HomeMy WebLinkAboutAPA1796-
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343
BEFORE THE
FEDERAL ENERGY REGULATORY COMMISSION
APPLICATION FOR LICENSE FOR MAJOR PROJECT
SUSITNA HYDROELECTRIC PROJECT
VOLUME 2C
RED MODEL (1983 VERSION)
TECHNICAL DOCUMENTATION REPORT
ARLIS
Alaska Resources
Librarv & tnformation SerVices
Ancl. . ~ ·~ ..tska
JULY 1983
ALASKA POWER AUTHORITY
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FINAL REPORT
RED MODEL (1983 VERSION)
DOCl..lt~ENTATIO N REPORT
M. J. Scott, Project Manager
M. J. King
B. L. Coles
B. J. Harrer
E. T. Marnell
R. J·. ~~e
T. J. Secrest
L. A • Sk uma t z
June 1983
Prepared for Harza-Ebasco Susitna
Joint Venture, Anchorage, Alaska
under Contract 2311205912
BATTELLE
Pacific Northwest Laboratories
Richland, 1..Jashington 99352
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SU~1~1ARY
This report describes the 1983 version of the Railbelt Electricity Demand
(REn) model, a partial end-use/econometric model for forecasting electricity
consumption in Alaska 1 s Railbelt region through the year 2010. It contains
complete documentation of the modeling approach, structure of the equations,
and selection of parameter values. In addition, information is presented on
the data bases used, supporting research, model output, and the Battelle-
~iorthwest residential energy-use survey conducted in the Railbelt during i1arch
and April, 1981. This survey was used to help calibrate the model.
RED has several unique capabilities: a t·1onte Carlo simulator for analysis
of uncertainty in key parameter values, a fuel price adjustment 1nechanisrn that
incorporates the impacts of fuel prices on demand, and the capability to
explicitly consider government.subsidized investments in conservation
measures. The 1933 version contains the following features:
an aggregate business electricity consumption forecasting
methoctology that is based on the model 1 sown forecast of commercial,
light industrial, and government building stock
calibration of the Residential sector end uses, appliances
saturation, and fuel mode splits on actual data
;) a variable price elasticity adjustment mechanism to faithfully
reflect consumer response to electricity, gas, and fuel oil prices
in both the Residential and Business Sectors
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a Housing r-bdule that transforms a forecast of the total nunber of
regional households into forecasts of the occupied and unoccupied
housing stock by four types of housing units
parameters updated to reflect 1980 Census infonnation and
construction and energy market activity between 1980 and 1982, as
well as additional energy research performed in several other parts
of the country
two load centers, Anchorage-Cook Inlet and Fairbanks-Tanana Valley
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o a report-writing module that reports price elasticities ~nd price
effects on consumption (price-induced conservation and fuel switch-
ing), as well as households served, saturation of appliances, elec-
tricity consumption by sector, peak demand, and the sensitivity of
forecast results to variation of key model parameters.
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CONTENTS
SUrH1ARY ••••••••••• • e • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • c • • • • _•
1.0
2.0
3.0
4.0
INTRO[)tJCTIOr~ ....... .c •••••••••• 1!1 ••••••••••••••••••••• ~ •••••••••••• e
OVERVIE\~ • • • • e • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •·
UNCERTAINTY ~1001JLE ••• • • • • • .fl •• Cl •••••••••••••••••• ·-· •••••••••••••••
THE HO\JS I NG f·100tJLE •••••••••••••••••••••••••••••••••••••••••••••••
RESinENTIAL CONSlH1PTION ~10DlJLE .................................. .
R I J S P~ E S S C 0 N S U ~1 P T IO N ~10 0 lJ L E • • • • .. • • • • • • • • • • • • • • • • • • • • • • • • • • • • • , ••
PROGRM1-INDlJCED CONSERVATION ~10rJ1JLt ••••••••••••••••••••••••••••••
r-1 I SCELLANEOUS CONSU~1PTION MODULE.
PEAK OE~1AND r--10DULEe ••••••••••••••••• · ••••••••.•••••••••••••••• _ ••••
U ~1 C E R T A I N TY ~10 0 U L E ••••• ,., •••••••••••••••••••••••••••••••••••••••••
r1ECHAN I St1
INPUTS AND OUTPUTS •••••••••••••••••••••••••••••••••••••••••••••••
nODULE STRUCTURE ••••••••••••••••••••••••••••••
PARJ1.~1ETERS ••••.••••••••••••••••••••••••••••••••
THE HOUSING r-100u·L E •.................................................
MECHANISM
I~PUTS AND OUTPUTS •••••••••••••••••••••••••••••••••••••••••••••••
r10DLILE STRUCTlJRE •• · ••••••••• ~~ .. ·e ••••••• s ••••••••••••••••••••• ~ ••• .,
PARAMETERS.
~1i l ita ry Households.
Household Size and Demographic TrendS· ••.•••••••••••••••
Historic and Projected Trends in Demand for Housing •••••••.•
Vacancies .•.••••••••••••••••.••••
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1.1
2.1
2 .3
~.4
2 .4
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2.7
2.7
3.1
3.1
3.1
3 .2
3.3
4.1
4.1
4 .1
4.1
4.8
4.8
4 .9
4.11
4 .16
rJepreciation and Removal. ••••eeeee••••••••••••••••e••••••••• 4.17
Rase Year Housing Stock ••••••••••••.•.•..•...•....•••.••• 4.19
5.0 THE RESIDENTIAL CONSUMPTION MODIJLE •••••••••••••••••••••.•••• 5.1 -
r1ECHAN I 91 •••••••••••••••o••••••••••••••o••••e•e•••e•••••eoGIIIIIIIIIII 5.1
INPUTS AND OUTPUTS ••••••••• ••••••••••c••••ooGoeee•e••••••••••e~e.,• 5.2
MODULE STRUCTURE •••• 5.2
PARAMETERS ••••••••••••••••••••• •••••••••••••••••••e••••••••6eeeee 5.10
Ap p1 i ance Saturations •. 5 .11
F u e 1 ~1o de S p 1 its • • .. • • • .. ... 5.26
Consumption of Electricity per Unit ........................ . 5 .28 -
E1 ectrical Capacity Growth. '). 3 3
Appliance Survival •..••.•... 5 .36
Household Size Adjustments •• • • • • • • • • e • • • • 11 • • • oG • • • e e • ., • •. •. • • • • 1),311 -P r i c e El as t i c i t i-e s ...... _ ..•............... Co .......... ~~ ....... . 5 .311
6.0 THE RUSINESS CONSUMPTION MODULE 11.1
r1ECHAN ISM .................................. •· ..................... . 6 .1
INPUTS AND OUTPUTS ••••••••••••••• . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . 6.1
MODULE snUCTURE ...... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ·• ............ " 6 .2
PARAMETERS ••••••••••••••••••••••••••• ~···················~······· 6.7
Fl o o r Space Stock Eq u at i on s •••••••••••••••••••••••••• · ••••••• 6 .8 -
Business Electrtcity usage Parameters ••••••••••••••••••••••• 6.16
Business Price Adjustment Parameters ••••••••.••••••.••••••.• 6 .20
7.0 PRICE ELASTICITY • • • • • • • • • • • • • • • • e • • • • • • • • • • • • c • • • • • • • • • • • • • • • • • • 7.1 -THE RED PRICE ADJUSTMENT MECHANISM ••••••••••••••••••••••••••••••• 7 .1
LITERATURE SlJRVEY ••••••••••••••••••••• · •••••••••••••••••• _. •.• • • • • • • 7.3
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SELECTION OF RED PRICE ADJIJSH1EIH MECHANIS~1 STRUCTURE
AND PARAt~ETER~ ••••••••••••••••••••••••••••••••••••••••••••••••••• 7 .1 0 -Sector Division ••••••• 7.10
Variable Elasticity ..............•...........•............. -. 7 .12
Adjustment Over Time ••••• 7 .P
~· Cross Price Elasticities ................................... . 7 .13
Parameter Estimates. 7.14
DERIVATION OF RED PRICE ADJUSTMENT MECHANISM EQUATIONS. 7 .15
GLOSSARY OF SYMBOLS ••••••.••••••••••• ., •••••••••••• "' ••••••••••••• "'. 7.22
8.0 THE PROGRAM-INDUCED CONSERVATION MODULE •••••••••••••••••••••••••• 8 .1
r·1E CHAN I S~1 8.1
INPUTS AND OUTPUTS ••••••••••••••••••••••••••••••••••• ~··········· 8.5
MODULE STRUCTURE. •••••••••••••••••••o•••v•••••••••e••••.•••••••••• 8.5
Scenario Preparation (CONSER Program) ••••••••••••••••••••••• 8. 7
Residential Conservation. • • • • • • • • • • • • • • • • • • • • • • a • .• • e • • • • • • • • H. 1 [) -Business Conservation 8 .1?.
Peak Correction Factors ••••••••••••••••••••••••••••••••••••• 8.16 -PARAMETERS •••• 8 .16
9.0 THE MISCELLANEOUS MODULE. • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • e • • • • • • • • 9. 1
MECHANISM 9 .1
-INPUTS AND OUTPUTS. •••••••••e••••••••••-~>••••·••••••o•o•••~••••••••. 9.1
MODULE STRUCTURE ••••••••••••••••••••••••• 9 .1 -PARAt1ETERS ••• 9.3
10.0 LARGE INDUSTRIAL DEMANn •••••••• ~ •••••••••••••••••••••••.••••• 10 .1 -t1ECHANIS~1, STRUCTURE, INPUTS AND OUTPUTS ••••••••••••••.•••••••.•• 10.1
PARAMETERS •••••••••••• 10.2 r
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11.0 THE PEAK DEt1AND rmDULE ••••••••••••••••••••••••••••••••••••••••••• 11.1
~1ECHANIS~1 ············································~~·········· 11.2
INPUTS AND OUTPUTS •••.••••••••••••••••••••••••••• "'. • • • • • • • • • • • . • • 11.1
MODULE STRUCTURE ••••••••••••••••••••••••••••••••••••••••••••••••• 11.2
PARAMETERS ••••••••••••••••••••••••••••••••••••••••••••••••••••••• 11.5
Quantitative Analysis of Trends in Load Factors
in the Railbelt ••••••••••••••••••••••••••••••••••••••••••••• 11.fi
Qualitative Analysis of Load Factors ........................ 11.10
12.0 r10DEL VALinATION .•••••••.•••.••••• ~~······e·•·····e·············e·• 12.1
ASSES9~ENT OF RED 1 S ACCliRAO.... ... •• • .. • • • • • • •• • .. •• • • • • .. • • • • .. 12.1
REASONABLENESS OF THE FORECASTS •••••••••••••••••••••••••••••••••• 12.3
13.0 t1ISCELLANEOUS TABLES ........................ .' .................... 13.1
REFERENCES ···············································~~········· R.1
APPENDIX A: BATTELLE-NORTHl~EST RESIDENTIAL SURVtv •••••••••••••••••••• A .1
SURVEY DESIGN •••••••••.•••••••.•• f!o ••••••••••••••••••••••••••••••• A.2
SA11PLE SIZE AND COt~POS ITIO N...................................... A .2
~1AILING PROCESS AND COLLECTION OF RESULTS........................ A.5
OUTPUT ~...................................................... A.6
APPENDIX B: CONSERVATION RESEARCH.................................... 13.1
PACIFIC NORTHWEST POWER PLANNING COUNCIL........................ 13.3
BONNEVILLE POWER AD11.1INISTRATION. ........................ ......... 13.4
CALIFORNIA ENERGY COMMISSION..................................... B .6
WISCONSIN ELECTRIC POWER COMPANY ••••••••••••••••••••••••••••••••• R .10
A LA S KA N R A I L B E L T. • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •• 13 .13
APPENDIX C: RED 110DEL OUTPUT......................................... C.1
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LIST OF TABLES ................................................... . c .3
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FIGlJRES
1.1 The Railbelt Region of Alaska................................... 1.2
2 .1 Information Flows in the RED rndel.............................. 2.2
3.1 RED Uncertainty Module •••••••••••• ~............................. 3.3
4 .1 RED Housing r1:Jdul e.............................................. 4 .3
5.1 REO Residential ConslJTiption Mod!Jle.............................. 5.4
6 .1 RED Business Consumption MJdul e................................. 6.3
""" 8.1 RED Program-Induced Conservation ~1odule. .• •• ••• ••••••• ••• ••••••• 8.2
9 .1 RED r~i s c e 11 an eo u s t--1o d u 1 e ••••• -· • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • 9 • 2
-11.1 RED Peak Demand Module •••••••••••••••••••••••••••••••••••••••••• 11.3
11.2 Oaily Load Profile in the Pacific Northwest ••••••••••••••••••••• 11.12
A.1 Battelle-Northwest Survey Form.................................. ~.3
-A.2 Saturation of Freezers in .Anchorage-Cook Inlet Load Center •••••• A.7
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3 .2
4.1
TABLES
Inputs and Outputs of the RED Uncertainty Module ••••••••••••••••
Parameters Generated by the Uncertainty rbdul e .•••••••••••••••••
Inputs and Outputs of the RED Housing Module ••••••••••••••••••••
4.2 Number of r~ilitary Households Assumed to Reside on
3.2
3.4
4.2
Base in Railbelt Load Centers................................... 4.8
4.3 Household Size Western U.S. and Railbelt 1950-1980 ••••••••.••••• 4.9
4.4 Forecast Size of Households, Railbelt Load Centers.............. 4.10
4.5 Impact of Householder Age and Household Size on Housing
Mix and Total Utility Sales, Anchorage-Cook Inlet............... 4.13
4.6 Single-Family Housing as Proportion Year-Round Housing
Stock by Type, Railbelt Load Centers, 1950-1982................. 4.14
4.7 Probability of Size of Households in Railbelt Load Centers...... 4.15
4.8 Regional Frequency of Age of Household Head
Divided by the State-Wide Frequency............................. 4.16
4.9 Housing Demand Equations: Parameters' Expected
Value, Range, and Variance...................................... 4.17 .
4.10 Assumed Normal and ~1aximum Vacancy Rates by Type of House....... 4.18
4.11 Assumed Five-Year Housing Removal Rates in Railbelt
Region, 1980-2010 •••••••••••••••••••••••••••••••.••••••••••.••• 4.18
4.12 Railbelt Housing Stock by load Center and Housing Type, 1980 •••• 4.19
5.1 Inputs and Outputs of the RED Residential Module •••••••••••••••• 5.3
5.2 Percent of Households Served by Electric Utilities in
Rail belt Load Centers, 1980-2010................................ 5.11
5.3 Appliance Saturation Rate Survey .............................. .
5.4 Market Saturations of Large Appliances with Fuel Substitution
Possibilities in Single-Family Homes, Railbelt Load Centers,
5 .12
1980-2010. e e e e e e e • e e e e e e e oil e e e e e e e e e e e e e e e e II e e e e e e e. e e e e e e-e e e·e "e e 5.14
5.5 r~arket Saturations of Large Appliances with Fuel Substitution
Possibilities in Mobile Homes, Railbelt Load Centers,
1980-2010......... .............................................. 5.15
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5.fi Market Saturations of Large Appliances with Fuel Substitution
Possibilities in Duplexes, Railbelt Load Centers, 1980-2010..... 5.16
5.7 Market Saturations of Large Appliances with Fuel Substitution
Possibilities in Multifamily Homes, Railbelt Load Centers,
1980-2010....................................................... ~ .17
5.8 t1arket Saturations of Large Electric Appliances in
Single-Family Homes, Rail belt Load Centers, 1980-2010... •• •• ••• • 5.21
5.9 i1arket Saturations of Large Electric Appliances in
Mobile Homes, Rail belt Load Centers, 1980-2010... •••••• ••••• •••• 5.22
5.10 i1arket Saturations of Large Electric Appliances in
Duplexes, Rail belt Load Centers, 1980-2010.. •• ••••• ••• ••••• ••• •• 5.23
5.11 t1arket Saturations of Large Electric Appliances in
~1ultifamily Homes, Railbelt Load Centers, 1980-2010 ••••••••••••• 5.24
5.12 Percentage of Appliances Using Electricity and Average
Annual Electricity Consumption, Railbelt Load Centers........... 5.27
5.13 Growth Rates in Electric Appliance Capacity and Initial
Annual Average Consumption for New Appliances................... 5.29
5.14 Comparison of Appliance Usage Estimates from Selected Studies ••• 5 .30
5~15 Electric New Appliance Efficiency Improvements 1972-1980. ••••••• 5.34
5.16 Percent of Appliances Remaining in Service Years After
Purchase, Railbelt Region....................................... 5.37
5.17 Equations to Determine Adjustments to Electricity
Consumption Resulting from Changes in Average
6.1
6 .2
Household Size .................................................. .
Inputs and Outputs of the Business Consumption ~1odul e •••••••••••
Calculation of 1978 Anchorage Commercial-Industrial
5 .38
6 •. 2
Floor.Space ••••••.••••••••••••••••••.••••.•.•.••••••••.•••.••••..• 6.5
6 .3
6.4
6.5
6.6
1978 Commercial-Industrial Floor Space Estimates ••••••••••••••••
Comparisons of Square Feet, Employment, and Energy
Use in Commercial Buildings: Alaska and U.S. Averages ••••••••••
Business Floor Space Forecasting Equation Parameters ••••••••••••
Original RED Floor Space Equation Parameters •••• · ••••••••••••••••
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6.6
6 .1 0
6.13
6 .14
6.7
6.8
6.9
7.1
7 .2
7.3
7 .4
7.5
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Predicted Versus Actual Stock of Commercial-Light
Industrial-Government Floor Space, 1975-1981 ••••••••••••••••••••
Business Consumption Equation Results •••••••••••••••••••••••••••
Electricity Consumption Per Employee and Square Foot and
Square Footage Per Employee for Greater Anchorage and
Fairbanks, 1974-1981 .••••.••••••.•.••.•••.••..••..••..•.••.•..•.
Residential Electricity Demand Survey •••••••••••••••••••••••••••
Residential Survey Parameter Estimates ••••••••••••••••••••••••••
Commercial Electricity Demand Survey ••••••••••••••••••••••••••••
Commercial Survey Parameter Estimates •••••••••••••••••••••••••••
Parameter Values in RED Price Adjustment ~1echanism ••••••••••••••
6 .15
6.17
6 .19
7.6
7 .8
7.11
7 .12
7.14
8.1 Inputs and Outputs of the Conservation MJdule ••••••••••••••••••• 8.6
8.2 Payback Periods and Assumed Market Saturation Rates for
Residential Conservation Options................................ 8.17
9.1 Inputs and Outputs of the r1iscellaneous Module.................. 9.1
9 • 2 Par arne t e r s f o r the r~i s cell an eo us MJ d u 1 e. • • • • • • • • • • • • • • • • • • • • • • • • 9 • 4
11.1 Inputs and Outputs of the Peak Demand Module •••••••••••••••••••• 11.2
11.2 Assumed Load Factors for Rai-lbelt Load Centers ••••••••••••.••••• 11.5
11.3 Computed Load Factors and Month of Peak Load Occurrence
for Anchorage and Fairbanks 1970-1981 ••••••••••••••••••••••••••• 11.7
11.4 Ttme Period of Peak Demands in Anchorage and Fairbanks •••••••••• 11.13
11.5 Percentages of Total Forecasted Railbelt Electrical
Consumption Comprised by Individual Customer Sector ••••••••••••• 11.14
11.6 Conservation ~"easures ~1ost Likely to be Implemented
in the Residential Sector of Alaska ••••••••••••••••••••••••••••• 11.14
12.1 Comparison of Actua 1 Base Case, and Backcast El ectri city
Consunption (GWh) 1982 ...••.•.••.•.•.•...•.............•.•••..•. 12.2
12.2 1982 Values of Input Variables •••••••••••.•••••••••••••••••••••• 12.3
12.3 Comparison of Recent Forecasts, 1980-2000 ••••••••••••••••••••••• 12.5
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13.1 Number of Year-Round Housing Units by Type, Railbelt
Load Centers, Selected Years.................................... 13.2
13.2 Railbelt Area Utility Total Energy and System Peak Demand ••••••• 13.3
13.3 ·Anchorage-Cook Inlet Load Center Utility Sales and Sales
Per Customer, 1965-1981 ••••••••••••••••••••••••••••••••••••••••• 13.4
13.4 Fairbanks-Tanana Valley Load Center Utility Sales and Sales
Per Customer, 1965-1981 ••••••••••••••••••••••••••••••••••••••••• 13.5
13.5 Adjustment for Industrial Load Anchorage-Cook
Inlet, 1973-1981 •••••••••••••••••••••••••••••••••••••••••••••••• 13.6
A .1 Customers, NUllber Surveyed, and Respondents for the
Residential Survey Battelle-Northwest ••••••••••••••••••••••••••• A.5
A.2 Weights Used in Battelle-Northwest Residential Survey........... A.6
8.1 PNPPC Likely Conservation Potential at 4.0 Cents/kWh by
the Year 2000......... .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5
8.2 BPA Budgeted Conservation Program Savings....................... 8.7
8.3 CEC Conservation Programs Electricity Savings in
the Year 2002. •••••••••••••••••••••••••••••••••••••••••••••••••• 8.9
8.4 CEC Potential Energy Savings by End-Use Sector by
the Year 2002 •• ~.................................................. R-elO
B .5 WEPC Conservation Potential by the Year 2000 ••••••••••••••••••••
B.6 Average Annual Electricity ConsUllption per Household
on the GVEA System, 1972-1982 •••••••••••••••••••••••••••••••••••
8.7 Progerammatic Versus Market-Driven Energy Conservation
Projections in the At~L&P Service Area •••••••••••••••••••••••••••
B .8 Programmatic Energy Conservation Projections for AML&P ••••••••••
Appendix C has a special list of tables •••••••••••••••••••••••••••••••
Xi i i
8.12
B .14
B .15
8.16
C.3
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1. 0 I NTR ODUC TI ON
This document describes the 1983 version of the Railbelt Electricity
Demand (RED) model, a computer model for forecasting electricity consumption in
Alaska•s Railbelt region through the year 2010 (see Figure 1.1). The original
version of this model was developed by Battelle, Pacific North\vest Laboratories
(Battelle-Northwest) as part of the Alaska Railbelt Electric Power Alternatives
Study (Railbelt Study). The Railbelt Study was an electric power planning
study performed by Battelle-Northwest for the State of Alaska, Office of the
Governor and the Governor•s Policy Review Committee bet\veen October 1980 and
December 1982.
In March 1983, Battelle-Northwest was asked by the Harza-Ebasco Susitna
Joint Venture of Anchorage, Alaska to review the REO model structure, to make
appropriate changes, to document the changes, and to validate the model. Dur-
ing the update,Harza-Ebasco assisted and guided in the v.ork performed. The 1983
version of the RED model is used as one of a series of linked models to produce
updated forecasts of electrical power needs in the Railbelt over the next
30 years. The other models used in the 1983 update foecasting methodology are
the State of ~aska•s PETREV petroleum revenue forecasting model, the
University of Alaska Institute of Social and Economic Research•s MAP economic
and population forecasting model, and the Optimized Generation Planning (OGP)
model for planning the Railbelt electricity generation system and for estimat-
ing electricity costs. Separate documentation is available for those models.
The outcome of the RED update process is contained in this documentation·
report. The report contains complete documentation on the model, information
on data bases used in model development, and a section on model validation.
The RED forecasting model documented in this report is a partial end-
use/econometric model. Initial estimates of total residential demand are
derived by forecasting the nunber of energy-using devices and aggregating their
potential electricity demand into preliminary end-use forecasts. The model
then modifies these preliminary forecasts, using econometric fuel price elas-
ticities, to develop final forecasts of total residential energy consumption.
The model thus uses both technical knowledge of end uses and econometrics to
1.1
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FAIRBANKS T VALl.E:NANA . ~£
FIGURE 1.1 The Railbelt Region ofA1
1.2
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50 100 MILES
-ask a
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produce the residential forecast. The business secto~ (commercial, small
industrial, and government load) is treated similarly. However, because little
information is available on end uses in the business sectors in Alaska, pre-
1 iminary demand is estimated on an aggregated basis rather than by detailed end
use. Miscellaneous demand is based on the demand of the other three sectors,
while large industrial load and military load is forecasted exogenously by the
model· user.
Other important features of the model are a mechanism for handling
uncertainty in some of the model parameters, a method for explicitly including
government programs designed to subsidize conservation and consumer-installed
dispersed energy options (i.e. microhydro and smal J wind energy syste1ns), and
the ability to forecast peak electric demand by load center. The 1983 version
of the model recognizes two load centers: Anchorage-Cook Inlet (including the
Matanuska-Susitna Borough and the Kenai Penninsula) and Fairbanks-Tanana
Valley. The model produces annual energy and peak demand forecasts for every
fifth year from 1980 to 2010, and then linearly interpolates to derive annual
energy and demand forecasts for years between the five-year forecasts.
To produce a forecast, the model user must supply the model with region-
specific estimates of total emplo~nent and total households for each forecast
period. A few statewide variables are also required, such as forecasts of the
age/sex distribution of the state 1 s population. All of these variables are
,produced by the University of Alaska Institute of Social and Economic Research
r~AP econometric model; however, they can be derived from other sources. The
user must also supply price estimates for natural gas, oil, and electricity.
The estimates used in the 1983 update are consistent with input and output data
of the other models used in the forecasting methodology. Finally, the model
user may select either ranges or default values for the model 1 s parameters and
may run the model in either a certainty-equivalent or uncertain (Monte Carlo)
-mode. The model then produces the forecasts.
,1/iHfl,
!
This report consists of 13 sections. In Section 2.0 an overview of the
RED model is presented. In Section 3.0 the Uncertainty rvbdule, which provides
the model with r1onte Carlo simulation capability, is described. Section 4.0
describes the Housing rudule, which forecasts the stock of residential housing
1.3
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units by type. These forecasts are used in the electricity demand forecasts of
the Residential Consumption ~bdule, discussed in Section 5.0. Forecasts of
demand in the business sector are produced by the Business Consumption ~~odule,
which is described in Section 6.0. The price adjustment mechanism is the
subject of Chapter 7.0. The effects of government market intervention to
develop conservation and dispersed generation options are covered by the
Program-Induced Conservation Module, Section 8.0. Section 9.0 discusses rnis-
cellaneous electricity demand (street 1 ighting, second homes, etc.). Large
industrial demand is covered in Section 10.0. The Peak Demand ~1odule, Section
11.0, concerns the relationship between annual electricity consumption and
annual peak demand. Section 12.0 covers model validation, and Section 13.0
provides miscellaneous statistics on Railbelt electrical demand. The report
also includes appendices on the Rattelle-Northwest residential electric energy
survey used to calibrate RED, conservation research conducted by Battelle-
Northwest in support of the study, and model output for the 1983 update.
1.4
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2.0 OVERVIEW
The Railbelt Electricity Demand (REO) model is a simulation model designed
to forecast annual electricity consumption for the residential, commercial-
1 ight industrial-government, heavy industrial, and miscellaneous end-use
sectors of Alaska's Railbelt region. The model also takes into account
tJOVernment intervention in the energy markets in Alaska and produces forecasts
of system annual peak demand. In the 1983 version of RED, forecasts of
consumption by sector and system peak demand are produced in five-year steps
for two Railbelt load centers:
a Anchorage-Cook Inlet (including Anchorage, Matanuska-Susitna Borough
and Kenai Peninsula)
• Fairbanks-Tanana Valley (including the Fairbanks-North Star Borough
and Southeast Fairbanks Census Area).
Between these five-year steps, the model linearly interpolates to estimate
annual energy and peak demand. When run in fv'onte Carlo mode, the model
produces a sample probability distribution of forecasts of electricity
consumption by end-use sector and peak demand for each load center for each
forecast year: 1985, 1990, 1995, 2000, 2005, 2010. This distribution of
forecasts can be used for planning electric power generating capacity.
Figure 2.1 shows the basic relationship among the seven modules that
comprise the RED modeL The modei begins a simulation with the Uncertainty
Module, selecting a trial set of model parameters, which are sent to the other
modules. These parameters include parameters to compute price elasticities,
appliance saturation parameters, and regional load factors. Exogenous
forecasts of population, economic activity, and retail prices for fuel oil,
gas, and electricity are used with the trial parameters to produce forecasts of
electricity consumption in the Residential Consumption and Business Consumption
Modules. These forecasts, along with additional trial parameters, are used in
the Policy-Induced Conservation f'k:ldule to model the effects on electricHy
sales of subsidized conservation and dispersed generating options. The revised
2.1
ECONOMIC UNCERTAINTY
FORECAST MODULE
HOUSING -;, IC STOCK
l -.. RESIDENTIAL
.._
) r ..... BUSINESS lC ,... .,..
I ...... u
IC PROGRAM-INDUCED
CONSERVATION .
\
LARGE
INDUSTRIAL MISC. -
) d
ANNUAL SALES
,.,;;;;,
...:> AND
PEAK DEMAND ~
~
FIGURE 2.1. Information Flows in the Red ''1odel
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2.2
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consumption forecasts of residential and business (commercial, small indus-
trial, and government) consumption are used to estimate future miscellaneous
consumption and total electricity sales. Finally, the unrevised and revised
consumption forecasts are used along with a user-supplied estimate of large
industrial load and trial system load factor forecast to estimate peak
demand. The model then returns to start the next r'Onte Carlo trial. 'v-lhen the
model is run in certainty-equivalent mode, a specific "default" set of
par~neters is used, and only one trial is run.
The RED model produces an output file of trial values for electricity
consumption by sector and system peak demand by year and load center. This
information can be used by the Optimized Generation Planning (OGP) model or
other.generation planning model to plan and dispatch electric generating
capacity for each load center and year.
The remainder of this section briefly describes each module. Detailed
documentation of each of the modules is contained in Sections 3.0 through 11.0
of this report.
UNCERTAINTY MODULE
The purpose of the Uncertainty Module is to randomly select values for
individual model parameters that are considered to be key factors underlying
forecast uncertainty. These parameters include the marke~ saturations for
major appliances in the residential sector; the parameters used to compute
price elasticity and cross-price elasticities of demand for electricity in the
residential and business sector; the market penetration of program-induced
conservation and dispersed generating technologies; the intensity of
electricity use per square foot of floor space in the business sector; and the
electric system load factors for each load center.
These parameters are generated by a r-bnte Carlo routine, which uses
information on the distribution of each parameter (such as its expected value
and range) and the computer• s random nLJllber generator to produce sets of
parameter values. Each set of generated parameters representsa "trial." By
running each successive trial set of ·generated parameters through the rest of
the modules, the model builds distributions of annual electricity consumption
2.3
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and peak demand. The end points of the distributions reflect the probable
range of annual electric consumption and peak demand, given the level of
uncertainty.
The Uncertainty t~odule need not be run every time REO is run. The
parameter file contains "default" values of the parameters that may be used to
conserve computation time.
HOUSING MOOULE
The Housing Module calculates the number of households and the stock of
-
housing by dwelling type in each load center of each forecast year in which the· -,
model is run. Using regional forecasts of households and total population, the
housing stock module first derives a forecast of the nunber of households
served by electricity in each load center. Next, using exogenous statewide
forecasts of household headship rates and the age distribution of Alaska's
population, it estimates the distribution of households by age of head and size
of household for each load center. Finally, it forecasts the demand for four
types of housing stock: single family, mobile homes, duplexes, and multifamily
units.
The supply of housing is calculated in two steps. First, the supply of
each type of housing from the previous period is adjusted for demolition and
compared to the demand. If demand exceeds supply, construction of additional
housing begins immediately. If excess supply of a given type of housing
exists, the model examines the vacancy rate in all types of houses. Each type
is assumed to have a maximum vacancy rate. If 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.
the Residential Consumption Module.
RESIDENTIAL CONSUMPTION MODULE
This forecast is passed to
The Residential Consumption Module forecasts the annual consumption of
electricity in the residential sector for each load center in each forecast
year. It does not, in general, take into account explicit government
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intervention to promote residential electric energy conservation or self-
sufficiency. Such intervention is covered in the Program-Induced Conservation
i~odule. The Residential Consumption t1odule employs an end-use approach that
recognizes nine major end uses of electricity, extra hot water for hJO of these
appliances, and a "small appliances" category that encompasses a large group of
other end uses. For a given forecast of occupied housing, the Residential
Consumption r1odule first forecasts the residential appliance stock and the
portion using electricity, stratified by the type of dwelling and vintage of
the appliance. Appliance efficiency standards and average electric consumption
rates are applied to that portion of the stock of each appliance using elec-
tricity. The stock of each electric appliance is then multiplied by its
corresponding consumption rate to derive a 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 "trial" values of parameters used to compute price elastic-
ities and cross-price elasticities of demand from the Uncertainty r"odule. It
adjusts the preliminary consumption forecast for both short-and long-run price
effects on appliance use and fuel switching. The adjusted forecast is passed
to the Program-Induced Conservation and Peak Demand ~1odul es.
RIJSINESS CONSUMPTION MODlJLE
The Business Consunption Module forecasts the consumption of electricity
by load center in commercial, small industrial, and government uses for each
forecast year (1980, 1985, 1990, 1995, 2000, 2005, 2010). Oi rect promotion of
conservation in this sector is covered in the Program-Induced Conservati~n
tbdule. Because the end uses of electricity in the commercial, small
industrial and government sectors are more diverse and less known than in the
residential sector, the Business Consumption Module forecasts electrical use on
an aggregate basis rather than by end use.
REO uses a proxy (the stock of commercial, small industrial floor, and
government space) for the stock of electricity-using capital equi pnent to
forecast the derived demand for electricity. Using an exogenous forecast of
regional employment, the module forecasts the regional stock of floor space.
2.5
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Next, econometric equations are used to predict the intensity of electricity
use for a given level of floor space in the absence of any relative price
changes. Finally, a price adjustment similar to that in the Residential
Consumption t~odule is applied to derive a forecast of business electricity
consumption (excluding large industrial demand, which must be exogenously
determined). The Business Consumption rvbdul e forecasts are passed to the
Program-Induced Conservation and Peak Demand Modules.
PROGRAM-INDUCED CONSERVATION MODULE
Because of the potential importance of government intervention in the
marketplace to encourage conservation of energy and substitution of other for,ns
of energy for electricity, the RED model includes a module that permits
explicit treatment of user-installed conservation technologies and government
programs that are designed to reduce the demand for utility-generated electric-
ity • Th i s mod u l e wa s des i g n e d f o r an a l y z i n g potent i a 1 f u t u r e con s e r vat i o n
programs for the' State of Alaska and was not used in the 1983 updated
forecasts. The module structure is designed to incorporate assumptions on the
technical performance, costs, and market penetration of electricity-saving
innovations in each end use, load center, and forecast year. The module
forecasts the aggregate electricity savings by end use, the costs associated
with these savings, and adjusted consumption in the residential and business
sectors.
The Program-Induced Conservation r~odule performs estimates of payback
period and penetration rate of commercial sector and residential sector
conservation options. In the residential sector, the model user supplies
information to the module on the technical efficiency (electricity savings),
electricity price, and costs of installation. The module then calculates the
internal rate of return on the option to the consumer, as well as the option's
payback period for technologies considered "acceptable" by the user. The
module's payback decision rule links the payback period to a range of market
saturations for the technologies. The savings per installation and market
saturation of each option are used to calculate residential sector electricity
savings and costs. In the business sector, the model user must specify the
2.6
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technical potential for new and retrofit energy-saving technologies. The user
r must also specify the range of conservation saturation as a percent of total
potential conservation. The Program-Induced Conservation Module then calcu-
r-· lates total electricity savings due to market intervention in new and retrofit
applications and adjusts residential and business consumption for each load
center and forecast year.
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r11 SCELLANEOUS CONSUMPTION MODULE
The r1iscellaneous Consumption ~1odule forecasts total miscellaneous
consumption for second (recreation) homes, vacant houses, and street
lighting. The module uses the forecast of residential consumption (adjusted
for conservation impacts) to predict electricity demand in. second homes and
vacant housing units. The sum of residential and business consumption is used
to forecast street lighting requirements. Finally, all three are sunmed
together to estimate miscellaneous demand.
PEAK DEMAND MODULE
The Peak Demand Module forecasts the annual peak load demand for
electricity. A two-stage approach using load factors is used. The unadjusted
residential and bu,siness consllllption, miscellaneous consumption, industrial
demand and load center load factors generated by the Uncertainty ~dule are
first used to forecast preliminary peak demand. Next, displaced consumption
(electricity savings) calculated by the Program-Induced Conservation r1odule is
multiplied by a peak correction factor supplied by the Uncertainty l~odul e to
allocate a portion of electricity savings from conservation to peak demand
periods. The al1ocated consllllption savings are then multiplied by the load
factor to forecast peak demand savings, and the savings are subtracted from
peak demand to forecast revised peak demand.
The following sections describe each module of the model in greater
detail.
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3.0 THE UNCERTAINTY MODULE
RE0 1 s Uncertainty Module allows the forecaster to incorporate uncertainty
in key parameters of the RED r-1odel forecast. In other words, the impact of
uncertain parameter values can be reflected in the forecast values.
RED allows generation of key subsets of the full set of parameters. It is
not practical to all ow all parameters to vary on all runs of the model, because
the total nunber of such parameter values required for a single pass through
the model is greater than 1000. For example, if the user wanted to generate 50
values for every uncertain parameter, over 50,000 values would have to be
produced. While this exercise is within RE0 1 S capabilities, the cost is very
high.
"1ECHANI91
A Monte Carlo routine uses the host computer 1 s pseudo random number
generator to translate user-supplied information on a parameter, such as its
expected value, its range, and its subjective probability distribution, into
random trial parameter values. By producing simulations using several such
randomly generated values of the parameter, the model will yield electricity
consumption forecasts that incorporate each parameter 1 s uncertainty.
INPUTS AND OUTPUTS
The Uncertainty Module requires three basic inputs:
• the nunber of values to be generated
• a selection of parameters to vary
• the parameter file.
The parameter file contains the default values, ranges, and (if required) the
expected value and variance of each parameter. Table 3.1 provides a summary of
the inputs and outputs of the module.
3.1
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TABLE 3.1. Inputs and Outputs of the RED Uncertainty ~~odule
(a) Inputs
Symbol
N
(see Table 3.2)
(b) Outputs
Symbol
(See Table 3.2)
N
MODULE STRUCTURE
Variable
Number of Values
to be Generated
P a r a mete r 1 s Ra n g e ,
Variance, and
Expected Values
Variable
Random Parameter
Values
Number of Times
M:>del is to be Run
In put From
User Interface
Parameter File
Output To
Other f"odules
Model Control Program
An overview of information flows within the Uncertainty Module is given in
Figure 3.1. First, the program asks whether the user would like to generate a
parameter. If the answer is no, then the default value (from the parameter
file) for each parameter is assigned. If a random parameter value is to be
generated, then the user is queried as to which parameters will be allowed to
vary.
The next step is to choose the number of values to be generated for each
parameter. This is the number oft imes the remainder of the model will be run,
each time with a different generated value for each parameter. Next, an
arbitrary seed for the random number generator is entered.
Next, the computer generates a random number for each value to be pro-
duced. This is accomplished by calling the computer 1 s "pseudo 11 random nUTJber
generator, which generates a random number between 0 and 1. From the parameter
file, the information on the range of the parameter, or (for parameters with a
normal distribDtion) the range, expected value, and variance is used to
3.2
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ASSUMED RANGE
EXPECTED VALUE
START
SELECT PARAMETERS
TO BE
GENERATED
RANDOMLY
SELECT NUMBER
OF VALUES TO.
BE GENERATED
(N]
COMPUTER
GENERATES N
RANDOM
NUMBERS
TRANSFORM
RANDOM NUMBERS
TO
PARAMETER
VALUES
OUTPUT
PARAMETER
VALUES
NO
ASSIGN DEFAULT
VALUE OF
UN SELECTED
PARAMETERS
FIGURE 3.1. RED Uncertainty Module
construct cumulative probability functions for each parameter. The random
values for each parameter are then generated by applying the random numbers to
these functions.
PARAMETERS
Table 3.2 provides a list of the parameters that can be generated by the
Uncertainty Module. Where information exists on parameter distributions from
3.3
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TABLE 3.2. Par~neters Generated by the Uncertainty Module(a)
Symbol
SAT
A; B; ;:... ; OSRz ;
GSRz
BBETA
CON SAT
LF
Name
Housing Demand Coefficients
Sat u rat i o n o f Re s i dent i a l Ap p l i an c e s
Residential, Business Parameters for
Own-, Oil-Cross and Gas-Cross Price
adjustment
Floor Space Consumption Parameter
Saturation of Conservation Technologies
Load Factor
Statistical
Distribution
Normal
Uniform
Normal
Normal
Uniform
Uniform
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(a) Values of these parameters (except CONSAT, which varies by case) are found ~
in Tables 4.9, 5.4 through 5.11, 6.8, 7.5, and 11.2.
econometric results, the distribution of values is assumed to be normally
distributed. Where no information exists on the shape of the parameter
distribution, all values within the range are considered equally likely and the
distribution is assumed uniform.
3.4
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4.0 THE HOUSING MODULE
The const.nning unit in the residential sector is the household, each of
which is assumed to occupy one housing unit. The Housing ibdule provides a
forecast of civilian households and the stock of housing by dwelling type in
each of the Railbelt's load centers. The type of dwelling is a major deter-
minant of energy use in residential space heating. Furthermore, the type of
dwelling is correlated with the stock of residential appliances. This module,
tl1erefore, provides essential inputs for the Residential Const.nnption i~odule.
r~ECHANISM
The Housing Module accepts as input an exogenous forecast of the regional
population and nlJllber of households to forecast household size. The total
households forecast is adjusted for military households and is then stratified
by t h e a g e o f t h e he ad o f h o u s e h o l d an d t h e n lJll be r o f h o u s e h o l d m emb e r s • Th e
housing demand equations then use this distribution of households by size and
age of head to predict thP. initial demand for housing by type of dwelling. Tne
initial demand for each housing type is compared with the remaining stock, and
adjustments in housing demand and construction occur until housing market
clearance is achieved •
INPUTS AND OUTPUTS
Table 4.1 presents the data used and generated within this module.
Exogenous forecasts of regional households, population, and the st~te-wide
distribution of households by age of head are needed as input, while the module
passes information on the occupied and vacant housing stock to the remainder of
RED.
MODULE STRUCTURE
The Housing Module's structure is shown in Figure 4.1. The module begins
each simulation with a user-supplied forecast of households and population for
the load center.· The asst.nned number of households for each load center is
r-first adjusted for military housing demand and multiplied by a decimal fraction
4.1
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TABLE 4.1. Inputs and Outputs of the RED Housing Module
(a) In~uts
Symbol Variable Variable Input From
THH Regional Household Forecast Forecast File
HHAta State Households by Age Group Forecast Fi 1 e
b, c' d Housing Demand Coefficients Uncertainty Mbdule
(b) Outputs
S.2::mbo 1 Variable Variable Out~ut From
HDTY Occupied Housing Stock by Type Residential Mbdul e
to obtain a forecast of households served by utilities. Total households are
then stratified by age and size of household, and then used to generate an
estimate of demand for each type of housing (TY). Demand is compared to the
initial stock, resulting in new construction or reallocation of demand as
appropriate.· The end result is a set of estimates of occupied and unoccupied
housing units by type. Finally, the housing stock is reinitialized for the
next forecast period.
The first step in the Housing Module is to find the number of civilian
households in a given Railbelt load center.
( 4 .1)
where
CHH =total number of civilian households
BHH = military households residing on base (exogenous)
THH = total households (exogenous)
:::; region subscript
t = forecast period subscript.
On-base military households are subtracted out because they do not signifi-
cantly affect off-base housing. In addition, since the military supplies
4.2
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DEMAND
PARAMETERS
(UNCERTAINTY
MODULE)
INITIAL HOUSING
·sTOCK TY
REINITIALIZE
HOUSING
STOCKS
L-------
REGIONAL
FORECAST
• POPULATION
• HOUSEHOLDS
STRATIFY
HOUSEHOLDS BY
AGE OF HEAD
SIZE OF HOUSEHOLD
CALCULATE
DEMAND FOR
HOUSING UNITS
BY TYPE TY
FORECASTS OF OCCUPIED,
UNOCCUPIED HOUSING
BY TYPE
FIGURE 4.1. RED Housing Module
4.3
• AGE DISTRIBUTION
OF HOUSEHOLD
HEADS
• SIZE DISTRIBUTION
OF HOUSEHOLDS
.NEW
CONSTRUCTION
OF TYPE TY
FILL VACANCIES
TYWITH
COMPLEMENTARY
DEMAND
I I 'II
electricity to them, on-base households have no impact on the residential
demand for utility-supplied electricity.(a)
Once the total number of civilian households in the load center has been
obtained, they are stratified by the size of the household and the age of the
household head. To obtai.n the distribution of households by size of household,
the total nunber of households is multiplied by the probabilities of four size
categories derived from information provided in the 19~0 Census of Popula-
tion. To estimate the distribution of households by the age of head, the 1980
Census ratio between the regional and state relative frequencies of age of head
is assumed to remain constant. The user supplies forecasts of the statewide
age distribution of heads of households from a forecasting mode1 or by some
other method. Using the state relative frequency distribution, therefore, and
applying the constant ratios of regional to statewide frequencies, the model
obtains forecasts of the regional distribution of households by age of head.
The joint distribution by size of household and age of head is obtained by
· inu 1 t i p 1 y i n g t h e two d i s t r i b u t i on s :
where
HH = number of households in an age/size class
THH =·total nunber of households
CHH =total civilian households
A.= subscript denoting aggregate state variable
P =regional household size probability (parameter)
R = ratio of the regional to state relative frequency of age of
household head (parameter)
a = age of head subscript
s = household size subscript.
( 4. 2)
(a) Military purchases of electricity from the utility system are handled as
industrial loads.
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The demand for a particular type of housing-single family, multifamily,
mobile home, or duplex-is hypothesized to be a function of the size of the
household and the age of the head (which serves as a proxy for household
wealth). Equations projecting demand for three of the types of housing (single
family, multifamily, mobile homes) were estimated by the Institute of Social
and Economic Research (ISER) from Anchorage data collected by the University of
Alaska's Urban Observatory (Goldsmith and Huskey 198Gb). The remaining
category (duplex) is filled with the remaining households.
The demand for a particular type of housing is given by the following
equations:
HDSFit = CHHit x bo + ba 1 x 51; t + ba2 x 5zit + ba4 X $4 it +
b2s x Azit + b3s x A3it + b4s x A4it
HDMFit = CHHit x co + cal X Slit + Ca2 X 52ft + ca4 x s4it +
c2 s x A2; t + C3 S X A3; t + c4s x A4it
H0~1Hi t = CHHit x do + dal x sl; t + da2 x 5zit + da4 x s4it +
dzs x A2it + d3s X A3; t + d4s x A4it
HOOP it = CHH;t -HDSFit -HDMFi t -HD~1Hi t
where
HD = housing demand
SF ::: index for single family
5 sit = a~l HHuas; s = 1,2,4
A a it = sfl HH;tas; a = 2,3,4
MF = index for multifamily
~1H = index for mobile home
DP = index for duplex
4.5
( 4. 3)
(4.4)
( 4. 5)
( 4. 6)
I', Ill
The model then adjusts the housing stock and housing demand so that the
housing market is cleared. Initially, the housing stock is calculated as the
previous period•s stock net of demolition:
where
HS = housing stock
TY = index denoting the type of hou~ing (SF, MF, MH, and DP)
r = period-specific removal rate (parameter).
( 4. 7)
-
-
Net demand for each type of dwelling is defined as the demand minus the housing ~.
stock:
NOTYit = HDTYit -HSTYit ( 4 .8)
where
ND = net demand.
If net demand for all types of housing is positive, then enough n~w construc-
tion immediately occurs to meet the net demand plus an equilibrium amount of
vacancies req~ired td ensure normal functioning of the housing market:
4.6
-
-
-
NCTYit = NDTYit ~ VTY x (HSTYit + NDTYit) ( 4. 9)
where
NC = new construction
V =normal vacancy rate (parameter).
The equilibrium vacant housing stock is the "normal 11 vacancy rate times the
stock of housing.
If the net demand for a particular type of housing is negative, however,
then the vacancy rate for that type of housing has to be calculated:
AVTYit = 1 -(4.10)
where
AV = actual vacancy rate.
If the actual vacancy rate is greater than its assumed maximum, then the excess
supply of that particular type of housing is assumed to drive down the price of
that type of dwelling. Individuals residing in other dwellings could be
induced to move to reduce mortgage or rent payments. An adjustment to the.·
distribution of housing demands, therefore, is appropriate.
Substitution first occurs, if possible, within groups of housing that are
close substitutes (single-family and mobile homes; duplexes and multifamily).
If not enough excess demand exists from the close substitutes to fill the
depressed market, then substitution occurs from all types. The procedure is as
fo 11 ows:
1. The number of excess vacancies within a type is calculated by subtracting
the housing demand from one minus the maximum vacancy rate, times the
stock.
2. The number of substitute units available to fill the excess supply is
given by subtracting one minus the normal vacancy rate, times the close
substitute stock from the close substitute demand.
4.7
I I !II
3. The minimum of 1 or 2 is subtracted from the complementary housing demand
and added to the depressed demand.
4. If excess supply persists (the actual vacancy rate is above its assumed
maximum), then the above procedure is repeated; only the number of housing
units available is now calculated using maximum vacancy rates and all
types of housing where the actual vacancy rate is less than their assuned
maximum. The available units are then allocated based on normalization
weights of the number available by type.
The final outputs of this module are occupied h6using by type (HDTYit) and
unoccupied housing:
VHit =
where
E
TY
VH =total vacant dwelling units.
PARAMETERS
Military Households
(4.11)
The number of on-base military households, presented in Table 4.2, is
assumed to remain constant over the forecast periods. The level of military
activity in Alaska has stabilized, and little indicates that a major shift will
occur in the future.
TABLE 4.2. Number of ~1ilitary Households Assumed to Reside
on Base in Railbe1t Load Centers
Anchorage
3 ,212
Fairbanks
3 ,062
Source: Supplied by I SER.
4.8
-
-
-·
-
.-
.-
-
Ho0sehold Size and Demographic Trends
A key factor in the residential demand for electricity is the number and
type of residential customers. The nunber of customers approximately equals
the number of households served by electricity, with the difference being
caused by such factors as vacant housing with electrical service. Thus, it is
important in forecasting the demand for electricity to forecast the number of
households. The nunber of households in a load center is, in turn, a function
of the size of the population and the rate of household formation. Household
formation depends on the nunber of persons of household fonnation age; certain
economic factors that may influence household formation, such as potential
household income, price of housing, interest rates; changing tastes for mar-
riage and housing; and government housing programs.
Table 4.3 shows how the size of households has changed in the United
Stntes and in the Railbelt since 1950. The table indicates that the average
nLmber of persons per housing unit has declined dramatically in both the II.S.
and the Rai]belt during the period. Since 1970, the size decline has been more
TABLE 4.3. Household Size l-.estern U.S. and Railbelt 1950-1980
(~ersons per Occupied Unit)
1950
1960
1970
1980
United
States
3.5(a)
3.3
3 .1
2.7
Anchorage-Fairbanks-
Cook In 1 et Tanana Valle~
3.4(a) 3.3(a)
3.4 3.6
3.4 3 .4
2.9 2.9
(a) Obtained by dividing total resident population by
total households~ Includes only urban places of
10,000 persons for Alaska locations.
Sources: U.S. Deparbnent of Commerce 1982; Goldsmith and
Huskey 1980b; Harrison 1979; and U.S. Bureau of
the Census 196.0.
4.9
I I !II
rapid in the Railbelt than in the nation as a whole, resulting from increasing
ntmbers and proportions of young, single adult householders and childless
couples. This trend toward smaller households headed by young arlults probably
has a practical 1 imit somewhere near the Western Census Region 1980 average
household size of 2.6. However, recent revisions have been made to the Univer-
sity of Alaska's r~AP economic and population model to forecast the nunber of
households based on the household formation rates implicit in the 1980 census
figures. These imply that the lower 1 imit may not be reached. Table 4.4 shows
the MAP forecast size of households in the Railbelt for the years 1980-2010 for
a typical economic scenario. The average size of households is relatively
insensitive to the scenario used, depending almost entirely on the age distri-
bution of population.
Household formation rates are thought to depend on the income of potential
householders, the price of housing, and borrowing costs implied by interest
rates. Unfortunately, Alaska economic data do not include time series on
Railbelt household income or housing prices; therefore, it has not proved
possible to estimate household formation rates based on these variables.
The RED model formerly estimated the nunber of households in each Railbelt
load center from a MAP model estimate of statewide households and the
TARLE 4.4. Forecast Size of Households, Railbelt Load Centers
Year Anchorage-Cook Inlet Fairbanks-Tanana Va1leJ::
1980 2.91 3.00
1985 2.73 2.89
1990 2.69 2 .85
1995 2.67 2.81
2000 2.64 2.79
2005 2.63 2. 76
2010 2 .62 2.71
Source: University of Alaska Institute of Social and
Economic Research, case HE.6, FERC 0% Real
Growth in Oil Prices
4.10
""""'
-
-
-
,....
relationship between the age distribution of the population in each load center
and the age distribution of Alaska's population. The 1983 version now simply
accepts a MAP model forecast of the number of households in each load center.
The n\Jllber of households served by electric utilities is estimated by multiply-
ing the numbers of households times a constant to reflect the proportion of
households served by electricity.(a) The nl.lllber of households served by
utility-generated electricity is virtually 100% in Anchorage. Rural areas of
the Matanuska-Susitna Borough and Kenai Peninsula Borough have a few residences
not served (mostly seasonal homes), but the Fairbanks North Star Borough and
Delta Junction areas have many year-round dwellings not served by utilities.
Historic and Projected Trends in Demand for Housing
The demand for a particular type of housing--single family, multifamily,
mobile home, or duplex--is hypothesized to be a function of the size of house-
hold and the age of the household head. The economics literature generally
also includes price of housing and household income in the demand for hous-
ing. However, Alaska economic information does not include time series on
family income and housing prices that could be used to forecast housing demand
by type. Cross-sectional data on household income do exist for Anchorage in
1977 by type of housing (Ender 1978); however, the lack of historical time
series on household income prevent the estimation of household income as a
function of economic growth over time in the Railbelt. However, the age of the
head of household serves to some extent as a proxy for household income, with
older household heads generally more wealthy and able to afford larger homes.
Larger households also require more space and larger homes. These factors are
included in the demand equations for individual types of houses contained in
the RED model.
Government Program Effects
ISER performed an analysis of State of Alaska housing programs in 1982
(ISER 1982) with the following findings. Alaska Housing Finance Corporation
(a) Although this calculation is actually performed in the Housing Module, its
·description is included in this doucment with the discussion of
residential electricity demand in Section 5.0.
4.11
(AHFC) operates several different housing programs on behalf of the state in
which it acts as a secondary lender to provide mortgage loan money at the
lowest possible interest rates. Between July 1980 and December of 1982, AHFC
had a substantial negative impact on mortgage interest rates in ~aska, ranging
from 2.5 percentage points in July, 1980 to slightly more than 4 percentage
points in December 1981. Average loan volume repurchased by AHFC increased
5 times between 1979 and 1981, and accounted for 85% of all Alaska home loans
from July 1980 to October 1981. ~1uch of the activity was due to the special
Mortgage Loan Purchase program enacted in June 1980. ISER found that the State
of Alaska's low interes~ housing loan programs caused construction of new homes
statewide to be about one thousand units higher (or one third higher) than it
would have been without the program and caused conversion of about 300 units
from rental to sales units. The other substantial effect was on the quality of
housing purchased. New homes built during 1980-1981 were an average $25,000
more expensive than existing homes. The proportion of multifamily construction
was not clearly affected one way or the other by the loan ~rograms. In 1980
and 1981 new multifamily construction in Arichorage was only JO% of total units
built, whereas it had been 50% or more every year from 1974 through 1979.
However, opposite effects were found in Fairbanks. Loan program impacts were
confounded with the 1 evel s of rents. These were depressed between 1979 and
1981 and failed to support the construction of new multifamily rental units.
Compared to a situation with6ut large-scale interest subsidies, ISER's
findings suggest that continuation of these large-scale subsidies would result
in the following: 1) more first-time home buyers and more expensive units
being built (though it is not clear that these would necessarily be single-
family detached houses rather than condominiums); and 2) downward pressure on
rents, reducing the incentive for building multifamily rental units. Depending
on people's tastes for single-family detached units versus condomini urns and the
builder's cost of providing units of each type, government programs could cause
single-family construction to increase .2...C.. decrease as a proportion of the
total. In the RED model, government programs are assumed to have no 1 ong-term
net effect on housing mix by type.
4.12
10"0\,
-
-
,_
' '
-
-
-
Housing Demand by Type of Housing
Table 4.5 compares the demand for types of housing in the Anchorage-Cook
Inlet load center with and without the influence of household age and household
size as reflected in the RED model structure. Wi~h the influence of household
size and age, relatively more households occupy single-family homes, which have
a lower electric fuel m·ode split than multifamily housing. By the year 2010,
residential electricity demand is about 3% lower with the effects of size and
age of households on housing mix than without these effects. As revealed by
the table, even fairly large differences in the proportions of households in
the various types of dwellings have little impact on electricity consumption
forecasts.
TABLE 4.5. Impact of Householder Age and Household Size on Housing t1ix
and Total Utility Sales, Anchorage-Cook Inlet
Single Family Proportion
of Served Households:
With Age and Size Effects
Without Age and Size Effects
Multifamily Proportion of
Served Households:
With Age and Size Effects
l~ithout Age and Size Effects
Mobile Home Proportion of
Served Households:
With Age and Size Effects
Without Age and Size Effects
Duplex Proportion
of Served Households:
With Age and Size Effects
Without Age and Size Effects
Residential GWH Sold by Utilities:
With Age and Size Effects
Without Age and ~ze Effects
1980
0.496
0.496
0.284
0.284
0.115
0.115
0.105
0 .105
979.5
979.5
1990
0.549
0 .461
0.245
0.383
0.126 .
0 .097
0.080
0.059
1336.1
1382.2
2000
0.549
0.461
0.261
0.383
0.127
0 .097
0.063
0 .059
1599.6
1656.4
Source: RED Model Runs, Case HE. 6, FERC 0% Real Price Increase.
4.13
2010
0.545
0.461
0.264
0.383
0.129
0.097
0.063
0 .059
1883.9
1955.0
Ill
After an initial adjustment, Table 4.5 also shows a slight downward trend
in the proportion of single-family households as the size of households
declines between 1990 and 2010. This is consistent with the falling historical
trend in the proportion of single-family houses in Railbelt communities from
1950-1980, as shown in Table 4.6. Although a short-term reversal of the
historical trend may have been occurring since 1980, especially in Fairbanks,
high vacancy rates and depressed rents probably explain the high proportion of
single-family homes constructed since 1980. In particular, the very high pro-
portion of single-family construction in Fairbanks since 1980 can be attributed
to high vacancy rates in multifamily units between 1977 and 1980. Vacancy
rates for multifamily dwellings in Fairbanks ranged upward from 0.5% in May
19 7 6 t o 13 • 5% i n au n e 19 8 0 • The v a can c y rate s h a v e fa 1 l e n d r am at i c a 1 l y s i n c e
(to 1.7% by June 1982), and building permits for new multifamily units have
recovered, increasing by over 50% in the North Star Borough from 1981 to 1982
(Community Research Quarterly, Winter 1982).
Tables 4.7 and 4.8 present the parameters used to derive the joint distri-
bution of households by size and age of head. The baseline figures for the
TABLE 4.6. Single-Family Housing as Proportion Year-Round Housing
Stock by Type, Railbelt Load Centers, 1950-1982
1950 (a)
1960
1970
1980
l982(a)
Proportion Single-
Family Housing
Built 1980-82
Anchorage -
Cook Inlet .
0.592
0.628
0.4 71
0.462
0.472
0.539
( a ) U r b an An c h o rage and Fa i r ban k s on l y •
(b) Fairbanks-North Star Borough only.
Source: Table 13.1.
4.14
Fairbanks -
Tanana Valley
0.713
0.518
0.389
0.450
0 .4 72
0.781(b)
-
-
-
-
-.
·-
TABLE 4.7. Probability of Size of Households
in Railbelt Load Centers
Year Size Anchora~e Fairbanks
198o(a) <2 0.476 0.455
3 0.190 0.210
4-5 0.291 0.287
6+ 0.042 0.048
1985 (b) <2 .489 .468
!""" 3 .188 .208
4-5 .282 .278
6+ .042 .048 -199o(b) <2 .502 .481
3 .185 .205
r--4-5 .27 2 .268
6+ .041 .047
1995(b) <2 .515 .494
3 .182 .202
4-5 .262 .258
6+ .041 .047
zooo(b) <2 .528 .so 7
3 .180 .200
4-5 .253 .249
6+ .041 .047
2005 (b) <2 .541 .520
3 .178 .198
4-5 .244 .240
6+ .041 .047
zo1o(b) <2 .554 .533
3 .175 .195
4-5 .234 .230
6+ .041 .04 7 -
(a) Source: Battelle-Northwest End-Use
Survey.
(b) The Anchorage ; niti al distribution
reaches the 1.-Jestern u.s. regional
average by 2010 (Bureau of the
Census 197 7). The Fairbanks dis-
tribution is assumed to have the
same rate of change as Anchorage.
!"""'
4.15
·-
TABLE 4.8. Regional Frequency of Age of Household Head
Divided by the State-Wide Frequency
Age of Head Anchorag_e Fairbanks
<25 1.064 1.108
25-30 1.013 1.103
31-54 1.018 0.988
55+ 0.867 0.842
Source: 1980 Census of Population
General Population Charac-
teristics: Alaska PCS0-1-83.
distribution of size parameters were derived from the Battelle Northwest end-
use survey. Those parameters were adjusted to approximately approach the 1977
Western Regional average household size of 2.6 (Bureau of Census 1977) by the
year 2010 in Anchorage in constant linear increments. Fairbanks uses the same
increments and converges to a household size of about 2.7. The ratio of
regional to statewide frequency of age of head was derived from the 1980 Census
of Population for Railbelt locations. These ratios are assumed to remain
constant over the forecast period.
-
-
The housing demand parameters were originally estimated by ISER using a !""'\
linear probability model. The expected values in Table 4.9 are the estimated
coefficients reported by ISER. The ranges were calculated as the width of the -
95% confidence intervals; the variance was backed out of the reported
F statistics.
Vacancies
Table 4.10 presents the assumed norma 1 and maximum vacancy rates by type
of house. ISER derived the normal vacancy rates by taking the ten-year u.S.
averages of vacancy rates for owner and renter units (Goldsmith and Huskey
198Gb). Single-family and mobile homes have the owner rate; multifamily homes
have the renter rate; and duplexes are the average of owner and renter rates.
For the maximum vacancy rates, Anchorage multifamily rates were available. The
relationship between the normal rates for multifamily and all other types was
used to derive the maximum rates.
4.16
-
-
TABLE 4.9. _Housing Demand Equations: Parameters' Expected Value,
Range, and Variance
Parameter Expected Value Range Variance
b0 0.461
bal -0.303 0.142 0.001
ba2
ba4
b2s
b3s
b4s
co
cal
ca2
ca4
c2s
c3s
c4s
do
dal
da2
da4
d2s
d3s
d4s
-0 .17 5
0.080
0.182
0.317
0.380
0.383
0 .225
0.086
-0.090
-0.203
-0.280
-0.352
0.097
0.068
0 .039
0.014
0.008
-0.020
-0.016
0.152
0.230
0 .205
0.182
0.226
0.124
0.133
0.202
0.180
0.159
0.198
0.101
0 .109
0.159
0.152
0.130
0.162
0.001
0.003
0.003
0.002
0.003
0.001
0.001
0.003
0.002
0.002
0 .• 003
0.001
0.001
0.002
0.001
0.001
0.002
Source: Goldsmith and Huskey 1980b, Table 8.6.
Depreciation and Removal
Housing demolition rates (Table 4.11) are a function of the age of the
housing stock and the demand for housing. ISER found that approximately 1% of
the housing stock was removed between 1975 and 1980 in Anchorage and Fairbanks
(Goldsmith and Huskey 1980b). As the existing stock ages, the removal rate is
assumed to grow toward the U.S. average, which has been estimated to be between
2 and 4% per forecast period (5 years).
4.17
TABLE 4.10. Assumed Normal and ~1aximum Vacancy Rates
by Type of House (Percent)
Tt~e
No rmf l) Rate a MaXi~~~ Rate
Single Family 1.1 3.3
fvbbi 1 e Home 1.1 3.3
Oupl ex. 3.3 10.0
Multifamily 5.4 16 .o
(a) Imputed by I SER from Bureau of
the Census (1980a).
(b) Imputed by ISER from Anchorage
Real Estimate Research Committee
(1979).
TABLE 4.11. Assumed Five-Year Housing Removal Rates in Railbelt
Region, 1980-2010 (Percent of Housing Stock at
Beginning of Period Removed During Period)
Years
1980-1985
1985-1990
1990-1995
1995-2000
2000-2005
2005-2010
Remova 1
Rate (percent)
1. 25
1.50
1. 75
2.00
2.25
2.50
Source: Author Assumption.
The professional economics literature has devoted some attention to
depreciation rates in housing. In an article in the Review of Economics and
Statistics, leigh (1980) used a perpetual inventory method of calculating the
national stock of efficiency-adjusted residential housing units and checked
these estimates against the Census of Housing for 1950, 1960, and 1970 as well
as other authors• estimates. The various sources sited in Leigh•s article show
values for economic depreciation/replacement ranging from 0.4 to 2.35%, with
m6st estimates grouped around 1.0 to 1.5%. Leigh herself calculates about 1%
4.18
-
-
~'
-
r
-
-
.. ...,
for the period 1950 through 1970. ISER calculated an approximate five-year 1%
r a t e o f rem o v a l f o r An c h o r a g e an d Fa i r b a n k s h o u s i n g u n it s by c o mp a r i n g t h e
estimated number of units in 1970 and 1979 with cumulative building permits
data. Because the housing stock ages and new houses provide more "services"
than old houses, the rate ot: economic depreciation for a given area is assumed
to be larger than the rate of physical depreciation. Consequently, housing
units are physically replaced 1 ess frequently than 1% per year. The U.S.
average physical depreciation rate was calculated by de Leeuw (1974) at between
2 and 4% per five-year period or 0.4 to 0.8% per year. It is assumed that as
the Alaska housing stock ages, the very low current removal rate of 1.0% per
five years wi 11 approach the national 1 ower bound rate, 2.0°/, by 2000 and 2.5%
by the year 2010.
Base Year Housing Stock
The base-year housing stock figures displayed in Table 4.12 are the counts
of year-round housing stock from the 1980 Census of Housing for Alaska.
TABLE 4.12.
Housin9 T.l:ee
Single Family
Mobile Homes
duplexes
Multifamily
Tot a 1
Railbelt Housing Stock by Load Centef Qnd
Housing Type, 1980 (number of units) a;
Anchorage Fairbanks
40,562 10 ,87 3
10,211 2,175
8,949 2 ,512
27,980 8,607
87,702 24,167
(a) A unit is occupied by one household. Thus,
a 4-plex is considered four housing units.
Source: 1980 Census of Housing, STF3 Data Tape.
4.19
-
-
-
-
5.0 THE RESIDENTIAL CONSUMPTION MODULE
The Residential Consumption r1odule provides forecasts of electricity
consumption for the Residential Sector. The forecasts of the residential
sector•s needs do not include the impacts of conservation produced by market
intervention by government. The potential for and impacts of such conservation
activities are handled in the Program-Induced Conservation Module (see Chapter
fl.O). Furthermore, the module•s forecast of residential requirements is the
amount of electricity that needs to be delivered to the residential sector-it
does not include allowances for line losses.
The Residential Consumption ~1odul e estimates the amount of electri·city
residential consumers use, with explicit consideration of the impacts of
electricity price changes and fuel switching among electricity, gas, and oil.
Impacts of fuel switching to ·and from other fuels (such as v.ood) are handled in
the Program-Induced Conservation Module.
r~ECHAN ISH
The Residential Consumption r~odul e ernpl oys an end-use approach. In an
en d-u s e an a l y s i s , t h e f i r s t s t e p i s t o i den t i f y t h e m aj o r u s e s o f e l e c t r i c -
ity. Future market saturations of the uses are forecasted so that the future
stock of electricity-consuming devices is defined. The next step is to esti-
mate the amount of electricity demanded to meet a future demand for the ser-
vices of the devices. The forecast of average consumption of the appliance
stock, therefore, reflects both the trend in the size of the device and its
utilization rate, as well as projected increases in the efficiency of the
device. Once the stock of major electricity-consuming devices and their
corresponding average annual per-unit consumption of electricity are forecast,
the future consumption of electricity by device type is obtained by multiplying
the nl.D1lber of devices by their predicted annual average consumption of
electricity. Using the same procedure for miscellaneous residential uses and
summing over all end-uses yields an aggregate forecast of electricity
requirements.
5.1
One major problem of the end-use approach is that the impacts of changes
in fuel prices (both electricity and alternatives) and income on electricity
usage are usually treated directly through the forecaster•s judgment. The RED
Residential Consumption Module addresses this problem differently. By adjust-
ing the aggregate residential consumption figure with variable price and cross-
price adjustment factors computed in the model from actual consumption data and
prices, RED accounts for price change and fuel-switching impacts in the resi-
dential sector. These adjustments can be interpreted as electricity conserva-
tion induced by changes in fuel prices.
INPUTS AND OUTPUTS
Table 5.1 presents the inputs and outputs of the module. The number of
households by dwelling type is the number of occupied civilian dwelling units
served by electricity predicted in the Housing Module. The price adjustment
parameters, as well as the appliance saturations, are generated in the Uncer-
tainty ~1odule. The output of the module is preliminary residential sales of
electricity.
MODULE STRUCTURE
The Residential Consumption Module identifies the following major uses of
electricity in the residential sector:
1. Water Heating
2. Cooking
3. Refrigeration
4. Freezing
5. Clothes Washing (and additional water heating)
6. Clothes Drying
7. Di shwashi ng (and additional water heating)
8. Saunas-Jacuzzi s
9. Space Heating
In addition, several other uses of electricity by households are captured by a
small appliance category. Small appliances include televisions, radios,
lighting, head-bolt heaters, kitchen appliances, heating pads, etc. The basic
5.2
-
-
-
-
-
~ ' i
-
(a)
(b)
TABLE 5.1. Inputs and Outputs of the RED Residential Module
In[2Ut s
S,tmbol
HDTY
A, B ,.A ,
OSR,GSR
SAT
0Ut[2Ut S
S~mbol
RESCON
Variable
Electrically Served Households
by Type of Dwelling
Price Adjustment Coefficients
Appliance Saturations
Variable
Residential Electricity
Requirements
From
Housing Stock Module
Uncertainty Module
Uncertainty r"bdul e
To
Miscellaneous, Peak Demand
and Conservation Modules
premise of this module is that the household is the primary consumer of elec-
~ tricity, not the individual. However, the number of individuals in the house-
hold significantly affects the consumption of energy for clothes washing,
clothes drying, and water heating. Therefore, an adjustment is included in the
model for changes in the average household size to recognize the impact of such
changes on the usage of these appliances.
For the nine major uses of electricity, the end-use approach is used (see
Figure 5.1). Figure 5.1 shows the calculations that take place in the Residen-
tial Consumption Module. Reginning with a regional estimate of occupied hous-
ing stock by type, the module uses appliance market saturation parameters to
estimate the stock of each of the major appliances recognized by the model.
The module then calculates the initial fuel mode split for multifuel appl i-
~-ances, calculates preliminary electric consumption for each appliance type
(including small appliances), and then sums these estimates together into a
r
preliminary consumption estimate for the residential sector. Price forecasts
for gas, oil, and electricity and "trial"-specific own-price and cross-price
adjustments are used to adjust the preliminary forecast. The adjustments are
described in Section 7.0.
5.3
CALCULATE
PREUMINAY
SMALL APPLIANCE
USE OF
ELECTRICITY
PRICE FORECASTS
(EXOGENOUS)
FORECAST OF
OCCUPIED HOUSING
STOCK BY TYPE
{HOUSING MODULE)
CALCULATE STOCK OF
LARGE APPLIANCES
8Y END USE.
DWELLING TYPE
CALCULATE INITIAL
SHARE OF EACH
APPLIANCE USING
ELECTRICITY
CALCULATE AVERAGE
ELECTRICAL USE IN
LARGE APPLIANCES
BY APPLJANCE
CALCULATE TOTAL
PRELlMINARY LARGE
APPLlANCE USE
BY
APPLIANCE
SUM PRELIMINARY
CONSUMPTION FOR
ALL APPLIANCES
PRICE AND
CROSS-PRICE
ADJUSTMENTS
RESIDENTIAL
CONSUMPTION
PRIOR TO
CONSERVATION
ADJUSTMENT
APPLJANCE
SATURATIONS
8Y HOUSING TYPE
(UNCERTAINTY
MODULE)
FUEL MODE
SPLIT
1980
~FFICIENCY
STANDARDS
PRICE
· ADJ. PARAMETERS.
RESIDENTIAL SECTOR
(UNCERTAINTY
MODULE)
FIGURE 5.1. REO Residential Consumption Module
5.4
-
-
!'-' I
,-
Results from the Battelle-Northwest (BNW) end-use survey (see Appendix A)
show significant differences in the saturations of these nine end uses by the
type of dwelling in which the household resides. The module, therefore, uses
the number of occupied housing units of each type of dwelling (single family,
multifamily, mobile home, and duplex) as predicted by the Housing Module as one
of the inputs to estimate the stock of appliances.
The Housing ~1odule predicts the number of occupied primary(a) residences
by type in a given region served by electric utilities. By multiplying the
number of occupied housing units by type by an assumed percentage served, the
Housing Consumption t-bdule forecasts the nlJllber of primary occupied housing
units served:
HHSTYit = SEit x HDTYit ( 5 .1)
where
HHS = households served
TY = denotes the type of dwe 11 in g
SE = proportion of households served by an electric utility
HD = stock of occupied dwellings from the Housing Module served by
electricity
i = region subscript
t = forecast period (t = 1, 2, 3, • • • • 7) •
Once the nunber of electrically served households by type of dwelling is
known, the appl icance stock can be estimated. The saturation rate for an
appliance is the' percentage of households residing in a certain type of dwell-
ing and having the appliance in question. By multiplying the housing-type-
specific saturation rate by the nLillber of households residing in that type of
housing and then summing across housing types, the model forecasts appliance
demand in each future forecast period t:
(a) Excluding second or recreation homes.
5.5
where
AD= appliance demand
4
= I (SATTYitk X HHSTYit)
TY=1
SAT = sat u rat i o n rat e ( p a r arne t e r)
k =end-use appliance.
(5.2)
Next, the model calculates the number of future additions to the stock. Assum-
ing demand is fully met, the nLJTlber of new appliances in period tis found by
calculating the stock of appliances surviving from all previous periods and
subtracting this surviving stock from appliance demand:
(5.3)
where
NA =number of new appliances
AS; ok = initial stock of appliances (198 0)
m dtk= vintage specific scrap rate in period t· ' for vintage m
(parameter) (m -= 1' 2, 3, ol!l •• ' 7) •
Equation 5.3 can be rearranged so that the stock equals the demand:
t
ADitk = ASiok x (1 -d~k) + m~1 NAimk x (1 -d~k)
The future appliance stock, therefore, can be stratified by vintage. Next, the
model calculates the initial stock of electricity-consllTling appliances by mul-
tiplying the number of appliances in each vintage by the percentage using
electricity:
( 5. 4)
ENAimk = FMSik x NA;mk (5.5)
5.6
-
-
-
-
.....
i
. I
( 5 • 6)
where
EAS = initial stock of electric appliances
Fr1S = fuel mode s p l it
ENA = additions to the electric appliance stock
EAO = total electric appliance stock.
The Residential Consumption nodule next calcu1ates the average annual
electricity consumption of each major appliance. ;Jifferent vintages of
appliances use different amounts of electricity, so the average consumption
:nust reflect the vintage composition of the stock. Furthennore, industry
energy efficiency standards for appliances could change in future years. The
future vintage specific consumption rate can be derived by rnu1tip1yin~ the
current (1980) consumption rate by a growth factor and adjusting for any
c h a n g e s i n e f f i c i en c y s tan d a r d s • By we i g h t i n g t h e s e fi g u r e s by t h e p r o po r t i o n
of the stock they represent, the av~rage consumption of each appliance type in
a forecast year is derived:
where
= ACiok x
EAS,.ok x ( 1-dt
0
k) ! ( . . ( ·1) Z
+ I AC. k x ( 1 +gk) m-x
. 10
EADitk m=l
m
ENAimk ( 1-d tk) ) x ( 1-c smk) x .;....._ ____ _
EADitk
ACitk =average consumption of appliance kin period t (parameter)
ACiok = average consumption of appliance k in the beginning period
( parameter)
Z =length of forecast periods t and min years (parameter) set
equal to 5 for this study.
g = growth rate of appliance k consumption (parameter)
5.7
( 5. 7)
Ill
cs = conservation standards target consumption reduction
( p a r arne t e r) •
Finally, the preliminary consumption for each major appliance can be
calculated by multiplying the stock of each appliance by its calculated average
consumption:
where
CONSitk = EADitk x ACitk x AHSitk
CONS= preliminary consumption of electricity prior to price
adjustments
AHS = household size adjustment parameter for clothes washing,
clothes drying, water heaters only.·
(5.8)
The Residential Module makes no distinction among the various types of
appliances in the small appliance category. The requirements for these units
are simply the product of the number of households in the region, the initial
consumption level, and a growth factor in consumption over time:
where
CONS;tsa = ~y HHSTYit x [ACiosa + (AfGitsa x t x Z)]
ACG = growth factor in small appliance consumption
sa= index denoting small appliances.
Total preliminary residential consumption is found by summing across end
uses:
9
RESPRE; t = I CONS. t k + CONS. t k=l 1 1 sa
(5.9)
(5.10)
-
where _
RESPRE =total preliminary residential consumption.
-5.8
.....
.....
RESPREit reflects mainly the physical characteristics of the stock of
electrical appliances and household income. Consumers, however, can respond
dramatically to changes in the prices of electricity and alternative fuels.
The own-and cross-price adjustment factors measure the responsiveness of
consumers to price changes. Specifically, the own-price adjustment factor is
the ratio of the percentage of change in the quantity taken of electricity
. during a five-year period to the weighted percentage change in price of
electricity relative to the prices of other goods during the period •
Similarly, the demand for electricity is also a function of the prices of
alternative fuels. For example, the cross-price adjustment factor for gas
~ measures the responsiveness of the quantity of electricity taken with respect
to change in the price of natural gas. In other ....ords, the cross-price adjust-
lnent factor predicts the percentage change in the quantity of electricity taken
for a one-percentage change in the relative price of an alternative fuel.
-
-
If the cross-price effect is positive, then the fuels are said to be
substitutes. As the price of another fuel rises, the quantity taken of el ec-
tricity rises. For example, natural gas and electricity are substitutes. If
the price of gas rises enough relative to the price of electricity, then some
natural gas customers will switch to electricity. If the cross-price effect is·
negative, the fuels are complements, implying that increases in the price of
the alternate fuel will cause reductions in the amount of the electricity that
is taken.
The RED model distinguishes between short-run and long-run responses to
price. In the short run, or the immediate future, consumers cannot alter their
usage as much as over longer periods of time, since their stock of appliances
is fixed. Over a longer period of time, they can replace elements of their
stock with devices that use less electricity, or perhaps use another fuel
source. Therefore, the speed with which consumers adjust from the short-run to
the 1 ong-run is important.
The price effects generated in RED are aged over the forecast period from
their short-run values to their long-run values, thus expliCitly modeling con-
sumers' changing the pattern of use in the short run and fuel sw.itching in the
long run. The Uncertainty r1Jdul e generates both the short-run values of the
5.9
I I Ill
price effect for specific trials and the coefficient of the speed of consumer
response. Chapter 7.0 discusses both the economic theory and literature under-
lying the estimation of the own-price effect and cross-price effects of gas and
oil on electricity consumption, as well as the manner in which the effects are
calculated.
The actual calculation of the price adjustment of residential consumption
i s a s f o 11 ow s :
where
RESCONit = RESPREit x ( 1 + OPAit) x ( 1 + PPAit)
x ( 1 + G P A it) .
RESCON =consumption of electricity in the residential sector
OPA = own-price adjustment for electricity
PPA = cross-price adjustment for fuel oil
GPA = cross-price adjustment for natural gas.
( 5 .11)
RESCON is the predicted electricity consumption in the residential sector
before adjustments for program-induced conservation. This figure is passed to
the Peak Demand and Program-Induced Conservation Modules. Note that RESCON is
a single number. The Residential Consumption r-bdule does not report price-
adjusted consumption of electricity by end use.
PARAMETERS
The percentage of households served by an electric utility (Table 5.2) is
an important parameter. ISER has estimated that only 91% of the occupied
housing in Fairbanks was connected to an electric utility (Goldsmith and Huskey
1980b). Due to the high emphasis the Alaska state 1 egi sl ature and governor
have placed on energy, the extension of electrical service to all who would
like service is highly probable. Therefore, electrical services are assumed to
be extended to the entire stock of housing in the Fairbanks load center by
1995. The Anchorage-Cook Inlet load center is assumed to be 100% served.
5.10
-
-I
-
-
-
....
-
-
r
-
TABLE 5. 2. Percent of Households Served by
Ap p l i a n c e Sat u rat i o n s
Electric Utilities in Rail belt
Load Centers, 1980-2010
Year Anchora9e Fairbanks
198o(a) 100 91
1985 (b) 100 93
199o(b) 100 96
1995(b) 100 100
zooo(b) 100 100
2005 (b) 100 100
2010( b) 100 100
(a)
(b)
Source: Goldsmith and Huskey 1980b,
Table C.13, C.14, 0.4, 0.5.
The state is assumed to extend
electrical service to all residents
by 1995.
Because historical growth and comparison with the lower forty-eight states
provide only limited guidance on both current and future market saturations of
major appliances, somewhat arbitrary maximum penetration rates have been est i-
rnated. The estimates were made by comparing recent utility saturation rate
studies by San Diego Gas & Electric (SDG&E) in 1982 and Southern Ca1ifornia
Edison (SCE) in 1981 (realizing their limited relevance in estimating Alaska
saturation rates), information from 1980 Census of Housin9 for Alaska,
informat-Ion from the Battelle-Northwest end-use survey, and other related
literature. Wide bands of uncertainty should be presumed for all appliances
examined since saturation rate data in the literature were not consistent.
Table 5.3 summarizes saturation rates examined.
t1arket penetration rates for many appliances in Alaska are already outside
the bounds of lower forty-eight state experience and have been increasing over
time. However, many of the major appliances will likely never reach 100%
market saturation for a variety of reasons, such as transient population, the
convenience of substitutes such as laundromats, srnal l housing units with
5.11
lT1 .
1-'
N
TABLE 5.3. Appliance Saturation Rate Survey (table values in percent of households)
SDG&E(l982)(a)
A~~l i ance (total market area)
Clothes Drier
Refrigerator 97.5
Freezer 26.2
Hot Tub/Jacuzzi/ Saunas · 11-39
Water Heater
Cooking Range 96.2
Dishwasher 55.4
Clothes Washer 68.9
t~icrowave Ovens 34.5
Space Heating 94.6
( a ) Ave rage v a 1 u e s f o r a·l 1 c u s t ome r s •
SCE (1981)
(range of values
observed in
market area} (b)
71.1-81.2
96.2-96.6
9.1-33.5
1.3-19.4
92.3-97.7
98.3-99.5
. 41.2-58 .o
75.6-89.3
17.9-38.9
Railbelt: Housing
Census (1980
(range of
values: lowest,
highest area)
92.0-97.7
99.5-99.9
99.9
Railbelt BNW End-lJse
Survey (1981)
(range of values:
lowest to highest
area and building ty~e)
61.0-90.2
99
57.2-94.8
2.5-16.9
86.9-100.0
95.7-100.0
23.3-78.2
63.8-92.5
(b) By building type. Types were single family, apartments/condominiums/town houses, and mobile homes.
(c) Areas were Anchorage (Anchorage, Matanuska-Susitna, and Kenai Peninsula Boroughs) and Fairbanks
(North Star Borough plus Southeast Fairbanks Census Area). Fairbanks was the lower value.
(d) Building types were single family, mobile home, multifamily, and duplex. See Tables 5.4-5.11.
Sources: See reference 1 i st.
J _I ] J
-
.....
-
....
--
inadequate space for some appliances, changing consumer perferences, etc. The
saturation rate estimates assumed in the RED model reflect a compromise between
1) rapid historical growth in appliance stocks in Alaska, 2) approaching
boundaries on market saturation and 3) comparable saturation data from other
sources.
Tables 5.4 through 5.7 show the default value and range for future market
saturations of major appliances that can use one of several fuels in normal
horne installation. The table values are the expected percentages of housing
units of a given type that will own the appliance in a given year (having
access to and owning an appliance may result in different saturation rates) and
market area, and the subjective uncertain range that can he used instead of the
default value if the Monte Carlo option is chosen. The table title indicates
the type of housing. The assumptions for each type of appliance are given
be 1 ow.
Hot Water
Hot water was available in nearly 99% of single-family homes in the
Anchorage market area, according to the Battelle-Northwest end-use survey.· It
is assumed that 99% is a maximum for two reasons: the market saturation of hot
water in the Western U.S. was 99% in the 1970 Census (Bureau of Census 1970);
and Alaska can be expected to have rural cabin-like structures with limited
electric service for some time to come. In the Fairbanks market area, single-
family saturations are projected to incr~ase to the Anchorage level by 1990.
The end-use survey and 1970 Census both show saturations in the vicinity of 90~h
in this area. Increasing urbanization in Fairbanks and better electric service
should increase this percentage.
The other types of structures in the Battelle-Northwest survey showed
market saturations of nearly 100% in all market areas. The exception was
multifamily housing. However, the wording of the question in the survey upon
which this calculation is based may have been interpreted as asking whether the
respondent had a hot water tank in his unit rather than (as was intended)
whether he had hot water available. A 100% market penetration for hot water in
duplexes and multifamily buildings was assumed. Mobile homes were considered
the same as single-family units.
5.13
TABLE 5.4. Market Saturations (percent) of Large Appliances with Fuel Substitution -
~
Possibilities in ~ngle-Family Homes, Railbelt Load Centers, 1980-2010
Water Heater Clothes Dr~ers Range (cookin9) Sa u n a s-J a c u zz i s
Load Center Year Default Range De fault Range Default Range De fault Range
a. Anchorage 1980 98.6(a) 90.2 99.9(a) 14.1
1985 98.8 95-100 91.2 88-94 100.0 100-100 16.3 13-19
1990 99.0 98-100 92.5 89-95 100.0 100-100 18.7 14-22
1995 99.0 98-100 93.7 90-96 100.0 100-100 21.0 16-26
2000 99.0 98-100 95.0 92-98 100.0 100-100 23.4 18-28
Ul . 2005 99.0 98-100 95.0 92-98 100.0 100-100 25.7 20-30
I--'
.p. 2010 99.0 98-100 95.0 92-98 100.0 100-100 28.1 23-33
b. Fairbanks 1980 86.9(a) 81.4 99.5(a) 7.9
1985 93.0 91-95 84.0 80-88 100.0 100-100 8.9 6-12
1990 99.0 98-100 87.5 82-92 100.0 100-100 10.0 6-14
1995 99.0 98-100 92.5 87-97 100.0 100-100 11.2 6-16
2000 99.0 98-100 95.0 92-98 100.0 100-100 12.4 7-17
2005 99.0 98-100 95.0 92-98 100.0 100-100 13.6 8-18
2010 99.0 98-100 95.0 92-98 100.0 100-100 14.8 9-19
(a) For hot water and cooking, missing values in the Battelle-Northwest survey were not counted.
.! -J ) ·.~ -J .. I J
J 1 -l
TABLE 5.5. Market Saturations (percent) of Large Appliances with Fuel Substitution
Possibilities in Mbbile Homes, Railbelt Load Centers, 1980-2010
Water Heater Clothes Orters Range (cooking) Saunas Jacuzzis
Load Center Year Default Range Default Range Oefaul t Range [)efaul t Range ----
a. Anchorage 1980 98.2(a) 79.0 95.7(a) 6.1
1985 99.0 98-100 80.0 79-81 100.0 100-100 6.9 3-11
1990 99.0 98-100 82.0 80-84 100.0 100-100 7.8 4-12
1995 99.0 98-100 84.0 82-86 100.0 100-100 8.7 5-13
2000 99.0 98-100 85.0 83-87 100.0 100-100 9.6 6-14
2005 99.0 98-100 90.0 85-95 100.0 100-100 10.5 6-14
U1 . 2010 99.0 98-100 95.0 91-99 100.0 100-100 11.4 7-15 ,__.
U1
b. Fairbanks 1980 99.0(a) 92.3 98.6(a) 2.5
1985 99.0 98-100 94.0 91-97 100.0 100-100 2.8 1-5
1990 99.0 98-100 95.0 92-98 100.0 100-100 3.1 1-7
1995 99.0 98-100 95.0 92-98 100.0 100-100 3.5 1-8
2000 99.0 98-100 95.0 92-98 100.0 100-100 3.8 1-8
2005 99.0 98-100 95.0 92-98 100.0 100-100 4.2 1-8
2010 99.0 98-100 95.0 92-98 100.0 100-100 4.5 1-9
(a) For water heat and cooking, missing values in the Rattelle-Northwest end-use survey were not
counter!.
TABLE 5.6. Market Saturations (percent) of Large Appliances with Fuel Substitution
Possibilities in Duplexest Railbelt load Centerst 1980-2010
--
~
Water Heater Clothes Dr~ers Range (cooking) Saunas Jacuzzis
Load Center Year Default Range Default Range Default Range Default Range
a. Anchorage 1980 100.o(a) 90.0 96.4 16.9
1985 100.0 100-100 91.0 90-92 100.0 100-100 19.0 16-22
1990 100.0 100-100 92.5 90-95 100.0 100-100 21.2 17-25
1995 100.0 100-100 93.0 91-96 100.0 100-100 23.4 18-28
2000 100.0 100-100 95.0 92-98 100.0 100-100 25.6 21-31
2005 100.0 100-100 95 .o 92-98 100.0 100-100 27.6 23-33
U1 2010 100.0 100-100 95.0 92-98 100.0 100-100 29.8 25-35 .
.......
100.o(a) 85.5(b) Q) b. Fairbanks 1980 100.0 8.2
1985 100.0 100-100 91.0 90-92 100.0 100-100 9.2 6-12
1990 100.0 100-100 92.5 90-95 100.0 100-100 10.3 6-14
1995 100.0 100-100 93.0 91-96 100.0 100-100 11.4 6-16
2000 100.0 100-100 95.0 92-98 100.0 100-100 12.5 8-18
2005 100.0 100-1QO 95.0 92-98 100.0 100-100 13.5 9-19
2010 100.0 100-100 95.0 92-98 100.0 100-100 14.6 10-20
(a) Values for Battelle-Northwest end-use survey were adjusted to 100 percent for water heaters in.
1980. For explanation, see text.
(b) 1980 clothes dryer penetration in Fairbanks for 1980 adjusted downward by one to match the nurnber of
washers in duplexes.
J J
••• 1
TABLE 5. 7 • Market Saturations (percent) of Large Appliances with Fuel Substitution
Possibilities in Multifamily Homes, Railbelt Load Centers, 1980-2010
Water Heater Clothes Dr~ers Range (cooking) Sa u n a s J a c u z z i s
Load Center Year Default Range Default Range Default ~~ Default Range
a. Anchorage 1980 10o.o(a) 75.7 98.2 13.6
1985 100.0 100-100 83.0 82-84 100.0 100-100 15.0 12-18
1990 100.0 100-100 83.5 82-85 100.0 100-100 16.4 12-20
1995 100.0 100-100 84.0 82-86 100.0 100-100 17 0 7 13-23
2000 100.0 100-100 85.0 83-87 100.0 100-100 18.9 14-24
Ul 2005 100.0 100-100 90.0 85-95 100.0 100-100 19.9 15-25 0
I--' -..... 2010 100.0 100-100 95.0 92-97 100.0 100-100 20.9 16-26
b. Fairbanks 1980 100.0(a) 61.0 100.0 5.7
1985 100.0 100-100 65.0 61-69 100.0 100-100 6.3 3-9
1990 100.0 100-100 70.0 65-75 100.0 100-100 6.9 3-11
1995 100.0 100-100 80.0 75-85 100.0 100-100 7.5 3-13
2000 100.0 100-100 85.0 80-90 100.0 100-100 fLO 3-13
2005 100.0 100-100 90.0 85-95 100.0 100-100 8.5 4-14
2010 100.0 100-100 95.0 92-97 100.0 100-100 8.9 4-14
(a} Water heat survey numbers adjusted to 100 percent for 1980. For explanation, see text.
i I Ill
Clothes Dryer
The Battelle-Northwest survey and 1970 Census both show Rail belt market
saturations for clothes dryers far above the IJ.S. average (Bureau of Census
1970). Information available from the 1980 U.S. Statistical Abstract for 1979
shows that abo~t 61.5% of electrically served housing units have an electric or
gas dryer (up from 44.6% in 1970) (Bureau of Census 1980b). In contrast, the
Battelle survey showed market saturations ranging from 61% in Fairbanks multi-
family structures to over 90% in other types of housing. Single-family dryer
saturations ranged from 81% in Fairbanks to 90% in Anchorage. Because Alaska
already has such high saturations, the forecast is outside the bounds of
historical experience. A reasonable estimate is that no more than 95% of
single-family homes, mobile homes, and duplexes will ever have dryers because
of the availability of laundromats and because of the room taken up by washer-
dryer combinations in small housing units. For multifamily units, penetration
is assumed to be much slower because of the space problem. Since washers and
dryers are now installed in pairs in most new housing, market saturations for
dryers (which are now about 2% below those for washers in most areas) will
approach that for washers as old housing stock is replaced. In general, the
lower the existing saturation, the greater is the uncertainty concerning its
future growth rate.
Cooking Ranges
Several data sources were examined to arrive at market saturation rate
-
-
-
-
-
estimates. The Battelle-Northwest end-use survey indicated that between 96 and -
100% of all h,auseholds surveyed had a range available. SOG&E (1982) reported a
96.2% saturation rate while SCE (1981) ranged from 98.3% for multi-family units
to 99.5% for single-family units. The substitution of hot plates, broiler
ovens (1979 estimated national saturation rate of 26%) and microwave ovens
(1979 estimated national saturation rate of 7.6%) may account for the differ-
ence between 90 and 100%. Therefore, 100% of all housing units currently are
assumed to have cooking facilities available by 1985. This percentage holds -
throughout the period.
-
5.18
Saunas, Jacuzzis, Etc.
These units are a relatively new phenomenon in private homes, almost all
having been installed since 1970. The Battelle-Northwest end-use survey found
-, market saturations ranging from 2.5 to 17%, SOG&E (1982) 11 to 39%, and SCE
(1981) 1.3 to 19.4%, all depending upon market area and housing type. Accord-
ing to the survey, 14% of Anchorage single family households reported having
one of these units, compared to 10.4 and 11.0%, respectively, for SCE and
SDG&E. Among single-family homes built since 197.5 in Anchorage, the saturation
l'las 21~~. while among single-family homes built since 1980 in the SDG&E survey
area, the saturation was 23.8%. To arrive at saturation rate estimates, a
target rate slightly larger than both was assumed for newly constructed single-
'~
'
·-
family homes in Anchorage to allow for the increasing popularity of saunas-
jacuzzis. Additional allowances were made for the existing stock of housing to
acquire saunas-jacuzzis. The additional allowances changed over time based on
the belief that saturation growth rates waul d fall as the newness of the item
>vore off. This phenomenon may happen with any relatively new technology. Once
it has reached that segment of the population initially desiring to own a sauna
or jacuzzi, additional growth will be slower since a lower maximum penetration
rate, when compared to other appliances, is assumed. Additional supportive
evidence for a lower maximum penetration rate is found from California. There,
saturation rates are lower than in Alaska and growth rates are slowing down.
One additional impact on the willingness of those individuals initially not
strongly desiring to own a sauna or jacuzzi may be the relatively high price,
at least when compared to other major appliances. Also, installation costs may
be higher in Alaska since poorer weather would necessitate that the unit be
enclosed. However, the inflation-adjusted cost of saunas and jacuzzis, whirl-
pools, etc. is expected to drop somewhat as it does with any new appliance
type. This could raise future market saturations above current levels. Ry
weighing these factors, and considering economic growth prospects for the
subregions, the estimated default values were chosen. They are presented in
Tables 5.4 through 5.7.
One potential problem exists in Table 5.7. The Battelle-Northwest end-use
survey created a slight ambiguity in terms of appliance ownership for
5.19
i I Ill
mu 1 t i family homes by not asking residents of this type of housing whether they
actually owned or had access to a sauna or jacuzzi. In some apartment
complexes, a central recreation building houses a sauna or jacuzzi that all
residents may use. If every individual in the apartment complex claims they
each have a sauna or jacuzzi when in fact only one exists, the saturation rate
is overstated. This phenomenon is brought out in the SCE (1981) data, where
19.4% of all apartment/condominium/townhouse occupants claimed a hot tub/-
jacuzzi. However, only 6.7% of that total had their own private hot tub/-
jacuzzi. A level of 19.4% gives an incorrect representation of the penetration
rate for saunas and jacuzzis and an overestimate of electricity consumption.
To correct for this problem, default values and ranges in Table 5.7 have been
adjusted downward for slower future growth.
Tables 5.8 through 5.11 indicate default market saturations and ranges of
values for large household appliances that are almost always electric. These
include refrigerators, freezers, dishwashers, and clothes washers. The table
title indicates the housing type, and the table values show an ·expected market
saturation for each appliance by market area and year. The ranges shown in the
tables reflect the degree of uncertainty attached to the default value. The
wider the range, the greater is this subjective uncertainty. The assumptions
supporting the table values are given below by appliance.
Refrigerators
The Battelle-Northwest end-use survey found that virtually 100% of all
households had a refrigerator. This is in agreement with several other studies
such as SDG&E (1982) at 97.5%, SCE at 96.2 to 96.6%, and the national Residen-
tial Energy Consumption Survey (RECS) at 99.8%. The California Energy Commis-
sion (CEC) found in 1976 that enough housing units had second refrigerators to
raise total California market saturation to 113-116%. ISER, in their report to
the Alaska State Legislature, assumed that this high percentage would likely
not prevail in Alaska because of the cooler climate (Goldsmith & Huskey
1980b). Therefore, a default value of 99% was chosen throughout. In the RED
model, the ISER assumption is modified to permit a range of values from 98 to
100%.
5.20
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-) --, }
TARLE 5.8. Market Saturations (percent) of Large Electric Appliances in Single-Family Homes,
Railbelt Load Centers, 1980-2010
Refrigerators Freezers Dishwashers Clothes Hashers
Load Center Year Default Range Default Range Default Range Default Range ----
a. Anchorage 1980 99.0 88.3 78.2 91.7 -
1985 99.0 98-100 90.0 85-95 85.0 80-90 92.0 90-94
1990 99.0 98-100 90.0 85-95 90.0 85-95 92.5 90-95
1995 99.0 98-100 90.0 85-95 90.0 85-95 93.7 91-96
2000 99.0 98-100 90.0 85-95 90.0 85-95 95.0 92-98
tJ1
0 2005 99.0 98-100 90.0 85-95 90.0 85-95 95.0 92-98 N ......
2010 99.0 98-100 90.0 85-95 90.0 85-95 95.0 92-98
b. Fairbanks 1980 99.0 84.9 53.8 84.9
1985 99.0 98-100 88.0 86-90 79.0 75-85 86.0 84-88
1990 99.0 98-100 90.0 85-95 90.0 85-95 87.5 85-90
1995 99.0 98-100 90.0 85-95 90.0 85-95 92.5 90-95
2000 99.0 98-100 90.0 85-95 90.0 85-95 95.0 92-98
2005 99.0 98-100 90.0 85-95 90.0 85-95 95.0 92-98
2010 99.0 98-100 90.0 85-95 90.0 85-95 95.0 92-98
TABLE 5.9. Market Saturations (percent) of Large Electric Appliances in Mobile Homes,
Railbelt Load Centers, 1980-2010
Refrigerators Freezers Dishwashers Clothes Washers
Load Center Year De fault Range Default Range Default Range Default ~~
a. Anchorage 1980 99.0 94.8 43.9 80.6
1985 99.0 98-.100 92 .o 90-95 6 7 .6 62-72 85 .o 80-90
1990 99.0 98-100 90.0 85-95 90.0 85-95 90.0 85-95
1995 99.0 98-100 90.0 85-95 90.0 85-95 90.0 85-95
Ul 2000 99.0 98-100 90.0 85-95 90.0 85-95 95.0 92-98 .
N 2005 99.0 98-100 90.0 85-95 90.0 85-95 95.0 92-98 N
2010 99.0 98-100 9.0.0 85-95 90.0 85-95 95.0 92-98
b. Fairbanks 1980 99.0 7 3 .o 48.6 92.3
1985 99.0 98-100 82.0 75-89 71.4 66-76 93.0 91-95
1990 99.0 98-100 90.0 85-95 90.0 85-95 92.5 91-96
1995 99.0 98-100 90.0 85-95 90.0 85-95 94.0 92-96
2000 99.0 98-100 90.0 85-95 90.0 85-95 95.0 92-98
2005 99.0 98-100 90.0 85-95 90.0 85-95 95.0 92-98
2010 99.0 98-100 90.0 85-95 90.0 85-95 95.0 92-98
) l l
... 1 1
TABLE 5.10. Market Saturations (percent) of Large Electric Appliances in Duplexes
Railbelt Load Centers, 1980-2010
Re fri ge rators Freezers Dishwashers Clothes Washers
Load Center Year Default Range Default Range Default Range Default ~~~
a. Anchorage 1980 99.0 66.5 76.5 92.5
1985 99.0 98-100 75.0 70-80 85.0 80-90 93 .o 91-95
1990 99.0 98-100 85.0 80-90 90.0 85-95 95.0 92-98
1995 99.0 98,..100 85.0 80-90 90.0 85-95 95.0 92-98
(J1 2000 99.0 98-100 85.0 80-90 90.0 85-95 95.0 92-98 .
N
Lo.J 2005 99.0 98-100 85.0 80-90 90.0 85-95 95.0 92-98
2010 99.0 98-100 85.0 80-90 90.0 85-95 95.0 92-98
b. Fairbanks 1980 99.0 75 .2 57.4 85.5
1985 99.0 98-100 80.0 75-85 85.0 80-90 91.0 90-92
1990 99.0 98-100 85 .o 80-90 90.0 85-95 92.5 90-95
1995 99.0 98-100 85.0 80-90 90.0 85-95 93.0 91-96
2000 99.0 98-100 85.0 80-90 90.0 85-95 95.0 92-98
2005 99.0 98-100 85.0 80-90 90.0 85-95 95.0 92-98
2010 99.0 98-100 85 .o 80-90 90.0 85-95 95.0 92-98
-
TABLE 5.11. Market Saturations (percent) of Large Electric Appliances in Multifamily Homes, ~
Ra i1 belt Load Centers, 1980-2010
Refri~erators Freezers Di sh1'1ashers Clothes Washers
Load Center Year Default Range Default Range Default Range Default Range
a. Anchorage 1980 99.0 62.5 73.3 76.5
1985 99.0 98-100 65.0 60-70 85 .o 80-90 85.0 80-90
1990 99.0 98-100 70.0 65-75 90.0 85-95 90.0 85-95
1995 99.0 98-100 70.0 65-75 90.0 85-95 92.0 90-94
(Jl 2000 99.0 98-100 70.0 65-75 90.0 85-95 95.0 92-98 .
N ...,. 2005 99.0 98-100 70.0 65-75 90.0 85-95 95.0 92-98
2010 99.0 98-100 70.0 65-75 90.0 85-95 95.0 92-98
b. Fairbanks 1980 99.0 57.2 23.3 63.8
1985 99.0 98-100 65.0 60-70 34.0 30-39 68.0 63-72
1990 99.0 98-100 70.0 65-75 50.0 45-55 70.0 65-75
1995 99.0 98-100 70.0 65-75 74.0 70-79 80.0 75-85
2000 99.0 98-100 70.0 65-75 90.0 85-95 85.0 80-90
2005 99.0 98-100 70.0 65-75 90.0 85-95 90.0 85-95
2010 99.0 98-100 70.0 65-75 90.0 85-95 95.0 . 92-98
I J J l _I J ) ~ .~ J I J .. -j
-'
Freezers
The end-use survey found market area-wide saturations of freezers ranging
from about 80% in Fairbanks to over 90% in Anchorage. These figures are 10 to
20% higher than assumed by ISER for 1980 for these areas, about 40~~ above 1970
Census values for the Railbelt, and 30 to 40% above the U.S. average. In other
-wo r d s , a rea-to-a r e a co mp a r i son s a n d h i s t o r i c a 1 e x p e r i en c e a r e n o t v e r y he 1 p f u l
for predicting future saturations. For single-family homes and mobile homes,
the maximum saturation has been assumed to have been just about reached because
r-
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.,....
'
with better shopping facilities and increased urbanization, fewer freezers will
be necessary for long-term food storage from bulk buying.
For duplexes and multifamily units, the percent of saturation should
remain significantly lower. The tenants in such units tend to be more
transient and are proba~ly less involved in Alaskan hunting, fishing, and
gardening pursuits than most Alaskans. Consequently, they would have less
demand for freezers. Second, rental units tend to be smaller. Consequently,
renters might tend to substitute rented commercial cold-storage locker space
for a freezer to conserve scarce living space in duplexes and multifamily
units. The range of uncertainty is shown to be quite broad, since market
penetration has been rapid in the last 10 years, but the maximum appears to
have been reached in some cases.
Dishwashers
The Battelle-Northwest end-use survey found market saturations for dish-
washers well above the existing U.S. average. In the U.S. as a whole, the 1979
saturation was about 41% of homes served by electricity (Bureau of Census
1980b), but this percentage ranged from 50% in Fairbanks to 75Yo in Anchorage
survey homes. Saturations have increased by about 50 percentage points in both
Railbelt load centers since 1970, again outside the range of historical experi-
ence. (Using this experience, ISER (Goldsmith and Huskey 1980b) projected 1978
market saturations of 50% in Anchorage and 36% in Fairbanks.) The rate of
increase in market saturation was very rapid in the 1970s, but further
increases in saturation in Anchorage in particular may be 1 imited since a high
proportion of some types of housing units already have dishwashers. A maximum
saturation of 90% was assumed for all homes. The annual rates of saturation
5.25
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growth for the 1970s were then projected for each· region: 9% per year for
Anchorage, and 8% per year for Fairbanks. Except for Fairbanks multifamily,
where historical growth rates are assumed, 90% maximum saturation is assumed to
occur in 1990. The growth rate was then assumed to fall to zero. A wide range
of uncertainty is assumed for dishwasher saturations because of the tenuous
nature of the required assumptions.
Clothes Washers
The Battelle-Northwest end-use survey found that area-wide clothes washer
saturations ranged from about 84% in Fairbanks to 89% in Anchorage. These
figures are well above the 73% reported for the U.S. in 1979 in the 1980
Statistical Abstract (Bureau of Census 1980b). It also represents about 10 to
15 percentage points growth since the 1970 Census. The rate of saturation
increase did not slow down appreciably in the 1970s compared to the 1960s;
consequently, market saturation may not have yet approached its maximum. For
forecasting, the maximum penetration is assumed to be 95%. Different types of
housing reach this maximum at different rates. In particular, since single-
family homes are already 85 to 90% saturated, they reach 95% slowly, achieving
this level by the year 2000. Some markets are closer to being completely
-
-
~· 1
saturated. Even at low rates of growth they reach 95% somewhat earlier. In no ~
case is clothes-washer saturation allowed to be below that for clothes
driers. The Battelle-Northwest survey generally found that washer saturation
was one to two percentage points higher than that for dryers. Where this was
not the case (e.g., duplexes in Fairbanks) the difference appears to have
occurred because of the small number of households in the category. The market
saturations for washers and driers gradually converge, since they are now
usually installed in pairs. Multifamily saturation of washers and driers grows
the slowest, reaching 95% by 2010 in Fairbanks.
Fuel Mode Splits
The fuel-mode splits presented in Table 5.12 were also derived from the
Battel1e-Northwest end-use survey and 1980 Census of Housing with the exception
noted below. These parameters are assumed to remain fixed over the forecast
period, as the cross-price elasticity adjustment handles fuel switching.
5.26
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TABLE 5.12. Percentage of Appliances Using Electricity and Average Annual
Electricity Consumption, Rai lbelt Load Centers
Anchorage Fairbanks
Percent age Using Electricit~{a} Annua 1 kWh Percentage Using Electric it.}' Annual kWh
A~~l i ance __jL_ MH OP MF Cons um~t ion SF MH OP MF Cons un~t ion
Space Heat (Existing Stock)
Single Family 16.0 NA NA NA 32,850 9. 7 NA NA NA 43,300
l-1ob i 1 e Home NA 0.7 NA NA 24,570 NA o.o NA NA 33 ,210
Duplex NA NA 22.8 NA 21,780 NA NA 11.7 NA 28. 7l 0
Multi Family NA NA NA 44.4 15 ,390 NA NA NA 14.8 19 ,080
Space Heat (New Stock: 1985)
Single Family 10.0 NA NA NA 40,100 9.7 NA NA NA 53,000
ttlb 11 e Home NA 0.7 NA NA 30,000 NA o.o NA NA 40,600
Ouplex NA NA 15.0 NA 26,600 NA NA 11.7 NA 35,100
MultI Family NA NA NA 25.0 18,800 NA NA NA 14.8 23,300
Water lleaters (Existing) 36.5 50.4 44.0 60.9 2,800 33.1 42.8 43.1 26.2 3,300
Water lleaters (New: 1985) 10.0 50.4 15.0 25 .o 3,000 33.1 42.8 4 3.1 26.2 3,4 75
(.J1 Clothes Dryers 84.3 88.1 81.3 86.6 1,032 96.2 94.6 94.4 100.0 1,032
N
'-1 Cooking Ranges 75.8 23.2 85.2 88.2 050 79.0 48.2 95.0 9 7 .1 850
Sauna-Jacuzzi s 93.5 100.0 93.7 81.8 1,600 61.8 100.0 60.8 100.0 1,600
Refrigerators 100.0 100.0 100 .o 100.0 1,636 100.0 100 .o 100.0 100.0 1,636
Freezers 100.0 100.0 100.0 100.0 1. 342 100.0 100.0 100.0 100.0 1, 342
Oi shwashers 100.0 100.0 . 100 .o 100.0 250 100 .o 100 .o 100 .o 100.0 250
Additional
Water fleating (Existing) 36.5 50.4 44.0 60.9 799 33.1 42.8 43.1 26.2 . 799
Water Heating (New: 1985) 10.0 50.4 15.0 25 .o 799 33.1 42.8 4 3.1 26.2 799
Clothes Washers 100.0 100.0 ]00.0 100.0 90 100.0 100.0 100.0 100.0 90
Additional
Water Heating (Existing) 36.5 50.4 44.0 60.9 1,202 33.1 42.R 43.1 26.2 1, 202
Water lleatl ng (New: 1985) 10.0 50.4 15.0 25.0 1 ,202 3 3 • I 42.!! 4 3 .1 26.2 1 ,202
t1i scell aneous 100.0 100.0 100.0 ]00.0 2,111) 100.0 100.0 100.0 100.0 2,466
(a) SF= sin!)le farni ly; ~I= mobile homes; OP duplexes; f.IF =multifamily.
I I Ill
Discussions were held with several Anchorage area home builders, the staff
of Anchorage Municipal Power and Light, ISER, and two real estate management
firms in Anchorage concerning incremental fuel mode splits for new housing
stock. The consensus was that very few units are being constructed in the
Anchorage area in 1983 with either electric heat or electric hot water where
gas is available because electric thermal units are considered to have
unattractively high operating costs. This is believed to be a phenomenon
caused by past electricity price increases and is therefore not accommodated i;y
the RED price adjustment coefficients after 1980. Accordingly, the 1983
version of the model judgmentally imposes reduced incremental electric fuel
mode splits in space heating and water heating for new housing units built in
the Anchorage-Cook Inlet load center since 1980. The fuel mode splits are kept
above zero to reflect construction in portions of the Anchorage-Cook Inlet load
center not served by gas. Where incremental fuel mode splits are shown, el ec-
tricity use rates for both the new and old stock are shown in Table 5.12.
Post-1985 use rates for all appliances appear in Table 5.13.
Comparison of Census and Battelle Northwest end-use survey results for the
percentage of water heaters using electricity in Fairbanks in 1980 revealed
lower values in the Census. The assumption was made that the Census results
were more accurate and additional time went into a further analysis of the
Battelle Northwest end-use survey. As a result of this and a study of the
methodology employed in the Census, original end-use survey fuel mode split
values have been scaled downward by a correction factor of 0.6 for hot water.
After the correction factor, the figures now reported in Table 5.12 are
believed to be accurate.
Consumption of Electricity per Unit
The average kilowatt hour consumption figures are primarily based on
values st.rnmarized from other studies presented in Henson (1982) and also SDG&E
(1982). Below is a brief discussion of each parameter. Studies reviewed are
shown in Table 5.14.
5.28
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\.()
TABLE 5.13. Growth Rates in Electric Appliance Capacity and Initial Annual
Average Consumption for New Appliances
Average Annual
kWh ConsLDll~tion for Grov1t h Rate in
New Ap~l i ances {198 5} Electric Capacity
A~~l i ance Anchorage Fairbanks Post-1g85 (annual)
Space Heat
Single Family 40,100 53,000 0.005
t-bb i 1 e Homes 30 ,ooo 40,600 0.005
Duplexes 26,600 35,100 0.005
Multifamily 18,800 23,300 0.005
Water Heaters 3,000 3,4 75 0.005
Clothes Dryers 1 ,032 1 ,032 o.o
Cooking Ranges 1,200 1,200 0.0
Saunas-Jacuzzi s 1 ,7 50 1 ,750 0.0
Refrigerators 1,560 1,560 0.00
Freezers 1 ,550 ·1 ,550 0.00
Dishwashers 230 230
Additional. Water Heating 740 740 0.005
Clothes Washers 70 70 0.0
Addition a 1 Water Heating 1 ,050 1 ,050 0.005
Small Appliances and Lighting 2,110 2,466 (a)
(a) Incremental growth of 50 ·kWh per custo,ner in Anchorage per 5-year period;
70 UJh in Fairhanks.
1
TABLE 5.14. Comparison of Appliance Usage Estimates from Selected Studies (measured in kWh)
Scanlon Parti &
SRI (b) MR I (b) CEC(b) Appliance Hoffard(a) Parti ~ George AHA11 SOG&E ------
Refrigerators 1,270 1,665
Frost Free 2,177 1,624 1,455 1,523 1,858 2,250 1,8!:10
Standard 869 684 681 933 893 1,500 906 ;;;;
Freezer 1,084 1,622 1, 294 1,47!:1 1,342 1,316
frost Free 2,252 1,820 1 ,210
Standard 1,881 1,190 811
Electric Range 1,024 804 1 ,083 753 1,180 782 674 700 671
Clothes Washer 98 88 70 103 259
Clothes Dryer 1,051 1 ,363 1,170 990 1,032 950 993 808
Washer/Oryer
Combination 2,680
Ul Water 1-leater 3,021 4,535 2,628 . 4 ,490 4 ,046 3,!:126 4 ,219 2,581
w Oi shwasher 1;539 538 360 149 250 363 259 0
Color Television 639 613 726 490 420
Space Hea.t i ng 11,966 3,441. 7,301 5,876 14,153 2,258 9,834 2,486 Sf(c)
7!:15 MF}
1,152 MH
Central Air
Conditioning 1, 505 1,809 1,596 2,183 5,494 3,573 2,924
14i see ll aneous 2,127 1 ,865 1,882 1,950 1 ,259
(a) ResuHs of final (7th) iteration.
(b) Engineering estimates.
(c) SF denotes single family units, MF multifamily units, and MH mobile homes units.
Sources for Table 5.13:
1) The Christian Science Monitor, 1981, pp. 15.
2) San Oiego Gas and Electric 1982.
3) Scanlon and lloffard 1981.
J ) -J
Space Heat
For space heating in the existing housing stock, the average annual
consumption figures derived by ISER are used (Goldsmith and Huskey 1980b).
These figures were derived based on heating degree days, floor space, and
average consumption of all electric homes within the Railbelt region and were
adjusted downward by 10% to allow for additional conservation in the building
stock since ISER 1 S study.
Water Heaters
The average consumption for water heaters is based on the California
Energy Commission 1 s (CEC 1 s) estimates and several engineering studies sum-
marized in Henson (1982). The figure separates out consumption for clothes
washers and dishwashers and has been adjusted upward by 15% to account for the
colder-water inlet temperature in Alaska. Anchorage values were also adjusted
downward for some heating of municipal water supplies (see Tillman 1983).
Clothes Dryers
1-For clothes dryers, average consumption is the figure reported by the
Midwest Research Institute U~RI). ISER (MRI 1979) picked a lower estimate.
~ based on household size, but the colder climate in Alaska should also raise the
estimated use of dryers. This is reflected in high saturation values for this
appliance.
Cooking-Ranges
This category is broadly interpreted as production of heat for cooking
purposes. The figure reported was derived by averaging the values from several
reports.
Saunas-Jacuzzis
The authors informally contacted several suppliers of saunas, jacuzzis and
hot tubs and were told that the consumption of these devices ranged from
-100-3000 kWh annually. Hunt and Jurewitz found 1300 kWh annual consumption for
new additions to the stock. However, SDG&E (1982) reported annual average con-
sumption at approximately 2700 kWh. A conservative consumption figure of
5.31
! I Ill
1600 kWh annually was chosen to reflect the presence of bathtub whirlpools and
other small units as well as larger units.
Refrigerators
An average value from SDG&E (1982) was used, allowing for a 75% saturation
of frost-free units in the Railbelt, as revealed by the Battelle-Northwest
residential survey.
Freezers
This figure showed little variation among ~1erchandising Week, r1RI, and
ISER. The MRI figure was chosen.
Dishwashers
The value assumed for dishwashers is the mean of several engineering
studies cited in Henson (1982) and SDG&E (1982). Additional water heating
associated with dishwashing has been separated out.
Dishwasher and Clothes Washer Water
These values are from the CEC, adjusted upward to account for colder water ~
inlet temperatures in Alaska.
Miscellaneous Appliances
For miscellaneous appliances, estimates of consumption were originally
prepared by ISER by subtracting estimated large appliance electricity consump-
tion for 1978 from total 1978 consumption/residential customer (Goldsmith and
Huskey 1980b). Lighting was inferred from national statistics and increased to
1000 k.Wh/year/customer. The remainder was charged to small appliances.
Research for the RED rvbdel checked ISER 1s work by assuming: 1) televisions
(rated at 400 kWh/year) are included in small appliances; and 2) the ISER
estimate of 480 kWh/year/customer for headbolt heaters is replaced with load
center-specific estimates derived from load-center specific utilization data
produced by the Battelle-Northwest end-use survey and National Oceanic and
Atmospheric Administration (NOAA) data on normal minimum temperatures (NOAA .-
1979); and 3) 1000 kWh/year lighting. The revised estimates for block heaters
5.32
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are as follows: Anchorage, 459 kWh/year/customer; Fairbanks, 1127 kWh/year/-
customer. Because the results were broadly consistent with ISER 1 S figures,
ISER 1 s totals were used (Goldsmith and Huskey 1980b).
E l e c t r i c a l Capac it y G r owt h
Table 5.15 presents average annual kWh consumption for new appliances in
1985. Revised numbers are presented reflecting the authors 1 belief that
improved efficiency ratings for appliances coming onto the market will largely
offset future increases in energy use brought about by increases in appliance
s i z e • Th i s i s n o t me r e l y a p hen orne no n o f A l a s ka f u e l p r i c e s ; r a t h e r , i t
reflects national energy market trends. Alaskans have little choice concerning
the purchase of more efficient appliance technologies since the available
appliance mix is dictated by national markets.
little information is available on changes in appliance efficiencies in
the absence of price effects in the Alaska market. However, the appliance
manufacturers associations and the U.S. Department of Energy (DOE) have
developed estimates of appliance efficiency for several types of new appliances
(see King et al. 1982). The major source for the efficiency ratings on new
appliances was a DOE survey of appliance manufacturers (Form CS-179) that asked
actual energy efficiency information on current models of appliances for 1972
and 1978. In addition, manufacturers were asked to make projections of new
~ appliance efficiency for 1980. The Association of Home Appliance Manufacturers
has since revised some of the estimated efficiencies of the 1980 (sometimes
1981) model~ and has found that estimated efficiencies have improved more than
was anticipated at the time of the CS-179 survey. In fact, refrigerators
freezers, dishwashers, and clothes washers have improved enough in average
efficiency to offset the effects of product size increases and new energy-using
features (such as the frost-free option on refrigerators), 1 eadi ng to a si g-
nificant net reduction in average kilowatt-hours used in the new models.(a)
Table 5.15 summarizes the findings of the CS-179 survey and appliance
~ manufacturers.
(a) Personal Communication, Jim Mct1ahon, Energy Analysis Program, lawrence
Berkeley laboratory, May 24, 1983.
5.33
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"""'\
TABLE 5.15. Electric New Appliance Efficiency Improvements 1972-1980
(percent impact on energy use, 1972 base) -
Aeel i ance
CS-179 Findings(a)
1972-19iS 1972-19SO
Aeeliance Manufacturers(b)
1972-1980 ~
1. Water Heat
Efficiency -1.1 -1.9 NA
Size Increase NA NA NA
Other Features NA NA NA
Net Energy Use NA NA NA
2. Ranges -Efficiency -15.7 -20.1 NA
Size Increase NA NA NA
Other Features NA NA NA
Net Energy Use NA NA NA
3. Clothes Dryers
Efficiency -0.0 -4.2 -3.1 -Size Increase NA NA 0.4
Other Features NA NA 0.4
Net Energy Use NA NA -2.7 -4. Refrigerators
Efficiency -20.5 -34.3 -45.6
Size Increase NA NA 8 .o
Other Features NA NA 11.6
Net Energy Use NA NA -26.0
5. Freezers -Efficiency -24.7 -32.8 -48.0( )
Size Increase NA NA -10.0 c
Other Features NA NA 18.5
Net Energy Use NA NA -39 .5
6. Dishwashers
-45.a(ct) Efficiency NA NA
Size Increase NA NA I
114.Q(d)
Other Features NA NA
Net Energy Use NA NA -3l.O(d)
7. Clothes Washers·
-51.6(d) Efficiency NA NA
Size Increase NA NA "slight" (d) -Other Features NA NA (d)
12.1(d)
Net Energy Use NA NA -39.5 -NA = Not Avail ab 1 e
(a) Source: King et al. 1982.
(b) Source: McMihon 1983 ••
(c) Net decrease in average size. More compact models sold. ,-
(d) 1972-1981.
5.34
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Even in the absence of further changes in Railbelt energy prices, residen-
tial consumers in the region are expected to have access to increasingly effi-
cient models of major appliances. In the recent past, efficiency improvements
~ .... , have more than offset increases in the size of these appliances. Far the
future, consumers are assumed to adopt more efficient available ~odels to just
offset increases in size of new models far the years after 1985. Two excep-
tions are allowed. Table 5.15 shows that water heaters have not improved
1""""-
!
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significantly in efficiency. Once properly installed (and then only if in an
unheated space), the limits of efficiency improvements will have been reached
on existing designs. From there on, further improvements are possible from
redesign of water-using appliances, tankless paint-of-use water heating, and
significant behavioral changes of household residents, but these are unlikely
without further price increases in the Railbelt. Thus, as household incomes
rise, it is assumed that hot water usage increases and efficiency improvements
do not offset these increases in the absence of price changes. A similar
factor is assumed to be at work in space heating. Rising household incomes are
assumed to increase the average size of the housing stock and comfort demands
at a faster rate than efficiency improvements can reduce demand in the absence
of energy price changes.
Prior to 1985, a mix of influences is expected to be operating on energy
use. Water heaters and space heating systems are assumed to increase in size
with little or no offsetting conservation effects in the absence of fuel price
increases. Clothes dryers are assumed to have about the same energy use as in
1980, with small increases in size offset by small improvements in effi-
ciency. New ranges are assumed to increase in size and in energy-using fea-
tures over the existing stock to surpass the existing upper bound usage in
Scanlon and Hoffard (1981) single-family homes. Refrigerators have gained
radically in energy efficiency historically and are assumed to continue to do
so between 1980 and 1985, offsetting size and energy-use increases. 1980
refrigerator energy usage rates already reflect a 1 arge proportion of frost-
free units. (Battelle-Northwest survey results show about 75 to 80% frost-free
units in the Anchorage load center, 65 to 70% frost-free in Fairbanks.) Thus,
little increase in energy use can be expected from penetration of frost-free
units. Mthough nationally freezers have become more efficient, additional
5.35
I I Ill
penetration of frost-free models·in the Railbelt is assumed before 1985, lead-
ing to a small increase in average energy use. Clothes washers and dishwashers
are assumed to continue their recent historic trend toward greater efficiency
and conservation of hot water before 1985. After that, water use increases
while efficiency improvements just offset increased capacity and use. Sauna
and jacuzzi 1985 energy use reflects additional market penetration of slightly ~
larger units than comprise the 1980 stock.
Agpliance Survival
Table 5.16 presents the percentage of appliances remaining in each five-
year period after their purchase. These figures were derived by ISER based on ·~
Hausman• s work (1979) with implicit discount rates for room air conditioners.
Hausman found that the stock of a particular vintage of air conditioners was
fairly well approximated by a Weibull distribution. By substituting differing
lifetimes (EPRI 1979) for alternative appliances, ISER used his results to
derive the figures in Table 5.16. For saunas and jacuzzis, RED assumes the
appliance lifetime was comparable to refrigerators.
Household Size Adjustments
Clothes washers, clothes dryers, and water heaters are used more inten-
sively by large families. Relying on a 1979 Midwest Research Institute study
of metered appliances and family size U1idwest Research Institute 1979), ISER
researchers calculated an adjustment factor for usage of electricity in clothes
washers, clothes washer water, clothes dryers, and water heaters (Goldsmith and
Huskey 1980b). As household size declines, so does energy use in these appli-
ances, other things equal. Table 5.17 shows the equations used. ISER annual-
ized the equations (which were based on daily use), normalized them to an
average household size of three persons, and calculated a ratio to adjust
calculated electricity consumption for average household size.
Price Elasticities
The final parameters used in the Residential Module are the parameters
used to compute the price effects described briefly in the module structure
section of this chapter. Because of the complexity of the algebra involved,
5.36
-
-
~~
,._
-!"""\
r-
_;o.""
-
.l"ll<A
-
--(
Tll,BLE 5.115. Percent of Appliances Remaining in
Purchase, Ra i 1 be 1 t Region
a. 01 d Aeeliances 5 10 15
Space Heat ( A l 1 ) 0.90 0.80 0.6
Water Heaters 0.6 0.3 0.1
Clothes Dryers 0.8 0.6 0 .3
Ranges-Cooking 0.6 0.3 0.1
Saunas-Jacuzzi s 0.8 0.6 0.3
Refrigerators 0.8 0.6 0.3
Freezers 0.9 0.8 0.6
Dishwashers 0.6 0.3 0.1
Clothes Washers 0.6 0.3 0 .1
b. New Aeeliances
Space Heat (Al 1) 0.89 0.73 0.56
\~ater Heaters 0 .7 5 0.35 0 .1
Clothes Oryers 1.00 0.75 0.35
Ranges-Cooking 0.7 5 0.35 0 .1
Saunas-Jacuzzi s 1.00 0.75 0.35
Re fr i gerato rs 1.00 0.7 5 0.3 5
Freezers 1.00 1.00 0.75
Dishwashers 0 .75 0.35 0 .1
Clothes \~ashers 0.75 0.35 0.1
Source: ISER (Goldsmith and Huskey 198Gb) except
which is author assumption.
5.37
Service Years After
20 25 30
0 .3 0 .1 0.0
0.0 0.0 0.0
0 .1 0.0 0.0
0.0 0.0 0.0
0 .1 0 .0 0.0
0.1 0.0 0.0
0.3 0 .1 0.0
0.0 0.0 0.0
0.0 0 .0 0.0
0.42 0.3 0.1
0.0 0 .0 0.0
0.1 0.0 0.0
0.0 0 .0 0.0
0.1 0.0 0.0
0 .1 0.0 0.0
0.35 0 .1 0.0
o.o 0 .0 0.0
0.0 o.o 0.0
for saunas-jacuzzis,
TABLE 5.17. Equations to Determine Adjustments to Electricity
Consumption Resulting from Changes in Average
Household Size
A~eliance Eg uat ion
Clothes Hasher AHs(a) = 1 x AHH(b)
Clothes Was her Water AHS = 0.25 + 0.75 AHH
. Clothes Dryer AHS = 0.2 5 + 0.75 AHH
Water Heater AHS = 0.51 + 0.49 AHH
(a) AHS = Adjustment factor.
(b) AHH = Average household size (Based on 3.0).
the discussion of this topic has been given its own chapter (Chapter 7.0),
where the parameters are reported. The values for the parameters came from
~1ount, Chapman, and Tyrell (1973).
5.38
_,
-
~
-
-
-
-I
·-
·~.
6.0 THE BUSINESS CONSUr1PTION ~10nlJLE
The Business Module forecasts the requirements for electricity in the
commercial, light industrial, and government sector of the Railbelt economy.
The figures predicted here do not consider the impacts of explicit program-
induced conservation. Program-induced conservation is handled in the Progra~
Induced Conservation rlodule. Heavy industrial use is forecasted exogenously,
as described in Section 10.0.
:·1ECHANI Sr1
The structure of the forecasting mechanism in the Business Consumption
i1odul e is dictated by the availability of data that can be used to produce
forecasts. Unlike many Lower 48 utility service areas, the Railbelt has a very
weak data base for estimating and forecasting commercial, 1 ight industrial, and
government electricity consumption. No information exists for consumption of
electricity by end use in this settor, so RED produces an aggregate forecast of
business electricity consumption. The Business Consumption r1odule uses a
forecast of total employment for each load center to forecast business
(commercial, light industrial, and government) floor space. The module then
uses this forecast of the stock of floor space (a proxy for the stock of
capital r.>quipment) to predict an initial level of business electricity
consumption. This initial prediction is then adjusted for price impacts to
yield a price-adjusted forecast of business electricity consumption.
INPUTS AND OUTPUTS
Table 6.1 presents the inputs and outputs of the Business Consumption
r,1odule. Load-center-specific forecasts of total employment are exogenous to
RED. Currently these come from forecasts of the ISER Man in the Arctic Program
(r1 A P ) mod e l. Th e e 1 a s t i c it y o f u s e p e r s q u a r e f o o t o f b u il d i n g s p a c e a n d p r i c e
adjustment parameters are assigned in the Uncertainty Module. The output of
the Business Consumption Module is the price-adjusted forecast of electricity
requirements of the business sector before the impacts of program-induced
conservation are considered.
6.1
I I 'II
TABLE 6.1. Inputs and Outputs of the Business Consumption Module
a) Inputs
Symbol
TE~1P
BBETA
A ,R ,;.. ,OSR ,GSR
b) Outputs
Symbol
BlJSCON
Name
Total Regional Employment
Electricity Consumption Fl oar
Space Elasticity
Price Adjustment Coefficients
Name
Price-Adjusted Business
Co nsLDTipt ion
From
Forecast File (exogenous)
Uncertainty Module
(parameter)
Uncertainty module
( p a rame t e r)
To
t~iscellaneous, Peak Demand
and Conservation r1odul es
t100ULE STRUCTURE -,
Figure 6.1 presents a flow chart of the module. The first step is to use
employment forecasts to construct estimates for the regional stock of floor
space by five-year forecast period. The predicted floor space stock is then
fed into an electricity consumption equation that is econometrically derived to
yield a preliminary forecast of business requirements, which is then adjusted
for price impacts.
After investigating several alternative methods for forecasting business
f1 oo r space, Batte ll e-Northwes t researchers decided to use a very simple
formulation of the floor space forecasting equation in the 1983 version of
REO. The floor space per employee in Anchorage and Fairbanks is ass LDTied to
increase at a constant rate to levels about 10% and 15%, respectively, above
today 1 s levels by the year 2010. This takes into account both the evidence of
historic increase in floor space per employee in Railbelt load centers and the
historic lower levels of floor space per employee in Alaska compared with the
nation as a whole. The assumption is still quite conservative, since Alaska 1 s
commercial floor space per employee is far below the national average. The
forecasting equation is shown as equation 6.1.
6.2
-
-
PRICE
FORECASTS
(EXOGENOUS)
FORECAST
EMPLOYMENT
CALCULATE
BUSINESS/
GOVERNMENT.!
LIGHT INDUSTRIAL
FLOOR SPACE
CALCULATE
PRELIMINARY
BUSINESS
ELECTRICAL
CONSUMPTION
PRICE AND
CROSS-PRICE
ADJUSTMENTS
CONSERVATION
PRELIMINARY
BUSINESS USE
COEFFCIENTS
(UNCERTAINTY
MODULE I
PRICE
ADJ. PARAMETERS
BUSINESS SECTOR
!UNCERTAINTY
MODULE!
,-_ ADJUSTMENTS
F I G U R E 6 • 1 • R E 0 8 u s i ness Cons iJn p t i on ~1o d u l e
where
-STOCK = floor space in business sector
a = initial (1980) fl oar space per employee
b = annual growth factor (1 plus growth rate) in floor space per
employee
TH1P = total employment
= index for the region
t = time index, t=1,2,3, ••• ,7
k = time index, k=1,2,3, ••• ,3L
6.3
(fS.1)
I I Ill
The controlling data series for the commercial forecast is an annual
estimate of commercial floor space, which is derived for the period 1974 to
1981. The beginning point is an estimate of commercial floor space in the two
locations developed by ISER (Table 6.2 and Table 6.3) that shows the 1978 stock
of energy-using commercial floor space in Anchorage to be about 42.3 million
square feet (from whith 860 thousand square feet of manufacturing floor space
were subtracted to yield 41.4 million) and in Fairbanks about 10.8 million
square feet. This estimate was adjusted backwards and forwards for the period
1974 to 1981 using a predicted construction series (Equation 6.4) to produce a
stock series for the two locations.
Once the forecast of the stock of floor space is found, the module then
predicts the annual business electricity requirements before price adjustments,
based on a regression equation:
where
PRECONit = exp(BETAi + BBETAi x 1n(STOCKit)] ( 6. 2)
PRECON = nonpri ce adjusted business consumption
BETA = parameter equal to regression equation intercept
BBETA =percentage change in business consumption for a one percent
change in stock (floor space elasticity).
exp,1n =exponentiation, logarithmic operators
t =index for the forecast year (1980, 1985, ••• , 2010).
Finally, price adjustments are made with the price adju-stment mechanism
i d en t i c a l t o t h a t i n t h e Res i den t i a 1 Con s ump t i o n MJ d u l e •
where
BUSCONit = PRECONit x (1 + OPAit) x (1 + PPAit) x (1 + GPAit) (6.3)
BUSCON price-adjusted business requirements (MWh)
OPA own-price adjustment factor
PPA = cross-price adjustment factor for fuel oil
GPA =cross-price adjustment factor for natural gas.
6.4
-
-
-
-
-f
-~·
i~-
TABLE 6.2. Calculation of 1978 Anchorage Commercial-Industrial Floor Space
10 3ft 2
M1ATS Survey (Anchorage Bm>Jl, 197 5)
t~inus Non-energy Using (parking lots,
c erne t e r i e s , e t c • )
Energy Using Floor Space
20 Percent Adjustment for Underreporting
Sectors
1.
2.
3.
4.
It em: ( e l
not Included in Survey:
Girdwood/Indian(a)
Eagle River/C~ugiak(b)
Ho t e l s I ~~o t e l s c !
Assorted Cultural Buildings(d)
Retail Trade
Warehousing
Education
\·Jho l e sal e Trade
Tran sport-Communication-
Public Utilitites
Government
Manu fact uri ng
Other
G r owt h Ret wee n 19 7 5 -1 9 7 8 ( f l ( a b out 2 5 % )
6 '148
3,722
3,528
3 ,131
2 ,663
1,405
706
7,331
1978 Estimated Commercial-Industrial Floor Space(g)
General 25 ,120
Education 5,000
Warehousing 4,520
Hotels 1,500
Manu fact uri ng 860
1978 Non-Manufacturing Floor Space, Anchorage
Source: Adapted from Goldsmith and Huskey (1980b).
6.5
42,067
18,918
23' 149
4,630
27,i79
53
300
1,000
500
29,632
7 ,400
37,000
36,140
I I I"
TABLE 6.2. (contd)
(a) Twenty-five businesses in 1975 acording to telephone book. Assume 2,50f)
square feet/business.
(b) Rased on the ratio of the housing stock in 1978 between Eagle River/Chugiak
and Anchorage.
(c) Assumes 2,000 rooms at 500 square feet/room. Based on Jackson and Johnson
1978, p. 40.
(d) Forty-six establishments identified in 1975 telephone book. Average size
assumed to be 10,000 square feet.
(e) Detail does not add to total in original. Total was assrmed correct.
(f) This is based upon two indicators. The first is the growth in employment
between 1974-75 and 1978. Civilian employment was as follows: 1974-
58,700, 1975-69,650, and 1978-76,900. Employment growth was 31% in the
period 1974 to 1978 and 10% in the period 1975 to 1978. (State of Alaska,
Department of Labor, Alaska Labor Force Estimates by Industry and Area,
various issues.) The second is the growth in the appraised value of
buildings over the period 1975 to 1978. After adjusting for inflation, the
increase was 48%. Based on the assumption that the rapid employment
increase in 1975 resulted in undersupply of floor space in that year, we
assume a 25% growth in floor space between the summer of 1975 and 1978.
(g) Independent estimates of floor space in 1978 in the educational category
and the hotel/motel category were available from the Anchorage School
District and Anchorage Chamber of Commerce, respectively. The remaining
growth was allocated proportionately among the other categories.
TABLE 6.3. 1978 Commercial-Industrial Floor Space Estimates
Greater Anchorage Area
Anchorage
Kenai-Cook Inlet
Matanuska-Susitna
Seward
Greater Fairbanks Area
Fairbanks
Southeast Fairbanks
Source: Adapted from Goldsmith and Huskey (1980b).
6.6
Mill ion
Square Feet
41.4
36.1
3.2
1.5
0.6
10.8
10.4
0.4
-
-,,
The price-adjusted business requirements are then passed to the Program-
!""" Induced Conservation and Peak Demand ivbdules.
c'f""", PARAI 1IETERS
-
As described in the subsection on MECHANISM, the data base available in
the Railbelt for forecasting business electricity consumption is very weak.
Among the principal problems in forecasting for this sector are the following:
• No information on electricity consumption by end use exists for this
sector in the Railbelt.
• Many of the Railbelt's large commercial users of electricity
(considered industrial users in many electricity demand forecasting
models) are primarily commercial users. In addition, many
government offices are in rented commercial space. This makes it
impossible to use employment by industry to forecast electricity
consumption separately for commercial, industrial, and government
end-use sectors since the Standard Industrial Classification (SIC)
codes in which employment is typically reported do not at all
correspond to the traditional end-use sectors of electricity-demand
models.
o While an e~timate exists for the stock of business floor space in
the Railbelt in 1978 and can be used to estimate the intensity of
commercial electricity use, the only comprehensive data base on
commercial (including industrial and government) building
• construction available to estimate changes in stock is subject to
tight copyright controls. It was necessary, therefore, to estimate
historic construction to derive historic series of the stock of
business floor space.
These problems made it reasonably clear that forecasts by end use or even
end-use sector were impossible. However, it was unclear whether stock or
employment was a better predictor of business electricity consumption.
The approach used to r;esolve the issue consisted of three steps. First,
the historical relationships of electricity consumption per employee and per
6.7
II Ill
square foot of commercial floor space were examined to determine the most
appropriate relationship on which to base the forecasts. Second, equations
developed for related work were applied to the two locations and examined as to
the plausibility of their forecasts. Finally, a less sophisticated forecasting
.methodology was devised due to data limitations. This methodology took maximum
advantage of the existing Railbelt data base.
The historical relationships of electricity consumption per square foot
and per employee in the commercial sector were examined to determine whether
one or the other of the two relationships was more appropriate as a basis for
consumption forecasting electrical energy consumption. This examination,
reported in the subsection on consumption below, concluded that floor space was
theoretically superior and a slightly more stable predictor of electricity
consumption.
Floor Space Stock Equations
Several different methods were used in an attempt to forecast commercial
building stock in the Railbelt. These methods included adapting forecast
equations from related work performed by Battelle-Northwest in the Pacific
Northwest and the nation as a whole. It was not possible to directly estimate
building stock equations for the Railbelt due to copyright restrictions on the
use of the data used to estimate the Pacific Northwest and national equations.
The forecast method used a relatively unsophisticated approach to develop
fioor space forecasts. Commercial sector energy consumption and building stock
figures for Anchorage and Fairbanks were compared to similar estimates in the
Lower 48. These comparisons then formed the basis for the method used for
forecasting floor space.
Data on "actual" floor space in the commercial sector are scarce; this
limited the comparison to one year (1979 for U.S. figures; 1978 for
6.8
-
Alaska) .(a) Some Lower 48 multistate regional estimates, but no independent
state-wide estimates, were available. Table 6.4 summarizes the results of
these comparisons to Railbelt estimates for a variety of sources.
An average 531 square feet per employee existed in commercial buildings in
the lJ.S. in 1979 (using Energy Information Administration data on square foot-
age and total U.S. employment, less mining and manufacturing employment).
Broken out by region, the figures ranged from 364 to 751. The highest space-
per-employee ratio occurs in the North Central region, and the smallest is in
the l~est. Comparable figures for 1978 in the Railbelt fall at the lower end of
that range. For comparison, the table shows estimates from a survey performed
by the Bonneville Power Administration (BPA) by commercial building type:
trade employees use 891 ft 2 ; services employees use 1194 ftz; and office
employees use 305 n 2 • Figures for the distribution of commercial square
footage by building type in the U.S. do not exist, but if the square footage
estimates in Table 6.4 are accurate, they may indicate a relatively higher
proportion of offices in the Railbelt on average than in the U.S.
Estimates for the Railbelt from historical data (1978) and the RED model
(1980) fall bel ow the U.S. national average for square footage per employee.
The estimates are reasonable, however, and the differences largely reflect
•differences in the precise definition of employees (U.S. Department of Commerce
or State of Alaska definition) in the available data used in the denominator.
The reasonableness of the square-footage-per-employee figure in the
Railbelt can also be evaluated by examining comparable figures for kWh/employee
and kWh/ft 2 in Table 6.4. The 1979 national average energy use shown is 7303
kl~h per employee. Regional averages range from 4468 kWh in the West to 9997 in
the North Central region. With California's moderate temperatures (low heating
(a) F. W. Dodge, a division of McGraw-Hill, Inc., markets 1 ocal historical
estimates of residential and nonresidential construction by building type,
from which estimates of historical building stock may be generated.
However, copyright restrictions on these data prevented their direct use
in RED model development unless they were purchased for use in the
project. Tests of the data base in other projects persuaded us that the
expense of purchasing the F. W. Dodge data set for use in RED Model
development was not justified.
6.9
II Ill
TABLE 6.4. Comparisons of Square Feet, Employment, and Energy Use
i n Comme rc i a l Buildings: Alaska and U.S. Averages
ft 2 /Em~l oyee kHh/ Em~ l oyee k~~h/ft 2
EIA(a,b)
IJ.S. ( 1 g 79)
NE
NC
s
w
Alaska(l978) (c)
Anchorage
Fairbanks
Climate Zone(a,b)
<2ooo coo(d) 7000+ HDD(e)
<2000 coo 5.5-7000 HOD
<2000 coo 4-5,500 HOD
<2000 COD <4000 ·HOD
>2000 COD <4000 HOD
PG&E (1981) (f)
Power Council (1983) (g)
Warehouse
Office
Hospital
BPA (1980) (h)
Trade
Services
Office
RED Alaska (1980)(i)
Anchorage
Fairbanks
(a) EIA 1983.
(b) U.S. Bureau of the Census 198Gb.
(c) Goldsmith and Huskey 1980b.
(d) COO= cooling degree days
(e) HOD =heating degree days
(f) Pacific Gas and Electric Co. 1981.
531
562
751
476
364
375
336
891
1 ,194
305
429
360
(g) Northwest Power Planning Council 1983.
(h) Bonneville Power Assocation 1982.
7,303
7 ,310
9,997
7 ,358
4,468
7,851
7 ,550
(range
Retail/Wholesale
Office
\4arehouse
real th
8,407
7 ,496
13.75
13 .02
13.31
15.45
12.27
20.9
22.5
10.21
13 .02
11.16
15 .15
16.80
22
5-65)
16
36
45
18.16
7 .75
5.34
24.31
19.57
20.80
(i) RED Model Run Case HE.6--FERC 0% Real Increase in Oil Prices (Employment
Alaska Department of Labor basis from MAP model).
6.10
-
-
.-
and low cooling load) in the West, and the large heating load in the North
Central, these figures are reasonable. Maska 1 s figures of 7851 and 7550 kWh
per employee are slightly higher than the national average, which follows,
given Maska•s hours of winter daylight and temperatures. No independent
utility survey-based estimate could be found.
The RED model (1980) predicts 8,407 and 7,496 kWh per business sector
employee in Anchorage and Fairbanks, respectively. The definition of employees
differs between the two estimates for the Railbelt, but a figure 10 to 15%
higher than the NC region for an area such as the Railbelt that has large
heating, lighting (due to shortened days), and· a reasonable cooling load is not
unacceptable.
The national average kilowatt-hour use per square foot in commercial
buildings shown in the table is 13.75 kWh/ft 2 • The regional averages vary from
12.27 kWh/ft 2 in the West up to 15.45 kWh/ft 2 in the South. ~aska•s figures
are almost double the Western regional average. This reflects the relatively
high consumption per employee and low square footage per employee. First
assumptions might attribute this to the relatively high heating load, but a
comparison of regions by climate zone [that is, by heating-degree (HOD) and
cooling-degree-days (COD)] does not support this hypothesis. t"ovi ng from the
coldest to the warmest climate, kWh/ft 2 figures basically increase. Assuming
Alaska belongs to the coldest climate classification, Railbelt averages might
be expected to fall at the bottom end of the range. Also, the Railbelt commer-
cial building stock is predominantly heated with gas or oil, which ought to put
the Railbelt at the bottom of the range, not the top.
An alternate explanation would examine the mix of commercial building
types within the regions. In all cases, warehouses are the least energy
intensive, while restaurants, grocery stores, and health facilities are
relatively energy intensive. Estimates by Pacific Gas and Electric (PG&E)
(1981) ranged from 5 to 65 kWh/ft 2 , with an average of 22. A report prepared
for the Pacific Northwest Power Planning Council (1983) showed existing
commercial stock consumption at 16 kWh/ft 2 in warehouses, 36 kWh/ft 2 in
offices, and 45 kWh/ft 2 in hospitals. BPA estimates (1982) show consumption in
warehouses around 5.5 kWh/ft 2 , offices at around 8, retail facilities around
6.11
II Ill
18.25, and health facilities at 24.5 kWh/ft2. As shown in Table 6.3, non-
energy using commercial space has been eliminated to the extent possible in the
Railbelt figures. These figures suggest (as in the ft 2/employee case) that the
Alaska mix of commercial buildings may lean relatively more heavily toward more
energy-intensive space 1 ike offices, restaurants, and hospitals. In addition,
the Alaska consumption data include some industrial sector consumption and
therefore inflate the estimates of kWh/ft2.
Lack of data in the area of square feet of stock of commercial buildings
severely 1 imited the depth of these comparisons. The comparisons that were
performed are only as good as the data from which they were derived, which
varied considerably in quality. However, figures for square foot, energy, and
employee ratios estimated from available data suggest that estimates from the
RED model are fairly reasonable, especially considering the level of
sophistication of the model and the quality of available data.
Given the problems reported below with a satisfactory statistical rela-
tionship for predicting floor space, a rather simplified approach to fore-
casting commercial floor space was used. This approach is that _square footage
per employee will grow from its current low level to reach current Lower 48
values by the end of the forecast period, 2010. Although this is not a very
satisfying alternative, professional judgment suggests this to be more appro-
priate than the other options. It recognizes a direct relationship between
floor space and employment and permits fairly easy use of sensitivity analysis.
This simplified formulation is derived by assuming that floor space per
employee grows by 10% in Anchorage by the year 2010 and by 15% in Fairbanks.
-
This is a conservative assumption since best estimates put Anchorage growth in ~,
stock per employee at about 11% for the 1970s, and Fairbanks• growth at 46%.
·The year 2010 stock-per-employee estimates (U.S. Department of Commerce
definition of employment} waul d then be 412 square feet and 386 square feet per
employee in Anchorage and Fairbanks, respectively. This brackets the 1979 U.S.
western regional average. These growth rates are then applied to the 1980
estimates of Railbelt load center floor space per employee (Alaska Department
of Labor employment definition). This provides commercial floorspace forecast
equations for the two cities as follows:
6.12
-
-
Anchorage
Fairbanks
429.5(1.0033)k x Emp~oyment
360.4(1.0046)k x Employment
~~here k is the forecast period in years. The only change necessary for
forecasting was to convert the annual growth rates into five-year forecasts.
Th e -co e f f i c i en t s a r e s h own i n T a b l e 6 • 5 •
TARLE 6.5. Business Floor Space Forecasting
Equation Parameters
Load Center
Anchorage
Fairbanks
Other i1=thods Tried
Parameter Values
a·
429.5
360.4
b·
1.0033
1.0046
In previous versions of the REO model, the parameters used to forecast the
annual' change in floor space stock were extracted from work at Battelle-
Northwest for BPA. Staloff and Adams developed a theoretical and empirical
formulation of a stock-flow model for the demand and supply of floor
space.(a) Using three-stage least squares multiple regression, they estimated
their system of equations using pooled cross-section/time-series data for the
years 1971-1977 for the 48 contiguous states and tested the equation on Alaska
data, among other regions.
In their formulation, the percentage change in the stock of floor space is
a function of the changes ·in the following: the annual change of the nominal
interest rate, the annual percentage changes of the Gross National Product
(GNP) deflator, the annual percentage change in regional income, and the annual
percentage change in regional population, as well as some cross-product terms:
( 6. 4)
(a) Staloff, S. J. and R. C. Adams. 1981 (Draft).
6.13
! I !II
where
Stock =
61-139 =
t. =
GNPDEF =
POP =
INC =
=
£ =
II =
r =
floor space stock
parameters
symbol for the first difference (annual
gross national product price deflator
population
income
index for the region
index for the year
symbol for the annual percentage change
nominal interest.
change)
( 6. 4)
contd
The Anchorage Consumer Price Index (CPI) was used as a proxy for the GNP
price deflators. It is assumed (as historically revealed) that the nominal
interest rate was approximately three percentage points above the measure of
inflation. A proxy for regional income was derived by multiplying regional.
employment by the statewide average wage rate. Parameter values are shown for
equation 6.4 in Table 6.6.
TABLE 6.6. Original RED Floo~ Space Equation Parameters
Parameter Coefficient Standard Error T-Stat i s t i c
61 -0.1291 0 •. 00345 -3.7 5
62 1 .27 53 0.2566 -4.9 7
63 0.3553 0.0302 11.76
64 -0.113 0 .0037 -3.04
65 0.1929 0.0355 5.43
66 -0.094 7 0.0078 -12.09
67 -0.0078 0.0008 -9.92
138 ~o .0116 0 .0253 -0.46
69 -0.0412 0.0061 -6.68
6.14
-
"""'I
-
-
,'\lllllilllll_
r-
.~
.-
Table 6.7 shows how well the stock-flow floor space relationship performed
in Anchorage and Fairbanks historically. Although the stock-fl mv equation
performs fairly well on backcast and could be used to predict stock of co1nmer-
cial space for the historical period, in forecasts of future years it predicted
virtually no growth in square footage per employee in Fairbanks and vigorous
growth in building stock per employee in Anchorage. Since Fairbanks 1 actual
commercial stock per employee grew faster between 1974 and 1981 than Anchor-
age's stock per employee, this forecast result appeared incorrect. For fore-
casting purposes, the equation was replaced with a simpler formulation that
trended square footage per employee from existing levels in the Railbelt to
near the current western average.
TABLE 6. 7. Predicted Versus Actual Stock of Commercial-Ll~~t
Industrial-Government Floor Space, 1975-1981,
(million square feet)
Forecast Error Forecast Error
Anchorage as Percent of Fairbanks as Percent of
Year Predicted Actual (%) Predicted Actual (%)
1975 31.2 -7 .2 6.6 -3.8
1976 33.8 -9.3 7.2 -18.1
1977 37 .o -6.9 7 .8 -23.0
1978 40~5 -2.4 8.2 -24.1
1979 42.3 -1.1 9 .4 -16 .0
1980 43.8 -0.7 9.9 -13.3
1981 44.7 -0.4 10 .4 -9 .2
..-(a) Because of the double lag structure of equation 6.1, only 1975-1981
-
can be compared.
Source: Unpublished test results of Staloff and Adams (1981 Draft).
Several other equations estimated for related national commercial
buildings work at Battelle-Northwest were also applied to the Railbelt to
determine their ability to forecast floor space. The equations used were
estimated using pooled Lower 48 Standard Metropolitan Statistical Area (SMSA)
and non-SMSA level data. The magnitude of the units of the independent
6.15
I', Ill
variables (primarily the population~ employment, and construction activity
variables) was within an order of magnitude of those in Alaska. However, the
magnitude of population, employment, and construction activity in the Railbelt
is still small compared to those in the U.S. data used to estimate the equa-
tions. This may partly explain why building stock equations estimated with
Lower 48 data do not perform well when applied to Alaska.
Annual additions to commercial floor space were estimated with several
linear, logrithmic, and difference forms as a function of the following:
• lagged commercial building stock additions
• AAA bond rate in two forms--current and first differences
o population, both lagged and first difference
• employment, both lagged and first difference
• income, both lagged and first difference.
The equations "fit" the data on which they were estimated reasonably well,
with R-square values generally above 0.9 and significant t-values on all
coefficients. However, the equations did not perform wel 1 when applied to the
two Alaska locations. All of the equations, in fact, produced negative levels
of construction in forecasts. As mentioned above, this may be partly due to
the magnitude of the units of the independent variables in relation to those
used to estimate the equations. r·'bre importantly, the special behavior of the
Alaskan economy may not be adequately described by equations estimated using
data from the Lower 48 states.
Business Electricity Usage Parameters
I
These parameters were estimated with regression analysis. Using predicted
historical floor space shown in Table 6.7(a) and using historical commercial-
light industrial-government electricity consumption, the following regression
equations were estimated:
ln(CONit) = BETA; + BBETAi x ln(STOCKit) +sit ( 6. 5)
(a)Copyright restrictions precluded the combining of "actual" data--that is,
estimated construction based on FW Dodge construction data and 1978 building
stock estimate produced by ISER. Predictions of historical floor space were
done with equation 6.4.
6.16
1!'1"<,
-
-
-
-
-
-
F"
~.
-
-
\>Jhere
CON= historical business sector consumption U1vlh)
BETA = intercept
BBETA = regression coefficient
STOCK= predicted stock of floor space, .hundreds of square feet
E = stochastic error term.
Table 6.8 presents the results of the regression analysis. ( a) The
parameters BBETA are allowed to vary within a normal distribution, truncated
the 95% confidence intervals in Anchorage and 90% in Fairbanks ••
TARLE 6.8. Business Consumption Equation Results
BETA
standard error
t-statistic
BBETA
standard error
t-statistic
GAI~I"1A
standard error
t-statistic
THETA
standard error
t-statistic
R 2
Anchorage
-4.7963
0.6280
-7.6368
1. 4288
0.0491
29.1159
0.9906
Fairbanks
-0.9611
3.6314
-0.264 7
1.1703
0 .32 9 3
3.5538
0.1629
0.0535
3 .0444
-0.0028
0.0024
-1.154 7
0.9121
at
The estimating equation (equation 6.5) was modified with dummy variables
for Fairbanks to capture and remove the effects of a rising trend in Fairbanks
electricity prices after 1974 and the effects of the pipeline boom on consump-
tion from 1975 to 1977. The regression equation estimated for Fairbanks is as
follows:
(a) Regression intercept was adjusted to calibrate consumption in the business
sector to its actual 1980 value for forecasting purposes.
6.17
ln(CONt) = BETA+ BBETA x· ln(STOCKt) +GAMMA x V
+ THETA X OT + Et
with CONt, BETA, BBETA, and s defined as above and where
D =Dummy variable (1974 through 1981 = 1)
V =Dummy variable (1975 through 1977 = 1)
T =Time index forT= 1, ••• , 9. (1973 through 1981)
GAMMA, THETA = regression coefficients.
The dummy variables were held at zero in forecasting.
(6.6)
The historical electricity consumption data were obtained from FERC Form
12s for the Railbelt utilities (supplied by ISER) and from Alaska Power
Administration. These data lump together commercial and industrial sales by
size of demand and there is no reliable way to disaggregate these two types of
consumers. This is fe1t to be a significant shortcoming of the data series.
Commercial and industrial loads should be separated because the typical
characteristics of industrial demand for electricity are different from the
demands of commercial and government users. Part of past Railbelt industrial
load identified by subtracting commercial consumption for users over 50 KVa
from the Homer Electric Association (HEA) service area load and assJining this
load was mainly industrial.(a) Historical loads are shown in Section 13.0.
Historical electrical consumption per square foot of estimated commercial
floor space and per employee·, and estimated floor space per employee are
displayed in Table 6.9. The consumption per estimated square foot in Anchorage
shows a 2.0% annual increase for the period, while Fairbanks shows an annual
decrease of 3.1%. The actual cause of this decrease in Fairbanks is unknown, -
but may be due to declines in space heating, or to priced-induced conservation,
or to growth in warehouses as a proportion of commercial stock. The floor
space is low at the beginning of the period on a per-employee basis relative to
Anchorage (as well as other·known estimates) but then increases at a faster
(a) The major industrial users in HEA 1 S service area include \Jnion Oil,
Phillips Petroleum, Chevron U.S.A., Tesoro-Alaskan Petroleum Corp., and
Collier Chemical. Other large commercial (non-industrial) users· are
included in HEA 1 s over-50 KVa figures, but could not be separated.
6.18
-.,
-
-
-
,~·
.-
TABLE 6.9. Electricity Consumption Per Employee and Square Foot and
Square Footage Per Employee for Greater Anchorage and
Fairbanks, 1974-1981
kl.~h/ft 2 k \~h/ Em~ 1 o.z:ee ft 2 I Em~ 1 o;tee
Year Anchorage Fairbanks Anchorage Fairbanks Anchorage Fairbanks
1973 19.9 27.7 6612 6631 332.6 217.8
1974 19.5 26.8 6414 5399 329.8 201.1
1975 21.1 31.7 6341 5368 300.0 169.1
1976 2 2.8 30.5 7044 5641 309 .1 185.2
1977 22.9 30.8 7445 6922 325.5 (24.1
1978 21.9 29.6 7847 7550 359 .1 255.1
1g79 20.8 23.5 7663 6858 369.2 292.4
1980 22.9 21.7 8644 6913 377.6 318 .3
1981 23.3 21.5 NA(a) NA NA NA
( a) Not applicable.
rate. Once the floor space per employee estimates for Fairbanks reach si1nilar
levels to those in Anchorage, the kWh/ft 2 figures for Fairbanks appear to
stabilize.
The energy consumption per employee figures show increases over time of
3.4~~ and 0.5% annually for Anchorage and Fairbanks, respectively.(a) These two
series show some instability with slight decreases in 1975 and 1979. The
growth rates are too high, too unstabl~, and too dis~arate for long-term appl i-
cation, reflecting a period of extreme growth within the state. With more
disaggregated data, employment may prove to be a suitable argtlTlent for
industrial electricity consumption. However, with a rather 1 imited Rail belt
industrial sector, forecasts of industrial demand are better handled on a
scenario building basis; i.e., identify industry expansion plans case by case.
Several regression equations were estimated in an attempt to develop a
-theoretically satisfying relationship to predict el~ctricity consumption
-(a) No data are available on consumption of electricity by SIC industry
code. ~1ultiple regression techniques proved unsuccessful in determining
the separate effects of each subsector' s employment on commercial demand,
due to high colinearity among explanatory variables.
6.19
I I Ill
separately in the commercial, light industrial, and government sectors. All
failed rnost normal statistical tests. The aggregate nature of the electricity
consumption data and employment data, the rather high trend exhibited for per-
employee consumption, and the limited data series prevented statistical
estimates of consumption on a per-employee basis. No further attempt was rnade
to estimate a statistical relationship between electricity consumption and
employment.
-
--
Business Price Adjustment Parameters ~
The parameters used in the price adjustment mechanism are an important
part of the business electricity forecasting mechanism. As in the Residential ~
Consumption ~·1odule, the parameter default values and ranges were picked fro1n
f,1ount, Char;man, and Tyrell (1973). Chapter 7.0 discusses these parameters and -
their use in the price adjustment mechanism.
-
-
-
6.20
.......
.-
-
-
7.0 PRICE ELASTlCITY
This section describes the price adjustment mechanism employed in the RED
model. In both the Residential and Business r1Jdules, this mechanism modifies
preliminary estimates of electricity consumption generated elsewhere in the
model. Changes in consumption are made to account for changes over time in
electricity, natural gas, and oil prices. The changes in electrical consump-
tion computed by the price adjustment methani sm can be considered price-induced
conservation of electricity.(a) Outputs from the price adjustment mechanism
are the final RED electricity consumption estimates for each sector, region,
and time period.
The remainder of this section is divided into four parts. A brief general
introduction to the RED price adjustment mechanism is given in the next sub-
section. This is followed by a survey of economic literature on electricity
demand. In the third part, the structure and parameters selected for the REO
price adjustment mechanism are discussed. Implementation of the selected
structure and parameters is described in the final subsection.
THE RED PRICE ADJUSTMENT MECHANISM
The RED price adjustment mechanism is motivated by economic theory, which
hypothesizes the following: consumption of any commodity is determined both by
"scale" variables such as population, income, and employment, as well by the
prices of the particular commodity, its substitutes, and its complements.
Elsewhere in the RED model, preliminary estimates of electricity consumption
are generated, with consideration only of "scale" variables. The price adjust-
-ment mechanism described in this section completes the analysis of consumption
-determinants suggested by economic theory.
The mechanism works in the following manner. Preliminary, non-'pri.ce
adjusted estimates of electricity consumption by region, sector, and time
(a) Of course, with falling electricity prices or increases in gas and oil
prices, the price adjustments could result in increased electricity
c o n sump t i o n o r " n ega t i v e c o n s e r vat i on " of e 1 e c t r i c i t y • Th e p r i c e
adjustments include fuel switching.
7.1
I I Ill
period are introduced into the model. These preliminary estimates were
generated under the assumption that 1g3o price levels are maintained through
the year 2010.
The price adjustment mechanism accounts for the fact that prices in any
forecast period K are not necessarily the same as prices in 1980, even in real
(inflation-adjusted) terms. If real electricity prices increase (decrease) in
any region and sector between 1980 and period K, economic theory suggests that
electricity consumption in that region and sector would decrease (increase)
relative to its non-price-adjusted preliminary estimate. Conversely, if real
natural gas or oil prices increase (decrease) in any region and sector between
1980 and period K, electricity consumption in that region and sector would
increase (decrease) relative to its non-price-adjusted preliminary estimate
because natural gas and oil are substitutes for electricity. Thus, the RED
price adjustment mechanism scales preliminary estimates of electricity
consumption upward or downward based on changes in real electricity, natural
gas, and oil prices.
The amount by which preliminary p_eriod K consumption is scaled upward or
downward depends on three general factors: 1) the percentage change in real
electricity, natural gas, and oil between forecast period K-1 and forecast
period K, as well as price changes occurring prior to period K-1; 2) the short-
run elasticities of electricity demand with respect to the three prices; and
3) the speed with which final consumers of electricity move toward their long-
run equilibrium consumption levels when these prices change, which is
represented by a "lagged adjustment coefficient", or alternatively, the long-
run demand elasticity. Short-run elasticities of demand are defined as the
percentage change in consumption in year t caused by a one percent increase in
price in year t. Own-price elasticities refer to changes in electricity
consumption caused by changes in electricity prices; cross-price elasticities
refer to changes in electricity consumption associated with changes in either
natural gas or oil prices. Short-run elasticities represent the instantaneous
adjustment that consumers make when prices change. Of course, in the case of
electricity, a significant period of time may pass before consumers have fully
responded to a price change in year t: time is required to change old habits,
7.2
-
!""":
I
""''I
-
-
.-
-
to replace old appliances with more energy-efficient ones, to weatherize
residences or commercial/industrial buildings, and to switch to other energy
sources. The lagged adjustment coefficient represents the rate at which
consumers move toward their final equilibrium consumption level; the higher
this coefficient, the more current consumption depends on past consumption, and
thus the slower consumers respond to current price changes. In fact, simple
algebra can show that the long-run demand elasticity (either own-or cross-
price), which is defined as the percentage change in electricity consumption in
year t +"" caused by a one percent change in price in year t, can be defined in
terms of the lagged adjustment coefficient and the short run elasticity. The
formula for the long-run elasticity ELR is given by
ELR = ESR
1-,\
where ESR is the short-run elasticity and,\ is the lagged adjustment
coefficient.
( 7 .1 )
Alternatively, a set of long-run price elasticities can be entered into
the mechanism. These elasticities describe the change in consumption caused by
a price change once the consumer has reached a point of equilibrium with that
price change.
LITERATURE SURVEY
Si nee the "energy crises" of the early 1970s, an extensive econorni c/
econometric literature on the demand for energy, and electricity in particular,
has been generated. A survey of this literature was performed with two primary
objectives: first, to identify possible structures of the RED price adjustment
mechanism; second, given the structure, to identify potential parameter values
for the mechanism. These objectives center around the concepts of elasticity
and adjustment coefficients. In performing the survey, the objectives led to
the following questions.
o Should the RED Residential and Business Sectors be combined or
modeled separately?
7.3
• Should the own-price elasticity be a constant or a function that
depends on the price level?
o Should both natural gas and oil cross-price elasticities be included
in the mechanism and should these elasticities be constant or vary
by the price levels of the two fuels?
• Should the relationship between short-run and long-run price elas-
ticities (both own-and cross-) be modeled explicitly by including
lagged adjustment coefficient in the mechanism, or should the t1~0
types of elasticities be included in the mechanism separately?
o nnce the structure is selected, what are the most appropriate values
for the parameters of the mechanism?
All of the studies surveyed were econometric in nature, in which electri-
city demand functions were estimated using statistical techniques. A variety
of data bases was used in these studies, and the fuctional forms, independent
variables, and estimation techniques employed varied substantially as 1vell.
Al 1 but a few of the studies modeled residential, commercial, and industrial
electricity demand separately; in many studies, only one of these sectors was
considered. Many of the studies estimate price elasticities that do not vary
according to price levels; this is accomplished by regressing the natural
logarithm of consumption on the natural logarithms of the prices and other
independent variables. The coefficients of the price terms can then be
interpreted as elasticities. Non-constant elasticities were estimated in a few
studies, using a variety of functional forms. One method of estimating
variable price elasticities is to regress the natural logarithm of quantity on
the natural logarithms of th·e prices, the natural logarithms of the other
independent variables, and the reciprocals of the prices:
log Q ~ a+ b log P +++ c 1/P +++ ( 7. 2)
where "log" denotes natural logarithm, Q is consumption of electricity and P
its price, a,b,c are parameters to be estimated, and 11 +++" denotes the other
price and independert variables in the equation. In this specification, the
own-price elasticity is equal to b-c/p, which depends on P.
7.4
-
-
-
-
I'""
Several studies include only natural gas as a substitute for electricity,
a smaller nllTlber include only oil, and some studies include both. The substi-
tute commodities included in an eq11ation depend on the intentions of tf]e
researcher and the type of data used: neither oil nor natural gas prices
typically vary much in cross-sectional samples, so their effects on electricity
consumption are difficult to discern when using this type of data.
Finally, the type of elasticity estimated (short-run, long-run, both)
varies across the studies survey. In studies using time-series data, the
coefficients on prices and the other independent variables are typically inter-
preted as short-run elasticities. An exception to this occurs when lagged
consumption is included as an independent variable in the estimation equation;
then, the coefficients in the prices represent short-run elasticities, and the
long-run elasticity is given by equation 7.1 with A the coefficient on lagged
consumption. In equations estimated using cross-sectional samples, the
coefficients are typically interpreted as long-run elasticities. Pooled time-
series--cross-section samples pose a bit more of a problem; the estimated
coefficients contain both long-run and short-run effects. However, when lagged
con s ump t i on i s i n c 1 ud e d a s a n ex p 1 an at o r y v a r i a b 1 e , t h e p r i c e co e f f i c i en t s
again represent short-run elasticities and long-run elasticities are again
given by equation 7 .1.
Table 7.1 summarizes the econometric studies of residential electricity
demand surveyed. For each study, the type of elasticity estimated (constant,
variable), the time period for which it is relevant (short-run, long-run,
both), and the type ofdata used (cross-section, time-series, pooled cross-
section--time-series) are presented. Also shown are the substitutes' prices
and non-price factors considered in each study. The own-and cross-price
elasticities estimated in each study are presented in Table 7.2. For those
studies in which lagged consumption was included in the equation, its coef-
ficient, the lagged adjustment coefficient, is also presented.
Estimates of the short-run own-price elasticity vary considerably. In
absolute values, the minimum estimate is 0.101, while the maximum is 0.3. Many
of these differences can be attributed to the data used in the estimation;
estimates based on national datct would be expected to differ from estimates for
7. 5
TAEiE 7 .1. Residential Electricity Demand Survey
Type of Cl:her Damnd
Author Elasticit~ Ti rre F r i:JT'e T~~e of Data Substitute Prices IX!tenni nants( a)
Alderson, K.P. (1972) Cbnstant Long run O'os s-sect ion Aver age price
Residential [)emnd for 1969,. states of Natural Gas -
~
Electric it~: Econmetri c
Est irmtes For Ca 1 ifomi a
and the lhited 9::ates.
The ~nd Cbqnration,
Santa ~bnica, CA
.llnderson, K.P. ( 1973) Qmstant 9lort run Cross-section Fuel oil, Y, HS, SHU, NU,
l€sidential Energy Use: long run 1969. states bottled gas, w, s
M EconOTEtric .llnal:tsis R-coal
1297-NSF. lhe ~nd Cbrp. ,
Santa fvbnica, CA
--.,1 . Raughnan, M.L., rnnstant 9lort run Tirre series Ener'gy' price Vi, N, NT, LT, c;n
Joskcw, P.L., Dilip, K.P. long run 1968-1972 index P;
1979 Electric POt.er in the 48 states
lhited 9::ates: ~txlels
and Polic~ ktalysis.
MlT Press, CQTbridge, MA
Blattenberg=r, G.R., Constant 91ortrun Tirre series ttirginal price rrpe, fee, x,
Taylor, L.D., 1 ong run 1960-1975 natura 1 gas, ddh, ddc
Rennhack , R .K. 1983, states fixed charg2
11 f\6tural Gas Availability natural gas,
and the Residential [)emnd price of fuel
for Energy 11
• The Energy oi 1
Journal. 4(1):23-45
1-k11 vorsen, Robert. 1976 Constant· Long run Cross-sect ion Average price cr • p nn • Y* • J •
"Darend For Electric 1969 p2r thenn for 0, Z, R, H, E
Energy in the United states all types of
States". Swthern Econ gas purchased
,Journa 1. 42( 4) :610-625. by sector
.... J ) J .J ) J
l 1 1 1
TAFlF.: 7 .1. (contd)
Type of Other De11and( )
Aithor Elast kit~ Tirre Frare T~~e of r:Hta SrJlStitute Prices rete nni nant s a
1-fll vorsen, Robert. 1978 Constant Long run Pooled Avera;)e real PR, Y~, A, 0,
&::onmetric Hxlels of U.S. 1961-1969 gas rrice for J, ll, 1, HA, T
Enerw Oemnd. D.C. Heath . 48 states all types of
and Co. , Lexington, f1A gas in cents
per thenn
Hirst, Eric, and G:lrney, (bnstant furt run eros s-sect ion HT, HSA, C, TI,
Janet. 1979. "The ORNL long run 1970 EU, U
Jes identi a 1 Energy-Use
f1:>de 1: Structure and
results". Land Econo-
........ rnics. 55(3):319-333 .
........
1-buthakker, H.S. and Constant 9lort run Tirre series 9t-1• \· p
Taylor, L.O. 1970.
Cons Lire r Damnd in the
United States. 1-flrvard
l.h i v. Press , Carbr idge, f1A
fbunt, T. D. , Olapnan, Variable 9-Jort run Cross-section Price of gas-Population, per
L. 0., and Tyrrell, T. J. 1 ong run 1947-1970 inc 1 uies capita incone,
1g73. Electricit~ Danand States natural, liquid avg. electricity
in the lhi ted c:tates: l'v1 ~Etrol eun, rrice, rrice index
Econmetric Analysis. nanufactured for appliances,
and mixed gas. nean ,January
tmperature
(a) For S)fnbols, see gl6ssary at end of section.
TABLE 7.2. Residential Survey Parameter Estimates
9-ort-lt.tn L..ong-~n La]ged G3s Oil
fu1 Price CW1 Price A:ljustJrent Cross-price Cross-price
Putror Elasticit~ Elastic it~ ())efficient (>.) Elasticit~ Elast icit~
Jlllderson (1972) -0.91 0.13...
l'fiderson (1973) -0.3 -1.12 0.732 0.3Q 0.27L
Ba~J,Jhllan, et al (1979) ..;0.19 -1.00 0.842 0.055, 0.17L 0.015, 0.009...
Blattenberger, et al (1983) -0.101 -1.052 0.904 0.0025' 0 J)l!l.
Halvorsen (1976) -0.97 O.Hi
--.J Halvorsen (1978) -1.14 0.0!1 .
00 Hirst, Carney (1979) -0.16 -0.83 0.025, 0.2Q 0.005, 0.04L
1-bJthakker, Taylor (1970) -0.13 -1.89 0.873
M:Junt, Olapnan, Tyrrell -0.14 -1.21 0.884 0.025, 0.21L
(1973)
J J J J
l'-'
individual states, and estimates for more recent periods would be expected to
differ from older estimates. The functional forms used and the set of indepen-
dent variables considered also appear to play a role. However, in neither case
does a clear relationship appear.
The long-run own-price elasticities display even greater variation,
largely because two methods of estimating these elasticities exist: 1) using a
cross-sectional sample, or 2) using a time-series or a pooled sample and
including a lagged endogenous variable. For the studies surveyed, the second
approach generally leads to larger (in absolute values) estimates of the l eng-
run own-price elasticity.
As expected, in studies in which both long-and short-run elasticities are
estimated, the long-run elasticity is larger in magnitude than the short-r11n
elasticity. The relationship reflects the fact that consLmers can n1anage only
a limited response to price changes in the short run, when their housing an1
appliance stocks are fixed, but r~spond more fully over time when these stocks
can be varied.
Esti1nates of the lagged adjustment coefficient do not vary as much as the
other parameters; most estimates are about .85. Oil and natural gas price
elasticities vary much less than the o.ther parameters of interest, but quite a
lot relative to their magnitudes and are considerably smaller than the own-
price elasticities.
Most of the literature surveyed considered commercial and industrial elec-
tricity demand separately. Industrial demand elasticities are typically larger
than those in the commercial sector because of the large amounts of electricity
used for purposes in which oil, natural gas, and coal serve as very good subs-
titutes. In the commercial sector, most electricity consumption is for light-
ing and cooling, uses in which fuel-switching is not as easy.
The RED Business sector is a combination of industrial and commercial
sectors. ~st business concerns in the Railbelt, however, are commercial or
light industrial. Therefore, the industrial electricity demand elasticities
were deemed in~ppropriate to the Railbelt~ and only the commercial electricity
demand literature was surveyed.
7.9
Only two studies that deal explicitly with the commercial sector were
found. These two studies are summarized in Tables 7.3 and 7 .4, which parallel
Tables 7.1 and 7.2. Even among these two studies the estimated price elasti-
cities vary considerably; the two short-run own-price elasticities are -.03 and
-.29. The cross-price elasticities again vary considerably less, and are much
smaller in magnitude than the own-price elasticities.
For both the residential and commercial sectors, the hypothesis that own-
price elasticities are constant was statistically tested and rejected by Mount,
Chapman, and Tyrrell (1973) (MCT). In that study, own-price elasticities were
found to increase in magnitude as the level of electricity prices increased.
Thus, the absolute value of the own-price elasticity of electricity demand is
higher in regions with high electricity prices than in areas with lower elec-
tricity prices and increases (decreases) over time as the real electricity
price increases (decreases) over time. In both sectors, oil and natural gas
were each found to significantly affect electricity consumption, and long-run
elasticities were found to be larger than short-run elasticities. However, the
parameter estimates do vary according to sector; ~1ount, Chapnan, and Tyrrell,
who estimated models for both sectors, found significantly greater price
responsiveness in the short run and long run in the commercial (Business)
sector, with approximately equal lagged adjustment coefficients.
SELECTION OF RED PRICE ADJUSTMENT MECHANISM STRUCTURE AND PARAMETERS
On the basis of the literature surveyed in the previous section and consi-
deration of the non-price modules of the RED model, the RED price adjustment
mechanism was specified in the following manner.
Sector D i vi s i on
1-m,
-
-
Separate price adjustment mechanisms are used for the two end-use sectors. -
In the only study surveyed in which both sectors were considered, MCT found
that the electricity demand elasticities for the two sectors were considerably
different. Thus, specifying a single mechanism to be applied to both sectors
would lead to biased estimates of the price adjustments in each sector. How-
ever, each of the two mechanisms has the same structure; only the parameters
and the price changes considered differ.
7.10
-
-
] l l
TAil.E 7 .3. Canrercial Electricity DBTBnd Survey
Type of ether Oemnd
Author El asticit.z:: Tine Frare T,tpe of Data 9Jbstitute Prices fletenni nants( a)
feierlei n, Jares G., [J..nn, lbnstant 9urt-run cross-sect ion Nlttral gas, Yj, PEj,
Jares W., fvtConnon, 1 ong-run ti1re series fue 1 oil Qit-1j
Jares C. 1981. 11 lhe 1967-1977
IBnand for Electricity regional NE
and N.ltll'al fils in the
l'b rtheastem IJn i ted
9::ates 11
• The Review of
-...I EconOTJi cs and Statistics • . ,_.. AugJst 1981, pp. 403-408. ,_..
tbunt, T. o. , Olapnan, Variable 9-lort-run Cross-sect ion Gas Y, P, PE, 1\_1 L. D., and Tyrell, T. J. long-run 1947-1970
1973. Electricit.z:: Demand States
in the Lhited 9:ates; k1
EConaretric Ala lysis.
Cbntract No. t.J-7405-eng-
26. ORNL, Oak Ridge,
Tennessee
(a) For synbols, see glossary at end of section.
I I Ill
TABLE 7.4. Commercial Survey Parameter Estimates
9-ort-fW Long-Run La;Jged GJ.s
oo Price CWn Price A:Jju strrent Cross-price
A.rtlnr Elasticit,:t Elasticit,:t r.oefficient (\) Elasticit,:t
Bierlein, et. al. (1981) -0.03 -0.37 0.9167 0.045, 0.4a
M:lmt, et. al. (1973) -0.29 -1.36 0.!3724 o.o15, o.oa
Variable Elasticity
Oil
Cross-price
Elasticity
-0.095, -1.0Sl
The own-price elasticity in each sector is not constant, but varies with
the level of the real electricity price. In the only study surveyed in which
variable elasticities were estimated, MCT rejected the hypothesis that own-
price elasticities were constant. Furthermore, a considerable amount of
variation was found in the estimated own-price elasticities during the litera-
ture survey. This variation could be caused in part by variations in the
e s t i mat i n g s amp l e s 1 p r i c e l eve l s •
These factors would be unimportant if the level of electricity prices in
the Railbelt region were fairly similar to the mean level of prices used in
estimating the constant elasticity equations, if the levels of electricity
prices within the Railbelt were uniform, and if real electricity prices in the
Railbelt were not expected to change during the forecast period. In such a
case~ the estimate from a constant-elasticity model might provide a reasonable
approximation to the true elasticity in the Railbelt. Even if the true
elasticity were variable, when evaluated at the mean level of prices, it would
be similar to a constant elasticity estimated with the same data. Unfortu-
nately, none of these conditions hold; the average level of Railbelt electri-
city prices in 1980 was significantly below U.S. average electricity price;
within the Railbelt, the level of Anchorage electricity prices was less than
half the level of Fairbanks prices in 1980; and in several of the RED price
scenarios, electricity prices increase rapidly enough that by the year 2000
they are 50 to 100% higher in real terms than they were in 1980.
Adjustment Over Time
Long-term price elasticities are not entered explicitly into the mecha-
nism; instead, short-run elasticities and a lagged adjustment coefficient are
7.12
_,
'
-
~-
~I
-
-
-
-
-
.....
-
-
employed. Thus, long-term elasticities appear explicitly in the mechanism via
the relationship given above. This choice was made for three reasons. First,
the explicit short-run elasticities are consistent with the implicit long-run
elasticities; that is, the elasticity estimates can be taken from the same
study, estimated with a lagged adjustment coeffici~nt. If the long-run
elasticity were entered explicitly, it could not be taken from the same study
as the short-run elasticity because it is impossible to obtain both elasti-
cities from one equation except via the lagged adjustment coefficient. Second,
since the lagged adjustment coefficient did not vary much across the studies,
whereas the long-run elasticities did, choosing a value for A was more
straightforward. Third, and most importantly, by including the lagged adjust-
ment coefficient the impact of price changes in year ton consumption in year t
+ 1, t + 2, ••• , t + 10 can be assessed directly; because t + 1, ••• t + 10 is
neither the short-run nor the long-run, with only the two sets of elasticities
and no lagged adjustment coefficient these impacts cannot be directly measured,
but only crudely guessed. This is particularly important in RED because it
forecasts electricity consumption at five-year intervals; price changes in the
first-year of the five-year period obviously have neither a long-run nor short-
run impact on consumption in the fifth year of the period, but an intermediate
impact.
Cross Price Elasticities
Short-and long-run natural gas and oil cross-price elasticities are
included in the mechanism. In several of the studies surveyed, one or the
other fuel was found to be a substitute for electricity, although due to data
1 imitations they were only considered simultaneously in a handful of studies.
Thus, the effect of oil and gas price changes on electricity consumption,
although small in relatinn to the effect of electricity prices, cannot be
ignored. It is important to include these prices in the RED price adjustement
mechanism for th_e following reasons. ~1uch of the own-price elasticity of
electricity demand can be attributed to "fuel switching." As real electricity
prices increase, some households and businesses will, the mechanism predicts,
"switch" from electricity to natural gas or oil for heating and other energy
uses. However, if real oil and gas prices are also increasing, the extent of
7.13
Ill
this fuel switching will be diminished. The cross-price elasticities are
employed in RED to account for this. One would think that the amount by which
this fuel switching is diminished because of rising gas and oil prices would be
a function of the level of oil and gas prices; in other words, that these
cross-price elasticities are not constant with respect to their corresponding
prices. Unfortunately, none of the studies surveyed employed variable cross-
price elasticity models; thus, the cross-price elasticities in each of the two
price mechanisms are constant.
Parameter Estimates
The parameter estimates for each of the two price adjustment rnechani sms
were taken from the study by rvbunt, Chapnan, Tyrrell (1973). Oil cross-price
elasticities, which were not estimated in the MCT study, were based on profes-
sional judgment and values taken from the 1 iterature survey. The parameter
values used in RED are presented in Table 7.5. The MCT parameter values were
used in RED for two reasons. First, their models were most consistent 1vith the
structure se1 ected for the RED price adjustment mechanisms; there are separate
equations for the residential and business ~ectors, variable own-price elasti-
cities are einployed, lagged adjustment coefficients are estimated, and a cross-
price elasticity (gas) is included. Second, the elasticities estimated by MCT, .
when evaluated at 1980 Anchorage and Fairbanks prices (in real 1970 dollars, as
in MCT), appear reasonable. In the residential sector, calculated short-run
elasticities were -.1462 in Anchorage and -.1507 in Fairbanks; calculated
TABLE 7 .5. Parameter Values in RED Price Adjustment ~chani sm
Short-Run El asti cities
Own-Price
Natural Gas
Oi 1
Lagged Adjustment
Residential
Sector
Business
Sector
-.1552 + .3304/P(a) -.2925 + 2.4014/P(a)
.0225
.01
.8837 .
.0082
.01
.8724
(a) Measured in mills per KWH, 1970 dollars.
7.14
-
-
-
-
-
~
I
I
j
-
"""
r
long-run elasticities were ~1.2571 and ~1.296, respectively. The short-run
elasticities are slightly bel ow the average of the estimates presented in
Table 7.2; since average prices are rather low in the Railbelt, this result is
satisfactory. The long-run elasticities are slightly above the average of the
studies surveyed, since the MCT lagged adjustment coefficient is at the high
end of the range of those surveyed. This is satisfactory for the Railbelt
because electricity comprises a large share of consumers 1 budgets due to the
climate and winter hours of darkness and because in the past residents of the
area have been conservation-minded. The business sector short-run own-price
elasticities evaluated at 1980 prices are -.2270 in t\nchorage and -.2600 in
Fairbanks, and the respective long-run elasticities are -1.7788 and -2.0378.
The short-run estimates are a little below the average MCT calculated, due to
below-average Railbelt prices, and the long-run elasticities are at the high
end of the range found in the survey.
DERIVATION OF RED PRICE-ADJUSTMENT MECHANISM EQUATIONS
The final outputs from the RED price adjustment mechanism are price-
adjusted consumption of electricity for each sector, region, and time period,
denoted RESCONiK and BUSCONiK" Each of these is equal to preliminary estimates
of consumption, denoted RESPRE;K and PRECONtK• multiplied by a series of price
adjustment factors:
where
( 7 • 3)
RUSCONiK = PRECONiK • (1 + OPAiki) • (1 + PPAiKJ.) • (1 + GPAiKR.) (7 .4)
= region index
K = time period index
t = sector index (=1 residential, = 2 business)
OPA = own-price adjustment factor
· PPA = oil (petroleLnll)-price adjustment factor
GPA =gas-price adjustment factor and denotes multiplication.
7.15
I I In
Thus, fina1 consumption in a sector is equal to preliminary, non-price
adjusted consumption scaled upward or downward depending on the signs and mag-
nitudes of the three corresponding adjustment factors. These factors combine
information on price changes in periods K, K-1,., own-and cross-price elasti-
cities in periods K, K-1, ••• ,and lagged adjustment coefficients in the fol-
lowing manner. First, denoting electricity, oil, and natura1 gas prices by
PE;Kz• POiKZ• and PGiK£• (define the five-year percentage change in prices):
PE,· K-1 z)IPEi K-1 t
' ' ' '
PO; K-1 z)IPOi K-1 t
' ' ' '
PCPGiK£ = (PGiK£ -PGi ,K-1,2 )/PGi ,K-1,2.
Then calculate the average annua1 percentage change in price during the
five-year period:
PCPEAiKZ = (1 + PCPEiKz)**.2 - 1
PCPOAiKZ = (1 + PCPOiKz)**.2-1
PCPGAiKZ = (1 + PCPGiK£)**.2 - 1
( 7 • 5)
( 7. 6)
( 7. 7)
( 7. 8)
( 7 .9)
(7.10)
where "**11 denotes exponentiation. Thus, during each of the years behJeen K-1
and K, prices increase' an average of 100 • PCPEAiK£, and 100 • PCPOAiK£, and
100 • PCPGA;Kz percent.
The impact of a change in the price of electricity in the first year of
the five-year period on consumption in the fifth year of the period can be
analyzed in steps. First, the impact of the price change on consumption in the
first year (denoted t) is given by
(7 .11)
7.16
-
-
-
-
-
-'
-
-
....
. ~
where ~~1:!. denotes percentage change, Ot is consumption in year t, sector t,
region i, Pit! is the price, and ESRitl is the short-run own-price of
electricity. Equation 7.9 states that consumption in year t falls (increases)
in percentage terms by an amount equal to the price increase (decrease) scaled
by the own-price elasticity (which is negative). The effect of the price
change in year ton consumption in year t + 1 is the sum of two components.
F i r s t , l a g g e d con s urn p t i o n h a s fa l l en by %1:!. Q i t1 , s o t h i s p e r i o d 1 s c on s um p t i o n
falls by l..%1:!.0iu· Second, the price change which occurred·in year t persists
(the price did not go back to its year t-1 level) so consl§llption in year t + 1
fa l l s by E SRi t + 1 1 • %1:!. Pi u . Thus , the change i n yea r t + 1 cons ump t i on of • •
electricity caused by a price change in year t is given by
%1:!.Qi t+1 t:: I..%1:!.QiU + ESRi t+11. "/ot.Piu
' ' ' '
(7.12)
= (!.. ESR;tz + ESR; t+1 t) • %~:!.Pit£ . ' (7.13)
Similarly, the change in year t + 2 consumption is equal to the sum of two
components:
~~1:!. 0 i , t + 2 , 2 = 1.. %Q i , t + 1 , 2 + E SRi , t + 2 , 2 • %1:!. p i t£
This process can be carried out to year t-+ 4, the final year of the
five-year period:
2 + >.: ESRi t+Z 2 + >.: ESR; t+3 1 ' , ' '
+ E SRi , t +4 ,1 )
7.17
(7.14)
( 7 .15)
(7.16)
which gives the percentage change in year t + 4 consumption resulting from the
price change %l1Piu in year t. Similar price changes occur in year
t + 1 ( %ll P i , t + 1 ,R. ) , t + 2 ( %ll P i , t + 2 ,R. ) , t + 3 ( %ll P i , t + 3 ,£ ) , and
t + 4 (%l1Pi,t+4 ,2 ), with equal percentage price changes assumed during each of
the five years. That is:
(7.17)
The impact of these individual price ch~nges on consumption in year t + 4
can be derived in a manner similar to that used to obtain equation 7.1fi. The
sum of the impacts of the five annual price changes is given by equation 7.18:
= PCPEA; Ia • ( >4
ESRit.! %t~Q,. t+4 R. . '
(7 .18)
+ 2A. 3 ESR 1. ·t+1 2 + 3A. 2 ESR; t+2 2 ' ' , '
+ 4A. ESR. t+3 " + 5 ESR; t+4 .e.)
1 ' ,... ' '
Equation 7.18 accounts for price changes which occur between period K-1
and K; price changes which occurred before K-1 also influence consumption in
period K, just as pricechanges in period t affect consumption in, for example,
period t + 9:
%LI.Qi. t+9 ,i (7.19)
+ ••• + A
5
ESRi ,t+4 ,R. + A
4
ESR; ,t+S ,R.
+ A ESR; t+8 2 + ESRi t+g 2 )
' ' ' '
+
The combined total impact of the five annual price changes in t, t+l, t+2,
t+3, t+4, on consumption in period t+9 (period K+l) is given by
7.18
-
.....
-
.....
-
-
-
~ 0 -vs~·o 10
/). ·i ,t+9,2 -I\ •oLl i ,t+4,2
3
ESRi ,t+S,2 + 2.\ ESR; ,t+6 ,2
+ 3>..
2
ESRi ,t+7 ,2 + 4.\ ESR; ,t+S,2
+ SESR; t+g ~)· • •
(7.20)
Extending this analysis forward, combining terms, and rearranging, one
obtains the percentage change in any five-year period K as a function of
average annual price changes between K-1 and K, K-2 and K-1, etc:
(7 .21)
(
K
+ I
m=1
3
E S R i • K 1 • 2 + 2.\ E S R i • K 2 • 2
2
+ 3:\ ESRi ,KJ ,.e. + 4:\ ESR; ,K 4 ,2
+ 5 ESR; ,KS,<)
Where the subscripts K1,,,K5 denote, respectively, the first year in the period
between K-1 and K, the second year in the period between K-1 and K, etc. The
summation over past price changes takes into account that these price changes
persist: that once prices have increased, the increase and its effects are
permanent, until and unless future price decreases offset them.
Equation 7.17 defines OPAi k .e. as the percentage adjustment to electricity • •
-consumption which must be made because of real electricity price changes.
Restated, -
7.19
OPAiKl = A5 OPAi,K-1,£
+ (.t PCPEAim.<) • (A 4
ESR i 'kl ,f
+ A 3 ESRi ,K2,l + A 2 ESR; ,K3,.e.
+ A ESR; ,K 4 ,, + ESR; ,KS ,t)
(7.22)
Similarly, price adjustment factors for oil a~d natural gas price changes can
be derived, with one simplification-the oil and gas cross-price elasticities
are constant. Thus,
PPAiKl = A
5
PPA; ,K-l ,.e.
+ • OSR .\!,
• (A 4 + 2A 3 + 3A 2 + 4A + 5 )
5
= .\ GPA; ,K-1 ,l
• (A 4 + 2J.. 3 + 3J.. 2 + 4A + 5 )
(7.23)
( 7 .24)
where OSRi is the short-run oil cross-price elasticity in sector~ and GSR.e. is
the short-run gas cross-price elasticity in sector .e..
7.20
-
"'"' ' I
-
....
-
-
-
--
-
-
,....
All that remains is to attach values to ESRi,Kj,t· In the r-1CT study,
short-run elasticities are defined by
ESR = a-b/P. (7.25)
Implementation of this requires calculating the average elasticity for a given
year Kj, so that
(7.26)
-.5 B0 /P. K. n ~ 1' J •"'
where Pi,Kj-1 ,£. is the price at the end of the year before Kj, and Pi ,Kj,J.. is
. the price at the end of year Kj.
7.21
I
y
HS
SHU
~u
= income per household
= average family size
GLOSSARY OF SYMBOLS
= single detached housing units (fraction of total)
= nonurban housing units (fraction of total)
W = mean December temperature
S mean July temperature
Yi = income per capita (67 dollars)
N
Pi
MT
LT
mpe
fee
= population density
"'energy price index relative to CPI (dollars per Btu)
= average temperature of warmest three months of year (°F)
= average temperature of coldest three months of year (°F)
= marginal price of electricity
= fixed charge for electricity
x = total personal income
ddh
ddc
Cr
heating degree days
= cooling degree days
= number of residential customers
Prm = marginal price of electricity
Y* = per capita personal income
J = average July temperature
0 = heating degree days
Z = population per square mile
R = percent rural population
H
E
percent of housing units in single-unit structures
= number of housing units per capita
PR =average real price of residential electricity, in cents per kwh
YH average real income per capita, in thousands of dollars
A = index of real wholesale prices of selected electric appliances
U =percentage of population living in rural areas
= percentage of housing units in multiunit structures
average size of households
=time
= stock of occupied housing units
7.22
-I
-
-
-
-
-
-
·~
!"""
I
~.
-
....
HSA = average size of housing units
C =the fraction of households with a particular type of equipment
T1
EU
u
9t-1
= thermal performance of housing units
= average annual energy use for the type of equipment
= usage factor
= lagged personal consumption expenditure for electricity per capita
in 1958 dollars.
Xt =total personal consumption expenditure per capita in 1958 dollars
p =implicit deflator for electricity/implicit deflator for PCE (1958=100)
Yj = value of retail sales
PEj =average deflated price per KWH of electricity
O;t-lj = lagged per capita fuel consumption
Y = income per capita
P =population
PE =price of electricity (mills per KWH)
Ot_1 =lagged demand in millions of KWH.
L =long run
7.23
-
-
8.0 THE PROGRAM-INDUCED CONSERVATION MODULE
The purpose of the Program-Induced Conservation 1'1odule is to account for
the electricity savings that can be obtained with a given set of consl!Tler-
installed conservation technologies and government policies, together with the
associated costs of these savings. The peak demand or capacity savings of the
technologies set are calculated in the Peak Demand ~1odule.
The module forecasts only those portions of conservation that are not
market-or price-induced. The module was developed as part of Battelle-
Northwest's Alaska Railbelt Electric Power Alternatives Study in 1981 and was
designed as a tool to enahle the State of Alaska to analyze the impact of
potentia1 large-sca1e conservation programs. The future of such programs in
Alaska is in doubt (Tillman 1983) and the data on the savings and costs of
existin.g programs are uncertain. The Program-Induced Conservation i"odule was
not used in the 1983 updated forecasts, but a description of the module is
given be1ow.
MECHANISt1
The fuel price adjustments in the Residential Consumption and Business
Consumption ~bdul es account for rna rket-i nduced technology-related <;onservat ion
impacts, as well as reductions in appliances use and changes in the way in
which they are used. The Program-Induced Conservation .t·'Ddule analyzes
government attempts to intervene in the marketplace to induce conservation via
loan programs, grants, or other policy actions. The module accounts for the
effects of this program-induced conservation on demands for electric energy and
generating capacity.
RED separates conserved energy into two parts: energy saved from the
actions of residential conslJTlers and energy saved from reduced energy use in
the business and government sectors. Figure 8.1 provides a flow ·chart of the
process employed.
A separate, interactive program developed with RED (CONSER) is called by
~"'" RED to prepare a conservation data file. This file contains information on the
8.1
START
CONSER
WRITE
0 SATURATION
•·PcF
TO CONSERVATION
Fl LE
CQNS~RVATION
DATA FILE
TECHNICAL INPUTS
•ELECTRICITY SAVED
•LifETIME
•ELECTRICITY
PRICES
TECHNICAL INPUTS
• SUBSIDIZED
INSTALLED COST
o O&M COST
GO TO NEXT
CONSERVATION
OPTION
TECHNICAL INPUT
• UNSUBSIOIZED
INSTALLED COST
TECHNICAL INPUTS
• MAXIMUM
SATURATION
.-PAYBACK RULE
TECHNICAL INPUT
• PEAK CORRECTION
•FACTOR IPCF)
AlJSINfSS INPUTS
[NEW cEXISTING USES)
•f-'OTENTIAL SAVINGS
•fo'RO?O~ TION SAVED
•Pi:.AI<. CORRECTION
FACTOR
LOAO DATA FILE
• SAVINGS
° COSTS
RESIDENTIAL
REQUIRE\1ENTS
I RESIDENTIAL
\IOOULE)
ADJUST
REQUl RE 1.~ENTS
FOR SUBSIDIZED
CONSERVATION
SALES
• SAVrNGS
• COSTS
IN NEW ANO EXISTING
USES
SUM OVER
USES
•SAVINGS.
• cosTS
ADJUST~
REQUIRE'.'E'lf_S I
FOR SUHSIOil~.? I
CONSERVATIO~
FIGURE 8.1. RED Program-Induced Conservation Module
costs, energy savings,
installed conservation
user for the technical
and the level
options. For
of market acceptance of
the residential sector,
various consumer-
CONSER the
parameters of each option (up to ten options
queries
may be
8.2
-
"'"'\
-
-
-
-
-
-
-
-
included). Based on a user-supplied forecast of electricity prices and the
costs associated with each option, CONSER calculates the internal rate of
return on each technology. The user compares this rate to a bank passbook
savings rate as a very loose minimum test of acceptability. If the user
decides, based on this comparison, that the option should be included in the
analysis, CONSER calculates the payback period for each option. CONSER then
writes the default values and range of values for the option 1 s market
s at u rat i o n rat e t o a n out p u t d at a f ·i l e • Th e u s e r i s t he n que r i e d f o r t h e
market saturation of electricity in the use that the conservation option
offsets (e.g., electric water heating). This market saturation is also written
to the output data file.
Government residential conservation programs primarily reduce the
effective purchase price of conservation options to the consumer. Therefore,
CONSER next requests the user 1 s estimate of consumer purchase and installation
costs for each option with and without government subsidization. The
saturation of each technology with and without subsidization is calculated and
is written to the output data file.
For the business sector, CONSER requests the potential proportion of
predicted electricity use that might be saved through conservation, the
estimated proportion of these potential conservation savings that are realized,
and the costs per kWh for conservation savings in existing and new buildings.
These values are also written to the output data file, which now becomes an
input data file for the Conservation r1Jdule.
RED uses the residential conservation infonnation in the CONSER data file
to account for the impacts of the conservation technologies under
consideration. First, the amounts of conservation occurring in the residential
sector with and without government subsidization are calculated by multiplying
together the electric use saturation rate, the conservation saturation rate,
and the nunber of households. Next, the level of program-induced conservation
is calculated by subtracting the nonsubsidized conservation savings from the
subsidized figure. Finally, this figure is subtracted from the price-adjusted
residential requirements to derive the utilities 1 total residential sales.
8.3
The business conservation calculation separately addresses the sales to
new and existing uses, and two potential pools of electricity savings are
calculated. For simplicity, existing uses are defined as the previous forecast
periods' electricity requirements, whereas new uses are defined as the
difference between the previous period's requirements and the current period's
requirements. The two potential pools of savings are the sales to new uses and
retrofits times user-supplied potential savings rates (for new uses and
retrofits). The predicted level of savings in each case is found by
multiplying the potential pools of savings times user-supplied conservation
saturations with and without government intervention. Finally, the total
program-induced savings are derived by subtracting the savings without
government intervention from sales with government intervention for both new
and existing uses. Total price adjusted requirements, minus program-induced
business conservation, equals utilities' total sales to business.
The economic costs of the residential conservation technology package are
found by multiplying together the government subs1dized conservation saturation
rate, the electric saturation rate, the number of households, and the cost to
consumers per installation without government intervention for each
conservation option, and summing over options. For the economic costs of
business conservation, the total megawatt hours saved by government~subsidized
conservation is multiplied by the cost per megawatt hour saved.
F i n a 1 1 y , the Cons e rv at i on r-.rb d u l e tiel p s cal c u l at e the effect o f
conservation on peak demand. Unfortunately, not all conservation technologies
can be given credit for displacing the demand for peak generating capacity.
Therefore, CONSER queries the user for a peak correction factor, a variable
that takes on a value between zero and one if the option receives credit for
producing some portion of its energy savings during the peak demand period;
otherwise the value is zero. These peak correction factors for each option are
aggregated in RED. First, they are weighted by the proportion of total
program-induced electricity savings each option represents during a given
forecast per1 od. Next, the weighted correction factors are summed together.
The resulting aggregated peak correction factor is sent to the peak demand
model to calculate the peak savings of the set of conservation technologies.
8.4
-""'"~:
-
-
-
-
-
INPUTS AND OUTPUTS
The inputs and outputs of the Program-Induced Conservation Module are
summarized in Table 8.1. The potential market for the conservation option is
defined by the total number of households served (HHS) and the saturation of
the electrical devices (ESAT) whose use of electricity can be displaced by
investment in a particular conservation option. ESAT equals the total market
saturation of the appliance times the fuel mode split. The total nUTiber of
households served is calculated in the housing module, while ESAT is
interactively entered by the user. RCSAT, the penetration of the potential
market by the conservation technology, is determined within the CONSER
parameter routine. The technical energy savings and the costs of residential
conservation devices (both installation and maintenance) are interactively
specified within CONSER by the user.
The business segments of CONSER also query the user for the potential and
actual saturations of electricity conservation in the business sector and the
costs per megawatt hour saved for business investments in conservation.
Finally, the correction factors are decimal fractions that are
interactively supplied by the user to CONSER and that reflect the extent to
which conservation options receive credit for peak savings.
The outputs of the Program-Induced Conservation ~1odul e are the final
electricity sales to the business and residential sectors, and ~he electricity
savings of the conservation technology set considered in a given run of the RED
model •
~ MODULE STRUCTURE
The price adjustment mechanisms used in the Business and Residential
,-. Consumption r-bdules employ price elasticities derived from studies that did not
distinguish among the impacts of conservation technologies and other effects of
energy price changes. Since conservation of electricity is argued to be
induced either by energy price changes or by market intervention designed to
encourage conservation, the treatment of conservation in REO was cautiously
developed to eliminate the possibility of double counting energy savings and
costs.
8.5
TABLE 8.1.
ai ~
Svmbo 1
HHS
TECH
COST!
COSTO
RCSAT
ESAT
PRES
RESCON
CF
BCSAT
COST
BUSCON
bl Outputs
Svmbal
TCONSAV
TCONCOST
ADRESCON
ADBUSCON
ACF
Inputs and Outputs of the Conservation Module
Nam~
Tota' households served
Technical energy savings
Installation and purchase cast
of the residential conservation
device
Ooeration and maintenance casts
of the residential conservation
device
Residential saturation of the
device !with and without govern-
ment intervention)
Residential electric use
saturation
Exoected residential electri-
city price
Price-adjusted residenti~l
consume t ian
Peak correction factor
Potential prooartion of elec-
tricity saved in bus1ness in
new and retrofit uses
Business conservation saturation
rate (with and without govern-
ment intervention)
Cost per megawatt hour saved
in business
Business price-adjusted
consumption ·
Name
Total electricity saved
(busin~ss plus residential)
Total cost of conservation
!business plus residential)
Adjusted residential consumption
Adjusted business consumption
~ggregate peak correcti an factor
8.6
From
Residential Module
CONSER, Interactive Input
CONSER, Interactive Input
CONSER, Interactive Jnout
CONSER, Interactive Input
CONSER, Interactive Input
CONSER, Interactive lnout
Residential Mc1ule
CONSER, Interactive Input
CONSER, Interactive Input
CONSER, Interactive Input
Uncertainty Module
CONSER, Interactive 1 nout
Business Modul~
To
Report
Report
~iscellaneous and Peak
Demand Modules
Miscellaneous and Peak
Demand Modu 1 es
Peak Demand Mode 1
-
-
-I
--
-
-
-
-
In RED's formulation, the Program-Induced Conservation Module serves
primarily as an accounting mechanism that tracks the impacts of a given set of
technology options in the residential sector and the aggregate level of
conservation in the business sector. However, since government policies and
programs could have a significant, direct impact upon the level of conservation
ad opt e cl , a n d s i n c e t h e i n c r erne n t a l i mp a c t s o f t h e s e act i on s a r e n o t
incorporated in the price adjustment process of the Residential and 8usiness
Consumption rtodules, the Program-Induced Conservation r'odule explicitly
calculates these impacts and accordingly adjusts the forecasted sales to
consumers.
Scenario Preparation (CONSER Program)
The calculations of the Conservation Module require scenarios of the
saturation of conservation options, the expected electricity savings, and their
associated costs. To reduce the amount of data entry in scenario preparation
and to facilitate the use of a broad set of conservation technologies and
government policy options, a separate program (CONSER) queries the user for
information necessary to calculate the saturations, savings, and costs. These
p a r arne t e r s a r e t h en w r it t e n t o a d a t a f i l e w h e r e t hey c a n be a c c e s s e d by t h e
remainder of the Conservation t'odule. Two steps are required: 1) determining
if an option will achieve market acceptance; and 2) calculating market
saturations for options gaining acceptance.
The first step is to determine whether a specific conservation option will
achieve market acceptance. For the residential sector, the way RED identifies
acceptable options is to compare them with other investments available to the
consumer. Conservation is an investment with a financial yield that can be
calculated and compared with other investment options. By comparing the
internal rate-of-return (IRR) of a conservation option with the market rate of
interest, one can determine whether conservation options' return is sufficient
to encourage market acceptance.
The market rate of interest to which RED compares the internal rate-of-
return is the standard commercial bank passbook interest rate. Passbook
accounts have several characteristics:
1. They are virtually risk free.
2. They are extremely 1 iquid.
8.7
I I Ill
· 3. They have trivial requirements as to the size of the initial deposit.
4 • Th e y a r e r e ad i l y a v a il a b l e t o eve r yon e •
Investments in conservation technologies, however, are characterized by
the foll m·ti ng:
1. risky
2. difficult to liquidate
3. (sometimes) require a large initial payment.
These factors would cause most homeowner-investors to require a higher rate of
return on conservation than those on passbook accounts to invest in
conservation. Therefore, a conservation option can pass the internal rate market
interest test even though it might not be adopted. Such a comparison insures that
every option that could achieve market acceptance is included in the portfolio of
conservation technologies to be considered.
where
The IRR is calculated with the following formula:
T = lifetime of the device (maximum of 30 years)
p = internal rate-of-return
i = subscript for the year. Takes on values 1 to 30
ES = value of electricity saved
c = total cost of the option in the year
= subscript for the 1 oad center
k = subscript for the option
( 8. 1)
The value of electricity savings is based on the energy prices the consumer
expects. It is calculated by querying the user for price forecasts and the
electricity savings (in k\~h) for each option and multiplying:
( 8 .2)
where
PRES; = dollars per kWh in load center 'i
TECHik =annual kWh savings in region i per installation of device k.
8.8
-I
~-1
-
--
The cost (Ci£k) is the 1980 dollar installation and purchase cost in the year
the device is purchased and the annual maintenance and operating 1980 dollar
costs in all remaining periods.
Recognizing that initial cost is a major barrier to conservation, the
Congress has provided incentives for individuals to install energy-conserving
equi~ent. Furthermore, the State of Alaska has also instituted several
programs aimed to promote installation of conservation equipnent. Because the
main impact of these programs is to reduce the initial cost of conservation,
CONSER uses the subsidized installation and purchase costs of the device to
forecast whether a device will achieve additional market acceptance over an
unsubsidized case.
As previously stated, CONSER requests the expected electricity price
forecast for each year, the operating and maintenance costs, the kWh savings
and the government subsidized purchase and installation costs of the device for
each region. CONSER calculates the internal rate of return of the option,
prints this information, and asks the user if the option is to be used. If it
is, then the unsubsidized costs of purchasing and installing the option are
also requested.
If the scenario to be considered does not include government intervention,
the installation and purchase costs entered for the subsidized and unsubsidized
cases should be the same (and equal to the unsubsidized costs).
The next step of scenario preparation is to determine the market
saturation rate of each conservation option. RED employs a payback decision
• rule to determine the default value and the range of the conservation
saturation rate. Since the expected value of electricity savings probably is
not constant across time, the payback period is calculated by dividing the
installation and purchase costs by the cumulative net value of electricity
savings (value of energy savings minus operating and maintenance costs),
starting with the first year and continuing until the ratio is less than one.
The nLITlber of years required to drive the ratio to less than one is the payback
period.
The payback period is calculated for both the subsidized and nonsubsi-
dized cases. Since the subsidized case usually will have lower installation
8.9
I I in
and purchase costs, the payback periods for the subsidized case will usually be
lower and the conservation saturation rates will usually be higher.
CONSER also requests the name of the conservation option, a forecast of
the market saturation rates for electric devices from which the option
displaces consumption, and the peak correction factor for each conservation
option. The saturation of electric devices is used within the Conservation
Module to define the potential market of the conservation option, whereas the
peak correction factor indicates the extent to which the option displaces
electricity cons~nption at the peak. This information, as well as the costs
and saturation of the conservation option (for the unsubsidized and subsidized ~
cases), is written to a data fi 1 e for 1 ater access by the remainder of the
Program-Induced Conservation Module.
Funding constraints in the Railbelt Alternatives Study prohibited the
development of detailed cost and performance data for business conservation
applications. CONSER, therefore, requires the user to provide the following
for both new and retrofit uses: the potential proportion of electricity that
conservation technology can displace and an estimate of the proportion of those
potential savings actually realized for subsidized and unsubsidized cases.
CONSER also requests the cost per·megawatt hour saved for both cases and the
peak correction factor for new and retrofit uses.
This business sector information is also written to CONSER 1 s output data
file. By running CONSER with several different technology packages and
government policy packages, conservation scenario files can be easily
constructed for later analysis within RED.
Residential Conservation
Using the information from the data file that CONSER creates, the
calculation of electricity saved by the set of technologies is
straightforward. By llJUltiplying the electric device saturation and the
incremental nunber of households served, the total nunber of potential
applications of the conservation device is found. The incremental number of
households served in the first forecast period (1980) is zero, since the
current consumption rates already include the current level of conservation.
8.10
-
-
-
-
-
-
-
By next multiplying the potential number of uses by the savings per
installation and the saturation of the conservation option, the amount of
electricity saved is derived:
CONSAVit~ = RCSATikj x TECHik x
(ESATitk x HHSit-ESATi(t-l)k x HHSi(t-1)
where
CONSAV = electricity saved (kWh)
RCSAT = conservation saturation rate
TECH = electricity savings per installation ( k \•lh)
ESAT = electric device saturation rates
HHS = total households served
t = denotes the forecast period (1,2,3, .•. ,7)
j = denotes subsidized (j=l) or nonsubsidized (j=o).
The total electricity displaced through the residential conservation set
considered is found by summing across the options (subscript k):
where
K
RCONSAVitl = I CONSAVitkl
k=l
RCONSAV =residential electricity conserved (kWh)
K =total number of residential opti'ons considered.
Since the price adjustment mechanism does not account for government-
induced conservation, the model next adjusts residential sales by the
,-incremental conservation attributable to government programs:
where
-
ADRESCON;t = RESCONit -(RCONSAVitl -RCONSAVito)
ADRESCON = final electricity requirements of residential consumers
RESCON = price-adjusted residential consumption.
8.11
( 8 • 3)
( 8 • 4)
(8.5)
I I Ill
The electrical device saturation and the incremental number of households
define the number of potential applications. The cost of purchasing and
installing the option is calculated by multiplying the potential number of new
uses by COSTI (the installation and purchase costs per option). Next, by
multiplying COSTO (annual operations and maintenance costs per option) by the
cumulation of previous forecast periods' potential uses, the operating and
maintenance costs are found. Finally, by summing all these components, the
total annual costs associated with conservation savings in a given forecast
period can be found. nuring any forecast year, the annual costs are equal to
one year's total installation costs, plus operating costs associated with all
previous additions to stock: ~
CONCOSTit kj
where
CONCOST =
COST I
COS TO =
t
=[COSTlikj x RCSATitkj x (ESATitk x HHSit-
ESATi(t-1)k x HHS 1(t-1 ));"5 + COSTOik x 2: RCSAT.k. x
h=1 1 J
(ESAT; hkj x HHS; h -ESAT; hkj x THHS;( h-l))]
the option's tot a 1 annual cost
unit cost in 198 0 do 1 1 a r s for purchasing and installing the
conservation option
unit cost in 1980 dollars of operating an0 maintaining the
conservation option
h = forecast period subscript. Can take on values 1 tot.
(8.6)
By summing over the options, the total costs of the residential conservation
set is found.
where
K
RCONCOST i tJ· = 2: CONCOST it. kJ.
k=l
RCONCOST = present value of the total costs of the set of
residential conservation options.
8.12
( 8. 7)
-
-
-
-
-
I~
The total costs of conservation are the unsubsidized total costs
(RCONCOSTit 0 ), consumers pay the subsidized costs (RCONSAVitl), and government
pays the difference (RCONCOSTito-RCONCOSTitl).
Business Conservation
For business conservation impacts, funding constraints prohibited
collection of detailed cost and performance data. Fortunately, a 1 imited
nunber of studies have estimated the potential energy savings and associated
costs for aggregate conservation investments in new and existing buildings.
RED separates the conservation impacts for the business sector into two
parts: those arising from retrofitting existing buildings, and those arising
from incorporating conservation technologies in nevJ construction. As in the
residential segment of the Program-Induced Conservation i,bdule, the potential
pool of electricity that can be displaced must be identified for both new
construction and retrofits. This "pool" is determined by the state of
conservation technology and is supplied to the conservation module from the
CONSER output file. The actual amount of conservation that occurs depends upon
the price of electricity and competing fuels and upon the cost and perfor~ance
characteristics of the options available.· This is also supplied by CONSER.
In RED, the potential pool of displaced electricity for businesses is
derived by first separating business sales into sales to existing structures
and sales to new structures. For simplicity, the change from the previous
periods• business requirements as calculated by the Business Consumption i'odule
is assumed to be the sales to new buildings:
SALNBit = BUSCONit-BUSCONi(t-1)
where
SALNB = sales to new buildings
BUSCON =business consumption prior to conservation adjustments.
Therefore, the sales to existing buildings are the sales in the previous
period:
8.13
( 8. 8)
SALEXit = BUSCONi(t-1) ( 8. 9)
where
SALEX = sales to existing buildings.
To find the potential pool of electricity use displaced through retrofits and
incorporation of conservation options in new buildings, the Program-Induced
Conservation Module multiplies the disaggregated sales figures times the
potential percentage of electricity saved in new and retrofit buildings:
where
POTNS;t = SALNBit x PPESitN (8 .lOa)
POTEX;t = SALEXit x PRESitE (8.10b)
POTNB = potential amount of displaced electricity in new buildings
PPES = proportion of electricity that technically can be displaced via
retrofit or incorporation of conservation options in new
buildings.
POT EX = potential amount of displaced electricity in existing buildings
E = subscript for existing buildings
N = subscript for new buildings.
These figures, however, only provide the technically feasible amount of
electricity that could be displaced. Market forces determine what level of the
potential electricity savings will be achieved.
In the residential segment of the Program-Induced Conservation Module, REO
used an internal rate-of-return test and a payback period decision rule to
determine first, whether an option would achieve market acceptance, and second,
what level of acceptance it would achieve. As mentioned above, the information
available fOr business conservation does not permit such an analysis.
Therefore, the model user is required to assume a level of potential market
saturation. The saturation rates (one for retrofits, one for new buildings)
must reflect the prices of fuels (including electricity), the costs of the
package of options employed, and the electricity savings expected for
subsidized and nonsubsidized cases.
8.14
-
.,....,.
-
.r--
The saturation rates are obtained from the data file CONSER creates. The
displaced electricity can be found by multiplying the total saturation rates by
the total potential pool of electricity savings:
where
BCONSAVitNj = BCSATitN x POTNBitj
BCONSAVitEj = BCSATitE x POTEXitj
BCONSAV = electricity savings
BCSAT = saturation rate for conservation options in business.
(8.lla)
(8.llb)
As in the residential sector, the business requirements must be adjusted
for the incremental impact of government programs:
ADBUSCONit = BUSCONit (BCONSAVitN 1 BCONSAVitNo) (8.12)
-(BCONSAVitE 1 -RCONSAVitEo)
where
ADBUSCON = adjusted business consumption.
The total cost of the conservation set in a given future forecast year is
given by multiplying the 1980 dollar cost per megawatt-hour saved by the
conservation savings in each use:
where
BCONCOSTitj - ( BCONSAV itEj x COST; Ej + BCONSAV itN1)
BCONCOST = business conservation costs, future forecast year
COST = 1980 dollar costs per megawatt hour saved.
(8.13)
The total costs of the conservation in a future forecast year to "society" is
the nonsubsidized costs (BCONCOSTit 0 ), whereas the value of the subsidy in that
yea_r is (BCONCOSTito-BCONCOSTitl), and businesses bear only the subsidized
costs (BCONCOSTit 1).
8.15
iiI Ill
Peak Correction Factors
The last item to be calculated is the aggregate peak correction factor for
the incremental impact of government conservation programs on peak demand.
This factor is calculated by weighting each option's peak correction factor by
the option's proportion of incremental conservation:
K (CONSAVitk 1 -CONSAVitko) x CFk
= k:1 ( RCONSAV i tl -RCOI~SAV ito) + ( RCONSAV i t 1 -BCONSAV~ (8.14)
(BCONSAVitE 1 -8CONSAVitEo) x CFE + (BCONSAVitN 1 • BCONSAVitNo) x CFN
+ (RCONSAVitl -RCONSAVito) + (BCONSAVit 1 -BCONSAVito)
where
--.
ACF = aggregate peak correction factor
CF =option-specific peak correction factor, equal to the proportion
of the electrical demand of displaced appliances that can be
displaced at the peak demand period of the year (e.g., January).
PARAI'1ETERS
One of the requirements of the Alaska state program whereby homeowners
request state money to install conservation measures is that the payback period
for the measure be less than seven years. Therefore, if a conservation
option's payback period is assumed to be greater than seven years, the options
market penetration will be very limited, effectively zero. However, if tf1e
option pays for itself within the first year, then the option would penetrate
the entire potential market immediately. The relationship between payback
period and penetration rate for payback periods between zero and seven years is
assumed to be linear. A range of 15% on these values is arbitrarily assumed.
Table 8.2 presents these market penetration parameters.
8.16
-
-
-
-·
TABLE 8.2. Payback Periods and Assumed Market Saturation
Rates for Resident i a 1 Conservation Options
r~.
Payback Assumed Assumed
Period Saturation Range
(years) ( %) (%)
!"'"'
0 100.0
1 87.5 80-95
2 75.0 6 7 .5-82 .5
3 62.5 55-70
,_, 4 50 .o 42 .5-5 7 .5
5 37.5 30-45
6 25.0 17 .5-32 .5
7 12.5 5-20
8 0 0-5 -"
Source: Author· Assumption
.-~
-
8.17
-
-
9.0 THE MISCELLANEOUS MODULE
t1ECHAN I sr1
The Miscellaneous Module uses outputs from several other modules to
forecast electricity used but not accounted for in the other modules, namely,
street lighting, second homes, and vacant housing.
INPUTS AND OUTPUTS
This module uses the forecasts of electrical requirements of the residen-
tial and business sectors and the vacant housing stock. The only output is
miscellaneous requirements. Table 9.1 provides a summary of the inputs and
outputs of this module.
a)
b)
TABLE 9.1. Inputs and Outputs of the Miscellaneous Module
Inputs
S~mbol Name
ADBUSCON Adjusted Rusiness Requirements
ADRESCON Adjusted Residential Requirements
VACHG Vacant Housing
Outputs
S.z:mbol Name
MISCON t~i scell aneous Requirements
From
Program-Induced
Conservation Module
Program-Induced
Conservation r1odul e
Ho u s i n g tvb d u l e
To
Peak Demand MJdul e
--MODULE STRUCTURE
Figure 9.1 provides a flowchart of this module. For street lighting, the
requirements are assumed to be a constant proportion of conservation-adjusted
business and residential requirements:
SRit = sl x (AOBUSCONit + ADRESCONit) (9.1)
9.1
where
'I Ill
(
RES I DENT I AL
PLUS
BUSINESS
CONSUMPTION
lF • ' CALCULATE CALCULATE CALCULATE
SECOND HOME STREET LIGHTING VACANT HOUSING
CONSUMPTION REQUIREMENTS CONSUMPTION
•
SUM FOR
MISCELLANEOUS
CONSUMPTION
•
( .
Ml SCE L LAN EO US
CONSUMPTION
FIGURE 9.1. RED Miscellaneous Module
SR =street lighting requirements
ADBUSCON =business requirements after adjustment for the incremental
conservation investments
ADRESCON = fi na 1 electricity requirements of residential consumers
i = subscript for load center
t = forecast period (1,2,3 ••• ,7)
sl = street lighting parameter.
For second-home consumption, RED calculates the number of second homes as
a fixed proportion of the total n1.111ber of households. A fixed consumption
factor is then applied:
SHR;t = sh x CHHit x shkWh (9.2)
9.2
-IOOii
'
-
-
-
!!"'!! ,.
-
-
where
SHR = second home requirements
CHH =total ncmber of civilian households
sh = proportion of total households having a second home
shkWh = consumption factor.
Finally, the use of electricity by vacant housing ts a fixed consumption
factor times the nt.mber of vacant houses:
where
VHRit = vh x VACHGit
VHR =vacant housing requir~nents
VACHG = n t.mbe r of vacant houses
vh =assumed consumption per vacant dwelling unit.
( 9. 3)
Total miscellaneous requirements are found by st.mming the three components
above:
(9.4)
where
MISCON =miscellaneous electricity consumption.
PARAMETERS
Table 9.2 gives the parameter values used for the Miscellaneous t~odule.
These parameters are all based on the authors 1 assumption because no other
source of information is available. Tillman (1983) found that Anchorage
Municipal Power and Light has a conservation program in place to convert city
street lights from mercury vapor lamps to high-pressure sodium lamps, resulting
in some savings of electric energy. This is considered to be a one-shot
success whose total impact grows proportionately to street lighting demand.
Even since this program was instituted, miscellaneous demand has continued
to grow. It is assumed that the effects of additional requirements for
street lighting will partially offset the effect of conservation, and that
9.3
Symbol
Sl
sh
shkWh
Vh
TABLE 9.2. Parameters for the Miscellaneous Module
Name
Street lighting(a)
Proportion of households having a seco~d home(b)
Per unit second-home consumption(b)
Consumption in vacant housing(c)
Value
0.01
0.025
500 k\4h
300 k\~h
(a) 1980 ratio of street lighting to business plus residential sales.
(b) 0. Scott Goldsmith, ISER, personal communication.
(c) Author assumption. Reflects reduced level of use of all
appliances.
this component of miscellaneous demand will continue to be about proportional
to residential and business use in the future.
9.4
-
--
;:-
10.0 LARGE INDUSTRIAL DEMAND
Large industrial demand for electricity in the RED model is not provided
by the model itself; rather, the model provides for a data file called EXTRA
OAT, which is read by the program each time a forecast is made. The model user
supplies a "most likely" default value forecast of electricity energy and
demand at system peak to the EXTRA OAT file for each load center he wishes to
include in the model run. If he wishes to develop a ibnte Carlo forecast, he
must also supply forecasts for higher and lower probability conditions. These
exogenous estimates can be assembled from any source; however, they should be
consistent with the economic scenario used in any giveh model forecast. This
was done for the 1983 update.
The EXTRA OAT data set has other uses. Although military demand for
electricity in the Railbelt historically has been self-supplied, the model user
could test the effect of military demand on utility sales or total Railbe1t
demand by adding military annual energy and peak to the exogenous forecast for
each load center. Self-supplied industrial energy can be handled in a similar
fashion. Finally, EXTRA OAT can he used to account for cogeneration of
electricity and for utility load management. The model user only needs to
estimate the effect of such projects for 1980, 1985, 1990, etc. on annual
energy sales and load at the time of year when the electrical system peak load
occurs. He then subtracts these estimates from his estimates of large indus-
trial (plus military) annual energy and demand at system peak and enters the
difference in EXTRA OAT for each forecast period and load center. This data
f i 1 e w i 1 1 accept n ega t i v e n urn be r s s how i n g n e t c on s e r vat i on • Ot h e r t y p e s o f
conservation or demand that cannot be analyzed in detail in other sectors of
the model can also be handled here. Examples might include agricultural and
transportation demand for electricity or the impacts of district heating
systems on electrical consumption.
MECHANISM, STRUCTURE, INPUTS AND OUTPUTS
The user supplies data for the file EXTRA OAT for each load center and
forecast period on net total industrial, military, agricultural, transportation
10.1
I I Ill
annual energy demand at system peak (net of cogeneration effects) for each load
center for cumulative probabilities of 0.75, 0.5 (default value), and 0.25 that
demand will be greater than. or equal to the value specified. The model then
adds these estimates to the appropriate reports in the forecast re~ults.
Inputs and outputs are identical. Outputs are supplied to the Peak Module (to
calculate system peak demand) and to the report writing routines.
PARM1ETERS
There are no parameters in the RED model large industrial demand
calculations.
10.2
~.
.~
11.0 THE PEAK DEMAND MODULE
Up to this point, only the method to forecast the total amount of electri-
city demanded in a year has been considered. However, forcapacity planning,
the maximum amount of electricity demanded (or peak demand) is probably more
important. Peak demand defines the highest rate of consumption of electric
energy during the year. As identified in RED, it does not include losses of
energy in transmission.
i1ECHANISM
Unlike the Lower 48, where utilities frequently have done extensive cus-
tomer time-of-day metering and other analyses to estimate. peak demand by
customer type and end use, the Railbelt utilities have virtually no information
on peak demand by type of customer and end use. Consequently, the RED model
does not forecast peak demand by end use; instead the Peak Demand Module uses
regional load factors to forecast peak demand. The load factor is the average
demand for capacity throughout the year divided by the peak demand for capacity
in the year. RED first calculates the peak demand without the peak savings of
program-induced conservation. Next, the peak savings of the incremental pro-
gram-induced conservation are calculated, taking into account the mix of con-.
servation technologies being considered. Finally, by netting out the peak
savings, RED calculates the peak demand the system must meet.
INPUTS AND OUTPUTS
Table 11.1 provides a summary of the inputs and outputs of the Peak Demand
~'lodul e. The load factors (LF) are generated by the Uncertainty M::ldul e, ~vhereas
the aggregate peak correction factor (ACF) comes from the Conservation
r'\odule. The business, residential, and miscellaneous requirements (BUSCON,
RESCON, and MISCON) come from the Business, Residential, and Miscellaneous
fv1odul es, whereas the conservation-adjusted requirements ( ADRESCON and ADBUSCON)
conie from the Conservation ~1odule. The outputs of this module are 1) the peak
demand in each regional load center at the point of sale to final users, and
~-2) the incremental peak savings of subsidized conservation.
-11.1
TABLE 11.1. Inputs and Outputs of the Peak Demand t1odul e
a) Inputs
Symbol
LF
RESCON ·
BUSCON
ADRESCON
ADBUSCON
ACF
b) Outputs
sxmbo l
FPD
PS
Name
Regional load factor
Residential requirements prior to
adjustment for subsidized conservation
Business requirements prior to adjustment
for subsidized conservation
Residential requ·irements adjusted for
subsidized conservation
Business requirements adjusted for sub-
sidized conservation
Aggregate peak correction factor
Name
Peak demand
Incremental peak savings
MODULE STRUCTURE
From
Uncertainty r·'odul e
Residential
Co n s ump t i o n r.'f.) d u l e
Rusiness
Consumption rndule
Conservation Module
Conservation Module
Conservation Module
To
Report
Report
Figure 11.1 provides a flow chart of this module. First, the peak demand
without subsidized conservation is calculated. This is done by dividing the
total electricity requirements in each region by the product of the load factor
-
times the nunber of hours in the year. Next, the same operation is performed -
using energy requirements adjusted for the energy savings resulting from sub-
sidized conservation investments. This yields the preliminary peak savings.
REO then adjusts the peak savings by multiplying the aggregate peak correction
factor times the peak savings. The corrected peak savings are then subtracted
from the peak demand calculated in the first step to derive the regional peak
demand at the point of sale.
The first step is to calculate the total electricity requirements without
subsidized conservation by adding the residential, business, and miscellaneous
requirements:
11.2
-
-·
-'
!""""
where
ANNUAL ELECTRICITY
REQUIREMENTS
• RESIDENTIAL
• BUSINESS
• MISCELLANEOUS
LOAD
FACTORS
[FROM UNCERTAINTY
MODULE)
CALCULATE
PRELIMINARY
PEAK DEMAND
CALCULATE
PEAK
SAVINGS
LARGE
INDUSTRIAL
DEMAND
PEAK
DEMAND
FIGURE 11.1. RED Peak Demand Module
TOTREQBit = BUSCONit + RESCONit + MISCONit
• ANNUAL SAVINGS
DUE TO SUBSIDY
• PEAK CORRECTION
FACTOR
(FROM CONSERVATION
MODULE]
(11.1)
TOTREQB =total electricity requirements before conservation adjustment
( 1"1Wh)
BUSCON = business requirements before conservation adjustment ( ~1Wh)
RESCON = residential requirements before conservation ad jus tmen t ( MWh)
~11 SCON = miscellaneous requirements (MWh)
= index for the load center
t = index for forecast period (t = 1,2, ••• ,7).
Next, the Peak Demand fvbdule calculates the peak demand without accounting
for the incremental conservation due to subsidized investments in conservation
by applying the load factor:
11.3
! I ill
TOTREQB; t
= ;-;:'---;;-=;'=" LF it X 8760 (11.2)
where
PD = peak demand (r1~1)
LF = load factor
8760 = number of hours in a year
p = index denoting preliminary.
To calculate the peak savings due to subsidized conservation investments,
RED first must find the incremental nunber of megawatt hours saved:
TOTREQSit = BUSCONit-ADBUSCON;t + RESCONit -ADRESCONit (11.3)
where
TOTREQS = incremental megawatt hours saved by subsidized conservation
investments
ADBUSCON = business requirements after adjustment for the incremental
impact of subsidized co.nservation
ADRESCON = residential requirements after adjustment for the incremental
impact of subsidized conservation.
Next, peak savings are found by multiplying the incrementa1 electricity
saved by the aggregate peak correction factor and app1ying the load factor:
TOTREO\ t
= ACFit X LFit X 8760 (11.4)
where
PS = peak savings (MW)
ACF = aggregate peak correction factor.
Finally, by subtracting the peak savings from the preliminary peak demand,
the final peak demand for each region is derived:
PD .t pl
11.4
(11.5)
.~,
-
-
-]'
·-
.-
-
-
.-
where
FPO = index denoting final peak demand.
PARM1ETERS
The only parameters in the Peak Demand Module are the system load factors
assumed for the Anchorage and Fairbanks load centers. These load factors are
shown in Table 11.2.
TABLE 11.2. Assumed Load Factors for Railbelt Load Centers
Load Center
Anchorage
Fairbanks
Load Factor (%)
Default Range
55.73 49.2-63.4
50.00 41.6-59.1
In the RED model, peak electricity demands are estimated as a function of
the seasonal load factors (average energy demands/peak energy demands) for the
major load centers in the Railbelt. Thus, identification of appropriate load
factors is crucial in determining the need for peak generating capacity for a
given amount of forecasted electrical energy demand.
Forecasting fut~re load factors and thus, peak ~lectrical energy deman~s.
is a difficult process because of the interaction among many factors that
determine the relationship between peak and average electrical demands. The
analysis conducted in support of the parameter estimates in Table 11.2 quanti-
tatively and qualitatively evaluated annual load factors for the Anchorage and
Fairbanks load centers. The impacts of the diversity between the two load
centers in the timing of the occurrence of peak loads is also briefly discussed
below.
Simple trend-line fitting and more complex ARIMA time series modeling were
used in an attempt to develop quantitative forecasts for future load factors
for the Anchorage and Fairbanks load centers. A qualitative analysis was also
11.5
conducted of the impacts of conservation programs, changes in customer mix, and
other variables as they may affect future load factors for the two load
centers.
The central conclusion ar1s1ng from the analysis is that no scientifically
defensible basis for projecting that future load factors for the Anchorage and
Fairbanks areas will either increase or decrease could be developed within the
resources of the study. (a) Thus, average 1 oad factors far the peri ad 1970-1981
of 0.56 for Anchorage and 0.50 for Fairbanks were used as default values in
developing peak demand estimates. Historic minimum and maximum values of the
·load factors of individual utilities in each load center were examined. The
lowest and highest of these in each load center were used as the minimum and
maximum load factor values for the load center.
Quantitative Analysis of Trends in Load Factors in the Railbelt
Trend analysis is not a preferred approach to forecasting future electri-
cal load factors and peak loads in the Railbelt. Ideally, the methodology for
forecasting future load factors over a long-range planning horizon (in RED,
30 years is the planning horizon) should incorporate information on structural
variables that determine the load factor. Examples of such structural vari-
ables are the forecasted demands of different customer classes (i.e., residen-
tial, commercial, and industrial) and the forecasted patterns and saturation
rates of appliances.
Developing a structural econometric model of l·oad factors and/or peak
loads is a complex task. In addition, while Anchorage Municipal Light and
Power has conducted very 1 imited metering of residential sector customers, in
general there is no data base in Alaska that associates patterns of residential
electrical use with appliance stock and socioeconomic characteristics. Even
less data are available on the commercial sector. Thus, the data necessary for
building a structural time-of-use model are not available for the Railbelt
(a) This is consistent with Anchorage Municipal Light and Power findings of no
trend in load factor (personal communication, Max Foster, At1LP economist,
to Mike King, June 11, 1981).
11.6
-
-
-'
-
-
-·
-!
-'
area. Thus, in this study, quantitative analysis of Anchorage and Fairbanks
load factors was l imi ted to trend analysis.
Simple Trend Analysis
Table 11.3 presents estimates of the annual load factors for areas
approximating the Anchorage and Fairbanks service areas and the month in which
the peak load occurred in the period 1970-1981. The load factors presented in
Table 11.3 were estimated by the following equation:
REG
PMW*8. 76
where
REG= regional energy generation for Anchorage or Fairbanks areas in
gigawatt hours
Pr1W =largest monthly peak regional energy demand for Anchorage or
Fairbanks areas in megawatts.
TABLE 11.3. Computed Load Factors and ~1onth of Pe9k) Load Occurrence
for Anchorage and Fairbanks 1970-1981 ~a
Year
1970
1971
1972
197 3
1974
1975
1976
1977
1978
1979
1980
1981
Anchorage
Load Factor Peak Load t>bnt h
0.524
0.575
0.562
0.585
0.589
0.495
0.583
·0.548
0.576
0.593
0.541
0.559
December
January
December
January
December
December
December
December
December
December
December
December
Fairbanks
Load Factor Peak Load r~onth
0.445
0.443
0.486
0.505
0.446
0 .4 7 4
0.555
0.466
0.553
0.5 7 4
0.488
0.511
December
December
January
January
December
December
January
December
January
January
December
December
(a) Computed from data presented i ri DOE/ APAdmi n (1982) •
11.7
! I Ill
All data for estimating the load factors were obtained from tables
developed by the Alaska Power Administration (APAdmin) (DOE-APAdmin 1982). The
area designated as the "Southcentral" region in the APAdmin statistics is
assumed to be representative of the Anchorage service area in the Railbelt and
the area designated as the "Yukon" is assumed to be representative of the Fair-
banks area.
The information presented in Table 11.3 clearly shows that the period when
Railbelt peak loads occur (and thus, when annual load factors are determined)
is in the winter, coinciding ~ith the timing of coldest winter weather and
maximum hours of darkness. It is desirable for forecasting purposes to stan--
dardi ze for weather-related impacts on the load factor. Including weather-
related impacts in the trend analysis could lead to erroneous conclusions if a
nonrepresentative mix of weather patterns occurred over the period of the time
series data. In addition, weather is such a random variable that it is almost
impossible to forecast.
Assuming that a strong correlation between non-weather-related load fac-
tors and time could be identified, future non-weather-related load factors
might be reasonably forecast using the coefficient in the time trend
equation. To correct the load factors for weather-related influences, the .
annual load factors for each year presented in Table 11.3 were multiplied by
the nlJllber of heating degree days in each corresponding year. The resulting
adjusted load factors for Anchorage and Fairbanks were then regressed against a
time variable using the following simple equation:
where
Y = a + bx
Y = load factor multiplied by heating degree days
x =time.
The explanatory power of time in explaining changes in the adjusted load
factor was low for both Anchorage and Fairbanks. The R2 values for the regres-
sions were 0.39 for Anchorage and 0.02 for Fairbanks, respectively. Both the t
and F values for time in the Anchorage equation were significant at 95% levels
11.8
-I
..... ,
-
-
r-·
of confidence. The time coefficient was negative, indicating that Anchorage 1 s
weather-adjusted load factor was declining over time. For reasons that will be
discussed later, it does not appear that forecasting a declining load factor in
either Anchorage or Fairbanks is realistic. In any case, the level of explana-
tory power provided by the time trend equations ~vas too low to base any fore-
casts of future load factors upon the results.
Trend Analysis Using an ARIMA Model
A more complex method of using time series data to forecast future load
factors in an ARH1A model (Autoregressive Integrated flbving Average) was also
attempted. The first step in this process was to calculate load factors by
month for the period 1970-1981. These monthly load factors were calculated in
a manner similar to that used in calculating the peak load factors presented in
Table 11.3. Calculating load factors for each month in the 12-year period pro-
vided a data base of 144 observations, which was more than sufficient for dev-
eloping an ARIMA model.
The next step was to attempt to identify the correct specification of the
ARH1A model in terms of the lag operators to be used and the degree of differ-
encing to be employed. The objective in identifying the model is to obtain a
st~tionary historical time series that wil 1 consistently represent the para-
meters underlying the trends in the time series.
The appropriate lag operators for the model were specified to be 1 and
12. That is, the load factor in a particular month should be correlated with
-the load factor in the previous month and the load factor in the previous
year. Computation of autocorrelation coefficients for the data using lag
operators of one and 12 and various levels of differencing revealed that using
-·
first differences on both lag operators produced a stationary time series with
small random residuals in a relatively short time for both Anchorage and Fair-
banks.
Thus, the ARIMA model for load factors was identified as the following:
11.9
where
at
B
cj>1
e1
8 12
Yt
::
-
:;::
=
=
=
ran d om e r r o r t e rm ( 11 w h i t e n o i s e" )
lag operator
sequential autoregressive parameter for the first difference
on the load factor of the previous month
sequential moving average parameter for the first difference
on t h e l o ad facto r o f the pre vi o u s month
seasonal moving average parameter for the first difference on
the load factor of the previous year
load factor in a particular month.
This model specification is similar to the one developed by Uri (Uri 1976) for
forecasting peak loads using an ARIMA time series model.
The model was applied to the monthly load factor data and relatively low
residual sum of squares (i.e., unexplained variation in the data) were
obtained. The coefficients of the ARH1A model were then input into an ARmA
forecasting routine that uses the most recent historical data and the coeffi-
cients to generate forecasts for specified forecasting periods.
The forecasts generated by the ARIMA forecasting model predicted that the
load factor for Anchorage over the next 30 years would increase from 0.56 to
0.66, whereas the load factor for Fairbanks would decrease from 0.51 to 0.42.
However, project resources were insufficient to permit validation and refine-.
ment of the ARIMA coefficients and the resulting forecasts. In addition,
qualitative analysis of the factors influencing load factors does not support
the conclusion that Fairbanks load factors are likely to decline over time.(a)
Qualitative Analysis Of Load Factors
Although peak load forecasting has received a substantial amount of
research attention, the relationship between peak 1 oads and average energy
(a) Whether the load factor is computed on a monthly basis, as in Table 11.3,
or on an annual basis, as in Table 13.2 it appears that Fairbanks• load
factor is increasing slightly. In any event, 0.42 appears unrealistically
low. Note also that simple trend analysis showed opposite results.
11.10
-
-i
-
demands has not received the same degree of attention. Locating research
literature on the relationship between peak loads and average loads and on the
factors that influence this relationship proved to be a difficult task. In
addition, it is questionable how applicable the results of studies from other
areas are to the Railbelt because of the unique characteristics of the area and
the fact that load factors tend to be unique to each utility system.
The following discussion represents an attempt to synthesize available
information into a useful form for evaluating potential changes in Anchorage
and Fairbanks load factors. r1uch of the discussion is somewhat subjective, and
empirical results on these topics are unavailable. Consequently, there was not
a strong enough basis for concluding that load factors will change substan-
tially from present levels in the major load centers of the Railbelt.
Impacts of Changes in the Customer Load Mix on the Load Factor
The customer mix, which can be measured by the proportion of total power
demands comprised by the residential, commercial and industrial sectors, is a
crucial factor in determining the load factor of an electrical service area.
The analysis of power demands by customer is important. If it could be
demonstrated that the demands of particular customer classes are the primary
cause of Railbelt system peak demands and that changes in the current mix of .
c~stomer demands are likely to occur in the future, future changes in the Rail-
belt system load factor could be evaluated.
In general, residential power demands have the greatest degree of vari-
ation both by time of day and by season of the year. Commercial power demands
demonstrate slightly less variation over time. Industrial power demands are
~-the most constant type of power demand over time.
-
A typical Lower 48 load pattern for residential, commercial, and indus-
trial customers on a peak day is shown by a daily load profile in the Pacific
Northwest in Figure 11.2. Note the substantial amount of variation in residen-
tial power demands by time of day relative to other sectors. The pattern of
demand illustrated in Figure 11.2 is typical for most utilities,
11.11
.J
30 1-
25 r-
20 '-
15 -
10 -
5 -
LOAD (1000 MW)
TOTAL
INDUSTRIAL ---COMMERCIAL RESIDENTIAL
···············
..... .. ·........ .. . ... ··. . .. .. . . . ... ··.
,• ·~~. a•'' '• .·· ··. . . . . . · . . · ··.......... · .. . . . . ~ .
~ ~
! ~ . ~ ! ~ . .
~ ·. .· ..,.,.. ----. .. .
~ . ·. .. . .
•, • 4141 I I I I I. t t t 41 41
,., -...
/ '
/ ' / ---.-----------______ .__. _,--~-.---..... .....,_,
/ . "' ~-..-.-.----~-'-----...------
o~-1~~~~-1~-J~-~~-1~-J~-J~-~~~J--~I--~1--~J--~1--~J--~1~
12 2 4 6 8 10 12 2 4 6 8 10 12
AM PM
FIGURE 11.2. Daily Load Profile in the Pacific Northwest
--
. -
-
since sectoral load patterns in most utility service areas will reveal substan-
tially greater variation in residential loads over time than for other sectors.
Data on load patterns by type of customer in Alaska were not avail~ble •
However, a limited amount of data on total utility system loads was avail-
able. An analysis of these data shows that highest power demands in Alaska
occur in the late afternoon and early evening. This is illustrated by the data
presented in Table 11.4 for two peak days during the winter of 1981-1982.
TABLE 11.4.
Service Area
An c h o rage ( b )
Fairbanks(c)
Time Period of Peak Oemgnds in
Anchorage and Fairbanks\a)
Time Period of Peak Demand
December 29 2 1981 Januar;t 2,
4 p.m. 5 p.m.
4 p.m. 5 p.m.
1982
(a) Source: r'1emorandum from ~~yl es C. Yerkes of the
Alaska Power Authority to the Committee on Load
Forecasts and Generation, Alaska Systems Coordi-
n at i n g Co u n c i l •
(b) Includes Anchorage ~1unicipal Power and Light and
Chugach Electric Association.
(c) Includes Fairbanks Municipal and Golden Valley
Electric Association.
The late afternoo~ timing of the occurrence of peak demand in the Railbelt
generally indicates that both residential and commercial demands are likely to
be important in determining the occurrence of peak demand. Thus, it does not
appear that the load factor of the Alaska power system waul d be particularly
sensitive to changes in the relative mix of residential and commercial power.
The percentages of total Railbelt forecasted power consumption comprised
by individual sectors for various future time periods are presented in Table
11 • 5 • Th e i n f o rma t i o n pres en ted i n t h i s tab 1 e d em on s t rate s t h a t i n t h e c a s e
examined there is no clear trend in the share relationship between commercial
and residential demand. Thus, even if Rai"lbelt residential and commercial use
had different load patterns, it is not clear that this would result in any
11.13
:I ill
TABLE 11.5. Percentages of Total Forecasted Railbe1t
Electrical Consumption ComDr~sed by
I n d i v i d u a 1 C u s t orne r Se c to r \ a J
Anchora9e Fairbanks
Year Residential Commercia 1 Residential Commercia 1
1980 52.8 47.2 44.8
1990 49.1 51.9 49.2
2000 47.9 52.1 51.8
2010 46.1 53.9 51.4
(a) Sectors add to 100% (excludes miscellaneous and
industrial demand).
Source: RED Model Run, Case HE6--FERC 0% Real
Growth in Price of Oil.
55.0
50.8
48.2
48.6
~'
-
clear trend in system load factor. Industrial demand could change the load ~
factor, but industrial demand is handled separately in RED (see Section 10.0).
Impacts of Conservation on the Load Factor
Future conservation efforts in the Rai1belt have the potential to improve
the annua 1 system 1 oad factor by reducing winter e 1 ectri ca 1 demands by a -
greater amount than average electrical demands. The residential energy conser-
vation measures that are most likely to be included in Alaska's long-term
energy conservation program are presented in Table 11.6.
TABLE 11.6. Conservation t-'easures r-'ost U kely to be
Implement~d)in the Residential Sector
of Alaska\a
Measure
Ceiling Insulation
Wall Insulation
Glass
Weatherstripping
Water Heater Improvement
R-38
R-11
Level
Storm Window Installation
Doors and windows
Blankets and Wraps
(a) Source: 1983 Alaska Long-Term Energy Plan
11.14
-
-
-
The measures listed in Table 11~6 are generally related to the overall
goal of improving thermal energy efficiency in the residential sector. Thus,
one would expect that the implementation of most of these conservation measures
would result in greater energy demand reductions in the winter than the average
demand reduction for the entire year.
However, it should be noted that electricity is used for space heating in
only a small percentage of the Railbelt 1 S residences and businesses. Thus, the
impact of improvements in thermal efficiency on the total electrical power
system load factor may not be large.(a)
Electrical demands for lighting are probably the major causal factor in
creating the large disparity between peak and average electrical demands in
Alaska. Currently, according to the 1983 Alaska 1 s Long-Term Energy Plan,
lighting is not targeted as an area for future conservation efforts in
Ala~ka. Without a sustained conservation effort in lighting, it appears
unlikely that conservation will result in a significant change in the annual
load factor in the Railbelt. ·
In summary, it appears that future conservation efforts in the Ra i lbel t
will result in positive, but very small, improvements in the power system load
factors. A successful program to increase lighting energy efficiency could
significantly increase the positive impacts of conservation upon the system
1 oad factor.
Load Center Diversity
.~ The diversity in the timing of peak electrical demands is important in
determining how changes in demand will affect the system load factor. The
impacts of demand diversity between Fairbanks and Anchorage will be particu-
larly important after the t~o load centers are intertied in 1984.
(a) Note also (from Section 5.0) that the incremental electric fuel mode
in space and water heat for the Anchorage service area is very 1 ow.
means that over time the measures shown in Table 11.6 will grow less
less effective in saving electricity, other things being equal~
11.15
split
This
and
Data on demand diversity among customer classes in Alaska were not avail-
able. A limited amount of data on demand diversity among untilities was avail-
able. These data, collected by the Alaska Systems Coordinating Council (Yerkes
1982), reveals that the diversity among utilities in the timing of peak demands
is not great. The ratio of the highest peak demand for the Alaska power system
as a whole (the coincident peak) to sum of the peaks for the individual utili-
ties (the noncoincident peak) was 0.98 for selected peak days in December, 1981
and January, 1982.
This high coincidence factor, which equates to a low level of diversity
among the various utilities in the timing of peak demands, implies that future
shifts in the mix of demand among the various load centers will have little
impact on overall peak demand. A primary cause of peak power demands that
occurs in Alaska is high-pressure Arctic weather systems that generally tend to
i_ncrease the demand for electric power in almost all areas of Alaska. Thus,
diversity in demand among utilities has little impact on total system peak
demand, although more research would be necessary to reach the same conclusion
for the various customer classes.
11.16
-
-
~
-,
-· I
-
-
-
ff"ii"'f'.
12.0 MODEL VALIDATION
The purpose of a model validation is to assess the accuracy and plausi-
bility of the model 1 s forecasts. In engineering or physical systems, this can
be accomplished via controlled experim~nts, where a systen can be character-
ized, simuJated, and compared to experimental results.
Unfortunately, demand forecasting models attempt to describe the inter-
actions of physical systems, individuals, and the environment. It is impos-
sible, therefore, to conduct the type of validation that typically accompanies
physical science models.
Validation of integrated economic/engineering models typically consists of
two tests: the ability of the model 11 Come close 11 to historical figures when
the actual inputs are used, and the 11 reasonableness 11 of the forecasts. This
section applies both of these tests to the RED model.
ASSESSt·1ENT OF RED 1 s ACCIJRACY
In order to assess the accuracy of a simulation model, the usual procedure
is to substitute historical values for the inputs or 11 drivers 11 of the model,
produce a backcast, and compare the predicted and actual values. Unfortun-
ately, the period for which this type of exercise can be produced is relatively
brief.
End-use forcasting models are very data intensive, and RED is no excep-
tion. Much of the data necessary to run the model (including fuel mode split
and appliance saturations) required a primary survey of the population. His-
torical data for these critical parameters is incomplete; therefore, the
accuracy tests which can be performed on the model are limited.
A partial validation of RED 1 s accuracy, therefore, was performed hy taking
the linearly interpolated forecast values from the case.
The 1 inearly interpolated forecasts were then compared with the actual
consumption levels in 1982. Table 12.1 presents a cross tabulation of these
values.
12.1
TABLE 12.1. Comparison of Actual Base Case, and Backcast Electricity
Consumption ( GWh) 1982
Anchorage-Cook Inlet Fairbanks-Tanana Valley
Base(b) Base{b)
Actual Case Backcast Actual Case Back cast
Residential 1 ,146 1 ,060 1 ,097 178 205 208
Business{ a) 1,072 1 '118 1,170 269 243 254
Other 23 25 23 5 7 6
Total 2,241 2,203 2,290 452 455 468
% Difference from Actua 1 -1.7% 2.2% 0.6%
(a) Including Industrial Demand.
{b) Sherman Clark No Supply Disruption. This value is a linear interpolation
beh1een the 1980 and 1985 forecast values.
Even though RED is designed to be a long-run model, it produces an inter-
polated forecast with an error of only 0.6% in Fairbanks, anrl an error of only
-1.7% in Anchorage when compared to actual data in the most recent year avail-
able.
The model was also run using best estimates of 1982 economic rlrivers anrl
fuel prices .shown in Table 12.2. These results are shown in Table 12.1 as the
Backcast case. The results are also very close to the actual values in most
cases for the individual sectors; the forecast of total consumption was within
3.5% of the actual value in both load centers. Given that the model is a long
run model, that forecasts of actual households and employment and to be used in
place of unknown actual data, and that the 1980 fuel mode splits, appliance
saturations, and use rates had to be used in place of 1982 values (which are
not available) the backcast performance for 1982 is very good.
The remaining discrepencies in the forecasts for the individual sectors
appear to be related to the quality of the input data. In general, however,
there are insufficient data available to determine whether the "actual" eco-
nomic data are correct until about two to three years after the fact. Maska
"actual" data periodically undergo substantial revision. Therefore, the per-
formance of individual sectors for a short-term forecast of this type should
12.2
3 .St
-.,
""''
.....,
.....,
-
-
TABLE 12.2. 1982 Values of Input Variables
Households (a)
Employment( a)
Electricity Prices
Residential
Business
Natural Gas Prices'
Res ide n t i a 1
Business
Fuel Oil Prices
Resident i a 1
Business
Anchorage
Cook-Inlet
83 ,677
120,533
($/kWh) (b)
0.45
0 .42
($/mcf) (b)
1.84
1. 61
($/gall on) (b)
1.19
1.12
Fairbanks-
Tanana Valley
22,922
33,500
.• 1 on
.095
12.53(c)
11.08
1.21
1.17
(a) Forecasts by r1AP model for Sherman Clark NSD case. Consis-
tent estimates of households and total employ-
ment are not available for 1982 from official sources.
( b ) A 1 1 p r i c e s a r e i n n om i n a 1 do 1 1 a r s •
(c) Propane price.
considered less important than the forecasts' long-term plausibility.The next
subsection covers the subject of long-term plausibility of the forecasts.
REASONABLENESS OF THE FORECASTS
In order to test the reasonableness of RED's long-term forecasts, we com-
pared the base case used in the 1983 update with three comparable 1 ong-term
forecasts. The three forecasts were: forecasts by Pacific Northwest Power
Planning Council (PNPPC) and Bonneville Power Administration for the Pacific
Northwest, an area with large electric space heat loads and rising prices; and
a forecast by Wisconsin Electric Power Company (WEPCO) for Wisconsin and Upper
r1ichtgan, an area with relatively stable electric prices and low electric space
heat penetration. The intent was to compare forecasts from areas similar to
the Railbelt Region. The Pacific Northwest forecasts were selected because of
12.3
the low electricity prices the region shares with the Anchorage load center,
while the Wisconsin area closely corresponds to the 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
l~orthwest, prepared a twenty-year forecast of electricity demand in the North-
v.Jest. PNPPC modelled four alternate load growth scenarios (low, medium low,
medium high, and high) for the purposes of generation planning. We chose the
medium high scenario for comparison because it corresponds more closely to the
economic conditions expected to occur in the Railbelt.
The Bonneville Power Administration (BPA) is the marketer of all federal
power in the Pacific Northwest. BPA, due to its adversarial relationship with
the PNPPC, recently completed construction of their own forecasting tools. We
chose to examine BPA's medium scenario as it represents their assessment of the
most probable situation.
-
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The Wisconsin Electric Power Company markets power to i--lilwaukee-Kenosha-......
Racine Standard ~tropolitan Statistical Area, plus selected counties in cen-
tral and northern Wisconsin and upper Michigan. Unlike the two Pacific North-
west organizations, WEPCO markets.to a service area with relatively little
electric space heating. As in the southern Railbelt, the primary fuel source
is natural gas, with electricity supplying only 4 to 5 percent of total energy
used. Consequently, there are fewer the opportunities for savings of electric
energy in conservation of building heat than exist in the Pacific Northwest.
In contrast to the Pacific Northwest, where annual residential electric
consumption in 1980 averaged 17,260 kWh per household, and 11,000 to 13,000 in -
the Railbelt WEPCO customers averaged 7,240. The fact that the electric load
in the WEPCO area is mostly not related to the thermal shell of the building is
reflected in the much higher growth rates of e 1 ectri city constJnpti on than in
the Pacific Northwest or the Railbelt. This increasing power forecast is also
caused by the assumption by WEPCO that electricity rates would rise at only 0.3
percent per year in real terms through the end of the century, much less than
in the PacificNorthwest or the Railbelt. In WEPCO's service area, it was
12.4
~I
·.~
assumed electricity would capture a high (40-65 percent) share of nev-1
testdential units due to its projected cost advantage over oil and gas.
Table 12.3 presents a decomposition of two commonly used metrics for the
BPA, PNPPC, WEPCO and RED forecasts: the annual growth rate in use per
employee and use per household. The RED forecasts both exhibit higher growth
rates than either of the Pacific Northwest forecasts, but lower than the rates·
in the WEPCO forecast.
TABLE 12.3. Comparison of Recent Forecasts, 1980-2000
Pacific Northwest Power Council
Bonneville Power Administration
Wisconsin 8ectric Power Company(a)
RED
Anchorage
Fairbanks
Average Percent
Growth Rate,
Use Per Household
-.64
-.f54
1.41
-.36
0.98
Average Percent
Growth Rate
Use Per Employee
.14
-.31
3 .9 7
1.04
0.93
(a) For Wisconsin Electric Power Company, the residential forecast is use
per customer • .
This is the expected relationship of the forecasts. The BPA and PNPPC
forecasts assume vigorous conservation programs and rising electricity prices
in a region characterized by high market penetration of electric space heat and
water heat in both the residential and commercial sector. Furthennore, because
Pacific Northwest electricity prices have been low historically, there are many
opportunities available for cheaply saving large amounts of electricity. In
contrast, the Railbelt and WEPCO regions do not have as many inexpensive
opportunities to save large amounts of power, since most thermal requirements
are being met with natural gas. Furthermore, the rate of increase in
electricity prices is expected to remain low in the WEPCO region, reducing
incentives to conserve. The RED forecasts occupy a middle ground, both in
terms of base year consumption and in terms of the rate of increase in
12.5
consumption. With moderate rates of electricity price increases and fewer
inexpensive conservation opportunities, RED shows 1 ower rates of conservation
than the Pacific Northwest. In comparison with the WEPCO area, the Railbelt is
expected to have a declininy electric share in space heat and water heat, so
the rate of increase in use per customer would be less. In addition, since
Railbelt customers on the average use more electricity than WEPCO customers and
are facing higher projected rates of electricity price increases, the
forecasted rate of increase in the rate of electricity consumption should be
lower. Based on this comparison, the results of the RED forecast, therefore,
seem to be in line with what other forecasters are predicting.
12.6
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13.0 MISCELLANEOUS TABLES
~-~ Ab b r e v i a t i o n s Used
APA = Alaska Power Authority
i"""' AP&T = Alaska Power and Telephone (TOK)
AP Adrni n = Alaska Power Administration
CEA = Chugach Electric Association -GVEA = Go 1 den Valley Electric Association
GWH = Gigawatt Hour -HEA = Homer Electric Association
k \~h = Kilowatt Hour
'"""' KVa = Kilovolt
MEA = ~1a tanuska Electric Association
~1\~ = Megawatt
MWH = Megawatt Hour
Fr~US = Fairbanks Municipal Utility System
SES = Seward Electric System
SO FT = Square Foot
~I>'~
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13.1
TABLE 13.1. Number of Year-Round Housing Units by Type,
Rai 1 belt Load Centers, Selected Years
Anchorage-Cook Inlet
( U r ban ) 1 9 50 ( A a )
196o{b)
197o(c)
1980( d)
1982(e)
Single
Family Duplex Multifamil,:t
Load Center:
3,325 964
19,195 1,552
21,935 3,981
40 ,562 8 ,949
47,610 9,899
1,128
8 ,033
14,259
27 ,980
31,893
Fairbanks-Tanana Valley Load Center:
(Urban) 1950(a)
1960(b)
1970(c)
1980(d)
1982Ce)
Rail belt:
1950{a)
1960(b)
1970(c)
·1980(d.)
1982Ce)
1,295
6 ,527
5,335
10,873
12,218
4,620
25 ,722
27,270
51 ,43 5
59,828
166
671
1,068
2 ,512
2,551
1,130
2,223
5,049
11 ,461
12,450
352
4,547
6,072
. 8 ,607
8,927
1,480
12 ,580
20,331
36 ,58 7
40,820
r1ob i 1 e
Home Tot a 1
202
1 ,783
6,403
10,211
11,379
2
853
1,254
2 ,17 5
2,193
204
2,636
7,657
12 ,386
13,572
5,1119
30 ,563
46,578
87 ,702
100,781
1,815
12 ,598
13,729
2 4 ,16 7
25,889
7,434
43 ,161
60,307
111 ,869
126,670
(A) Excludes Kenai-Cook Inlet Census Division, Seward Census Division,
~1atanuska-Susitna Census Division.
(a) U.S. Department of Commerce Census of Housing 1950; Alaska, General
Characteristics, Table 14. These are all dwelling units.
(b) U.S. Department of Commerce Census of Housing 1960: Alaska, Table 28.
These are all housing units.
{c) U.S. Department of Commerce Census of Housing 1970: Alaska, Table 62.
These are all year-round housing units. ·
(d) U.S. Department of Commerce Census of Housing, 1980: STF3 data tapes.
All year-round housing-units.
(e) 1980 Census, plus estimated 1980-1982 construction from Mr. Al Robinson,
economist, U.S. Department of Housing and Urban Development, Anchorage.
13.2
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F'""
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!""'"
TABLE 13.2. Railbelt Area Utility Total Energy and System Peak Demand
Anchora9e-Cook Inlet Fairbanks-Tanana Vallet
Annual Peak Load Annual Peak Load
Energy ( Gt-ih) Demand UH~) Factor Energy ( GWh) Demand ( ~1W) Factor ---
1965 369 82.1 0.51 98 24.6 0.45
1966 415 93.2 0 .51 108* 26.7 0 .46
1967 461 100.8 0.52 NA NA NA
1968 519 118.0 0.50 141* 42.7 0 .38
1969 587 124.4 0.54 170* 45.6 0.43
1970 684 152.5 0.51 213 57 .1 0 .43
1971 797 166.5 0.55 251* 70.6 0.41
1972 906 195.4 0 .53 262 71.2 0 .42
1973 1,010 211.5 0.55 290 71.5 0.46
1974 1 ,086 225.9 0 .55 322 89 .0 0 .41
1975 1,270 311.7 0.47 413 108.8 0.43
1976 1 ,463 311.0 0.56 423 101 .0 0 .48
1977 1,603 375'.4 0.49 447 117.5 0.43
1978 1, 7 4 7 382.8 0 .52 432 95.8 0.51
1979 1,821 409.6 0.51 418 100.7 0.47
1980 1 ,940 444.4 0.50 402 9 5 .4 0 .48
1981 2,005 444.7 0.51 422 93.1 0.52
1982 2,254 471.7 0.55 452 94 .4 0.55
13.3
-
TABLE 13.3. Anchorage-Cook Inlet Load Center Utility Sales and ~
Sales Per Customer, 1965-1981
Residential Commercia 1-Industria 1-Government
Sa 1 es Sales Per Sales Sa 1 es Per
(GI·IH) Customers C u s t orne r ( k Wh ) (GWH) Customers Customer (kWh)
1965 174 2 7 ,016 6 ,42 5 189 3,994 4 7 ,235 -1966 194 28,028 6,937 215 4,147 ·51,909
1967 208 30 ,028 6 ,941 241 4,363 55 ,206
·~ 1968 233 34,443 6,766 277 4,804 57,715
1969 262 37 ,653 6,971 316 5 ,125 61 ,656
1970 309 41,151 7,517 363 5,784 62,713 -
1971 369 43 ,486 8,487 415 6,006 69 ,05 7
1972 419 47,707 8,788 473 6,420 73,704
1973 457 49 ,433 9 ,239 539 6 ,693 80 ,55 7
1974 494 54,606 9,044 577 7,232 79,791 -1975 592 58 ,326 10 ,14 7 659 7 ,750 85 ,073
1976 675 62,413 10,817 769 8,789 87,598
1977 739 71 ,27 5 10 ,37 5 846 9,860 85,753 -.
1978 841 76,999 10,928 884 10,219 86,542
1979 845 76 ,494 11,047 878 10 ,368 84 ,684 -
1980 936( a) 77,743 12,040 1 002(a)
' 10,629 94,270
1981 916(b) 80 ,089 11 ,437 1 ,o3o(b) 11 ,021 93 ,458
Annual Growth
Rate 1965-81
10.9% 7.0% 3.7% 11.2% • 6.5% 4.4%
.....
(a) 1979 data used for SES. ~
I
(b) Based on 1980 ~~EA, 1979 SES data.
13.4
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,..,..
-
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-
,..,..
J!"ll""
I
!"'!"'
....
'
TABLE 13.4. Fairbanks-Tanana Valley Load Center Utility Sales
and Sales per Customer, 1965-1981
Residential Commercial-Industrial-Government
Sales Sales Per Sales Sales Per
( G\IJH) Customers Customer (kltJh) ~ GvJh) Cu st orner s Customer (k',.Jh)
1965 39 8183 4,804 55.198 1,313 41,880
1966 47 8170 5,712 59 .37 6 1 ,467 40 ,4 7 4
1967 NA NA NA NA NA NA
1968 61 9 ,344 6 ,569 77 .906 1 ,469 53 ,03 3
1969 77 10,023 7,672 91.212 1, 579 57,766
1970 91 10 '7 56 8 ,418 118 .560 1,888 6 2 ,79 7
1971 106 11,184 9,515 133.056 1, 929 68.977
1972 121 11 ,48 7 10,529 135 .873 2 ,002 6 7 ,86 9
1973 133 11,825 11,233 150.823 2,054 73,429
1974 154 13 ,261 11 ,600 161 .615 2 ,242 7 2 ,08 5
1975 190 13,877 13,719 210.759 2,342 89,991
1976 194 15,419 12 ,561 219.175 2 ,530 86 ,630
1977 198 17,197 11,500 240.463 2,834 84,849
1978 178 17,524 10 ,153 242.668 2 ,854 35,027
1979 169 18,070 9,344 219.335 2 795(a) • 7.'3,474
1980 160 18 ,054 8,890 214 .263 2 ,73 7 78 ,283
1981 159 19,379 8,219 224.354 2. 942 76,259
Annual Growth
Rate 1965-81
9.2% 5.5 3.4 9.2% 5.1 3.8
(a) Includes 1979 estimated 70 customers for AP&T.
13.5
TABLE 13.5. Adjustment for Industrial Load Anchorage-Cook Inlet, 1973-1981
1973
1974
1975
1976
1977
1978
1979
1980
1981
1973
1974
1975
1976
1977
1978
1979
1980
1981
Tot a 1 Achor age Homer Electric t(W~ Anchorage
Comm-Ind-Govt ~~WH Demand Industrial Load a "Commercia 1"
540,476 56 ,130 484 ,346
579,068 58,298 520' 770 29,660,900
661,192 62,806 598 ,386 3 3 ,4 71 ,800
771,054 72,063 698,991 37,049,800
846 ,939 83,989 762,950 39 ,618,900
896,072 82,984 813,088 41,440,000
904,851 87,955 816 ,896 42,733,800
988,957 99,103 889,854 44,042,700
1,030,753130,318 900 ,435 44,817 ,400
MWH Use/Sg Ft. kWh/ SO FT %6. From Previous Yr
0 .0179 17.9
0.0176 17.6 -1.7
0 .0179 17.9 1.7
0.0189 18.9 5.6
0.0193 19 .3 2.1
0.0196 19.6 1.6
0.0191 19 .1 -2.6
0.0202 20.2 5.8
0.0201 20.1 -0.5
Anchorage
Sq Ft. (b)
(a) Commercial-Industrial Load over 50 KVA (commercial users included)
(b) Predicted value. See Chapter 6.0.
13.6
....
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!l!I!IIIU"
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I
REFERENCES -
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Bonneville Power Administration. 1982c. Technical Documentation of Final BPA
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-
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Department of Commerce and Economic Development. 1983c. 1983 Long Term Energy ~
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R.2
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Elrick and Lavidge, Inc. 1980. The Pacific Northwest Residential Energy
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California. · """\
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Public Service Commission of Wisconsin, Milwaukee, Wisconsin.
R.S
!''""
APPENDIX A
BATTELLE-NORTHWEST RESIDENTIAL SURVEY
r-
1:
-
;-..
·-
~ J
i~
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APPENDIX A
BATTELLE-NORTHWEST RESIDENTIAL SURVEY
To calibrate an end-use model of electricity demand, the initial nunber of
appliances that use electricity must be known. At the time the RED model was
undergoing initial develo~ent (1981), there was no adequate information
available in the Railbelt concerning either residential appliance stock and
fuel mode split or uses of electricity in the commercial sector. v/hile it did
not appear possible to collect significant useful information on the commercial
sector within project resource constraints, BNW researchers concluded that a
residential survey was both possible and desirable. This initial evaluation
was reinforced when it became clear that data would not be available from the
1980 Census of Housing on detailed housing characteristics until 1982 at the
earliest, and that reporting on appliances would be less complete than in
1970. Accordingly, plans were made to survey the residential sector.
Although a lot of new infonnation of good quality was developed in the
survey, there were several constraints on the survey process. First, the
resources available to design, test, run, and analyze the survey were extremely
limited. This precluded in-person interviews, large samples, or follow-up of
non-respondents. Second, it was not possible to stratify the survey sample,
both because there was no accurate information on types of dwellings in any
Railbelt community except Anchorage and because utility customers could not be
matched to dwelling types or demographic characteristics. To conserve project
resources for analysis, we chose to do a blind mailing of the survey instrument
with no follow-up to random samples of each utility•s residential customers.
Where possible, the random mailings were done by the utilities themselves.
Where Battelle-Northwest did the mailings, random subsets of customers or
complete customers lists were supplied by the utilities to Battelle-Northwest.
A .1
SURVEY DESIGN
Because budget limitations precluded follow-up interviewing as a means to
improve survey response rate and to check errors, it was very important to have
a survey instrument that required minimal respondent effort and time, gathered
only the least controversial and highest priority information, and was easy to
understand. Questions considered controversial items (income). questions
difficult to understand (insulation values or energy efficiency of appliances),
and questions requiring substantial respondent effort (estimates of annual
electrical bills) were dropped. The highest priority questions concerning
appliance stock and fuel mode split were retained. A draft of the question-
naire was sent to the Railbelt utilities and other interested parties in
Alaska, and was reviewed by several senior Battelle-Northwest researchers.
Based on their comments and the results of a pretest with uncoached clerical
staff, the questionnaire was simplified to the point that it required the
~\
-
average test respondent only two to five minutes to answer all questions. A ~
copy of the survey form is shown in Figure A.l.
SAMPLE SIZE AND COMPOSITION
Because of the high labor costs of selecting respondents, addressing the
mailings, and key punching and verifying the survey results, it was decided
that an acceptable level of accuracy for survey results would be plus or minus
6 percent with 95 percent confidence on the entire sample for a load center.
In order to obtain utility cooperation in mailing the questionnaire, we
considered it necessary to achieve this level of accuracy for each utility's
service area to provide them with usable data. Thus, accuracy of survey
results for load centers that contain more than one utility is somewhat greater
than the sampling error for each utility would suggest. Because of the care
taken in survey design to maximize response rate, we believed that an average
response rate of 50 percent was possible with no follow up. The desired number
of respondents was therefore doubled to obtain the nunber of mailings in each
utility service area. A total of 4,000 questionnaires were sent to the respon-
dents, of which 1764 usable responses were received, for an average response
A.2
~!
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-
. ~.
1"""
I'
Alaska Railbelt Electric Power Alternatives Study
Dear Alaskan:
()Battelle
Pacific Northwest Laboratories
P.O. Box 999
Richland, Washington U.S.A. 99352
Telephone (509)
Telex 15-2874
Battelle, Pacific Northwest Laboratories is working under contract to the
State of Alaska to help determine the future needs for electricity in the
Railbelt Region, and the best way to meet those needs.
Many individuals believe that the Susitna hydroelectric power project is
the best way. Others think that these needs can be better met by employing
coal, conservation, or some other means. First, however, we need to estimate
future electric energy needs in the Railbelt. We can only d~ this properly if
\'le know how people in the region use electricity.
That 1 s where you can help us.
Please take a few minutes to complete the questionnaire on the other side--
it is only one page long and will take only 5 minutes or so to answer.
Why should you help? First, the information you provide will be vital in
decisions your state government will make over the next year and a half to
build or not build the Susitna project. Either way, your electricity bill will
be affected. Second, whether or not the Susitna project is built, the
confidential information you provide will help your lq_cal utility plan ways in
which to meet your future electricity needs.
Since this is an issue of such importance to you and Alaska, every response
is vital. All responses will be strictly confidential. There will be no way
anyone can tell who you are from your response. The results of this survey
will be published in your local newspaper.
Please respond as accurately as you can. Thank you for your cooperation .
Sincerely,
Michael J. King
Research Economist
P.S. In order for us to consider your response, you will need to return the
questionnaire within three weeks. For your convenience, you will find a
postage paid envelope enclosed.
FIGURE A.l. Battelle-Northwest Survey Form
A.3
)> .
Please complete the following quest lonna Ire and return It In the enclosed
en~elope. If you have 4lready completed and returned a que~t lonnalre, please
disregard this request.
1. What type of building do you res Ide lnl
() single family home () duplex
()mobile home ()multifamily (lor more units)
2. Humber pf persons In your hou~ehold (please respond In each category):
Adu lls 18t Children 5-18 Ch lldren Under 5
0 1 2-34 or more 0 -1--z-----ro; more o -1--2--j--4or more
() () () () () () () () () () () () () ()
l. How many rooms are In your res idence1 ___ llow many bedrooms 1 __ _
4. Approximate square feet of ltvlng space (just your est !mate):
II
less than 700
701-1000
1001-1300
1301-1600
il 1601-2000
2001-2400
greater than 2400
5. In what year was your house (building) bulltl (just your estimate)
(
() before 1g50
) l950-}g59
() 1960-1969
() 1970-1974
() 1975-1980
6. What h the main fuel used for heating your homel
() natura 1 gas
() propane-butane
() fuel otl, kerosene, or coal oil
() solar collectors
() passive solar (check one: () south
(I electricity
( co a 1 or coke
wood ~ district heating system
facing windows () custom solar design)
1. In addition to your main fuel, what additional fuels do you use to heat
your homel
() none
fuel oil, kerosene, or coal oil
solar collectors
(I electricity
( coal or coke
( wood
( district heating
(i( I natura 1 gas propane-butane
() passive solar (check one: () south facing windows () custom solar design)
FIGURE A. l.
} l
8. ~hat. proportion of your heating needs are u~et by:
0-1/4 !/4-1/2 !f_?.::)/4 lL~_:..i!.!!.
malo fuel () () () ()
secood fue 1 () () () ()
other fuels () () () ()
9. lllaat type of heating distribution system do you use1
() forced air () radiant or convection () hot water or steam.
10. Please Indicate the fuel your appliances use:
L >.
QJ ... D
> ·~ ~QJ
nS u
.<: ;: ~ ,., ·~ c:
"' QJC: DQJ
.~ ~ L c: ... ,_ ....
u :a ltl£1 '0 ~ "' -;;:;e
'" QJ '"'"' ... CI Cl ... 0 0 ~ IQ'U :J&... 0 0 "'"' '0 QJ c: "' .l:l 0. ~ u "' ..... ..><
water heater () () () () () () () ()
range/stove () () () () ()
sauoa/jaculzi/etc. () () ()
clothes dryer () () () ()
clothes w~sher () ()
freezer () ()
dishwasher () ()
Do you hA~e an electric refrl~erator1 () yes
If yes, i~ It frost freel () yes () no
() no 11.
12. If you use plug-Ins for vehicles:
llo.~ many vehicles do you usually plug-In? () 1 () 2 () l or more
Do you plug the vehlcle(s) In: () overnl~ht () just In the mornlngl
At approximately what temperature do you· start plugging them lnl ___ _
11. The uses de~crlbed above are for my:
() primary residence () second or vacation home.
(contd)
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rate of 44.1 percent. Table A.1 shows the total number of residential
customers in each utility, the nunber and percent surveyed, the nunber and
percent responding.
RESIDENTIAL
TABLE A.l. Customers, Nunber Surveyed, and Respondents for
the Residential Survey Battelle-Northwest
1980 Year End Customers Surve~ed Customers Res ~ond i ng
Utility(a) Customers(b) Number Percent Number Percent
Chugach Electric (CEA) 42 ,567 530 1.2 222
Anchorage Municipal (AMPL) 13,7 44 522 3.8 214
Seward Electric (SES) 1 ,090 424 38.9 185
Homer Electric (HEA) 8,620 518 6.0 249
Matanus ka Electric (MEA) 11 ,722 520 4.4 268
Goblen Valley (GVEA) 13,591 524 3.9 252
Fairbanks Municipal (FMUS) 4,463 504 11.3 156
Copper Valley (CVEA) 12588 458 28.8 252
Tot a 1 97 ,385 4,000 """""4.1 1 ,798
Tot a 1 Used 97,385 4,000 4.1 1,764
(a) CVEA is not part of the interconnected Railbelt, since it serves
Glennallen and Valdez. This utility and load center were eventually
dropped from the analysis.
41.9
41.0
43 .6
48.1
51.5
55.0
31.0
55.0
44.9
44.1
(b) Source: Alaska Power Administration. 1979 customer totals were used for
CVEA, HEA, and GVEA. Residential customers only.
MAILING PROCESS AND COLLECTION OF RESULTS
The survey questionnaire was administered in one of three ways. In some
cases the utilities randomly selected a list of residential customers and
performed the mailing. In these cases, Battelle-Northwest provided the utility
an appropriate nunber of mailings, consisting of the questionnaire and pre-
stamped, self-addressed return envelope. To ensure confidentiality, the ques-
tionnaire was stamped only with the initials of the utility, providing identi-
fication of the service area. No other identification of the respondent was
possible from the survey form or the return envelope. When Battelle-Northwest
performed the mailings, the utilities provided either a random sample of
A.5
customer addresses or their complete mailing list of residential customers,
from which a random sample was drawn. No known geographic bias was introduced
by the sampling technique. Finally, Fairbanks Municipal Utility System (Ft~US)
provided neither a mailing list nor mailing services to the project. In this
case, the Fairbanks telephone directory was used as a source of customer
addresses. Although an attempt was made to exclude addresses outside the City
of Fairbanks served by Golden Valley Electrical Association, unknown biases
were probably introduced into the Fairbanks sample by the sampling procedure.
The response rate was also signficantly lower for the Ft1US sample.
As the survey forms were received, they were coded, keypunched and veri-
fied. The raw card image data file was recorded on magnetic tape and loaded
into an SPSS data file, organized by subfiles corresponding to each utility.
The results for each utility were weighted according to the total number of
residential customers in each load center in 1980, the last year 1 s count
available at the time the file was assembled. The weights are shown in
Table A.2.
TABLE A.2~ Weights Used in Battelle-Northwest Residential Survey
Util itt Weight
Chugach 2.81
Anchorage ~~un i c i pa 1 1.17
Seward Electric .06
Homer Electric .45
Matanuska Electric .54
Golden Valley 1.21
Fairbanks Municipal .6 7
Copper Valley 1.00
OUTPUT
The output of the survey was organized in SPSS files and printed in
frequency distributions and standard SPSS CROSSTABS tables. An example of
-
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-
typical output is shown in Figure A.2 for freezer saturation. In the figure, 8M
712 out of 807 Anchorage area single family households are shown to have
A.6
) -)
,... . ...,, .. _.,_......_.,.._....---..------· '" ~-~~·····.· "····
STATISTICAL PACKAGE FOR THE SOCIAL SCIENCES
FILE ENDUSE.D (CHEATION DAT~ = Ob/17/91)
SUBFILE C~A AMLP SEA HEA MEA
* * * * * * * * * * * * * * * • * * Ff FH~EZER fUEL
C R 0 S S T A B U L A T I U N
B'i TYPE
07/28/IH
0 F
* * * * * * * * * * * * * * * * * * * * * * * * • * * * • * * * * * * * * * * * * * * ~
ff
COUNT
IWW PC f
COL PCT
'l'OT PCT
TYPE
I
I
I
I
SINGLE f MOBILE H DUPLEX
AM I LY OMf~
-1.1 1.1 2.I
MU 1,1' If AM
J LY
3.1 4.1
---~---·I·-·---·-I-•·--··-1·------·1--------I--------1
•1.
MISSING
o.
DO NOT HAVE
HAVE
1 •
COLUMN
TOTAL
1
I
I
I
0 I
0.7 I
6.7 I
o.o 1
36 I
52.8 I
4.4 I
3.1 I
0 I
o.7 1
0.7 I
0.0 I
11 I
16.0 l
9.8 I
0.9 1
20 I
29.9 I
1 3 • 1 I
1 • B I
-1··-----·I~w·~---·I------·-1-·------l--------1
1
I
I
I
1 I
0.4 I
8.1 I
o.o I
59 1
46.8 I
1. 3 l
5.2 I
3 I 26 I
:l.4 I 20.7 1
4.5 I 23.7 I
0.3 l 2.3 I
37 l
29.7 I
24.4 I
3.3 I
-1··-·--4 ~1-···--••I-~-··-•-I-------~I--------1
1 b I
1 U.6 I
I 85.2 I
I o.s I
7
0.6
712
75.1
88.3
62.4
807
70.6
l
1
1
I
62
6.5
94.8
5.4
5.7
1
I
I
I
73
7.7
66.5
6.4
110
9.b
l
I
I
I
96
10.1
62,5
8.4
15]
13.4
1
1
I
I
HOW
TOTAL,
b1
5.9
126
11.0
949
IH .1
1142
too.o
CHI SQUARE = 91.30715 wiTH 9 DEGREES OF FHEEDOH SIGNIFICANCE = u.OOOO
FIGURE A.2. Saturation of Freezers in Anchorage-Cook Inlet Load Center
Figure Note: Subfiles for each surveyed utility were combined and weighted by weights
in Table A.2. Seven households were unidentified by type of house and were ignored.
freezers (missing values were counted as 11 do not have 11 ). The computer shows
this as 88.3 percent saturation of single family households. This percentage
was used in Table 5.8. In practice, these computer estimates were usually
modified with professional judgment; however the Battelle-Northwest survey
supplied the raw data on which the judgment was made.
A.8
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APPENDIX B
CONSERVATION RESEARCH
r r
r
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,-
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APPENDIX B
CONSERVATION RESEARCH
The Railbelt area has limited ability to adopt conservation measures that
would result in large-scale electricity savings. According to Tillman (1983),
past conservation in load centers like Fairbanks has been largely the result of
price increases for electricity. In addition, Railbelt utility managers
believe that future electrical conservation will be largely the result of
price, not conservation programs. The impact of conservation programs in the
Railbelt has been taken into account in the fuel mode splits, use rates, and
price effects incorporated in the 1983 update. In addition, selected conserva-
tion programs in the Lower 48 states were analyzed to determine if anything
could be learned about program impacts in the Railbelt.
An attempt was made to compare conservation of electricity in the Railbelt
with conservation effects as forecasted by four policy-making bodies elsewhere
in the United States. The goal was to obtain a range of potential energy sav-
ings due to price-and program-induced conservation and determine if such esti-
mates would be applicable (and to what degree) in Alaska. The four policy-
making bodies chosen were the Pacific Northwest Power Planning Council, the
Bonneville Power Administration, the California Energy Commission and the Wis-
consin Electric Power Company. The first three entities were chosen because
~ they represented regions in the Western U.S. and because conservation programs
--'
played a signficant role in their regional planning. Wisconsin Electric Power
Company was chosen as an example of a utility in a colder climate where natural
gas was the predominant fuel source. However, Wisconsin has its peak demand
for electricity in the summer when natural gas cannot fuel air conditioning.
It became clear upon examination of the various programs that direct com-
parison of the forecasts was not possible at the end-use level nor was it pos-
sible to compare the assumptions supporting the forecasts (e.g., heating/ cool-
B .1
ing degree days, appliance standards, etc.). The following list touches on
some of the differences among forec.asts which made either direct or indirect
comparison difficult.
o Definitions of conservation differed.
o Variables were not consistent across regions.
• Programs were not consistent across regions.
• Some documentation showed a lack of internal consistency in report-
ing values.
• One entity reported savings in peak capacity while the others
reported both capacity and energy forecasts.
• Direct comparison of baseline, high, and low load growth scenarios
was not possible because of the level of conservation implied in the
forecasts; i.e., in a low demand case more conservation is assumed
than in the high demand case, or conservation instead may be asslJlled
in a sensitivity case.
• Savings could be projected either by program, or appliance, or end-
use sector.
In addition, each of the four Lower 48 entities quantifies the components
of conservation effects differently. The Northwest Power Counc i 1 1 s approach is
to assume no change in technological efficiency; therefore, there is no price-
induced conservation. Conservation is treated as an energy resource. A
separate supply function (with price and program components) determines the
value of potential conservation. The difference between the forecast demand
and the supply function is the value of conservation potential. The program
and price components of the conservation increment cannot be readily sepa-
rated. Potential savings are reported at the appliance level.
The California Energy Commission also forecasts a conservation increment
~I
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-
~-
-
in which price and program shares are not easily discernible. Part of the ~
program-induced savings has been quantified and double counting of price-
induced conservation is subtracted by a 20% implicit reduction in savings
estimates. The Bon nevi 11 e Power Admi ni strati on forecast has both technol ogi ca 1
8.2
change and price response imbedded in their model, but only part of their pro-
gram-induced conservation is quantifiable.
The Wisconsin Electric Power Company lacks the more sophisticated end-use
~ models used by the other three and focuses more on the peak demand savings
potential. Trend analysis driven by population projections is used to estimate
capacity requirements. There is some conservation implicit in the demand
growth estimated by the model. For example, air conditioning efficiency
improvements are assumed, and three 11 adjustments 11 are made to total demand for
rate structure reform, solar water heat, and solar space heat; but in general,
only fragments of the conservation response are quantified.
The literature provides some idea of the energy use attributable to bud-
geted and proposed programs, however. The following subsection discusses the
separate definitions of conservation adopted by _the four policy-making bodies,
the forecasts of program-induced energy savings, and the methods adopted to
avoid double counting of competing programs and double counting of price and
program effects. The last subsection looks at current estimates for Alaska and
determines whether the conservation program savings have relevance to Alaskan
forecasts.
PACIFIC NORTHWEST POWER PLANNING COUNCIL
The Pacific Northwest Power Planning Council (PNPPC) was created in 1981
in accordance with the Pacific Northwest Electric Power Planning and Conserva-
tion Act (the Act) to encourage conservation and the development of renewable
resources in the Northwest and to assure an adequate and economical power sup-
ply. Conservation is defined by the PNPPC as the more efficient use of elec-
tricity by the consl.l1ler through replacing existing structures with electricity-
saving technologies or the use of new, more energy-efficient devices and pro-
cesses in the residential, commerical, industrial, and agricultural sectors.
The PNPPC assessments do not distinquish between price-induced conservation and
program-induced conservation. The forecast power supply estimates are based on
the high market penetration rates the PNPPC assumes for each conservation pro-
gram available under the Act. A conservation measure is asslJlled cost-effective
at costs below 4.0 cents per kilowatt-hour (roughly the cost of power from
8.3
regional coal plants). Not all of the economically achievable savings can be
realized, however, due to constraints such as consumer resistance, quality con-
trol, and unforeseen technical problems. The PNPPC believes that given the
wide range of measures permitted by the Act, over 75% of the economically
achievable levels are possible (ranging from 56% for residential appliances to
100% in the industrial sector). Table B.l lists the likely conservation sav-
ings at a cost equal to or 1 ess than 4.0 cents per kilowatt hours by the year
2000. r-tlst of the savings in the residential sector come from building shell
or hot water tank improvements. Electricity has a larger share of space and
water heating loads in the PNPPC region than it does in the Railbelt. Thus,
many of the conservation savings of electricity in the PNPPC could not be
achieved in the Railbelt.
The PNPPC decided that all technically achievable conservation estimated
for the industrial sector could be realized since the savings represented less
-
-
then 10% of the region•s current industrial electricity demand. This level was --,
considered a reasonable goal for the industrial sector.
Including all conservation along with other available resource choices can
avoid double counting of conservation induced by prices in the demand model and
conservation counted as potential resources on the supply side. This implies
that price-induced efficiency improvements within the end-use sectors and elec-
tricity uses where conservation programs are proposed are included in resource
potential, not demand reductions. In the residential and commercial sectors
technology efficiencies were frozen at 1983 levels so that the PNPPC models
forecast future energy use as if no efficiency improvements were made. Unfor-
tunately, once a conservation program or measure is available, savings in
response to price changes cannot be separated from those derived from the pro-~
gram. Running the PNPPC demand model for individual programs will quantify the
impact for each measure under a given fuel price and supply scenario.
BONNEVILLE POWER ADMINISTRATION
The Bonneville Power Administration (BPA) supplies about half of the elec-
tric power production in the Pacific Northwest. Its service area is
B .4
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,_
'
TABLE B .1. PNPPC Likely Conservation Potential at 4.0
Cents/kWh by the Year 2000
Residential (kWh/household)
Ex i s t i n g Space Heat 854
New Space Heat 1404
Water Heating 1364
Air Conditioning 0
Re fr i gera tors 259
Freezers 108
Cooking 15
Lighting 150
Other 229
4383
Commercial (kWh/em~OJee}(a)
Existing Structure 1199
New Structures 825
2024
Industrial (kWh/emel o~ee) (a)
$1000-3000 subsidy/kW 655-3282
(a) Includes federal, state and local government,
transportation, communication, public utilities,
wholesale and retail trade, finance insurance,
real estate, services.··
(b) Includes mining, manufacturing, and construction.
Source: Pacific Northwest Power Planning
Council , 1983.
roughly equivalent to the area covered by the PNPPC power planning efforts
(Oregon, Washington, Idaho, Western M:lntana). Long-range electricity demand
forecasts are made by BPA to assist in utility power planning. Projections are
expressed as a baseline case to which alternative cases are added for a high-
low range of electricity conslUllption. Forecasts made by BPA covering the
region defined by the Pacific Northwest Electric Power Planning and Conserva-
tion Act of 1980 (P.L. 96-501) were done primarily to assist regional decision
making until the publication of the PNPPC official 20-year energy forecast and
plan in the spring of 1983.
B.5
BPA estimates of conservation potential savings include price-induced sav-
ings and savings from existing governmental, utility, and BPA conservation pro-
grams. Conservation programs that have yet to be initiated or budgeted are not
included. Some improvements in technology efficiencies are implicitly included ""'"',
as part of the consumer price response.
The types of programs represented by the base, low, and high forecasts
i n c 1 ud e t h e f o 11 owi n g :
• home energy efficiency improvement
• commercial energy efficiency improvement
• street and area lighting efficiency improvement
• institutional building efficiency improvement
o utility customer service system efficiency improvement
• support of direct application renewable resources projects.
The BPA currently sponsors weatherizing of electrically heated dwellings
(primarily retrofit of existing housing), wrapping electric water heaters,
encouraging the distribution and use of shower water flow restraints, and
installing faucet flow control devices, ·low-flow shower heads, and solar hot
water/heat pump water heater conversions. Table B.2 summarizes the savings
estimates by program for residential and commercial sectors. Currently, there-
are no budgeted programs in the Industrial sector.
BPA • s Office of Conservation estimated the savings from conservation
measures that could not be explicitly modeled and subtracted that amount from
computed demand. To avoid double counting of price-induced conservaton, the
measure-specific savings were reduced by 20%. Again, most savings were found
in space conditioning and water heating.
CALIFORNIA ENERGY COMMISSION
The California Energy Commission (CEC) is required by the Warren-Alquist
Act of 1974 (Public Resources Code, Section 25309) to 11 identify emerging trends
related to energy supply demand and conservation and public health and safety
factors, to specify the level of statewide and service area electrical energy
demand for each year in the forthcoming 5-, 12-, and 20-year periods, and to
provide the basis for state policy and actions in relation thereto ...... In
8.6
Alio\,
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-
)'''"··
TABLE B.2. BPA Budgeted Conservation Program Savings
(annual kWh savings by the year 2000)
Residential (kWh/household)
Region Wide Weatherization
Low Income Weatherization
Water Heater Wrap
Shower Flow Restrictor
Re s i den t i a l Fl ow Con t r o 1
Shower Heads
Faucet Heads
Solar/Heat Pump Water
Commercia 1 (kWh/ e~pl oyee) (a)
Pub 1 i c
Heating
Cooling
Water Heating
Lighting
Other
Private
Heating
Cooling
Water Heating
Lighting
Other
4,933
4,933
435
400
600
270
2,200
13,771
537
0
0
36
0
916
0
0
43
0
1,532
(a) Includes local and state government, trans-
portation and utilities, trade, finances,
insurance, real estate, services and con-
struction. High growth figures were used
for tot a 1 number of emp 1 oyees.
Source:
1982a.
23.
Bonneville Power Administration.
Table 5.6 and Appendix II, Table
B. 7
compliance with the code, the CEC prepares a biennial report containing updated
energy supply/demand projections and a supplemental electricity report. Infor-
mation in this section reflects the fourth and most recent report (1983) in the
series.
The C£C has adopted the following definition of conservation.
11 Conservation savings from local, utility, state, and Federal
programs in place or approved, and savings resulting from private
utilization of conservation measures in response to prices, and sav-
ings from programs on which analytical work is well advanced and for
which there is a substantial likelihood they will be in effect by
January 1985."
The code requires the CEC to include all conservation that is reasonably
expected to occur based on credible evidence within the framework provided by
their definition. Conservation programs and savings are categorized into three
classes: 1) conservation reasonably expected to occur, 2) additional achiev-
able conservation, and 3) conservation potential. Savings in Category 1 are
used to reduce the demand estimate. Those in Category 2 are cons ide red to have
a moderate probability of occurring because of a higher uncertainty factor.
Category 3 includes both 1 and 2 and any other conservation thought to be cost
effective when compared to new generation sources. All conservation savings
reasonably expected to occur must be included in the CEC's adopted forecast.
Quantifying additional achievable conservation can help to establish new con-
servation programs. Table B.3 summarizes the savings reasonably expected to
occur for each program or measure. Table B.4 lists the savings by end-use sec-
tor.
The C£C feels that because programs are the causative agent for many
measures adopted, forecasts should report savings by program. Double counting
of programs is eliminated by analyzing how specific conservation measures
affect end uses of energy and reconciling competing programs' influence on each
measure. A "sharingu structure is set up which includes effects of programs
and price fluctuations. Price-and program-induced conservation becomes "dis-
jointed." For example, in general the residential sector model does not have
price-induced savings from consumer choice of more efficient appliances,
B.8
''''"'\
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TABLE B.3. CEC Conservation Program(Electricity
Savings in the Year 2002 a)
Sector Oemand(GWH)
Residential
Existing Retrofit and
Programs
1975 HCO Building Standards·
1978 CEC Building Standards
1982 CEC Building Standards
1978 CEC Appliance
OI I-42 Programs
Other Retrofit Programs
Load Management Cycling
Commercial
1978 CEC Building Standards
1983 CEC Building Standards
1983 CEC Equipment Standards
Schools and Hospitals
Load Management Audits
Other Commercial
In dust rial
1978 CEC Building Standards
391
2 ,292
644
5 ,108
6,069
0
301
1 ,160
15 ,96 5
6,011
1,083
1,057
234
1,683
1 ,846
11,914
323
kWh/house ho 1 d
34
201
57
449
533
0
26
102
1 ,403
kWh/employee
549
99
97
21
154
169
1,088
97
(a) Reasonably expected to occur. Street lighting and agriculture sectors
exc 1 uded.
Source: California Energy Commission 1983, Table 3-IV-1,2,3. Household
and employment projections used were taken from U.S. Department of Com-
merce, Bureau of Economic Analysis, 1980 Regional Projections. Households
at 11,377,270: commercial employment at 10,950,677; industrial employment
at 3,321,917.
B.9
TABLE 8.4. CEC Potential Energy Savings by End-Use
Sector by the Year 2002
Sector GWh kWh/ HH 0 r erne 1 o.zee
Residential 23 ,313 2,049
Commercial Bldg 12,849 1,173
Other Commercia 1 1 ,593 145
Street Lighting 983 86
Process Industry 0 0
Assembly Industry 4,985 1,501
Extraction Industry 0 0
Total 43,723 ~
Source: California Energy Commission, Volume I Technical Report, 1982,
Table 3-7. Agriculture not included.
but estimates savings based on mandatory standards. In the commercial sector,
CEC 1 oan management audits compete with price to motivate customers to make
efficiency improvements. However, as more programs are introduced this separa-
tion becomes more difficult. Once again, heavy reliance is placed on building
shell improvements to achieve conservation of electricity.
WISCONSIN ELECTRIC POWER COMPANY
The Wisconsin Electric Power Company (WEPC) is an investor-owned utility
serving the Milwaukee, Kenosha, and Racine Standard r-'etropol it an Areas, Centra 1
and Northern Wisconsin, and the Upper Peninsula of Michigan. Wisconsin's pri-
mary fuel source {70%) has been natural gas since 1977. Electricity accounts
for only 4 to 5% of total energy used. WEPC has adopted a very broad defini-
tion of conservation, covering not only more efficient end use of electricity
but also energy saved at the supply and conversion levels, e.g., fuel switch-
ing, time-of-use rates, load management, etc., although load management was not
modeled. It should be noted that there is currently an on-going debate between
WEPC and the Wisconsin Public Services Commission regarding this definition.
Basically the problem centers around WEPC's desire to raise rates to pay for
programs they define as conservation measures. The Commission uses the defini-
tion of improvement in efficiency of energy end use by the customer. The Com-
B.10
-
-
-
-
-
mission feels that WEPC emphasizes load management over incentives to the cus-
tomer and thereby serves the company objectives first.(a) WEPC counters with
the following argument:
"Staff has been critical of Wisconsin 1 S Electric 1 s perspective
on conservation. It is true that Wisconsin Electric has viewed con-
servation in context of the over-all planning process. That process
seeks to anticipate and influence load patterns in order to maximize
efficiency and maintain financial strength with the ultimate purpose
of insuring that reliable service can be delivered at the lowest
reasonable cost. The encouragement of efficient end-use of electri-
city contributes to the achievement of planning goals to the extent
that peak use is constrained. It may be detrimental to the extent
that it results .in inefficient plant utilization.u~b)
Two points about this controversy are important to this study. First,
total state or regional energy planning will be less efficient unti.l a unified
policy position is adopted. Such a situation occurred in the past between BPA
and PNPPC and was resolved through guidelines provided by the Regional Power
Act. Second, the WEPC conservation forecasts will include end-use efficiency
improvements, price-induced and program-induced conservation, and energy sav-
ings from fuel switching.
WEPC uses trend analysis to estimate peak demand. The WEPC system is pri-
marily concerned with prbviding adequate capacity and their modeling effort
~'""' reflects that concern; there is very little disaggregation at the end-use
level. The energy forecast is derived directly from demand and contains some
conservation from an implicit reduction for improved air conditioning effi-
ciencies. Then, adjustments in hourly energy use for rate structure reform and
solar water and space heat are made. These adjustments are summed for monthly
and annual energy forecasts. The adjustments were allocated to each sector in
the following manner:
(a) Post Hearing Brief on Docket 6630-ER-14.
(b) Hearings before the Public Service Commission of Wisconsin Docket 6630-
ER-14. 11 Application of Wisconsin Electric Power Company for Authority to
Increase Rates for Electric Service Based on Projected 1983 Operations,11
1982.
8.11
• rate structure reform to general secondary (commercial)
• solar to residential
• air conditioning efficiency improvements to residential and general
secondary according to the percent of the efficiency reduction at
summer peak demand attributable to each sector (62% residential, 38%
c omme rc i a 1 ) •
Table B.5 presents the energy savings by customer for the year 2000.
Energy savings per household or employee were not available.
TABLE B.S. WEPC Conservation Potential by the Year 2000 (Base Case)
Sector
Residential
General Secondary
( c omme rc i a 1 )
Savings
13 kWh/customer
447 kWh/customer
Source: Number of customers from
Response to Item 7 of the Public Ser-
vice Commission of Wisconsin Docket
6630-ER-14 Regarding Conservation.
Estimated savings from Wisconsin Elec-
tric Power Company 20-year Demand and
Energy Forecast 1981-2000,
Table 2-1.2. Air Conditioning load
reduction developed from Table 1-3.1
and Table 2-1.4.
These conservation estimates represent only part of the total potential.
Although the a.ir conditioning component includes price response, the solar and
-
rate structure components do not. The forecast does not include reductions for ~
improved efficiency in other appliances. Double counting occurs in adjusting
for improved appliance efficiency resulting from federally mandated standards
and the associated response to the econometric pricing assumptions. WEPC
avoided double counting (or rather discounted for it) by not quantifying
separate adjustments for baseload and water heating efficiencies.
-B .12
ALASKAN RAILBELT
The State of Alaska, various utilities in the Railbelt region, and the
1"1unicipal ity of Anchorage have implemented energy conservation programs that
include measures for conserving electricity that have already reduced electri-
city consumption.
Major conservation programs currently available in the Railbelt include
the State Division of Energy and Power Development energy audit and loan (DEPD)
program; the Golden Valley Electric Association program (primarily education in
support of the market place); similar education programs by the Chugach Elec-
tric Association and the Fairbanks Municipal Utility System; and the City of
Anchorage Program involving audits, weatherization, and educational efforts.
The Golden Valley program was partly responsible for a reduction of electricity
use in this Fairbanks service area from 17,332 kWh/household in 1975 to 9303
kWh/household in 1982 (see Table B.6). In the past, however, the DEPD program
has been the most extensive with an estimated 24% of all Railbelt houses having
had· an energy audit performed. The program has saved an estimated average of
1,582. kwh/year of electricity per Alaska household, with electricity equaling
about 18% of total energy savings from the program. No reliable data on DEPD
program electricity savings are available in the Railbelt load centers.
According to Tillman (1983), almost all of the Rail belt programs have been
aimed at the residential sector, with con~ervation in the commercial and indus-
trial sectors being accomplished primarily through market conditions. Price-
induced conservation is then more easily distinguishable in those two
sectors. In the AML&P program, total conservation potential through 1987 has
been disaggregated into program-and price-induced components (see Table 8.7)
with approximately a 40 and 60% share, respectively. For a breakdown by pro-
g r am , s e e Tab l e 8 • 8 •
Tillman indicates that price-induced electricity conservation will be more
important in the future than programmatic conservation for the following
reasons:
B.13
TABLE 8.6. Average Annual Electricity Consumption per
Household on the GVEA System, 1972-1982
Annual Monthly
Consumption Consumption Percent
Year (kWh) (kWh) Change
1972 13,919 1 ,160 +5.6
1973 14,479 1,207 +4.0
1974 15 ,822 1 ,319 +9.3
1975 17,332 1,444 +9.5
1976 15 ,203 1,267 -12.3
1977 14,255 1,188 -6.2
1978 11 ,57 4 965 -18.8
1979 10,519 877 -9.1
1980 9,767 814 -7.1
1981 9,080 757 -7.0
1982 9,303 775 +2.5
Source: GVEA, as reported by Tillman (1983).
• It has the dominant share of impacts.
• Subsidized audits and investments programs for residences are being
phased out.
• Practical impact limits are being achieved in institutional build-
ings and systems programs.
• Current plans for future programs are predominantly educational pro-
grams designed to support price or market-induced conservation.
Tillman ( 1983) notes that two miscellaneous M~L&P programs are expected to
save considerable electric energy by the year 1987. These are street lighting
improvements, whose impact is taken into account in Section 9.0, and heating of
the Anchorage municipal water supply to reduce the electricity use of water
heaters. The water heater impact is factored into the use rates for Anchorage
water heaters in Section 5.0
In attempting to determine the level of conservation potential, the ques-
tion arises as to whether further investment in energy-savings programs
8.14
-
-
-
-
, .... _
-
TABLE 8.7. Programmatic Versus Market-Driven Energy Conservation
Projections in the M~L&P Service Area
Year
Programmati~
Conservation\a)
(MWh)(% of Total)
Market Dr i v~g)
Conservation\
(MWh) (%)
Total(a)
( MWh) ( ~fc)
1981
1982
1983
1984
1985
1986
1987
12,735 39.5 19,558 60.5 32 ,294 100
19,609 34.9 27,243 65.1 46,853 100
Cumulative
20,896
27,619
30 ,195
32,614
35 ,421
179,089
37 .1
41.1
40.4
40.6
41.0 -
40.3
35 ,37 4
39,560
44,536
48,133
50,940
265,344
62.9
58.9
59 .6
59.4
59 .0
59.7
56 ,289
67 '133
7 4 ,730
81,015
86,363
444_,677
(a) Detail does not add to total in the orginal. 1981 programs
inc 1 uded:
Residential
We at he r i za t i o n
State Programs
Wa t e r Fl ow Rest r i c to r
Water Heat Injection
Industrial
Boiler Feed Pumps
MWh/yr
586
879
200
3,921
5,586
7' 148
kWh/Customer
42
63
14
281
400
2298
Planned conservation programs include hot water
wraps ~n the residential sector and street light
conversion and utility transmission conversion in
the commercial sector. The number of customers was
provided by the 1982 Alaska Electric Power Statis-
tics of the Alaska Power Administration.
(b) 1981 Price elasticity effects equaled 19~58 MWh/yr.
Source: AML&P 1982.
B.15
100
100
100
100
100
TABLE B.S. Programmatic Energy Conservation Projections for Ar-1L&P (MWh/yr)
Program
Weatherization
State Programs
Water Flow
Restrictions
Water Heat
Injection
Hot Water
Heater Wrap
Street Light
Conversion
Transmission
Conversion
Boiler Pump
Conversion
TOTAL
% Change From
Previous Year
1981 1982
586 762
879 1 ,759
.· 200'. 464
3,922 3,922
NA NA
0 555
0 0
1983
938
2 ,199
464
3,922
249
1,859
4,119
1984
1' 114
2,683
464
3,922
249
3,307
8,732
1985
1,290
3,078
464
3,922
249
4,788
9,256
1986
1,466
3 ,518
464
3,922
249
1987
1,641
3 ,73 7
464
3,922
249
6,306 7,861
9,811 10,399
7,148 7,148 7,148 7,148 7,148 7,148' 7,148
12,735 14,609 20,896 27,619 30,195 32,614 35,421
NA 14.7 43.0 32.2 9.J 9.8 8.6
Source: AML&P, as.reported by Tillman (1983).
would be cost effective. An investigation of program-induced versus price-
induced conservation forecasted by other regions could indicate if current mar-
ket penetration levels in the Railbelt are realistic. Unfortunately, as we
have seen, total separation of price and program effects forecasted by PNPPC,
BPA, CEC, and WEPC has not yet been achieved. We have some indication that
these forecasts do show programmatic contributions by the year 2000 in residen-
tial commercial, and industrial sectors. However, the extent to which techni-
cally achievable conservation limits can be approached in Alaska through
programs and what proportion would be due to market actions is not clear. In
general, because of differences in housing stock, fuel mode splits, fuel
prices, climate, and other factors, forecasted program savings for other
regions may have only limited relevance for the Railbelt.
B .16
--.
~I
~
I
APPENDIX C
RED MODEL OUTPUT
-~
-
-t
.-
APPENDIX C
RED MODEL OUTPUT
This appendix displays selected RED model output produced for the 1983
update. Included in the following tables are information on the number of
households served by electricity in each load center, housing vacancies, fuel
price forecasts, electricity used per household and per employee, as well as
SLDTimaries of price effects and programrriat·ic conservation, annual electricity
requirements by sector and load center, and total peak demand. The figures
presented in these tables are at the point of sale and include estimates
supplied by Harza.;.Ebasco of military and industrial demand. They do not
include an adjustment for transmission losses. However, for the 1983 update of
the alternative generation plans these reported figures were adjusted for
transmission losses.
c .1
-
.-
-
LIST OF TABLES
H-12--SHERMAN CLARK NO SUPPLY DISRUPTION •••••••••••••••••••••••••••••••••• C.11
Households Served, Anchorage-Cook Inlet •••••••••••••••••••••••••••• C.l3
Households Served, Greater Fairbanks ••••••••••••••••••••••••••••••••• C.14
Housing Vacancies, Anchorage -Cook Inlet •••••••••••••••••••••••••••• C .15
Housing Vacancies, Greater Fairbanks ••••••••••••••••••••••••••••••••• C.16
Fuel Price Forecasts Employed, Electricity ($/kWh) ••••••••••••••••••• C.17
Fuel Price Forecasts Employed, Natural Gas ($/M~1Btu) ••••••••••••••••• C.18
Fuel Price Forecasts Employed, Fuel Oil ($/Mt1Btu) •••••••••••••••••••• C.19
Residential Use Per Household (kWh) (Without Adjustment
for Price), Anchorage -Cook Inlet ••••••••••••••••••••••••••••••••••• C .20
Residential Use Per Household (k\~h) (Without Adjustment
for Price), Greater Fairbanks •••••••••••••••••••••••••••••••••••••••• C.21
Business Use Per Employee (kWh) (Without Large Industrial)
(Without Adjustment for Price) ••••••••••••••••••••••••••••••••••••••• C .22
Summary of Price Effects and Programmatic Conservation in
GWh, Anchorage -Cook In 1 et •••••••••••••••••••••••••••••••••••••••••• C .2 3
Summary of Price Effects and Programmatic Conservation in
GWh, Greater Fairbanks ••••••••••••••••••••••••••••••••••••••••••••••• C.24
Breakdown of Electricity Requirements (GWh) (Total Includes
Large Industrial Consumption), Anchorage-Cook Inlet •••••••••••••••• C.25
Breakdown of Electricity Requirements (GWh) (Total Includes
Large Industrial Consumption), Greater Fairbanks ••••••••••••••••••••• C.26
Total Electrical Requirements (GWh) (Net of Conservation)
(Includes Large Industrial Consumption) r-'edium Range (PR = .5) ••••••• C.27
Peak Electric Requirements (MW) (Net of Conservation)
(Includes Large Industrial Demand) r-'edium Range (PR = .5) •••••••••••• C.28
HE3--DOR AVG SCENARIO ••••••••••••••••••••••••••••••••••••••••••••••••••••• C.29
Households Served, Anchorage-Cook Inlet •••••••••••••••••••••••••••• C.31
Households Served, Greater Fairbanks ••••••••••••••••••••••••••••••••• C.32
C.3
Housing Vacancies, Anchorage-Cook Inlet •••••••••••••••••••••••••••• C.33
Housing Vacancies, Greater Fairbanks ••••••••••••••••••••••••••••••••• c .34
Fuel Price Forecasts Employed, Electricity ($/kWh) ••••••••••••••••••• C.35
Fuel Price Forecasts Employed, Natural Gas ($/~1\~Btu) ••••••••••••••••• C .36
Fuel Price Forecasts Employed, Fuel Oil ($/Mf.'\Btu) •••••••••••••••••••• C.37
Residential Use Per Household (kWh) (Without Adjustment
for Price), Anchorage -Cook Inlet ••••••••••••••••••••••••••••••••••• C.38
Residential Use Per Household (kWh) (Without Adjustment
for Price), Greater Fairbanks •••••••••••••••••••••••••••••••••••••••• C.39
Business Use Per Employee (kWh) (Without Large Industrial)
(Without Adjustment for Price) ••••••••••••••••••••••••••••••••••••••• C.40
SLmmary of Price Effects and Programmatic Conservation in
GWh,· Anchorage-Cook Inlet .•••••.•••••.••.•••.•••••••••••••••••••••• · C.41
Summary of Price Effects and Programmatic Conservation in
GWh, Greater Fairbanks ••••••••••••••••••••••••••••••••••••••••••••••• C.42
Breakdown of Electricity Requirements (GWh) (Total Includes
Large Industrial Consumption), Anchorage -Cook Inlet •••••••••••••••• C.43
Breakdown of Electricity Requirements (GWh) (Total Includes
Large Industrial Consumption), Greater Fairbanks ••••••••••••••••••••• C.44
Total Electrical Requirements (GWh) (Net of Conservation)
(Includes Large Industrial Consumption) Medium Range (PR = .5) ••••••• C.45
Peak Electric Require111ents (MW) (Net of Conservation)
(Includes Large Industrial Demand) Medium Range (PR = .5) •••••••••••• C.46
HE9--DOR 50% •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• C.47
Households Served, Anchorage-Cook Inlet •••••••••••••••••••••••••••• C.49
Households Served, Greater Fairbanks ••••••••••••••••••••••••••••••••• C.SO
Housing Vacancies, Anchorage-Cook Inlet •••••••••••••••••••••••••••• C.Sl
Housing Vacancies, Greater Fairbanks ••••••••••••••••••••••••••••••••• C.52
Fuel Price Forecasts Employed, Electricity ($/kWh) ••••••••••••••••••• C.53
Fuel Price Forecasts Employed, Natural Gas ($/~1MBtu) ••••••••••••••••• C.54
C.4
-J
,...
'
-
-
Fuel Price Forecasts Employed, Fuel Oil ($/1'1t~Btu) •••••••••••••••••••• C.55
Residential Use Per Household (kWh) (Without Adjustment
for Price), Anchorage-Cook Inlet ••••••••••••••••••••••••••••••••••• C.56
Residential Use Per Household (kWh) (Without Adjustment
for Price), Greater Fairbanks •••••••••••••••••••••••••••••••••••••••• C.57
Business Use Per Employee (kWh) (Without Large Industrial)
( ~I i t h out Ad j u s tm en t f o r P r i c e) • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • C • 5 8
Summary of Price Effects and Programmatic Conservation in
GWh, Anchorage-Cook Inlet •••••••••••••••••••••••••••••••••••••••••• C.59
Summary of Price Effects and Programmatic Conservation in
GWh, Greater Fairbanks ••••••••••••••••••••••••••••••••••••••••••••••• C.60
Breakdown of Electricity Requirements (GWh) (Total Includes
Large Industrial Consumption), Anchorage-Cook Inlet ................ C.61
Breakdown of Electricity Requirements (GWh) (Total Includes
Large Industrial Consumption), Greater Fairbanks ••••••••••••••••••••• C.62
Total Electrical Requirements (GWh) (Net of Conservation)
(Includes Large Industrial Consumption) Medium Range (PR = .5) ••••••• C.63
Peak Electric Requirements (M~n (Net of Conservation)
(Includes Large Industrial Demand) ~~edium Range (PR = .5) •••••••••••• C.64
HlO--DOR 30% •••••••••••••••••••••••••••••••••••••••••• ,., ••••••••••••••••••• C.65
Households Served, Anchorage-Cook Inlet •••••••••••••••••••••••••••• C.67
Households Served, Greater Fairbanks ••••••••••••••••••••••••••••••••• C.68
Housing Vacancies, Anchorage-Cook Inlet •••••••••••••••••••••••••••• C.69
Housing Vacancies, Greater Fairbanks ••••••••••••••••••••••••••••••••• C.70
Fuel Price Forecasts Employed, Electricity ($/kWh) ••••••••••••••••••• C.71
Fuel Price Forecasts Employed, Natural Gas ($/MMBtu) ••••••••••••••••• C.72
Fuel Price Forecasts Employed, Fuel Oil ($/MMBtu) •••••••••••••••••••• C.73
Residential Use Per Household (kWh) (Without Adjustment
for Price), Anchorage-Cook Inlet ••••••••••••••••••••••••••••••••••• C.74
Residential Use Per Household (kWh) (Without Adjustment
for Price), Greater Fairbanks •••••••••••••••••••••••••••••••••••••••• C.75
c.s
-
Business Use Per Employee (kWh) (Without Large Industrial)
(l~ithout Adjustment for Price) ••••••••••••••••••••••••••••••••••••••• C.76
Summary of Price Effects and Programmatic Conservation in
GWh, Anchorage -Cook In 1 et •••••••••••.••••••• ., ••••••••••••• " ••••••••• C .7 7
Summary of Price Effects and Programmatic Conservation in
GWh, Greater Fairbanks ••••••••••••••••••••••••••••••••••••••••••••••• C.78
Breakdown of Electricity Requirements (GVJh) (Total Includes
Large Industrial Consumption), Anchorage-Cook Inlet ................ C.79
Rreakdown of Electricity Requirements (G\-Jh) (Total Includes
Large Industrial Consumption), Greater Fairbanks ..................... C.SO
Total Electrical Requirements (GWh) (Net of Conservation)
(Includes Large Industrial Consumption) ~dium Range (PR = .5) ••••••• C.81
Peak Electric Requirements (i'~W) (Net of Conservation) -(Includes Large Industrial Demand) Medium Range (PR = .5) ............ C.82
Hl3--DRI SCENARIO ••••••••••••••••••••••••••••••••••••••••••••••••••••••••• C.83
Households Served, Anchorage-Cook Inlet •••••••••••••••••••••••••••• C.85
Households Served, Greater Fairbanks ••••••••••••••••••••••••••••••••• C.86
Housing Vacancies, Anchorage-Cook Inlet •••••••••••••••••••••••••••• C.87
Housing Vacancies, Greater Fairbanks •••••••• ~ •••••••••••••••••••••••• C.88
Fuel Price Forecasts Employed, Electricity ($/U~h) ................... C.89
Fuel Price Forecasts Employed, Natural Gas ($/MMBtu) ................. C.90
Fuel Price Forecasts Employed, Fuel Oil ($/MMBtu) .................... C .91
Residential Use Per Household (kWh) (Without Adjustment
for Price), Anchorage-Cook Inlet ••••••••••••••••••••••••••••••••••• C.92
Residential Use Per Household (kWh) (Without Adjustment
for Price), Greater Fairbanks •••••••••••••••••••••••••••••••••••••••• C .93 -
Business Use Per Employee (kWh) (Without Large Industrial)
(Without Adjustment for Price) ....................................... C.94
Summary of Price Effects and Programmatic Conservation in
GWh, Anchorage -Cook Inlet •••••••••••••••••••••••••••••••••••••••••• C .9 5
C.6
-
-
-
Summary of Price Effects and Programmatic Conservation in
GWh, Greater Fairbanks •••••••••••••••••••••••••••••••••••••••••.••••• C.96
Breakdown of Electricity Requirements (GWh) (Total Includes
Large Industrial Consumption), Anchorage-Cook Inlet •••••••••••••••• C.97
Breakdown of Electricity Requirements (GWh) (Total Includes
Large Industrial Consumption), Greater Fairbanks ••••••••••••••••••••• C.98
Total Electrical Requirements (GWh) (Net of Conservation)
(Includes Large Industrial Consumption) r~dium Range (PR = .5) ••••••• C.99
Peak Electric Requirements (MW) (Net of Conservation)
(Includes Large Industrial Demand) rv'edium Range (PR = .5) •••••••••••• C.100
HE4--FERC +2% ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• C.101
Households Served, Anchorage-Cook Inlet •••••••••••••••••••••••••••• C.103
Households Served, Greater Fairbanks ••••••••••••••••••••••••••••••••• C .104
Housing Vacancies, Anchorage-Cook ·Inlet •••••••••••••••••••••••••••• C.105
Housing Vacancies, Greater Fairbanks ••••••••••••••••••••••••••••••••• C.106
Fuel Price Forecasts Employed, Electricity ($/kWh) ••••••••••••••••••• C.107
Fuel Price Forecasts Empl eyed, Natural Gas ($/MMBtu) H·• •••••••••••••• C.108
Fuel Price Forecasts Employed, Fuel Oil ($/MMBtu) •••••••••••••••••••• C.109
Residential Use Per Household (kWh) (Without Adjustment
for Price), Anchorage -Cook Inlet ••••••••••••••••••••••••••••••••••• C .110
Residential Use Per Household (kWh) (Without Adjustment
for Price), Greater Fairbanks •••••••••••••••••••••••••••••••••••••••• C.111
Business Use Per Employee (kWh) (Without Large Industrial)
(Without Ad-justment for Price) ••••••••••••••••••••••••••••••••••••••• C .112
Summary of Price Effects and Programmatic Conservation in
GWh, Anchorage-Cook Inlet •••••••••••••••••••••••••••••••••••••••••• C.113
Summary of Price Effects and Programmatic Conservation in
GWh, Greater Fairbanks •••••••••••••••••••••••••••••••••••••••.••••••• c .114
Breakdown of Electricity Requirements (GWh) (Total Includes
Large Industrial Consumption), Anchorage-Cook Inlet •••••••••••••••• C.l15
Breakdown of Electricity Requi rernents (GWh) (Total Includes
Large Industrial Consumption), Greater Fairbanks ••••••••••••••••••••• C.116
C.7
Total Electrical Requirements (GWh) (Net of Conservation)
(Includes Large Industrial Consumption) Medium Range (PR = .5) ••••••• C.117 -·
Peak Electric Requirements (i~W) (l~et of Conservation)
(Includes Large Industrial Demand) ~"ledium Range (PR = .5) •••••••••••• C.ll8
HE6--FERC O% •••••••••••••••••••••••••••.•••••••••••••••••••••••••••••••••• C.ll9
Households Served, Anchorage-Cook Inlet •••••••••••••••••••••••••••• C.121 ~
Households Served, Greater Fairbanks ••••••••••••••••••••••••••••••••• C.122
Housing Vacancies, Anchorage-Cook Inlet •••••••••••••••••••••••••••• C.123
Housing Vacancies, Greater Fairbanks ••••••••••••••••••••••••••••••••• C.124
Fuel Price Forecasts Employed, Electricity ($/kWh) ••••••••••••••••••• C.125
Fuel Price Forecasts Employed, Natural Gas ($/t~MBtu) ••••••••••••••••• C.126 """"
Fuel Price Forecasts Employed, Fuel Oil ($/MMBtu) •••••••••••••••••••• C.127
Residential Use Per Household (kWh) (Without Adjustment
for Price), Anchorage -Cook Inlet ••••••••••••••••••••••••••••••••••• C .128
Residential Use Per Household (kWh) (Without Adjustment
for Price), Greater Fairbanks •••••••••••••••••••••••••••••••••••••••• C.l29
Business Use Per Employee (kWh) (Without Large Industrial)
(Without Adjustment for Price) ••••••••••••••••••••••••••••••••••••••• C.130
Summary of Price Effects and Programmatic Conservation in
GWh, Anchorage -Cook Inlet •••••••••••••••••••••••••••••••••••••••••• C .131
Summary of Price Effects and Programmatic Conservation in
GWh, Greater Fairbanks ••••••••••••••••••••••••••••••••••••••••••••••• c·.132
Breakdown of Electricity Requirements (GWh) (Total Includes
Large Industrial Consumption), Anchorage -Cook Inlet •••••••••••••••• c .133
Breakdown of Electricity Requirements (GWh) (Total Includes
Large Industrial Consumption), Greater Fairbanks ••••••••••••••••••••• C.134
Total Electrical Requirements (GWh) (Net of Conservation)
(Includes Large Industrial Consumption) Medium Range (PR = .5) ••••••• C.135
Peak Electric Requirements (MW) (Net of Conservation)
(Includes Large Industrial Demand) Medium Range (PR = .5) •••••••••••• C.l36
C.8
-\
-
-
-
HE7--FERC -1% ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• C.137
Households Served, Anchorage-Cook Inlet •••••••••••••••••••••••••••• C.139
House h o 1 d s Se r v e d, Greater Fa i r bank s ••••••••••••••••••••••••••••••••• C .14 0
Ho us i n g V a can c i e s , Anchorage -Cook In 1 e t •••••••••••••••••••••••••••• C .14 1
Housing Vacancies, Greater Fairbanks ••••••••••••••••••••••••••••••••• c .142
Fuel Price Forecasts Employed, Electricity ($/kWh) ••••••••••••••••••• C.143
Fuel Price Forecasts Employed, l~atural Gas ($/MMBtu) ••••••••••••••••• C.144
Fuel Price Forecasts Employed, Fuel Oil ($/~1MBtu) •••••••••••••••••••• C.145
Residential Use Per Household (kWh) (Without Adjustment
for Price), Anchorage-Cook Inlet ••••••••••••••••••••••••••••••••••• C.146
Residential Use Per Household (kWh) (Without Adjustment
f o r P r i c e) , G r e ate r Fa i rb an k s • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • C .14 7
Business Use Per Employee (kWh) (Without Large Industrial)
· ( Wi thou t Ad jus tme n t f o r P r i c e) ••••••••••••••••••••••••••••••••••••••• C .14 8
Summary of Price Effects and Programmatic Conservation in
GWh, Anchorage -Cook In 1 et •••••••••••••••••••••••••••••••••••••••••• C .149
Summary of Price Effects and Programmatic Conservation in
GWh, Greater Fairbanks ••••••••••••••••••••••••••••••••••••••••••••••• C.lSO
Br~akdown of Electricity Requirements (GWh) (Total Includes
Large Industrial Consumption), Anchorage-Cook Inlet •••••••••••••••• C.151
Breakdown of Electricity Requirements (GWh) (Total Includes
Large Industrial Consumption), Greater Fairbanks ••••••••••••••••••••• C.152
Total Electrical Requirements (GWh) (Net of Conservation)
(Includes Large Industrial Consumption) Medium Range (PR = .S) ••••••• C.153
Peak Electric Requirements (MW) (Net of Conservation)
(Includes Large Industrial Demand) Medium Range (PR = .S) •••••••••••• C.154
HEB~~FERC -2% •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• C.155
Households Served, Anchorage-Cook Inlet •••••••••••••••••••••••••••• C.157
l-louseholds Served, Greater Fairbanks ••••••••••••••••••••••••••••••••• C.158
Ho us i n g Va can c i e s , An c h o rage -Cook In 1 e t •••••••••••••••••••••••••••• C .15 9
C.9
Housing Vacancies, Greater Fairbanks ••••••••••••••••••••••••••••••••• C.160
Fuel Price Forecasts Employed, Electricity ($/kWh) ................... C .161
Fuel Price Forecasts Employed, Natural Gas ($/W1Btu) ................. C.162 """
Fuel Price Forecasts Employed, Fuel Oil ($/~1HBtu) .................... C.163 -·Residential Use Per Household (kWh) (Without Adjustment
for Price), Anchorage -Cook In 1 et ................................... C .164
Residential Use Per Household (kWh) (Hithout Adjustment
for Price), Greater Fairbanks •••••••••••••••••••••••••••••••••••••••• C.165
Business Use Per Employee (kWh) (Without Large Industrial)
(\~ithout Adjustment for Price) ....................................... C.166
Summary of Price Effects and Programmatic Conservation in
GWh, Anchorage-Cook Inlet .............. ·:···························C.167
Summary of Price Effects and Programmatic Conservation in
GWh, Greater Fairbanks ••••••••••••••••••••••••••••••.•••••••••••••••• C.168
Breakdown of Electricity Requirements (GWti) (Total Includes
Large Industrial Consumption), .llnchorage-Cook Inlet ................ C.169
Breakdown of Electricity Requirements {GWh) (Total Includes
Large Industrial Consumption), Greater Fairbanks ••••••••••••••••••••• C.170
Total Electrical Requirements (GWh) (Net of Con·servation)
(Includes Large Industrial Consumption) Medium~ange (PR = .5) ....... C.171 -Peak Electric Requirements (MW) (Net of Conservation)
(Includes Large Industrial Demand) i<ledium Range (PR = .5) ............ C.172
C.10
Hl2--SHERMAN CLARK NO SUPPLY DISRUPTION
,~""'
C.ll
J 1
SCENARIO I HEO I Hl2••SH£RMAN CLARM NO BUPPLl' ll ISRIJPT I ON••III2 II II 98 J
HOUSfHOLOS SfRVEO
ANCiiOAAGE • COOK tttLE T
----··----------------
V£lR SJtiGL£ FAMILY HIJLTifAHILY HOHil.E HOMES OUPt.EXES TOTAL ···-""""""'""""····----................. ................ ~---------------...............
ueo J5IIH 1 2113111. 11::!11). 711811. 11'30J. o.ooo) o.ooo) o.OIJO) o.OOII) n.OOO)
1985 llb22LI. i!b2011 1 tnqsfl. 6Sb1, 9 I 951.
0 o.ootl) o.nuo) n.noo) o.ooo) o.oool
I--' 1990 '3117110. i!bJttq. 1]50'!1. 811b(l, 1070511. w o.oon) o.ooo) o.ooo) o.noo) o.oon)
1'195 61177". j!IJ9]l, 1&19111. 8]]], 1179811,
n.ooll) o.OOI)) o.nOOJ o.OOO) o.ooo)
2000 b<J822. H259. 'lb201)1 8022. 127102. o.ooo) o.ooo) o.OOO) o.ooo) n·.ooo)
2005 75777. lf:tHA, 11711'. 873R, l38blll. n.ooo) n.olln) n.ooo) 0.000) o.ooo)
i!OIO "HLIJ. II 0 Ill I • 1'H2t. 9611'1, 1511211.
0.1101)) o.nooJ 0.000) O.OOOJ o.onn)
SCEN6RIOt HEll I H12•·5H£R~AN CLARK NO SUPPLY OISRUPTI0~·•612111198]
HULISEHOLOS SERVED
GREATER FAIRAANKS
~w•••••••••~••••w•••••
YEAR SINGLE FAMILY 14UL TIHMILY HUBILE HOtiES OUPI,.El!ES TOTAL -··· ·--····-~··--................. -·------·----................. ········---···
1980 nzo. 5287. I \89 • uu. 15111,
0 0 0011) o.ooo) o.ooo) o.ooo) o.noo)
t98S IObllfl 0 Sllbl'. 2110. lU5, 201107.
n.ooo) o.noo) o.ono) o.ooo) o.OOO)
("")
1990 111211. 7qc,o. 2270. 2315, C!t1H2.
I--' ll.OOO) o.ooo) 1'1.000) o.ooo) 0.0011) ~
1995 I 117lb. 711111. H28. nn. ?.IIC!IItl 0 o.ooo) o.non) ll.OOO) c 0,000) 0 0 0011)
2000 lb521!. 7701. ]1!11115. 2298, 303711.
0 0 IHJ0) o.ooO) o.ooo) o.ooo) o.ooo)
C!OOS 17951. 8b"'t. 11220, i!Ul, 321f73.
o.ooo) o.noo) o.oooJ 0.000) n.ooo)
C!OIO t9b75. 9bU 0 11&7]. 2134, 1bC!911 •.
o.ooo) o.OOO) 0,000) o.OOO) o.onn)
] ) J J
l .··· .. 1 l 1 l ]
!ICENARJO! HED • HlZ••SHERHAN CLAR~ NO StJPPL Y OJSRUPTJON••6/lqJt~81
HUUSIIHJ VACANCIES
ANCHORAGE • coo~ INLET
·-----·-·····---~----·
'fEAR S I UGLE FAHILY HULTJFAI41LY '101.11l E HOMES DUPLEXES TOT'L .... . ............. . ............. ~. ·--.. •···•·••· . .............. ................ ~
nAo 5089. 1bbb, I 991, lllbl. IUOtJ,
o.ooo) o.OOO) o.oOQ) o.OOO) n.OOO)
1985 '5011. lll9b, li! I. ~qi!, 2111'1',
("") 0,000) 0,000) 0,000) 0,000) o.noll)
1--' t9(JO e.qb, IOO!i, lqCI• l89, i!OBCI. (Jl
o.ooo) 0,000) o.ooo) o.noo) o •• ooo)
19(J5 711, uu. ttJq, 2811, 2777.
o.ooo) 0,000) 0,000) 0,000) 0,0011)
2000 71J8, IHIJ, 17e. qqs, 1187.
o.ooo) 0,000) 0,000) o.ooo) 0,000)
2005 (1]0, IIJIJII, 195, 288. ]281.
o.ooo) 0,000) 0,000) 0,000) 0,000)
i!OlO t1l1, 2182, 217. 119. ]b]ll,
o.ooo) 0,000) 0,1100) 0,000) 0,000)
SCHllRIOI l-IED I Hll••RHERM4N CLARK NO SUPPLY OISRUPTION••bl?.41l98J
HOUSING V4CMICIES
GREATER FAIRBANKS
----·······-·-···-----
YEAR SINGLE FAMilY I-lUI.. Tl F AHIL V MORILE llOI-IF!:S OUPLEICES TOTAL
-----·~·-···-............... ··-···------· .............. .................
1980 J~SJ, 3HD. 98f,. 895, 88511.
o.noo) o.oon) o.ooo) 0 0 000) 11.000)
1985 ue. 2b511, 24, 722. 151!1.
o.ooo) o.OOO) o.ooo) o.OOO) 0.000)
0 199() IC!9, 11511. 25. 81 • . e,aq • ....... o.ooo) o.ooo) n.ooo) n.ooo) o.noo) en
1995 I IIi!. 4411. :n, 80, 726.
o.ooo) o.ooo) n.noo) o.ooo) o.ooo)
i!OOO 182. 11110 0 42. 78, 711~.
n.ooo) o.nooJ o.oooJ o.ooo) o.oon)
zoos 197. at.q. lib, i!09, q2t.
o.noo) o.noo) o.ooo) 0,000) n.ooo)
Zolo 2lt.. 519. 51. 77, 8bll.
o.ooo) o.oon) o.ooo) o.oooJ o.ooo)
J .J J
l 1 l J l l
SCENARIO! MfO 1 Hli!••SHERMAt-1 CLARK NO SUPPLY 018RUPJ1Utl••bli!lllliJ8]
FUEL PRICf FO~ECASTS EMPLOY~O
ELEtTRICITY (S I KWHJ
ANCHOFIAGE . COlli< INLET GREATER FA I PRANKS
··-······--·-·-··-·····--·~----·-·--R -·~-·~--------~---···--·-·········-··
YEAR RESIDENTIAL BUSINESS RESIDENTIAL AilS 1 NE S!1 ............. ............. ............... ----·····-·
1980 o.nH o.osu o.oqs o.OCJO
IIJSS o.o1111 0.1)1.15 o.o<J5 0.090
1990 o.os2 n.OII9 o.o'J2 o.oBJ
19CJ5 o.os~ o.oss o.oCJI.I o.oe9
?000 o.oe.2 n.oSCJ n.oqb o.o9l
2005 o.l'e.5 n.oe.i! n.oCJB o.o~JJ
2010 o.oe.Y o.ne-4 o.too 0.095
("') .
.B j
SCENARIOI ~EO I Ht2•·SHERMAN CLARK NO SUPPLV OISRUPTION••bi2QI198J
YEAR .....
1980
1985
1990
1995
lOOO
2005
21110
ANCHORAGE • COOK INLET
fUEL PRICE FORECASTS EMPLOYED
NATURAL GAS (SIMMBTU)
GREATER FAtR9ANKS
--····················--·····-··-···~ --~···-·---·-··---~···-·---·-------··
AE&lOENTUl BUSINF.SS AESIDENTI AL IHlSJNfSS .•.••...••. ·······-"~··-·-·~·--.. ·-· ... ............
I. 7]0 1.soo u. 7110 1t.l90
1.950 t.7'lo IO.bOO 9.150
2.1180 i'.bSil 11.2110 9.790
a.oso 3.820 13.030 lt.580
11.290 a.ot>O 15.110 1 J.uo
u.9bO 11. 7l0 17. 521'1 U.07t'l
5.)80 5.150 21l.J10 18.1161'1
} J __ } .J -] J
1 J 1 J l l
SCENARIO! MEO I Hli!,.•IIHERM4N CLMU< NO SUPPLY UISRllPTJON••bl2111196]
Fllt:l P~ICE FQRf:CASTS EHPLOYFD
FUEL OIL (S/HH8TUJ
GREATER FAJRRANK~
---·-·················-···-······-~·~ --------··········-------·--~-·--·--·
YEAR Rf.SIDENTUL BUSINESS RESIDENTt4L FillS I Nf!JS .... ------····· ·····--···· ............. . .............
1980 7.750 7.2fl0 7.830 7.'501)
1985 e..uso '5.900 e..510 e..uo
1990 &1 8110 ll.i!'ln fl.9l0 &.580
1995 7.910 7.1811 11.010 7.e.eo
2000 9.190 ll 0 bliO 9.190 "· 9110
2005 I o. b!. o 10.100 10.770 10 .IIIlO
2010 12 • JSO 11.800 12.1180 12.150
SCENARIO I ~ED I Iii i!••8HEAMAN CLARK NO SUPPLY 0ISRUPTJOH••6/2~/I~BJ
RESlOENTlAL USE PEA HOUSEHOLD (KWH)
tWlTtiOilT AOJUSTI1ENT 'OR PRICE)
ANCHQPAGE • COOK INLET
····-~·······-----·-~~
St-ULL LARGE SPACE
YEAR APPliANCES APPll ANCES HEAT TOTAL ..... -······--· . ........... . .......... . ...........
19811 2110.1)0 tosou.u sou.sa Ub'19.15
0 0 1JOO) O.IJOO) o.oooJ 0~000)
(""') 1985 i!lbO.OO h15l.l.l9 ~821.1'3 lJIH.H . u.oooJ o.ooo' 0.000) 0.000) N
0
1990 2210.00 b019 .• 7b 451!~. 35 U8111.U o.non) o.oooJ 1).000) o.OOO)
1995 221>0.00 1!1959~31 1.1'51~.'5b 12734.87
o.ooo) o.oouJ o.nooJ o.ooo)
2000 2310.1)0 5 1HI9.J8 1.11151.811 liil7'B. l l
n.uooJ o.ooo) o.onn) 0.000)
2005 i!Juo.oo 60'59.12 lllli!O.OI.I 12839.17
o.oonJ 0.000) O.OOO) o.ooo)
i!OlO aaao.ou U~3.98 11114].'55 ll971. 52
o.ooo) 0.1)00) o.ooo) o.ooo)
J j -) .J J ]
1 -]
SCf.NARIOI HED I H12-••SHEIHU~ CLARK ~0 SUPPLY OISRUPTION••b/21111983
RESIOE~TJAL USE PER HOUSEHOLD (IOHO
(WITHOUT AOJUST'11::NT FOR PIHCE)
GREATER FAIRBANKS
••••~••••••••~•••••aa•
SHALL LARGE SPACE
YEAR APPLIANCES APPLI'NCE!I HEAT TOTAL ..... ........... ---·-·---· ........... . ............ .,.,.
191\0 j!Ub6.00 Hlq.52 HIJ.6b 115tq.18
o.ooo) 0.000) o.ooo) 0.000)
n 1985 253S.q9 6178.911 ]b06.11 121'-&.au
N o.ooo) o.ooo1 o.ooo) O.OOO) I--'
1990 2uo.no 6115].56 1812.'52 I293Z.o7
o.ooo) o.ooo) o.ooo) 0.0001
1995 i!bH.oo t.bfl6.87 11050.111 1 HU.oo
o.OOO) o.ooo1 o.ooo) OoOOO)
20110 27116.00 U95.115 11Jl0 0 JO 11651.75
11.0001 o.oooJ o.ooo) 0~000)
20!15 l~'U.OO 68J8~8b 11'535.80 111190.6.
o.ooo) 0.0001 n.ono) 0.000)
2010 i!81Jo.oo 6887.85 111:155.96 11•112q.a1
o.ooo) 0.000) o.ooo) o.OOO)
0
N
N
J
SCENARIUt MEO I Hll--SHERMAN CLUlK NO SUPPLY DlSRUPTIOII• .. b/2111198)
YEAR ANCHORAGE • COOK INLET ...... ·····~·-··············
1980 8407.04
0.000)
1985 9580.18
o.ooo)
1990 10]55.0& o.ooo)
1995 109&8.115 o.ooo)
2000 ll'.llfi.IIO o.oon)
2005 li!069.U o.ooo)
2010 12932.U o.ooo)
J
BUSINESS USE PER EHPLOVff (KWH)
(WITHOUT LA~OE INOUBTRIAL!
(WITHOUT ADJUSTMENT FQP PRJCf)
GREATE~ FAIRRANK9 .•••••.......•....•..•
7~Q5.70
o.ooo)
Hli!.tl n.oou)
8127.15 o.ooo)
8obt!.ZT o.ooo)
8957.9i! o.oocl)
CIJOB,OJ
11.0011)
97ll.o5 o.ono)
-····-·] l
SCENARIO I MEO I H12•·SHfRMAN CLARK NO SUPPLY OISRUPTJOII••6/i!llll98J
SU"4t-4ARV OF PRICE EFFECTS AND PROGRAH&TIC CONSERVATtON
IN GWH
AUCHOQAGE • COlli< IULET
RESIDENTI•L Rlllii~IESS ................. . ............
OHII•PRICE PRUGR Al1· I N()IICFO CAOSS-PRJCE OHN•PRICI! PROORAI'I•INDIICf:D (:ROSS•PRJCf
YEAR P[DIJCTION CONSERV_A ~lOri RED_UCTIO.N RE~q.c_T 10~. C0~4~~~~!!9~---_ AEfiUCTION ........ .................. .. ............................ ...................... .. ................ .. ............................ .. ....................
1980 o.oon o.ono n.ooo o.ooo o.oon n.ooo
1981 6.169 n.ooo .. o.5b7 9. 327 o.noo n .''ni!
1982 12. JH o.ooo ·1.115 111.6'51 o.ooo l. 061
l 981 111.'506 O.OO!J Rl.702 21.980 o.noo 1.'595
19811 i!ll.f.711 o.oon -2.271) 31.107 o.ooo 2.12b
1985 30.B'll o.ooll •i!.8]7 11!..631 o.ooo 2oh58
19Bb ]!1.1176 o.oon •10.645 58.180 o.ooo -o.3Sb
1987 llb.l09 o.ono •l8.115q &9. 726 o.noo •3.370
1988 51. hi! o.ooo •2b.i!bi! Ill. 213 o.ooo -6.3115
1989 61.]75 n.ooo ·311,071 92.1119 o.ooo -9.399
1990 1)9.001! o.ooo •111.1179 1011.366 n.ooo -12.1111
n
N 1991 11'5.0116 o.noo ·91.197 lt9.CJIIO o.ooo •19.060
w 1992 lbi.OI!II o.ooo •1110.51'5 1)'1.'!1111 o.ooo •2'!1.707
1991 207.121 o.ooo -ts9.AH 151.088 o.ooo -u. JsJ
19911 251.159 o.ooo •239.150 16b.6b3 o.ooo •19.00(1
1995 i!99.1 1H o.ooo •i!ll8.11!.8 IB2.in7 o.ooo •115.6117
199& i!lii.Ol<J o.oon •?25.008 198.278 o.ooo •'52.5811
19'H 1611.8112 o.oon •161.5117 i!lll.\20 o.oao •59.530
1998 103.6!.5 o.noo .CJA.o86 no.Jbl o.ooo •b6oll71
1999 lR.II88 o.ooo ·311.626 2111>.1101 o.ooo •73.1112
2000 •26.681J 0.(100 i!fl.lll5 262.1JIIII o.ooo -80.1'511
2001 -7.502 o.ooo «».1170 282.1J89 o.ooo •90.ZIIS
2002 11.68'5 o.ooo ·15.895 302.'515 o.ooo •100.111
ZOO) 10.872 o.ooo •lR 1 2b0 !22.sao o.ooo •llO.OZII
20011 so.o59 o.ooo •bll.b25 ~~~<'.625 O.C'IOO ·119.920
lOOS b9.2111> o.ooo ·82.~90 ~1>2.670 o.ooo •129.1111
i!OOb "18.151 o.ooo ·9'5.9011 ]fiA • lli! o.ooo •I Ill. HI"
2007 117.055 (1.(100 -· 1)8 .Ill 9 ul].'iQ5 o.ooo -ts6.B611
2006 95.9bl) o.ooo •li!l.7H 1119.1157 o.ooC'I ·170.391
2009 1011.81111 n.ooo •IJII 0 hll7 llbll.'ii!O o.ooo •183.917
20.10 ll1.1b!J o.non • I II 7 0 Sbt' IJR9.982 o.ooo ·197.111111
SCENARIO! MEO 1 ~12••8HER~AN CLAR~ ~0 SUPPLY Ol8RUPTION··6!2111198)
YEAR ........
1980
1981
1982
198l
19811
1985
19h
1987
1988
1989
1990
1991
1992
19'H
19911
1995
1996
1997
1998
1999
2000
2001
2002
2003
20011
2005
i!OOb
2007
iOOB
2009
2010
]
OWN•PAlCE
REOUC TJ ON
.. .............. ..
0,000
o.ooo
0,1'100
11,001)
0,1)00
0,000
•0,200
.. 1),400
•0,600
•0,800
•1,000
•\,008
•I,01o
•1,0211
•l,oH
•0,86"
-o,o915
•0,522
•0,149
•0,176
0,129
O,II:U
o. 738
1,0112
\,3117
',772
2,19~
2,6211
1,0119
J .I
SUMMARY Of PRICE EfFECTS AND PROGRAMATIC CON8EAVATt0N
IN GWH
GREATER FAIRBANKS
FIESIDENTIAL .............
OWN•PFIICF.
BUSINESS . ............ .
PROGAAH•lNOUCEO PAOGFI.U1•1 NOUC[O
CONSERVA~lllll
.............................
CROSS•PRICE
REDUCTION
......................
AED~p_.ON_ CONHf.I~~JJpN ___ '
(1,000
1),1)00
0,000
o.noo
0,000
o.ooo
11,000
0,01)1)
0,000
0,04.10
0,000
0,000
o.ooo
0,000
11,000
0,000
0,001)
o.ooo o.ooo
n,ooo
0,0011
0,000
0,000
o.noo
n.ooo
n,ooo
fi,OtiO
o.ooo
0,000
0,0(10
]
o,ooo
0,758
1,5U
2,n11
1. on
1,789
11,1811
11.'378
11,972
•• JI»J
'!1. HI
5,176
11.592
11,008
J,lli!ll
2.8)9
1 0 150
•O,ltlO
•l,bJO
-1,119
.. a,ea5
·9.0112
•11,2'58
-1),1175
-t8.btli!
·21.~]]
•i!ll,bOil
·27,1i75
1 I
..................
o.ooo
0,000
0,000
o.ooo
o.ooo
0,000
•O,]IIi!
.o.68S
-1.027
-1,309
-1,712
-1,61J
•I,UII
•1.595
-l.!i5b
.. t.517
•1,2117
-0,078
·0.708
·11,1119
o.2CJ7
0, H3
1,&!28
1,6911
2,810
1.417
11.11o
11.795
.. ......................... ..
0,000
0,000
0,000
o.ooo
O,CIOII
0,000
0,000
0.(1110
o,ooo n,ooo
o.ono
n,ooo
o,ooo
o.ooo
o.oon
o.ooo
0,1100
0,000
0,000
0,000
0,000
o.ooo
o.ooo
0,000
o,ooo
0,000
0,000 o.ooo
0,000
n.noo
0,000
CROSS•PRICE
REDUCTION
.. ................ ..
o.noo
o,Slll
1,028
I 0 'SII2
2,1156
2,758
io0146
J,lJII
3,!23
3,0811
2.b57
e. 2:u
1,8011
t 0 318
0,556
•0,265
-t.08b
•lo90T
M],CJ10
.s.o•n
•b. 271
•7,1l!i2
-10.215
•I I ,836
•11.11)8
•15,010
1 1 ---. 1
SCENARIDI H[O I Hli!ooooSHEF111AN Cll~K NO SUPPLY OISRUPTIOI~·-b/21111981
BAEAKOnwH Ot ELECTHICITY REQUIRfM~NTS ( GWI1)
(TOT~L INCLUDES URGE INDUSTRIAL CONSUMPTION)
ANCHflRAIH. • COOK JNLET ··------N·········-··-
lo1EOIUH RAtiGf (PR•,5J --·--··-····4·4·----
RESJOEIITJ&l 8USIN£SS M ISCELUNEOUS EJ(OG. JNOllSTRJAL
YEAR R(.QlJJREIIENTS RfQIJIREHENTS REQUUif.HENU LOAO TOTAL
·---~---·---·····---·-·-----·-·-···--·-····-····-····· ···-~-~-·--·····------··--··--------
t«~IIO 97'1,'51 875,1b 24.:JI 84,00 1961,19
1981 1019.55 QIJIJ,5S 211.64 92.011 2082,82
1982 105'1,57 l017.B 211,91.1 100,16 2202,11o;
198] I099,t.O 1088,92 25. ll 108.211 i!UZ.o7
1984 113'1,62 1160,11 !5,6'5 IU,l2 211111.70
1'185 117'1,611 li!JI.JO 25,98 I 211,40 2'5l>l~l2
1986 t212.b5 1280,79 26.8] 137. "9 2658.16
1'187 li!II'S,6!i 11]0,28 27.67 151,]8 '7511,99
1988 ti!76,bfJ 1179,77 28.51 1611,88 i!851.8i'
n 1989 llll.b7 l1129,2b n.u. 17".17 29118,bb
N 19'10 I]IIII,U
Ul
11171.1,75 ]0,.20 191,8b ]0115,119
1991 IHII,IO 1510,4b 30,8e I 95 • I] lll0,5b
1'192 1110],52 15112.17 Jl,S6 IQ8,40 :1175.611
19'11 !1132,911 I '571,87 3l.24 201.66 ]2110~ 72
19qll lllbi!.lfl UO'S,-;8 12.92 2011.9] ]]05.79
1995 lll91,78 1~17.29 B.~o 208.20 ]]70.87
l'19b 1'511.'70 lt»b].oq ]ll,lb 2111.111 ]1129,0/J
19'17 I Slll,bi lbtl4.80 111. n 220.fl8 11187~22
19q8 ISbq.sJ 17111.55 1'5.29 Ub,Oi! 15115,110
1999 !595.11111 17110,11 )5,8b 2l'. 9b 1b01.51
2000 lb2l,)f> l1bb.OtJ .. JtJ.42 217.90 JUl. 7'5
2001 11155,85 IAii!'.b'J ]7 .27 i!llll,9b J750.7t>
i!002 ltJ911,H li\5CI.]I 1 tl. I I 252.02 3819.78
200] 17211.81 1905.911 l8.9b 259.08 1928.79
i!'OOII 1759.]0 19'52.'57 ]9,80 i!bb.lll 11017.81
2005 1791.711 lC19CI.21) IIO,b5 273.20 11106.82
i!OOb lfll9.i? i!Ot.9.1l2 111.117 2'81.58 IIZli!,llll
i!OOJ 11illll.b5 21110 ,11<; 41.08 2119.9b 11]58.15
2008 t'llo,oq 22II,Oii 1111.10 298.111 114111.81
2ooq I 'HS .51 ~281,71 115.52 J0~.72 llb09,1111
i!OIO i!O?O,'Jb 215<! .1'1 llb,711 115.10 11715.111
SCENARIOt MED I HI2••SHERMAN CLARK NO SUPPLY DISRUPTION••bi2QJ1983
aRE AKOIIWN OF ELFCTRlCITV REQUIAfMfNTS (GWH)
CTUUL HlCLUDES LAIH~E JNOUSTHIAl CONSUHPTJON)
GREATER FAIRBA~KS
--------··--···~-----·
MEDJIJH RAIIGE (PR•,S)
···--------~~··-·-··
RESJOENTJAL BUSJNF.SS MISCELLANEOUS EliOG. INDUSTRIAl
VEAR IU.!Wl RF.MENT 8 REQUIREMENTS REQUJRF.HENTS LOAD TOTAL
••• "!"' -·--~··---~·~····------~·······~-~--·--·~---~-·-·-···· ··-···----·---··--····--·····----·--
JU() l7b. 39 21?.1CI &.78 o.oo Q00,31
1981 l9Q.bQ 229.11Q b.15 o.no Q27.21
JU2 20Q.91) 2Qi!.5S o.7l o.oo 115Q.J5
1983 219.1S 25'5.25 b.b1 o.oo Q8l. OJ
l98Q !.!B.qO 2b1.9b b .u o.oo 507.99
191!15 247.b5 280.h b,59 o.no 5]11.91
l98b 2bO.IO 289.CI5 fl.&s 10.oo 561>.20
1987 il72.55 298.i!CI fl.70 2o.no S 1H.SO
1988 i!RS.OO 307.04 b.l! JO.oo 628.79
1989 297.Q5 315.83 &.eo QO.OO uo.oe
(") . 1990 ](19.90
N 3211.6C! b.Bb 51).00 b9l.38
())
199t lU.22 132.113 7.U8 sn.oo 7ll.lQ
1992 :Ub.S.J Hl.n5 7.lt sn.oo 1]11.89
1991 l119.8S JCI9. C'1 7.SQ 5o.no 75a.b'5
19911 3bl.U 157.111! 1. 7J so.oo 718.111
1995 31b.li? 3a'5.7o 7.99 so.oo 800.17
l99b 1811.!8 111.79 8.111 SQ.OO eu.n
1997 Ho.o<~ 371.87 e.n so.oo 832.29
1998 1105.90 181.9b 8.49 50.110 848.3Q
1999 415.71 un.o11 8,b5 so.no 8bQ.IIO
2000 Uj!5.5! ,.., J9b.li! 8.8?. so.no 81\0 ·.lib
2001 IJ]b 0 8b QOS.bl 9.011 so.oo 901.52
2002 CIIHI.il IIIS.IO 9.27 50,1)0 l:li!2.S8
i!003 11';9.Sb 11211.59 9.50 sn.oo 9113.a5
i!OOCI 1170.91 QJa.oe 9.72 so.oo 9all.7t
zoos 482.25 Qll1.57 9.9'5 so.oo 985.77
i!OOb 11q5. 91> 457.05 111.22 so.no 1013.23
!007 'i09.t.7 QJn.sl 10.50 so.no 10110.70
i!008 521.37 4811.01 t0.7A sn.oo IOb8.16
2009 517.013 llll7 0 llq II. US sn.no 1095.62
2010 '5"i0.79 sto.cn II.H so.oo ttl!l.09
1 J ·· . .J I ) I I I ] J ) .J ]
--] l -1
SC£NAR10t ~EO I H12••BH~RHAN CLAR~ NO SUPPLY OISRUPTJON••b/2~/198!
YEAR
1980
1981
I9Bi!
1911]
I911Q
19115
198b
1987
1988
19A'il
1990
1991
199i!
199]
l991l
1995
1996
19'H
1998
1999
i!OOO
i!OOI
i!OOi!
i!OO]
i!OOII
i!005
i!OOb
i!OOJ
i!008
i!009
i!O In
ANttiORAGE • COUll
TOTAL ~LECTR1CITY PE~UIREHENTB (GWH)
fNET OF CONSERVATION)
fJNCLUDES LARGE JNOUSTAIAL CONBU~PTIOH)
HfOIU" PANGF (PR • .Sl
INlET OAtATER fAIRAANkS TnTAl
····-----···········-· ----~--~··~-·R-·--···--···"·-~~---~-~·-~---~
19t-J.I9 Ill) II. Jl 2]6]~ 51
i!OIJi!.Bl "i!7.u Z'511J,0'5
Zi!IJ2.115 asa.ts i!f.'5b.bll
U2i!.nf 1161.07 i!BIIl.lll
211111.711 '5(11.'119 Z9IIO.b9
zsu.u ~]11.91 ]09&~21
2«»'58. u 5bb.l0 Ji!t'll~]ll
17'511.99 S'H. 511 l lSi!, 119
?851.112 bi!B.J'I lf!IIO.III
i!9QB.U 6110.118 JbOB. JQ
]0115.11'11 b'H. 18 1111>.81
)110.'511 Jll.lll )Ai!J. 70
]175.bQ 7]11 .89 ]910.51
]2110. 71 75b.fl5 )997. Jf
])05.79 n11.111 QI)8Q~21J
J'JO.!IJ ,.00.17 II 11 a". 011
H29.0U 11111.21 lli!US~i!J
Jllll7 .u 8U.29 11]19.51
]5115.110 81111. )q 0]9]~7~
]biiJ. SJ ll&ll.QI) 01!111.91
lbbl. JS fUIO.IIb 05112~21
]7'50. u 901.5i! llb!li!.i!A
]1119.711 9i!i!.58 117b2.lb
]9~8. J'il 90).b5 IIIIJ 2·.1111
0011.81 9bll.JI 119111(.51
111116.82 'iiB'i • .,., o;nqz.sq
ozJi!.o9 llllJ.2l '5;1115~ 1'2
11)511.15 lllllO.JO '5]Qft.AII
11011].81 IObtl.lb sso;a.n
llbll9.1l!l 109'5.bi! 'i705 .• 10
liTH. Ill tli!].(lq -;fl"iA.i']
n
N co
J
SCENARIO! ~ED 1 HIZ•·BHERHA~ CLlR~ NO SUPPLY OISRUPTION••~Ill!ll98J
PEAK ELECTRIC REQUIREMENTS (MW)
lNET 0~ CONSERVATIO"J
UNCLI.ID£8 LAHI'if III0116TIH4L DEMAND)
HED!UH RANGE fPR a .5)
····-··-·············~
VEAR ANCHORAGE • COOIC INLET GREATER 'AlRBANKS TOTH
•u•••••••••••••••••••• ·-·~··-············--~ ~·-·····-·-··-·····-·-
1 1UO Jqb.5t 91.40 41'17.911
1981 1120.118 97.511 518.21
l9ll2 1!114.81:1 to3.b9 5111'1.5'5
198] llb9.0U tn.u S78~1J7
1984 qqJ.21 115.98 bll9.l9
1985 517.3'1 U2.1J t.H.52
IQ8t. 537.82 li!9.2J bb7~08
lQ87 558.21! llb. Ill b91!.b5
19ll8 578.;b7 14].55 722~22
1989 599.10 150.b'J 7119.79
1'~90 bl9.51 157.8] 777~ lb
1991 uz.n te.z.~to 795~55
IIJ92 bll5.97 lb1. 77 813.111
199] b'i9.19 I U.TII 81 I~ 92
lQ911 o?i!.'H 171.70 850.11
1995 b85.t>J 162.&7 l.lbl'l .• 30
199& b97.ll tee..H IIIJ3~1J5
1997 706.99 191).00 898~99
1998 720.b7. 191.67 9111.311
lQ99 nz.l!i 191.111 929. b8
2000 7411.01 >.ot.oo 945~03
2001 7b2.00 205.81 9b7.81
200i! 779. 9b !ll).bi! 9ql).511
i!003 797 .'n i!l'!i.IU IOil.J6
20011 815.90 221J.24 IOJb~ll
zoos 8H.Bb 225.05 I0'58~'JI
200o 859.29 2ll.JZ I090~b0
2007 881.1.71 2l7.'59 1122.30
2008 9IO.l'l 2tU.eo 1153'. 99
2009 9l5.Sb ~50. t3 IIIIS .• bll
2010 9bQ.98 i!S&.IIO I Zl7~311
c ) ·~ I J 1 J ] 1 ) ] ... I J 1 l J
-
-l
-
'i"
I
!""'\
HE3--DOR AVG SCENARIO
C.29
) l I
SCEIIARIUI l-IED I Hfl•·OOR •vc SCENARIO••b/2qll98]
HOUSEHOLDS SF.RVED
,UICHOIHGE • COOl< INLET
---·-···-·----·-···-~·
YEAR SINitLE FAMILY HIJL TIF AHILY HOfliLE HottES DUPLEXES TOTAL -----------··----............... ............... ................ . ..................
1980 l5"H. 211]111. 8210. JUI:Ib, 11501. o.ooo) o.ooo) o.ooo) o.ooo) o.ooo)
1985 115&7'5. 2bi.!OII 0 10857, 85b1, 91]03.
o.oonJ 0,.000) n.OQO) o.ooo) o.ooo)
n
w 1990 '511)199. 25877. 12721. BilbO. 102157.
I--' 11.000) o.ooo) 0,.000) o.nooJ o.noo)
l9q5 6\0I:Iq .. 27629. lqOb6 0 8131, 111117. n.ooo) n.OOO) o.ooo) o.ooo) o.ooo)
2000 bb029, ]082'5. 1511~. 811H, li!O]bO, o.ooo) n.ooo) o.ooo) o.ooo) o.oonJ
zoos 111qb. Jlll.lb7. 1&822. 82fi3. 13llb9.
n.oOO) o.nl)o) o.ooo) o.ooo) o.nno)
ZOIO Jq0bb 0 38 J"i I • l81l5. 91SCJ. 1 115~91.
o.onoJ o.ooo) o.OOO) 0,.000) o.oooJ
SCENARIO a MED I UEJ••I>OR A\10 SCEN4RIO·~b/i!~ltq8]
HOUSEHOLDS SER\IED
GREATF.R FAlRRANKS
······~--······-······
VEAP SltiGLE FAHILV ~UL T fF·AnlLV M09llE HOHES DUPLUES TOT4L ···-.. ,. ........... ············· .............. .............. -~·-·········
1980 72ZO. Si81. ttn, 11.111, l'illl.
o.oon) o.oonl 0,000) 0,000) 0.000)
\985 1061ib, '568tl, ZllO, t no. 2otan.
o.noo) 0,000) 11,000) 0,000) o.ooo)
0
1990 10852. HoO, l\0), 2'115, 23291', w o,noo) 0,000) 0,000) 0. 000) 1',000) N
1'~95 l]tiQl\, 78til, 2~""· nu. 2b)7!i.
0,000) o.oon) 0,000) 0,000) o.OOII)
2000 1503~. '7701, 311011, 2UB, i!8tlt11,
0,000) 0,000) o.OOO) 0,000) 0,1'00)
200'5 lb8bi. 78QS, ]9bb, a nz. 30975.
0,000) 0,000) 0,000) 0,000) 0,000)
2010 181§20. CJOSl, l~tiOl, 2198, Jill 69.
0,000) 0,000) 0,000) 0,000) 0,00(1)
-J l J
SCENARIO I MED I HE]-.. OUR AVO SCENARI0••61~111lq81
HOUSING VACANCIES
ANCHORAGE ... COOK INLET
--·-·---·-····--·-·-·-
YEAR SINGLE FAMILY MULTifAMILY MORILE HOMf.S DUPL£l«ES TOTAL ·-·--·-----......... .................... ··-·-----.. --.. ............... . ..................
tqao so sq. 7tsbo. lqql. lllb3. tblOCJ•
o.oool o.oooJ o.ooo1 o.ooo) o.ooo)
(q85 SOl. lll'lb. II q I zqz. 21110.
n o.oooJ 0 1 1'100) o.ooo) 0 0 000) o.oooJ
w w aqqo bOA. l !117. 140. zaq. 215111.
o.ooo) o.oooJ o.ooo) o.ooo) o.ooo)
Jqqs b72 0 1119P.. ass. 2611. 2601.
o.noo) o.oooJ o.oooJ O.OOO) 11.000)
2000 72b. lbb'!'. lbq. 2H, illlCJ.
o.oOOJ o.oooJ n.OOO) o.OOO) O.OOOl
200'5 790. 111&1. 18~. '"· 2ll5o.
o.oOOJ o.OOO) o.ooo) o.ooo) o.ooo)
2010 870. l011. 20&. 102, 1/lllq.
o.oi)O) o.ooo) o.ooo) 0 0 000) o.ooo)
SCENARIO I !o4EO I HEl••OOR AVG 8CEHARJO••b/211/lq8J
HUUSING VACANCIES
GREATER fAIR6ANKS
·····-················
YEAR SINGlf FAMILY MULTifAHllV MOBILE HmtES OUPLEXES TOTAL ···-... ~ ............ . .............. ............... ----·-··---... ····~·-··----
aqao Jb51. H20. 98b. 895. 88511.
O.OOOJ o.oool 1).000) O.OOOl o.ooo)
0
1qas ll "· i?8 J7. 24. 7b7. 3145. . o.OOO) O.OI)O) n.ooo) o.ono) o.ooo)
w +» 1990 tl 9. 11511. 21. 81. 618. o.ooo) o.OOO) o.ooo) OollOO) n.oon)
t99S lllq. 44l'. 30. eo. 10b.
o.ooo) o.ooo) o.ooo) o.ooo) o.non)
i?OOO lb'S. 11110 1 3"· 78. 721.
0 0 0011) o.ooo) o.ooo) o.ooo) o.ooo)
2005 185. 85. 114. n. ·HI.
o.oon) o.ooo) o.ooo) o.nool ll.OOO)
2010 i?OII. ~~av. 118. 79. 819.
o.o()O) 0 0 000) 0 0 000) o.ooo) c n.non)
J J _J J j
n
w
t1l
YEAR
1980
nss
1990
1995
2000
lOOS
2010
1 ) l
,UEL PRICE FO~ECASTS EHPLOYfO
ELECTRICITY lS I KWH)
ANCI'fOFIAGE ,. COOK INLET GPEUER FAIRBANKS
--~--·-·-··--···-~---·~···--~----·-·-··-·-·---··-~·~--·-·-·---·~-----·----
RESIDENTIAL AU5 Itl!S!I RESIDENTIAL BIJSINfSS
··--·--·----·--·--···· -·--·····-·
_______ 411 ___
0.11]7 o.olu o.o9!5 o.oqo
o.nll8 n.n115 0.090 o.oa5
o.nst o.oqa o.o9o o.OBIJ
n.os1.1 o.o'Jt o.o90 o.ne"5
n.o57 n.nsb o.o9o o.oa5
o.obt o.osa 0,09i! o.OBJ
o.obl o.o&o o.o95 n.oqn
/
J
w m
J j
SCENARJOI MEO I HEJ••OUP AVO 8CENARIO••h/2~1lq8J
YEAR ....
1980
tqes
1990
1995
2000
lOOS
2010
] . __ J
ANCHQRAGE • COOK INLET
FUEL PRICE FORECASTS E~PLOYFD
NATURAL GAS (S/HMBTUJ
GRfATER FAIRRANKS
--~----···----·······-·-·----------·-
RESIOENTJ AL RliSINfSS RESIDENTIAL BUSINES~
-----·-----. ............ ·---··---------~~~----·-
1. no 1.!00 u. 740 ll.l90
I.'JbO t.no 9.810 B.3b0
l. 710 2.4RO 9.1b0 e.:uo
1.no J.020 to.:Uil 8.920
3.1110 3.180 II. 220 9.170
J.5b0 J~HO li.UO 10.520
J. 71n 1.11'10 12.770 11.320
) J j I J J
l
n
w .......
hl
SCENARIOI MEO I HEJ•·OO~ AVG SCEHARJO••b/lllll98l
YI!:AR .....
1980
1985
1990
1995
i!OOO
zoos
21110
ANCHORAGE • COOK INLET
FUEL PRICE 'ORECA!T! EMPLOYED
FUEL OIL (S/MMBTU)
GREATER FAIRBANKS
R•······--~------·--·--·-·····-·---·· ---····-··-----------------·····--·-·
RESIDENTIAL HUS I NESS RESIDENTIAL BUst NESS . ............ --········-·-·-···-··--.............
7.750 1.lOO 7.810 7.~00
11.970 115.4ll) b.OJII 5.700
5.91111 5.Ho 11.ooo 5.ft'70
11 0 ]10 5. JbO b. 370 b.nun
&.8]0 ~.i!80 ,.890 b 0 5b0
7.290 6.7'10 7.1110 7.1110
7.'780 7.i!JO 7.eso 7.'520
SCENARIUI HED I HEl .. OOQ AVG ~CENARIO••fl/241198]
RESIDENTIAL USE PER ~OUSEHOLD (lUI H)
tWJTHOtiT AI)JIISTI'tfNT FOR PRICE)
A~ICHOR4GE • COOK INLET
·~~-·-·---·-····--····
Sr-1ALL I..ARGF. SP4CE
YEAR APP1.14NCES APPLI A !ICES tiE AT TOHL --·-............ -·-······-........... -···-·----
UBO allll,OU f1Soo.e.3 5088,51! Ul:l99 .15
0.000) o.·oooJ 0,000) 0.000)
1985 i!lb\1,00 &154.71 '1811,81 1114&,51
0,000) 0.000) 0,000) 0.000)
0
w 191JO 22111,00 602b,l8 46i!1, 92 11860,10 co 0,000) 0.000) ( 1),000) 0.000)
1995 ZZbO,OO 15958,98 4'ill.98 127l0,9b
O.OOOJ O.OOI)) 0,000) o.OIIOJ
2000 &!HO,OO 5988,97 41.141,29 12740,2&
o.ooo) 0;000) 0,000) 0,000)
i!005 i!]bO,OO 60&0,87 1!42l,lt 12841,98
0,000) o.oool 0,000) Q,OOO)
i!OlO 21110,00 fll2b,8l 4llliO,U 12977,44
0,000) 0 ,•000 l o.ooo) 0,000)
J J .J J
1
SCENARIO I MED I HE l••O(IR AVG SCE~ARJO••b/2411981
RESIDENTIAL USE Pf.~ HUUSEHOLO (I<WH)
r WITHOUT ADJIJSTHENT FUR PRICE")
GREAT!~ FAIRBANKS
-~·---·---------------
SHlll LARGE SPACE
HAR APPU ANCFS APPLIANCES HEAT TOfo\L --·····-·· ............ ·----·----·----·--·-
1980 zu&t..oo 15739.5? H 11obh 11519.11\
o.oon) 0~000) o.ooo) 0.000)
1985 2'5J&.oo UAI.]/J 3593.90 12111.23
0 o.ooo) 0.000) o.ooo) 0.001))
w
1..0 1990 2&o&.oo &1.140.&1 31!119.117 1281)5.29
o.noo) 0.0(1()) o.noo) 0.000)
1995 2&H.OI &&5&.15 11088.11 13420.27
1).0(10) o.onoJ o.ooo) 0.000)
2000 2711&.00 &793.05 11]20.70 t:J859.15
O.OOO) 0.000) o.OIJO) o.ooo)
2005 2lllb.(ICJ b8Sl.Sb 11507.5(1 IUI77 0 0b
o.ooo) o.oooJ n.oooJ o.OOO)
2010 21\06.0(1 b89].Jb llbSb.'H lllllliJ.ll
o.I'IOO) 0.000) 0.000) 0.000)
J
SCENARIOI HEO I HE3••00R AV~ SC£NARIO••b/2111t~81
YEAR ANCHORAGE • COOK INLET ...... ······-··--·-·-~----··
1~80 s1Jo7.ota
o.oooJ
1985 9518.'18
o.ooo)
1990 10089.60
o.ooo)
1995 IOb011.9l
o.ooo)
i!OOO lll7i.ll4
o.ooo)
i!OOS 11850.11
o.oool
2010 ti!b715.U o.ooo)
-~
RUSINESS USE PER EHPLOVEE (KWH)
(WITHOUT LARGE INOUSTHIALl
(WITHOUT ADJUSTMENT fOR PRICE)
GREATEP FAtPBANKS
•*· .. ···-···~····-··---
Jtlq5.7D
o.ooo,
791J7.4l
o.oooJ
BZ119.111
0.1)00)
1\558.611
0.000)
l\6711.75
o.oou\
9227 0 q~
o.oooJ
qb28 .ll
o.oou)
_} J .J
) l
SCENAIHOI HEO I HE)•ooOOR AVG SCE~AR10••6/~ll/l96l
!UJHHARY OF PRICE EFFECTS AND PROt;RAHA TJC CONSERVATION
IN GWH
ANCHOR A liE • COOl< INLET
RU IIJEI'ITI AL IHJSINf!IS .............. .. ...... ~ ......
OWII•PP 1 CE PROGRAH .. JNDUCED CROSS•PRIC£ OI<IN•PP ICE PROGR H1•J NDUCf'D (:ROSS•PRJCF
YEAR REOUCTIQN CONSERVA Tl or~ REDUCTION REI'lliCTtON CONH~VATI~N REOUCTION ....... .................. .............................. .. .................... .. ................ .. ............................ .............. ._ ......
1980 0,1100 0,000 o,ooo o.ooo 0,000 o.ooo
1981 6,120 0,1)00 -0,175 9 ·"] o.ooo 1.002
1982 12,2110 o.oon .. (1,]50 lli.U7 0,000 ~.005
198] 18,160 0,000 -0,5211 27. )110 0,000 ],f)OT
14811 211,1180 0,000 -o,t.99 :\6,115) 0,(1011 tl,OOQ
t985 ]0,594 0,000 •0,1\711 115, "itlb 0,000 15, o II
I 98b lf>,711'i 0,000 •b,I9J 'i11,11139 1),000 1,581
1987 112,1190 0,1100 •11,512 bl,llll 0,000 2.1511
19118 119.035 O,OIJO •lf>.ll]l 72.]]11 0,000 o. uo
1989 S'i,II!O 11,000 •22,150 P.l,257 1),000 -o. 111
1990 61,125 0,000 •27,11&Q 911,179 o.ooo •!. till n
.p. 1991 b8,!I09 0,1100 -1'3,7911 99,7811 n.ooo -s.ooo
I--' 1992 7f>,292 o,ooo ·1111,119 109,189 0,000 •1,'158
199] 8J,77b 0,01)0 •52,111111 118.q9q o.ooo •10.117
19911 . 91 ,2bO o.ooo ·60,7b9 128.'599 o,ooo •ll,'H5
t<J95 98,7111 o,ooo ·69,0911 1]11,2011 0,000 oolf>0 11]11
199t> I 08,81H 0,1100 •74,0Sb 151,908 0,000 •19,7]0
I9Q] ttR,QSt 0,000 ·1!9,1117 te.'i.bll .. 0,000 •21.026
1998 129,055 0,001) -98.978 179,1111 o.ooo ·2f·. 122
1999 I)Q,t5'l 1),111)0 •IOA,~J9 19],017 0,0011 •29,blll
2000 IIIQ,2tll n.oo!l ·1113,901 ?Ob.120 0,000 •l2,QIIJ
2001 lbl,975 0,000 •llO,O'lb 221,1122 0,1!00 •lb,Bbb
2002 l1ll,b~1 O,ll')O •1111,111 1'16. 125 o.ooo •IIO,BIQ
200] t117,H8 0,000 •152,2&7 2511~~27 0,001) •1111,772
i!OOII 200, II II o.oon •lbl,l;!2 2e.-;,'529 0,000 •1111,7211
2005 21i',ll2?. o.ooo •1711,1127 2A0,2lt 0,0110 •';?,671
i!OOb ?.29,0211 o.ooo •189.201 2Q8,900 0,000 -s7,f'Q6
2007 2'15,22b 0,000 -21)1,QJ5 ]17.G;f;,Q 0,000 •bl.llb
2008 h1,1~2e 0,1100 -218,7119 ]31..'-111 0,000 •bll, HS
2009 U7 ,fill 0~1100 -~H,521 '\<;11.9011 0,0011 -71,5511
2010 2Q].A]] 11,00(1 •21113,2Qb 171,"77 o. ono ·711,7711
SCUUIUOI MEO I HEl•ooOOR AVG 8C£NAAIO••bl21111963
SIJHHARY OF PRICE EFFECTS AND PROGH4MATIC CON8ERVA TTO"!
IN GWH
GRB TF.R FAIREUNKS
~ESJOEIITIAL A.UUNf!IS .. ,. ........ ...............
OWN•PRICE PRUGRAH·INOLJCEO CROSS·PRICE IIWN~PAJCf PROGR AM• I NOIIC F D C:ROS!I•PRIC:£
'fEAR REDUC Tl ON CUNSFRV~TIOII REDUCTION REDUCTION C:ONH~V~HnN PEOUC:TJON
•••• .......... ............... .-. . ................ . ......... .................... . .............
1980 o.ooo (1.1)00 o.ooo o.ooo !l.ooo o.oon
19111 ~o.aoo o.ooo l.l)bl .o.11q1 o.ooo 0.12~
1982 •D.5U o.ooo 2 .12& .(1.~86 0,000 1.1157
1983 •0.7fl7 o,ooo 3.1152 -t.ll7q o.noo 2.181>
lq&q •t.Obl o.o()o 11.au .. 1.~72 o,ooo 2.qlll
1985 •1.129 o.ooo 5.3011 -~.1111'5 o.ooo 1.1>113
198f, •t.-soo o.ooo b,211ll -1.110'5 o.oon II, 15.11
1981 •I. Ht !).ooo ., • 18'5 ·1.111'5 o.ooo II.Uo
1988 •2.1122 o.oon 8,125 •1.1185 n,ooo 5,1711
1989 .. 2.253 o.ooo fi,06b .. ,,82ft 0,000 !;,~sqo
1990 •2.11811 o.ooo ao.oob -ll.lbh 11.000 b,~O?
0
+>-lq91 .. z,t.e5 o.ooo IO,IIbll .11,1115 0,000 bol8$
N 1992 -~.88b o.noo &0,1122 .1.1,7011 0,000 b 1 S&7
1993 ·3.0&7 o.ooo &1.380 .11.97;! o.ooo b.7SO
19911 •l.i!'Rq o.oof) 11,1138 -5.2111 0,0011 b.IIH
\995 •3.4'10 P.ooo lii! 0 iHib -5,510 0,000 '.115
199o .. 3,b38 n.ooo li!.llb .11.9711 0,00(1 ~.2115
1997 •3.787 o.noo ll.ql7 B~~.~~~~b o.ooo s. 375
\998 •],93& 0.0011 11.757 -1.qt'i 0,1)(1(1 11.505
1999 ·II .«'Ifill o.ooo 11,578 .. ],183 0,0011 !,6]5
2000 .. 11.211 o.oon II. 398 •2,851 o,oon 2. 7&5
2001 ·11.175 o.oon 10,8911 •l. B'5 o.ooo 1.050
2002 ·11.117 o.ollo 10.382 .,]0 1J19 o.oon 3,315
200] ·11.059 n.noo 9.1175 .. 11.30~ 0,000 ],(119
20011 •11,000 n.oo'l 9,161 .. 11.78& o.ooo J,9011
2005 .],9112 0. (100 11,859 .5.2711 0,000 11.1eq
200b •1.&23 0.11011 S,Obll .11.8111 0,0011 3,79q
2007 .. ],3(15 o. o IJ n 7,~6q -11.1111 o.ooo ]0 11011
2008 ·2.98(1 o.ooo b,II7J a].qll'-o.ooo 1,018
2009 •2.11b7 n.ooo s.&7A -3.'55:? o.ooo i!,b28
2010 •i,]IIR u.oon 11.8111 -l.12J 0,000 i!,i!]A
) J J J ' J l .. J J , I - J
J
) )
SCENARIOI 1-4EO I HEl••OOR AVG SCENARJO••b/~411198]
BREAKDOWN 0~ ELECTRICITY REQUIREMENTS fG"fH)
(TOfAL lllCLIIDES LARilE JtlllUSTRUL CONSUMPTION)
ANCHORAr.E • COOK I rJLE. T
··-·------~--~-----·--
MEDIUM RANGE (PR•.'i)
····-----·-··-------
RESIOENTJ•L BIISitlESS HI!ICELLANEOUS EliOG. JNDIISTRIAL
YEAR REQIJJREtiENTS REQUIREMENTS REDUIREMENTS LOAn TOTAl
·-------------·--· --------·---·~----~------···----------···-~----------------·------------
1980 979.53 8'75.3b 211.31 811.110 19,3.1'1
1981 1017.711 940.84 24.5b 92.08 l'll75.21
1982 I05'5.CJ"!i IOOb.Jl 24.112 IOO.Ib 2187.21>
1983 10911.17 1071.81 25.08 1011.24 2299.30
1984 1132.)R 11]'7.29 25.311 llb.32 2411l.H
1985 1170.59 ll!02.78 25.bO 124.410 <'SH.H
1986 ll92.97 1232.72 2b.l5 111.89 2589. n
1987 tal5.]41 12U.b5 i!b.71 151.38 h!ib.09
1988 1211. n 1292.59 27.27 lbll.88 2U2. 415
1989 ti!bo.oll 1322.'53 27.83 t1ll.l7 27118.81
n 19110 l2Ai!.ll7 1152.416 211.11' I 9 I • 86 PA'55.17 .
.p.
w 1991 1)02.97 I 179.'57 28.89 19'5.13 290b.55
1992 I Ul.47 I110b.68 21t. ]Q 198.110 2957.93
1993 1]413.q7 11131.'711 29.89 i!OI.bb 30il9.31
19911 lJ64.417 l11b0.8Q ]0.110 2041.H ~Obll.6q
1995 111411.911 14187.99 30.90 208.20 1112.07
l99b 1408.59 1!518.20 31.411 214.14 ]I 72 .Ill
1997 lqJa.21 151111.112 12.05 220.(18 3212.76
1998 14~5.1'12 1~78.b) 32.bJ 226.02 3293,10
1999 11119.4141 lbOII.AII H.20 l'1l.llb 13'53. 4Ci
2000 1511].01, 16H,nb Jl. 711 237.90 34113.7q
2001 I'B2.t7 U81.35 341.511 21141,9b 3l!CJ5,112
2002 1Sbl.29 17iJ.bq 35.31) 252.02 157&.211
200] 15~0.4111 1771.93 lb.Ob 25CJ.08 1(157.117
20011 lbi9,Si' l8lb.22 ]b. 83 2bt.. t4 "]7]8, 70
2005 l&llij,61 l8b0.5l 37.59 ?13.20 ~IIICJ,91
200b lb8b.CJO 192'1.15 ]8.b8 i'81.56 ~911,30
i!007 1125.1'7 1987.79 39,17 ?M.qb 410112.611
i!QOB 17&1.11] 2osl.lll 110,86 ji9A,]4 111511,06
2009 1801.711 i!ll5.0b 111.95 ~06.72 11265.413
2010 l839.•H 217~.70 lt],OQ :H5,10 Q]76,81
n
.j:::o.
~
~~ ,
SCENARlOI MED 1 HEl••OOR •vG &CENARJO••b/21111983
····-·---~-·-··-··--
RESIDENTIAL.
YEAR Rf!11JIREMFNT&
BREA~DOWN OF ELECTRICITY REDUIR!MENTS (GWH)
(TOTAL INCLUDES L•RGE INDUSTRIAL CONSUMPTION)
GREATER FAlRB.NK&
···-·······---··-~·-·-
RUSJNESS MISCELLANEOUS
REQUIREMENTS REQUIREMENTS
······-····---···· ••••a••••a•••••••• ······-·----------
1980 !7&.39 217,111 6,78
1981 190.01 i!j!R, 93 b, 7il
1982 203,bi' iflf(l,71 b,70
198] 217.211 25l,SO b,bb
19811 i!10,8'S 2bll,2'1 b,62
t985 2111.1.117 2711,011 6,58
19116 2511.11 28t,U 6,'56
1987 2U,80 287.27 6,53
1988 2H.II7 292,8o 6,51
1989 2113.111 298,115 6,119
1990 292,80 ]Oil,04 b,4fa
1991 l0l.i!7 uo.u b,bll
1992 313.74 JU.U b,81
1991 ]11.1,21 J22.fal b,99
I 9 91.1 ]]4,b8 1211,110 7. 17
1995 ]115.15 1]15,00 7,]0
199b 351.53 ]Ill • 71\ 7.50
1997 lbl • .., t 11.111.so 7.6fa
1998 ]70,j!0 JS'S,B 7.82
,1999 ]11j. b7 lbi!. tl 7,97
i!OOO ]117.05 Jo8,1!9 6.13
i!OOl 39&.1111 377.71 BolO
2002 1105,92 18o,52 8,47
2003 1115. !5 395,311 8.611
20011 11~11. 71' 11011.15 11,61
2005 lllll.i'l 1112,97 8.98
i!OOo 11115.52 11211,75 9,24
2007 115b.BJ 11]6,5\ 9,51
i!008 1166.1] 1148,31 9,7'7
2009 1179.1111 111:.0,08 10,01
2010 11~0.711 £171,8b 10,]0
] ) • l J ' J )
El'OG, JNDUSTRJAL
LOAD TOTAl
-----~----········ •••••~•••••••••••w
n,no 1100,]1
0,11(1 1125,68
o,oo 1151,0/J
(1,00 il7b,IIO
'1,00 501,.,.,
o.oo 527 .u
10,00 552.11
20,00 571.&0
111,00 &02,811
40,00 &28,01
'50,00 6c;3,]0
50,110 b70,14
50,00 686,98
50,00 703,82
50,00 no ,6t;
50,00 737.49
so.oo 752.81
so.oo 768.12
so.oo 783,114
50, OQ 798.7&
50,00 814.07
so.oo 832,1.19
so.oo 8'10,91
50,00 8~9.33
50,00 8&f. 75
'50.110 906,U
50,00 929 • S I
so.oo 9'U,U
so.oo 970.21
so.oo 999,56
so,oo 1022,90
I .. ~ ~ .. J .J }
SCENARIO! MED I HE3••0UP JVn 9CENARIO••bl2111l98J
YEAR
1980
1981
1982
1983
19811
1985
198b
1987
1'188
1989
1990
1991
1992
1991
19911
1995
199b
1997
1998
1999
2000
2001
2002
2001
20011
2005
200b
2007
2008
i!OO'l
2010
ANCHORAGE • COOK
TOTAL ELECTRICITY REQUJREHENTS (GWH)
(NET OF CONSERVATION)
fJNCLUOEB LAPG£ IHOUSTRJAL CONSUMPTION)
~IEOIIJM RANGE CPR • .5)
INLET GPEATER fAJAKANKS ToTAL
······--·--·····p·----··p··-----------·~---· ---·---·-··-~·-----·-·
l'lfJJ.I9 IIOO.JI .?]1.3.51
l075.ll IIZ5.b8 25110~91)
2U7 .21:1 IJSI.OII i!b]R .• 30
;?299 .30 117&.110 i'715~ 70
ZIIII.Jl '501.17 2•Hl.IO
25i!3.37 527.13 '\or;n·.so
i'589.7J '552.37 31112~10
2fl'5b.09 !177.t.O J2H.b9
2722.11!; 602.84 312'5~29
271111.81 bi!B.07 )lllb~8R
2855.17 b'il.lO ]508~118
290t..S5 h70.111 1.,7b-.b9
2957.91 t.8b.98 1bllll,91
3009.31 701.82 J71l.ll
]ObO.b'l 721l.b5 17111~311
)I 12.07 737.119 1RII9~5b
1172.111 752.81 H25~i!i!
HH.7b 7bll.li! IIOOO.AB
3293.10 781.1111 II07b~51J
]]'5].1111 798.7b 111!12.20
31113.79 Alll.n7 11227~86
3119'5.02 1132.119 4327.51
15U.i!ll 850.91 111127.15
]b57.'17 llfi9.U uo;~t..an
Hl8. 70 887.75 4b'.6-.llll
3619.93 90b.lb 1172b.09
3'111.30 q;!9.51 111\bO.III
110'12. bll 9S2.8b 11<195.511
111511.0& <~7b.2l 'H30.2b
1121>5.111 Q9<l.Sb 5?611.9<1
IIJ7t..81 J022.90 ';]'~9-.71
.'11. i l •
• ,)
SCENARIO! ~EO I HEl••OOR AVG SCEHARI0••&/241198)
YEAR ANCHORAGE • COOl< 114LET
·-··-·-----·-··-··-···
1980 J9t..5l
1981 419. u
1982 4111.75
1983 lltt4.37
19811 ll&b.99
1985 51)9.bl
198b 52:5.80
1987 537.99
1988 552.11
1989 5&&.36
l990 5A0.511
1991 590.9o
1'~92 &0 I • 37
199) &11.19
l991j &22.20
1995 &32.&Z
199& o411.711
1997 &5&.87
1998 bb8.99
1999 ~~~ 1.1 t
2000 b9).211
2001 '709.&1
2002 725.98
i!003 7112.35
20011 7'511. 73
2005 175.10
ZOO& 191. bO
2007 8i!0.09
2008 81J2.59
2009 111>5.09
2nto 887.59
J J • J
PEt.K ELECTRIC REQ!JJREHENTS CHW)
CNET UF CONSERVATION)
CINCLIJOES LAili'H~ INDUSTRIAL DEHANO)
HED l1JI1 RANGE (PR a .s,
··------------········
GREATFR ~AIRBANKS TOTAL
--·····--·--··--·---·· ---·-····--------·----
91.1.10 1187~90
'H. I'J 5U~J2
102.98 5114,11
108.77 '571~111
1111.56 b0l.51J
120. )5 &29 .• 9?
12&.11 649~91
131.87 &69.85
tH.bi! 689~80
tii1.J8 709.711
1119.111 729~1>8
152.98 7113~911
IS&. 8J ?58,20
l&O.U 772~4b
11111.52 7116.72
I bll. 36 800~98
171.8b 81&~60
l75.1b 1112.21
I 78 • 85 811'7~811
182.15 86l.llb
185.85 an·.oe
190.05 899~66
l911.2b 920.211
198.11& 9110.81
aoa.u 9bl.39
i!Ob.87 981 ~ 91
212.20 10119~80
217.53 1031,&1
Z22.8b 10&5.4'5
::!28.19 I0 1U~2~
2:U.'5i! lt21 .• ll
j J .. J J .J
-
r
-
,.....
I
HE9--DOR SO%
C.47
)
SCENARIO I HED I t4E9•-000R SOX•-etllll/1'111
HOUSEHOLDS SERVED
ANCIHJRAGE • COOl< INLET
---·····--·---~·--~-·-
YEAR SINGLE IF AHILY HULTIFUtiLY HORILE HOHES OUPLEXU TOTAL .... ··-··----·-·· ········-·--· ............... -·------·--·· ..............
1980 )5471, i!OJIII, 8i30, 11Hl6, 71503,
o.ooo) 0,000) 0,000) 0,000) O.OOO)
1985 115b65, i!bi!OII, 10659, 1:15&7, ql]l'5,
o.ooo) 0,000) 0.001)) 0,000) 0.000)
n l990 550]13, 25817. llbb!, 811&0, 102036.
+:> o.noo) o.ooo) o.ooo) 0,000) o.ooo)
1.0
lH5 59QIJ7, lh8'JO, I J78q, 111H, to8Qsq.
o.ooo) o.ooo) o.ooo) o.ooo) o.OOO)
lOOO &II'Ul. ttnss. tllqlo, 8187, ll1l63,
o.OOO) O.OOII) o.ooo) 0,000) 0~000)
i!005 69574. JHbl, lU95, IIOC!II, 127255.
o.OOO) o.OOO) o.ooo) 0,000) o.oon)
i!OIO 7&160. HOI?., 1807i!. 8845, 14028A 0
n.ooo) o.OOO) o.ooo) 0,000) o.noo)
SCEN.RIOI t-IE.O I HE9••DOOR 50X••61241198J
HOUSEiiOLOS SERVED
GREATER F•IRRANKS
·················-····
YEAR SltlGLE FAHILV MULTIFAMILY MOBILE HOMES DUPLEXES TOT•L ..... ............... • •••••••••••• . ............. . .............. . ..............
1980 Ti!C!O. 5287. 11~9. tbP. 151l1.
o.oon) 0.000) o.ooo) o.OOO) o.ooo)
1985 !Ob4b 1 SUfi. 2130. 1721. ;!01815. n o.ooo) o.oooJ o.ooo) o.OOQ) O.OOO)
U1
0 1990 11)725. 7CJII0 1 2101. 2175. .2llbl·.
o.ooo) o.ooo) o.ooo) o.ooo) o.oon)
1995 12980. 78111. 2571. 2l3CJ. 25'7H.
o.oon) o.oon) o.ooo) o.ooo) o.oon)
2000 1'1124. 7103. 1194. 2298. 2!~20.
o.ooo) o.ooo) n.OOO) O.OOO) o.ooo)
2005 t620b. 7549. :nos. 2252. iCJ8l5.
O.OOO) ( o.ooo) o.ooo) o.OOO) o.ooo1
2010 t7773. 8661. 112i!l. 2109, ]2'781.1.
o.ooo) o.ooo) o.ooo) o.OOO) o.noo)
) -) -J
l J )
BCEN~RIOI MEO I HECJ••OliOR 'SOX••t./lll/l9Bl
H£11191 NQ VACANCIES
ANCHORAGE •·COOl< INLET
-----·----~-·-~·-····-
YEAR SJNGLf f~HllY HUL Tlf A"'ILY H081LE HOMES OIJPL[l(£8 TOTAL --·---·---···----............... ---··---··--· ............... ...............
19fiO 5089, 1bl>b. t<Jql. I lib], 16209. o.ooo) o.ooo) o.ooo) o.OOO) o.ooo)
1985 501. lliqb, 120. 2Cfi!, i!IHO.
n o.noo) o.oon) n.ooo) o.ooo) o·.ooo)
Ul ,___. uqo b05. lll 71. 119. i!89. 2'll 0. o.noo) 0,000) o.OOO) 0,000) o.oon)
\995 tt59. su. 152, 2811, IIIIQ,
o.ooo) o.ooo) o.ooo) o.ooo) 0,000)
zooo 101. uo1. 1611. 279, 275~. n.oOI)) ( o.ooo) o.ooo) 0,000) 0,000)-
i!OO'!i 1b'l. 1802, lH. 2711. ]020.
o.ooo) o.ooo) 0,000) o.ooo) 0,000)
lDI 0 Bill). 1999, 1<19, 292, H29.
0,000) o.ooo) 0,000) 0,000) 0.001'1)
SCENARIO I MED I HE9••DOOR SOX--6/241198!
HOUSING VACANCIES
GREATER FAIRBANKS
--~··-·····---~·-··-··
Y(AR SINGLE FAMJL Y HULTIFAHILY H081LE HOH£8 OUPLEICES TOTAL
•••• .............. ···········-· ···~········· ·-·-········· ..................
1980 31b5l. nao. 986. ns. 88SII.
o.noo) o.noo) o.ooo) o.ooo) 0.1'100)
1985 ll8. 2RH. 211. 7btJ. ,74,.
o.ooo) o.ooo) fl.OOO) o.ooo) o.oon)
n 1990 tlA. 11511. n. 81, . 677 • .
U1 ( n.oon) o.noo) o.ooo) o.ooo) o.oooJ
N
1995 143. 1.1411. 28. eo. bqq.
O,OOQ) 0.1100) o.ooo) o.ooo) n.OOO)
zooo 158. IIllO. l!. 78, . '7lt. o.OOO) o.ooo) o.ooo) o.ooo) o.OOO)
2005 178. 1.1]1. 42. 77. . 728.
o.ooo) o.noo) n,.nooJ o.ooo) o.ooo)
2010 19&. 11()9. 116. lb7. . 878.
o.ooo) o.ooo) o.ooo) 0,000) o.noo}
) J .J
YE:AR
-~--
1980
CJ 1985
m w 1qqo
1995
2000
2005
2010
FUEL PRICE FORECASTS EMPLOYED
ELECTRICITY ($ I ~WH)
ANC~ORAOE • COOK INLET GREATER fAIRBANKS
································~--·· ~---··-·-···--·------······-------·--
RE81DENT IAL BUSINESS RESIDENTIAl RIIS INFSS
·--------·· ···~W••••••• ............ -~---·-----
o.o.n o.oH o.o95 n.oq11
0 0 0118 0.0115 o.oCJ5 n.nqf)
o.niiCJ 00 0116 o.oCJo o.oalj
o.osn 0.1111? o.o~Jo 0.085
o.n51 o.o11e o.o9o n.os'5
o.ost o.o11e 0,090 0.085
11.1151 n.oll& o,oqo n.oa1111
YEAR .....
1980
0 19BS .
Ul
~ lfi90
tfl95
2000
2005
2010
.J
ANCHORAGE • COOK INLET
FUEL PRtC! FORECASTS EMPLOYED
NATURAL OAS CS/MMBTU)
GREATER FAIRBANKS
·······-····························· ········-·······--·-·······-·~·-·····
RESIOENTUL BUSINESS RESIDENTIAL BUSINESS ............ ............. .. ............ ··········-
I. 730 t.soo 12.740 l t .no
2.oon '· 770 lO.UO c,.uo
i!.fJlO 1.1100 '·090 '7.6110
2.1\to 2.~60 e.uo fl.610
2.710 2.1181) ?.flU 6.210
2.un 1.uoo 1.210 5.820
1.115&0 2. no e..890 5.1140
J'
' \
-·--" }
YEAR ·-·-
1980
IIU'i
n 1990
U"1
U"1 1995
2000
2005
2010
ANCHORAGE • COOK INLET
FUEL PRICE FORECASTS EMPLOYED
'U£L OIL (./MMBTUJ
GREATER FAIRRANKS
··········~·-·······-········~--····· ········-·-------···-········--------
RESJOENTJAL BUSINESS RESIOENTUL BUSINESS ............ ···-··--··· ---····--·-··--·····-·
1,75(1 7.200 T.no 7.5011
fl.lllll) 5.Qtt(l b,550 b.220
s.5lo il.l'i80 l§.sc:,o 5.2b0
... c:,so 11.1100 II•C:,C:,Q u.e.e,o
ll.bbO II. ItO 11,710 11.)80
11,0)0 1.880 o.11e.o ~.~.no
11,200 :..e.so 11,2110 l,qto
l
SCENARIO I 1-4ED I tif9--DOOR 50"••6121111 Ul
RESIDENTIAL USE PER HOUUHOLO (KWH)
(WITHOUT AflJUSTMENT ,OR PRJCEl
ANCHORAGE • COO~ INlET
········~·······--~···
SMALL LARGE. SPACE
VEAR APPLIANCES APPLIANCES HEAT TOTAL ·-·-.......•.. . .......... ·····-···· ............
t98o 2110.00 uoo·.u 5088.52 11b99.15
o.OOO) ( o;ooO) ( o.OOO) 0.000)
1985 atu.oo 6154.flll 4 .. 31.62 131116.27
o.ooo) 0 ··0 0 0) n.ooo) 0.000)
n . 1990 i!Z1o.oo 6026.77 4b27.fl2 128,11.60
U1
O'l 0.000) Oo'OOO) f o.ooo) ( o.ooo,
1995 22b0 1 00 5998~47 4509. ]9 12727.87
o.ooo) 0.000) o.OOOJ ( 0.000)
2000 a:u.,.oo '5C)88.lS 44]6.117 12731.1,111
0.0011) O.UOO) o.ooo) 0.000)
2005 i!}bO.OO 6060.911 41121.117 128~2.40
o.OOtl) 0.000) o.ooo) o.OOO)
2010 21110.00 6l27 .57 111139,13 &2976. 70
o.ooo) o.·noo) o.ooo) o.OOO)
) J _J J
) ,• 1
SCEN4R!OI MED I tiE 9••DOOR 50;(••6/lll/l98J
RESIDENTIAL USE PER HOUSEHOLD (KWH)
(WITHOUT AOJUSTHENT FOR PRICE)
GREATER FAIRBAN~B
~---··--··············
St.ULL LARGE SPACE
YEAR APPLIANCES APPLlAI~CES HEAT TOTAL -··-.......... . ......... ····~--··· ..............
1980 21lbb.oo .57H.Si' H13 0 6b 11519,18
n.oooJ 0.000) o.noo) 0.000)
1985 2 s H. 'n t~lat'.a6 35qll.lll 12311.110 n l n.oooJ 0.000) l o.oOo) o.OOOJ
Ul
-....! 191JO 2606.01 MH.31 38110.88 12886,20
o.ooo) o.ooo1 o.ooo) O.OOOJ
1995 2676,01 bfl51,89 11081,97 131109,87
l o.ooo) 0,000) 0,000) O.OOOJ
2000 27116,01 67')0.8CJ 11325,95 138bi!,85
o.ooo) Oo'OOOJ o.ouo) 0,000)
2005 2816.00 68SB.J2 11497,119 111l1l.81
o.ooo) o.oooJ o.OOO) o.oooJ
2010 2885,9CJ 1!895.94 4656.78 llllll8, 72
o.oonJ O.OOQJ o.noo) 0.000)
n
Ul co
-J
SCENARIOI HED 1 HE9••DOOR SOX••6/ZI.I/lq83
ANCHORAGE • COOK INLET .... ·-··--·~··············
1980 8tluJ~o4
o.ooo)
t98S 95lfl.9b o.ooo)
I0059.b4
o.nooJ
t0482.b0 o.ooo)
2000 11021.1.92
f o.ooo)
2005 llbliO.U o.ooo)
2010 12483.97
11.000)
J J
BUSINESS USE PER EMPLOYEE (KWH)
(WITHOUT LARGE INDUSTRIAL)
(WITHOUT AOJUST~ENT fOR PRICEl
GREATER FAIRBANKS
············~··-······
7'195.70
o.ooo1
791.17.93
0.000)
8l37 .2t
0.000)
asts.os o.ooo)
8822.88
0.000)
9lb9.82
o.ooo)
q5btl.ti'P
o.ooo1
J J J
' )
SCENARIO I H£0 I HEII••!lOOR 50X••b/?ll II 98 3
SIJMHARV OF PRICE EFHCTS AND PROGRAM•JIC CONSERVATJON
IN GWH
UICHORAGE • COOl< INLET
RESIOEtHJAL RUSJNEU .............. . ............. .,.
QIIN•PRlCE PROGIUH•l NDUCED CROSS•PRICE OW~I .. PR ICE PRMRAti•INOUCED CROSS•PRtr.E
VEAR REOIICTIO"' CONSERVATION REDUCTION PEnuCTJON • ~IJNSERVAUQ~ .. REOUCTION ........ ............. ............................... .. ................. .. ............... .. ........................ .. ..................
1980 11,000 o,ooo 0,000 11.ooo 0,000 o,ooo
11181 b,\11'5 0,000 •0,9bq fl,l ]9 0,00!) {1. 126
lfl82 12.2110 0,000 •1,928 !8,271 Q,ooo O,bH
198] 18,11JS o.ooo •2 .• 892 27,1116 0,000 0 .fiH
lfl811 211,'5811 o,ooo .],1!56 36,5511 o.ooo !,lOb
19~5 Jll, 725 o,oon •11,820 ~~~.6fl1 o.ooo 1.632
I flAb ]'5,681 o,oon -8.771 511,1180 o.ooo 1,288
lfl87 1111,6111 0,000 ... 2.721 Sfl. 2bb 0,000 O,fllll
lfl88 115.5119 0,000 •16,672 66,051 o,ooo 0,59fl
lfl89 50,557 0,000 •i!0,6i!] 72.flll(l o,oon o.2ss
19110 5'5,515 0,000 •i!II,S7J 7fl,fl27 n,ooo •0,090
n
U1 lflfll 59.111S 0,000 •i!J,IIIO Al!,flbO o.ooo o.221
1.0 1992 b1,3111 o.ooo •)O,i!llb fi0,2flll o,ooo o.SJ6
19fl] tJ7,211 0,000 -u. on 9'5,627 0,000 O,AII9
19911 11.111 0,1100 .)5,919 totl,9bl (1,000 1.162
11195 7S,IIIi! 0,0011 •J8,l'55 106,2911 ti,OOO 1,1175
1911b 18,11112 0,000 ·]9,'5119 112,1188 o.ooo <?.5711
lflfl7 61,811 0,000 ·110,]11] 117.883 0,000 ],683
19fl8 1\5,100 0,000 ·III,IH 121,677 0,001) 11,78b
lllflfl 88,729 0,000 .. 111,931 129,1171 0,000 !5,890
i!OOO 92,15'1 0,000 ·lli!,Y25 l]ll,i!b5 0,000 6,flfl]
i!OOI 9~,081 0,000 •lli!,SIIO 1110,0 85 0,000 1!,6b]
i!OOi! 96,11011 0,1100 ·112,355 1116,705 0,000 I O,Hi!
i!OOl IOO,fli!7 o.ooo •42,170 152.1126 0,000 ll,002
i!OOII 103,650 0,000 •111,9(15 1511,1116 0,1100 U.HI
i!005 106,7711 0,(100 ·111,800 lb],llbf> n.ooo 15.1111
i!OOb IOfl,7oll o.ooo ~111,1108 170,710 0,000 17.7b7
i!007 112.7'55 0,000 .uo.?t5 177,6711 o,ooo 20. I 1111
i!008 115,7116 o.ooo .. ]fl,ll21 Hlll.~71'1 o,ooo 22.621
i!009 118,7]7 0,000 .. ]8,6]1 191 ,IIIli! o.oon i!'i,OII!I
2010 121,7211 o.oon -37,638 19R,l8b 0,000 iP ,117LI
SCI!:IHRIOI HED I HE9•·DDOR 'iOX•-t.li!llll 963
SUf1HARY OF PRICE EfFECTS AND PROGRAHATIC CONSERVATJUN
IN GWH
GREATER fAlREUII!<S
RfSIOEtiTJAL f'USINESS .............. .. ...........
QINN•PPICE PROGR~M·INDUCED CAOSS•PRICE OWN.PRICF. PROGRAM• INDUCED CAOSS•PRJCE
YEAR REDUC fiON CONSER\/ A TI ON REDUCTION ~-EI'IUC TIIJN CONSER\/ AT ION ~EDUCTION . -~ .................... .: ...... ........ .................. .............................. .. .................... .. ................ .. ....................
1980 o.ono o.ooo o,oon o.non o.ooo n,ooo
198& o.ooo o.ooo 0,72b o.ooo o.ooo 0.118(1
1982 o.ooo o.ooo 1,1152 o.ooo tl.ooo 0.97!i
l98J n.ooo o.oon 2.178 o.ooo o.ooo l.llbJ
1984 o.ooo o.ooo 2,9011 n.ooo 0,000 1,950
1985 o.ooo 0,000 :s.uo o.ooo n.noo 2.11111
198& ·0.3l9 o.ooo 5,0l9 .o.!iU 0,000 3,250
198'7 •O.b]B o,oon 6.1127 -t.Obll o.ooo II,OU
1988 •0.457 o.ooo 7,82b .. t.'59b o.ooo 11,873
1989 •l.na o.ooo 9.225 .. 2.129 o.ooo 1§0 &85
0 1990 •1.595 0,1)011 to.b211 ·2.bbl o.ooo &.1197 .
()) 1991 •1.8116 o.ooo u.ns •Z.998 o.ooo 7,395
0 1992 ·2.097 0,000 lii,Ul •!. 335 0,000 e.n2
1993 •2.31l8 o.oon l5 1 R78 ·1.Ul o.ooo 9.189
19911 ·2.599 o.ooo l7.t.30 .11.008 o.ooo 10,087
1995 ·2.1150 o.ooo l9.J81 .11.145 o,ooo l0,9811
· I 99& ·1.1131 o.ooo 20.99& .11.5811. o.ooo 11.1139
1997 -3.2ll o.ooo 22,U l .. 11.8)2 o.ooo u.a95
1998 ·3.39? 0,000 211.2;u -s.o75 o.ooo lJ. 551
1999 ·1.'572 o.noo 25.8110 .s. 318 0,000 111.401
2000 ·1.751 o.ooo 27.455 .s.'UI o.ooo l '5 • 2U
2001 ·1.905 o.ooo 29.12& ·5.779 o.ooo u,aqe
2002 •11.058 n.ooo lll.79? .. 5.997 0.1100 11.1311
2003 ·11.211 o.ooo li'.IIU .. ~t.2lb o.ooo !11,070
2004 •11.3113 n.ooo 311.139 -t..ll311 o.ooo 19.00.
2005 •11.51& o.ooP 35.810 -ll.aSii! o,ooo 19.9/li
20011 ·ll.e.bll o.ooo 37.725 -&.1192 0.(100 21.091
2007 -4.1120 o.ooo H,blll -7 .I H 0,00(1 u.n9
2008 •11,973 o.noo 111 1 5'511 -7.37.? o.ooq 23.3811
2009 ·5.125 0,000 111.1172 .. 7.1>12 o.ooo 211.536
2010 •5.277 n.noo 4S.3tl8 .7.R52 n.ooo 25,685
) ) J J J ] J J ~• J J ·" -~ I
l I
SCEtUR I 01 I-lEO I HE'I•-OOOR 50¥•-~/iiiii'IRl
6~0KOUWN OF ELfCTRtCTTV REQUJREMfNTS (GWH)
(TOTAL INCLUDES URGE INDIJSTRJAL CONSUMPTION)
ANCHUR~GE • COOK TNLET
-··-···-------~-----··
MEDIUM R&NGF. (Ph,;)
••••••••••••--•••••w
RESII!ENTI ll BUSINESS MISCELLANEOUS EM on. PIDUSTR1U
VEAR REQUIREMENTS RF.UlllREME"'TS RECIUIREMENT8 LOAD TOTAL
·--~----··-----··· ---------·--·~-~--···-------·---·---·····-----·--·-··· ---·-··---··--·-·-
1980 979,53 !175.:.& 211.11 811,00 19b],lq
19111 1018.53 •uu.t.o 241.58 92,08 207b,7fl
lfiA2 I057,SLI 1(107.8] 211.85 IOOalb 2190,H
lfl8l IO'IIl,511 10741.07 25.1~ 1011,241 230],98
1981! IIJ5.5LI IIIIO.'Jl ;!5.40 llb,J2 ~1117.51
I'll'S 11111.5'1 li!Ob.'i5 25.118 t2LI.IIO i!'.i:\(,17
lf/811 11'15.99 li! ]4. '§q 211 • .!0 !l1,89 i!59LI.II7
1987 1217.111 Ut.l.llll 211.73 151 • 38 2658,1b
1988 lll'!.llll U9o.t.8 27.2b 11111.118 2721.&&
1989 li&0.28 1Jifl.1l 27.7'1 178,]7 iDIIS,Ib
("")
1990 1281.71 IJ11b,77 28.]1 191 0 8& 211118.&5
Ol
I--' 1991 1295.118 llb'5,61 28.58 I 9'5. ll 28811.79
199(! tJO'I. 25 118LI.IIIl i!6,8LI 198,LIO ~920,91
199] 1Hl,02 11101.211 29, I o 20I.bb i!9'57,0b
19911 I ]]~~79 11122.11 jl9 .3b i!OLI,9] 2993.20
1995 1]';0.56 llllln.IIS lll.oi! 208.20 3029. H
199(1 llb8,91 11171.17 JO.i!l 2111.111 10811,50
1997 13117,]? \'501.39 ](),8] 220,08 11J9.oll
1998 tqo5.7A Uli.U Jl 0 41 J i!2b.02 111111,8'5
19911 111211.19 15ol.811 12.011 2ll,9b 1250,0?.
2000 111112,511 1'5?2,0!1 J2,bLI 231.90 ]305,111
2001 lllb7,113 lb3S,87 Jl.37 21111,96 HAi!,ILI
2002 lllfl3.27 lb79,b9 ]11.10 252,02 1459,08
2001 ISIA.bl 1121.50 311,1111 2511.08 ]5]11;0]
20011 1511],95 17o7.l2 ]5. 57 i!b6.1LI ]012.111
2005 1'5oii,ZII 1811.1) lb.]O 27J.20 3bfl'l,112
i!OOb 1on2,75 111711.00 37.12 281.'58 37115,611
2007 lt>3b,i!l lfl3b.Bb JB.H 2811,116 Jqlll.lb
2008 lbb9,f.o1 1999. 7l JQ,]II i!'lfl.]ll 11007,08
20119 1701,1] 20&2.5'1 LIO.lb l0b,72 Ill I i'. 8 0
i!OIO I7Jb,511 i!lc?'S,UI) Lll. 31 115,10 11211\,5()
n
0"1
N
~ Jl •
SCENARIOI MED I HE9··000R 50X••bi241196J
MEDIUM RANGE (PR•.SJ
--·-----···-········
RESJDEtlTJAL
YEAR REQUIREMENTS
BREAKDOWN 0~ !LECTRICITY REQUIREMENTS (GWH)
(TOTAL INCLUDES LARG~ JI~OUSTRIAL CONSUMPTION)
GREATFR FA1R8ANKS ---------·············
81JSINfSS MISCELLANEOUS
REQUlRE.'4ENT!I REQUIREMENTS
·····-~··········· ···········-·---~-···-···--··-······
1980 17t..l9 ll7.111 b.18
19111 190.09 Ull.70 o.74
1982 203.79 240.2{1 t..7o
U83 217.1111 251.82 t..bb
19811 231.111 2bJ.18 6.61
1985 i:!1111.87 2H.9S b.57
19Bb 2'51.79 279.17 t..SJ
1987 i!t.2.70 28li 0 1J9 o.49
19118 21l.U 289.111 b 0 4b
1989 280.511 2911.U 6.42
1990 2119.4115 299.05 t..le
1991 ~fl7 0 i1 102.90 t..so
199i! ]05.09 30t..75 t..oJ
1993 312.91 llO.t.ll 6.75
1994 H0.71 :UII.IIS 11.88
1995 31?8.511 Jlll.10 7.00
199t.J )14.40 12J.55 7~ l2
1997 ]110.25 328.1!1) 1.H
1998 )llb.IO JJII.OS 7.35
1999 lSI.fiS 'Ufi.JO 7.46
2000 l'H .eo 1411.55 7.58
2001 ]011.119 3SI.Ab 7.71
2002 311.111 559.17 7.67
200) 377.Hb \bb.lll! 8.02
20011 ]AII.SCj Hl.H 8.17
2005 ]fll.i!ll 381.10 a.lt
i!OOt.J ]CI9.b5 191. :u 8.52
2007 IIOfl.oS IIOI.S2 8.72
2008 lltb.llb 1.11 I • 7 II A.<Ji'
2009 4211.81 1121.Q5 9.12
2010 4B.i'l! IIP.Ib 9.H
) ) l ) ) .. i )
EliOG. INDUSTRIAL
LOAD TOTAL
--·---------·-···~ -·~·-·---·~·-----~
o.oo IIOO.]t
o.oo 1125.51
o.oo 4,0.711
o.oo 475,9t.
o.oo !IOl.; U
o.oo su:1~t
lO.OO 550.09
20.00 sn. 79
30.00 1)97.119
110.00 t.2t.t8
so.oo 61111·. 88
so.oo b5b.U
so·. oo bb8.47
so.oo 6@0.26
so.oo t.92.0t.
so.oo 703.85
50,00 715~U
so.oo 720,211
so.oo 711 .so
so.oo 7118.7t
so.oo 7S9.U
so.no 1111.01
sn.oo 788.22
so.oo 802.37
so.oo au.st
so.no 830.bt.
so.on 8119.118
so.oo 8b8.JO
sn.oo 887.12
so.no 905.911
so.oo 9211.7t.
J } ) } J
n
(J)
w
-~ l
YEAR
1980
1981
1982
11183
19811
1985
ICJSb
1987
ICJtl8
ICJ89
11190
1991
1992
1993
ICJ911
1995
l99b
1997
1998
1999
i!OOO
2001
2002
200]
20011
2005
i!OOb
2007
2008
i!OO<I
i!OIO
ANCHOA~GE • COOK
TOTAL ELECTRICITY REQUIREMENTS (GWH)
f~ET OF CONSERVATION)
(INCLUDES LARGE INDUSTRIAL CONSUMPliONJ
rtEDJUM RANGE CPA • 0 5) ·········--·-·-------·
INLET GREATER FAIASANKS
l
TOTAL ··---·-····-·········· --------------·---··---·--------·------~---·
I'H•3.19 1100.11 i_13b3~51
211H. n 1125.'53 ~51lif.32
i'I90.l8 1150. Til 211111~11
230].98 1175.9fl 2779~9/J
.?1117.57 501.18 2918~75
1531.17 Si!fl.39 1os1:s•
i!5911,b7 sso.o9 ]IIJII~Jfl
i!b'5B.lt> sn.n ~211,95
Ui!l.t>b 597.119 nu, 1s
1.785. u fl21.18 HOllo 311
i!BIIB.b'!i fiiiii.BB 31191.511
18811.79 fl5fl.fl8 ]5111~117
11120.93 bb4.117 3589,39
2957.01) b80.2b H37, 32
299].20 b9i!.Ob Jbl\5. 25
3029. 3l 703.115 n:n: u
30811.50 715. Ob 3799~57
Jll9.b8 7lb.28 l8b5.9b
31911.115 7 H. so 3932, ]II
3250.02 711111.71 3998.73
B05.19 759.93 IIOb5~ 12
HRZ. I 1.1 1711.07 Ill %,21
)1159. 08 788.22 112117,30
JSJb.Ol 802.31 11]]8, 39
Jt.t2.97 8H.51 11112'J.I.III
3t>A<J.92 lllO.bb 115?0~511
JH5.bll 11119.118 1Jbll5~12
J90 I. 311 8b8.3(1 117b9.6b
11007.08 A87.12 118911~20
11IIZ.60 905.911 'iOIA,711
11218.51! <1211.7b l!iJII],i!A
ICENARIOI HEO I HE9•·~UOR SOX••&/21111983
YEAR ANCHORAGE • COOK INLET ··--·--~··--·-··----·····-
1980 ]9&.51
1981 1119.115
1982 11£12.39
t98J 11&5.]]
19811 IIIIB.ii!7
1985 511.21
l98b 52'1.81
1987 518.41
1988 552.ut
1989 5o5.bt
1990 579.21
1991 511b.511
1992 593. H
1993 bOI.09
1'~94 bOB.H
1995 biS.b7
l99b &2&.73
1997 bJ7.80
1998 1)1.18.86
1999 &li9.91
2000 b 10.99
2001 ~tllb.ll9
i!002 70 I • 98
2003 717.11'3
20011 732.97
zoos 7118.117
200b 7&9. 8 I
2007 791.1'5
2008 8li?.IIA
2009 1!13.82
i!O l 0 BSS.tb
,I ) .J ·. )
PEAK ELECTRIC RfQUIREHENTS CMW)
fNEf Of CONSERVATION)
(INCLUDES LARGE JNOUSTRIAL DEHANO)
MEDIUM RANGE (PR • .5)
········-········---~-
GREATER fAIRBANKS TOTAL
··-···--·-·-··-····-·· ~-·-··-··-··-·····--·-
9t.ao 1187.90
97.15 51b.&O
IU.9l 5115 .• 30
toe.u 5711~00
'111.112 &02.711
1211.18 &31~39
125.59 bSO~IIO
13t.oo &t-9.111
lJ&.IIO bBI!.IIl
lilt .81 707.112
1117.22 72&~113
I 119.91 Tlb~Cil
!52.&0 711b,IIO
155.30 711&. 38
157.99 HIJ. 37
lbO.b8 11,~ n
1&5.211 u•f. 911
1&5.80 flll.J.bO
lbB.lb Bl7~l3
110.9j! 1110.8'5
1n.u 81111.118
17b.7l 86l~i!O
179.9lf 881~93
181.11 9110 0 b5
18&.110 •tt9~38
t89.U one·.,,
191.9.5 9&1~ 711
1911.21 91!9 ~ 37
&'02.52 1015.01
2•lf>.82 IOIIO~bl.l
211.12 lOtib .• i?@
.1 -_) I -J
HlO--DOR 30%
-
-
-
C.65
) l
SCENARIO I HEO I HIO•-OUR JOJI••61211/IIJ!IJ
HflUSEtiOLDS SERVED
ANCHORAGE • COOK INLET
----~---------~-~-----
YEAR SINGLE 'AHJLY HULTIFAI11LY 'iORILE HOMES DUPLEXES TOTAL ···-········--··· .............. ---··--.... -~--................. ................
1980 35473. i!Ollll. 82lo. 7486, 7l'50~.
o.ooo) D.OOO) n.OOO) o.ooo) o.OOII)
1985 IISHB. lbi'Oil. 10803. 8'5b'l'. qoqsJ. n o.ooo) o.oooJ o.ooo) o.oooJ o.ooo)
0\ ....... 1990 5]135. 2SR77 • 1228'1'. 8460, q99se.
o.ooo) o.ooo) o.OOO) 0,000) n.ooo)
1995 5832~. i5,qJ. 13401. 8lH. 10595t..
o.ooo) o.oooJ o.ooo) o.ooo) o.ooo)
2000 625b'5, 26717. 1'1'505, 8181, 11;4975.
o.ooo) o.ooo) o.ooo) 0,000) o.ooo)
i!005 tt7Rqo. Ji!5bR • 15906. 7833, u~ 1 q7.
o.ooo) o.ooo) o.OOO) o.ooo) o.ooo)
i!OIO 7q7J9. 1o272. 1170'5, 86&7. I:U42ll'.
o.ouo) o.OOO) o.ooo) 0,000) o.ooo)
SCENARIO I 1-4EO I HlO••OOR 1011--6/24/' 9fl]
HOUSEHOLDS SERVED
GREATER FAIARANKS
-······-···-··········
YEAR SINGLE f'AiolllY HULTIFAMILY MOBILE HOMES DUPLE XU TOTAL .... . .............. . .............. ···········-· .... ., ......... . ...............
1980 uzo. 5287. 1 I 89 • lill1, 1~113.
o.ooo) o.ooo) 0.000) o.OOO) 0.000)
1985 tOb'lb. ssn. 2tln. l69l. 201)42, n 0,1100) o.noo) o.ooo) o,OOO) o.ooo) .
0"1
(X) 1990 10513. 7741. 2103, 2197. ll!Sb.
o.ooo) O,ROO) 0,000) 0,000) o.ooo)
1995 12~'~2. 7f1Lit. 2410. 2339, 211881.
o.ooo) o.ooo) 0.,000) o.ooo) o.ooo)
2000 13bH. 770]. 101')6. 2298, 26641.
0,000) o.non) 0,000) 0,000) o.OOO]
2005 tssso. 7!549. ]bl8. 2252. 2~990.
o.noo) o.nooJ ( o.ooo) o.ooo) o.noo)
2010 t nss. 8LI83. 4126. 2061. 32026.
o.o'JO) o.OOO) n.oooJ o.ooo) o.oon)
.) J J ... l J J 1
1 ) 1
SCENARIO I Ht.O I HIO••DOR 10¥--b/2111198.5
HOUSIUG VACANCIES
ANCHURARl • COOK INLET
------··-···-········-
YEAR SJI4GL£ FHIILY HULTIFAHILV HORILE HOMES DUPLEXES TOTAL ·------·--·----·· -··-···-··-·-··-·-··-···--........ ., ......... ·--~~-------·
1980 5o8CJ. ?&bE>. liJ91. 111&3, t&j!OQ•
o.ooo) n.ooo) n.OIIO) n.ooo) o.OOO)
1985 q9CJ. IIICJ&. I t 9 • 21Jl. ii!IIOb.
o.ooo) o.oooJ o.ooo) o.ooo) o.ooo1
n
m 1990 587. a" n. 135. ii!89, ii!IIRB.
1.0 o.ooo) o.ooo) o.ooo) o.ooo) o.oooJ
l91J5 b'll. 1050. 1117. 2811. 21&!11.
o.oonJ o.ooo) o.ooo) o.ooo) o.ooo1
2000 &88. 1551. l&o. 279. 2b78.
o.ooo) O.,OOOJ o.ooo) o.ooo) 0.000)
2005 1111. 175q. 175. 11611, ]IIlLI.
o.non) ( o.ooo) ( o.ooo) o.ono) o.oooJ
lOIO 823. 1959. 195. 28ft, l2E>l.
o.ooo) o.ooo) o.ooo) n.oooJ o.oonJ
SCENARIO a 14ED I HtO .. DOR l0X,..b/Z4/19fll
HOUSING VACANCIES
GREATER FAIRBAN~S
········~····-········
YEAR SINGLE FAMILY MULTIFAMILY MOBILE HOHF.S DUPLEXES TOTAL .... .............. . ............. ··-···-······ .. .............. ..............
1980 Jb5J, UlO, 086, 8~'5. 8854.
0,1)00) o.ooo) 0,000) 0,000) o.ooo)
1985 lliJ 0 29ljf~. l4. 794, ]884, n o.ooo) o.ooo) 0,000) 0,000) O,OOC)) .
"--.1
0 t990 117. bTl. n. 259. 1070.
0,000) 0,000) ·( 0,000) o.ooo) 0,000)
1995 us. 4148, H. 811, 689,
0,000) 0,000) 0,000) 0,000) 0,000)
2000 150, till 0 • ll. 78. 701,
0,000) 0,000) 0,.000) O,OilO) 0,000)
2005 \71. ll]t, ItO, 17, 719.
o.ooo) 0,000) 0,000) 0,000) 0,000)
lOIO 191. .QSA, 16'5, 216, 9to.
0,000) o.ooo) 0,000) 0,000) o.oon)
)
1
n .
l l i
SC!NARIDI ~EO I HIO••DOR 10~••6/ill/198)
ANCHORAGE .. COOK INLET
J
fUEL PRJC! 'DREC•STS EMPLOYF.O
ELECTRICITY (t I KWH)
GREATER FAIRBANKS
·-1
·--······················~---···-----······~-----~--·--·····-~----------~-
YEAR RESlDENJI AL BUS IIIESS RE 81 DENTI AL RIISINEU ···-......•...• ···--·-···· ............. ·•········•
U80 o. 0)7 O.OJII 0,091J 0.090
U85 o .• o 119 0.01115 0,095 o.oqo
1990 0,11119 o.oii~J o.o9o 0,085
tns o.nso o.oqJ o.o9o 0,085
aooo o.o5o 11 0 0117 0.090 o.08!i
2005 o.oso o.047 o.o9o 0,08'5
aoto o.oso o.oll7 0,090 0,085
YEAR ....
1980
1985
1990
1995
2000
2005
2010
)
ANCHORAGE • CODK INLET
FUEL PRICE FORECASTS EMPLOYEO
NATURAL GAS (1/HHBTU)
GREATER FAIRBANKS
·····-~--····-········~····---······· ·--·-·····-················-······--~
RESlOENTIAL BlJSI NESS qESIDENTIAL RIJSINF.SS .....••...• .. ........... ............. . ...........
a. no 1 8 500 12.140 11.290
I. Q]O 1.700 9.090 1.6110
2.480 2. 2'50 7. 7b0 6.310
2.510 2.100 6.Uo 5.290
2.450 i.i!i!O 6.290 4.8110
2. hi) 2.130 5.820 11.11o
z •. hn l.030 5.190 3.940
J
YEAR ·---
19M
1985
liJIJO
1995
2000
2005
2010
ANCHORAGE • COOK INLET
FUEL PRICE FO~ECASTB EHPLOYEO
'UEL OIL ('IMMRTU)
GRfATER FAJRIUNKS
---·--·-·-··-··-·-~···-····----·~---· ---····--·---·-------···---·~--·--·--
REUnEHTI AL BUSINESS RESIDENTIAL RUSINfSS ............ . .......... ., ·---·-····--·----·-·-·
1,150 7,200 7,R30 7,500
l§.t;lO 11,980 5,590 5,2&0
11,7)1) 0,180 "· 770 U,U40
U,ll 0 1,!1&0 U,II~O 3,810
1,830 J .no J,ebo 3,5]0
1,550 1,000 1,580 1~250
1.280 l. 7JO 1.110 2,980
SCENARIO I MEO I HlO••DOR JU .... b/ilt/I'UJ
RESinENTIAL USE PEA HOUSfHOLD (KWH)
(WITHOUT AOJUSTHENT FOR PRICE)
~NCHURAQE • COO~ INLET
--··----~·-·-·······-·
SHALL LARGE SPACE
YEAR APPLIA"'CES APPLlA~ICES HI! AT TOTAL
•••• . ............ .. ............ . ........... . ...........
1980 2111\,00 bSOO, ttl 5088,52 llbqq,ts
0.000) o.·nooJ 0,000) 0~000)
n 1985 ZlbO,OO ust~.H 4831,U 13151.75 . 0,000) o.-ooo) ( 0,00(1) ( 0,000) .......
"""' tCJIJO 2210,00 ti010,9l 11651,£14 U8~4.34
0,000) 0.000) 0,000) 0;.000)
1995 2'.b0,00 sqse.ss 4!07.7l l27U.l5
0,000) o .·o o o) ( 0,000) 0,000)
2000 2119.00 51J88 .u 411U,bCJ 12130.82
0,000) o.-ooo) 0,000) o,OOO)
2005 2160,00 60&2,19 1.1421,68 128£14,811
o.OOO) OoOOO) 0,000) 0~000)
2010 21&11),00 6l2q~]6 44]8,60 l2CJ77 1 CJ6
0,000) 0.'000) 0,000) o'. oOO)
j J l ) .J
.. 1 l
SCEtURIOI to~ EO I HIO••OOR ]I)X ... b/2!1/1~81
AESIDENTIAL US£ PER HOUSEHOLD (t<WH)
(WI THOIJT ADJUSTMENT ,OR PRICEJ
GRE.TER FAIRBANKS
--·-·--~----·-----···-
S"'ALL I.~RGE SPACE
YEAR \ APPLHNCES APPL HNCES HF.:H TOTAL ----------···---····---------··--· -·---·-·-·
1980 i!llbb.OO ~7H.S2 llll.6b 11519,18
o.ooo) o.ooo) o.ono) o.ooo)
(""") 1985 i!Sl'i.QIJ 6lAi,9l 351\&.15 l2305,0l' . 0 0 001)) 0.000) 0.000) O.OOOJ ........
tn
IIJIJO ibo&.oo &IJH.oo 3822,(1] 12862,111
o.OOO) o.OOQ) o.ooo> o.·oooJ
1995 2t-76.00 bbll1~01 11075.11 1139ll,li!
n.OOO) o.ooo) n.(IOO) ( 0.000)
2000 2711&,00 67139.50 11129,67 lJ865.18 o.OOO) 0,1)00) n.ooo) o·;.ooo'
2005 2816,00 68159,08 11502,ll 1111]7.30 o.ooo) o ,;o o o 1 o.ooo) o.OOO)
2010 2~86,01 68"9.116 li65!,1JR llllllll.'HI
1'1.1)00) 06000) o.ooo) 0~000)
YEAR .....
1980
0 1985 .
........ en 1990
1995
lOOO
i!005
2010
] .J J
ANCHORAGE • COOk INLET
··················-···
(
(
(
99Je.11
O,IJOO)
IOJIH,97
D,OOO)
I0908,4l
n,oOo)
BUSIN[SS USE PER fMPLOYEE (kWH)
(WITHOUT lARGE INDUSTRIAl)
(WITHOUT ADJUSTMENT fOR pqiCE)
GREATER 'AIR8ANKS
············-···-····-
7495,70
0,000)
~782.72
0,000)
953&,3]
0,000)
J J
.... l l 1
SCEI-URIOI 14EO I Hto-~ouR lU••b/21111 ~113
SUHHAR'I' Of' PFIJCE E'f'ECTS ~NO PROGRAHAliC CONSE:RVATJON
IN GWH
ANCHOR ME • COOK INLET
RF.liJOENTlAL BUSINESS ............. ····-~-----UwN•PRICE PIIOGRAH·lNDUCfD CROSS•PRJCE OWN•PIIICE PROGRAJ-1• J NOUCf 0 CROSS~PRICE
YEAR REDUCT_JO~ CONSERVATION RE!l_U_C T 1_Q~-----. REOu{:TJ9f:! __ CONSERVATION REDUCTION ....... .................. .......... -.: ................... .. ....................... .. ................ ···~;;t:t. .. t:;:f. .... .. ...................
IIJRO OoOOO 0,000 OoOOO 0,000 0 0 00o OoOOO
tcUII 41o0811 OoOOO ·0.1189 8,982 OoOOO I o '5H
I ~82 l2,11J7 OoOOO Oo9711 17. 9bll 0,000 lo1'51
1981 18o251 o.ooo I 1 111J8 2bo9111J OoOOO llo727
U811 211, Jl5 0,000 I o 95 7 15,928 OoOOO bo101
1985 l0olll8 o.ooo i!ollllb 1111,1H I n.ooo 7,8H
1~8b lii,Q89 OoOOO Oo021 5loiiS OoOOO 8o51~
1987 JQoSf.IO o.ooo -2,11011 '37oll9 OoOOO 9, uo
l988 1111 0 Ill 00(11}1) •lloll29 blo5211 OoOOO 9oll00
I981J 118o702 OoOOO •7o2'5'5 b9,728 OoOOO IOolllll
1990 51o27l 00000 ·9obfl0 7'5oHl OoOOO llo082 n
'--J 1991 '5b,blb 0,(11)1) •IOolll8 eooqlo 0,(100 l2o'591
'--J 1992 59,9b0 0,000 ·11.19'3 fl'5oll87 OoOOO 111,101
199] Uo303 0,000 -11.95] 90,8b] o.ooo 1'5obll
1994 bbobll1 0,000 •l2o1ll 9'5o8ll0 noOOO t1,1i!O
tns b9o990 11,000 •IJ.ae.~ IOOo8l1 OoOOO l8obl0
19911 H',6i!t 0,000 •12o9119 IO'i,IIOO OoOOO i!Oo78b
1997 75 .• 25 I noooo •U,1128 I09o983 OoOOO 22,9112
1998 17 0 882 OoOOO ·11,908 1111,565 0,000 2'5,098
1999 80o'512 n,ooo •llol87 119,148 0,000 27,2511
ilOOO 8301112 n,ooo ·IO,flb7 123,711 0,000 29,1110
2001 85,612 0,0\10 •9oli!9 li!l\,697 0,000 ]2,1171
2002 88,122 OoOOO -7.'191 I J],6bll 0,000 ]5.532
2003 90o612 OoiiOO •bo2SII t18,bl0 o.ooo u.sn
20011 93,102 00000 .. 4,71b 1111,597 0,000 Ill ,IJ511
2005 950592 OoOOO .•lol78 1118,561 noooo 1111,715
200b 98o2b7 0,110~ .. oobll 154,705 o.ooo 119oiSO
2007 10!10911] (1,(100 1,'15<' un. A lib 0,000 5lo585
2008 IOJo618 0,000 11,5\7 166,987 OoOOO 58.021
2009 l0bo29l o.ooo 70082 171.1211 0,00(1 62oll5b
2010 I08 0 9bq oonoo 9obll7 I 7Q.i!70 0,(101) bb ,1191
SCENARIO I MEP I liiO•wDDFI ]Q!( .... b/i!llll98]
SUMHARl' OF PRICE EF'FECTS AIIID PROGUMAT1C CONSERVATJON
IN GWH
GROTER fA JRBM~KS
RESIDENTIAL BUSINEU
-·-·-····-~ ---------·· OWN•PR!CE PROGIUH~ INDUCED CROSS-PRICE OWN•PAICE PPOGRAM•JNDUCED CROSS•PRTCE
Y[AFI REDUCTJOtl CIJNSf'JtiiAT lOti REDUCTION RE!'.U~ Tl Qt-.1.~-CON~!~Y~!ION REnucTION ......... ......... -.:-.. f:~.:.-........ . --..,.. ". ' __ , __
.................. ...................... .................. .. ............................ .. ....................
1980 n.ooo o.ooo o.ooo o.ooo o.oon n.ooo
1981 o.ooo o.n9n •• 338 o.ooo o.ooo 0.899
U82 o.ooo o.ooo 2 0 Ub n.ooo o.ooo I, 798
I 983 o.ooo o.ooo 11.0111 o.ooo o.noo 2,69?
19811 o.ooo 0 ·"')fl 5.152 o.ooo o.ooo 3.59b
l985 o.ooo o.ooo o.b9l o.ooo o.ooo 11.119!!
Uh •0.310 o.oon 8.527 .. o.•nt n.ooo !!.52b
U87 •'l.b20 o.ooo l0.3t»3 .. t.o22 o.ooo 6.557
19118 •0.910 n.ooo 12.199 -t. 5]] o.ooo 7.588
IU9 -1.240 o.ooo 111.035 -2.01111 o.ooo ft.bl9
n 1990 •1.550 o.ooo 15.872 -2.555 o.oon 9,6'50
........ tnt •1.791 o.ooo t8.o«U -2.878 o.ooo t "· 763 (X) 1992 -2.031 o.ooo 20.115 -1.200 o.ooo 11.9lb
199] •2 .27 I n.ooo ii!2.53b -3.522 o.ooo u.nll9
1994 ·2.5l2 11.ooo 211.758 -3.844 o.ooo 111. 18 3
~~~95 •2.75~ o.ooo 26.979 -ll.lb6 n.ooo t5o116
1996 •2.928 o.oo11 29.0111 -11.1107 o.ooo 16.1123
1997 •].tOll o.ooo 11.11119 .. 11.648 o.ooo 11.530
1998 -3.2811 o.ooo u.on -11.88<1 o.ooo t8.U?
1999 •3.115& o.ooo H.tte -5.1]11 n.ooo 19.71111
2000 •l.&lL' o.ooo 37.153 -5.111 o.ooo 20.851
1!001 -3.7811 o.ooo 39 • 3& I -5.'59\ o.ooo 22,128
2002 ·3,93'5 1),000 111.'\7(1 .. s. 811 o.ooo 21.1105
2003 •11.1187 o.IIOO 113.778 •b.O]I o.ooo i?ll,b82
20011 -4.219 o.ooo 115.980 ·6.251 o.ooo 25.959
2005 ·11.391 n.ooo IIA 0 19S •b.ll71 o.ooo 27. 231>
200& -11.'5113 o.ooo so. 797 -~.712 o.ooo 28.eq8
2007 •ll.&9b o.IIOO 53. ]99 .. o.9S2 o.ooo 30.11&0
2008 •4.8111! O.llOO 56.111)1 -7.t'n o.ooo 32.072
2009 •5.1101 11.ooo 5A.bOII •7.11]1 o.ooo n.~>ell
2010 -5.1511 o.nqn l>l.?Of> .. 7.fi7U o.ooo 35.29h
J J CJ J _J _j J J J CJ J ) ) -,J -
l ] l ~) ll 1 .. ] -] 1 l 1 j 1 1 l I
SCENARIO! MEO I HIO•·OOR JOl••61211/198J
BREAI<DOWII OF EL~CTRICITY REQUIRfHENTS (t;WH)
(TOTAL IUCLIJDU LARGE INDUSTRIAL CONSUHPTION)
ANCHORAGE • COOl< INLET
----~-~-·········---·-
HEDIUH RANGE (PR•,5)
·······--·-···------
RE$1DENTJAL BUSINESS MISCELLANEOUS EWQG, INDUSTRIAL
nAR REGUIREHENTS REQUIREME'iTS REQUIRE HEN TS LnAD TOTAL ...... ·~··········R····· ..•...•........... -----·~·~····--~--••••••••••••••~a•• ----~---··········
1980 '179.5] IUS, 30 ZII,JI 811,00 19bl,l9
19111 IOI6,J] 917,25 Zll, 5I 92,08 2070,11
1982 1053.12 999,111 211,12 I 00, U 2177.111
1983 10119,92 1061,03 211,93 108,211 L12811,11
19811 112b.71 1122,92 2S.IJ ll6,1Z 2HI,08
1985 1163.51 11811,81 2'.5.14 124,110 21198,0b
1986 ll7q.87 UOII,H zs. 7J 1]7',89 25117,92
1987 II 9b,i2 l2a4,0b 26,12 151,]8 2597,79
1988 1212.511 1241,69 26,50 164.88 h117,b'§
1989 1228,911 12bl, ]j! 16,1!19 111!1,]1 i!b97,52
n 1990 12115.30 128!.95 17.21!1 191 • Bb 27117,18
-.....j
lD 1991 li!5tl,b2 1299.15 21,5) I 95, l] 2770.11)
1992 12b].911 1Jl'5,l6 27,18 198.110 2805,117
1991 li!71.2b &JJI.57 28,02 20 I, b6 28lii,5Z
19911 121.'2,511 13117,78 28,27 i!OII,9J 28b],5b
1995 I i!'H. qo llU,99 28,52 208,20 2892,bl
l99b 1]09,27 IJ<I5,110 i!9,0b i!lll,lll 29117,87
1997 ll2b,6J l112b.BZ 29.bO 220,08 ]00],11
1998 l]ll],qq 11158.23 ]O,lll 221!,02 10'!8, H
l99q llbl.]fl tlla9.b5 ]0.68 i!ll,96 ]11],65
20110 1]78,72 1521,07 ]I ,23 1]1,90 1168,91
i!OOI l110],5'!i l'ib'i.~S H,97 21111,9b ]2115,8]
i!OOi! 11128.311 lbOq,b] ]i!, 72 i!5i!,02 ]l22. 7'S
2001 11151.21 1651,91 ]],lib 259,08 nqq,67
20011 11178.011 11!911.20 ]11,20 ~bb,lll 1117b,59
2005 1502,88 l7112.111J 311,95 27l. 20 155],50
2006 l5l'i •. H 18011,20 15,9] 281,58 3b5b,98
2007 l567,b6 1865.93 1b.'ll 289,96 l7bO,IIb
2008 lb00.06 1927,65 l7 ,89 <!98,311 ]8b],QII
2009 1632,115 l98q, HI 18,87 101>,72 ]967.112
2010 166 11. 8 q 21151.10 H,tlb ]15,10 11o1o,qo
n
())
0
) J
SCE~ARIOI ~~0 I HIO••DOR JOX••b/24/1983
····················
RESIDENTIAL
YEAR RE.QUJREMEIHS
BREAKDOWN OF ELECTRICITY REQUIREMENTS (GWHJ
(TOTAL INCLUDES LARGE !NDUSTRIJ.L CONSUMPTION)
GREATER FAIAaAHKS
·····---~---··········
BUSINESS MISCI!:LLANEOUS
REilUIREI'IENTS REQUIREMENTS .... . ...........•••..• ···············-·· ··•·····•·•····•·•
1980 llb,;34 il1.14 &.18
198l 189.10 227.53 "· 73 198i 201.80 237.93 Cl 0 b7
1983 211.1.51 iii&.'U &.&2
1981.1 227.U 251\.72 &.So
1985 219.92 i!o9.11 &.51
198& 2117.10 272.22 &.1.15
1981 2511.211 2715.33 o.34
IUS i!bt.llf• 211\.11] o.H
1989 2f.l8.&3 281.51.1 &.27
1990 275.81 281.1. 64 & • i!l
1991 U2.1.17 28t1.03 &.i!9
1992 289.11.1 291.1.11 &.31
1943 245.80 291.1.79 &.4&
1991,1 ]112.1.17 24~.17 b.51.1
1995 309.U )01.55 0 0 &2
199& 311.1.118 JO&.IJI &.711
1997 319.82 3U.Ob &.85
1998 325.11 317.32 &.9&
1949 310.52 );HI. 58 1'.0'7
2000 335.1!& 327.811 7.18
2001 3112.11 us.o9 7.32
200l 3118.110 )112.311 7.11&
i!OOJ 1511.&& 1119.59 7.&1
2004 Jb0.9) )5&.81.1 7.75
2005 l~>7.i0 3611.08 1'.84
200& 375.05 373.94 a.oq
2007 382.41 383.79 8.2'1
2008 )90.7b 393.611 1!.48
2009 JCI~.b2 1103.50 R.&s
2010 411b.IJ8 lll!.l'i 8.81
J .I .J J J
E I! Dr. • INDUSTRIAL
LOAO TOTAL
·-·······-········ R·····-···--·----·
o.oo 1100.1\
o.oo 4U.Ib
o.oo 1111&.110
o.oo llb4.11'5
o.oo 1192,'50
o.oo 515.511
lO. 00 535.71
21J.OO 555.qQ
30.00 57&.21
11o.oo !i9b,llll
so.oo &l&.bfl
so.oo &h.n
50,00 &3&.92
so.oo 1)117.0!
u.oo U7.U
so.oo ~tU.Jt
so.oo &78.02
5o.oo ~ae8. n
so.oo &49.115
so.oo 710.16
50,00 7l!0,88
so·. oo 1l4.SIJ
so.oo 71J8.20
so.oo 1'bt.85
so.oo 715.51
so.oo 7119.11'
so.oo 807,08
so.oo Ut1.98
so.oo 84i!.8Q
so.oo uo.7c;
50.110 876.70
I J -.J J I J
n
00
l ) . -l
VEAR
1980
1961
19112
198]
19811
1985
1986
1987
1988
1989
1990
1991
1992
1993
19911
1995
199b
1997
1998
1999
2000
2001
2002
200]
20011
zoos
200b
i!007
2008
i!009
2010
-l
4NCHOR4GE • COOK
--l
TOTAL ELECTRICITY REQUIREMENTS (GWH)
(NET OF CONSERV.TIDN)
(INCLUDES L4RGE I~DU~TRIAL CONSUMPTION)
~EDIUH R4NGE CPR • ,5)
·······--·-------·----
INLET GREATER FAIRBANKS
1
TOTAL
••••••••••w••••••••••------~---······-----·· ·-~---··-····--·R·-···
t9U,19 1100,31 <'3fll~51
2070,17 lli!J,U (1119] ~52
i!l11,11J Clll6,110 llb2]~511
22811,11 llb9,115 ~75l.5fl
2]CH,Oil 1192,50 iHIA),58
i!ll98.1lb !i15,511 30 t:f. bll
25'17. 92 535.17 3083~69
2597.79 555,99 1153,78
h117 ,b'! Cli7b,i! I 121!3,117
h97 .51 59b,llll !293.9b
27117,18 bl b, bb 3_3bll~ O'i
Z77b,IIJ bi!b, 79 ]110]~22
2805,111 blb,92 311112~ ]9
28111.52 bll7,05 ]111!1,57
UbJ, 5b b57. 18 1520,711
2892,61 t.t.7,JI 1559.,92
29117,87 fl78,0i! Jb25~89
1003,1 J 668,711 ]b91,87
]0~11.]9 699,115 ]757~611
JIIJ,b5 7lO,Ib 38i!J,81?
Jlb8,91 720,88 ]8119.,79
ll115,83 1311.511 ]9110·, J1
JUi!.7'5 7118,20 11070:9'5
]]99, b7 7b1,85 l.llbl,52
Jll7b, 59 775,51 112'5?.~10
)55],50 789,17 1.1]112.fl8
lb'5b • 9A 807,08 llllfii.!~Ofl
]JbO,II& 8211,98 11585,1111
]1!&],911 11112,119 1.170e.,81
]9b7.112 8&0. 79 llfli?8,21
11070,90 818,70 119119~b0
n .
OJ
N
J
SCENARIUI HED I HIO•·OOR JO¥••bli!41lq&]
'I' EAR ANCHORAGE ., COOK lllLET
-----~----~·-··---····
tqAo lqb.SI
lqBI 1118.0'1
1982 1.!39.08
198] llb1.2b
lCJ84 482.8'!5
1985 504.11)
19811 515.24
1987 52&.04
1988 Slb.85
1989 5111.bb
1990 558.4b
19'11 564.10
1992 570.1 1~
l9CI] 575.98
1994 5111.82
1995 5~1.bb
I !ill& 59!!.75
1997 b09.8J
1998 uo.•H
1999 Ui!.OO
2000 &113.011
lOOl 1>58.57
2002 &111.0&
2001 &89.55
2004 HS.OII
2005 720.5]
200& 7 1·11 .Ill
2007 7&2.28
2008 783.1&
2009 8011.011
2010 8211.9i!
PEA~ ELECTRIC RfQUIREMENTS (MW)
(NET OF CUNSERVATlOHl
(INCLUDES LARGE l~DUSTRIAL OEHAND)
MEDillH RANGE CPR • .5)
--·--··-----·····-··--
GREATER fAIRB~NKS TOTAL
-~~-·--~-------~~-----·----·------~···----··
q1.110 487.90
9b.bb 5111~ 75
l01.CJi! 5lll~b0
107.18 5&8.114
112.114 595 .• 29
ll7.70 bi!.i!". u
122 .H U7~5b
1211.93 652.98
l] l. 55 668~110
lH.lb 683'.8!
1110.71 U9~24
1111.09 707.1'
llfS.IIO 715.511
JIH.Jl 723~ 70
150.03 Ht'.n
152.111 7110~00
I 511.78 7Sl~SJ
lS7.i!l TU 1 06
159.68 780.59
tbl!.t2 794 .• 12
1&11.57 801~ bl!
1&7.69 82b,25
170.81 BIJII.Bb
I 73.92 8&1'.117
171.04 eAi. (lA
18ll.lb 900 .• 69
1811.25 925.&5
'88. 14 9'i0~b2
192.1.12 975~59
19b.SI I000~5b
200.1)0 I02'i~52
J ] .J
H13--0RI SCENARIO
, ....
-
~·
C.83
1 l l -1
SCW.RIOI Mf:.O I HIJ••ORI SCENARIO••bll~/1~81
HOUS£110LOS SEPVEO
ANCHORA~E -COOK INLET
--~·······-·-·······-·
YEAR SINGLE F M-1 Jl V MIJL TIFAHJL V MQRILE H0'1ES DUPLEXES TOTAL ··-· ·----·~-.. -------·-·------.. -·---·-------······-·-···-...............
1980 35~73. 20]1''· aBo. T~aet. 71503.
1).000) O.O'JO) o.oonJ o.ooo) o.noo)
1985 llb22t. 2b20II. 10957, 85b7, 91950.
\) O.OOfl) o.ooo) 0,000) 0,000) o.oooJ .
00
(J"l 1990 57A90. 25871, 1 no 1. BilbO, 105528.
0 0 000) o.ooo) ll.OOO) o.oooJ 0.000)
1995 b5 1H7. 30~211. 15120. 8]]3, 119]511.
0.000) o.OOO) o.ooo) n.OOOJ 0~000)
2000 7!9bq. ]'5~'5'.. llll5, 8532, 1351&7.
0.001)) n.ooo) o.ooo) 0.000) o.oooJ
zoos 8]]57. 1102b7. 19580. 9bll~. 15281lf'.
11,000) n.OOO) ll.oooJ 0,000) 0.000)
lOIO 9Sli!7. ~&1155, U589. 11057, 175327.
O.OfJO) 1).001)) n.ooo) 0,000) n.oooJ
SCENARlOI MfO I Hll••OR I SCENARIO••b/241!q8J
HOUSEHOLDS Sf:RVED
GREATER FAJRSANMS.
··············-·······
YEAR SWGLE FAMILY t1ULTIFA1'11LV MOIULE HOH!S OUPLEMES TOTAL ···-··-----····-· .................. .............. ·--·-·-··-··-................
1980 1no. ':i287. 1189. 16\7, tS!B. o.oor)) 4).1)00) 0.001)) 0.000) 0.1100)
1985 I IJ6.46. 5866, auo. 11b4, 20406.
0 n.OOO) O.I)QO) o.ono) o.ooo) o.ooo) .
co
0'1 1990 It 458. noo. 2204, 2175, ~H97.
o.ooo) o.ooo) 0.001)) O.OOO) 0.000)
1995 14936. 7841. :SHi!. H:U, 28507.
o.ooo) o.ooo) o.ooo) o.noO) o.ooo)
2000 I 76 to • 8272. 41 u. 2298, 32292.
o.OOO) o.ooo) o.ooo) o.oon) o.ooo)
2005 lq820. 96lb. 11612. 2349, 3647'1'.
o.oon) O.I)OO) o.ooo) o.ooo) 0.000)
2010 ~2579. 11088. SJ75, 2686, 41'1'28.
o.ooo) o.ooo) o.noo) o.ooo) n.oon)
J .. J c~ .. -.J . __ ) J
1 ) ... 1 ] l .... I I
SCENARIO I HED I lii:S•·ORI SCENARIO••bl~llllq8)
HOUSING VACANCIES
ANCHUPAQE • COOK INLET ···········-·····-····
VElA SlllGLE FAMILY HUL TIFAHIL V HORILE !-IOH£5 DUPLEXES TOTAL ..... ········---·-................ . ........ ..,.., ..... ·----·---···· ···-·-------..
1980 5(181J. 7bbb. lq91. 14~3. 16209. o.oiJOJ o.non) o.oooJ 0.1100) o.oonJ
1'185 5o A. Jllllf>. 121. 291. 21117. n o.oon) n.oun) n.ooo) o.OOO) 0~000) . co
'-I 1990 bl7. 1 an. lilt!. i!81J. i!!illq.
0.1100) o.non) o.ouo) 11.000) O.OOOJ
1995 un. l~4J. lbb. i!84. C!81·11. n.oon) n.oooJ o.oonJ o.OOO) o.nooJ
C!OOO 8111. 1q111. a sq. 282. 319'l.
o.ooo) o.oonJ 0 0 0011) n.OOOJ o.oo•'IJ
2005 Ql1. l!t7a. 2 tiS. ]18, ltt25.
n.onoJ O.OO(I) O.OOOJ o.onoJ o.nonJ
lOlO ant~~t. 250IJ. 2111J. lb'!l. 41bq.
o.noo) o.ooo) o.noo) o.ooo) 0.000)
SCENARIO I MED I Hl)••DR! 5C£11ARIO••bli!~llfJI\]
UOU9ING VACANCIES
GREATER FAIRRANI<S •.•••....•....•......•
VEAR SINGLE FAMILY r.tULTIF&t11LV MOBILE H(lMES OUPLE)(ES TOTAL ..... . .............. .. ............. ..., ............ . ................ . ...............
1~80 3651. nao. CJ6~. 895, 8854.
o.ooo) o.onol 1).1100) o.oon) I).OOO)
1985 II A. 2655. a~. 7ll. l!itq.
\) o.OOO) o.OOO) o.oooJ o.ooo) o.noo)
OJ
OJ 1990 lib. 1151.1. 2~. 81, b86.
o.ooo) o.ooo) o.ooo) o.OOO) o.ooo)
1995 lb4. lUll! 8 H. eo. . 729.
o.ooo) o.ooo) o.ooo) n.ooo) o.ooo)
2000 1911. 441. liS. 78. 764.
o.ooo) o.oooJ o.oon) o.ooo) 0.000)
2005 21A. sao. 51. 18. 861.
o.ooo) 0.000) O.l'fiOJ o.ooo) o.ooo)
2010 l~". I§QCJ. 51f. BCJ. CJCJ'3.
o.ooo) o.ooo) 0.0110) o.ooo) o. 000)
J . _c_J J 1
] 1
FUEl PRICE FOREC~STS EHPLOYEn
ELECTRICITY (S I KWH)
ANCHORAGE • COOK INLET GRf4TEA FAIP8ANKS
·----··-·-·---·-·······-----~····-----~~------------·-------------·-·--·-·
YEAR RESJOENTIAl BUSINE'SS RESlOENTtAL RliSINESS ..... ............ .............. ............... -------·-·-
1980 0,031 o.oJtJ 0,095 o.oqo
n 1985 0,0&18 o.nqt; o.o95 o·.oqo
00
lO 1990 o.os11 11.051 o.o92 0,087
1995 O,ObJ o.o&o o.091i o·,oeq
2000 O,Ob9 0,1)611 o.o96 o.091
zoos o.ou n.ob' 0,098 o.o111
i!OIO o.o7'5 n.on 0,100 n.o95
YEAR ....
1980
n . 1985
\.0
0 1990
1995
2000
2005
20l0
.1
A~CHOPAGE • COOK INLET
fUEL PRICE FORECASTS EMPLOYED
NATURAL GAS (1/MMRTU)
GREATER fAIRBANKS
·····~···-·············--·-·······~·· ········-·------··-···-~·-······--···
RESIOENTlAL ~US I NESS RESIDENTIAL RLJSINES9
·-·--·····-............ ............. . ...........
'· nn 1,500 12. no lt,l90
2.030 1.eoo ll, b'fO 111,2'10
J,IISO J.zan U•.OlO lli,IJU
'S,ton 11,470 19,8'10 18,]91)
'5.750 '5,'520 21,120 ll ~670
b.OlO l!l,180 24 0 410 n~n2o
b 0 1bO 6,llo 26,2]0 2'1. 780
.I .J ] J J
1
FUEL PRICE FOREC4STS EHPLOYEO
FIJEL OIL C'I14MBTU)
4NCHOR4GE • COOK JIll ET GRE~TER FAJRBANKS
··----------···-··-----··-~-·-·-···---·-···~----···-----·~---·--··-···-··-
VEAR RE.SlOENTUL ftUSitiESII RESIOENTIAL fHISINESS ··-· ................ .............. ·-----···--.. .............
1980 7.750 7.?.00 7.830 7 0 1i00
1985 7.120 6.57n 1.1eo b.A50
n 1990 9.750 Q.200 9.1:1110 q."''IO .
1.0 1995 u.oao lt.l§3n 12.190 t ... 8&0 --'
2000 1u.nso 11.5)0 111.210 u.sao
2005 lll.qOO 111.350 15.0110 lll.710
2010 15.9711 l'i.l120 1&.120 I5 0 7«JO
SCEN.RIOI "4ED I till•·OPI SC£NAR10q•bliqllq81
RESIDENTIAL USE PER HOUSEIIOLD (KWH)
fWlT~OUT ADJUSTMENT fOR PRICE)
ANCHORAGE • COOK INLET
~--···-~··········-···
SHALL LARGE SPAC!
YEAR APPLJA.NCES APPLIANCES HEAT TOTAL .... ........... . ........... ...•...•.. . •...••...
1980 lllO.OO e,soo.bJ soae.sz UU9.U
0 0 001)) o.ooO) o.ooo) 0~000)
n 1985 61'51 ~119 118;!1.87 tllll.H . 2lbO.OO
1..0 o.oooJ o.ooo1 o.ooo) o.OOO) N
t99Q &'211).00 6020.51 115Bf>.U U811.141
o.ooo) 0.•000) o.ooo) o.ooo)
1995 Ubl)0 00 5960.28 4518.86 12739.14
o.oon) o .. noo) o.ooo1 o.ooo)
2000 2110.00 SqH.I q 41151.51 12756.U
n.OOO) 0.1100) o.OOO) o.OOOJ
i!OOS 2Jbo.oo 6062.'51 4ti.U.21 128/Hio 72
o.ono1 o.noo) n.ooo) 0. 000)
2010 lllto.oo U27 .• 20 4111'5o.M 12987 .sa
o.oon> o.ooo1 o.onn) o.ooo1
.·. __ J
] 1 l
SCENARIO I HED I HB••OHI SCE~ARIO••bi24JlqA1
RESIOfNTI ~l USE PER HOUSEHOLO (IOIIH
I WITHOUT AOJIISTME~T F"OA PIHCE)
GRE&T'-R fAI~RANKS
·-····--------~----·--
SHALl L&~GE SPACE
YHR APPLIANCES APPL I AllCES tlfAT TOTAl. ..... ·····~----........... . ........... . ..........
1980 i!4bh,IIO '51 Jq·. 52 HlJ,t.~ ll51'l.l8
0,000) 0,000) 0,001)) o.ol)oJ
1485 2SJb,OO e.t 1a·,qR 360t.,28 UU1,25 n 0,000) 0.000) ( o.ooo) 0,000)
\.0 w 1490 2606,0(1 t.'l'18,88 38&7, H 12922,21
o.ooo) o.·ooo) 0.000) 0,000)
IHS 21176.00 bb&9.21 '1051,13 11H7,oo
0,0011) o.ooo) o.ooo) 0,000)
2000 H'lt..OI 6792~9!1 '11]&,15 1]875,10
0,000) OoOOO) 0,0011) o.oonJ
zoos i!BU,9q &818, 511 lj5LI],81j Jill q5. 38
11.000). OoOOO) 0,000) o.oooJ
2010 2886,01 f.88t>.76 '1654,68 1'11.1]2 ,lib
0,1100) O.OOOJ 0,000) o.OOO)
YEAR ....
1980
1985
1990
2000
2005
2010
ANCHORAGE ~ COOK INLET
······~---············
9500. t3
o.ooo)
IOlbl.ll
0.1100)
11031.,!11 o.non)
J ]
BUSINESS USE PER EMPLOYEE (~WH)
(WITHOUT LARGE INDUSTRIAL)
(WITHOUT AOJUST~ENT FOR PRICE)
GREATER fAIR9ANKS
•••••~•••••••••••~•••w
H95. 70 o.ooo1
90138.00 o.oooJ
«JSOO.al o.oooJ
99b8. H
o.ooo)
J --]
l l
SCEIURIOt ~-!ED I Hl]•·flRJ SCE~ARI0··~/21111983
9U"1 1URV OF PRICf. EFFECTS AND PRUORAIAATIC CONSERVATION
IN GWH
ANCHORAGE • COOK INLET
RESIDENTIAL .ausiNE!S .............. ---·---~~-... OW~I•PRJ[E PROGR A!4·l'HlliC E £l CROSS·PRJCE OWII·PRICE PROGR Al-l• INDllCFO CROSS•PRJCE
YEA II REOIIC TI ON CONSERVHIU'l REDUCTION AEJIIJ.t. T1 ON CONS~~Y~H()t:' ---!lEDUC liON
~---..... --..... .................. ........................... . .................. ............... .. ......................... ...................
1980 n.ooo o.ooo ll,oon o.oon o.ooo o.noo
1981 b,215 o,ouo -1.7t~J 9.]5q o.ooo -n.J911
IU2 12.1129 o.ooo .. ),5(!5 111.719 o.ooo .n. Hb
U8J t8.bllll o.ooo oo5,288 211.078 o.ooo -t.lqq
lq8q 211.A511 o.ooo •7,051 H.II]IIJ o.non -t,sq2
tq85 31.111) 0,000 ·8,8111 11&.797 0,000 •1.990
198& 12,181 0,000 15,970 &0.]119 o.ooo -9.2011
1987 -&.710 o,noo 20. 75J 73,900 n.ooo -lb,lll8
1988 •25.&01 o.noo 3'5, Slit 87.1152 0,000 •23,631
19119 .. qll.llql 0,000 50,119 101.001.1 0,000 •]0,81.15
19qo oob]0 ]1JQ 0,000 n u, to2 lll1.55S o,ooo •]8.059 .
1..0 1991 oo]ll,229 o.ooo 30,178 ue.•ne 0,000 •50.751
01 1992 •5.075 o.oon •11,74& tU.IIIlO o.ooo -6].1111)
199) 211,083 0,000 .. Jq,!t70 1117.1182 0,00(1 -a.us
19911 SJ,i!]A 0,(100 ·711.,94 Zl i!. 1211 o.ooo •88. 82?
19q5 Iii!, 3911 0,000 •I 09 0 'lA ii.'Jb. 1bb n.ooo •101.518
199& llll,qtll 0,000 -uo.ou U1,ql7 o.ooo •ll!t.Bib
l 997 9"'.5211 o.oon -uo.sos 294.0U o,ooo ·112.113
1998 102.045 o,noo •1110,998 ]30,219 o,ooo •1117,1110
19q9 108,&&2 o.noo •151,1191 :u1. no o,noo •lb2.101
2000 115.(1lQ 0,000 •l!tl,911J5 Hi!,521 o.ooo ·1?8,0011
2001 120,1111 0,(100 -t~9.b98 1121.317 n.ooo •1411.10&
2002 125.597 0,000 ... 77.1111 llbi!.llJ 0,000 ·210.6011
200) 130,781 11,1100 ·J~S.I211 496.qo9 o.ooo •226,911
zooq 13!1,9bll o.non •lq2,83fl li]l.105 o.ooo ·l!lll.i!U
2005 I II I. I qa o.ooo •?.110.551 o;bft.502 o.oon ·25q,'51'5
i!OOb lllb,bOII 11,000 ·2011,1130 ftl].Ob!l 0,1100 ~280,qli!
2007 15i!,Ot>A 0.0110 •;?16,308 659.b]ll 0,1)00 •lOi',]IO
2008 15?.52Q o.ooo •2211 0 IB7 70,..201 o.ooo •123.707
2009 lbi!.qAq 0,(100 •.?li!,ObS 152.1&1 0,000 •111S.IO"l
2010 lbA 0 IIQQ u.ooo •:.!19,91111 HQ.BII o,~oo •lbb,'50i!
SCE~lRlOa ~<ED I HI3•·~'~RI SCENARI0••612Q/lQ81
SUM'URY OF PRICE EffECTS lND PROGRAMATIC CONSERVATION
IN GWH
GREATER FAIRBANKS
RUIDfNTilL IIIUSINESS ............ .. •..••.•...
lli'Hl•PP I Cf. PAOGRAH.JNDUCED CROSS-PRICE OWN•PRICE PROGRAM•INOUCF.D CROSS-PRICE
YEAR REDUCHON COtiSfRYATION AEDUCTIO_N _ R.E DIJC TIO"!_ CON~{f!Y~.!J.QN __ • PEDUCTlON ........ ................... ............................ -.. .. .................... .. ................ .. .... *" ...................... .. ....................
1qeo 0.0011 o.non n.oon o.ooo o.oon n.non
1981 n.oon o.ooo o.n1 n.oon 0.(100 (l.l!lll
1982 o.ooo 0 0 01)0 o.YU n.ooo o.ooo o.ll85
lq8J o.ooo o.ooo 1.n1o o.ooo o.ooo o.ue
19811 o.noo o.ooo l.lla? o.ool) o.QOO o, qll
l985 o.ooo o.oon 1.1811 o.ooo o.ooo lol!ll
1986 -o.l97 o.ooo o.lll4 .. o.nJ o.ooo 0. lllb
1987 ·0. 3911 o.ooo ·0.95b .. n.b65 o.ooo •Oo521
1988 ·0.591) n.ooo •2,U5 .. n.99tl o.ooo -1.388
1989 -o.1s1 I).OOO •3,US ., . no o,noo •2.256
l990 •0.91111 o.ooo -s.ou •l.M13 n.oon -1.123
n
lD I9Ql •0.997 o.ooo ·1.6q1 ·1.651 o.ooo -11.5811
(]) I IIIli! •1.010 1'1.01111 •10.3]0 •1.651 o.ooo •6.0116
199] •l.ni!J o.ooo •li!.9b2 •1.6115 o.ooo •1.501
19911 •l.nl& o.n11o •15.595 -1.639 o.noo •8 1 1lb8
l91l5 •l.OIIIl o.noo ·18.228 .. t.&U o.ooo -tn,IIJO
1996 •0.871 o.ooo .. 21.578 .. t.31.1l o.ooo •12.209
1997 •0.7011 o.ooo ·211.Q29 •1 1 0511 o.ooo -u.qaq
1998 •0.'512 o.ooo ·28.280 -o. hli o.ooo •15.7&8
1999 •O.lbO 0.11011 ·31.6131 -0.1176 o.ooo •11.5118
i!OOO •0.187 o.ooo ·311.1f81 .o.t87 o.ooo •19.]27
2001 11.11111 o.ooo •38.2bB o.JIIB o.ooo •ZioOSO
2002 0.111111 o.ooo •III.S55 o.BBJ o.ooo •22.713
200J o.P.zn n.ooo •llll.8111 1.1118 o.ooo •211.1196
20011 I.IS'i o.noo -118.128 1.9511 o.ooo •26.21Cf
2005 I.1JQI 11.non •'ii.IJIII 2.118Q o.noo •27.9112
zooea 1.qn o.oo(l oo5'5 1 168 1.}00 o.ooo ·30.0311
2007 2.1.1911 o.ooo -se.•a? II. 112 (1.1)00 ·32.126
2008 2.991§ o.noo •b2 .b16 1.1.9211 0.1100 •311.217
2009 l.ll9b n.noo •bb.ll]l) 5.7.3'! 0.1100 ·36. 309
ZOIO l. 991\ o.onn ·1fl.l83 6.'5117 o.ooo •31\.1.101
I J
l 1 ] I i
] I --J
SCENARIO I MEO I H13••0Rt SCENARIO••b/2111198)
BREAKDOWN 0~ ELECTRJCITY REQUIREMENTS IGWH)
(TOTAL INCLUD~B LARGE INOIJBTRtAl CONSUMPTION)
ANCHOR ARE " COOK INLET ··-···----·----·······
HEOIWI RANGE rPI"h.S)
········------------
RESIOF.NTIAL BUSJN~SS H ISCELLANEOUS Ex or.. PJOIJSTRIAL
YEAR RE.QUIRH'ENTS REQUIREMENTS REQUIREMEIHS LOAD ToTAL
·············•·•·• ----~-·-·········· ··············-··· ··········-······· ·············-···-
1980 9H.5J 875.3& 211.31 84 0 j)O 1961.19
191H 1020.70 11117.112 211.66 92.118 !0811. 86
1982 1061.8& 101".118 25.02 IOO.Ib 220b.52
1983 t I 03.02 1091.55 25.37 1011.211 2328.18
1984 111111.19 llbJ.bl 25. 7J llb.Ji! 24119.85
1985 1185.'35 UH.U 2&.08 1211.110 2571.51
1986 1218.45 1277.9& 26,88 137.89 26&1.21
19117 us1.ss 1121).30 u.u 151.]8 2750.91
ICJ88 li!All.o5 llU.fll 28.117 lbtl.e& 28IIO.lll
I981J 1117.75 liiOtt.9i! 29.27 178.37 2910.10
n 1990 1350.85 11147.23 30.06 1~1.U 1ozo.oo .
<.D
........ 1991 '190.20 11198.51 'Sl. 02 195.1S 31111.86
1992 11129.55 1'3119.79 31.98 198.110 3209.71
1993 111&8.89 11101.07 ll.93 201.66 ]]011.57
t 9911 150!1.211 lb'52,lb H.89 2011.•3 J]99.11jl
1995 ISII7.S9 IJOJ.bll ]q.8'5 208.20 ]11911.2A
199& 1592.28 l761.89 35.911 2111.111 36011.25
1997 lb3&.97 IRi!o.IS l7 • OJ l20.08 ]7lll.i!J
1998 lbAI.bl• 1878.110 38.12 226.02 !8211.21
191J9 IJi'IJ.]II 19lb.bl:l ]9.2? 231.9& 39111.1~
2000 1771.01 19911.92 40.31 237.90 IIOIIII.Ib
2001 1821.37 i!Ob7.29 lll.b1 24ll,'1b 11175.22
2002 1871.71) 2139.6& 112.90 1!52,0i! 1130&.28
2003 1'122.0] 21'12.01 411.20 259.1)8 11417.311
20011 l'Hi!,]h ;!2811,111 IIS.SO (!hb.lll 115&8.111
2005 C,I022.1>Q 235&.78 11&.79 :?73.20 11699,117
200b C,IOI\7,811 211bi!,l9 48.59 281.58 IJ81110,2l
2007 215],0b C!S&7.59 50.18 289.9& SObi.OO
2008 UI8.2S 2&n.oo '52 .18 2911.111 52111.77
2009 .?281.111 277A.III Sl.<H )Oh.72 'i1122.'il
·2010 21118,61 i'R81.112 55.77 11'5.10 %01.]0
0 .
1.0
(X)
J
8CENARI08 ~~0 I Hll•·D~I SC~NARIO••b/~Q/lQ6]
·-·-········---·~·-·
RESJIJF.NTIAL
YEAR REQti1REMEIITS
BREAKDOwN 0~ ELECTRICITY ~EQUJRF.~ENTS (GWH)
(TOTAL JNCLUD!S LARGE INDUSTRIAL CONSUHPTJONJ
GAEATEH fAJRBAN~S
-----·~---~·····~·-···
BUSIIH!SS ~UCELLANEOIJS
RF. Qlll AE ME NTS REQUIREMENTS .... ···········--·~--· ···~·····~·-······ ···---~·~·-·······
1980 l?t•.JII i!U.tll "·"
11181 191.011 lJO.ll &.n
1982 20S.t>9 241.08 b. 7Z
'983 2i!O.)'l i!9fi.05 flofl9
l98Q 2311.99 2b9. OJ &.&b
1985 lllll.bS 282.00 b.bJ
l98b 2fl2.9'5 29o.QO fl.&e
1987 Zl'b.~4 2?8. n &.711
11188 2811.511 307. t9 &.eo
1989 l112.8'l :U5.59 &.8'3
1990 ]lb. !II ]23.98 &.91
~~~91 3!3.15 Jh.U 7.22
l99i! J'i(l.lh ]1.1 9. i!9 7.53
l993 H7.17 3111.94 7. 811
19911 ]811.18 3711.511 e.1s
11195 1101.18 3B7.25 8.4&
l'fc:lfl 1117.59 qon.o;q a. ,.,.
1997 1134.00 'liJ.IIl CJ.oe
1998 IISO.Ill 427.11 11.]8
11199 llbfl.lll 4110 0 1.10 9.b9
2000 'l81 • .?2 1151.69 to.nn
2001 500.15 Clbll.65 lO.]Q
200i 517.07 48J.60 IO.b1
200] 5]3.9Q 119A.55 11.0 l
20011 sso.CJl! su.u tl.l'l
2005 Sb7.81l '528 0 11b 11.68
l!OO& 5117.'H' 5111!. H 12.10
2007 b08.o7 5b9.o5 12.53
2008 tJ28.1Q 589.35 12.115
2009 biiA.ll b09.b4 13.38
zoto &b8.Qi! b29.911 u. an
_) J I J I I J ) 1 )
F.XOG. INDUSTRIAL
LOAD TOTAL
·····----·-······· ----········-----·
o.oo 1100.31
o.oo 427.90
o.oo 1155.50
o.oo 4113.09
o.oo 5IO.b8
o.oo '518. 27
u.oo 570.03
ao.oo 601.78
JO.oo f1)].5)
40.00 6fl5.28
so.oo 697.0]
so.oo 727 .oo
so.oo 75fl.98
so.oo 78&.9'5
so.oo BU.9Z
so.oo 811b.89
so.oo 87b 0 90
5o.oo CJOb.CJO
so.no 9lb. 9 I
5o.oo 9bfl.91
so.oo 99b.9i'
so.oo 1029.13
so.oo IOfll.]ll
so.oo tnCJJ.Sb
sn.oo IUIJ.'H
!So.oo 11'57.98
so.oo 1198.82 so.oo 1239.t.S
511.00 li!80.Q9
so.oo 132l.H
so.oo 13b2.17
I j -.I -J J J
YEAR .......
1980
1981
1982
198)
1984
1985
t9U
1987
1988
1989
('")
1990 .
1991
1992
1993
19911
1995
IH6
1997
1998
1999
2001)
2001
2002
200)
2004
2005
2006
201)7
2008
2o09
2010
1
ANCHORAGE
----~
TOTAL EUCTRICITY REQUIRfMEIITS (OWH)
(NET OF CONSERVATION)
l
(INCLUDES LARG~ INDUSTRIAL CONSUMPTION)
MEDIUM PAtiGE (PR • .5)
~-~---·---~-----------
• COOK INLET GREATER FAIRBANKS TOTAL
····················---·--·----·---~------·· --~----···········----
I Ul. 19 400.31 2ll>f.51
2084.86 421.90 i!Sil~76
UU.Si! 4'iS.SO 2&62.02
Ui!IJ.19 483.09 Ull~27
211119.8'1 'ii0.66 2960.51
2571~51 518.27 1109~79
21161.21 570.01 3211 ~211
27!50.91 601.78 :U'52.b9
28110.61 6H.!53 1474, I J
nJo.JO 66'!1.28 ]595. 58
lOi!O.OO 1>97.03 3711~01
Jll11.8& 727.00 1841~86
)209. Jl 756.98 H6b.b9
)304.57 786.95 11091.'52
))99.G2 8U.9i! Gi!l6.)4
Jqqll.2" 846.89 11)111~11
]6011.25 876.90 41481~15
Hlll.2l 906.90 IIUI,IJ
18211.21 9H.91 11761,11
1914.1" 9b6.91 11901.09
11044.16 1'196. 9i! 5041~01
11l7'S.U 1029.11 5204.35
4)06.-28 I06l.l11 5JU~b2
IIIU7.J4 1093.56 55~9~90
11568.111 112'1.71 '5694~11
4699.117 1157.Q8 5A'57~4Cj
IH3IIO. i!l 1198.82 6079~05
'50111.00 10!19.6'5 UOO.bS
52111.77 1280.119 65~i!.2!,
'51122.51 1)21.]] 1>711].8!,
'560].)0 IJ6i!.ll:l !,96'!;'. Qf:,
--····]
n .
0
0
SCENARIOI t-IED 1 Hl] ... llRI SCE~URIII••b/lll/1983
VEAR ANCHIIRAGE • COOK INLET ..... ---·~·-··-~·~····-····
l980 39t..51
1981 lllt.IO
1982 11115.111
1983 1170.29
ueq 11911.88
1985 'i19.11R
1986 538.1111
1987 557.111
1988 S7b.J1
1989 sos.34
1990 tll11.31
1991 oJl.U
1992 6Si!.9'S
l 991 U2.l7
19911 691.59
1995 710.91
1996 7)).20
1997 755.119
1998 717.78
1999 eoo.01
2000 IJ22. ]6
2001 811!!.911
2002 875.52
i!OO] 90j!.J(I
20011 91£1.68
2005 9'55.26
200b qq I. 9 7
2007 1028.611
2008 IOh5.3Q
2009 1102.10
lOIO 1138.81
J
PEAK ELECTRIC REQUIREMENTS (MW)
fNET OF CONSERVATION)
(JNCLUOES LARGE INDUSTRIAL DEMAND)
H~OIU~ RANGE (PR • 0 51
····----------~····--·
GRE4TER FAIRBANKS TOTAl
-~---··M·--~·-········ ·····---·----~------~-
91.110 1!87.110
97.69 !1111~80
tOJ.n Sll9~69
110.29 seo.se
llfl.!9 t.1i'. 118
t22.A9 6112'. )1
130.111 U8~58
137.39 6911,79
liiii.U Ul .• OI
151.88 7117.22
159.12 773~111
US.97 799~5Q
t72.111 ns,u
179.65 ll$1~92
186.50 878.011
193.311 9011~25
200.14 913~39
ii!07.oll 96lf53
Ul.B9 90l.fl7
220.74 !020~81
227.59 10119~95
2311.95 1083,89
2112.30 ltl7.8i!
&!119.115 11'51~75
257.01 I U5~fl9
i!611.36 1219~62
27J.b'J 1265.6&
283.01 1311,69
292.33 1]157.73
lOI.6fJ 1 II Of. 7&
3IO.Q8 t 4119 .• 80
J .J ]
-
HE4--FERC +2% -
-
-
,....
I c. 101
) ] .... ] 1
SCENARIO I MED • HEII-·'ERC +U••b/21111981
~IJUSEHOLOS SERVED
~NC~ORAGE • COOK INLET
--~---···-·----·-·----
YEAR SINGLE fAt-liLY HULTtfU•ILY MOBILE HO~If'S DUPLEXES TOTAL --·-·-------.. -----··---··----.. -·--·-----·----------------------------
1980 151173. i'OlllJ 0 8210. 711~b. 7.1501,
O.OOO) o.oon) n.ooo) o.ooo) 0.000)
1985 /JCJ087. 2b20II. lt«P~2. 8567, 95)50.
o.ollo) o.ooo) o.ooo) o.ooo) o.oon)
0 I9CJO 6017?.. 271511. I 3825. 81160, IOCJbtn.
o.ooo) n.ooo) ( o.ono) o.ooo) 0.000)
0 w 1995 b&oH. Jl'IP. ts7to. 78)8. 1211018.
n.llllOJ o.ooo) o.ooo) o.ooo) o.noo)
aooo 17'1b7. 171115. t8t'H. 9000, llli!SH~
o.oooJ 0.000) tJ.OOO) n.ooo) o.ooo)
2005 113b8~. 1102311. 19bOCJ. •usa, 153181.
1'1 0 001'1) o.oon) o.oon) O.OOOJ o.ooo)
2010 897~11. 11)11115. 21~111. 103711, lbll81b.
o.oonJ (l.flOOJ o.oonJ o.ooo) o.non)
SCENARfOI 11(0 I HE4••FERC ti!X••&/2411983
HOIJSEIIOLOS SERVED
GRE~TER FAIRRAHKS
-----·-4····-·--······
YEAR SINGLE FAMILY HULTIFHI)LY MOAILE HmtES DUPLEXES TOTAL -·-· ····-·-----... .................... ................. -·-···-~-···· . ................
1980 7?20. 5287. 1189. 1 &17. 15113.
( o.ooo) 0.000) o.OOO) o.oonJ n.ooo)
1985 l()bQb. 5Af.lll 0 &!llo, 17&15. i!OliOA.
o.ooo) n.ooo) o.OOO) n.ooo) o.ooo)
n 1990 111111. 79&0. nos. 2375. 240IJ. . o.ooo) o.ooo) o.ooo) o.noo) o.ooo) _,
0 .p. 1995 lqQ]Q. J81U • lJ91. 2139. 28'505.
o.ooo) o.ooo) o.ooo) o.ooo) o.ooo)
2000 171\59. 81132. 4173. 2298. 327of!. · o.ooo) o.ooo) o.ooo) o.ooo) O.OOO)
zoos l'HlB. 1Jl57. 4ll9b, 225~. 35129. o.ooo) o.ooo) o.ooo) o.ooo) o.noo)
2010 20455. 99U. 4852. 21122. 31705.
o.ooo) o.noo) o.ooo) o.ooo) o.ooo)
j ) ._J
-··· -1 --l ··-l 1 J ]
SCENARIO I MEO I HEII• .. FEQC t2X••bl?l.l1198]
HOUStfiG VACANCIES
ANCI'IOIUGE • COOK INLET
····----~---~--·--·-··
YEAR SINGLE FAMILY "'ULTifAHtLY MO~JLE tiOMES DUPLEXES TOTAL ·-·· .............. ~---· -··-~----···· ··-···-···-·-~--~----~---.. ...................
1980 50119. 1bbb. 199l. I lib], lb209.
1).0~0) o.ooo) o.ooo) o.noo) o.ooo)
1qss 51.10. IIAqb • lib. 292. 21155.
o.ooo) o.ooo) o.OOI)) 0.000) 1'1.000)
n 11190 bb~. 2on. 152. 289, 1303 • .
-J n.noo) o.ooo) o.ooo) o.OOil) o.ooo)
0 m 1995 711~. 1751. 17). 780. ]115~.
o.oon) o.ooo) o.ooO) n.ooo) 0.000)
2000 ~SII. 2020. 200. z•n. H75. o.oon) o.ooo) o.ooo) 0.000) o.ooo)
2005 921. 2173. 216. 119. ]1,27.
o.OOO) o.ooo) O.OQOJ o.ooo) 0.00(1)
2010 q~a. 2)116. 2H. ]lf2. ]909.
0 0 1'100) ( o.ooo) o.ooo) o.ooo) o.ooo)
SCENARIO I l-IED I HEII••FERC tC!X••olliU\IJR]
HOUSIIH~ VACA'-~CIES
GREATER FAIRSAHKS
·-·------··-··~--~-·--
YEAR SINGLE FAMILY HliL TIFAMJLY HOFIILE HOHF.S DUPLEXES TOTAl --·-................. -------.. ·-·--. .............. ................ . .............
1980 36'B. H2o. 966. 89S, 8A5a.
o.nooJ n.onoJ o.ooo) o.ooo) o. 00(1)
1985 tl8 1 2b'H. ll&. ?ll. 3'§17.
n.OOO) o.oooJ o.nool o.ooo) o.ooo)
n 1990 Ufl. liSa. 21&. 81. 686. . o.ooo) o.ooo) o.ooo) 0.000) o.ooo) ___..
0
0'1 l9~5 lb4. tltlfl 1 17. eo. 729.
o.oOOl 0.000) o.ooo) n.ooo) o.ooo)
2000 t9o. liS!Ii. 416. 78. . 7111.
(1.000) o.oon) 11.000) o.OOO) 0 0 0011)
2005 210. -;oo. so. 70, fllO.
o.ooo) o.oooJ o.ooo) o.ooo) o.oooJ
zoto llS. '539. 53. eo. 697.
o.o9n) o.ooo) o.ooo) o.ooo) o.ooo)
.I ] _] _j .J
1 l l 1
FUEL PRICE FORECASTS EHPLOVF.O
ELECTRICITY (' I KWH)
··--------··----~--------~~---~------
YEAR lllSJOENTUL 8liS II~ESS RESIDENTIAL RIIS INf'SS ............... ·---------.. ·-·-·------·----·-··--
1980 o. 031 o.nlll o.n9S 'l.(IQJ
("") 1985 o.oll8 o.o~ts o.o91J 0.090 .
o 1990 '-l (I.OSJ o.o'5o o.oqz o.oe7
1995 o.ose o.o'55 O.II9Q o.oa9
i!OOO 0. ou 0.1159 o.o?& 0.091
zoos o.o65 O.llbi! 0.1198 0.091
i!OIO ll.(lt.J 0.0611 o.too 0.095
n . __,
0 co
J
YEAR
••••
1980
1985
Jfi90
1995
2000
zoos
2010
J
FUEL PRICE FoRECASTS EHPLOYFO
NATURAL GAS (J/HMBTUJ
ANCHORAGE • COOK INLET GREATER 'AJRBANKS
····················~·-·············~ ····~·-······-~················-~---~
RESIDENTIAL BUS INF.SS RESIDENTIAL lUlSI NESS ............ ............ ............... . ........... ~-
1 .no t.soo 12.140 ll.290
2.030 I.BOO u.ollo ll.biiO
3.190 i!.qbO 111.390 12.850
11.2b0 1.1.030 15.890 li.I.IQO
1.1.590 ~.~.uo l7.511(1 15.670
11 0 Q50 11.120 19.370 17. ]00
5.3110 s.u 0 2t.390 19.tOO
.1 .I J -_) l J
0
1..0
YEAR ·-·-
1980
198'5
UllO
Ull5
zooo
i!OOS
i!OIO
1
ANC~ORAGE • COOK INLET
FUEL PRICE FORECASTS EHPLOYED
'UEL OIL ($/MMBTU)
]
GRfATER FATRRANKS
••~w••••••••••••••••••••••~•••••••••• -----·---·---~---··----------···-----
RESIIlENTI Al HUSINESS RESJOENTUL RIISJNESS ................ .............. .............. ·------.......
7.750 7.200 7.11Jil 7.500
?.quo 7.1.120 s.oto 1. no
11.7b0 9.190 8.8110 8.510
9.68(1 Q.oqo q.760 Q.LIZO
IO.flllO Q.9AO 10.7811 IO.LIOO
11.791' 11.021' 11.900 li.LI80
13.020 U.17o IJ.IIfll 12.b80
... -1
SCENARIOa MEO I Hf~t .. ~FERC u~ .. ·~li!l4'1qsJ
RESIOENfiAL USE PER HQIJSfHOLD (KWH)
(~lTHOUT ADJUSTMENT FOR PRICE)
ANCHORARE • COOK INLET
--~····--·-······--···
SI·ULL LARGE SPAC[
YEAR APPLIAr-ICES APPLJAI~CES HEAT TOTAL ..... ........... . .......... ········-· ····-··-··
1980 2110.00 1.1500.~] sose.•u l3bq9.15
o.ooo) 0.1)00) 0.000) o.ooo)
1985 21&0.00 &092.SJ 1H1l.bl U0211,1U
o.OOO) 0.000) o.oool 0,000)
0 1990 2210.00 5'U5.91i 41519.116 U765 0 1l(l .
--' o.ooo) o.ooo) o.ooo) o.OOO)
0 1995 2i!hO.oo 592l~JO 4!5H.II7 1271U,77
o.oon) o.oOO) o.ooo) o.OOOl
2000 Htn.oo ~'lST,aZ 441117.611 U1111,8t.
0.000) o.ooo) o.ooo) 0.000)
2005 zlbo.oo 6020.37 111109.15 l27eti.53
o.OOO) o.onoJ 0,;000) o.ooo)
i!O 10 zutn.oo e.osa.oo 11436.52 12928.52
o.ooo) 0.000) o.ooo) o.OOO)
.J ... I J J .1 l
n .
-~
Jl -l
SCHI~RIOI "'EO
't'E A R
tqeo
PI 8 5
1990
tt;J9S
2000
2005
2010
1
I HEII••FERC
SM4lL
APPLI~NCfS
-·-·-·····
C!llbf).OO
o.OOI))
2Sl5.qq
o.ooo)
2ftOb.OO
0.001))
2fl7b.OI
o.ooo)
2HS.Qq
o.nooJ
i!Blfi.OI
o.OOO)
288,.00
1).000)
l J
+U••612lt1&qaJ
RESlnENTI~l USE PER HOIJS[HOLO (KWH)
(o<~ITHOIIT ADJUSTMENT FOR PRICE)
GREATER FAIRBANKS
-·-··-·-····~---------
LARGE SP4CE
APPLIANCES HEAT TOTAL
-·····-··· ·-··--·-.... ----------
5719.52 HIJ.Mt 1151'il.ll'
0.000) o.ooo) 0~000)
bl78.q2 lb0b 0 J7 l2l?l.i!8
o.oooJ 0.000) 0.000)
bLIII9 0 0} 38bJ.SIJ 12'il22.b2
0.000) o.oooJ o.OOO)
flht;l.2l 11051.72 lUQb.qS
0.000) 0.000) 0.000)
f>H2.90 111111.48 1181'2. 31
o.oool 0.000) 0.000)
b8J4.8Q 11510.1211 IUIIJI.SJ
0.000) 0.0011) o.ooo,
6882.91 llbll9,8l 141118.78
0.000) n.oooJ 0.000)
YEAR ···-
l'US
n .
1'~90
N
2000
200S
2010
J J
ANCHORAGE • COOK I~LET
······~·········-·····
8U01.0ll.
11.000)
CJsao.u o.oool
1lOJl.7S o.oon)
lt9b2.09
1).000)
121102. Ol
o.oOfl)
llOU.'il o.oon)
.I
BUSINESS USE PER EMPLOYEE (KW~)
(wiTHOUT LARGE INOUSTRIALl
(WITHOUT AOJUSTMENT FOR PRICE)
GHEATER 'AIRBANKS
~-············-·-·····
11195.70
0.000)
1972. tq
O.OOO)
8b9t1.21
0.000)
9lto.ta9 o.ooo)
q]9b.8l o.ooo)
9714.70 o.ooo)
l l l l ··~ 1 1 l
StEIURIOI HI::D I HECI•·FERC tlX··t.nvl96l
SU"1"'4RV OF PAJCE EFFECTS UID PRIJGR4HUTC CONSERVATION
)14 GwH
AllCHIIR4GE " COOK INLET
RESIOENTJAL RUSINESS ·-·--·-·-· .. . ..............
OWN•PAICE PROG!t All .. PH>U!:ED CIWSSwPRJCE OWti•PRICE PROGRAM•JNOUCfO CROSS.•PRICE
YUR PEDUCT IUN CONSER VAT IO'l REDUCTION REDUCTION CONSERVATION REilUCTION . -----. ·-' .. ,. ....... ................. .............................. .. ................... .. ................ .. ............................ ............
19110 o,ooo o.ooo o.ooo o.ooo o.oon o.ooo
1981 &.CIJi' 0.1100 •i!. S]5 9.395 o.ooo •l.l'~i!
I ~fBi! l2. Ball n.noo •5.070 111. HI o.ooo -0!,3811
198] 19.295 o,ooo ·7.605 i!8.18b o.ooo .. J,S77
19811 as. 121 o.ooo •10.1110 H ,581 11,000 .. Q, 7b9
1985 U,151l 0.(1011 .. u.us llb,lf11 o,ooo -'5,9bl
198ft 1111,581 o.noo •li,IJ117 59,313 o,ono •11.1811
1987 fllj,OOb 0,111111 ·50,219 11,bll6 0 ,ooo •lb,IIOb
lll88 81,11111 o,oon •&8, 991 8J,Il811 0,000 •i!lob29
IU9 97,8511 0,000 ·81,1bll 9(),320 o,ooo •i!b,85i'
(") ICJIJO 1111,0!78 o.ooo •10&,!13b I08,b5b 0,000 •H,0711
IIJ91 1011,1121 0,1100 •100,1385 12b.217 o.ooo •IIO.lllb w lll92 llii,SbJ o,ooo ·95,2]1 1113,178 o.ooo •118,bl7
1993 811,70& o.ooo ·89.582 lbi,JIIO 0.001) ·5o,888
191JII 111.8118 0,000 ·83,931 1711. 9•11 o.ooo -bS,IbO
1995 &11,991 0,(101) ·7R 0 i!!IO I!Jb,llbl! 0,000 •H,IIJI
199& 70.999 o.oon. ·8b,l39] 2ZO,IIbi! o,ooo •83,Hb
1'~97 71,007 o.ooo ·95.505 i!llll,llbl 0,000 •911,1flf)
1998 8'5,015 0,000 •IOII,ll8 i'b8,11b0 o.ooo •1011,525
1999 89,021 o.ooo •Ill, HI l.'lll!,1159 o.ooo •liii,A90
l!ono 95,0.51 o.ooo •li!I,JliJ 31b,ll58 o,ooo •125.255
2001 99,1Z2 o.noo •127 ,11811 1JJ,111l o.ooo •133.1'50
2002 101,212 o.ooo •llJ.US 351,2811 0,000 •IIJI,IIIIS
2003 107,]03 o.ooo •t39,1bb 3t.A,b97 0,000 •1119,5/JO
20011 III,Hl 0,000 •1115,907 lAb.IIO 0,000 ·IS7,&Jb
2005 115,11113 o.noo •ISi!.OilA 110],'521 0.1100 -lt."i. 711
200& li!ll,i!29 o.ooo •159,1108 11211.11111 o,oon -l7b.lll0
i!007 1211.9711 o,ooo •lb7.tb8 111111,713 0,000 ·l!lb. 51J8
2008 12q.?l9 o.ooo •1711,7211 llb~.]I'J8 11,01)0 . -19b,q57
i!OIIIJ I 111. lib II 11,000 ·182,i'88 1111a,o211 0,000 •i!07,lt.b
2010 IJil,i!IO o.noo ·111'1,13118 so&,bllq o.oon •217.17'5
SCENo\RIUI MEO I HEII•.FERC tin:--~:~n•llt CJ63 ITfRATIO~IS I:
SUMHARV Of PRICE EFFECTS AND PROQR 4H 4 Tf C CONSERVATION
IN GwH
t;AfAHR fAlRAANKS
RESIDFrlTOL AUSINESS .............. . ...............
OWI.•PIHCE PROGRHI• p40UCE!l CROSS•PRtCE OWN•PRIC~ PROGRAM•INOlJCfD CROSS•PRJCF:
YEAR REOIICTION CONSI!:RVATIOtl REOUCTtON AEOIJC TION CONSERVATIIlN PEDUCTICl~J ........ .................. .............................. .. .................... .................. .. ............................ .. ....................
1980 o.noo o.ooo o.oi)O o.ooo o.ooo o.ooo
1981 o.ooo o.ooo .0.097 -0.097 o.ooo •11.080
1982 0.(100 o.ooo ·0.195 .o.t94 o.ooo •0.159
1983 0.01)0 o.oon .o.29Z -0.292 o.ooo •0,239
19811 o.noo n.nQn -0.]90 .. o.J89 o.ooo •0.]19
IUS o.ooo o.ooo ·0.1187 .. o.ll86 o.ooo -0,)98
l98b ·0,197 o.ooo •I 0 0Q'l .. o.886 n.ooo .. o.7so
1987 •O.HII o,noo -1,702 -1.'.86 o.ooo •1,102
11188 -0.591 o.ooo ·i!. )I 0 -t.686 11.1100 •I 0 11153
1989 .. o.78ll o.ooo ·2. 918 -~.086 o.non •1.805
("") 19QO •0,9RII o.non .. ].525 ·2.1186 o.ooo •i',IS7
1-' 19 1H •II,9Cil7 9.oon -4.7l] -i!.54J n.ooo •2.786 1-'
·~'=-1992 •l.oto 0,01)11 •'l,9i!l ·2.599 o,ooo •l.llltl
1993 ·1.021 o.ooo ... ., • It 9 -2.b55 o.noo ·11,011]
19911 •1.030 o.ooo .a. ll7 ·2. 71 t tl.non -11,672
l995 ·1.049 0.(190 .. 9 .'H 5 -i'!.7b1 o,ooo -~.:sot
t996 -o.en o.ooo •ll.lll -i'.54t o.noo •b.i!liO
1997 -o.us o.ooo •ll.IIO •2.JIS o.noo -7.179
1998 ·0.5311 o.ooQ •lii.Q08 ·2.089 o.oon .. a. tt 7
1999 •ll.1bt' o.ooo ·16.705 ·1.8bi! o.ono ·9,056
2000 -o.l911 o.noll •18.50] •l,6]b o.ooo ·9.9911
ZOO I o.us o.ooll •20.54] •l.lbO o.ooo •10.919
2002 O.llbO o.ooo ... zz.~ei! .o.t.811 o.ooo •tl.8411
2003 o.1aq o.non •ZII.6ZZ ·0.207 ll 0 1l00 •12.769
20011 I.II)Cil o.noo ·H.t:.bi! n.i!69 o.ooo ·13.6911
zoos 1.11311 o.ooo ·28.702 o.111~ o.ooo •111.619
200b I. flb9 n.ooo -.H. ua t.lb6 o.olln •1'i.78l
2007 2.1011 0. (If) I) ·33.51!!2 1.987 o.noo •16.9117
2008 2,1311 o.ooo ·1S.9•H! 2.607 ll.noo -tl! .I 12
2009 3 0 I 7] 0. 01)1) ·H.uai! J.~28 o.ooo •ICil.l7b
2010 1.608 o.noo •1.10.1152 1.8119 n.ooo -zn.11110
--) J j ~~ J J J J ,J J I J
1 l l
SCENARIO I HEO I HEII••FERC t2X••bli!llll 981
8RfAI(00Wtl llF ELECTPICITY REQUIREMENTS (GWH)
(TOTAL JNCLliOES LARGE JHOUSTIUAL CONSUMPTION)
ANCHORAGE • COOIC INLET
-·-······----------·--
HEDJUH FUNGE (PR•.'i) ······-·-·····--··--
RESIDENTIAL BUSINESS HJSCHLANEOUS HOG. JNOUSTAlAl
YEAR REAUJRE~'ENTS RI!:QtlJAEHENTS REDUIREHF.NTS L.IJAO TOTAL
--~-----····----·-·-----------·-----·····-····-----------·-~------·-----·····~·····-······
U80 979.53 111'5. l& ttll.l1 811.00 19113.19
1981 10i8.10 9111\.P.l 211.75 92.118 ~091.1'5
198i! 11)7b.b7 1021.08 2'5.20 100.16 i'2 B. ll
1983 1125.2) 109].95 iS.b5 IOA.i!ll H'B.07
19811 ll1lo'll0 11116.111 2b.IO llb.li! 211!13.01
1985 1222.]1 llH,67 26.5' 1211.110 21112.90
19Bo li.''5b.l9 1281,'50 21.27 137.89 27112.91
1987 1290.01 IJB.IIII 28.00 1'51.]8 2792.81
1988 132).81 l]b5,12 28. H 1611.88 2882.75
(""') . 1989 1557.&5 11107.21 29,1111 178. H 2'H2.~7 ,_. ,_. 1990 IJOI.'U7 111110,09 ]0.17 191.116 10~2.50 c.n
1991 llf31.'21 1502.08 ll.26 I 95. 13 ~·1'59,bR
1992 114711.911 155'5.07 32.1'5 1911.110 H'Sb. Jt.
!993 ISIO.b~ lbO~.IJb 33.1111 20I.U :n•.n. 8'5
19911 1'5~1).111 1661.05 )11.511 2011.93 ]lt!i0.9]
1995 1590.1~ 171'1.011 35.61 2011.~0 )SUA, 02
1996 lbl9.115 17AI!.i'l . 3b,91 2111.111 ]b79.12
19~7 lbM.56 l8b2.3A 38.19 220.08 1810,21
199& 17J9.27 193b.S5 )9,117 22l·. 02 39111.31
1999 171\8.97 21'110.7] 110.75 231.96 11072.111
2000 18J8.bA 2(1BCI.90 uz.oJ 217.90 11201.50
2001 llt70.0II ZIM ... H 112.bO 2«4.9b 112bii,02
i'OO~ 1901.119 2127."5 II] .11 252.112 11]2C1,53
2001 1932.119 21 qo. H a].7u LJ5CI.Ol\ II'Sli5.0U
200Q 19bll.]l) ZI70.III 1111.11 26b,IU 1111115.5b
2005 l99'L70 Zlo2.29 1111.61\ 273.20 1150b.07
2006 2012.81, 2Zlb.OO 115.711 281,58 11'59b.l7
2007 2070,01 i!279.72 u&.59 j!l\Q.Qb Ub8b.i!f!.
2008 2107.11.. 2121.11] u 1.115 Jl!O~.]II 11171>.3!1
2009 211JII.]I 2~b1.15 111\.]0 306.72 lll\bb,ll!l
ZOIO 211H ,111 211tO.Ilb IIQ.Ib )1'5,10 /19'51>. 'ill
n
BCfNAHIOI ~EO l HE4~·FERC +2X•·~I2111IQAJ
HEOJUM RANGE (PR•.5l
~--············----~
RESIDHITIAL
YEAR REQIIl REtiErn 8
8REAKDO~~ nF ELECTRICITY REQOJREHENTS (GW~)
(TOUL INCLUDES LARQ£ lNOUSTRUL CONSUMPTION)
·--··--·-·········-···
BUSINESS HJSCELUNEOUS
RfQIJIPEMENTS RE13UIREHENTS ...... ····-·-~--······-· •w••••••~•••·~•••• ...................
1980 &76,]9 2\7,14 &,78
1981 t91.'30 230.511 &, 76
1'~82 i!Ob.bl iiiJ,94 &.711
t983 221.72 257,311 b. 72
1984 Hb,8J 270,711 6,69
1985 25l. 911 2841,111 6,61
l98b 21111.52 292,111 6.72
1987 an.oQ 300,111 &.16
1988 21!9,·U ]Ofl,lll &,81
\989 302.25 116,t5 b,85
!990 3111~81 Jlll .IS b,90
1991 :uo.n 335,92 7,18
1992 345,81 H7,&9 ?,117
1993 31>1,110 159,416 ., • 1f,
1994 31b,9i? 371.25 8,011
1995 392.44 J8J,OO 8,3]
199(1 ll08,b6 397,415 8,t>S
1997 4C!II,87 II II. 90 8.97
1998 <ILII.Oll 42&.35 9.29
1999 1157.30 441).111) 9,61
zooo 413.51 uss.25 9.•n
C!Otll ~~~3.90 4bO.I9 10.09
2002 /1911.29 llb'5.H IO,U
2003 5011,&7 uJn.17 10.42
20011 515,06 U7'5 0 4l tn.sll
zoos 525,45 Ul:\0,4b to. n
2006 5111,511 IJ6Q.;LII'I 10,96
2007 547,&3 498.10 11.17
2008 55~.72 SOI.I.ql I l.H
i009 SI:Jq,8l 51S.75 11.59
2010 SR0,9fl t;?.ll,o;ll 11.60
J J J J J J J I ]
EICOG. INDUSTFIUL
LOAD TOTAL
··------------·~--~--·········-·----
0,110 IIOO,Jt
0,00 428.80 o,no IJ'!I7,29 o,oo u!s.n
0,00 5111,26
o,no 542,'715
to.oo 51.5,31
20,00 6011,00
30,00 &34,61?
110,00 &~5.25
50,00 695,87
sn,oo ?B,IIS
50,00 751~03
50,00 7'78 .• 61
511,00 806,19
50·. 00 8ll~17
50p00 8611,7'5
so.oo 895.74
so.oo 926 ~ 1&1
sn.oo 9!.7.70
so.oo 9111!.69
50,00 !Otl4,i!8
5o.no 1019,87
50,110 10]5,47
50.00 1051,06
50.110 IObb.bl§
sn.no 10~6.71.1
50,00 110&.90
so.nn 1127.03
so.no 1147.1!i
':io.no 1167.28
J J J I .I J
1 ] ]
YEAR
1980
1981
19112
198)
~~~Bq
~~~85
19611
ICJ87
1988
1989
n 1990 .
1991
1992
199)
19911
1995
l99fl
19CJ7
1998
1999
2000
2001
2002
200)
20011
2005
ZOOt!
2007
zooe
200'}
lOIO
ANCHOFIAGE • COOK
TIIT.Al EUCTRICITY REQUIREHENTS (GWH)
!NET 0~ CO~SERVAIION)
(INCL'lnES LARGE JIIOUSTFIIAl COI'lSUHPflONl
JNLE. T GREATER FAIRAANKS TOTAL
·-----··---···------·~ --~-~·----··-------~---·-··--------------·-·
t9ol.l9 11011.11 216]~'31
20"3." 11&!11.110 2521 .• 9!;
22<13.11 1157.~9 20~11~110
2)51.01 116S.77 <!AlA.BII
21181.01 'illl.i6 i!9q7.29
i_lbl2.99 5112.75 li~S.71f
2702.91 sn.:n H76.211
2792.61 6011.00 )396~113
28~2.7!i fl)11.6&! ~517.]1
2972.67 665.?.5 11>17.92
1062.59 69'5.117 1758~116
l1'59.t>8 7 i!l .. ll'j 1881,13
l2Sb.76 751.113 111!07,79
31SJ.85 776.61 lll]ii!~IH.1
)1151'.91 1106.19 11257.13
)5118.0?. 8)3.77 11)191.79
Jo 19. 12 81111.75 115111,87
1610.21 119S.711 11?05.9'5
19 111.11 926.7a 111168·. OJ
11072.111 957.711 SolO~ II
1121l3.50 CJ813.69 5192 .• 19
112flii.02 100''.28 526''· ]I)
Ill«' II. 5J 1019.87 IJJIIII~I.IO
IIJR5.0U 1035.117 ~CI20.51
1111115.5(1, 1051.01) '51196.6?.
11506.07 10611.65 5512·. H
11596.17 108&.78 '56£12.95
Ub!lb.Z~ 1106.90 S7H.tll
11776.]11 1\27.113 "i90l.lfl
ll!li!>b.llll \1117.15 6011.61
1195t>,'ill llh7.21! hliB.R6
]
n . ...... ......
00
YEAR .....
1980
l981
1982
1983
19811
198'5
198b
1987
1988
1989
1990
1991
1992
1993
1904
1'~95
P'l'ib
19117
1998
1999
i!OOO
i!OOI
2002
2003
2001.1
2005
200b
2007
2008
2009
2010
PE4K flECTRIC REQUIREMENTS (MW)
«NET UF CO~SfRVATIONJ
( JIICl!JDE S l4RGE I NDUSTR l AL DEMAND J
HEOIUH AANGF CPR • 0 5)
--~·····-·····-~--··-·
ANCHORAGE • COUIC INLET GREATER FAIRRANKS TOTAL
·--------·---.. ····--·· --~-············---··· ···---------··--------
Ht.. 51 91,110 11117:90
422.811 97.90 ~i!0~70
11119,n 104.110 S!if. SO
11'75.39 ll11.91 58b ·.29
'.i(.II.U 117.41 bt9:n
527.97 123,'H &lllt.n
511&,98 uo. 91) b77~89
S&b.OO 137,119 7031'89
58S,OI 1411,88 729.89
b04,02 151.87 1S5~89
bi!3.0! 158,8b 7111.89
blli!,lll lbl!i.lb M7~9b
bH.SB 171.115 IHII,OII
b8Z.1b 177.75 et-c). It
702,111 1811,05 llh:to
7ll. 92 190,H 9U:i!b
7118,53 197.112 qus:9s
77'5. 1'5 ?.011,119 979~b4
eo 1. 77 2ll.5b 1013~33
818.38 2111,64 1047~02
855,0(1 i!2S.71 1081),71
8U.n i!Z9.27 lo9b,40
819. ib 231.83 1112.09
891 • .5' 23b.39 1 tn. 7J?.
9(13,52 1]9,95 ltll] .• ll7
9tS.65 i'43,51 tl'!ilil~lt.
0]!.7Q i!llfl,ll 11tll~f1Cjl
951.9! 252.70 li!OII,b!
970.06 257.~11 12?7~!b.
Qlll\,20 2bl,99 1?.511~10
IOOb,]ll i!bb,119 1212 .• 8~
J .J .... J )
HE6--FERC 0%
-
c. 119
l ~--1 -1 ] l
SCENARIUI MfO I HEb•ooFEAC Ol--bJlllll 98]
HOUSF.tiOLOS S£RVED
~NCHORAGE .. COOl< INLEJ
---····~--··--··-·-··-
YEAR SINGLE FAMILY MULTIFAMILY 1-tORILE HOMES DUPLEXES TOTAL ·-·-.... ~ ... ····-··· ··-~········~ .................. ··········---... ----····--·-
1980 151171. 20]lll. 8210. 71186, 71'503. o.oonJ o.oon) 0,000) 0,01)1)) 0.000)
n . 1985 llb227. 2b2011 0 10958. fl5b7. q I q5b •.
N l).non) n,OOO) 0,000) 0.000) c 0.(100)
1990 SHOb, 25877, IH05, BilbO, 1055118.
0.000) O.lli)O) o.oooJ o.ooo) 0.1'100)
tens bbfl911, ]I)Aif). 15lb1, 8131, 120'50/J.
0.001)) o.oon) ll.OOO) o.oon) o.ooo)
2000 b9bb8. HI qo. lbl5t, 799b, li!b!JSS, n.oon) fi,Of)O) 0.0011) o.ooo) 0,000)
2005 7111iOJ, 15&69. 17412, 8579, t lb207. o.ooo) n,OOO) o.ono) o.ooo) 0,000)
2010 8011111. HI SA, 19111. 91b0 0 11185911,
0,000) n.llOO) 11,000) 0,001)) o.ooo)
SCENARIO I MED I HEb••FERC U••b/211/t 941
HOUSEHOLnS SERVED
GREATER FAIRSlNKS
········---····-·~····
YEAR SINGlE FAMILY MUL TIFAI11LY MOAilE HOliES DUPlfXES TOTAL .... ................. . ................ ............... ·····---··-·· .. ................
PUO 7220. 5187. 1189. lld 7 0 1511'5.
O.OOI)) o.n(IO) ( o.ooo) 0,000) o.noo)
!'iSS IObllb. SAh7 0 lllO. t7b5. 20IIOT.
0,1)00) o.ono) o.ooo) 0,001)) o.nool
0 . 1990 lll.lbl. 19t~o. UOI:I. 2375. 211001. _.
N o.QOI)) O.QOO) o.ooo) 0.000) o.ooo)
N
1995 I SUR. 7Rllt. 111118. 2339, l81bb.
n.ooo) o.oon) o.ooo) o.ooo) o.ooo)
lOOO lb184. 7101. ]8()7. 2298. 1~191..
o.ooo) o.ooo) o.ooo) n.ooo) 11.1100)
200'5 17555. 8293. 4123. 2252, 3~?.21.
1'1.000) O.QOO) 0 0 000) o.ooo) 0.1100)
lOIO l897b. 925~. 4!iOl. 22119, ]11981.
o.ODII) o.oon) o.ooo) o.OOO) c o.ooo)
. _) .I .I -)
~-~ ~---] ----] .----_I
~ -" -1 I I J 1 j
~·a --) -1 l -]
SCENARIO I 14[0 I HEb••F"ERC O"••b/2111 t CJfll
•wus JUG V~CANCIES
ANCtiiJR~r;[ . COOl( INLET
••••••••••M•~·-·----~-
YE~R SINGLE FA"~ILY MUL TIF" AM Il V MOBILE HOMES OUPLf)(ES T.,TAL ·----------·-···-...... ,.. ........ --------·· .. -· -----·---·---·------·------
ICJ8o 5089. 76bb. l qq I, l'lbJ. 1&.?09, o.oon) O.OOil) o.ooo) o.onn) o.non)
('"") lCJ85 lj0A 0 14q&. U!l. 2q2, i!lltJ. o.ooo) 11,000) o.oon) o.oQn) o.oon)
N w t9CJO 113'7, 1471. I lib. 28CJ. 2514~. o.oooJ o.nonJ o.ooo) o.ooo> 0,000)
IH5 727, 1661.1, loB. 2811. 2841,
0,000) 0.01)11) o.ooo) o.oooJ 0.1)00)
2000 1bb. IJ'lO. 178. 117' • ~(1011,
o.noo) o.oon) 0. 00.0, n.ooo) 0,000)
2005 820. 1921. 192, i!83, 1222.
o.oOOJ o.ooo) o.ooo) 0,000) o.non)
i!OIO 8CJO, 2 t 15. 211. )C)CJ, .~524. n,QOO) o.ooo) o.noo) 0,000) o.ooo)
SCENARIO I MEO I HEo••fERC U••bli!lllt983
HOUSING VACANCif.S
GR[ATER f•IRRANKS
-···-···-········--·-·
YEAR SINGLE FAMILY MULTIFAMILY MOBILE tlOMES DUPLEXES TOUL .... ............... ······-······ ········-··---············ ..............
1980 3&51. llln. 98&. 895, 88511.
o.noo) o.oon) o.OOO) o.ooo) n.oool
0 . 1985 118. Zb5ll, 211. 722. ]'it fl. _.
N o.ooll) n.ooo) ( o.ooo) o.oooJ o.ooo) .p.,
1990 t2b. liSt~. 24. 8 I • b8b.
o.noo) o.ooo) o.non) o.ooo) n.ooo)
1995 1&7. 41111. 38. 8(1. 711.
o.noo) o.ooo) 0.000) o.OOO) o.oOOl
2000 180. ll4n. 42. 78, 740.
o.non) o.ooo) 0.00(1) o.ooo) 11 0 001))
2005 tH. li411 0 il'i. 77. 763.
n.oon) n.QOO) o.ooo) o.ooo) o. oorn
zoto ii'OQo soo. so. 28. . 1 lib 0
o.ooo) o.oon) 0.000) o.ooo) o.oorn
__ ) -)
n
N
Ul
HAR ·--·
1980
1985·
1990
t995
2000
zoos
2010
1
FUEL PRICE FORECASTS EMPLOYEO
ELECTRICITY (S I KWH)
ANCI-IllRAGE .. COUK 1NLE T GRf.ATER HIRBANKB
--~-------~·······-····-···-········· ······-·--~M•••••••••·-··---~-~------
RESIOENTI Al BlJSI tiE SS REBIOENTIAL RUSINESS ·······-··· ·--··---~--............... -..............
o.o:n o.O]U n.n95 0.090
o.ouR o.ou~ 0.091) o.oqo
o.os~ (1 0 0U9 0.090 0.011'5
o.ns? n.oso o.oqo o.oes
o.ns9 11.056 n.o9o o.nA'i
o.obt o.ose o.o9o o.<lll5
o.obl o.obo 1'1.090 o.ns5
I
n . __.
N
0'1
.~
SCENARJUI MEO I HEb••FERC OX••b/2411985
ANCHORAGE • COOl< INLET
fUEL PRICE FORECASTS EHPLOVEO
NATURAL GAS (S/MI'IBTUJ
GREATER FAIRBANKS
·········-·············-··--···--·~·· -···-···········-··-····--~·-·······-
YEAR RESIOENT Ul BUSINESS RESI OENTI Al BIISINUS .... ............ ···--······ ······-·--· . .............
1980 '· 730 a.soo u.Jqo 11.290
1985 z.oto t.l'80 12.'!130 11.190
1990 2.9bO z.uo u.s1n t l .190
1995 l.bOO 1.110 u.sJO 11.190
2000 l.bOO J.:no U.!ilO tt.l911
zoos 1.1100 1.170 U.l!i30 tt.uo
2010 1.6011 l.Ho 12.1§10 II. 190
I ) --~· - -
J I I _)
-
] 1
FUEl PRICE ,ORECASTS EHPLOYfn
FUEL 01l (tiHHBTU)
ANCHOPARE • COOK INLET
~-·----~-----····---··-··-··~------·-••~••••••••••••w••••••••••••••••~••••
YEAR RESIDENTIAL HUSINI!SS RESIOENTJ Al AIJSHIESS
n -~·-............. ··-··---· ... ····--·-··· . .................
1980 7.750 7.20!1 7.810 7.1liOO
1985 1 ... 10 7.130 1.100 7.1130
1990 T.&JO 7. t .~n J.?oo 7.030
1995 1. bJO 1.11n 7.700 7.a~n
iOOO 7.bl0 7.130 7.700 7.030
zoos 7.630 7.110 7.700 7.1130
i!OIO 7.bJ(I 7.110 7.700 7.1130
SCENARIO I l-IED I Hfb••FERC OX••*»/24/ I 98]
RfSIOENTIAL USE PER HOUSEHOLD (KWH)
(WIT~OUT ADJUSTMENT FOR PRICE)
.UIC HORAr.E • COOl< INLET
--~····--·-····~~-~---
SMALL LARGE SPACE
YEAR 4PPLJ ANCES 4PPLIA~ICES HEAT TOTAL .... . ............. ·······-·· -~·-··-··· ~ ............
n 1980 211n.no &sno,ol 50811,'52 l ]bq9,15
N o.nonJ o.ono> o.ooo) o.oou
co
ICJ85 2lb0 0 00 U5l~t~b 482\,78 llll3. 211
n.ooo) o .;uno) 0,000) o.ooo)
ICJQO 2210.00 bOi!'O .• I.Ifl 458&,110 12816.88
o..ooo) o.·noo) o.ooo) 0.000)
IQ95 22bo.oo 59110,98 415tCJ,9(1 12740,94
n.OOII) 0.000) n.OOO) 0.000)
2000 2110 0 00 IJIJAa.o6 qaqB,08 127~&. u o.noo) o.ono) o.ooo) 0.000)
2005 2-,&o.oo b058.14 44l8,19 12816. 7-J.
o.oon) o .•ooo, o.ooo) 0.000)
2010 2UlO.nO 1.11:!3.90 441.1(1,09 1297'5,0c:l
o.OOO) o.ooo> o.ooo) 0.000)
.I ]
1 1 l
SCENARIO I MED I HEb••FlRC IJl••&/i!l.l/' ql)]
RESIDENTIAL USE PER HOUSEHOLD (KWH)
(WITHOUT ADJUSTH~NT FOR PRICE)
GAEATfR FAlRBAN~S
--~---··-···---~·--·-·
SHALl LARGE SPACE
YEAR APPLIANCES APPLIANCES HF.AT TOTAL ...... -~--.. ···--• .... til ........ ............ . ...........
n 1980 2llbh.no 1§719. 5:! Htl.6b 11519,18 . _..
N
O.llOO) 0.000) 0,000) 0.000)
1.0
1985 .i!SH.qq bl71'1.9b 3606,11 12lZl~2b o.ooo) . 0 .·000) c 0,00(1) 0.006)
1990 i!f>06.oo 6448~8'1 38&7,1.12 129?2,31
o.non) o .·ooo J 0,000) o.OOO)
1995 267&.01 bbl'l~SO '1053, H 134~0,81
o.ooo) n.:ooo) o.ooo) li.OOO)
2000 i!74&,00 &793. "' 43011,7?. 138114,90
o.OOO) 0;000) o.ooo) 0.000)
2005 28tf:~.no &81Pr. 7tl t15t7,ZO 11.1178,90
o.noo) 0.'0001 (1.0011) 0.000)
ZOIO 2"8~.no 68f17'. 911 '1656,67 11.1430,tll
o.ooo) 0.'000) O.OOO) 0.000)
.......
w
0
J
SCENARIOI MED I HE~·-rERC O¥••bl24/tq8l
YE~R ···-
1980
1985
.1990
2000
aoos
i!OlO
~NCHOR~GE • COOK INLET
•••••••~~••••••••w••••
9S80.5J
n.oon)
10'-bi.I'C! n.ooo)
t l OFIIS •'JZ o.ooo)
BUSINESS USE PER EMPLOYEE (M~H)
(WITHOUT LARGE INOUSTRill)
(WITHOUT ADJUSTME~T FOR PRICE)
GREATER 'AIR8~NKS
1495. 7n
0.000)
79U. 111
o.otHl)
.uoo.ss o.ooo,
BH3.7l
ll.nOO)
921§2.04
0.000)
9~1b.lJ
O.OOO)
J I I
l 1
SCENioRIOa HED I HEb·~FERC OIC••t./211/1983
SIJHIHRV OF PRIU EHECTS loNO PROGRioMioTIC CONSERVlTtON
IN GWH
ANCHOR loGE .. COOK 11-llET
RESID£NTJAL ~U!INESS .............. . ...........
OWtl•PR ICE PRUGRioH•INOUCEO CROSS•PRICE QWN•PRJCE PRQGR~H·INUIJCFI> CROSS•PRTCf
VHR IIEOUCTlON CON Sf A~ 4!]9_11_ R~f!UCTIO!!. _ REflllP ION_ CON9f'!Y~HQ~ PEDUrTtON ..... . ......... .. .................. . ........... ........... ••••••••••••••• • ••••••••••
1980 n,ooo 0,000 0,000 o.ooo n,1100 o.non
1981 b,2JII 0,000 •2,058 9.3811 11,ooo -n,Bb7
l9U 12,11b0 o,noo -11,115 111,761 (1,000 -1.1!11
1983 IR,b91J 0,01)11 •b,l13 28,1111 n,oo11 •l'0 hOI
19811 211,921 n,oon .. 8,231 11,'521 o,ooo •3 0 llb8
1985 31,151 1),000 ·10,2119 llb.CIOI 0,001) ·11,]1'5
198b H,bB 0,000 ·19,595 '57,9b5 n,onn -7.9211
1987 118,1111 o,oon ·28,901 bQ,028 o.noo •11.'5111
1'988 5&.~9b 0,000 ·313. 207 811,091 n,1111n •1'5.1011
1989 b'5,071! 0,1100 -117,513 91.1511 0,11110 •111,6911
(")
1990 n,st.IJ o.ooo •56,1119 l02.2l1 o,non •i!l,i!IHI .
1991 97,6911 o,noo ·81,1198 '111,11]1 o.o11o •27,i!2l w l99i! 121,827 0,0011 •106,117 135,115& 11,ooo ~1?.11>11
1993 I IIS,hl n.ooo •1)11,856 152,075 n,noo .. ·p, l'f9R
1991! 110,1)95 11,(100 •1515,535 lbR,69'5 o,on11 •II;?,O]h
1995 1911,;!211 0,(11)0 •1811,i!l1 18'5,3111 n,noo •llf-,9111
l99b 2111,11119 n,oon •t99,9j!'f 1911,bllll n,noo •119,21\R
1997 215,11b9 o,noo •219,611(1 203,973 0,000 •SI,bOI
19911 0?5f>,089 0,000 -~.J9,J'5l 21],30] 0,000 •'5],9111
1999 ~H:..709 0,1100 •259,1167 U2,f>l1 o,noo •%.221
2000 ;.n.uo o.noo .;!7ll, 78(1 ~]1,9b1 0,1100 •SIJ."illl
t'OOI JO(I,'liiS 0,000 -;H9,9~5 illlll,b711 o,nnn •f>t,n72
i!OOi! 1o3,7bn n,ooo •i'AI,070 2,7,377 n.non •bl.t.Q]
2003 ]06,fl1"-0,000 -~82.216 AIJO.ORII n,no11 •b~.1111
211011 310,199 n.ooo •i!!Al,Jal :PA2,191 n.no11 •bA,bi>S
2005 ~1],11011 0,11011 •2811,506 ;t9'5,119A n,ooo •71.1Qf-
lOOCI 111),619 o.non -~811,bAI 112.729 0,0011 -711.2~2
2007 llq.AB o,non •2EIII,8Sb ~29,9(10 o,11nn ·71.1611
t'OOII 12~.0111 1),0110 ·'-85,1)]0 ~117,190 0,000 •1'10,1153
200Q Hb, 2b I n,nuo ·:!8'5,;?1)«; 1f>U,II21 n.nllo ·Rl,5Jfl
2010 ]2q,117b o.nnn •lA5,11Jil 1RI,65i! o,oo11 •Af>.~2'!i
SCENAIUOa ~EO I HEb••FEflC OIC••Il/i!4111HIJ
SU"'I1ARV or PRICE EFfECTS AND PROGRAMATJC CONSERVATION
IN QWH
GREATER FAIRBAI>lKS
RESlOfNTl-l AU!IJNE8S ............... . ...........
OWN-PRICE PROGR 411• J NUlJCEII CROSS•PRICE OWN•PR!CE PAOGR AM• J NDIJCFD CRf\S.S•PRTa
YEAR PEDUCTION CONSFJ~VA!_ION REDUCTION REOU(:TJON. CON~fRyA IJ QN AEDUrT 10N ........ .................. .............................. ...................... .................. .. ............................ .. ....................
1980 o.oon 0,000 o.ooo o,ooo o.ooo 0,0011
1981 .. o.i'b7 o,noo 11.010 0,0011 o.ooo 0,024
1982 .. o,"ill 0,11011 0,11111 0,0011 o,noo 0,1\48
t983 •II.AI)O o,noo 0,209 n.ooo n.noo n.072
1984 •I,OI)b 0,000 0.2711 n.ono n.noo 0,1191>
1985 -t.:UJ n.ooo o. JQ9 n.noo n.noo o,t2n
198b •l.'i7i! n.ooo o.au .. o.s5z n,ooo O,l]b
1987 ·• .au n.oon 0,1114 •t.IOS n.ooo 0.153
1968 •2.1151 o.oon o.s:u -t.t.51 o.oo11 o.110
I 989 •4'.291 n.oon 0.599 -2.210 o.ooo n.tAb
n 1990 •2 .'§JO 11,1100 o.fl&2 -~.7b2 o.ono o.201
__,
w 1991 ·2.772 n.ooo 0,72'5 -1.201 o.nnn o,.?tq
N 1992 •1.011 0,000 o. 788 .. ).641) o.ooo O,BII
1991 •1.2511 0,0911 0,1151 .. 4.079 o.ooo O,i'S(i
1991! ·11.491> o.ooo 0 0 9111 -4.511 n.ooo o,2bl>
1995 -J. 7]? 0,1100 O,C171 -4.956 o,noo o.2Bi?
t911b •J.IIb9 0,000 I.OU -5.14? o.ooo n.2A7
~~~Q7 •14.oot o,ooo 1.046 .. S,:UA n.noo n.29~
&1198 ooii,I]J (l.ooo 1.081 -5.110211 o.ooo 0,?97
1999 .. 4.1.&1:> n.oon I ,lt 5 •'!1.720 o.ooo o.~o1
i!OIIO •11,3911 o.non i,ISO .. -..qtt o.oon 11,3011
lOOt ·ll.'i(?l) o.noo ' I If!&! -6.109 o.ooo 0,11'5
2001. •1!.1141 o.noo 1.2111 .6.306 n.ooo n.323
2003 •11.7bt> o.ono 1,246 -b.B;(I4 0,0011 o, HI
2004 •11.1188 n,ooo • .ne ~fo,701 n.oon O,HII
2005 -s.ott n.ooo l • HO -t..ll911 n.nno o.JIIb
200b ·'5.1110 o,nno 1.3411 .7.t31 o.ooo o.JSt.
2007 ·"i.2b9 11,000 t .177 .. 7.]bl! o.ooo o.31:>7
200A .. '5.39Q n.noo a. a It .. 7.1:i9b o.nno n.J77
2009 •S,B;c!A n,ooll \1 11115 .. J.A29 n.oon n.3A7
i!OIO .. c;.b'i7 o.ooo t,ll79 .. R.Ob~ o.ooo o.,97
I J J ~.~ J J I J _j J } ) --I .I J . _)
"l ----J "1 »> "l J l 1 ---l ] 1 I l l -~ "]
SCEN.fHOs t-tEIJ I HH••FERC OX••ti/211/I'HI]
IIP[&KOOIIHI OF ELHTRTCITV REl1Uif!EHENTS (GWH)
(TilT&L !NCLIIDES URr.E I~OIJSTRIAl CONSUHPTION)
.U'CH!lR&GE • COUI< IUl[T
•w•w•••~••••~-~--~N••P
HEOlUH RANGE (PR•.'i)
-·-···-·----~~·--···
RE'SIOf.IITJ&l IIUSINESS HJSCELLANEOU!I BOG • JNOUSTRJ•L
VEAR RE~liiRI':~If.NTS REIWIRE"HENTS REQIJTREHENTS LOAO TOTAl
····-~------··-··---·---------···-·· •••w••••••••••••~• ------····N·-··-·· -----·----------·-
1980 9H.Sl ~7'!i.1b 24~]1 84.00 196],19
1981 10?.0.99 9117.90 24.t.7 92.08 t'085.bll
1982 101>2.4'; 1020.115 25.1)] IOO.It. noa.o9
I 983 II 0].90 1091,00 25.411 tne.i?a ?330.511
19114 ttas.n llt15,5S 25.U. 111>.!2 ;ta"i2.99
1985 ll'lt..lli! 12]11,fl9 2t.,l2 1211.110 li57S,IIl
19l'b 121b.b7 1279. ]I) 2b.8f4 I 37.89 ;.b~0.711
19A7 1211b.51 1120. 'H 27 .u 151.18 .?7111>.04
1988 li!7b.]l> 1Jbl,72 211.38 lbll.ll8 iiiiJI.Jll
1989 l]nb.i!l 1402,91 l!9,U 178.H ;t91b,l>ll
n IIJ90 IHb.Ot. 141!11,111 29,89 t91.111> 'oot".911
w 19"1 1311.11 1500,112 10.811 I 95. 13 lOIIII.IIa
w 1992 IIIIO.lb 1557.'H 11.87 1911.110 1197.114
IIIQ] 111117.21 Ul11.19 H.8fo 20l.bb ~295.9)
191111 141111.27 tun.'ll\ H,81> ?04.9] H91,'lll
\9115 15il.li' 1727.'5b 34.85 .,08,?0 1491.11,
19116 151b.QII 172Q.95 15.07 ;q u. '4 ]5tb.lll
1917 IS"i2.t~l\ l7H,1S 35.211 220.08 l51!0.]6
1998 IShti.JI 173'1,711 ,5.51) ::!2t..02 ,5611.57
li:IH ISIH.97 1717.11 15.72 i!ll.<l& '51111.79
2000 159'l.t.ll 17)11,151 ]5,Qil ?]7,90 ltd J.oo
t'OOI I o .. l.U 177'1. 72 3b.S'i i?411.9t. :!1&71'1~811
2002 I bll7.1.10 11107,91 J7. I 5 liSi-'.02 Hllll,f>ll
2001 lt.7l.511 ll.l42.n9 H.H ?SII.nR llll0,52
2004 tt.95.57 11171..28 38.]b ?61>.111 l87&.1ft
2005 1719.5'5 1910.'17 J6. 97 ll73,?.0 ,9112.20
lOOt. 17"l.ll] 19bA.illl 11:1.9? 281. "i8 110112.17
2007 17115.30 2(12&.01 110,811 ;t119,9f, 111112,1'5
2U08 I A I A. I_, 20S3, 78 IH ,811 ?.911.14 lli_llli!.ll
?009 IR5I.O'S 2 I Ill 0 ~4 42. n l0~.72 11]112.11
2010 I fllll. q;_t 2199.11 1!],7~ liS. Ill 111111?.08
ICENARlOt HED I HEb··'ERC o~--~li!lllt9Al
BREAKOOWN OF ELECTRICITY REQUIREMENTS (GWH)
(TOTAl INCLUDES L&Rr.f. INIIUSTR!Al tONSU11PTION)
QREATF.R FAIRBANKS
·-·--·~···--------·-~·
MEDIUM RANGf (PR•.S)
-------·--·--·---~--
RESIDI!NfiAL BUSINESS MISCELLANEOUS EXOG. INDliSTRJAl
YEAR REQIJIRfMEIITB · REQUIREHEIJTS REIJUtRE~ENTS LOAO TOTAL
····----·-····-··~ ---·-·-····-·---~· ····--·····--····-••~•••••••w••~•••• ·---·-····-·······
1980 Pb.JO l!l7.14 b. 111 o.no IIOD.Jt
1981 I q I. bO no.n b .u o.oo 1128 .• 1:>9
1982 20b.81 !41.53 6.74 o.oo 41Sl'.07
198] 222.01 25~:>. n 6.71 o.no IIAS 1 ll'!i
1084 2l7.2i! 1&9.03 6 0 1:>0 o.oo !513.8~
1985 2152.111 281.12 6.b7 0.110 5112.21
tUb 2114.3'1 no.e& b.10 to.llo Sl'l.91
1087 27b.27 29B.b0 4.711 zo.no (o!ll.tol
19811 21111.10 30~.14 '· 77
311.00 ua_:u
1989 JOO. U 114.08 4.81 llo.oo Ul.lll
("")
___. t990 312 .'Oil 121.112 ~.84 5o.oo ~90.7t
w
+:> toot 327.28 nu.ao 7. Ill 50.00 718.b0
19CJ2 311~.52 )11~.55 7.43 so~oo 746.50
199] 157.71; JSA • 9 I J.H 5o.oo 711.1. 39
19011 )7].01 171.27 e.ot so.oo 1102.211
1995 JM.i!5 381.64 8.30 sn.oo 1130.111
l9'Jb ]04.85 181i. ii!3 8.38 so.oo 11111.411
1997 1101.11"1 J86.tl2 8.11? sn.oo 8ilh~l'll
19911 11011. OS 188.'H 8.5h !So.oo 8'!i5.01
1999 ljjij.tl5 J90.fl0 e.~4 so.oo 863.20
2000 1121 .n 191.'5~ e. n sn.oo Ul.lliY
2001 4i!9.tl! UB.II7 e.e8 5n.oo AA6.117
i!002 u'\o.99 uos.h 9.011 so.oo oot,3A
i!OOJ tiiiQ.IIIj Uli!.25 CJ.to so.oo ot6.29
i!OOtl 415i!. H 4111.13 9.311 so.oo •U1.211
2005 1160.59 Ui!fJ 0 0i! 9.so so.on 11116.1\
200~ 470.27 US7.tO 9.11 sn.nn 9U.oll
2007 IIH.oU IIQII.17 9.93 so.oo 98R~O'!i
2008 IIA9 .'b'. 459.215 10.115 so.no IOOII.O:t
i!I)OQ qiiQ.]O 1110. n 10.36 so.no t0211.90
2oto snA.o~t 1181 • Ill tn.se so.oo IOS0.9f>
J J -J
w
(J1
l l 1 l l -~
SCE~ARIOt MEO 1 HEb••fEAC Ol••b/~~11985
YEAR AN!;HORAGE .. [IIOK
TOTAL ElECTRICITY REQtJIREMEilTS (GWH)
(NET OF C04SfRVATJON)
ClNCLIHlES lARGE INOU!ITA! AL COt~SUHPT!ONl
~EDIU~ RANGE (PA • ,5)
INLET GREATER FAlABAN~S
)
TOTAL
-~--------··---------·
_______ R ______________
-----M···--------··-··
ICJ80 19li].ICJ ~on.JI 216'f.51
U81 21lfl5.b~ 1128.69 C!5111 1 JJ
19112 2208.0IJ «57.07 ~665.16
198] i'Hil.Sil 1185.115 ~81"i~9Q
19811 ii!ll"\2.'19 51J.8J ~9bb.82
1985 2575.111 511:!.21 :u 11~ b'5
l98b '.bbi).7G 511.91 l2J2~b5
1987 Hllb. oa 1,01.61 na7.b5
l988 (!llll.lll Ul .31 'Ubi!~b!i
19119 291b.bU bol.nl JS71.b"!i
t990 1001.911 b91l.11 161J2~b'5
1991 ]099.'911 718.U 111111~511.
1992 1197.911 111b.50 1911~.111
I99J 12Q5. 9) 1711.19 11010 .• 11
19QII '\lQJ. 91 1102.29 1119&~22
1995 11191.9) A ]0 • 18 113<'2~11
t99o 'Uib.l~ flll'.llb IIHII~bO
IIJ'I7 15IIO.lb 111111.711 11]117.09
1998 15"11.57 85'5.(11 111119~59
1999 JljA8. 79 Ae.l.29 1111"!12~08
:i!OOO lbi].OO 1171.117 1111111(.57
2001 11;1711. 811 Allb.ll7 115b'5~H
2002 1JO~.bli '101.111 ~bUb~Ot.
2001 3810.5~ 91&.?9 IIJ?.b.81
20011 J87b. ]b 9ll.i'O uAn7~511
2005 19112.20 911ft. II IIIIAII.]O
lOOb 00112.11 9o7.oa '500'1.25
2007 IIIOi?.l'i CJ611.!15 ~110.20
2008 112112.11 101)9.02 ~2"il~t5
2009 11]112.11 lfl2q.qq c;]12.09
2010 1111112.0tl 1osn.q& "iiiCJJ.O~
l
J
w m
j
SCENAPIUI MEO I HE&·-FE~C OX••bl2q/1983
YEAR ANCH(JfUGE -COOK INLU
.,.. .. •••••••••~••••~••w••••
1980 .59&.51
l98l liZ\ .lb
1982 que..oz
"1983 470.77
1984 1195.51
IQIIS 520.28
l98t> 518.15
11187 5'1&.111
1988 574.48
19~9 592.511
1990 biO.&I
1991 uo.S7
1992 b50.53
1991 &7o.sn
1994 b9(1.11b
1995 710.11]
199o 715.15
1997 719.81
1998 Uq.bO
1999 P9. 3i!
2000 7)11.011
2001 1111.i!h
2002 1bO.qll
I?.OO] 713.7(1
20011 711&.92
2005 1100.111
200b 8?0.]1
1?007 1140.111
2008 flbo.u
20D9 680.79
l!OlO 900.Cilb
J :J, ,,)
PEAK ELECTRIC ~EQUIREHENTS (HW)
(NET OF CONSERVATIOHl
( JNCLUIIES LARGE lNOUSTRUL OEHANO)
H~OJUM RANGE (PA • .5)
GREATER FAIRBANKS TOTAl.
-~·---·-·-·--·····--·· ··-·-·--·----·-·------
'H.IIO 487'.90
97.87 519 .• 14
1011.]5 550.37
1111.83 liill(.bl
111.:11 bl2.811
t.u.n &1111~07
uo.•u b68~9~
ll1.l5 &U.7b
1411.13 118.110
150.90 74l.qll
'n. ~>8 7b8~29
lbii.OS 7911~U
1711.41! U0~9S
176.79 847.24
181.15 87!'.&2
169.52 1\99~9'5
191.111 90&~56
191.10 91].111
I 95.l9 919.79
197.n8 92b~40
J9A.'J7 913~02
202.111 9CI9~bl1
20'5.113 9&6.2b
~09.18 1'.1112~119
2li!.S9 999~51
11'5.911 1011>~11
uo. 78 10111~08
t!i!';.57 I06b~01
;:t]O.lS 1091)~98
2H.111 111ili.9l
'.39.9] 111.10 .• 811
J I J J J J
HE7--FERC -1%
-
-
-
-c. 137
-
1 J .. -·~ l .. , l
SC[NAHIOI ~EO I HE7•·'ERC • lX••&IlqJI CJHl
HIJUSFHOLOS SERVED
~NC~ORAGE • COOK INLET
···-···-·-···---------
YEAR SINGLE fAMilY HIJLTIFAMILY MURllE HOHfS OUPLEXES TOTAL
-~----·------·-·· ............... ................... . ................ -~··-·-------
1980 ]54 n. 201111. B2lo. 7ll8fl. 71'501.
O.OOO) o.noo) o.noo) o.ooo) 0~000)
n 1985 . 119118. 2&2011. 11502. ~567. CJ:;IIt~ •
w o.ooo) o.ooo) · o.oon) o.noo) 0.01)0)
lD
1990 bOH7 • 21257. t38b5, Bll&o. lOfiCJ2fl.
1.1,000) n.ooo) o.oocl) o.ooo) n·.non)
ans 6&711~. llOOII. UH2. 81H. 1211126.
n.oun) n.ooo) o.nno) n.ooo) 0.000)
2000 707118. HbOA. t63H, Rlll5 0 1288&1.
o.noo) n.oon) n,ooo) o.oooJ · n.noo)
2005 757311. l&2b1, l17l9. 87C!I. ll81Jl2 0
0,000) 0,000) o.noo) 0,000) o.noo)
i!OlO 82]117. 3qsun. tqQ&9. 952&. 1'51181,
(1,000) O.llOO) 0,000) 0,000) n,OOO)
SCENARIO I HED I HE7 .. FERC ·1~·-&/241198]
HOUSEHOLDS SERVED
GREATER FAIRBANKS
··--···-~-~---·········
YEAR SINOLE FAMILY foiULTIFAHILY "109llE H011ES DUPlEXES TOTAL ··--................... ................. ··-··---····· ................ ........... "' ..
1980 7220. SlAl'. un. lb17 0 15311.
o.ooo) (. fl.noo) o.ooo) ( o.oon) ( o.ooo)
1985 IOb4b 0 5880. i!llO. l7b8. 201.124.
o.ooo1 o.ooo) o.ooo) 0,000) o.ooo)
n
1990 I ISH. 19b0. U22. 2175. 24090.
-"" o.ooo) o.ooo) o.ooo) o.noo) n.ooo) 0
1995 141.107. 7841. 3236. 2319. 27823. o.oon) 0,000) o.oon) o.ooo) o.ooo)
i!OOO 15712. 7701. 3~14. 2298. 29348.
o.oon) o.IIOO) ( O.OOO) o.ooo) o.ooo)
2005 171 04. 8020. 401?. 2252, 31391.
o.ooo) o.ooo) o.ooo) o.ooo) c o.noo) .
2010 18524. 90]1. 4197. 2t9b. ]IHSCJ.
o.noo) n.oon) n.ooo) o.ooo) o.ooo)
] l
1 -l ) 1
SCENARIO I HED I .. E7••FERC •I ~· .. blil!J 1981
tiiiUSING VACANCIES
ANCHORAGE . COOK HILEY
----~·-·-··-··········
YEAR SINGLE FAI-IILV HUL TJFAMIL v HORILE HOMES DUPLEXES TOTAL ... .., ................ ······-~-·--· . ................ ................... .. .............
1980 5069, 7~&~. l9clt o tl.lol. t&i101J,
n 0,0011) 0,001)) 0,000) o.ooo) n.OOO) . _, 1985 51.11, 11.19ft. 127. 292, 245'5, .p. _, 0,000) o.ooo) 0,000) 0,000) 0,000)
1990 ~bU, 91, 151. 289. 120?.
0,000) 0,000) o.ooo) 0,000) O.OOO)
1995 7H, 1ft7tJ, lb9, 2tU. 26&1,
0,000) 0.001)) 0,000) O,fiOO) 0,000)
2000 718. 181111, ton. 152. 312b.
0,000) 0,000) 0,000) 0,()00) 0,000)
zoos 831. 1958, 195, 2B~. 3274.
0,000) o.ooo.) 0,000) 0,000) 0,000)
ZOIO 90&. 21St, 2111. 114, J58b,
o.oon) O,OC)O) 0,000) 0,000) (1.000)
8CEN4RIOI MED 1 HE7••FERC •lX••~IZQ/1~81
HOUSING VACANCIES
OREATfR FAIRBANKS
·-·~---···---·········
Y£4R SINGLE FAMILY HULT IF A"11LY 1-108ILE HOMES OUPLEXES TOTAL ···-·········--·· ·····---~---· ............. ········--·-· .•......•....
1980 ]b51. Hco. 98b. 895, 88Sll. o.noo) o.noo) ( o.ono) o.oon) n.ooo)
....... 1985 liB. ·2641. 2Q. 719, 3902 •
o.oonJ o.ooo) o.ooo) o.ooo) o.oon)
1990 t 21. asu. 25. 81. b61.
o.ooo) o.ooo) o.ooo) o.ooo) ( o.oon)
1995 159. IIllA. lb. so. '722.
o.ooo) o.ooo) o.ooo) o.ooo) o.ooo)
2000 t n. tJ4o. 40. 78, 13t ~
o.OOO) o.oooJ o.ooo) c o.oooJ 0.000)
zoos lBB. 431. 44. n. 111?..
o.ooo) n.ooo) O.OOO) o.ooo) o.oon)
ZOIO 204. IIRA 1 118. at. 82t.
o.ooo) o.OOOJ o.ooo) o.ooo) o.ooo)
.J
.··)
YEAR ....
1980
1985
1990
1995
C!OOO
2005
iOIO
l 1 -·» I J -1
FUEL PRICE fORECASTS EMPLOYED
ELECTRICITY (! I KWH)
ANCHORAGE • COOK INLET GRfATER FATRAANkS
··-·-····--~-·-·········-·-····-····· ~-····--~···········-····--·R·-······
RESIDENTIAL BUSINESS RE8JDENTI AL BUSINESS
·--------·· ................ . ........... -·-·-~--.. -·
o.ol7 o.o111 o.o9~ 0.!190
o.o48 o.o4~ 0.095 0.090
1).052 0 .ou .0,090 o.oa5
o.os11 0.0'51 o.o9o o.oas
o.os5 o.os2 0,090 o.on
O,OST o.osa 0,090 o.oe'S
n.o59 o.ostt 0.090 0.08'5
l
SCENARtOI HEO I HE7••FERC •l¥••oli41l98l
ANCHORAGE • COOK INLET
FUEL PRICE FORECASTS EMPLOYFD
NATURAL GAS (S/HMBTU)
GREATER FAIRRANKS
············--~---··················· ·-·······-··----····--~-----------·-·
YEAR RESIDENT UL BUSINESS AESIDENTJ AL BUSINF.:SS ···-••....•.... ······-··-· . ........... . .............
1980 •• 730 1.500 12,740 11,290
1985 2.noo •• 710 12.280 tn~~eo
1990 i.fi70 i'.&£10 tt.uo 10,11]0
1995 1.120 1.090 lt.tto 9.920
2000 ],noo 2.830 to.se.o 9,1130
2005 2, 4ha0 2,130 10,040 ~.970
2010 2,Boo 2.630 9.550 R,5]0
J }. J J
l
/
0 .
-J )
SCEtURIOI t-4fl) I
ANCHORAGE • COOl< INLET
FUEL PRICE FORECASTS EHPLOV£0
'UEL OIL (S/H~RTU)
GREATER FAIRBANKS
····-~--·-···-···--···-····-·-··-···· ·············------·---·--·····--·--·
YEAR RES IOEIH IAL BUSINESS RESlllENTIAL BU81NfSS ·-·-.............. ............. ·---···-··""' ··--·-·-···
1~80 7.750 7.200 1.e1o 7.1300
1985 ?.1180 fi.Ho 7.sso 7.280
19"0 7 .II 0 &.uo 7.180 &.qln
P~"s &.7ol) 6.Uo b.lli!O 6. ~590
2000 o.ll3n ft.oto 6.1.190 6.1&0
2005 &. un c;. uo 6.170 ~~qbO
lOlO 15.11i!O ~.1.1110 s.e7o 1§.6b0
SCENARIOI MfD 1 HE7••FERC •l~~•bi~Q/ICJ83
&l-ULL
YEAR APPI.IANCE& .... . ..............
tUO .2tto.no
D.!'liO)
1985 .21bO.oO
o.ooo)
tno 2210,00
o.ooo)
1995 2'.bO.OO o.ooo)
2000 2310.00
0.000.)
2005 2JbO.OO o.oooJ
i!OlO 21110.00
0.000)
J
RESIOENTIAL USE PER HOUSEHOLD CKWHl
PUTHOIJT AOJUSTMF.NT II='OR PRJC[)
ANCHORAGE • COOK INLET ········-·············
LUGE SPACE
APPLllNCES HEAT TOTAL
·······-·· . ............ ...........
bSno.ol 5088.52 tlb99,15
0.·000) o.ooo) 0,000)
1.09.2~311 4770,11 130?3,0';
0,000) n,O!lO) o.ooo)
597S~fl0 4S79,19 127611. 7CJ
0~1)01)) o.noo) ( o.ono)
5919~57 450,35 li!b92,91
( o.o(IO) o.oon) 0,000)
59149.2.2 ij1146,9ij 1270b,lb
( o'.ooo) 0,000) o.ooot
Ul9~tl lllltb,)8 U71J5,St
0.000) o.ooo) 0,000)
601\11,0'7 lliiUO,bB U!9H,7S
0.(100) ( 0,000) o!.'o oo 1
J } i
1 l
SC[N&IHOI HEO I HE7••FEFIC •IX••6/~IIIl98l
RESJOENTJAL USE PER HOUSEHOLD (KWH)
(WITHOUT AOJ\JS T MUIT FOR PRICE)
GREAT!R FAIRBANKS
··~---------~---~-~---
SMALL lARGE SPACE
YEAR APPLIHICES APPLIANCES HEAT TOTAL ...... ·--·---··-·--·--·--· . ........... ·---·-----
1980 Zllbb.nO 5719.52 3)1 1,66 11519.11~
o.ooo) o.ooo) n.oon) 0.000) n
__. 1985 215JS. 99 6178,78 3&07,U U322.00 _p. o.non) o.ooo) o.noo) 0~000) '-J
1990 2Mb.IIO bQLit,l.91 38t.8. 80 12921f.7t
0.1100) o.ooo) 0.000) o.oooJ
lt,Jt,l5 i!fl7b.Ol 6b611~6B 110ll8, H 11389,02
c o.ooo) 0.0(10) o.noo) O.OOI))
2000 271ft>.Ol b792.07 11]08,98 IJ81.17. 06
o.ooo) o.ooo) o.ooo) 0.000)
2005 21.116,00 6Mcr. oo ll'5tO,IO 11.1175.10
(1.000) 0.000) o.OOO) o.OOO)
2010 2886,00 68fiQ~70 11656,]9 lllllli!, 09
o.ooo) (1.•000) o.OOO) o.OOOJ
···-
1980
IUS
2000
zoos
2010
-J J J J ) J
4NCHOR4GE • COOK INLET
·············-········
euo'7.oll
· o.nooJ
10"23.18 o.noo)
lt82Q.e,9
o.ooo)
12blJ.CJ5
o.ooo)
) ,) l
BIISHIESS liSF PER EMPLOYEE (KWH)
(WITHOUT l4~GE INDUSTRIAL)
(WITHOUT ADJUSTMENT FOR PRICE)
GR'-ATER ,AJRBANKS
·····---·······-····-·
11
J
'711CJ5.70
0.000)
nn. TS o.ooo)
8Ub 0 08 o.ooo)
qasq.oil' o.ooo,
Qb01§0 75
o.ooo,
J ) J 'I
,_ 1 l )
SCEIUR 101 MEO I HE7••FEAC •IX--fd2111l98]
SUMI-lAR't' OF PRICE EFFECTS AND PIWGRU1ATJC COrHIERV&T JON
lN GWH
ANCHORAGE • COOK INLET
RESIIlEtHlil FIUSINE!IS
·---~·----~ .............
OW'i•PAlCE PROGRAH·lNDUCEO CROSS•PRICI!: OWN•PRICE PROGIUM•INOIJCED t:ROSS•PAtC:E
YEAR REDUCTION CONSE~IIATJON REOIJC TION RE!>t}r.T ION CMI~F~Y~JH!~ • _ REDUCTION ....... .............. ............................ .. ................... .................. .............................. .. ....................
1980 n,ooo !l,oon o.ooo n.oon o.ooo o.noo
1981 b. 399 o.ooo •1,910 9,]89 o.ooo •0.707
~~~82 U,798 0,001) •3,1320 t I!. 779 o.ooo •I,C11'5
1981 19,1CI7 o.ooo -s.no 28.168 0,000 •2. Ui!
19811 25,"19b o.ooo ·7.bll0 ]7.557 o.ooo •2.829
1985 Jl,99b o.noo ·9,550 116.9116 o.oon •l.'5U ·
198& 110,087 o.ooo •17,528 157,988 o.ooo •6.]118
1987 118,179 o.ooo •25.4305 b9. 030 0,000 •9.260
1988 Sb,i!7t 0,000 .]].118) 811,1172 0.001) •12.122
19!19 bii,]IJ] n.ooo ·111,1161 91,11" o.ooo •I a. 98·11
1990 12.11511 o.ooo •119,11)8 lo2.15b o.noo •17.8116
n
_, 1991 8l,a29 o.ooo ·611.97b 112.153 0,000 •211,591
+:> 1992 <JII,aoJ o.ooo -72.'!11] I H. ISO o.oon -;n. Hf> \.0 199] 105,178 0,000 •811,0511 1]2.1116 1),000 •2b.081
19911 llf>,l'i2 0,000 -95,'587 111?.1111 0,01)0 •lR,IIi!b
1995 127.127 11,000 •l07,1i!5 1'52.1110 o,ooo •]1,57\
1996 131,1178 0,000 •I09,'55fl 159,1111 o.ooo -12.o11a
l9'n ll6,bl9 0,000 •111,987 166.1112 o.ooo •:Jil.518
1998 1111.281 n.ooo •lla.a19 113. 1113 o.ooo •l2.H2
1999 111'5,9];! o.noo •116.850 tAn. tall o.ooo •lJ,IIbS
zooo 150,58] o,ooo •119,?81 187. Ill'S o.ooo •]],9]11
2001 1511,551 o.ooo •ll9olb'!l 197.2116 o.noo . •3J,95a
i!OOi! 158.5111 n.oon ·119,01.19 zn7.1117 n.noo •3l.9bll
200] lbl,ll"b o.ooo •118,9]) 211.11118 0,0011 •JJ,98a
i!OOII 166,11'511 o.noo •ll8,1H7 227.5119 0,000 •H. 999
2005 170,1121 11.!1011 •llA, TOI 2]7.650 0,000 -1a.n1a
i!006 17'5,1111 n.ooo ••• ,.i!80 2'51. 715 o.ooo •Jl.ba5
i!007 181,2011 0,001) •IJ1,fl59 ?65,1119 0,000 •31.21'5
zoo a lAb,595 I),OI)O •ll7,11H i'79,11011 o.ooo -32.1106
200'1 19l.9~b o.ooo •llT,OIII 291.989 0,000 •l2.'Bb
2010 197.178 o.ooo •llb,S95 10A,n711 o.oon •li!olbT
GREATER F&IRBAIIKS
AESII>ENTI ~L BUSINESS
-·-·-----·-.............
IIWNooPRICE PRoGIH l"i·l ROOtED CROSS•PRICE ,. pWN·PRICE PPOI;RAHooiNOIICED CROSS•PRlCf
YEAR IIEOUCTJOI~ C:ONS£RY& TI!Jtl RE~~Jr 1 TOr-~ D~ rliJrTflhi -.-' ""enNS!'FI'IIUIIllll ••. --REDIJrT TO~I ....... --· -.. -.., .... --,--.-··-.. ~-··-~;; .......... ~t: ................ . ....................... .. ................. _. .. ................ .. ............................
11180 0.1100 o.ooo o.ooo 0,001) n,ooo 11,0011
t981 o.ooo o,ouo 0,1511 o.ooo o,ooo 0,07111
1982 o.ooo o.ooo 0,307 o.ooo 0,000 n.tSI
1981 o.ooo o.ooo 0,1161 o.ooo o.ooo 0,226
19811 n.ooo 11,000 0,615 o.ooo o,ono o.1n2
1985 0,001) 0,000 o.ue 1),000 o.noo 0,177
1986 .. o.335 o.ooo I • I 711 .o,55o o.ooo 0,!17'5
1987 •0,670 o,noo 1,579 •1.099 o,ooo 0,771
1988 •1,1)0'5 o.ooo '. 9811 -1,6119 0,000 0,971
1989 •1,3111 0,01)0 2,189 ·2,t99 o,ooo 1,169
1990 •1,676 0,0011 2 0 ?9'5 •2.7~1(1 0,000 I, 166
('") 1991 •l,9bO o.ooo 3,1.159 .. ].109 o,ooo l.U7
Ul 1992 •2,211~ 0,000 ll,llll .. ].1169 0,001) 1.9111
0 1993 •2.52(j o.oon 11,788 -1,829 0,000 2,231
19911 •2,8111 o.ooo 5,1153 .a.aqo o,ooo 2.!527
1995 .. ],0911 o.non 6,118 .a.sso 0,000 2.8l7
1996 •1.282 o.ooo 6,896 .,11,7ll 0,000 1.117
1997 •l,ll6b 0,000 7,6711 .. 11.1172 o.ooo 1,1117
1998 •l,bSO o.ooo 8.1152 .. s.oJJ o.ooo 1.717
1999 ·3,8H o.ooo 9,230 -!i.l(jll 0,000 II,Olf>
2000 •11.011 n,ooo 10,008 ·'5,356 o,ono II, Jl 6
2001 •11.168 o.ooo 10,9611 -6,896 0,000 6, ll I
2002 ·11.)19 n.ooo II. 920 .. 8.1137 o,ooo 7,907
2003 ·11.1171 o.ooo 12.876 .. 9.•ns 0,000 '~• 702
20011 •11,622 0,000 u.~:n •tl.Sill 0,000 lt,ll97
zoos •11,711 o.ooo 111,789 •11.(159 o.ooo 13,292
ZOOb •11.920 o.ooo 15.971 ~t2.1bl 0,000 12o176
&!007 ·5.0b7 o.ooo 17 ·'52 •ll,2b2 o.ooo 12.259
2008 .. s.i!tll o.noo t8,ll41 .. ,0,363 o,ooo 11.141J
i!OO!J .. S.'Sbl o.ono 19.Slb .9.11b5 o.ooo 11.21!6
lOlO ·5.508 o.ooo 20.6CJ8 MR.'Sf>b o.ooo 10.110
_I ]
l l 1 -. 'l '1 1 l -l "j 'l l "J ~ .
l' f ~ •
SCENARIO I HEn I HE7••FERC •l¥••6/~ll/lCI81
BRE.KOOWN OF EUCTRICJTV REQUIREMENTS (GWil)
(TOTAL llli:LIJDES LAPGE HIIJUSTRUL CONSIJHPTJON)
ANCHOPAGE • COOl< INLET
------~---·--·--·-----
MEDIU!ol RANGE (PR•,5)
·-·----------~·-·---
RESJOENTJ AL BUSINESS HJIC!LLANEOU!I [)COG, INI'lliSTRUL
YEAR Rf:QIJJRE11ENT6 RE llU IRE HE. N TS AEQIJJREMEIHS LOAD TOTAL
····---·-·····-··· ·-·--·-··-·-····--·-·--------------~ ---------~-------~ ··---·------------
1980 979,51 8H,Jb 211,11 8LI,00 19b1,19
1981 1027 ,b'S 9118,17 211,75 92,08 2092,65
1982 I 075,·71! IOZ0,99 25,111 tOO,Ib 22<'2 .. 10
198] 1123,88 I 091,80 25,611 108,211 2351,5'5
19811 1171,99 llbb,bl 26,08 II b ,12 211Rl,OI
19115 lC!ZO,It 11)9.11] 2b,52 1211,ll0 2bl0,ll6
1986 12'52,1) 1280,bll 27,22 117,89 ?697,811
19117 128ll,l5 1121,115 27.91 151,]8 l785,29
1988 1116,17 1163,06 28,60 lbll,88 2872,71
1989 IJLI8,t9 111011,27 29,)0 118,]7 291>0,11
() 1990 I HO, 21 111115,119 29,99 I'H, 86 101.!7,511
--'
Ul 1991 11108,]1 l117f.t,llb JO. 72 195,11 1111,08
1992 11116.511 1508,211 li,CIII 198,110 )1111,61
199] 111114,71 1539,1.11 32. lb 20l,f.tb 1218,1"1
19911 1'192,88 1'570,09 12,89 ;!011,93 HOI,68
1995 1521,05 U02,1b )],61 208,20 Hl>'i, 22
199& 1518,05 Ul9,111 33.98 2111,111 1405,51
1997 1555,05 IUb,U 311.1b i!i!0.08 1445,81'1
1998 1572.0 .. U53.29 111.11 22b~02 ]118b,09
1999 1589.0'5 U70.h 15 ,II 231.96 JIS2b, 38
2000 lb0b."05 11>87.211 15.11@1 237,90 :1566,67
i!OOI 1&28,7b 17211~211 J&.ll 21111.96 ]6]1.1,11
2002 lb51.117 t7bi,3J u. 711 252.02 1701,56
200] lll111.11 1798. n 11.17 259,08 ]169,00
2004 lb'Jb,8tl 1111S.ll2 :JB.oo 2bb. '4 ]83b.llll
2005 1719.59 IIIH.IIb 1A.b1 273.20 H03,8A
200b 11'50."61 1929.<;7 19,56 281,'58 1.1001,12
2007 1781.bl t98b,b7 110,50 289,9/) LI098,7b
2008 IRli!.bb 20111,77 111,11] 298,111 Lll'l6,20
2009 llit.!l.bll 2100.88 112,3b l0b,72 11291,&LI
2010 I!HII.70 .!151.'lll 11],2Q 11'5,10 11]91,011
n
_.
U1
N
J .. ~
MEDIUM PANGE (PR•.S)
-·-·····---·······-~
RESIDEiiTlAL
YfAR Pf.DUJf:IPtfNU
BPEAKOOWN OF ELECTRICITY REQUIREMENTS CGWH)
(TOUL INtl.IIDES LAROE INDUSTRIAL CONSUMPTION)
GREATER '~!PBANKS
~--~-------·----------
811S li~F SS MISCELLANEOUS
REQIIIREMENTS REAUIREHENTS ..... ·-----------·-···· ··-···--·-·····-~--·····--·-······--
1980 l7b.]Q 211.l4 11.78
1981 191.29 2]0. Jb 11.711
1982 2011.19 143.58 fl. 7J
1981 221.09 i!H.Bl fl.7o
19811 235.99 uo.nJ bob7
1985 250.89 281.1.11 ~~~n
198& ibl.h 290.93 fl.&e
1987 271.1.11] 298.59 11.12
1986 28&.50 lOo •. h b. 75
1969 298.11 111.93 II.H
1990 )IO.i!ll 121.110 b,8l
1991 122.09 Ji!IJ.&b 7.01
1992 Jll.9ll us.n 7.21
1991 1115.80 )112.80 7.41
1994 551.&5 149.1111 7.111
1995 3&9.50 1511.93 7.81
199b 375.f!A :s&n.Ja 7~9]
!9C17 JFJI.811 JbJ.U 8.03
1998 388.011 Jb7.1.8 e. u
1999 !94.21 no.H 8.21
i!OOO 1100.]9 1711.18 a.n
2001 1107.)1 ]81.13 8.118
2002 IIIII. 21 l8fi.08 e.o2
200) 1121.15 195 0 0] a. 77
i!Oil4 1128.(111 1101.08 8.91
2005 11111.98 1108,93 CI.OS
i!OOb 11111.52 1.119,112 9.2&
2007 IIS?..Ob lli!'1.C1l 9.llfl
i!008 llll•J.SCI llllfl.]Q 9.611
2009 llb9.1l 450.118 9.BII
iOI() 1171.117 ll&t.1o IO,Ob
"I t J cl J J ) ) I J
EliOG. INDUSTRIAL
LOAn TOTAl
···-··------·--------------·-·····--
o.oo lJf)O.JI
o.oo IIC!8.41
o.oo 456~51
o.oo 484.110
o.oo stz.7n
o.oo 5110.80
to.oo 570 .• 37
ao.oo 599.94
lo .• oo IIH.52
411.00 ~~~9.09
sn.no lll\8·.u
so.oo 7n7.71'
sn.oo , 7211.90
50.00 711&.02
so.oo 7115.111
so.oo 7811.211
so.oo H J. 99
5n.oo 801.12
so.oo 8l3.11!i
so.oo 8i.'J.I8
so.oo 812.<!1
so.no 8IIII.C1i!
so.oo e11o. en
so.oo 8711.911!
sn.oo 888.911
so.no qn.f.97
so.oo 9i!2.20
oso.no 9111.112
50.00 9taO.bQ
so.oo q79,87
50,00 qqq,oq
ll J J J i .,
("") .
01 w
1 --~ --l l
SCEN4RIOt HEh 1 HE7••FERC -l~··b/2ijJI981
YE4R --·-
1980
1981
1982
198]
19811
1985
198b
1987
1988
1989
1990
1991
1992
1993
19911
1995
1996
1997
1998
1999
2000
2001
2002
200]
i!OQII
zoos
200b
2007
aooe
2009
zoto
4NCHUIUGE • COOK
TOT4L El!CTRICITV REQUIREMENTS (GWH)
UIET OF CIINSERV4TJ0tl)
(l~CLUOES l4RGE INOUITRIAL CONSUMPTION)
~EOIIIM R.ANGE (PR • .5)
-·-···------·~-·-·····
tNLE l GqE&TER F41RB&NK9 TUT4l
----·-------····------·---------·-·-··-~·-·· -··---·-···--····--·-·
19bl.l9 llOO. 31 iHbl.SI
ZO'Jil.tt5 1128.111 i!Sll~Ob
2UZ.IO 11Stt.5l ~tt76.bl
2351.55 Q811.b0 28H~lb
21181.01 'JU.70 2QH .71
i!bi0.11b "11.111.80 1151 .• 2b
Zt.'H .88 970.11 Hb8~25
i!7A5. 29 599.911 ])85.211
Z87Z.7l 6ZIJ.52 1502.21
29bO .IJ e.s9.o9 1619.22
10117.511 b88.ttit 17lb .• 21
lllt.08 707.78 1818~86
31711.bl 1211.90 1901~52
l2liJ.l5 711b.02 ]9811. 11
noa.e.e 7b5.1Q anu·.u
llb5.22 7811.2b 111119 .• 118
11105.51 791.1i19 11199~50
]11115.80 801.72 112119.52
111R6. 09 IJIJ.IIS 11299~511
Hh. H 821.18 11)119·. 'ib
l'il>b.bJ 8H.9l ti]Cillii~Sll
Ulll.ll 811b.9Z ll111U 1 0l
370t.Sb 8b0. 93 11'56i!.l.llil
17119.01) 87il.95 QttiJf.qq
]8lb.llll 86 ... 96 llHS .• I.IO
190].1!1\ qoz.q7 1.1806 .• 86
1.11101. ]2 92f!.i!l) II<JH.Si!
1109tl. 7b 91H .az 50/JO~IIt
1Jl9&.20 9&D.bll 'H So. 611
l.li!9l.bll q7q.A1 'liP :f. 5 t
1.1191.011 99Q.oq •nq•l .. t 1
n .
J
SCENARIO! HED 1 H£7-•FEAC •IX••b/2411~8)
YEAR ANCHORAGE • COOK INLET
·-·~ ···-···-··············
1980 )96,51
1981 41!2,70
1982 11116,89
1983 415,08
1984 501.27
\f~85 5i!7,4b
1986 545,95
1987 5114,45
1988 5Ai!,95
1989 601,45
1990 bl9,9!1
1991 bl2.8S
1992 bll5,7b
199) b58,bb
l99Q 071.57
~~~"S bAll ."47
199b b9i!,119
11197 700,50
19118 708,52
1999 7h.511
aooo 7lii,S'5
2001 738,10
2002 751 • b5
200] 7bS.ZO
20011 778.7'5
2005 792,3fl
200b 811,911
2007 6]l,58
2008 fl5t.IU
2009 870,87
i!OlO ~~~0~51
I _.J J j -~;;m
PEAk ELECTRIC RF.QUIREHENTS (HWJ
(NET OF CONSERVATION)
(INCLUO~S LARGE INDUSTRIAL DEMAND)
HEDlUH RANGE (PR • .5)
---~--------~-~---··~-
GREATER fAIRBANKS TOTAL •·•·••···•·•··•·····•· ••.••.....•........•.•
91,110 11"7~~0
97,81 520~'51
IOII,i!J '553~11
IIO,bll !385~ 72
I 11,05 bl8,l2
li!J,47 650~91
l30,22 b7b,l7
lJo,97 70l.42
1111,72 726~67
t51'1,4b 7!11,91
157,21 777.16
lbl,58 7911~4)
tb'5,94 Rll,70
170,]1 828~97
l1li,U 811b,24
179,011 IIU~SI
161 .2b 873~ 75
18),118 81\],99
18'5,70 8911~U
187.92 9011~11b
190,15 9111~ 70
191,)11 11]1.1.1'5
1911,'511 9118,19
199,7ll 9bll,911
2U,911 9lll,b9
i!Ob,lil 11911~1111
211J,Sl 1022~117
2111,'l2 J(llii>.Sfl
21'l,1t !070~5J
2i!J.70 IOIIII,Sb
228,09 tltA'.t.o
] J • I J _I ,JJ ,) ]
HES--FERC -2%
-
-
-
-
r
C.155
l • l -l l
ICENARIOI HED I HE8••FERC •U••b/liJIUAl
HOUSE:HOLOS SERVED
ANCHORAGE • COOl< Hlli;:T
··-·····-·············
YEAR SINGLE FA11ILY lo1ULTJFAMILY ~OBILE HOHES DU~'LI!XES TOTAL --·· ··---··-~·-·· ................ .. .............. ., .. ~ ........ ~--· .. ···--···--··-
1980 JSIIH, 20311.1. eno. 7118&, 71501.
o.oooJ o.ooo) 0,000) o.ooo) 0.000)
n 1985 1.1908&. lb20I.I. ltU92, 65&1, qs11.1q,
U1 O.OOO) 0.00(1) o.oooJ o.oooJ o.oon)
........
1990 b0/.169. 21JII1. I 3897, 811&0, ttotn.
0,000) 0,000) o.ooo) o.ooo) 0.000)
1995 b521.15, ]00&1. 15018. 83]], llB&'H.
O.OOO) o.OOO) 0.000) 0,000) o.ooo)
2000 6929b, 12901, I&OS!i. 7QII8, l2b201.
0,000) o.oooJ 0.000) 0,000) 11,000)
zoos 71.126&, 15571. t nau. 8557, useoo.
0,000) 0.000) o.ooo) O,IJOO) O.OOO)
2010 eoq 12. HIS&, Ul]ll, q)bJ. 11.18'5&'5.
o.ooo) o.oooJ 0,000) 0,00(1) O.OOO)
SCEN4RJOI MEO I HE8 ... FERC -n••bl21.11l98l
HOUSEHOLDS SERVED
GREATER FAIRBANkS
·······-·······------·
VE4R SINGLE FAMILY I'IULTifAMILY MOBILE HOMES DUPLEXES TOTAL .... . ............. ............... . ............. . ................. . ...............
1980 nao. 5287. !189. 1617. 15113.
0 o.ooo) o.poo) o.ooo) o.ooo) o.oooJ .
U1 IUS 10b'i6, 5~67, auo. 1765, 201.107.
CX> o.ooo) o.ooo) o.OOO) 0.000) 0.000)
1990 11575, 1~b0, 2!3!. 2175. 24141..
o.ooo) o.ooo) o.ooo) o.oool 0.000)
uqs Ull86. 7841. 3083. 23H, 27l4q.
o.ooo) o.ooo) n.OOO) o.ooo) t o.oon)
i!OOO 15152. 770J, 3487. 2298, 28b40.
o.ooo) o.ooo) o.ooo) o.ooo) o.ooo)
2005 tb?l?, 7791A 0 392'1. 2252. 30702.
( 0.(100) o.ooo) o.ooo) o.OOO) n.OOI\)
2010 18155. fiRS!§. 4]10. 215!. ll'17l'.
o.ooo) 0.000) o.oooJ 0.000) o.ooo)
j
1 1
SCENARIO I MED I HE8--FEFIC •i¥••6/2411981
HOUSING VACANCIES
ANCiiUR4GE • COOl< HILET ···-··----····-··-····
YEAR SINGLE FAMILY HULT If AH Jl V MOBILE HOMES OUPLF.XES. TOTAL ...... ·--·-·-······ ................. -·-··-----··· ·······-----· ..............
n 1980 51)61J, 1&66, 1991, l4bJ,; lb~O<J, . 0,000) n.ooo) 0,000) 0,000) O.OOO)
U1
1.0 l'fBS 540, lll'lb, li!b. 2q2. 2115'5.
0,000) o.ooo) 0,000) 0,000) 0~000)
1990 bb'S, "· ISJ, 289. l I I II •
0,000) fl,OOO) 0,000) 0,000) o.oooJ
1995 718. 1621. tbS, 261.1, 2790,
0,000) 0,000) 0,000) 0,000) n.ooo)
2000 Jb2. 1177. 1 n. 519, 323'5.
0,000) 0,000) 0,000) 0,000) 0,000)
i!OOS 811. I 'Ill. 191, 28i', 321 ~.
0,000) 0,000) ( 0,000) 0,0011) 0,000)
2010 890, 2115. 211. Joq. 3'i211.
o.ooo) o.ooo) ll,OOO) o.ooo) 0.000)
SC!:NARIOI lo!ED I HE8••'ERC •U••tlli!111l983
HOUSING VACANCIES
G~EATER FAIRBANKS
~-····-·--~~---··-~···
YEAR SINGLE f AI-IlLY MULTifAMILY f.IOA ll E HOME'S DUPLf)([S TOTAL ..... ~-····-····-· ............... ............... . .............. ···----~-----
1980 ns1. 3320. 98~. 895, 81\511.
o.ooo) o.noo) o.ooo) o.ooo) o.ooo) n .
........ 1985 tlR, 26511. 211, 'Pi!!. '511! • 0"1 o.oon) o.onoJ o.ooo) 0 0 000) o.oon) 0
1990 127. 11511. 25, ea. 687. o.ooo) o.ooo) 0,000) 0 1 001)) n.oon)
1995 151. (1(11~. ]a, 80, 7111. o.oonJ o.OOO) o.ooo) o.ooo) o.OOO)
i!OOO t67. 1.1'10. ]"· 78, 723. o.ooo) o.oooJ o.ooo) 0,000) 0.000)
2005 1811. 187. Ill. n. 1191.
o.noo1 o.ooo) o.ooo) o.ooo) 0.000)
2010 200. 1171!1. 117. 1211. 8119.
o.ooo) o.oooJ o.noo) o.ooo) o.OOO)
J } -l ) j
-------) --·--l
n
~--. ---1 --
~ J 1 ----1 l J ~'1
p
ICENARIOI HED I HE8••rERC •2X••b/lq/lq8)
YEAR
~---
1980
1985
1990
1995
2000
2005
2010
'
FUEL PRICE FORECASTS EMPLDYEO
!LECTRICITY (S I KWH)
ANCHORAGE • COOK ltll ET r.RfATER F'AIR9ANI<S
·······--···~~--~------~~---···---~·-·····-······--·--··---···~-----------
RESIDENTIAL BUSHIESS RESIOENTI AL !WiliNESS ............ ............. ............ -·---·-----
o.oH n.03/J 0.095 o.nqo
o.oqe o.OIJS o.o9'5 n.OQO
o.os1 o.oll8 0,090 1).085
o.o53 o.oso 0.090 0.085
0.115'5 0.052 o.oqo 1'1.095
o.ose, o.osJ o.n9o 0 0 0811J
o.o57 0.05/J o.o9o o.o,s
l ----l
]
n . _.
())
N
·-I ___ ]
YEAR ···-
1980
1985
1990
1995
2000
zoos
2010
-J cc~
ANCHORAGE • COO~ lNlET
FUEL PRlCE FORECASTS [HPLOYEO
NATURAL GAS (1/MMBTU)
GRE4TER FATRAANKS
······--·····--·······-··~-~------~-· ---~-·----········-··-·········~·-··-
RESlDENTI4l BUS ltJES$ RESIDENTIAL IHJSINFSS
··---~----· ............. ............... ..............
'· 730 t."iOO l2.5l0 11.290
t.QB(I 1.750 12 • 0 lO l0.7SO
2 .no 1.540 10.880 9. 710
1.070 2.840 9.830 11.780
2.A80 ~.bSO 8.890 '7 0
1HIO
2. 720 2.490 a.olo 7.170
~.~t~O 1. HO 7.i!b0 b.480
--) ~-J I ) J ---J _I -I .J ---J ~-·-
) --J
l
n
0'1 w
... l
YEAR ··--
1980
1985
1990
19q5
~000
iii'005
2010
ANCHORAGE • COOK INLET
FUEL PRICE FORECASTS EMPLOY[O
FUEL OIL (1/HMBTU)
GRF.ATER FAIR~ANKS
------~-----·--··-····---··-··-····-· ••••••••••••••w••••••••••••••••••••••
RE!IOEI'HIAL BIJSINES! RESIDENTIAL AIJSJNESS
···-···-·-· ............. .............. -------.. ···
7.750 7 0 l00 7.830 7.'500
7.Uo t:t.8so 7 0 390 7.1]0
t:t.uo t:t.lqo ~:t.f:tAO 6.11'30
o;.Q90 s.ttoo 6.0110 -;.fi]O
5.1110 5.0b0 I!S.IIbO '5.no
4."90 11.570 ~.9110 11.760
4.1120 11.110 lf.lltJO 11.310
8CENAR101 M[O I HEB••FfRC •i"••tl/2 till 98]
RESinENTUL liSE PER HOUSEHOLD (I<WH)
(WITHOUT AnJUSTHENT FOR PRICE)
ANCHORAGE ~ COOK INLET
-······-~----·······-·
SMALL LARGE SPACE
YEAR APPLIANCES APPliANCES HHT TOTAL ..... ......... ~ . --·······-·-·-·-··--............
n 1980 ii!IIO.IIO f»Soo'.b3 5088,52 l3b~9.1 s .
0'\ o.noo) o.ooO) o.ooo) o.OOO)
+:>
1985 ihO.OO ttoqz.5l 47JI.U 110~4.11
( o.ooo) o.·onol ( o.ooll) o.ooo)
1990 2210.00 597b~2l 4579.27 ll7~5.4Q
( o.oooJ o.oool o.ooo) 0.000)
1995 22b0.(10 IJQ 111". 59 4510.05 l2688.b4
o.OOO) 0.000) o.ooo) o.oooJ
zooo 2110.00 59119.30 41l51 0 1] U710.113
( o.OO'l) 0.000) 1)0 0110) o.OOOl
2005 2lbO.OO 6019.52 44l1.o3 U796 0 5'!
o.ooo) o.ooo) o.ooo) 0,000)
2010 21110.00 6085~02 "'~ao;u 12935.22
O.OOO) 0.1)00) o.ooo) o.OOO)
~' j ,J .. J l J J J ) J J J ~ . J
.... 1 --) )
SCENARIO I lo!EO I HEB••FERC •il¥••bl2llllq8J
~ESIDENTI At liSE PER I-IOIJSntOLD (KWH)
( W ITHOIJT ADJUSTMENT FOR PRICE)
GAEAT£R FAIRBA~KS ·····-··----··-···-···
SMALL LARGE SPAtE
YEAR APPLJ 4NCE8 APPLIANCES l-iEU TOTAL ···-............ . ...... ., ... ···--·-----··---····
n
1980 i!ttbb,OO 57H~ 5? HU.bb ll5t9.l8
m o.nooJ o.-oooJ 0.000) 0.000)
lTI
1985 2535.99 bl78.9f» HOb. 1i! li!3ll,2fl
o.nooJ o.-oooJ o.oooJ 0~01'10)
1990 260b.OO flii50.9tt 3Eib9. '59 129~6.53 n.oooJ o.ooo) o.ooo) 0,000)
1995 267f».OI bflbO.lS 110tl5 0 07 IU81.H o.noo) 0,1)110) 0.000) o.oooJ
i!OOO Hllb.OO b79t,29 4Jll.'59 l:J8118.88
n .not') o.-ooo) o.ooo) o.oooJ
zoos Z8l6.00 b852,5b li504,J9 14172.911 o.oooJ o.ol\o) n.ooo) o.oooJ
i!O 10 2881».00 1»891~75 llb5b.IJ9 144~11.35
1).000) Q.OOO) o.non) o.ooo)
n
SCENARlOt MEO I ~E8••FfRC •l~··bll41\961
YEAR ANCHORAGE • COOK INLET ·-·-···~·-·-·--···-·······
1980 8407.04
1'1.000)
1985 9'580.48 o.ooo)
U90 1010'1.51 n.ooo)
U95 IOMO.llb
o.OOQ)
i!OOO IIIH.f~S o.ooo)
2005 11752.91 o.oon)
2010 l2539.i!]
o.ooo)
-J J
BUSINESS USE PER EMPLOYEE (K~~)
(WITHOUT LARGE INDUSTRIAL)
(~ITHOUT ADJUSTMENT FOR PRICE)
GREATER FAIRBANKS
············-········-
7495.70 o.ooo)
7972 .u o.nno1
All3.01
0.000)
8585.26 o.nooJ
88139.70 o.ooo)
9IH.l7 o.oooJ
9581.lb o.ooo)
l ·····~··) 1 1 l . 1 l
StEtURJOI !olEO I HEB••FERC -n:-·6121111981
9UHHAPY o,-PRICE. EFFECTS AND PROGRAHATIC C0N9ERVA TtON
IN GHH
ANCHUIHilE " COOK INLET
RES HI HIT J Al IIUSINEU ................ ..----···-·· OWN•PRICE PROGIUH·I NDUCEil CROSS•PRICE OWN•PRICE PROGRAH•INOUCFO CAOSS•PATCE
YEAR REDUCT JON CONSERVA Tl ON REDUCTION RE[)UCTION CONSF,f'!VAT19N REDUCTION ........ .................. ............................. ...................... .. ................ .. ............................ .. ....................
1980 o.ooo o.ooo o.ooo O.OOj) o.ooo o.ooo
1981 t..lfl2 o.ooo •l.fiU q. ]b~ ll.ooo -o.su
1982 u. 7&3 o.ooo ·l. 232 18.710 o.ooll •1.0211
l98l 19.1115 o.ooo ·11.8117 28.09'3 o.ooll ·1.'335
19811 25.52b o.noo •b.lll)] H.llt.O o.ooo •2.0117
1985 31.908 o.ooo ·8.079 tlb.ll25 o.ono -1'.5~9
198b 39.052 o.ooo ·lll,b89 Sb.9bll o.ooo -11.b811
1987 llb.t9b o.oon oo21.299 b7.110 0.0011 •boR09
1988 53. J]9 o.ooo ·27.9119 71.252 o.ooo -8.9]5
1989 bO.IIBJ o.ouo •111.519 87.195 o.ooo •lloObO
n 1990 b7 ,b27 o.ooo ·llt.lt!9 97.531 o.ooo •11,18~
0'1 1991 711.1171 o.ooo ·117.290 Jo5.Jt.7 o.ooo •111,]88 ....... 1992 at. HS o.ooo ·51.1151 I t3.19b o.ooo •15.590
199) 88.159 0.11011 •S9.t.U 121.j)i!f, o.ooo •11>.792
1'1911 95.003 n.ooo ·b5.77l 128 0 R5b n.ooo •17,9911
1995 101 0 8117 o.ooo ·71.9311 136.1.185 o.ooo ·19.Ub
199b I06,6H o.ooo ·72.911l 11111.571 n.oll(l •18.592
1997 111.427 n.o11o -71.9119 1511.1158 o.ooo ·17.988
1998 llb.217 11.11011 ·711.9Sb lbll.]llll 11,00(1 •17.18]
1999 121.007 o.ooo •7S.~bl I bA.ll!l o.ooo -u,.779
2000 125.197 11.000 -71>.971) 176.1lb o.ooo •16.175
2001 129.'107 o.ooo •7'5.HS 11111.9115 o.ooo ·111,1179
2002 IJII.OI8 o.ooo ·711.1179 1 •n. 1111 o.ooo •12 ,7811
ZOO] 118. 128 o.ooo •71,?.311 2o2.b02 o.ooo •11.088
20011 ll1t'.218 11,1100 •71.988 lll .11]1 o.noo -9.192
2005 Ill b. ]II~ n.oon •70, 7111 i!2!1.2bll o.ooo -7,696
20011 150.'175 n.ooo ·6ll.i!t7 211.1>01 o.ooo •11.837
2007 155,bOt o.ooo ·65.691 2112.QII;? o.ooo •1.978
,zoo a lb0.221 o.noo •bl,lb~ ?SII.(I8] 0,000 0 • IIIH
i!009 lbii.R51 0,01)0 •b0 0 b]8 O?b~.b2r; o.ooo 3.711()
2010 lb9.1179 11,000 ·SA. IIi! 27b.9bb o.ooo ... 599
SCEN~IUOt HEO I HE"e••f'EPC •U••&/2411 983
SIJMMA~Y OF PRICE EFI"ECTS AND PROGRUH,flC CrJNSERVA TJ ON
IN GI'4H
GREATER fAIRBAI~KS
FIES IllfNTI AL RUStNfliS ............ -----··--· .. Oj<jN•PRJCE PROGR AI~-J NDUCED CROSS-PRICE OWN•PRICE PRORRAM•INDUCED CROSSMPRJCE
YEAR PEDUCTJON CUI~SEHVATIOU REDUCTION REDI!PION CONSERV~TION REDUCTION ........ .................. .............................. .. .................... .. ................ .. ;+:;;. .. ;;.;;; .. ;;; ......................
1980 0,000 11,000 o.ooo 0,1100 11,000 o,oon
lUI 0,000 0,000 0,192 0,000 o,ooo 0,130
1982 0,000 0,000 0,385 0,000 0,000 0,259
IUS o.noo o.ooo 0,577 o.ooo o.ooo 0,389
lUll o.ooo 0,000 0,1b9 o,oon 0,000 0,519
IUS 0,001} 0,000 0,962 0,1100 o,ooo 0,6118
usc. •0,3311 0,000 1.bb2 .. o.ll95 o.ooo n o'n9
1987 •O.Cib9 (),000 2. 3&2 .. n.990 0,000 1,309
198& •l,003 o.noo 1,11bi! .. t,ll8'5 1),000 1.&39
1989 •1.337 1),000 J, 7&2 •1.98\ o,ooo t.970
0 1990 ool,6U 0,00(1 11,116] .2.1176 0,000 2.100
m l99l ool,011)9 0,00(1 S,Ul •2,95b n,ooo 2,997 co IU2 •2,206 0,000 6,799 .. ],1136 0,000 1,69]
1993 .. 2,117) o,ooo 7 ,9b7 .. ],9\b o.ooo 4. 390
19911 •2. 7 ]9 o,ooo 9,135 .. 11,39& o,oon s.oe6
t995 -J.006 1),1)00 10,]0] -ll,llh 0,000 1;,783
l99b •3,18b 0,000 11.7119 -5.066 o.ooo &,IIIlO
1991 •1,3b6 0,000 11,195 .. 5,256 n,ooo 1.097
1998 -3,546 o.ooo lll,&lll -5,41f7 0,000 7,753
1999 •3.72b o,ooo 16.087 ·'5,637 o.ooo 8.1110
2000 •3.906 0,000 -17,533 .. §,827 o.oon 9.067
ZOO I •11,1)56 o.ooo 19,3311 •b.027 o,ooo 9o960
2002 -11.200 o.ooo 21,1311 .. 6.227 o.ooo 10,853
2003 wll 0 ]'31!1ii «~.ooo a2,9]5 -6.11;!& o.ooo 11.14§
2004 -11.50!5 0,000 211.'735 -t..626 n,OOI'I 12.618
zoos •11.654 o.noo i!6,!iJ6 .. &.82& o.ooo llo'530
i!OO«a ·11.1!01 o.ooo i!8,1811 -7,057 o.ono 111.717
2007 •4.~1111 0,000 ]l,032 -7.i!fl~ o.oon 15.9011
2008 .r;.o9u o.noo 13,279 -7.SIQ 0,000 l7 1091
2009 •!i.2Ut o.ooo 35,521 -1.750 o.ooo 111.278
20l0 -5.3611 o.ooo 37,775 •7,1182 n.oon 19,1£65
j .1 J J .J j .J J .1 ] ] J
~-·~ . l l l l
ICENAfllOI MEO I HEB·-FERC ·2~·-~/2q/198]
IJRE .U<OOWN OF ELECTRICITY REQUIREMENTS (GWH)
(ruT AL fi~CLUOES URGE INOUBTRIAL C:ONSUHPTION)
ANCHORAGE • COO~ INLET
-·-···---··-·-···--···
MEOiliH RANGE (PR•.'S)
··------··-··---·-·-
RES I DHITI AL 81JS I I~E SS MISCELLANEOUS f)IOG. INDUSTRJ.\L
YEAR REQUlREt'1ENT8 REQUIREMENTS REQUIREMENTS LflAO TOTAL
-------~----·-··-----·-·-~--~--------·-··-------·-N·-· ·---------·---------~---------------
1980 97'1,53 1115.31.! 211.11 811,00 tCJhl.ICJ
1981 10?.7.?3 CJII1. 5& 211.71! qz.na 1!091,&0
ICJ82 10711,92 1011.10 25.11 100.1& n2o.o~
198] 1122.&~ 1091.911 25,111 108.1!1.1 H118,1.13
19811 117U,31 llbl.l.l& 26.04 ""· u 1!117&.114
19115 1218.01 12lb.11 ill.li1 121.1,1.10 l'605.25
19h 1250.39 1281.28 21.20 131.89 2f.,CJ6, 77
1987 1282.71 13Z&.20 21.9] 151.18 1!788.29
19!18 1115.15 1371.12 28.U 1&11.8.8 i'6H.I!I
n 1989 IJIH.53 11116,011 29.39 178.37 ?.•HI~B
(J) I9QO 1379.91 1111111.95 30.12 t •n. 8& 3062.85
\.0
1991 1199.07 1111'5.95 30.511 195.11 3100.74
1992 ltllR.2J 11191),95 Jl. 05 198,40 31]8.61
199] 1•07.']9 IS05,95 31.51 201.&6 31 76,52
19911 ''~'5b,5'i I'Si!fl.qS 1 I • 91 <!01.1,9] ]i!lll.lll
1995 11175,71 153S.95 32.1111 2011,20 H52 .lo
19911 11191.62 ISS"i.n 32.83 2111.111 ]291.87
1997 1'507.52 IS711.6U H.23 <'20.11~ H15,11J
1998 1521.11] 1591.92 H.&l i.'26.112 nn.oo
1999 1539.]11 1611.25 111.01 2H .9& ]1.118.57
2000 1555.211 1612.57 111.11] ;t17,90 ]llf>O.II.I
2001 157b.b1 tb69.115 15.0/J 2111.1,<16 1526.1.17
2002 15Q8.DI 1707.13 35.611 252.112 '1592.81
200] tbt9.110 171111,1.11 311.25 259.08 ]6'59.10
2004 lb 1HI,78 l7111.6Q 111.86 2&6.111 3725.1.111
2005 lb62.17 IIIIA,Q7 11.1.11 ~n. i!O 3791 .e1
i!OO& lb9l,80 187'5.70 ]8,]9 2fll."i8 18117,1.17
2007 1721,'111 1912.111 H.lo 1!~9.96 H83.11
2008 175l.OA l989,11:J llll.2i.' ;:IQI!.11.1 1.1071\.79
2009 l7Ro.H 20II5,Bq 111.11 ]06.72 01711.115
2010 1810.]1> 2102,bl 112.011 ]15,10 Lla7o.ll
0
........
0
J ]
SCENARIOI HEO 1 HE8•·FERC •2X••6/~Il/l983
MEDIUH RANGE (PR•.Sl
-----·~·--·----~·--·
RESIDENTJAL
YEAR REGlltREMENTS
BREAKDOWN UF ELECTRICITY REQUIREHENTS (GWHI
(TOTAL INCLIIOES LARGE lNOI.ISTRUL CONSUMPTION)
GREATER FAJRRANKS
--------~-------------
BUSINf.SII HISCEll4NEOUS
RfQIJl REHENTS REiilUIREt1ENTS ·-·---··--··---·--~·-· --·--·-·---------· ·--····--~------··
1980 Po. 39 217.14 6.78
1981 19l.i!l 2JO,i!J b,75
l98l 20b.OJ 243.3a 6.1J
1481 220.1J4 .i!S&.IIl b.7~
1984 HS.ob i'o9.so &.b1
1985 i!S0.48 162.'59 b.bll
. l98b 2b2.211 29().62 b,b8
198 7 2H.oo 298,65 b. 72
1968 i!B'S.H JO&.b8 o.7S
1989 2 1H.S2 Jill. 71 o,79
1990 ]09.28 322.75 o.81
1991 118.62 J2b.78 6,97
199,2 .U7,97 JJI),8l 7.11
1993 U7.Jt 1311.8/l 7.26
19911 11l6.65 JH.87 'l'ollO
1995 155.99 111'1,90 7.54
1996 lbi.J9 )46.58 7,&11
1997 366.60 JSO,i!b 7. 7J
&998 512.2() 151,91 7,83
1999 377 .oO JlS7.61 7.92
2000 JBJ.ol lot.?9 8.02
2001 389,0b U7,9o 8.111
2002 ]95.11 )711.bl 8.25
i!OOJ 1101.16 181.10 s. ]'p
201111 1107.21 181.07 8,49
2005 111l.Z6 1911.64 8.&1
200b II?0.7b 1104.51 8.81
2007 'li!8. Zb 4111.38 9,01
2008 4\5.7b 1.1~11.&15 9.2i!
2009 411]. 26 11]/J. 12 9.4;?
2010 il!'iO.Ho l!llli.?IJ 'l.h~
J J ~J I j . __ ] CJ J 1
E~oG. INDUSTRIAL
lClAO
··----------------
o.oo
o.oo n.oo
n,oo
o.oo
o.oo
ao.no
ao.oo
3(1,00
40.00
so.oo
so.oo so.oo
5o.oo
so.oo
so.oo
so.oo
so.oo
so.oo
511.1)0
50,00
so.oo
so.oo
50,00
5(1,00
50,00
so.oo
50,1)0
so.no
so.oo
so.oo
J I J
TOTAl
400,}\
11211.14
1156~07
48],95
511,61
'539,1t
S*>q.s11
599,]7
t.29.20
6!19,0!
688,86
702 .• !7
715 .e,
729~110
7112.92
756,41
·76'5.61
774~78
78J.qf>
791.111
80.i!,U
815.15
827.99
8110 .• 81
8'U.61
8bb.5t
8811.08
90l.e.!5
919.21
93b.eo
qlijll ,]7
J J · ....... 1
l
'(E 4R
1980
1981
1982
198J
19811
19115
19Bb
1987
1988
1989
J990
I 99 I
1992
199]
19911
1995
199b
1997
1998
1999
2000
2001
i!002
i!OII]
i!004
2005
200b
2007
2008
2009
i!OIO
J
ANCHORAGE • COO~
1 -1 1
TOTAL ELECTRICITY REQUIPEHEHTS (G~H)
(NET OF CO~SEAVATlOH)
( JN(.LIJOES l~RGE Ji'H>USTRUL CONSUMPTION)
H£OIU~ RANGE CPR • .5)
---------·----·----··-
INLET GREAT!R FAIRAANkS
-]
TOTAL
·-------·-··--·····----·-·---~-----·-·-----· ---------------·-----~
19~].19 1100.11 .i'JU ·.51
2091.&0 1128.19 2519~80
2220.02 115b.07 i!e.H,09
23118.113 118].95 21112, l8
i!ll 7e.. 811 '511.8] ~988.67
i!&0'5.25 539. 71 Hllll .9ft
2t>9t>.77 '5tJ9.!ill 32bt>~31
2188.29 599.37 J]IJ7, bb
2879.81 629.20 3SII9.01
2911.33 t>59.03 h10~3b
10b2.8!5 &88.86 11'it'. 71
1100.711 702.37 :HI03,11
Jl36.b] 11'1.89 Jf:I'511,SI
Jl7t>.Si! 729.110 Jqos,9i'
12111.111 7112.92 19'57. 32
JZ5i!.l0 75&.113 1101'11\~ 7!
li!H.B7 7b5.bl 110'5q~q7
1HS.lll 1711.16 lltiO.U
1177.00 763.9b lllt>0~9b
]1118.57 793.111 0211.11
H&O.III IJ02.32 lli!b2.11'5
15i'b.ll7 81!i.l5 ll]lll~b]
)592.111 1127.99 111120~80
)1..59.111 8110.83 111199.97
3725.118 853.U 11'579~1'5
1791.81 8t>b.lil llb58.li!
]8117.111 8811.(18 11711:ss
198l.IJ 901.65 118811~79
(1078.79 919.2] 11998 .• 02
111711.115 931J.AO 'HI1 .• 2'5
11270.11 9511.17 1§2211 .• 119
n
'-l
N
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SCEN&RJOI HED a HE8•·FERC •2X••II1241198J
YEAR UICWOR~GE • COOl< Ill LET .... •••••••••••••••••••w••
uao ]91o.SI
l98l lf2i!.118
l98l 11118.4&
I 983 qu.ao
19811 500.111
1985 520.39
l98& 5115. 7J
tl'l&7 5&5.07
I91U 5811.40
1989 &AJ.711
1990 &23.0A.
l9'n b30. 7J
1992 b38.l'il
UH &a&.OII
1994 &5].69
1995 b&J.lll
1996 &69.U
1997 &17 .90
1998 ollo.IIJ
1999 694.45
2000 10Z. 7J
zoo a 7111.05
2002 729.37
200:S 7112.70
i!OOII 75b.Oii!
i!OOS 7&9.3(1
ZOO& 7A8.bii!
2007 807.90
aooe 8i!7.11
i!009 8~6.11'5
2010 ee.s.n
j I 1 ..... 1
PEAk £L!CTRJC REQUJREHENTB tHW)
fNEf O' CONSERVATION)
(INCLIIOES l~RGE INDUSTRIAL DEMAND)
HfDIUH RANGE (p~ • .5J
--~----·-···--------~-
GREATER FAIRBANKS TOTAL
···---·~·~-----·------••••••••••••••••••v•~•
91.40 487~90
97.71.1 920~211
1011.11 5'52.59
ll11.119 S811~·n
IU.SO 617.27
li!J.2i! bll9.11t
130.03 675~ 1&
1311.811 701.90
1113.&11 728~05
uo.as 7511.19
t51.i!b 7fiO~lll
1110. H 7'H ~ 08
IU.IIl 801.82
lbb.'H 'iti!.ss
169.60 821.29
I 72.69 UII .• OJ
1111.78 811(.1111 .
176.88 8511.77
178.•H "65~15
181.117 875~52
uu.u 1185.139
186.09 9(12~l5
189.02 9111~1.10
t 9'. 95 9lll.b5
1911.89 950.91)
tn.si! 9b1~1&
i!OI.IIl 990~45
2015.13~ 1013.711
209.85 IOH~OJ
2lJ.81 I ObO. H
2l7.88 to8J.61
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