I-200
«
...J
;:)
a.
0a.
I-150
...J
UJ
CO:::«
a:
100
50
o
3.~%/y/
3 °7 v
3·V3.6%,/
V
1960 1965 1970
YEAR
1975 1980
HISTORICAL POPULATION GROWTH
FIGURE 8.81
3000
,......
s:
5:2500(J
'-'za
I-
<t:cr::
IJ.J 2000z
IJ.J
(J
>-
(J
cr::
IJ.Jz 1500IJ.J
o
cr::
I-o
IJ.J
...J 1000IJ.J
...J
<t:
I-a
l-
I--
...J 500IJ.Jco=-ccr::
•
94%/YJ
1107%1/V
V
1960 1965 1970
YEAR
1975 1980
HISTORICAL GROWTH IN NET GENERATION
FIGURE 8.82
SCENARIO
GE~JERAT()H
f.1UDf.l
INPUT VARIABLES.
•INDUSTRIAL CASE
FILES
• PE TROLEUM
REVENUE
FORECASTS
I
I
·ECONOMIC MODULE
·FI~>CAL M''OULE
•POPULA Tlon I,IUDULE
'HOUSEHOLD
FORMA TiON MODULE
INPUT VARIABLES'
• U.S.INFLA TION RA TE
~--4
• U.S.UNEMPLOYMENT
RATE
•OTHERS
PARAMETERS.
·STATE FISCAL POLICY
PARAMETERS
.STOCHASTIC
PAR AME TE.RS
oNONSTOCHASTIC
PARAMETERS
NONSTOCHASTIC ~I-__'"
PARAMETERS
MAP MODEL SYSTEM
FIGURE B.84
FISCAL MODULEr - --1
STATE PETROLEW
GOVT ACTIVITY
r----
LOCAL
GOVT.
BASIC SECTORS
FORESTRY
FISHERIES
FEDERAL GOVT
AGRICUL TURE
MAl\lUF.FOR EXPORT
MINING
TOURISM
POPULA TION MODULE
r
I
I
I
I
I
I
I
I
I
I
I
I
I
L_
..,
I
I
I
I I
I I~-J
HOUSEHOLD FORMATION MODULE
CONSTRUCTION
..,
SUPPORT SECTORS
TRADE
FINANCE
SERVICES
TRANSPORT A TION
COMMUNICATIONS
MANUFACTURING
PUBLIC UTILITIES
MAP ECONOMIC SUB-MODEL STRUCTURE
FIGURE B.85
ECONOMIC UNCERTAINTY
FORECAST MODULE
-HOUSING -.STOCK
,
~RESIDENTIAL -
,
~-
\.BUSINESS
,-
--.PROGRAM INDUCED ..--CONSERVATION
1·
LARGE
INDUSTRIAL MISCELLANEOUS
1
ANNUAL SALES ~--t -.---PEAK DEMAND -
RED INFORMATION FLOWS
FIGURE 8.87
START
SELECT PARAMETERS
TO BE
GENERATED
RANDOMLY
SELECT NUMBER OF
VALUES TO BE
GENERATED
eN)
COMPUTER
GENERATES IN
RANDOM NUMBERS
TRANSFORM
RANDOM NUMBERS
TO PARAMETER VALUES
NO
OUTPUT
PARAMETER
VALUES
ASSIGN DEFAULT
VALUE OF
UNSELECTED
PARAMETERS
RED UNCERTAINTY MODULE
FIGURE B.88
DEMAND
PARAMETERS
(UNCERT AINTY
MODULE)
INITIAL HOUSING'
STOCK TY
REGIONAL
FORECAST
• POPULATION
•HOUSEHOLDS
STRATIFY
HOUSEHOLDS BY
AGE OF HEAD
SIZE OF HOUSEHOLD
CALCULATE
DEMAND FOR
HOUSI~G UNITS
BY TYPE TY
IS
DEMAND TV
>STOCK TY
7
'AGE DISTRIBUTION
OF HOUSEHOLD
YEADS
·S I ZE DIS TRIB UTI 0 N
OF HOUSEHOLDS
NEW
C ONSTRUC TIO N
OF TYPE TY
REINITIALIZE
HOUSING
STOCKS
IL..-__-
FILL VACANCIES
TY WITH)------4~COMPLEMENTARY
DEMAND
FORECASTS OF OCCUPIED
UNOCCUPIED HOUSING I....~...
BY TYPE
RED HOUSING MODULE
FlGUREB.89
FORECAST OF
OCCUPIED HOUSING
STOCK BY TYPE
(HOUSING MODULE)
+
!cALCULA TE STOCK OF
APPLIANCE
LARGE APPLIANCES
....SATURATIONS
BY END USE -BY HOUSING TYPE
DWELLING TYPE .(UNCERTAINTY MODULE)
~
CALCULATE INITIAL FUEL MODE
SHARE OF EACH ...SPLITAPPLIANCE USING -1980
ELECTRICITY•CALCULATE AVERAGE /"
EFFICIENCY
ELECTRICAL USE IN
....
LARGE APPLIANCES ...STANDARDS
BY APPLIANCE -,•SUM PRELIMINARY
CONSUMPTION FOR
APPLIANCE USE
BY APPLIANCE,,~
CALCULATE SUM PRELIMINARY
PRELIMINARY ...CONSUMPTION
SMALL APPliANCE ...FOR ALL
USE OF ELEC.
APPLIANCES•/"PRICE PRICE ADJ. PARAMETERSPRICEAND
FORECASTS ...CROSS-PRICE ...RESIDENTIAL SECTOR
(EXOGENOUS)...ADJUSTMENTS
...
(UNCERTAINTY MODULE)
\..•RESIDENTIAL
C ONSUMPT 10 N
PRIOR TO
PROGRAM-INDUCED
'-CONSERVATION
RED RESIDENTIAL CONSUMPTION MODULE
FIGURE 8.90
EMPLOYMENT
FORECAST
•
CALCULATE
BUSINESS
GOVERMENT
LIGHT INDUSTRIAL
FLOOR SPACE
•
CALCULATE PRELIMINARY
PRELIMINARY BUSINESS USE--COEFFCIENTSBUSINESS
ELECTRICAL (UNCERTAINTY
CONSUMPTION MODULE)
,
PRICEPRICEPRICEANDADJPARAMETERS
FORECASTS CROSS PRICE !.-BUSINESS SECTOR...~
(EXOGENOUS)ADJUSTMENTS (UNCERT AINTY
MODULE)
,
BUSINESS
CONSUMPTION PRIOR
TO
PROGRAM-INDUCED
CONSERVATION
RED BUSINESS CONSUMPTION MODULE
FIGURE 8.91
BUSINESS
REQUIREMENTS
(BUSINESS
MODULE)
.NEW USES
•EXISTI'4G uses
HOUSEHOt.OS
SERVED
CRESDENTlAL
WOOUU)
CALCULATE TOTAl.
RESDENTIAL
ELECTRICITY
SAVINGS
BY OPTION
TECHNICAL INPUTS
-ELECTRICITY SAVED
-lIFETIoE
-ELECTRICITY
PRICES
SELECT
RESDENTlAL
CONSERVATION
OPTION
START CONSER
CALCULATE
VALUE
OF ELECTRICITY
SAVED
SlIM ovEft
USES
-SAVINGS
• COSTS
CAl.CULATE
•SAVINGS
-COSTS
IN NEW AND EXISTING
USES
•SAVINGS
•COSTS
SUM OVER
OPTIONS
CALCULAte
TOTAL COST
C#CONSERV ATlON
BY OPTION
GO TONDT
CONSERVATION
OPTION
'UNSU8S~ED
INSTALLED COST
TECHNICAL INf'UT
TECHNICAl.INPUTS
- SUBSIOIZED
INSTALLED COST
CALCULATE
PAYBACK
PERIODS
CALCULATE
INTERNAL RAn!
OF REn.AN
RETURN (Al)
CALCULATE
MAflKn
SATURAOON
•MAXIMUM
SATURATION
.PAYBACX ROLE
AO.JJST
REOUREMENTS
FOR SUBSDIZED
CONSERV A TION
WRITI:
•SAT\JRATION
.PCS
TO CONSERVATION
FI.E
CONSERVATlON
DATA FI.E
TECHNICAL INPUT CAl.CULATE
AGGREGATE
puN CORRECTION
FACTOR
RED CONSERVATION MODULE
FIGURE 8.92
/'
RESIDENTIAL
PLUS
BUSINESS
-,CONSUMPTION
,
CALCULATE CALCULATE CALCULATE
SECOND HOME STREET LIGHTING VACANT HOUSING
CONSUMPTION REQUIREMENTS CONSUMPTION
1
SUM FOR
MISCELLANEQUS ~-
CONSUMPTION
n
/-,
MISCELLANEOUS
CONSUMPTION
RED MISCELLANEOUS CONSUMPTION MODULE
FIGURE 8.93
LOAD
FACTORS
(FROM UNCERTAINTY
MODULE)
ANNUAL ELECTRICITY
REQUIREMENTS
•RESIDENTIAL
•BUSINESS
•MISCELLANEOUS
CALCULATE
PRELIMINARY
PEAK DEMAND
CALCULATE
PEAK
SAVINGS
•ANNUAL SAVINGS
DUE TO SUBSIDY
• PEAK CORRECTION
FACTOR
(FROM CONSERVATION
MODULE)
LARGE
INDUSTRIAL
DEMAND
CALCULATE
REVISED
PEAK DEMAND
PEAK
DEMAND
RED PEAK DEMAND MODULE
FIGURE 8.94
LOAD
FORECAST
HOURLY BASED
PEAKS &ENERGIES
GENERATION
SYSTEM
EXISTING UNITS &
ALLOWABLE
TECHNOLOGIES
STUDY DATA
FUTURE ECONOMICS &
OPERATING GUIDELINES
OPTIMIZED GENERATION PLANNING COGP)•EVALUATE RELIABILITY I~
-I •EVALUATE SELECT UNIT SIZES &TYPES
ALL CHOICES •WITH "LOOK-AHEAD"STUDY
~CALCULATE OPERATING &INVESTMENT COSTS ALL YEARS
USING "LOOK-AHEAD"~
~r•
CHOOSE LOWEST COST ADDITIONS
&CALCULATE CURRENT YEAR'S COSTS
I -..
RESULTANT OPTIMUM EXPANSION PATTERN ----E::]&DOCUMENTATION OF NEAR-OPTIMUM PLANS,
FINANCIAL SIMULATION PROGRAM (FSP)•FINANCIAL ANALYSIS OF EXPANSION PLAN -OUTPUT
OPTIMIZED GENERATION PLANNING COGP)PROGRAM
INFORMATION FLOWS
FIGURE B.95
WEEKDAY WEEKEND DAY
20161284
P =MAXIMUM MINUS
2-1 MINIMUM RATING (MW)
PI =MINIMUM RATING (MW)
24
INITIAL
LOAD
16 20128
MODIFIED
LOAD
4
~'r'77""7'"".~~'""7'"7"!r:;~~"7"'7'7?'"7"7"'7'/'"7"7"'7'/'7'7'77""T77'/~..L
Pl
24To
HOUR HOUR
OPTIMIZED GENERA TION PLANNING
EXAMPLE OF CONVENTIONAL HYDRO OPERA TIONS
FIGURE 8.96
SOl SOl SOl
TEARS
-----------------------------._-._-----------------------------IZTO 82 TO 90 TO
1981 1982 19&3 1984 1985 1990 1995 2000 200s 83 90 2000
R£fIMflS ACQUISITION COSTS (S l'U BARJU:L)
Average ao.estic 34.33 31.21 26.82 25.93 29.55 55.23 n.70 ' 141.81 199.29 -1 ••1 7.4 '.9Lower48ConventiORal35.61 32.22 27.31 26.40 :leU7 55.95 92.46 142.41 ZOO.16 -15.2 7.1 9.8
AIm..31.60 28.84 24.81 24.09 27.57 52.48 88.67 139.56 196.16 -1 ••0 7.8 10.3
Shal.33.SO 29.5a 24.73 23.97 27.38 51.68 86.63 135.29 190.15 -16.4 7.2 10.1
Coal Ltquids 31.01 34.37 2'.61 27.62 31.42 51.08 !S.38 145.91 205.16 -16.8 6.a 9.7
Average IlI9O"tld 37.05 33.55 2a.60 21.SO 31.00 55.95 n."142.41 ZOO.16 -14.a 6.6 9.8
Average AcquisitiOA Cost 35.24 31.11 21.24 2S.48 30.03 55.4'n.99 142.05 1".67 -14.5 7.2 9.9
lUIJl£RS ACQUISITIOll COSTS (1982 DOLUAS PER 1IARAll)
Average eo.estic 34.38 31.22 25.61 23.55 ZS.3t 35.16 43.65 50.95 53.lIt -18.0 1.5 3.8
Lower 48 Conventtonal 37.82 32.22 26.08 23.97 25.83 35.62 44.02 51.16 54.13 -19.0 1.3 3.7
Alask..33.49 2a.84 23.70 21.81 23.69 33.n 42.%1 50.14 53.05 -11.8 1.9.4.1
~l.35.SO 29.51 23.61 21.77 23.52 32.91 41.24 48.61 51.42 -20.2 1.3 4.0
eoal Ltquids 41.42 le.37 27.32 ZS.OI 26.91 34.91 45••1 ·52.44 55.48 -ZO.5 0.9 3.6 .
Average I..,0rte4 3t.Z7 33.55 21.31 24.97 2S.63 35.62 44.02 51.16 54.13 -18.6 0.8 3.7
Average Acquisition Cost 37.35 31.11 2S.02 24.04 25.80 35.33 43.19 51.03 54.00 -18.4 1.3 3.7
lUIM£JlS ACQUISITlOll COSTS (1981 DOLUAS PER 1IARAll)
Average Ooaestic 34.33 29.45 24.16 22.22 23.95 33.18 41.19 48.07 SO.85 -18.0 1.5 3.8
Lower 48 Conventional 35.61 30.40 24.61 22.62 24.38 33.61 41.53 48.27 51.07 -19.0 1.3 3.7
Al ask.31.60 27.21 ZZ.34 ZO.64 22.35 31.53 39.83 47.31 SO.05 -17.a 1.9 4.1
Shal.33.SO 27.91 22.21 ZO.54 22.11 31.05 38.9l'45.86 48.52 -ZO.2 1.3 4.0
eoal Ltquids 39.01 32.43 25.7'23.66 25.47 34.90 42.84 49.48 52.35 -ZO.5 0.9 3.6
Average IJapOrtld 37.05 31.65 25.77 23.56 25.13 33.61 41.53 48.27 51.07 -18.6 0.8 3.7
Average AcqutstiOl1 Cost 35.24 30.07 24.55 22.61 24.34 33.33 41.32 48.15 SO.95 -la.4 1.3 3.7
PIOOOCT 1011 '....80)eo.estt e 5411)9 11a
Lower 48 ConwntiOl1al 6.96 6.98 6.93 6••6.81 6.72 6.65 6.42 6.01 -0.7 -0.5 -0.5
Alm.1.61 1.70 1.72 1.75 1.7'1.15 1.63 1.48 1.le 1.5 0.4 -1.7
.----
Total eonVetlt1onal a.57 ••67 a.65 8.60 '.51 8.47 8.28 7.90 7.43 -0.3 -0.3 -0.7
.Synthetic
eoal Liquids 0.00 0.00 0.00 0.00 0.00 0.01 0.03 0.10
O.ZO IlC IlC 25.9
Shal.0.00 0.00 0.00 0.01 0.01 0.02 0.05 0.15 0.25 IlC NC ZZ.3
----
OoMstic Crllde a.57 a.67 8.65 1.61 1.60 a.50 a.37 I.IS 7.88 -0.3 -0.2 -0.4
butic II6l'S 1.61 1.54 1.54 1.54 1.54 1.30 1.14 0.91 0.74 0.2 ·2.1 -3.4
OaMstic Liquids 10.1a 10.21 10.1'10.16 10.14 1.80 '.51 '.06 a.61 -0.2 -0.5 -0.8
Crllde [-,ports 0.23 0.24 0.23 0.23 0.23 0.23 0.2%0.21 O.ZO -3.3 -0.5 -0.9
Product E)tJlorts 0.37 0.51 0.5'0.62 0.64 0.61 0.61 0.68 0.68 1.7 2.0 0.0
Uaported Supplies
Ciross
CI"IIde 4.41 3.lt 3.91 4.28 4.43 4.88 5.11 5.45 6.20 21.5 5.1 1.1
Products-1.60 1.53 1.19 1.92 1.91 2.11 2.30 2.45 2.79 17.0 4.6 1.1
Total 6.01 4.82 5.79 6.20 6.42 7.07 7.40 7.'1 8.99 ZO.l 4.9 1.1
Met
CrllcM 4.18 3.05 3.76 4.04 4.19 4.65 4.89 5.24 6.01 23.4 5.4 1.2
Products 1.23 0.95 1.20 1.30 1.35 1.51 1.62 1.17 2.11 26.4 5.9 1.6
Total 5.41 4.00 4.97 5.35 5.54 6.16 6.SO 7.02 s.u 24.1 5.6 1.3
U.S. OIL OUTLOOK
CRUDE OIL PRICES AND PRODUCTION
FIGURE B.97
nn I«lllJl PEnoULII DDWIll AND aROAD IOUICU or SUPPLY
(HlttD)
UU-2040
~••C•••!O lup.I,DI.ruptlo.un l!!1 .!.lli ius !!!!.!!!2 ~lli2 .!2.!!1!l!lli2 1!!§lli2 .!ill ~20)0 1m
h04uctlo.
)Ion-orte
Cru4a U.I 20.4 20.'U.S 22.1 22.S 21..20.'17.1 14.7 I1.J 21.'26.1 21.4 24.1 U.'14.4
IICL 2.S 2.4 2.4 2.'2.4 2.4 2.S 2.1 2.0 1.4 1.1 2.4 2.4 2.1 2.4 1.'1.4
SJ1ItheUc -.2d ..!:l ..!:.!..!:l ...M -2.:.!-L!-Ll ...Ll ..L!...iJ ...2:.!..w -W.~..l:.!~
totd ".-oPEC 22.S n.l 21.1 24.S n.l n.4 26.0 n.7 n.l 20.1 u.s 24.t .)0.2 n.)u.s n.l 20.)
orte
Cru4a 11.4 U.S U.4 II.S U.2 U.s 22.)24.4 n.7 n.l 21.4 U ••n.o H.2 24.1 n.)22.1
NCL O.t 0.'1.0 1.0 1.0 1.1 1.1 1.S 1.4 1.)1.2 1.1 I.S 1... 1.1 1.J 1.4
IJ1Ithetlc ----J:l ~-2.:l -2:.!-l&...M ..!:l .Jh!...2:.!-2.:l------------toul OPEC .lli.!.!ld ~lli!~20.S ill .M ill lli1 ill l!.:.2 lh!lL.i l!:!ll:.!~
toul ,r04ucUoa 41.a ·40.4 44.2 4).'U.S 46.1 ".1 n.)41.'41.'41.1 H.t S4.1 40.7 H.t SO.7 U.7
••t l.,.rt.fr~SI..-Iovlat
Iloe loS 1.'I.'1..1.2 1.0 1.0 1.0 0.'0.7 O.S 1.1 1.1 1.1 1.1 1.4 1.2
.'rlc•••I ••••1.O.S O.S O.S O.S O.S O.S O.S O.S O.S O.S 0.4 O.S O.S O.S O.S O.S O.S
Incr ••••(d.er••••)I.ItOCke (2.0)(1.4)0.7 0.)0.1 0.1
0.)0.4 (0.4)(0.4)(O.S)0.2 O.S 0.'(0.S)(0.4)(0.4)
Itatl.tlcal dllt.rlftct ..!:l -W.------ --
------------ ---------
Co"....ptloa 41.'44.4 41.'46.1 41.1 41.S 51.0 n.4 SO.7 41.2 42.S ".0 56.'42.4 sa;7 n.4 47.0
Notl'D.talll .a,not .dd to total.dUI to roundl.l.
FREE WORLD PETROLEUM DEMAND AND BROAD SOURCES OF SUPPLY
(MMBD)
1982-2040
FIGURE B.98
80 ~--I
I
I I flO
'1/ii i
')/H i
70 I I ..._---
---
ORr Spring --5/33 ---;60
Reference Case -4/83 __150
2%
ORr LOWOIL -5/83
SHeA ZEG -4/83
--2%
DOR 50%
DOR Mean -4/83 130
0%
-1%
.'
~/
//
/'
./
I,
/-"
///~L~/I ?/'
r:--~-»<>.>/////~~.~
»:.:>:<>:
//,/'/,---",/
,~./
//~./-,>-'---',./
""./,/-/
.--------.-'-.-'--,---.-'/.//
-~-".>//
~-
30 --------~-._----/---
~'
-,..---
'i -~~=---~-7~"-O
20
50
40
60
4-lo
(lJ
o
,.-j
l-lp..
'0
rl
l-lo
;:3:
rl
•.-jo
,...,
C1J
C1'
rl
rl
,0
..0--0,
DOR 30%
lO I I I I ,-------f--------------+,------------110
I \I J I I I I 0o
1983 1985 1990 1995 2000 2005 2010
ALTERNATIVE OIL PRICE PROJECTIONS-$/bbl (1983 $)
FIGURE 8.99
o
1985198019821975
YEARS
19701965
_.
L--/
NOTE:PERCENT,A,GESARE /AVERAGE ANNUAL GROWTH
IRATESFOR5-YEAR PERIODS
/!
-/--
/v /'/
//
0\0
to
to'
/
f V
Q 0\;
/1C:J<'v '0:'
PROJECTIONS i /,~~
V::/'~V --I
I//V'"~~.~.;/~Q ~I
~~~i
~~i
IOOw
/
,---~0\0 ---,
!
""I
""
I
'"?,
200y85199019952000 201
YEARS~ORICAL 209.4 %--
179.3%191.70/<1'
6
4
o
1960
2
19
22
24
26
28
20
J'
:::>
Ei7
~18
0z
(j)
w 160:
::l
f-
az
w 14o,
><
IJ..I
az
::l 12u,
-'
<t
0:
W
z 10w
CJ
W
f-
<t
f-8
(j)
ALTERNATIVE STATE GENERAL FUND
EXPENDITURE FORECASTS
FIGURE 8.100
7 0 0;----------,-----------,---------,--------,-----------,
600+--------j--------I--
2 0 1 0
1985
2005
1980 1982
2000
1 975
YEARS
1970
1 995
1 965
1 990 ./
YEARS /'2>010~
HISTORICAL »>'2>'
'2>0/0~
~'2>'
0%---I
0 %.----
l.---'2>..
NOT E:
PERCENTAGES ARE
V--'2>.AVERAGE ANNUAL GIlOWTH
-~RATES FOR 5-YEAR PERIODS
!
I
I!
I
200
100
o
1960
4 OO+-·-------+--
r-1985
~300
ell
-I
<{
a:
z
o
r-
<{
-I
:J
Q..
o
Q..
r--«
en
o
o
o
ALTERNATIVE RAILBEL T POPULATION FORECASTS
FIGURE B.10 1
2 2 5 I I I --tl---------
2001 I I I I :;;».-/I
2010200520001995
-1.9%
-I »>:YEARS ~v
1 0/0______D'
1
010
-----D·--3.8%
-3.8%-HISTORICALf--
50
75
25
1251~~1 I I I I
1985
100
(f)
o
....Jo
I
W
(f)
::::J
o
I
f-
....J
W
OJ
....J
«
0:
o
1::)60 1965 1970 1975 1980 1982 1985
YEARS
NOTE:PERCENTAGES ARE
/'.VERAGE ANNUAL GROWTH
RATES FOR 5-YEAR PERIODS
ALTERNATIVE RAILBEL T HOUSEHOLDS FORECASTS
FIGURE B.102
7,000....,---,,I ,--:J
o~\
6,000 I I I I I 7/I
2010
198 519821980
2005
'2..'3
%
1975
2000
YEARS
PROJECTIONS
1970
1995
1965
1990
HISTORICAL
.>:~
6 °/0
0%-----
y\)'
"\'3 .»>
4%----
V
i 4.
YEARS
Io
I
1960
1,000
5,000 I I I
____4 ~•.•."~t,t~t~G~~
oo~'3 0 °/0
4,000 I I:;;>--::=:..;;;;>'"...-----I=---===I:::;:;;>'".........-:I I
3,000 P-:=I I I I I
1985
,---.
.I:::.
3=
CJ
'-'
zo
I-
o,
~
:::::>
Cf)
zoo
>-
CJ
0:W 2,000
Z
W
NOTE:PERCENTAGES ARE
AVERAGE ANNUAL GROWTH
RATES FOR 5-YEAR PERIODS
ALTERNATIVE ELECTRIC ENERGY DEMAND FORECASTS
FIG U REB.1 0 3
1600.i I ._,
1400 I I I I I ;;./I
PROJECTIONS
20102005
\~~Op---I E-?-.~f',S
t\ctc
Bt.rt.Bt._~:;>.......----I
'3 °/0 --.-WI t.j\
?-.nOB~~'300/0
OOB
2000
1.1%
19951990
800 I I ..--=......_f-.-r==__--r-=I I
600 I I I I I I
1985
1000 +I~~~~
!C!.<"11200III
~
~
oz«
~
w
o
~«
w
0..
YEARS
HISTORICAL
8.6 %
0°10
\3·
~_.--~
________i A .A°/0
--.-----~----
400
200
NOTE:PERCENTAGES
ARE AVERAGE ANNUAL
GROWTH RATES FOR
5-YEAR PERIODS 0
1960 1965 1970
YEARS
1975 19801982 1985
ALTERNATIVE ELECTRIC PEAK DEMAND FORECASTS
FIGURE B.1 04