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ALASKA POWER AUTHORITV SUSITNA HYDROELECTRIC PRO.JECT TASK 1 -PO\NER STUCIES SUBTASK 1.01 CLOSEOUT REPORT REVIEW OF ISER WORK DECEMBER I '1880 -I <': I r t r -i -! r/< ,--I'/::?-:;, ~s8 A d·3 11()#{J)tJl ALASKA POWER AUTHORITY SUSITNA HVDROELECTRIC PRO..JECT TASK.,-POWER STUCIES SUBTASK 1.01 CLOSEOUT REPORT REVIEW OF ISER WORK OECEMBER I IIsac ARLIS Alaska Resources Library &Information ServIces .;,~,Anchorage,Alaska ACRES .'..I j AMERICAN",INCCRPO~ATEC >'j t·•." r ALASKA POWER AUTHORITY SUSITNA HYDROELECTRIC PROJECT TASK 1 -POWER STUDIES SUBTASK 1.01 -CLOSEOUT REPORT REVIEW OF ISER WORK -TABLE OF CONTENTS Paqe-'- !"'"LIST OF TABLES......................................................iii L1ST OF FIGURES .....•...•....••......•..•.•.•.•.......•......••••...i v 1 INTRODUCTION.. . • . . .••.•. . . . . . . . . . . . . . • . . . • .•.. . . . • . . . . . . . . . . ...1-1 1.1 Background •...•.•...•...•........•.•.....•.•........•.•1-1 1.2 -Report Contents........................................1-1 2 SUMMARY 1.1 -Scope of Work ..............•...•..•.•.•.•.•.•....•.••..2-1 2.2 -Electricity Consumption Forecasts ....•.•.•.•.•...•..•..2-1 2.3 -ISER Forecasting Model.................................2-1 2.4 -Findings and Recommendations .•.•.......•.•......•...•..2-2 r - 3 -SCOPE OF WORK 3.1 Db j ec t i ve s .•....•.•..•...•.•.•.•.........•....•........3-1 3.2 -Approac'h •....•....••.•..•.•...•.•...•.....•......•.•..•3-1 3.3 -Record of Events 3-1 4 -CRITIQUE OF ISER MODEL -I, I 4.1 -Model Structure •......•...•..-........•..••....•........ 4.2 Database ••....•........•.....•.•.•...•......•...•.•...• 4.3 Economic Scenarios ............•....•.•.•.•........•.... 4.4 Model Parameters .•.•.•.•.....•..........•.•.•.•....•.•. 4.5 Implications of Model Limitations ....•.••..•..•........ 4.6 Critiques by Others ....•....•...•••.......•....•.•.•... 4-1 4-4 4-5 4-6 4-7 4-10 5-1 5-2 5-2 6-1 6-2 FINDINGS AND RECOMMENDATIONS 6.1 -Findings ...•.....•••.•...•.....•...•...•.•..•.•........ 6.2 -Recommendations •...•••......•.•.....•..•.....•.•.•.•.•. -COMPARISON OF RAILBELT ELECTRICTIY CONSUMPTION FORECASTS 5.1 -Recent Forec ast s ..•.•..•..•.••...............•.•...•..• 5.2 Comparison of Forecasts .. 5.3 -Differences between Current ISER Forecasts and Previous Forecasts .•...•.•.•.•.....•..... 5 ..q eno f""o. ~ oo06- LO LO f""o. M M I'""" I I""'" I - r ...... I I - ,.... ! r I ..... i -r ALASKA POWER AUTHORITY SUSITNA HYDROELECTRIC PROJECT TASK 1 -POWER STUDIES SUBTASK 1.01 -CLOSEOUT REPORT REVIEW OF ISER WORK TABLE OF CONTENTS (Cont1d) APPENDIX A-REVIEW OF ISER FORECASTING MODEL B -CRITIQUE OF ISER REPORT BY ALASKA PACIFIC BANK C -CRITIQUES OF ISER REPORTS BY WOODWARD CLYDE CONSULTANTS o -CRITIQUE OF ISER REPORT BY ENERGY PROBE E -CRITIQUES OF ISER AND TUSSING REPORTS BY ALASKA UTILITY MANAGERS F -ISSUES RAISED DURING ENERGY REQUIREMENTS FORECAST WORKSHOPS i i - - ""'", - -i - -I, l - - I!""" I LIST OF TABLES Number 2.1 4.1 4.2 4.3 4.4 Ll.5 4.6 4.7 5.1 5.2 5.3 5.4 5.5 Title Page Summary of ISER Forecasts (GWh)..••.••.•••..•.••..•••2-4 Railbelt Employment Estimates for Lower and Upper Bounds (10 3 )4-11 Estimating Railbelt Households for Lower and Upper Bounds (10 3 )4-12 Railbelt Housing Stock Estimates for Lower and Upper Bounds (10 3 )4-13 Railbelt Residential Nonspace Heating Electricity Estimates for Lower and Upper Bounds 4-14 Railbelt Residential Space Heating Electricity Es t imates for Lower and Upper Bounds 4-15 Commercial-Industrial Government Electricity Estimates for Lower and Upper Bounds 4-16 Railbelt Utility Sales Estimates for Lower and Upper Bound s (10 3 fvlWh)•..•••.•......••..•....••••4-17 Alaska Power Administration Forecasts,1975 .._._.....5-3 1976 ISER Electric Power Requirements Projections ....5-6 Range of Railbelt Annual Consumption 5-7 Railbelt Area Energy Forecast (GvJh)..•.••...••••...•.5-8 Comparison of Past Projects of Railbelt , Electric Power Requirements (1980 Base Year)(1)..;....5-9 iii LIST OF FIGURES Number Title Page.- 4-1 LO""I-Econorni c Growth Employment Scenarios 4-18 4-2 Moderate Economic Growth Ernp 1oyment Scenarios 4-19 ~4-3 High Economic Growth Employment Scenarios 4-20 4-4 Total Exogenous Employment Scenari os 4-21-4-5 Alternative Utility Sal es Forecasts 4-22 - - -I - iv - - - r, p;r!IiIl i -i -I 1 INTRODUCTION 1.1 -Background The Acres American Incorporated Plan of Study (POS)for the Susitna Hydro- electric Project was issued by the Alaska Power Authority for public review and comment in February 1980.Revision 1 to the pas was issued in September 1980 to take account of modifications made as a result of comments received from various sources.These modifications included removal from the original scope of work a substantial portion of Task 1,Power Studies in which load forecasting and polt/er alternatives had originally been proposed.Load forecasting was consequently carried out under separate contract by the Institute of Social and Economic Research of the University of Alaska (ISER).However,Subtask 1.01 which deals with the selection of an energy growth forecast for the south-central Alaska Railbelt Region was retained in its entirety as a part of the POS. This report presents the work undertaken by Acres American Inc.(Acres)and its sub-contractor Woodward-Clyde Consultants (WCe)in Subtask 1.01,Review of ISER Work Plan and Methodologies.The report contains a review of ISER's projections of electricity power consumption for the Railbelt and addresses specific issues related to the methodology.The report also recommends a range of electricity consumption projections,for use in subsequent tasks of the PUS for the proposed Susitna Hydroelectric Project. 1.2 -Report Contents This report is structur"ed in five sections.Section 2 is a summary of the work undertaken and the findings that have been obtained.Section 3 describes in detail the scope of work,approach employed and a historical record of significant events that occurred during the study period.A critique of ISER's work is presented in Section 4 where specific lssues are raised and implications discussed.In Section 5 a comparison is made of ISERls forecast with those developed by others in recent years and the main reasons for the differences between them are discussed.Finally,Section 6 concludes with the main findings that have been obtained and recommendations for further work. An in-depth review of ISER's model structure,assumptions and results is conducted in Appendix A.In a further series of Appendices,B through F, critiques of the ISER forecast,by wee and other agencies,are also presented. 1-1 .....2 SUMMARY I"'"This section summarizes the work undertaken in Subtask 1.01 and the findings that have been obtained. 2.1 -Scope of Work As stated in the February 1980 Pl an of Study for the Susitna Project,one of the main objectives of Task 1 -Power Studies was to select forecasts of electric load growth in the Railbelt and determine the future need for electric power in the region.To support this overall objective,Subtask 1.01 was defined as a review of the ISER forecasting methodology with the intent to develop a sound understanding of the methodology and projections.An additional objective of this study is to establish a basis for development of appropriate future generation expansion scenarios for the Railbelt Region. 2.2 -Electricity Consumption Forecasts The results of ISER I S work were documented in a fi nal report to the House Power Alternatives Study Committee of the State of Alaska and the Alaska Power Authority,which was issued in June 1980.The ISER forecasts were based on economic growth proj ect ions for three are as wh i ch compr i se the Ra 11 belt:the Anchorage reg ion,inc 1ud ing the Matanuska-Sus itna,Kenai and Seward Census Divisions;the Fairbanks and Southeast Fairbanks Census Divisions;and the Valdez Census Division.ISER expects electric power consumption of the Alaskan Rai lbelt to cont inue to grow over the next 30 years as the economy expands.The rates of growth will be conditioned by a large number of economic,demographic, and electricity consumption factors.To bound the alternative rates of growth, ISER defined three alternative economic futures as the likely economic conditions under which future electricity power consumption would occur. Electrical energy sales for the Railbelt Region are projected to grow from a 1980 value of 3,101 GWh to a minimum of 4,807 GWh and respective-medium and maxim~l values of 6,141 GWh and 8,927 GWh,by 2010.The results are summarized in Table 2.1.and are substantially less than previous forecasts prepared by other agencies such as the Alaska Power Administration. 2.3 -ISER Forecasting Model ISER1s projections of electricity power consumption for the Railbelt are a product of five 1 inked components: MAP,which is a state-of-the-art econometric model of Alaska's economy. - A model of household formation. A regional allocation model which estimates economic activity and population in the Railbelt and its subregions given the MAP forecasts. ..... - A housing stock model which estimates housing stock by type. - A model of ut il ity load growth us i ng a detail ed end-use approach for the residential sector,and aggregate consumption approaches for the commercial- i ndustri a l-government and mi scell aneous sectors. 2-1 To run these model components,two sets of input information were used.The first is a set of assumptions concerning future economic activity in Alaska's basic industries and future levels of State Government expenditure.The second is a series of assumptions concerning fuel and appliance choice and capacity for the end-use sectors.These different assumptions resulted in nine economic growth futures and two electricity end-use scenarios.Only three of the nine economic growth futures were run entirely through the models. 2.4 -Findings and Recommendations ISER's model was subjected to a detail evaluation.This evaluation was focused on three areas:model structure,data base and economic scenarios,and model parameters.A number of significant areas of concern emerged: -The full range of economic scenarios has not been estimated by the model. On ly three scenar io s represent ing moderate government expend it ure and alternative economic growth were used in projecting future Railbelt electricity power consumption. -The existing set of scenarios does not sufficiently cover a feasible range of alternat ive futures.The high economic growth and high government expend iture scenario is conservative.A higher growth scenario can be formulated to represent stable industrial growth with higher government expenditure without necessarily depleting the State's fund bal ance.At the same time,the low growth scenario can be made lower by formulating a scenario representing a contract ion of the Alaskan economy. -There is a paucity of data for calibrating an econometric end-use model in Alaska.The data base limits the robustness of the model,particularly the end-use components of the model. Recognizing that the poor data base in Alaska limits the ability to structure an econometric end-use model in sufficient detail,the existing model is weakest in the commercial-industrial-government component.This component at present is highly aggregated and cannot sufficiently respond to the specific _ assumpt ions developed in the economic scenar ios.Since th is component currently accounts for about 52 percent of total Railbelt utility sales,this deficiency is significant. The parameter fuel mode spl it presently employed in the model is based on judgmental assumptions.This parameter is too important to be made on this basis.Fuel choice is determined on the basis of relative fuel prices and fuel availability,which also change over time.These have not been explicitly entered into the fuel mode split parameter.Hence,the accuracy of the assumed existing fuel mode split values cannot be realistically tested. -The model contains many assumptions about other parameter values,such as household formation rates,appliance saturation rates,growth in appliance size,etc.which have not been fully tested.At the same time no sensitivity anal ys i 5 was conducted to understand the impact of these ass umpt ions. 2-2 """ !""'" I .... !""'" I ! .... The inpl ications of these problems in predicting future Railbelt electricity requirements require a thorough analysis.However,some of the outcomes of these problems can be estimated or deduced.These are: -The upper and lower bounds of the existing scenario set have been evaluated as approximations to the model estimations.These are estimated to be 14.0 million MWh for the upper bound and 5.4 million MWh for the lower bound in year 2010.The range of forecasts is therefore wider than that provided by ISER.(See Figure 2.1). -The inclusion of a scenario with stable industrial growth and a higher government expenditure,will drive electricity consumption projections to a level higher than the ISER upper bound.The opposite results would occur for an economic depression scenario,where future consumption levels would be lower than the ISER lower bound.The range of forecasts will therefore be wider than the existing set,covering a more feasible range of alternatives. - A thorough investigation of the fuel mode split parameter may lead to different values than those assumed.If an analysis of price and availability of fuels indicates that future electricity prices will be lower than those of other substitutable fuels,the fuel mode split would drive the electricity projections to levels higher than the existing set.If the price of electricity is more expensive relative to other fuels,the opposite results would occur.. Other problems such as a poor data base,inadequate structural detail in the commercial-industrial-government component,and untested parameter assumptions cannot be reliably assessed without further detailed analysis.The qual ity of the data base can only improve with time,but for the present only reasonable assumptions in the model can alleviate the problem.Other prob1~ms require a thorough analysis to understand the extent of the imp1 ications. In view of these problems,there are doubts concerning the efficacy of ISER1s projections.However,many of these problems are not insurmountable and can be put to rest with time and effort.Hence it is recommended that these issues be investigated and resolved so that applying these projections to electricity generation planning in the Railbelt can be undertaken with reasonable confidence. 2-3 TABLE 2.1 SUMMARY OF ISER FORECASTS (GWh) Railbelt Utility Sales Military Net Gen- eration Self-supplied Industry Net Generation 6,179 7,952 11,736 10,142 2,390 2,390 2,390 2,921 3,171 3,561 3,236 3,599 4,282 3,976 4,601 5,789 5,101 5,730 7,192 5,617 6,742 9,177 1980 1985 1990 1995 2000 2005 2010 L M H ME 2,390 3,171 3,599 4,617 6,525 8,219 M 334 334 334 334 334 334 334 L 414 414 414 414 414 414 414 M 414 571 571 571 571 571 571 414 847 981 981 981 981 981 ME 414 571 571 571 571 571 571 Abb rev iat ions: L =Low Economic Growth -Moderate Government Expenditure. M =Moderate Economic Growth -Moderate Government Expenditure. H =High Economic Growth -Moderate Government Expenditure. ME =Moderate Economic Growth -Moderate Government Expenditure with shift to electric space heat and appliances in residential sector. 2-4 - r I l r I""'" ! r 3 SCOPE OF WORK 3.1 -Objectives As stated in the POS,this task has the following objectives: -Critically review the work of ISER in forecasting electricity power consumption in the Railbelt. -Reach a thorough understanding of the assumptions used by ISER in its work. -Coordinate with ISER on electricity consumption forecasts required by Acres in its subsequent work. 3.2 -Approach The approach that has been adopted in satisfying these objectives is straight- forward.The approach is comprised of the following steps: Review ISERls forecasting methodology to develop a sound understanding of model structure,assumptions,and the data base. -Evaluate ISER IS methodology and identify issues that affect the efficacy of the projections. Assess the implications of these issues in projecting electricity power consumption for the Railbelt. Compare Railbelt electricity projections in recent years to posit ISER's forecasts and identify the basic differences between them. These steps lead to understanding of the efficacy of the ISER projections so that subsequent tasks in the proposed Hydroe 1ectr ic Power Proj ect can proceed apace. The work was undertaken by Acres through its subcontractor Woodward Clyde Consultants (WCC)and in close consultation with ISER and other Alaska agencies staff. 3.3 -Record of Events A number of events took place during the execution of the Subtask which had significant impact on the outcome of the study.The major events are summarized below: r ...... I r January 1,1980 January 7,1980 -Commencement of Susitna Hyroe1ectric Project. -Meeting at ISER offices in Anchorage to discuss ISER approach to energy use forec ast i ng.Attendees:Scott Goldsmith,Lee Huskey (ISER);Jim Landman (Acres);Craig Kirkwood (WCC). 3-1 February 15,1980 -Meeting of Alaskan economists at ISER offices in Anchorage,to participate in discussions of the State's economic future. February 20,1980 -Meeting at WCC offices in San Francisco to exchange ideas on improvement of ISER methodology.Attendees:Craig Kirkwood,Perry Sioshansi,Gary Smith (WCC);Peter Sandor (Acres);Scott Goldsmith ISER). March 14,1980 March 20 and 21, 1980 -ISER releases draft report. -Meeting at Acres I and ISER offices in Anchorage to discuss ISER draft report and possible improvements.Attendees: Jim Landman (part time),C.A.Debelius (part time),Peter Sandor (Acres);Scott Goldsmith (ISER);Robert Mohn (APA, part time). Apri 1 1980 April 14 and 16, 1980 -Woodward-Clyde issues critique on ISER draft report. -Meet i ng at ISER off ices in Anchor age to discuss ISER draft findings.Attendees:C.A.Debelius,John Hayden,Jim Landman,John Lawrence (all part time),Songthara Omkar, Alex Vircol (Acres);Scott Goldsmith (ISER). April 18,1980 -Meeting at wce offices in San Francisco to discuss ways of improv ing Subtask 1.01 work.Attendees:Jim Landman, Songthara Omkar,Alex Vircol (Acres);Craig Kirkwood, Perry Sioshansi,Gary Smith (part time)(WCe). May and June,1980 -ISER releases final report. June,1980 -Al askan Legisl ature vJithdraws funding from Acres Susitna Hydroelectric Project study for power study work. June 10,1980 -Meeting held in APA offices in Anchorage with Railbelt ut i1 ity managers and ISER to di scuss ISER forecast. June 11,1980 -Public workshop held at Anchorage Community College for discuss ion of ISER forecast. 3-2 -.J - 4 CRITIQUE OF ISER MODEL In this section a summary review and critique of ISER's forecasting methodology on future Railbelt electricity requirements is presented.The purpose is to address specific issues in the methodology that may have a significant impact on future electricity requirements in the Railbelt.The critique covers three main areas:model structure;database;and economic scenarios and model parameters . .An assessment of the impl ications of the issues raised in the critique is also included.Critiques made by others will also be discussed. 4.1 -Model Structure ISER's econometric end-use model is based on the proposition that energy is consumed to pursue human activities which are dependent on underlying economic conditions.ISER's model,therefore,begins with a projection of State employment,population and households (MAP and Household Formation Models). State projections are then regional ized to produce Railbelt projections (Regional Allocation ~10del).Railbelt projections of households and population are then input into the hous ing stock model to determine future hous ing stock and d istr ibut ion by type.Finally,all economic project ions together with end-use parameters are entered into the end-use models to forecast electricity consumption for residential space heating,residential nonspace heating, commerc ial-industr i al-government requ irements,and miscell aneous requirements in the Railbelt.A review of the ISER model is presented in Appendix A and illustrated diagrammatically in Figure A.I. The basic structure of the model is quite logical and allows a delineation between economic models and electricity consumption models.The critique follows this delineation. r- I' j' ..... (a)Economic ~1ode 1s Economic models in this context consist of the MAP,Household Formation, Regional Allocation and Housing Stock models.The present relationship between these models implies that the Regional Allocation Model and Househould Format ion Model are both 1 inked to the MAP Model and Hous ing Stock Model,but not to one another.This means that the Regional Allocat ion Model only reg ional i zed the MAP project ions of popul at ion and employment while future household formation is regional ized downstream in the Housing Stock Model.This is unnecessarily compl icated and it would be simpler to regionalize households in the Regional Allocation Model.Hence this requires the Household Formation Model to be 1 inked to the Regional Allocation Model and not the Housing Stock Model.Although this modification would lead to a more elegant structure,it would not necessarily improve the quality of the forecasts. The structure of the individual economic models deserves some comment.The MAP model is probably the best macroeconomic model avail able in Al aska at this time and hence its use is highly appropriate.The remaining economic models are developed specifically for the task at hand and these are simple and practical models.For example,the Household Formation Model is an accounting model which is driven by assumed household formation rates, while the Regional Allocation Model is a regional shares model which refl ects the comparative advantage and seal e of the reg ianal economy.In 4-1 the Housing Stock Model a stock adjustment procedure is employed which is driven by scrappage rates,vacancy rates,and housing choice.Scrappage rates and vacancy rates are assumed to follow the national trend while housing choice is a function of family size and age of household head in the region. Because of the simplicity of these models,the question arises whether those other than the MAP model sufficiently explain the variation in the endogenous variables and whether more underlying economic factors should be incorporated.One way of investigating this is to review the statistical performance of those models that were subject to statistical estimation. The Regional Allocation Model and Housing Stock Model were two such models and the results obtained by the former satisfied most statistical criteria while the latter did not.Specifically,the housing choice equations of the Housing Stock Model gave poor explanatory power.Nevertheless the independent variables such as family size and age of household head were statistically significant in some cases.A respecification of housing choice equations employing housing income and family size variables,might be more appropriate and lead to statistically val id results.In the case of the Household Format ion Model no stat i st ical est imat ion was conducted as th is is an account ing model based on assumed househo 1d format ion rates. (b)End-Use Models The end-use models have six components: -Residential Nonspace Heating Electricity Requirements. -Residential Space Heating Electricity Requirements. -Commercia1-Industrial-Government Electricity Requirements. -Miscellaneous Electricity Sales. -Military Net Generation. -Self-supplied Industry Net Generation. The present relationship between economic models and end-use models is that economic models would predict the basic consumption units such as household,housing stock and employees,for input into electricity end-use models.The structure at present implies a direct linkage from economic project ions to electr icity requirements,hence bypass ing the development of a total energy demand component by end-use sectors and the spl itting of energy demand by fuel type.The drawback with such an approach is that it does not allow an explicit investigation of interfuel substitution for various end-use sectors Which can take pl ace because of technological considerations,price changes,or government energy policies.These are crucial factors in determining future energy demand by fuel type, particularly in the long run. The first four models listed above forecast total utility sales for the Railbe1t,while the two remaining models forecast self-supplied electricity requirements.In discussing these individual models~the focus will be on sales models. 4-2 - r (i)Residential Nonspace Heating Electricity Requirements This sector in 1978 consumed 635,000 MWh which accounted for 29 percent of total electricity sales in the Railbelt.To forecast how much electricity this sector will consume in future years,a detailed end-use model was developed.The model disaggregates household appl iances into water heaters,clothes dryers,refrigerators,etc,and the electricity requirement for each type of appl iance is calcul ated as a function of households,appl iance saturation rate,fuel mode split,average annual consumption and household size.Several of these variables such as the appl iance saturation rate and fuel mode split have economic content and require an analysis of consumer preferences.Th is is important in order to understand future changes. However,this has not been done in any explicit way. r- I \ r- I' (ii)Residential Space Heating Electricity Requirements In 1978,residential space heating in the Railbelt consumed 395,000 MWh.This represents 18 percent of total utililty sales in the Railbelt.Future electricity requirements for residential space heating are projected by an end-use model which is disaggregated into housing types such as single family,duplex,multi-family,etc. Electricity space heating requirements for each housing type are determined as a function of number of housing units,fuel mode spl it and average consumption levels.This model is therefore quite detailed.Again,several of these factors have economic content, particularly fuel mode spl it,but the model does not incorporate them in any explicit way.Hence,this model is more of an engineering end-use model. (iii)Commercial-Industrial-Government Electricity Reguirement Electricity consumption in this sector amounted to 1,156 MWh in 1978 or 52 percent of Railbelt util ity sales.This is a 1 arge end-"use sector compared to 29 percent for residential nonspace heating and 19 percent for residential space heating.However,the model employed to forecast future electricity requirements for this sector is simply a funct ion of non agricul tur al wage and sal ary employment and aver age electricity consumption per employee.Future levels of employment are obtained through the economic models while average electricity consumpt ion per employee is.calcul ated as a function of time and energy conservation standards of new buildings. This model is highly simpl ified and is not a true end-use model.It does not distinguish between space heating and nonspace heating,nor does it differentiate between various types of buildings (e.g. shopping plazas,office buildings,institutional buildings,industrial facilities,etc.)which have heterogeneous electricity requirements. A number of reasons warrant a departure from the existing structure to a more detailed structure.First,the electricity consumption per 4-3 employee variable as presently defined,assumes a homogeneous electricity consumption pattern for commercial,industrial and government sectors while in reality the intensity of electricity usage varies substantially between sectors and type of buildings.Second, fut ure emp 1ojTl1ent grows at different rates for different econom ic sectors with different electricity intensities,thus rendering the use of an average electricity consumption per employee highly restrictive. Third,an end-use sector such as this which accounts for 52 percent of Railbelt utility sales warrants a more rigorous analysis with a greater level of disaggregation of end-uses. (iv)Miscellaneous Electricity Requirements This is a small component of Railbelt electricity consumption which accounted for about 1 percent in 1978.The model is disaggregated into street lighting and recreational home electricity requirements. Street 1 ighting is calcul ated as a fixed percentage of future residential and commercial-industrial-government requirements while recreational home is calculated as a function of household electricity consumption and a proportion of households.Because it is a small component,the model does not require a detailed structure. 4.2 -Database In general an EEU model requires a 1 arge database for cal ibrat ion.The data base must have a sufficiently long time series with information at a micro level.In this section some observations are made on the data base used for calibrating the ISER model. (a)Economic and Demograph ic Data This refers to all of the data required to cal ibrate the economic models. The MAP Model,which is a moderately detailed econometric model of the Al askan economy,is run on time series data.The data series,however,is limited in length but the statistical results of the estimated equations are adequate by conventional statistical criteria.The Household Formation Model is an account ing model based on Al askan household format ion rates derived from the 1970 census with yearly changes to these rates keyed to future national trends prepared by the Bureau of the Census.The Regional Allocation Model employs pooled time series cross section data based on Alaska labor divisions from 1965 to lS76 to capture the effect of time and structural differences in the regional economy.The Housing Stock Model requires more substantive data because of the larger number of variables. Future housing vacancy rates and removal rates are assumed to follow nat ional trends;an add it ion al set of vacancy rates was al so used to represent maximum rates based on the Anchorage Real Estate Report,1979. Data for housing choice equations were based on the 1978 survey of Anchorage population conducted by the Urban Observatory of the University of Al aska. 4-4 ., j J r It is therefore apparent that only the MAP and Regional Allocation Models were run on time series data.The Household Formation and Housing Stock Models were run on data based on assumptions keyed to national trends or cross section data for a specific year.There is a need to collect more Al askan data covering a longer historical period for cal ibration so that the robustness of the models can be ascertained. (b)End-Use Model Data The database required to run end-use models is extensive.A 1 arge number of parameters such as appl iance saturation rates,fuel mode spl it, electricity consumption levels,etc.were included.To obtain the current values for most of these parameters,calculations were performed with data from a variety of sources.These sources include utility authorities,the Alaskan Census of housing statistics,studies conducted elsewhere,etc. Some of the data used covered a sufficiently long historical period (e.g. utility sales)while others were older data covering a shorter period (i.e. 1960 and 1979 Census of Housing,Detailed Housing Characteristics:Al aska). There is indeed a paucity of data especially at the micro level,to calibrate the end-use models.Little can be done at this point,except to make reasonable assumptions for the models. 4.3 -Economic Scenarios Three economic growth scenarios and three state government expend iture scenarios were formulated to provide a total of nine scenarios.Economic growth scenarios and state government scenarios are addressed separately below. ...... -( (a)Economic Growth Scenarios Three alternative futures of basic sector industry growth were formulated to represent high,moderate and low scenarios.These were based on different assumptions concerning future employment and output of basic sector industries.It is useful to understand the nature of these scenarios by examining the timing and growth of basic sector industries. The assumed growth of basic sector industries for the three scenarios are illustrated on an industry-by-industry basis in Figures 4.1 to 4.3.By comparing between figures,the relative rate of development for each industry for the different growth scenarios can be inspected.Basic sector industry employment are also consolidated for the high,moderate and low scenarios and illustrated in Figure 4.4.It is evident from these figures that the economic development is assumed to take pl ace mainly during 1980 to 1985 and thereafter grow more slowly to 2000. Annual growth rates of total exogenous employment for these scenarios are also calculated at 5-year intervals and shown in Figure 4.4.These growth rates illustrate more clearly the bunching of speci al projects and economic events during 1980 to 1985.This is most conspicuous for the high scenario which has an annual growth rate of 5.4 percent during this period.This is followed by a decl ine until the period of 1990 to 1995 when economic growth increases again,part icul arly in the moderate and high scenarios when Outer Continental Shelf Petroleum developnent takes pl ace.The period 1995 to 2000 experiences a decline. 4-5 It is clear that these scenarlOS do not depict two other possibilities: stable industrial growth in the State,and economic depression in the State.These two scenarios warrant inclusion to the existing set,so that the alternative economic futures of Alaska are bounded consistently.In formul ating these scenarios,it is possible that the stable industrial growth scenar io should at 1east incl ude a "spec i al project"to supply the State energy base (such as the Susitna Hydroelectric Project or other alternative[s])together with a stable expansion of the industrial base. On the other hand,the depression scenario could depict an economic contract ion of the Al askan economy. (b)State Government Expenditure Scenarios Three State government expenditure levels were defined to represent high, moderate and low scenarios.These scenarios assume that real per capita expenditure consume a growing,constant,and decl ining proportion of real per capita income.All scenarios lead to a positive fund balance for the State by year 2000,with the lowest level at $48.8 bill ion (current dollars)for the high economic growth and high government expenditure scenario.Although the State is currently legislativly bound to retain a fixed proportion of oil royalties in this fund,it is reasonable to expect that this legislation maybe altered in the future to have some limit on the total amount retained. (c)Scenarios for Model Runs Although nine economic scenarios were defined,not all were run through the entire model.The MAP and Household Formation Models were run on all nine scenarios.However,the Regional Allocation Model and the end-use models were run on only three economic scenar ios.These scenarios are those with the moderate State Government expend iture and the al ternative economic growth scenarios.The reason given is that these were the more likely scenarios.It is difficult to rational ize why this course of action was chosen.At the very least the upper and lower bounds of the nine scenarios should have been included so that the full range of forecasts can be defined. 4.4 -Mode 1 Par ameters (a)Fuel mode Split This parameter is defined as the proportion of appl iances using a particu1ar energy fuel in the ISER model.Current values of this parameter were estimated from the 1960 and 1970 Al askan Census of housing data and information from utility,home construction and real estate personnel. Future values were estimated in increments --that is,fuel mode split for new appliances coming into the stock --based on assumptions reflecting past trends observed in 1960 and 1970,and future preferences for electric appliances.However,in reality,the future fuel mode split will be influenced by relative prices of fuels,relative prices of appliances, consumer tastes,etc.The methods employed have not made these factors exp 1ic it. In the case of relative fuel prices,it has been implied that the current price of electricity relative to other fuels will remain in effect throughout the forecasting period.An alternative to this scenario was 4-6 -- I""" I i (b) also formulated whereby a price induced shift toward electricity woul.d take place.Neither of these assumptions are considered adequate for forecasting future electricity consumption.Relative fuel prices playa crucial role in interfuel Substitution and consumer purchase decisions,and therefore must be analyzed expl icity.The analysis must provide a better understanding of the prices and avail abil ity of fuels that could compete with electricity in end-use applications. Other End-Use Parameters These include a large number of parameters such as appliance saturation rates,appliance size,appliance electricity consumption rates,etc.In calculating the future values of these parameters,a number of assumptions were made.These assumptions are important as the end-use models are driven by them.However,no analysis was conducted to determine whether the electricity forecasts were sensitive to these assumptions.Some sensitivity analysis is warranted to provide an understanding of the sensitivities involved. ..- - r 4.5 -Implications of Model Limitations In this section,the limitations that have been pointed out above are assessed. Some of these limitations will have significant implications on the forecast, while others are difficult to assess without a thorough investigation.For those limitations that can be assessed at the present time,the implications will be expressed in terms of directional change of electricity forecasts. (a)Economic Scenarios There are three problems associated with economic scenarios: -The Upper Bound (High Economic Growth and High Government Expenditure) and Lower Bound (Low.Economic Growth and Low Government Expenditure)of scenario sets were not run. -The existing scenario set does not incorporate alternative futures depicting stable industrial growth ·and economic depression . The future State Government expenditure for the high scenario is not sufficiently high. (i)Upper and Lower Bound Limitations To assess the impact of the upper and lower bounds of the existing scenario set,estimates of electricity consumption for these scenarios have been prepared.These estimates were calcul ated by the fol1owing steps: -Regional ize future State projections of employment,households and housing stock (Tables 4.1 to 4.3). 4-7 -Calculate future electricity requirements per consumption unit for residential space heating,residential non space heating,and commercial-industrial-government electricity requirements (Tables 4.4 to 4.6) -Multiply future electricity requirements per consumption unit for residential space heating,residential non space heating,and commercial-industrial-government electricity requirements (Table 4.4 to 4.6) -Multiply the sum of residential electricity requirements and commercial-industrial-government electricity requirements by a fixed percentage to obtain miscellaneous utility sales (Table 4.7). -Sum all end-use components to obtain future ut i1 ity sales (Table 4.7). These estimates,which are approximations of bounds of the scenario set,indicate a wider utility sales than those developed by ISER. these differences graphically. (ii)Economic Growth Scenario Alternatives the upper and lower range of alternative Figure 4.5 i 11 ustrates The second problem is associated with the formulation of economic growth scenarios.All of these scenarios indicate a relatively rapid increase in economic growth during the period 1980 to 1985 followed by a much slower growth thereafter.However,none represent the two possible extreme economic futures in which future industrialization occurs with greater expansion of energy intensive industries to tap the State1s vast energy resources or that of a gloomy future with ~ economic contraction.J Treatment of these futures is essential so that the implications for electricity generation planning can properly be assessed.It is possible to deduce what the impact on util ity sales will be for these extreme futures.The industrialization case will lead to greater economic growth and hence drive up future ut i1 ity sal es to a 1evel higher than the existing upper bound.In the other extreme,an economic contraction will depress future utility sales to a level lower than the existing lower bound. (iii)Future State Government Expenditures The ass urnpt ion on future State Government expend itures al so warrants a reexamination.At present,the high State Government expenditure and high economic growth scenario gives a fund balance of $48.8 billion (current dollars)in the year 2000,which is the lowest of all scenarios.This is a large surplus which indicates that the State Government could still increase its expenditure substantially. Admittedly,this is largely a matter of government policy and hence difficult to predict.However,this is well within the realm of possibil ity and should be included so that the range of alternative 4-8 - -I II -I - futures can be ascertained.The implication of a higher State Government expenditure is that the econometric end-use model will produce higher electricity sales forecasts. (b)Model Structure Although a number of problems on model structure have been identified,the most serious lies with the comrnercial-industrial-government component. Currently,this is an aggregate consumption model which combines the three sectors together.It would be far more mean ingful and sens ible to disaggregate into individual sectors and relate electricity requirements to square footage per employee.The main reason for such a disaggregation is that the degrees of energy intensiveness between different sectors are widely different.Even in the industri~sector,energy intensiveness varies according to specific industries,e.g.the aluminum industry is highly electricity intensive compared to forestry or fisheries.This approach is warranted even if it means collecting some original data. About 52 percent of current electricity sales is attributed to this component and hence greater effort is required.At present it is not possible to deduce whether a more detailed approach would lead to higher or lower forecasts,but the need for such efforts is clearly required. ~ ! -I I l -- l""" f F'" I (c) (d) Fuel ModeSpl it Two crucial but interrelated factors affecting the fuel mode split,are relative prices and the availability of fuel.The concept of opportunity cost could be applied to determine future fuel prices and hence lead to a better understanding of consumer preferences on alternative fuels.At present,the future values of the fuel mode split are predicated on two assumptions:that existing relative prices of fuels will continue into the future,and a shift towards electricity will occur because electricity will become rel at ively inexpens ive.These assumpt ions need to be tested through such an analysis.If it is found that electricity prices are less expensive than other competing fuels,then the fuel mode split for electricity will shift upwards and drive electricity sales to higher levels.If the relative price of electricity is higher,then future electricity sales will be lower.It is not possible to predict which event will occur in the absence of a thorough analysis,but it is clear what the alternative directional changes could be. Other Model Parameters A large number of parameters employed in the electricity end-use and electricity aggregate consumption models are engineering end-use data. Others such as appliance saturation rates and utilization rates have economic content.Since the values of these parameters were based on assumptions derived from a limited data base,the degree of certainty that can be attached to these values is difficult to ascertain.At a min imum,a detailed sensitivity analysis should have been conducted based on altern at ive assumpt ions.Altern at ively,a more rigorous approach could be adopted in which these parameter values are statistically estimated with microdata on price,income,temperature,etc.Without further analyses it is not possible at this time to state what the implications will be in regard to future electricity consumption. 4-9 (e)Data Base In general,there is a paucity of data in Alaska to adequately calibrate an econometric end-use model of electricity power consumption.The problem is most acute in the household formation,housing stock,and end-use models. The quality of the data base will improve with time,as the time series lengthens and more data is accumulated at the micro level.For the present,sound judgement on assumptions and sensitivity analysis are the only tools available to counter this problem. 4.6 -Critigues b~Others Apart from the foregoing,a number of critiques on the ISER forecasts have also been made by i ndi vi dual sand agenci es whi ch are i nvo 1ved inane way or another in the future growth of electrical demand in the Railbelt.Copies of these critiques are attached as Appendices B through F to this report.The sources of these critiques are: -the Alaska Pacific Bank -Woodward-Clyde Consultants -Energy Probe -Golden Valley Electric Association -Alaska Rural Electric Cooperative Association -Anchorage Municipal Electric Light &Power· Public issues raised during workshops The points raised in these critiques are many and varied.Although each critique differs somewhat in perspective,two main points emerge which essenti ally support the fi ndi ng of the Acres revi ew: -the ISER model has certain deficiencies which require resolution -the range of ISER forecasts is not sufficiently wide. 4-10 ., ",,~ 1 1 ~._~)..,~)·····1 ·_·······1 ~'~1 ",'1 ~--]-~'1 ·....1 J 'J ."'1 TABLE 4.1:RAIlBELT EMPLOYMENT ESTIMATES fOR LOWER AND UPPER BOUNDS(10 3) LOWER BOUN0 1 Low Economic Growth -Moderate Government ExpendIture low I:.conomic Growth -Low Government Expenditure State Rail belt Ratio state Railbelt Year (1)(2)(J)::(2)~(1)(4)C»::O)x(4) 1980 210 1J3 .6J 210 133 1985 243 150 .62 230 143 1990 254 157 .62 238 147 1995 287 180 .63 259 163 2000 332 209 .63 207 180 2005 -220 --169 2010 -230 --199 UPPER BOUND 2 +:> I...... HiQh Economic Growth -Moderate Government Expenditure High Economic Growth -High Government Expenditure otate Kal1belt Kat 10 otate Hailbelt Year (1)(2)(3)::(2h(1)(4)(5)::(J)x(4) 1980 210 lJ3 .63 210 133 1985 293 183 .63 304 191 1990 no 203 .62 354 219 1995 404 249 .62 445 276 2000 454 285 .63 510 321 2005 -334 --390 2010 -393 --475 (l)ro1'2000 and beyond,estimates based on 1 percent annual growth rate (2).for 2000 and beyond,estImates based on 4 percent annual growth rate TABLE 4.2:ESTIMATING RAILBELT HOUSEHOLDS fOR LOWER AND UPPER BOUNOS(10 3) LOWER BOUND 1 Low t.conomlc lirowth -l10derate liovernment t.xpendlture Low t.conomIc lirowth -Low liovernment Expendlture State Hallbelt Hat 10 State Rallbelt Year (1)(2)(3)=(2)+(1)(4)(5)::(3)x(4) 1980 133 87 .65 133 87 1985 158 '108 .68 153 104 1990 174 122 .69 167 115 1995 200 138 .69 186 128 2000 235 163 .69 211 146 2005 -171 --153 2010 -179 --161 UPPER aOUNo2 Hiqh Economic Growth -Moderate Government Expenditure High Economic Growth -Hiqh Government Expenditure ::itate Hailbelt Hat 10 ::it ate tta110elt Year (1)(2)(3)::(2).;.(1)(4)(5)=(3)x(4) 1980 133 87 .65 133 87 1985 175 116 .66 179 118 1990 210 141 .67 221 148 1995 262 177 .67 282 189 2000 312 212 .68 343 233 2005 -249 --283 2010 -294 --345.- (1)ror 2000 and beyond,estimates based on 1 percent annual growth rate (2)r .4 tor2000andbeyond,estImates based on percent annual grow h rate T.... N ~;.,~..J 1 '~---l -~l '~l '~l '~-~l ~1 -'-1 --'~1 ---,----]1 '---I -'-~)---I -1 TABLE 4.3:RAILHELT HOUSING STOCK ESTIMATES rOR LOWER AND UPPER BOUNDS(10 3) LOWER BOUND High Economic Gro~th -Moderate Government Expenditure High Economic Growth -Hioh Government Expenditure Railbelt Households Rallbelt Housing Stock RatlO Railbelt Households Railbelt HouSin~stock Year (1)(2)(3):::(1 )+(2)(4)(5):::(4 +(3) 1980 87 90 .96 87 90 1985 116 116 1.0 118 118 1990 141 141 1.0 148 148 1995 177 177 1.0 189 189 2000 212 213 1.0 233 233 2005 249 249 1.0 283 283 2010 294 296 .99 345 348 TABLE 4.4:RAILBELT RESIDENTIAL NONSPACE HEATING ELECTRICITY ESTIMATES fOR LOWER AND UPPER BOUNDS LOWER BOUND .:..:>~~]'~,_,,.J .,","~",-¥~j ",~_",-l ""..,~,J """,J -1 1 -1 ~'~l -,-~'J F'~-l ~-'1 --,--'I 0-'1 ~"'---l ··----~l,---=1 '~~1 1 TABLE 4.5:RAILBELT RESIDENTIAL SPACE HEATING ELECTRICIfY ESTIMATES FOR LOWER AND UPPER BOUNDS LOWER BOUND low economic lirowth -Moderate liovernment expenditure low Lconomic Gl'owth -low Government Expenditure nousing :>tOCI<Uectncity Keg.ElectrIcity Heg./nouslng unu Houslng litock .neetnclty Reg. 3 (10 3 MWh)3 (10 3 )(10 3 MWh)(10 )(10 MWh) Year (1)(2)0)=(2h(l)(4)(5)=(4)xO) 1980 90 446 4.95 90 446 1985 108 524 4.85 104 505 1990 117 583 4.98 111 553 1995 138 680 4.93 128 631 2000 161 841 5.22 144 752 200'1 171 917 5.36 153 820 2010 178 995 5.59 161 900 ~- UPPER BOUND ,p. I -' (Jl HiQh lconomlC lirowth -Moderate liovernment lx.r.enditul'e ,Hiqh lconomic lirowth -High liovernment EXJRienditure Houslng litock Uectncity Keg.UectrlClty Reg./Hou51Og Umt •HouslIlg Slack rfeetnclty'ego 3 3 3 (10 3 )(10 3 MWh)(10 )(10 MWh)(lo MWh) Year (1)____(2)(3)=(2).,.(1)(4)('1)=(4)x(3) 1980 90 446 4.95 90 446 1985 110 569 5.17 118 610 1990 141 686 4.86 148 720 1995-177 881 4.98 189 941 2000 213 1104 5.18 233 1208 2005 249 1350 5.42 283 1534 2010 296 1646 5.56 345 1918--...........-------.- TABLE 4.6:COMMERCIAL-INDUSTRIAL GOVERNMENT ELECTRICITY ESTIMArES fOR LOWER AND UPPER BOUNDS LOWER BOUND low I:.cooomic lirowth -Moderate liovernment Expenditure low I:.conomic lirowth -low liovernmenl lxpenditure I:.mploylllent l:1ectJ.'lclty Keg.t.lectflclty Keg./I:.mployee Employment Elecfrlcl{y Reg. (103 )(10 3 MWh)(10}MWh)(lO J )(10 3 MWh) Year (1)(.2)--.-......_......-(3):::;(2)+(1)(4)(5):::;(4)x(3)-- 19BO 133 1248 9•.18 133 1248 1985 150 1541 10.27 143 1469 1990 157 1670 10.64 147 1563 1995 lBO 2119 11.77 163 1919 2000 209 2803 13.41 180 2414 2005 220 30B9 14.04 189 2654 2010 230 3410 14.83 199 2950- UPPER BOUND diture--_.High Economlc urowth -Moderate Government !::.xoenditure HIQh I:.conomlC lirowth -Hiqh liovernment---r;(peo Employment -Electnclty Reg.I:.lectri.city Reg./tmployee tmployment Uectncity Heg. 3 3 J (10 3 )(1O}MWh)(1a )(1 0 MWh)(10 MWh) Year (1) ----~_.'"-,...(2)(3):::;(2)+(1)(4)(5):::;(4)x(3) 1980 133 1248 9.38 133 1248 1985 183 2042 11.16 191 2131 1990 20}2423 11.93 219 2614 1995 249 3370 1}.53 276 }735 2000 2B5 4163 14.61 J21 4689 2005 B4 5451 16.32 390 6365 2010 31n 7136 18.16 475 8626 ..p. I...... O'l ~~,",~":I,,,,..;"J "",.:,..,];"",,:0.,,')~,.'o~:]0,~~••1 ~"..J 1...'...,e ...••]~.c~~I -~'-1 --'~1 ..<"•••'-'1 .~-~l c"-']"-~l -1 TABLE 4.7:RAILBELT UTILITY SALES ESTIMATES FOR LOWER AND UPPER BOUNDS (10)MWh) LOWER BOUND Residential Nonspace Residential Space Commercial-Industrial Miscellaneous Total Utility Heating .Heating -Government Reg.Reg.Sales Year (1)(2)(3)(4)=[(1)+(2)+(3)]x.D1 (5)=(1)+(2)+(3)+(4) 1980 671 446 1248 24 2389 1985 796 505 1469 28 2798 1990 895 553 1563 30 3041 1995 1054 631 1919 36 3640 2000 1258 752 2414 44 4468 2005 1389 B20 2654 49 4912 2010 153B 900 2950 54 5442 UPPER BOUND ,- Residential Nonspace Residential Space Commercial-Industrial Miscellaneous Tot a1 Ut Hit y.p,.Heating Heating -Government Reg.Reg.SalesI.......Year (1)(2)0)(4)=[(1)+(2~+(3)]x.01 (5)=(1)+(2)+(3)+(4).....--.t1980671 446 124B 24 2389 1985 929 610 2131 37 3707 1990 1065 720 2614 44 4443 1995 1578 941 3735 63 .6317 2000 2034 120B 4689 79 8010 2005 2592 1534 6365 105 1059 2010 3329 1918 8626 139 14009 r r ! :44:~----------_•FEDERAL GOVERNMENT 42 32 30 25 .., o r ,... ! I-20 Z I.1.J :t>-o -Ia.. ~ w 15 10 5 __----MANUFACTURING "'---------------MINING EXOGENOUS CONSTRUCTION ,...., i I ! I-::-:f:.===~===============EXOGENOUS TRANSPORTATIONr*'AGRICULTURE -FORESTRY-o IIL..J-.!t-...L........--I FISHERIES 1980 1985 1990 1995 2000 YEAR LOW ECONOMIC GROWTH EMPLOYMENT SCENARIOS FIGURE 4.1 • 4-18 1995 20001990 YEAR 1985 b~b==~==::!:::;;::::::~:::::~======EXOGENOUS TRANSPORTATIONAGRICULTURE-FORESTRY- .....'\.FISHERIES .....,--_-EXOGENOUS CONSTRUCTION O~.----"------"-----_010--';;;;""----' 1980 5 r i r ,.... r I""'" ""'":[I FEDERAL GOVERNMENT ".....42 i 32 ""'"I :1 30l '!"'" I i ,-.25 ~MANUFACT URING rtl 0 I-20 zw :E 15 ....Ja.. ~ fI-w 15I "'"'"i l 10,... MINING MODERATE ECONOMIC GROWTH EMPLOYMENT SCENARIOS FIGURE 4.2 IAIIR I 4-19 - r :;~__~__----------------------------------FEDERAL GOVERNMENT 42 b- YEAR MANUFACTURING MININGA '\, I , I , I \,,,,,\, ,-,r '"AGRICULTURE·FORESTRY \ I \FISHERIES \ I \ "-./"'""_~EXOGENOUS TRANSPORTATION ~J "1:::-======;;;..----" .J '-_EXOGENOUS CONSTRUCTlON O .....----"------"------"'"-----.....,j 1980 1985 1990 1995 2000 5 10 30 25 32 ~20zw ~>-o ..Ja..:e w 15 It>o r r HIGH ECONOMIC GROWTH EMPLOYMENT SCENARIOS FIGURE 4.3 [j] 4-20 Iro FIGURE 4.4 20001995 AVERAGE GROWTH RATES(%l 1980·1985-1990-1995- 1985 1990 1995 2000 LOW 2.0 O.I 0.5 0.6 MEO.3.0 0.9 1.3 0.8 HIGH 5.4 1.7 2.3 1.3 1990 YEAR 1985 4-21 Oi.--i.-~~_..J 1980 TOTAL EXOGENOUS EMPLOYMENT SCENARIOS I-:z lJJ :!E>-o -la.. ~w 10o - -18 17 16 r--15 14 -13 ::I: ~ :2:12-rt) 0 -II enw ...J 10cten >-9l- t) 0::Bl- t) I..LI-...J W 7 6 5 r 4 I 3 2 LEGEND 2010200520001995 YEAR 19901985 OL..-.L.-J-."'--............__----I 1980-, j r- I ALTERNATIVE UTILITY SALES FORECASTS FIGURE 4.5 4-22 (""" r '"'" 5 CONPARISON OF RAILBELT ELECTRICITY CONSUMPTION FORECASTS In this section electricity consumption forecasts developed for the Railbelt in recent years are reviewed.These reviews will be brief and include a comparison of forecasts.The purpose is to compare ISER's forecasts with previous work and to understand some of the f actors that cause bas ic differences between them. 5.1 -Recent Forecasts Electricity consumption forecasts developed since 1975 are reviewed briefly below: r - - - (a) (b) (c) Southcentral Railbelt area~Alaska:Interim Feasibility Report: Hydroelectric Power and Rel ated Purposes for the Upper Sus itna River Bas in, Alaska District Corps of Engineers ~Department of the Army,1975. This study is an update of a previous Alaska Power Administration report* with some changes in assumptions which result in a lower demand projection. In this study,only the Railbelt was analyzed and hence the Southcentral and Yukon regions defined in the previous study were exluded.The most significant change is a reduction in the projected industrial load.A population growth rate of 3 percent annually was assumed in the study, resulting in estimates of 410,000 in 1980 and rising to 740~000 in 2000. Projections of load growth are shown in Table 5.1 Electric Power in Alaska.1976-l995~Institute of Economic and Social Research,University of Alaska.1976. This study forecasted electricity requirements for Alaska based on a model of the State economy and detailed assumptions concerning customer growth and average consumption rates.Two sets of economic assumptions and four set of electricity use assumptions were employed.Economic assumptions were based on 3.4 percent and 4.8 percent population growth.Military electricity requirements were assumed to remain constant over time while self-supplied industrial.requirements were not forecasted.The range of utility sales is shown in Table 5.2. Alaskan Electric Power:An Analysis of Future Requirements and Supply Alternatives for the Railbelt Region~Battelle Pacific Northwest Laboratories for Alaska Division of Energy and Power Development and the Alaskan Power Authority.1978. Th is study did not conduct an anal ys is of load growth but instead based its forecasts on earlier studies,including those of ISER,Alaska Power Administration and several Railbelt util ities.Industrial scenarios were modified to reflect developments at the time.The resulting projections are shown in Table 5.3. -t *1974 Alaska Power Survey~U.S.Department of Interior,Alaska Power Administration,1974. 5-1 {d)Upper Susitna River Project Market Analyses,U.S.Department of Energy, Alaska Power Administration,1979 This study is a continuation of the previous work done by the Alaska Power Administration.Electricity power consumption was projected for utility, mil itary,and self-supplied industry sectors.Utility requirements were projected on the basis of population estimates (provided by ISER econometric and demographic models)and average annual consumption per capita estimates.Future annual consumption per capita were assumed to decline because of conservation measures,appliance saturation,etc.Three different growth rates were used to represent this trend.Future population estimates were based on high and low growth. Mil itary requirements were projected on the bas is of ass umpt ions represent ing high,moderate and low growth.The growth rates of +1%,0% and -1%were used to represent high,moderate and low growth respectively. The self-supplied industrial load projections were based upon earlier work by Battelle and the Al aska Power Administrat ion. The results are summarized in Table 5.4. 5.2 -Comparison of Forecasts A comparison of these recent projections is shown in Table 5.5.These figures are extracted from ISER's recent study.Using 1980 as the base year for comparison,it can be seen that all of the previous forecasts were significantly in excess of the actual 1980 demand. 5.3 -Differences between Current ISER Forecasts and Previous Forecasts The growth rates of the current ISER forecasts are significantly lower than previous forecasts.The differences are mainly attributed to assumptions concerning economic growth and el ectr ic ity consumpt ion rates. Differences in economic growth among the various studies have given rise to widely different economic projections.These differences are mainly due to inconsistent assumptions on the type,size and timing of projects and other economic events.In general,the present ISER projection of economic activities is lower than previous studies. Differences in electricity consumption projections are mainly due to different assumptions on per capita consumption growth rate.The present ISER study has generally lower growth rate assumptions because of expl icit estimates of saturation,end-use patterns and conservation measures. 5-2 -j )'--1 ~--I '---,]---1 e----'--)~"'--'l --1 1 --1 --J ----1 1 ~---)---I J TABLE 5.1:ALASKA POWER ADMINISTRATION FORECASIS,1975 Actual Requirements Estimated future Requirements Type of Load 1974 1980 1990 2000 Peak Annual Peak Annual Peak Annual Peak Annual Demand Energy Demand Energy Demand Energy Demand Energy Area 1000 kw Million/kwh 1000 kw Million/kwh 1000kw Mill ion/kwh 1000 kw Million/kwh Utilities High Rate of Growth Anchorage 284 1,305 650 2,850 1,570 6,800 4,430 15,020 fairbanks 83 330 160 700 380 1,660 800 l~:~~gTotal-rr;r ,-;-m 'iTTIT ~T;9"5IT "B,"51iO"4;"2"3"!T Likely Mid-Ra~e Growth VI Anchorage 590 2,580 1,190 5,210 2,150 9,420 I Fairbanks 150 660 290 1 z278 510 2,230'"Total 740 3,240 1,480 6,48 2,660 11,650 Lower Rate of Growth Anchorage 550 2,410 1 ,OlD 4,420 1,500 6,570 Fairbanks 140 610 240 1,050 350 1.530 Total 69IT ~"1,25IT "5";47IT T;B'5tr B;ltITI TABLE 5.1:(continued) ,,-~J ,".:~."l ___.J fu ","J ~"-~J ,,=~..J 'b~,._._J ;;,.~~J ,~_J ~..~"J "".~-I 1 """"")r~--]~"",'l 1 '-"1 --1 --'1 "~l .'1 --J -I ---1 ..1 -.'J 1 TABLE 5.1:'(continued) Actual Requir~~ents Estimated future Requirements Type of Load 1974 19BO 1990 2000 Peak Annual ,Peak Annual Peak Annual Peak Annual Demand Energy Demand Energy Demand Energy Demand Energy Area 1000 kw Mill ion/kwh 1000 kw Million/kwh ~Million/kwh 1000 kw Mill ion/kwh Combined Utility,National Defense,and Industrial Power Requirements Higher Growth Rate Anchorage 327 1,505 7B5 3,730 4,520 27,460 6,395 35,700 fairbanks 124 527 2.05 920 430 1,900 BS5 3,760 Total ZiTI -z,m!9'9IT 'Zi;b5lT 7I;"9'5lr ~P5IT J9';ZibIT likely Mid-Range Growth Rate V> I I,J'O Anchorage 675 3,100 1,330 6,110 2,605 12,510 fairbanks 195 B80 340 1,510 565 2,490 Total iflIT 3,980 1,670 7,620 ~~ Lower Growth Rate Anchorage 605 2,720 1,100 4,960 1,645 7,500 fairbanks 1BS 830 290 1,290 405 ~:~~gTolal79IT1';'55(T T;J9lJ ~'2,i15tr TABLE 5.2:1976 ISER ELECTRIC POWER REQUIREMENTS PROJECTIONS AncharElge, Southcentral , &Fairbanks LOWEST HIGHEST Average Annual Total Energy Sales Growth Rates 1975-1975- 1975- 1974 1985 1995 1980 1985 1995 1,468 3,697 8,092 9.1 8.8 8.5 1,468 7,787 20,984 17.6 16.4 13.5 , .J ., j 5-6 "..., I""'" I r t I"'" I -I[ it. -i TA8LE 5.3:RANGE OF RAILBELT ANNUAL CONSUMPTION (Includes use by utility and industrial customers likely to be part of an intertied system.Excludes national defense and non-intertied users). Year Annual Consumption Compound Annual Growth Rate 1974 1.6 BkWh 1980 2.6 to 3.4 8 kWh 8.4 to 13.4 (1974-1980) 1990 8.5 to 10.8 B K'itl 9.6 to 15.3 (1980-1990) 2000 16.0 to 22.5 B kWh 4.0 to 10.2 (1990-2000) 5-7 TABLE 5.4:RAILBELT AREA ENERGY fORECAST (GWH) 1977 (Historic) 1980 1990 2000 2025 Utility: High Mid Low National Defense: High Mid Low 2,273 338 3,410 3,155 2,920 348 338 330 8,200 16,920 38,020 6,110 10,940 17,770 4,550 7,070 B,110 384 425 544 338 33B 338 299 270 210 Trend @ 1973-77 annual/growth:(3,215)(10,270) Self~Supplied Industry: High Mid Low Total: High Mid Low 70 2,681 170 170 141 3,928 3,663 3,391 5-8 2,100 630 370 10,684 7,078 5,219 3,590 8,490 1,460 3,470 550 1,310 20,935 47,054 12,738 21,578 7,890 9,630 (33,000)(601,000) - - ..... i i TABLE 5.5:COMPARISON OF PAST PROJECTIONS OF RAIlBELT ELECTRIC POWER REQUIREMENTS (1980 Base Year){1) Annual Growth Rate 0 f 3 (2)Net Energy Between Percent Net Energy (10 MWH)Forecast Year &1980 Error in Forecast of Year of Year of Forecast Implicit in Growth Rate Case Publication Forecast for 1980 Forecast Actual to 1980 U~) (a)1975 1,851 3,240 11.9 7.3 +63 (b)1976 2,093 2,985 9.3 5.9 +58 (c)1978 2,397 3,000 11.9 4.8 +148 (d)1979 2,469 3,155 27.8 6.5 +328 (1)Assuming 1980 Net Energy consisting of 2,390 MWh of sales plus 10 percent losses. (2)Net Energy figures calculated from sales plus 10 percent for losses • 5-9 6 -FINDINGS AND RECOMMENDATIONS 6.1 -Findings In the preceding sections of this report ISER's projections of electric power consumption for the Railbelt have been reviewed and evaluated.A number of significant areas of concern have emerged: -The full range of economic scenarios has not been estimated by the model. Only three scenarjos representing moderate government expenditure and alternative economic growth were used in projecting future Railbelt electricity power consumption. The existing set of scenarios does not sufficiently cover a feasible range of al ternat ive futures.The high economic growth and high government expenditure scenario is conservative.A higher growth scenario can be formulated to represent stable industrial growth with higher government expenditure without necessarily depleting the State's fund balance.At the same time,the low growth scenario can be made lower by formulating a scenario representing a contraction of the Alaskan economy. There is a paucity of data for cal ibrating an econometric end-use model in Alaska.The data base limits the robustness of the model,particularly the end-use components of the model. Recogni zing that the poor data base in Al aska 1 imits the ab il ity to structure an econometric end-use model in sufficient detail,the existing model is weakest in the commercial-industrial-government component.This component at present is highly aggregated and cannot sufficiently respond to the specific assumptions developed in the economic scenarios.Since this component currently accounts for about 52 percent of total Railbelt utility sales,this deficiency is significant. The parameter fuel mode spl it presently employed in the model is based on judgemental assumptions.This parameter is too important to be made on this basis.Fuel choice is determined on the basis of relative fuel prices and fuel availability,which also change over time.These have not been expl ic ity entered into the fuel mode spl it parameter.Hence,the accuracy of the assumed existing fuel mode split values cannot be realistically tested. The model contains many assumptions about other parameter values,such as household formation rates,appliance saturation rates,growth in appliance size etc.which have not been fuJly tested.At the same time no sensitivity analysis was conducted to understand the impact of these assumptions. The implications of these problems in predicting future Railbelt electricity requirements are not easy to assess.They require a thorough analysis. However,some of the outcomes of these problems can be estimated or deduced. These are: 6-1 I.':,.., ,... 1'"'", f""'! ! r- !, r- I 1 r t r-- ! The upper and lower bounds of the existing scenario set have been evaluated as approximations to the model estimations.These are estimated to be 14.0 million MWh for the upper bound and 5.4 million MWh for the lower bound in year 2010.The range of forecasts is therefore wider than that provided by ISER. The inclusion of a scenario 'with stable industrial growth and higher government expenditure,will drive electricity consumption projections to a level higher than the ISER upper bound.The opposite results would occur for an economic depression scenario,where future consumption levels would be lower than the ISER lower bound.The range of forecasts will therefore be wider than the existing set,covering a more feasible range of alternatives. A thorough investigation of the fuel mode spl it parameter may lead to different values than those assumed.If an analysis of price and availability of fuels indicates that future electricity prices will be lower than those of other subsitutable fuels,the fuel mode split would drive the electricity projections to levels higher than the existing set.If the price of electricity is more expensive relative to other fuels,the opposite results would occur. Other problems such as a poor data base,inadequate structural detail in the commercial-industrial-government component,and untested parameter assumptions cannot be reliably assessed without futher detailed analysis.The qual ity of data base can only improve with time,but for the present only reasonable assumptions in the model can alleviate the problem.Other problems require a thorough analysis to understand the extent of the implications. 6.2 -Recommendations In view of these problems,the efficacy of ISER's projections of Railbelt electricity power consumption is questioned.However,many of these problems are not insurmountable and can be put to rest with greater effort.Hence we recommend that these issues be further investigated and resolved so that the application of the resulting projections to electricity generation planning in the Railbelt can be undertaken with reasonable confidence. 6-2 - - ~ i :f- ! .r J.' .-. I \ r -! APPENDIX A REVIEW OF ISER FORECASTING MODEL ·~ APPENDIX A -REVIEW OF ISER FORECASTING MODEL r-TABLE OF CONTENTS r, -I r -! A.I Structure of the ISER t"!ode1 ••.••••••••••••••••••••••••••••••••••A-I A.I.I -The MAP Economic r~ode1 ••••••••••••••••••••••••••••••••A-2 A.I.2 -Household Formation Model .•••••••.•.•••••••••••••••.••A-3 A.l.3 -Regional All ocati on Model A-3 A.l.4 -Housing Stock i~odel •••••••••••••••••••••••••••••••••••,8,-4 A.I.5 -Residential Nonspace Heating Electricity Requirements Model ...••...••.•••..•..••••••.••.•.•.•~.A-4 A.I.6 -Residential Space Heating E1 ectricity Requi rements ••••••••••••••••••••••••••••••A-6 A.I.?Commercial-Industrial-Government Electricity Requirement Model •••••••.•••••••••••••••.•A-6 A.I.B -Miscellaneous Electricity Utility Sales Model •••••••••A-? A.I.9 -Military Electricity Requirements •••••••••••••••••••••A-? A.I.IO -Self-Supplied Industrial Electrical Requirements ••••••A-? A.2 -Economic Scenarios •..•••.••....•.•.•••.•.•.•..•••.•••.••.•.•••.•A-? A.2.1 Low Economic Growth Scenarios •••••••••••••••••••••••••A-? A.2.2 -Moderate Economic Growth Scenario A-9 A.2.3 -High Economic Growth Scenario •••••••••••••••••••••••••A-ll A.2.4 -State Government Scenarios ••••••••••••••••••••••••••••A-12 A.3 -Housing and Population Model Parameters •••••••••••••••••••••••••A-13 A.3.1 -Household Formation •••••••••••••••••••••••••••••••••••A-13 A.3.2 Regional Allocation •••••••••••••••••••••••••••••••••••A-13 A.3.3 Housing Stock •••••••••••••••••••••••••••••••••••••••••A-13 A.4 -Electricity Model Parameters ••••••••••••••••••••••••••••••••••••A-14 A.4.I -Base Case .•.•..•....•.•......•.•...•....••.••.•....•.•A-14 A.4.2 Case Price Induced Shift to Electricity ••.•.••••••••••A-16 A.5 -Economic Proj ecti ons .....•...•••..•...•...•.•.••.•...•.•....•...A-I? A.5.l -MAP Projections ••.•••.•....•...•...•.•....•.•.•.......A-I? If""'"A.5.2 -Future Household Formation A-17 A:5.3 Regional Growth •••••••••••••••••••••••••••••••••••••••A-I? A.5.4 -Future Ra il belt HOllS i ng Stock •••••••••••••••••••••••••A-18 -Ba se Ca 5 e .......•..••.•.•..•..•.•......•.•.•••.•..•... The Case of Price-Induced Shift Toward Electricity •••• -Summary of Electricity Consumption Forecasts ••••••••••r r r A.6 -Future A.6.1 A.6.2 A.6.3 Railbelt Electricity Consumption .........................A-18 A-IS A-19 A-19 LIST OF APPENDICES TABLES Number Title El ectr ic ity Requirements Model s A-23 1980 Alaska Civil ian Population Household Formation Rates (HHRij)A-24 Yearly Percent Change in Household Formation Rate CHHRij)A-25 ..... A.I A.2 A.3 A.4 A.5 A.6 Household Formation Model Regional Allocation Model Housing Stock Model A-20 A-21 A-22 Assumed Housing Removal and Vacancy Rates .,A-28 Housing Choice Regressions A-29 Initial People per Occupied Dwelling Units A-27 Parameter Values:The Price Induced Shift Toward Electricity Consumption in the Residential Sector Case A-39 A-38 A-26 Commercial-Industrial-Government Model Parameters Model Parameters:Residential Non-Space He at App 1 i ances A-30 Model Parameters:Small Residential Appliances A-35 Model Parameters:Residential Space Heating A-36 Regional Allocation Model ParametersA.7 A.8 A.9 A.lO A.l1 A.12 A.13 A.14 A.15r - A.16 A.17 A.18 A.19 A.20 MAP Projections A-40 Household Formation A-41 Regional Projections.................................A-42 Railbelt Housing Stock Forecasts (10 3 )A-43 Future Railbelt Residential Non-Space Heating Electricity Requirements A-44 A.2I Future Railbelt Residential Space Heating El ectrical Requirements A-45 r- I l LIST OF APPENDICES TABLES -Continued ~ A.22 A.23 A.24-! A.25 I""'" i I A.26 r - I""'" I r r I Commercial-Industrial-Government Requirements A-46 Miscellaneous Electricity Requirements A-47 Future Military and Self-Supplied Industrial Requ i rements ........................••...............A-48 Railbelt Utility Sales Projections by End Use Section (10 3 MWh)A-49 Project Electric utility Sales and Military Plus Self-Supplied Industrial Net Generation (10 3 MWh)....A-50 LIST OF APPENDICES FIGURES r I -i l -I r Number A-I Title ISER Economic End Use Forecasting Models A-51 - ,- ".. ".. ..... ".. APPENDIX A -REVIEW OF ISER FORECASTING MODEL In this Appendix a detailed review of ISER's electric power consumption model for the Railbelt region is presented.The purpose is to obtain a comprehensive understanding of how electric power consumption forecasts in the Railbelt were developed by ISER.The review is organi zed in three parts: -Model Structure -Scenar ios and Model Parameters -Economic Projections and Electricity Consumption Forecasts. A.l -Structure of the ISER Model ISERls electric power consumption forecasting model for the Railbelt region employs an econometric end-use (EEU)approach.EEU is based on the proposition that energy is consumed by capital items (e.g.household appliances,space heating systems,automobiles etc.)which perform specific activities (e.g. cooking,heating,transportation,etc.).It has two distinct features: econometric models which forecast future levels of economic activities and end-use model s which forecast future amount and type of energy consumed in the pursuit of economic act ivit ies.. In ISER's model structure,the prediction of future levels of economic activity (e.g.employment,population,households,and housing stock)by region is obtained by four models in the following sequence: -the Man-in-the-Arctic Model (MAP) -Household Formation Model -Regional Allocation Model -Housing Stock Model. Future levels of electricity consumption are projected by end-use models which consist of six components: -Residential Non-space Heating Electricity Requirement -Residential Space Heating Electricity Requirement -Commercial Industrial Electricity Requirement -Miscell aneous Electricity Requirement -Military Net Generation. -Self-sufficient Industry Net Generation The summation of the first four end-use components will produce total utility sales for the Railbelt region:the total electric power consumption for the A-I region is obtained by adding Mil itary and Self-Supplied Industry Net Generation to tot a1 ut i 1 i ty sal es . The basic structure of ISER1s econometric end-use model is illustrated in Figure A.I.A brief description of the individual moaels is provided below. A.I.I -The MAP Economic Model MAP is an econometric model of the State of Alaska developed by ISER for the Man-in-the-Artic Program.The model consists of three interrelated components: economic model -demographic model - f i s cal mod e 1 The significance of MAP lies in its projection of future economic activity which is used for electricity consumption forecasting. In the economic model,the economy is divided into basic and non-basic sectors.The basic sector is comprised of the following industries: -min ing -agriculture -forestry -fisheries -manufacturing -federal government -export component of construction and transportation. The level of output in the basic sectors is determined outside the economic model.The non-basic sectors of the model are: -who 1es ale and ret a i1 tr ade finance -insurance -real estate -serv ices -commun icat ion -utilities -endogenous construction and transportation. The level of economic model simultaneously determines income,output and employment levels for the State. The demographic model projects population levels on the basis of future births,deaths and migration.Future births and deaths of the civilian A-2 -'~ ~I j -.:1 - - - .... .... - population are determined by age-sex-race fertility rates and survival rates;the national increase in population is the number of births net of deaths.Total civilian population is obtained by adding net civil ian migration to the nation~increase.Net migration is determined by the relative economic opportunities in the state.Finally,total population is obtained by adding total civilian population with an exogenous estimate of military population. The fiscal model is the final component in the MAP model.The model cal- culates taxes,personal expenditures,state government employment and government expend itures on capital improvements.These calcul at ions are based on an assumed state spending rule.The model is 1 inked to the as- sumed economic model by providing information on taxes and capital improve- ment expenditures;the former is used to calculate disposable income,while the latter is used to determine part of construction emplo~TIent. In the present study,the MAP model has been updated to incorporate the most recent information.This updating included a respecification of equations in order to capture the buoyancy of the economy in the post pipe- line period.The fiscal model has also been modified to reflect changes in tax regulations which essentially eliminated individual income taxes for State residents. A.l.2 -Household Formation Model The household formation model determines future household 1evels on the basis of future population and the age-sex distribution.It is an accounting model which depends on the cohort specific rate of household formation,and changes in those rates.Input is required from the MAP model in the form of projected level and age-sex distr"ibution of the pop- ulation.The structure of the model is presented in Table A.L A.l.3 -Regional Allocation Model This model reg ional izes the economic projections to determine the level of economic activity in the Railbelt.The method used is a regional shares model.Regional shares are estimated as a function of basic sector activity and dummy variables representing comparative advantage and scale of regional economy.The estimation is based on pooled-time series cross- section technique because of short data series and the need to capture regional variation. The model consists of four equations which regional ize the following: -direct support sector emplo~ent including construction and trans- portation (RESA) other support sector employment,e.g.trade services,finance.etc. {RES B) -population (RPOP) -State and local government employment (REGSL). A-3 The functions of these equations are specified as shown in Table A.2. The regions defined in this model are Anchorage,Kenai,Seward,Matanuska, Fairbanks and Valdez.Regional totals are obtained by multiplying State totals with regional shares. A.l.4 -Housing Stock Model The housing stock mOdel projects the number of households by region and the distribution of households by housing type.The housing types included in this model are single-family,duplex,multi-family,and mobile homes. The projection of total number of households for different regions by the model is obtained by dividing the regional population by population per oc- cupied dwelling unit.On-base households are assumed to be constant over the forecasting period and subtracted from total households to find total off-base households (see Table A.3). Housing stocks are projected for off-base households only.The methodology is based on stock adjustment technique.The technique attempts to incor- porate factors such as income,family size,tenure,and changes in housing type distribution. A series of equations make up the model as shown in Table A.3.Initially, the model postul ates that the demand for housing type T (HT)is the product of the total housing units (HHi)and the demand coefficient for type T (HOT).Next,the initial stock of housing of type i in any period is defined as last period's housing stock of that type (Si ())minus the removals from the stock since the previous period.The net demand forT type T (NO i )is defined as the total demand of ?ousing type T (Hi)less the initial housing stock of type eT (Si). Construction of new housing is determined by the net demand.If the net demfnd for all types of housing is positive,new construction (NC i )equals net demand plus the equilibrium amount of vacant housing.However,if the net demand for a particular housing type is neg- ative,an adjustment is required.The adjustment assumes that single- family mobile homes,multi-family,and duplexes are close subsitutes. This means that when one housing type has excess supply,it is filled by households with the other housing type demand.Once these adjustments are made,new construct i on occurs. A.l.5 -Residential Nonspace Heating Electricity Requirements Model This model includes the following appliances: -water heater -range (cooking) A-4 - "'""i - r- I r -I - -clothes dryer -refrigerator freezer -dishwasher -clothes washer -television -air conditioner -small appliances The electricity requirement for appliance type j at time t for region r is the product of five factors: -number of households (HHt) -appl iance saturation rate (Sjt) -fuel mode split (FMS j,e,t) -average annual consumption (KWHj,d average household size (AHSjt). The equation is shown in Table A.4 (a). Note that AHSjt equals 1 for clothes washer,water heater and clothes dryer,and 0 for others.Total electricity requirements is the sum of all app 1 i ances j. This model is linked to the MAP model through the household variable (HHt).The remaining variables such as appliance saturation rates (Sjt),fuel mode spl it (FMS jet)and average annual consumption (KWHj t)are exogenously determined.A brief explantion is given below.' Saturation rates on major appliances were estimated for 1978 to represent current usage rates since current data on saturation rates were not available.The estimation was based on past trends in saturation rates in Alaska,other States and national trends;two years'data',1960 and 1970, were used.Future Alaskan saturation rates were projected in a simil ar manner following the long-run nation~trend. Fuel mode split was calculated as the proportion of appliances of type j, which use electricity.Data to calculate current fuel mode split for major appl iances were not avail able and hence 1960 and 1970 Al askan Census of Housing data were used.An important element in calculating future mode split is the future stock of appliances using electricity.The future A-5 stock is estimated by a stock adjustment method which embodies an explicit assumption on proportion of new appliances using electricity. Average annual electricity consumption of appliances is calculated as a function of the age distribution of the appliance stock and the electicity requirement for each vintage.In estimating electricity requirements for new appliances added to the stock,Federal mandatory improvements in appl ianceefficiency and changes in appl iance size are two important factors taken into account. A.1.6 -Residential Space Heating Electricity Requirements The residential space heating model is disaggregated into four housing types: -single family -duplex -mu 1t i -f am il y -mobile home. The electricity space heating requirement for housing type j is defined as the product of the following factors: -number of housing units of type j (HTj) -fuel mode split (FMSj,e,t) -average level of consumption (KWHj,t). The model is linked to the housing stock model through the housing unit v ar i ab 1e.Other components are determ ined exogenous ly and a br ief explanation is given below. The fuel mode split is calculated as the proportion of houses using electricity for space heating in housing type j.The number of houses using electricity in future years is obtained by a stock adjustment method. Impl icit in this method is the assumption concerning fuel mode spl it of new houses of type j added to the stock. Electricity requirement per unit of housing type j is calculated as the weighted average of the per unit requirement of space heating appliance of the different vintages in the stock.The electricity requirement for each vintage is based on,among other factors,the Federal mandatory energy s av i ngs . A.i.?-Commercial-Industrial-Government Electricity Requirem~nt Model Total electricity requirements for the commercial-industrial sector are defined as the product of non-agr icultural wage and sal ary employment and A-6 ,.... I ,.... f r ,.... r- ! - - r - average electricity consumption per employee (see Table 4.4.(b)). Electricity consumption per employee is a function of time and implementation of conservation standards.This implies that new electricity users in this sector will have different electricity requirements than previous customers. A.l.B -Miscellaneous Electricity Utility Sales Model This model estimates two remaining sectors of electricity consumption: street lighting and recreational homes.Street lighting requirement in time t is calculated as a fixed percentage of the total of residential (space heating and non-space heating)and commercial-industrial-government electricity requirement.Recreational home consumption is calculated as the product of a fixed level of electricity consumption and a fixed proportion of households in time t. A.l.9 -Military Electricity Requirements For a number of reasons,including a lack of historical data series,no model was built to correlate military electricity consumption with causal factors.Hence,future electricity requirements for the military are assumed to be the same as the current level. A.l.10 ~Self-Supplied Industrial Electrical Requirements No model was built to project future self-generated electricity for industry.Existing users are identified and current electricity consumption is determined from APA sources.New users and consumption levels are identified fran economic scenarios. A.2 -Economic Scenarios Three economic growth scenarios and three state government fiscal scenarios were formulated for MAP.These'represent a total of nine economic scenarios. The economic growth scenarios describe the alternative futures of basic sector industries in the state economy.Different assumptions on future employment and output for basic sector industries such as mining manufacturing,agriculture, forestry,fisheries,federal government,exogenous construction and exogenous transportation define high,moderate and low growth scenarios.In defining these scenar ios,spec i al projects and other economic events expected to occur prior to 2000 were identified.The following is a brief description of the timing and nature of future projects for each scenario. A.2.l -Low Economic Growth Scenarios Low growth assumes the following events to take pl ace for each of the exogenous industries.(See also Figure 4.2). A-7 (a)Min ing Prudhoe Bay Petroleum Production -Production from the Sadlerochit formatlon and Kaparuk formatlOn is assumed.Construction of the project will take place during 1982 to 1984 with peak employment of 2917 in 1983.Mining employment for 1980 to 2000 assumes a long run average of 1802 per year. -Upper Cook Inlet Petroleum Production -Decl ining oil production will be replaced by rising gas production to maintain current levels of employment.Employment for 1980 to 2000 will be 705 workers per year. Other Mining -Reduction in mining employment will take place as a result of land policy or world market conditions.Employment will decl ine at 1 percent per year from present levels. (b)Agriculture --Forestry -.Fisheries -A~riculture -Unfavorable conditions for agricultural development w11l occur.Agriculture will disappear in Alaska by 1992. Forestry -This is a small component and is discussed under manufacturing industry. -Fisheries -Existing fishery industry levels will be maintained but no bottom fish development will occur.Employment will remain at 1000 per ye ar . (c)Manufacturing Seafood Processing -Moderate growth in seafood processing will take place to accommodate the expanding catch in existing fisheries.A 22 percent increase is assumed during 1980 to 2000. Lumber--Wood Products--Pulp -Japanese market condit ions and the Forest Service allowable annual cut will increase employment levels to accommodate product ion of 960 mi 11 ion board feet of 1 umber. -Petrochemicals -Current developments in Kenai will continue.No expans 10n 1S expected. Other l\1anufacturing -Extension of eXisting production for local markets is assumed.Output will grow at 1 percent per year. (d)Federal Government ., i Civilian employment is assumed to grow at .05 percent per year while mil itary emplo.Yffient levels will stay constant.,.., 1 2 (e)Exogenous Construction This portion of the industry is that which serves special projects. Two projects are envisaged: A-8 ,..... I - "... i 1""'\ ! ! (f) -TransA1aska Pipeline -Although completed in 1977,additional construction of four pump stations is assumed.Construction will be completed by 1982 with employment for 90 workers annually. Pipeline operations will also employ 1000 people annually during the forecast period. -Northwest Gasline -Construction of a natural gas pipeline from Prudhoe Bay and an associated gas faci1 ity on the North slope from 1981 to 1985 will take place with peak employment of 7823 in 1983. Operat ions will begin in 1986 cont inuing to 2000 with employment for 400 petroleum workers and 200 transport workers. Exogenous Transportation This portion of transportation is that which serves special construction projects.These are the TransAlaska Pipe1 ine and the Northwest Gas1ine.Only the operations employment levels are included as exogenous transportation. f""'" I A.2.2 -Moderate Economic Growth Scenar io This scenario reflects a faster growth rate than the low scenario.The economic events envisaged to take p1 ace during 1980 to 2000 are described below (see also Figure 4.2). (a)~lining Prudhoe Bay Petroleum Production -Same as in Low Growth Scenario. -Upper Cook Inlet Petroleum Production -Same as in Low Growth Scenario. -National Petroleum Reserve in Alaska Petroleum Production - Pertro1eum production will continue in two fields with 1.2 bill ion barrels equivalent of oil and gas.Leased between 1995 and 2013, development will begin in 1998.Average mining employment of 286 a ye ar from 1998 to 2000 is ass umed . Outer Continental Shelf (OCS)Petroleum Production -PrOduction in six OCSlease scale areas is assumed,with mining employment peaking at 4,900 workers in 1990. Beluga Coal Production -Moderate development of coal for export is assumed,with operations employment of 210 per year from 1988 to 2000. (b) Other Mining -No expansion is assumed.Employment will stay constant at current level of 2350 per year. Agricu1ture--Forestry--Fisheries -Agriculture -low·development is assumed because of priorities to recreation or lack of markets.Employment will grow to 1037 by 2000. A-9 -Forestry -Discussed in manufacturing sector. -Fisheries -Existing fishery levels will be maintained and bottom-fishery industry will expand.Employment levels will increase to 1228 by 2000. (c)Manufacturing -Seafood Processing -Expansion of existing fisheries and bottom-fishery will lead to increased outputs for existing fisheries by 149 percent ~d for bottom-fishery by 49 percent,between 1980 and 2000. -Lumber-Wood Products-Pulp -Same as in low scenario. Petrochemicals -Expansion is assumed to take place with the development of a Pacific LNG facility,a fuels refinery in the Alpetco project and LNG facilities associated with oes activity in Western Alaska.The Alpetco and Pacific LNG projects will create operations employment of 518 per year starting in 1985 and 100 per year starting in 1986,respectively. -Other Manufacturing -Expansion of manufacturing of locally consumed goods will take pl ace.Output will increase at 2 percent per year. (d)Federal Government Same growth as in low scenario. (e)Exogenous Construction -Northwest Gasl ine -Same as low scenario. -Alpetco Project -Construction employment will be 900 per year from 1982 to 1984. -Pacific LNG Project -Construction will take pl ace during the period from 1982 to 1984,with peak employment of 1323 per year in 1984. -Outer Continental Shelf Petroleum Production -Construction employment will peak at 3,300 workers in 1992. -National Petroleum Reserve in Alaska -Construction employment is mentioned but no figures are given. -Beluga Coal Production -Construction will take place during the period from 1985 to 1990,w.ith peak employment of 400 in 1987. (f)Exogenous Transportation As in low scenario,this sector includes the operations employment for the TransAlaska Pipeline and the Northwest Gasline. Transporation employment in oes petroleum development is also incl uded. A-10 ., ,j .,, 1 !""'" i !"""\ I - - - """i I I r A.2.3 -High Economic Growth Scenario This scenario represents the fastest rate of economic growth.Greater economic expansion in the state is envisaged.The nature and timing of economic events are described below (see also Figure 4.3) (a)Mining -Prudhoe Bay Petroleum Production -Same as low and medium scenario. -Upper Cook Inlet Petroleum Production -Same as low and medium scenar ios. National Petroleum Reserve in Alaska -Production is assumed in five fields with a total reserve of 2.5 million barrels equivalent of oil and gas.This project will begin in 1985 with average mining employment of 460 per year. -Outer Continental Shelf Petroleum Production -Production is assumed in eleven des lease scale areas with different start-up dates beginning in 1979.Mining employment will peak at 9066 per year in 2000. -Beluga Coal Production -Major development of Beluga coal for export will take place during 1988 to 2000,with mining employment of 379 per year. U.S.Borax Mining -Development and exploration is assumed to begin in 1980.r'1ining employment of 440 per year will begin in 1993. -Other Mining -Other mining opportunities will expand with employment growing at 1 percent per year. (b)Agriculture--Forestry--Fisheries -Arriculture -Major development of agriculture will take place in A aska.Employment will reach 4600 by 2000. -Forestry -Discussed in manufacturing sector. -Fisheries -Level of employment in existing fisheries will be maintained.Major development of bottom fishery will take place, with employment in fisheries increasing to 1350 by 2000. (c)Manufacturin~ Seafood Processing -Because of expansion in fisheries,the seafood processing industry will increase output by 157 percent between 1980 and 2000. A-ll -Lumber-Wood Products-Pul p -Due to favorable markets and increased annual allowable cut,employment will expand to acconmodate an annual cut of approximately 1.3 mill ion board feet by 2000. -Petrochemicals -Two petrochemical projects over and above that described in the medium scenario are included.The first is a moderate petrochemical facility at Fairbanks employing 600 workers per year between 1987 and 2000.The second is a major development of the Alpetco project employing 1925 workers per year between 1987 and 2000. -Other Manufacturing -Output in other manufacturing is assumed to 1 ncrease at 3 percent per year to serve 1oc al markets. (d)Federal Government Civil ian federal government employment is assumed to grow at 1 percent per year.Mil itary employment will remain constant. (e)Exogenous Construction Northwest Gasline -Same as low and medium scenarios. -Alpetco Project -Major development will increase construction activlty from 1982 to 1986.Construction employment will peak at 3,500 per year. -Pacific LNG -Same as medium scenario. -Outer Continental Shelf Petroleum Production -Increased development will require construction employment to peak at 5,300 per year in 1992. -Nat ional Petroleum Reserve -Construction employment wi 11 increase because of increased development. -Belu~a Coal Production -Construction will peak at employment of 400 in 1 87. -State Capital Move -The movement of the state capital to Willow will begin in 1983 and be completed in 1996.Construction employment will reach a peak of 1560 p~r year in 1990. (f)Exogenous Transportation Employment in exogenous transportation is assumed to be higher than the medium scenario.This increase is attributed td"expansion of oes production in eleven lease scale areas. A.2.4.-State Government Scenarios In defining scenarios for State government expenditures,ISER mentions that past state fiscal policy is not appropriate for determining future A-12 .!- r - ,,- ! - policies.Two reasons are given:first,the production at Prudhoe Bay has led to state revenues overtaking state expenditures and this will continue to increase in future;second,the establ ishment of the Permanent Fund,tax reduction programs,and wealth sharing programs will constrain the use of petroleum revenues. ISER,therefore,assumes that future state fiscal pol icy can follow one of three separate directions.These directions are defined by the growth of real per capita state expenditures.They assume that real per capita expenditures consume a growing,constant,and declining proportion of real per capita income.Hence,three scenarios for state government expenditure representing high,medium and low are established. A.3 -Housing and Population Model Parameters A.3.1 -Household Formation This model requires two sets of parameter assumptions:initial household formation rates and yearly changes in those rates.The initial set of household formation rates is derived from the 1970 census (Bureau of Census,1970 Census of Population Detailed Characteristics:Alaska,1972). The yearly changes in household formation rates are based on estimates by the Bureau of Census (Bureau of Census,Projections of the Number of Households and Families 1979 to 1995,1979)and are assumed to be constant throughout the forecast per iod.Both sets of par ameters are shown in Tables A.5 and A.6. A.3.2 -Regional Allocation Regional shares for population (RPOP),direct support sector employment (RESA),other support sector employment (RESB),and State and local government empl~yment (REESL)are estimated by a pooled time series-cross- section technique.These equations are reproduced in Table A.7 (DA,OK, OS,OM,OF and DF represent dummy variables for Anchorage,Kenai,Seward, Matanuska,Fairbanks,and Valdez,respect ively). A.3.3 -Housing Stock Housing stock projections are determined by the followi~g parameters: -number of people per occupied dwelling -number of people per occupied dwelling unit (PPOOU)to determine the number of households in a given regional population -removal rates to determine proportion of houses removed from the housing stock -vacancy rates to determine number of vacant housing housing demand coefficents (HOT)to determine demand distribution by housing type. A-13 The initial people per occupied dwelling unit rates are shown in Table A.8.Future housing removal rates were assumed to grow toward the U.S. average of between two and four percent for a five-year period.These rates are shown in Table A.9. Two vacancy rates representing normal and maximum vacancies were used.The normal vacancy rates were based on ten-year U.S.averages for owner and renter units (Bureau of the Census,Housin Vacancies:Fourth Quarter, 1979,1980).Maximum rates were based on Anchorage experience Anchorage Real Estate Research Report,1979).The assumed rates are al so shown in Tab 1e A.9. Housing d~nand coefficients by housing type were determined by housing choice regressions.Data from existing survey data for Anchorage and a 1978 Anchorage Popul ation survey conducted by the Urban Observatory of Al aska were used in the regress ion.Hous i ng cho ice was spec ified as a function of age of household head and family size.The derived equations are shown in Table A.10. A.4 -Electricity Model Parameters: A.4.1 -Base Case Energy consumption.behavior in the base case is based on a number of assumptions which generally reflect a continuation of existing trends and federal energy conservation programs.One Of the most important assumptions in the base case is that the present relative price of electricity is projected to continue into the future so that no major shift toward or away from el ectr ic ity usage occurs.Deta i1 ed ass umpt ions employed are discussed in the ISER report.The following presents the parameter val ues assumed by ISER: (a)Residential Non-Space Heating A 1arge number of parameters enter this model.These are divided into major appl i ances and small appl i ances.For major appl i ances the par ameter s are: -appliance saturation rates -conservation target for new appliance -appliance lifetime -appliance capacity growth rates -incremental mode split -household size adjustment factor -appliance consumption in 1980 -average annual new appliance consumption -historical electric appliance stock growth rates. A-14 till! j j I' \ ,..... i 1""" r - -I r ..... r - ,.... I ,... I \' For small appl iances,the parameters include: -average annual consumption level in 1980 -annual increment to small appl iance consumption. These parameters are shown in Table A.ll and A.12.The assumptions and data used to calculate these parameters are discussed extensively in Appendix E.l of the ISER report. (b)Residential Space Heating Parameters used in this model consist of the following: -average annual unit consumption in 1980 by housing type -average annual unit consumption in 1985 by housing type -growth in unit size -average unit 1 ifetime -incremental mode split -conservation target for new appliances -retrofitting co-efficients. These parameters are reproduced in Table A.13.The assumptions and data sources used in the calculation are comprehensively discussed in Appendix E of ISER's report . (c)-Commercial-Industrial-Government Parameters used in this model are: -average consumption per employee for 1980 -average consumption per employee for 1980-1985 -subsequent increases to incremental consumption per employee -design performance efficiency targets. Electricity consumption parameters are estimated by using historical Railbelt employment data and historical electricity consumption data for commerci al-industr i a l-government customers.Incremental consumption in future are assumed to reflect trends during the period 1973 to 1978.Design and performance efficiency standards are based on a review of national studies on potential energy conservation impacts of Federal conservat ion programs in the commerci al,industri al and government sections.The review leads to a set of assumptions on A-15 electricity requirement reduction for new buildings.The calculated parameters are shown in Table A.14. (d)-Miscellaneous This small category consists of street lighting and second homes electricity sales.Street lighting is assumed to 1 percent of all other end-use components.For second homes,the parameters are difficult to est imate because of intractable data.It is therefore assumed that 25 percent of households have second homes (based on census informat ion)and that 50 percent are located in the Ra i1 belt. A.4.2 -Case of Price Induced Shift to Electricity For this scenario,ISER conducted a background review of factors involved in the choice of space heating.A number of factors were identified, the most important being the system cost,which includes the initial capital outlay and lifetime fuel costs.Recent relative prices of fuel in the Railbelt were examined and it was discovered that natural gas was cheapest wherever it was available,while fuel oil and electricity varied according to different parts in the Railbelt.In most areas,the price of electricity was higher than fuel oil.Where the prices of fuel oil and electricity were comparable the electricity was produced by cheap natural gas or coal. For electricity to become the lease expensive space heating fuel in different parts of the Railbelt,it was found that the prices of fuel oil and natural gas would have to change in the following directions: -Anchorage -relative price of natural gas to be increased 3 times -Fairbanks -relative price of fuel oil to be increased 2-1/2 times -Glennallen-Valdez -relative price of fuel oil to be increased 3-1/2 times. In an attempt to provide greater insight into future changes of relative prices and availabity of fuel,the conditions under which such changes could take place were reviewed.However,no attempt was made to analyze how these conditions would change in future;instead,it was assumed that electricity would become less expensive than other fuels. (a)-Parameters for Price Induced Shift Parameter values are based on the assumption that the price advantage of electricity will occur during the forecast period but will not be of a substantial magnitude.This leads to a shift towards electricity for space heating beginning in the period 1995 to 2000 for Anchorage and Fairbanks,and 1990 to 1995 for Glennal1en-Valdez.The shift is assumed to follow the natural gas pattern observed in Fairbanks in the early 1970's where new A-16 ,~ 1 j ..... -\ !""" [ installations were predominant but existing units were not retrofitted.In addition,the relative price advantage of electricity will enable electric appliances to be more attractive. Therefore,the incremental mode splits for water heaters and cooking appliances also increase during the period.It is also assumed that the shift towards electricity will take place only in the residential sector. A.5 -Economic Projections The forecasts obtained by the ISER model are presented below: A.5.1 -MAP Projections The MAP model projects future economic activity for the period 1980 to 2000.Nine economic scenarios were formulated to bound the continuum of alternative economic growth scenarios in Alaska.Accordingly,nine projections of population and employment were obtained.Three projections representing high,moderate and low economic growth plus moderate government expenditure scenarios and two projections representing upper (high economic growth and high government expenditure)and lower bounds (low economic growth and low government expenditure)are shown in Table A.16. As a check for consistency in these projections,ISER examined whether the State could make the required level of expenditure without running out of money or requiring large increases in taxes.The check indicated that the State's fund balance is positive in all scenarios in 2000.The lowest level of fund balance is $48.8 billion (current dollars)and this occurs in 2000 for the high economic growth-high government expenditure scenario. A.5.2 -Future Household Formation Nine projections of household formation based on alternative economic scenarios were produced by the household formation model for the period 1980 to 2000~Three projections representing moderate government expenditure plus alternative economic growth scenarios,and two projections representing upper and lower bounds are shown in Table A.17. A.5.3 -Regional Growth The projections of state population and employment were regional ized by the regional shares model for Anchorage,Fairbanks and Valdez regions. Although nine economic scenarios were formulated,only three scenarios were run through the regional shares model for the period 1980 to 2000.These scenarios are those representing high,moderate,and low economic growth with mOderate government expenditure.The reason given for choosing these scenarios is that they reflect the most likely range of future growth. percent Projections beyond 2000 were assumed to grow at 1 percent,2 percent and 3 percent for the three scenarios.These projections together with future households by regions (estimated by housing stock model),are shown in Table A.18. A-17 A.5.4 -Future Railbelt Housing Stock Future Railbelt housing stocks are projected by the housing stock model for three economic scenarios.The scenarios are the same as those used in regional projections,and the forecasts are broken down by housing type and different regions of the Railbelt.The projections are shown in Table A.19. A.6 -Future Railbelt Electricity Consumption A.6.l -Base Case Model parameters representing the base case are combined with three economic scenarios to produce electricity consumption forecasts by end-use sectors.The forecasts are discussed below: (a)-Residential Nonspace Heating Electricity consumption forecasts are produced for major and small appliances for Anchorage,Fairbanks and Valdez regions.These forecasts are shown in Table A.20.Large appl iances consistently consume more electricity than small appl iances for all scenarios. In terms of regions,Anchorage consumes more electricity for appl iances compared to other reg ions because of greater househo 1d concentration.For the Railbelt as a whole,this end-use sector is expected to consume electricity ranging from 0.7 million l\il~jh to 1.1 million MWh in 2010. (b)-Residential Space Heating Future residential space heating electricity requirements for Anchorage,Fairbanks and Valdez regions are shown in Table A.21. Anchorage accounts for a substantial portion of electricity consumption because of greater household concentration.Electricity consumption in this end-use sector for the Railbelt will range from a low of 1 million MWh to a high of 1.6 mill ion MWh in 2010. (c)-Commercial-Industrial-Government Future electricity requirement in this sector is sho~m in Table A.22.Again,Anchorage accounts for a substantial portion followed by Fairbanks and Valdez.For the Railbelt region,electricity requirement is expected to range from a low of 3.4 million MWh to a high of 7.1 mill ion MWh in 2010. (d)-~1iscellaneous This sector which accounts for street lighting and electricity for recreat ianal homes is rel at ively small.Forecasts are shown in Table A.23. A-18 I j. - .... r -, - -i - - (e)-~1i 1 itar y and Se If-Supp 1 ied Industrial The forecasts for these two sectors are shown in Tab1e A.24. A.6.2 -The Case of Price-Induced Shift Toward Electricity The shift toward electricity case is estimated only for the residential sector with a moderate growth scenario.The forecasts are shown in Table A.25. A.6.3 -Summary of Electricity Consumption Forecasts Future total utilitly sales for the Railbelt by end-use sectors are shown in Table A.25.Future utility sales by regions in the Railbelt and military plus self-supplied industrial'net generation are summarized and shown in Table A.26. A-19 HHij where: HH CNNP ij CPGO ij NATP ij NPGQij ~jHHRij TABLE A.1 -HOUSEHOLD ~ORMATION MODEL =total number of households in the State =civilian non-natives in sex cohort j and age cohort i. =civilian non-natives in group quarters in sex cohort i and age cohort j. =civilian non-natives household formation rate in sex cohort i and age cohort j. =natives in sex cohort i age cohort j. =natives in group quarters in sex cohort i and age cohort j. =natives houseMaId formation rates in sex cohort i and age coMort j. =military household in sex cohort A-20 and age cohort j. - .1 r ""'"i RESA j RESS i RPOP i REGSL i TABLE A.2 -REGIONAL ALLOCATION MODEL =f (LRPOP i ,L298 i ,Oi) =f (RR3EB i •LRPOP i ,Oi) =f (RESB j •RReEB i ,Oi) =f (LRPOP i ,Di) ""'"I where: RESA =direct support sector employment including construction and t I'anspo rt at ion RESB =other support sector employment,e.g.trade services,finance etc. RPOP =population r -f I ~ I I - REGSL LRPOP i L29Bi °i RR3EB i =state and local government employment =lagged share of population in region i =lagged share of change in total employment Ln region =dummy variable for region 1 =share of basic sector employment in region i A-21 TABLE A.3 -HOUSING STOCK MODEL (a)Number of Households THH' HH 1 1 where: THH iPOP· PP06Ui HH; BHA i (b)Demand T T H,=HH *HD • 1 i i =POP/PPOOU =THH i -BHH i =total number of households in region i =population in region i =population per occupied dwelling unit in region i =total off-base households in reqion i=on-base households in region i - sT =ST (-1)-5 T*r' 1 i i T H:TNO,=-5,. 1 1 1 NC T =NOT +V iii where: HT =demand for housing type T in region i 1 HoT =demand coefficent for type T in region i 15:=initial stock of housing 1 r =number of removals ND:=net demand1 NC:=new unit housing 1 viH:=unit ,minimum amount of vacant homes.1 1 A-22 , J TABLE A.4:ELECTRICITY REQUIRMENTS MODELS (a)Residential NonSpace Heating - - - REQj,t REQt where: HH c Sjt FMSj,e,t KNAj,t AHS j ,t REQt =HH t *Sjt *FMS *KWHj,t *AHSj,t =1:j REQj't =The electricity requirement for appliance type j at time t for region r =number of households =appliance saturation rate =fuel model split =average annual consumption =average household size =total electricty requirements for appliances j (b)Commercial-Industrial-Government 1"""\ i ~i ..... -! - CIREQt where: CIREQt EM t CIKWH t =commercial-industrial-electricity =nonagricultural wage and salary employment in time t =average electricity consumption in time t A-Z3 SOURCE.:Bureau of the Census,1970 Census of Population Detailed Characteristics:Alaska,1972,fable 153. A-24 ~ 1 IJ, .~ ..- - - TABLE A.6:YEARLY PERCENT CHANGE IN HOUSEHOLD FORMATION RATE (CHHRij) NON-NA TIVE NATIVE Male Female Male Female pili! 0 - 1 0 0 0 0 1 - 5 0 0 0 0 5 - 9 0 0 0 0-1.002 1.04510-14 1.001 1.028 15-19 1.002 1.045 1.001 1.028 20-14 1.002 1.045 1.001 1.028 25-29 1.000 1.045 1.002 1.028 30-34 1.001 1.040 1.001 1.024 35-39 1.000 1.027 1.000 1.016 40-44 1.000 1,027 1.000 1.016r45-49 1.001 1.012 1.000 1.006 50-54 1.001 1.012 1.000 1.006 55-59 1.001 1.000 1.000 1.000 60-64 1.001 1.000 1.000 1.000 65 +1.001 1.000 1.000 1.000 ..... SOURCE:Bureau of the Census,Current Population Reports Series P-25,No.805, Projections of the Number of Household and Families,1979 to 1995,May 1979. - -A-25 TABLE A.7:REGIONAL ALLOCATION MODEL PARAMETERS REGSL =.246 i LRPOP +.224 i DA +.124 *9F +.025 *10K (3.48)(6.99)(10.07)(3.56 ) +.0~1 *OM +.009 *OS +.018 *OV R2 =.987 0.15)(1.41)(2.x8)1 -"Ji RESA =1.357 1*L298 +.744 i LRPOP +.14~...DA +.045 *DF '~ )ijj (4.73)(3.44)(1.67)(1.27) +.025 1 DK -.008 ...DM -.003 ...DS +.003 ...OV R2 =.987 (2.36)(-1.0n (-.45)(.45) RESB =.269 ...LRPOP +.086 *RR3EB +.409 "DA +.11 1,i OF 0.26)1 (1.31)(1i.46)1 (1.94) +.017 *10K +.007 ...DM +.004 ...DS +.006 i DV R2 =.997 0.74)(1.95)1 (1.19)(1.94) RPOP =.290 ...RESB +.157 "RR3EB +.213 *OA +.073 *DF (4.70)1 (2.93)1 (5.78)(4.58)1 +.0291*OK +.022 ...9M +.005 *DS +.012 i DV R2 =.995 (7.38 )(6.81) (1.59) (3.45) 1 t statistic in parentheses signiFicant at greater than 95 percent. .....i .~ ,1\,-26 ~ ! .j - '"""i r""I I TABLE A.8 INITIAL PEOPLE PER OCCUPIED DWELLING UNITS -l 1 Greater Anchorage 3.03 Z Fairbanks 3.01 3 Valdez 3.1 4 Rest of State 3.5 census Un Lted r -I ...... I I I ~ I: I, r - 1Weighted average of rates found in:Anchorage r1micipality,1978 Population Profile,1978 (for Anchorage):Kenai Borough,Profile of Five Kenal Penlnsula Towns,1977 (for Kenai and Seaward):and Rivkin Assoclates,Workbook on the rcanomic and Social Impacts of the Capital Move on Juneau and the Mat-Su Borough,1977 (for Matanuska-Susltna). ZAssumes Fairbanks people per occupied dwelling decreased at same rate as the U.S.average between 1970 and 1977;the 1977 rate was .91 less than in 1970 for the United States. 3M•Baring-Gould,Valdez City Cenus,1978 4Weighted average for the nonrailbelt area of the state in the 1970 (3.7)assumed to decline at one-half the rate of the decline of the States . A-27 TABLE A.9 ASSUMED HOUSING REMOVAL AND VACANCY RATES (a)Removal Rates 1975-1980 1.O~i 1980-1985 1985-1990 1.50?~ 1990-1995 1.75% 1992-2000 2.mi (b)Vacancy Rates Single Family 1.1 3.3 Multi family 5.4 16.0 Duplex 3.3 10.0 Mobile Home 1.1 3.3 1'.-28 ,.... I"'"' I - TABLE A.1o:HOUSING CHOICE REGRESSIONS Single Family SF ~.461 -.303 *51 -.175 *52 +.OB *54 +.182 *A2 (70.36)1 (20.52)1 (1.87)(12.24)1 +.317 *A3 +.380 *A4 (47.33)1 (43.B5)1 Multifamil y MF ~.383 +.225 *51 +.086 *S2 -.09 *S4 -.203 *A2 (50.75)1 (6.46)1 0.07)(19.84)1 .153 - Mobile Home -.280 *1 (47.96) A3 -.352 *A4 1 (49.02) .128 MH ~.097 +.068 *51 +.039 *52 +.014 *54 +.00B *A2 (7.01)1 (1.98) (.121)(.043) -I ,.... I I i-I .020 *A3 -.016 *A4 (.366)(.151) A-29 -2R ~.005 TABLE A.11:MODEL PARAMETERS:RESIDENTIAL NON-SPACE HEAT APPLIANCES Parameter Region -Greater Greater Glennallen-,:1 Anchorage Area Fairbanks Area Valdez Area ':J Saturation Rates (Sj,t)~ I , WATER 1980 .99 .97 .91 '1 HEATER 1985 1.00 .99 .94 J 1990+1.00 1.00 1.00 COOKING 1980+1.00 1.00 1.00 CLOTHES 1980 .71 .67 .49 DRYER 1985 .72 .69 .52 1990 .73 .71 .54 1995 .74 .72 .56 2000 .75 .73 .58 2005 .76 .74 .60 2010 .77 .75 .62 1REFRIG-1980+1.00 1.00 1.00 .'~ ERATOR 1980 .46 .45 .46 1985 .48 .48 .49 1990 .51 .51 .52 1995 .52 .53 .54 2000 .55 .55 .56 2005 .57 .57 .58 2010 .58 .59 .60 DISHWASHER 1980 .49 .38 .15 1985 .54 .44 .24 1990 .59 .50 .32 1995 .63 .55 .39 2000 .67 .60 .45 2005 .71 .64 .51 2010 .74 .68 .56 A-3D ------------------------ - -" TABLE A.ll:(continued)I r-Parameter Regioni Greater Greater Glennallen- Anchorage Area rairbanks Area Valdez Area I"'" Saturation Rates (Sj,t) CLOTHES 19BO .77 .75 .66 WASHER 1985 .7B .76 .68 I"'"1990 .79 .77 .70 1995 .BO .78 .72 2000 .Bl .79 .73 2005 .82 .80 .74-2010 .B3 .81 .75 , i TELE-1980 1.50 1.51 .85 VISION 1985 1.55 1.56 1.00 1990 1.60 1.60 1.10 ~1995 1.64 1.64 1.19 2000 1.68 1.68 1.27 200S 1.71 1.71 1.34 2010 1.74 1.74 1.41-AIR-CON-1980+0 .01 0, I""'"Incremental Electrical ApplIance Mode SplIt \msi j e t),, msi WH 19BO+.35 .05 .04-I, msiC 1980+.66 .85 .04 !"""msiCD 1980+.90 .98 .75 msi (other)1980+1.0 1.0 1.0 I""'" I L- A-31 TABLE A.11:(continued) Parameter Greater Anchorage Area Avera~e Annual Appliance for (ew ApplIances (cSj,1980) Water Heater Cooking Clothes Dryer Re Fr igerator Freezer Dishwasher Clothes Washer Television Air Conditioner Average Annual New ~ppliance Consumption (kWhj,1985) Water Heater Cooking Clothes Dryer ReFrigerator Freezer Dishwasher Water Clothes Washer Telev ision Air Conditioner A-52 Region Greater Fairbanks Area .14 .03 .06 .29 .21 .18 .29 .32 .21 .005 o o .01 .01 .005o o a Glennallen- Valdez Area , J TABLE A.11:(continued) !""""' Parameter Region Greater Greater Glennallen- Anchoraqe Area Fairbanks Area Valdez Area Conservative Target for New ApplIances (CSj,1985) It/ater Heater 3,475 Cooking 1,200 Clothes Dryer 1,000-Re fr iger ator 1,250 Freezer 1,350 Dishwasher 230 Diswasher Water 700.....Clothes Washer 70 I Clothes Washer Water 1,050I Television 400 Air Conditioner 400 Growth in AP)liance Size (kwhg j Water Heater 3.650 Cooking 1,250 Clothes Dryer 1,000 Re fr igerator 1,560 Freezer 1,550 r-'Dishwasher 230 Dishwasher Water 740 Clothes Washer 70 r Clothes Washer Water 1,050 Television 400 Ai I'Condit ioner 400 ..... ,.... I r 1\-33 TABLE A.11:(continued) Parameter Greater Anchorage Area Average Annual Appliance for New Appliances (cSj,1980) Water Heater Cooking Clothes Dryer Refrigerator Freezer Dishwasher Clothes Washer Television Air Conditioner Avera~e Annual New Appllance Consumption (kWh j ,1985 ) Water Heater Cooking Clothes Dryer Refrigerator Freezer Dishwasher Water Clothes Washer Television Air Conditioner A-34 Region Greater Fairbanks Area .14 .03 .06 .29 .21 .18 .29 .32 .21 .005 o o .01 .01 .005 o o o Glennallen- Valdez Area , j TABLE A.l1 :(continued)- Parameter Region ""'"Greater Greater Glennallen- Anchorage Area Fairbanks Area Valdez Area Average Lifetime-of ApplIance (exj) Water Heater 10 Cooking 10 Clothes Dryer 15 ""'"Re fr igerator 15 Freezer 20 Dishwasher 10 Clothes Washer 10r-Television 10 Air Conditioner 10 Historical Electric ApplIance Stock Growth Rates (gj) Water Heater .05 .03 .15....Cooking .05 .03 .10 Clothes Dryer .06 .04 .14 Refrigerator .05 .03 .10 Freezer .05 .03 .11,...Dishwasher Water .09 .04 .25, Clothes Washer .06 .04 .12 Television .07 .04 .20 Air Conditioner 0 .03 0 f""'> I I t r- -,I,, - - A-35 TABLE A.12:MODEL PARAMETERS:SMALL RESIDENTIAL APPLIANCES Parameter Region Greater Greater Glennallen- Anchorage Area Fairbanks Area Valdez Area Average Annual Con- sumption Level (kwh)1980 Electric lights 1,000 1,000 1,000 Assorted appliances 1,010 1,466 1,333 Annual Increment to Small ilrPllance Consump Ion (nKWH)50 70 70 A-36 TABLE A.13:MODEL PARAMETERS:RESIDENTIAL SPACE HEATING Greater Anchorage Area-, - "... I -i r Parameter Averace Annual Unltonsumptlon (lxlstlng Onlts)(kwh j ,1980) Single Family Duplex Multifamily Mobile Home Avera~e Annual Unitonsumption (new OmEs)(kWhj,19~) Single Family Duplex Multifamily r~ob il e Home Growth in Unit Size (kwhg j ) Single Family Duplex Multifamily Mobile Home Average Unit lifetime (eXj) Single Family Duplex Multifamily Mobile Home 36,700 24,200 17,100 27,300 40,100 26,600 18,800 30,000 A-37 Region Greater Fairbanks Area 48,200 .31,900 21,200 36,900 53,000 35,100 23,300 40,600 .01 .01 .01 .01 20 20 20 20 Glennallen- Valdez Area 33,300 21,900 14,600 25,400 36,600 24,100 16,100 27,900 TABLE A.13:(continued) Parameter Greater Anchorage Area Incremental Electrical ~ppllance Mode SplIt <msi J,e t ).,, , Region Greater Fairbanks Area Slennallen- Valdez Area -1 mSi SF ,1980+ msi DP ,1980+ msi MF ,1980+ msi MH,1980+ Conservation Target for New ApplIances (CSj,1985) Single Family Duplex Multifamily Mobile Home Utilization Rates (UTj,e,t) UT SF ,1980+ UT OP ,1980+ UT MF ,1980+ UT MH,1980+ .19 .19 .19 .19 A-38 .01 o o .01 .05 .05 .05 o .02 o o o TABLE A.13:(continued) r- Parameter Region Greater Greater Glennallen- !"""Anchorage Area Fairbanks Area Valdez Area Retrofitting Coefficients ""'"m(ret.t)J,e, t 1980 .02 .04 .03 re SF,1985 1980 0 0 0 i"""ret DP ,1985 t 1980 0 0 0reMF,1985 1980 0retMH,1985 0 0 I""" I - r A-39 -I 1 ( TABLE A.14:COMMERCIAL-INDUSTRIAL-GOVERNMENT MODEL PARAMETER r - r"'" " r Parameter Average Consumption per employee 10 1980 Average Consumption rate for 1981 to 1985 1ncremental employees Subsequent Increases to Incremental Consump- bon Rate (nKwh) Desitn and Performance Ef iC1ency TArgets Region Greater Greater Glennallen- Anchorage Area Fairbanks Area Valdez Area 10.675 10.983 9.178 15.156 18.537 12.979 3.020 3.707 2.596 I"'" i l !""'" I 1985 1990 1995+ o .05 .1 A-40 o .05 ~1 o .05 •1 r ~TABLE A.15:PARAMETER VAUlES:THE PRICE INDUCED SHIFT TOWARD ELECTRICITYItDNSUMPJIONINIHERESIDENilALSEClORCASE Parameter Reqion Greater Greater Glennallen- Anchorage Area Fairbanks Area Valdez Area ~ I f SPACE HEAT Incremental mode split (msij,t) r SINGLE FAMILY 1985 .19 .01 .02 1990 .19 .01 .02-1995 .19 .01 .9 2000+.19 .9 .9 DUPLEX 1985 .19 0 0 1990 .19 0 0 1995 .19 0 .9 2000+.9 .9 .9-MULTIFAMILY 1985 .19 0 0 1990 .19 0 0 1995 .19 0 .9 2000+.9 .9 .9 MOBILE HOME i 1985 .19 .01 0I 1990 .19 .01 0 1995 .19 .01 .9 2000+.9 .9 .9 APPLIANCES Incremental mode split (msij,e,t) I""" II WATER HE!HING 1985 .35 .5 .4 ~1990 .35 .5 .4- 1995 .35 .5 .9 2000+.9 .9 .9 COOKING ~ 1985 .66 .85 .4- 1990 .66 .85 .4- 1995 .66 .85 .9--2000+.9 .85 .9 -I A-41 -J c-~-l -~l -..--J ~··-l ----I I --I ----J ~--l ----]--1 ---)--J TABLE A.16:MAP PROJEC TIONS LE-LG LE-MG ME-MG HE-MG HE-HG Employment 1980 211 21 J 211 211 211 1985 251 244 262 291 304 1990 238 254 281 330 354 1995 259 287 329 405 445 2000 2BB 3~2 372 455 510 ):>Population: I +0- N 1980 422 422 422 422 422 1985 467 481 504 536 550 1990 490 512 5117 615 645 1995 528 565 625 733 786 2000 514 636 70(J 831 908 LE-LG -Low Economic Growth -Low Government Expenditure LE-MG -Low Economic Growth -Moderate Government Expenditure ME-MG -Nodet'ate Economic Growth -Modet'ate Government Expendit:ut'e HE-MG -High Economic Growth -Moderate Government Expenditure HE-HG -High Econom ic Gro~lth -High Government Expend iture TABLE A.17:HOUSEHOLD FORMATION* , 1 Year LE-LG LE-I\1G ME-MG HE-MG HE-HG j ~ 1980 133 133 133 133 133 198~153 1~8 165 175 179 1990 167 174 187 210 221 1995 186 200 222 262 261 2000 211 235 260 312 343 *LE-LG LE-MG ME-MG HE-MG HE-HG Low Economic Growth -Low Government Expediture Low Economic Groy~h -Moderate Government Expenditure Moderate Economic Growth -Moderate Government Expenditure -High Economic Growth -Moderate Government Expenditure High Economic Growth -High Government Expenditure A-43 1 ~---J e---J -~·1 --1 ~--l -)~--1 ~·-l ----1 --,r·-'C---1 j '--1 ] TABLE A.18:REGIONAL PROJECTIONS Low EconolTlic Growth-~lod.t>1od.Economic Growth-Mud.High Economic Govt:.__EXPElrldi tur:e~____Govt.Expenditures Growth-Mod.Govt.Expend ilures Employment ~ulation Households Employment Population Households Employmefjt Population Households ANCHORAGE 1980 102,529 219,303 68,224 102,529 219,303 68,224 102,529 219,303 68,224 1985 111,118 248,850 85,177 119,352 260,034 85,805 132,186 275,848 89,515 1990 116,939 265,539 94,528 128,267 282,766 97,827 148,498 314,247 lOB ,048 1995 134,425 293,381 108,377 151,735 322,582 116,718 185,601 375.483 136,364 2000 157,268 329,865 127,099 173,021 361,239 137,172 21 J,011 427,146 163,560 2005 165,290 346,691 133,582 191,029 398,837 151,449 248,203 502,433 192,388 2010 173,722 364,376 140,396 210,912 440,348 167,212 291,950 590,989 226,278 fAIflBANKS 1980 29,641 59,26B 17,114 29,641 59,268 17,1Il~29,641 59,26B 17,114 ;<..1985 36,508 70,276 21,152 38,813 73,072 22,118 43,223 78,354 24,121I+>1990 37 ,270 74,187 13,530 40,485 78,911 25,330 47,638 88,555 28,711+> 1995 41,729 81,966 27,433 46,840 09,840 30,414 57,492 104,871 36,287 2000 48,326 92,159 32,712 53,068 TOO,111 35,843 65,852 118,836 43,716 2005 50,791 96,861 3LI,381 58,591 110,531 39,574 77 ,459 139,782 51,422 2010 53,382 101,802 36,134 64,134 122,035 LI3,692 91,111 164,419 60,836 VALDEZ 1980 2,146 5,821 1,876 2,146 5,821 1,87B 2,146 5,821 1,878 198')2,967 6,739 2,255 3,782 8,063 2,698 7,46LI 9,660 3,182 1990 3,328 7,163 2,491 4,241 8,768 3,059 7,323 11,080 3,830 1995 3,532 7,914 2,853 4,713 10,003 3,628 7,358 12,467 4,522 2000 4,033 0,898 3,354 5,237 11,201 4,197 7,717 13,296 5,060 ZOO5 4,259 9,352 3,525 5,7H2 12,367 4,634 9.077 15,640 5,952 2010 4,455 9,829 3,705 6,384 13,654 5,1'16 TO,677 18,396 7,001 TABLE A.19:RAILBELT HOUSING STOCK FORECASTS (000) (8)Low Eeon.Growth-Mod.Govt.Expend. Aneh.Fair.Vald.Total Aneh.Fair.Vald.Total Aneh.Fair.Vald.Total Aneh.Fair.Vald.Total Single Family 37 9 .5 47 50 11 .7 62 66 1S 1.1 82 72 17 1.3 90 Multi-Family i9 S .2 24 25 7 .3 32 36 10 .5 47 40 11 .6 52 l40bHe Home 9 2 .6 12 12 3 .6 15 \6 4 .6 21 18 5 .7 24 Duplex 6 1 .2 7 6 1 .2 7 9 2 .2 12 10 2 .2 12 (b)Mod.Eeon.Growth-Mod.Govl.Expend. ------- Aneh.Fair.Vald.Total Aneh.Fa ir.Val d.Total An~_Fair.Vald.Total Aneh.Fair.Vald.Total._------------- Single Family 47 S3 12 .9 65 72 17 1.5 91 88 21 1.9 111 Multi-Family 24 28 7 .4 35 4\10 .7 S2 50 '14 .9 65, -l:> \J1 Mob ile Horne 12 12 3 .7 16 18 5 .6 24 22 6 .8 29 Duplex 7 7 1 .2 8 10 2 .2 12 12 2 .3 14 (e)High feon.Growth-Mod.Govt.Expend. -_.........-.---------------- Aneh.Fair:..Vald.ToLal Anell.Fair.Vald.Total Aneh.Fair.Vald.Total Aneh.Fair.Vald.Total Single Family 47 58 14 1.2 73 85 20 1.9 107 118 29 2.7 150 Mull i Fam i.l y 24 31 9 .5 41 49 14 .9 64 68 19 1.3 08 Mobile Family 12 1L~4 .8 19 21 6 .8 28 29 8 1.1 38 Duplex 7 7 1 .2 8 12 2 .2 14 17 3 .3 20 "~,,J ,,~_~_J ~_._cJ ~,~J c_.~_J - r"" TABLE A.20:FUTURE Ril,ILBEL T RESIDENTIAL NO~-SPACE HEATING ELECTRIC IIY REQUIREHENTS r-(a)Low Economic Growth-Noderate Government ExpenditureI Large i;\ppliance All Appliance Year Anc.fan.Vald.Total Anc.Fair.Vald.Total for Railbelt !""" 1980 382 95 is 483 144 41 3 188 671 1985 444 118 8 570 193 58 5 256 826-1990 489 135 9 633 238 73 6 317 950 2000 679 194 12 885 385 125 9 519 1404-2010 795 230 15 1040 494 165 12 671 1711II I (b)Moderate Economic Growth -Moderate Government Expenditure /""'"Laroe Apgliance All Appliance Year Anc.ta~laId.Total Anc.Fair.Vald.Total for Railbelt 1980 382 95 6 483 144 41 3 188 67-1,.... 1985 464 123 9 596 203 60 5 268 864 1990 523 142 10 675 255 78 7 340 1015 ~2000 753 211 15 979 427 137 12 576 1555, 2010 97'"::>278 21 1274 604 200 17 821 209'"::>-(c)Hlgh Economic Growth -Moderate Government Expenditure LaE.g.~_!~.pe Iiance All Appliance Year Anc.tau.ald.Total Anc.Fair.Vald.Total for Railbel t-- -- 1980 B2 95 6 483 144 41 3 18B 671 1985 48'"::>134 11 630 211 66 6 283 913 r'"1990 '"::>74 162 13 749 282 89 9 380 1129 2000 886 257 18 1161 509 167 14 690 1851 F-'2010 1302 387 28 1717 817 278 23 1118 283'"::> I""'" ! A-46 TABLE A.21:FUTURE RAILBELT RESIDENTIAL SPACE HEATING ELECTRICAL REQUIREMENTS Vald.Total Vald.Tctal 0 446 0 524 0 583 840 ., 'ilf~ 995 jM (a)Low Growth Year Anc.Fair. 1980 395 51 1985 476 48 1990 539 44 2000 816 24 2010 982 12 (b)Moderate Growth Year Anc.Fair. 1980 395 51 1985 508 48 1990 578 44 2000 906 25 2010 1 198 15 (b)High Growth Year Anc.fair. 1980 395 51 1985 220 48 1990 640 45 2000 1076 27 2010 1623 21 A-47 2 Vald. o 2 446 556 623 932 1215 Total 4Lc6 569 686 1104 1646 -TABLE ,~.22:COMMERCIA,L-INDUSTR I Al-GOVERN~lD:l REQU IREHENTS (a)Low Economic Growth-Moderate Government Expenditure Year Anc.Fair.Vald.Total Moderate Economic Growth -r'loderat e Government Expenditure Anc.fair.Vald.Total 966 255 27 1248 1238 431 49 1718 1397 470 56 1923 2319 792 73 3184 3301 1~61 99 4561 - 1980 1985 1990 2000 2010 (D) Year 1980 1985 1990 2000 2010 966 1113 1218 2060 2487 255 389 1:08 686 857 27 39 44 57 66 1248 1541 1670 2803 3410 (el High Economic Growth -Moder3te Government Expenditure Year Anc.Fair.Vald.Total 1980 1985 1990 2000 2010 966 1432 1719 2991 5094 255 513 609 i070 1874 27 97 95 102 168 A-48 1248 2042 2423 4163 7136 TABLE A.23:MISCELLANEOUS ELECTRIC ITV REQU ~REMO;TS (a)Low Economic Growth-Moderate Government Expenditure Year Anc.Fair.Vald.Total 1980 20 4 2.5 1985 23 6 30 199C 26 7 34 2000 41 11 ')3 2010 49 13 63 (b)Moderate :::conomic Growth Moderate Government Expenditure .., '! Year Anc.Fair.Vald.Total j 1980 20 4 25 'III!!! ,:;;~ 1985 25 7 33 1 ,jl 1990 29 8 38 2000 46 12 59 2010 63 17 81 (c)High Economic Growth -Moderate Government Expenditure Year Anc.fair.Vald.Total 1980 20 4 25 1985 28 8 37 1990 34 9 44 2000 57 16 74 2010 91 26 2 119 '\-49 - - ""'"TABLE A.Z4:FUTURE MILITARY AND SELF -SUPPLIED INDUSTRiAL REQU IRE~lENTS -Mil Lt ary Se 1f -Supp li ed Year Net Generation Industry Net Generation low t10derate ~-1980 334 414 571 847 1985 334 414 571 847 1990 334 414 571 981..... 1995 334 414 571 981 2000 334 414 571 981-2005 334 414 571 981 2010 334 414 571 981 !"'" A-SO TABLE A.2~;RAILBELI UTILITY SALES PROJECTIONS BY END USE SECTION (10 3 MWh).- (a)THE BASE CASE Low Economic Growth Moderate Economic Growth High Feonomic Growth I·loderate Equipment ExpendIture Moderate Equipmef!t [xpenditur~_Moderate Egt~ipment ExpendIture__ Commerieal Commercial Commercial Year Residential Industrial Misc.lut al Res idential Industrial Misc.Total Residential Industrial t4isc.Total----- "19110 1117 1248 25 2390 1117 1248 2~2390 117 12118 2~23'10 1,){]~13~0 1541 Xl 2921 1~33 171H 33 3171 1482 2042 ,-J 3':>61 1990 1':>33 1670 34 32.37 163H 1932 38 3')99 1815 2423 44 42fJ2 20na 2244 2803 53 5100 24Bl 3184 59 5730 2955 4163 74 7166 2010 27D6 3410 63 6179 n10 4561 81 7952 4481 7136 119 11136 Annual Growth Hate 3~O~~3.4$3.1~~3.2~~3.7~o L~.4~~4.mo 4.1%4.7~o 6.O?,5.3~~'J.4~o :l> I V' (b)PRICE INDUCED SHIn TnWAHDS [LLURICI TY MODf.~E FCDNOMIC GRmllH SCENARIO -MODEHAH GOVfRNMENT EXPE~DlTURE Year COI1lJl!er':.lal -IndusLrIal -Government l')UO 12413 1')8')171H 19')0 1923 21J1J(],Wll ZOlU 3')61 .;...~J ~"_J ~__~::.CJ I,,"~.:':,I ~.~~J F'''~],~",.J - -I i l i""" ! "~"'~1 1 --~J -1 .---]~l -1 --"--1 --I -J ~--l ,,-"--1 ~-]I --] INPUT VARIABLES MODEL OUTPUT ~Of HOUSEHOLD ELECTRICITY REQUIRE GROWTH SCENARIOS fOR: •MINING •EXOGENOUS CONST. •MANUfACTURING AND TRANSPORTATION -1 MAP MACRO MODEL I I POPU LATlON •EMPLOYMENT,fiSCAL •AGRICULTURE •STATE GOV'T.I VARIABLES •fEDERAL GOV'T.•STATE GOV'T.CON ST.~-~-'l~_____.r-.•CIVILIAN NON-NATIVE HOUSEHOLDSHOUSEHOLDFORMATIONRATE HOUSEHOLD fORMATlO~~~/•NATIVE HOUSEHOLDS t •MILITARY HOUSEHOLDS I ~DUMMY VARS.fOR POOLED TIME SERIES REGIONAL ALLOCATION MODEL ~~///-•REGIONAL SHARE Of POPULATION •INITIAL PEOPLE·PER DWELLING UNIT •REGIONAL SHARE Of STATE EMPLOYMENT •HOUSING REMOVAL RATES •REGIONAL SHARE OF DISTRICT SUPPORT •VACANCY RATE N •EMPLOYMENT (E.G.CONST,a TRANSP.) HOUSING STOCK MODEL •REGIONAL SHARE Of OTHER SUPPORT •HOUSING CHOICE (REGRESSIONS)~~~-~---EMPLOYMENT (E.G,RHAIL,fiNANCE,ETC.) •TOTAL NO Of HOUSEHOLDS BY REGION I ELECTRICITY END USE MODEL •HOUSING TYPES BY REGION: I •SINGLE FAMILY •MULTI-FAMILY •DUPLEX •MOBILE HOMES •APPLIANCE SATURATION RATES RE:SIDENTIAL NON-SPACE ,.--ELECTRICITY REQUIRED BY REGION fOR: •FUEL MODE SPLIT HEATING ELECTRICITY •WATER HEATER •DISHWASHER •APPLIANCE AvERAGE ANNUAL ELECTRICITY REQUIREMENT MODULE •CLOTHF.S WASHER·TELEVISION CONSUMPTION •CLOTHES DRYER •fREEZER •CooKI NG RANGE •AIR CONDITIONER •REFRIGERATOR •SMALL APPLIANCES •PROPORTION USING ELECTRIC SPACE HEATING RESIDENTIAL SPACE HEATING ELECTRICITY REQ.FOR SPACE HEAT fOR: •AVERAGE LEVEL OF CONSUMPTION I ELECTRICITY REQUIREMENT ,.--•SINGLE FAMILY •MULTI-FAMILY •UTILIZATION RATE MODULE •DUPLEX •MOBILE HOMES •AVERAGE ELECTRICITY 1\t COMMERCIAL-INDUSTRIAL ELECTRICITY REQUIRED FOR COMMERCIAL. CONSUMPTION PER EMPLOYEE I ELECTRICITY REQIREMENT MODULE INDUSTRIAL-GOV'T SECTORS BY REGION I ·'L ~STREET LIGHTING a RECREATIONAL STREET LIGHTING a RECREATIONAL I I HOME MODULE HOMES ELECTRICITY REQ.BY REGION·'D ):> I U1 W ISER ECONOMETRIC END·USE FORECASTING MODELS FIGURE A-I [Ii] - ,.... I i -i (. - - APPENDIX B CRITIQUE OF ISER REPORT BY ALASKA PACIFIC BANK AUGUST 27,1980 - AJaskaPacificBank A SlIbsjdjury a/Alaska Pacific lJWlcorporaljO/l-t August 27,1980 Mr.Eric YouldrExecutiveDirector Alaska Power Authority 333 West Fourth Avenue ~Anchorage,Alaska 99501 Dear Eric: I""" I, - -r i i Concerning the employment and population projections prepared by ISER which you sent for our review,I would agree with you that they appear low.Generally speaking,we can make a strong case for a version closer to their high economic development/high government spending scenario. \~hile we are not prepared to provide you with detailed projectons,it·is my comment that a more aggressive estimate for Alaska average annual employment growth between 1980 and 1985 would be about 6.5%.I would expect this average annual.rate of growth to slow somewhat during the 1985-1990 period,to perhaps the 5.5%area I unless,of course "we can count on some definite progress in our resource development projects.That represents at least a 6%average annual rate of growth for Alaska employment during the decade of the 1980's. Beyond 1990,an average annual rate of growth in the neighborhood of 3.5%for each of the five-year periods may be a conservative number,but the current uncertainty associated with our future development makes it a minimum rate.Keep in mind,also,that any growth during that period will be advancing from a higher base which,no doubt,will translate into a slower rate of change. Obviously,these rates of growth reflect the assumption for gasline construction in the mid-1980 's.However,as you know, there are a myriad of other energy resource related proj ects possible in Alaska in the future,and although the employment impact of these separate projects probably will be less significant than the trans-Alaska oil pipeline construction project,each event can be expected to make its own contribution. Unfortunately,the timing of these major events is the one factor missing frpm any analysis 1 and,therefore 1 more precise employment forecasting at this time is difficult and perhaps Mr.Eric Yould August 27,1980 Page 2 of 2 misleading.I note,however,that ISER omits discussion of a gas liquids project per se,and I find it difficult to believe that federal government employment in Alaska will slow as suggested. In terms of the population impact of future development in Alaska,the average annual rates of growth may be closer to 4% for the 1980 I sand 2%for the 1990 IS.Obviously I once again these numbers depend on various resource development assumptions, but for your information they result from a population to employment ratio of 2.2 in 1985 and 2.0 in 1990. The chart below summarizes this brief analysis. Estimated Average Annual Rates of Growth Generally speaking,the rSER pattern of growth appears reasonable if the majority of our resource development for the time being takes place in the mid-80!s I followed by a period of slower growth in the late 1980's absent any new,maJor projects. However I as stated above,the huge quanti ty of Alaska's energy-related resources augers for our future development at some point.Therefore fit would seem reasonable that Alaska IS near-time future employment growth can be expected to remain at least as healthy as it has been in the recent past. I remain available to you if you wish to discuss these estimates further. Sincerel\{, ,'- ),/'//1~~ ('M.C.Couch Assistant Vice President S36A/X , :J I""'" I If"«', i I, r-,•. -I . r- ! APPENDIX C CRITIQUES OF ISER REPORTS BY WOODWARD CLYDE CONSULTANTS APRIL/JULY,1980 ,,- ~ [ -- -- I""" I Review of The University of Alaska Institute of Social and Economic Research Report "Electric Power Conswnption for the Railbelt: A Projection of Requirements" by Craig W.Kirkwood F.Perry Sioshansi July 1980 Woodward-Clyde Consultants 3 Embarcadero Center,Suite 700 San Francisco,CA 94111 INTRODUCTION This document constitutes the wTitten critique of the University of Alaska Institute of Social and Economic Research (ISER)final report (5.Goldsmith and L.Huskey,"Electric Power Consumption for the Railbe1t: A Projection of Requirements,"May and June 1980J as required by Section 1.1.5 of the Scope of Work for agreement no.P5700.l0.21 between Woodward- Clyde Consultants (WCC)and Acres American Incorporated (Acres).In accordance with a letter of May 14,1980 from Acres,this review is brief. Primarily it is an update of WCC's review of the ISER draft report IC.W. Kirkwood and F.P.Sioshansi,"Review of ISER Draft Report",April 1980J. For a complete review of the ISER electric demand forecasting work,this earlier document should be read in conjunction with the current critique. The conclusions reported here are based on a review of all three parts of the ISER final report:the Executive Summary dated May 16,1980, the main body dated June 19$0 and the Technical Appendices dated May 23, 1980.Additional perspective was gained by WCC attendance at a workshop for Railbelt utility representatives on June 10,1980 and a public work- shop on June 11,1980.At these workshops Scott Go:ldsmith of ISER present- ed the results of the ISER study and answered questions. 1 - ~ I I i r - REVIEW CONCLUSIONS ISER's work is the first attempt to construct an econometric/end use electric energy demand forecasting model for the Alaska Railbelt.It is the most comprehensive look at future Railbelt electric energy needs to date.Given the difficulty of obtaining much of the needed data and the limited time available,the ISER work is a maj or achiev'ement.However, there are significant limitations in the work which restrict its useful- ness in a study of alternatives for meeting the Railbelt's future need for electric power. Most of our conclusions reported earlier regarding the work discussed in ISER's draft report apply to the final report as well.In particular, we conclude the following: •ISER's overall approach,utilizing economic and population projections coupled with an end-use model to forecast total electric energy demand,is sound. •The modeling work suffers from a lack of some important data and the poor quality of other data.Substantial improvements in this would require an ongoing data collection program over a period of years. •It does not appear that a structured approach was used to develop input scenarios regarding possible future development in the Railbelt.In particular,the scenarios appear to represent only the personal professional views of the authors with no systematic attempt to incorporate other points of view. •Uncertainties associated with the forecasts are treated in a crude manner.Because of this it is difficult to determine the significance of these uncertainties for power system planning. •Only very limited sensitivity analysis was carried out to study the implications of varying the input asst~ptions used in the forecasting model. •For the'above reasons,the forecasts made by ISER are not neces- sarily superior to those provided by a simpler analysis approach. 2 IMPLICATIONS OF OTHER WORK The ISER final report contains a swnmary of other electric demand forecasting studies that have been carried out for the Railbelt.In general,previous studies have forecast greater future demand than the current ISER study. At the utility and public workshops,Professor Goldsmith commented that he believes other studies done during the last decade were overly influenced by the high rate of development occurring during the oil pipeline construction period.However,he also noted that the scenario approach to forecasting,which is used in the ISER work,may be myopic and,as a result of this,underestimate future growth.He discussed steps taken in the ISER study to counter this tendency.In addition, he noted that previous studies that used the scenario approach have not systematically underestimated the Railbelt growth that has actually occurred to date,although the details of the growth have turned out to be some- what different than what was forecast. An important reason for the differences in forecasted energy demand- growth between the ISER study and previous studies is the difference in forecasted popularion growth.The factors influencing future popula- tion growth in the Railbelt are subject to many uncertainties.The assumptions about these factors that were made in the ISER study should be given careful consideration since the authors of the study have considerable knowledge and experience regarding Railbelt development. However,as the utility and public workshops made clear,there are other reasonable points of view about these factors that might lead to substan- tially different forecasts of future electric energy demand. 3 - , I, .., I ..... r r STRENGTHS fu~D LIMITATIONS OF ISER WORK A well-constructed econometric/end-use model can be a powerful tool for studying the possible implications of proposed energy-related actions or policy changes.However,past experience indicates that substantial time and resources must be invested to achieve reasonably defensible results with such a model. The ISER work to date provides a solid basis for development of an econometric/end-use model.However,we believe that at its current stage of development,the ISER model does not give results that are more defensible than those of previous forecasting studies.The previous studies were,however,limited,one-time efforts while the ISER work could form the basis for an ongoing modeling and data collection effort to develop a sophisticated energy forecasting tool for the Railbelt. Regardless of what model is used to forecast future electric energy demand,there will be substantial uncertainties about many of the input assumptions made in the model.These will lead to substantial uncertainties in the forecasted electric energy demand.For example,Professor Gold- smith commented in the public workshop that he believed there was approx- imately a 20 or 25 percent chance the actual future demand would be below the "lown forecast presented in the ISER final report and a similar chance it would be above the "high"forecast. With this degree of uncertainty,there are reasonably likely levels of future electric demand so low that the Susitna Project could provide more electric energy than would be needed.There are also reasonably likely levels of demand for which substantially more capacity would be needed than could be provided by the Susitna Project. 4 It appears desirable to analyze the oveT-and under-capacity risks associated with Susitna Project planning in the presence of these large uncertainties about future demand for electricity.Such an analysis requires that uncertainties be treated explicitly in the demand forecasts. This is not done in the ISER work;this is a major limitation of the work with regard to its usefulness for the Susitna Project. A second,related limitation is that the input assumptions and scenarios used in the modeling work represent only the judgments of ISER professionals.While these experts are very knowledgeable about potential future Railbelt developments,it appears from the utility and public workshops that there are other knowledgeable individuals who have somewhat different views about the future of the Railbelt.It seems desirable to have these views incorporated into the demand forecasting work.This was not done systematically in the ISER work. 5 ,i -i r- i, " ~ I -! - - - ~ i, REVIEW OF ISER DRAFT REPORT by Craig W.Kirkwood F.PerrySioshansi April 1980 Woodward-Clyde Consultants Three Embarcadero Center,Suite 700,San Francisco,CA 94111 273/11 TABLE OF CONTENTS 3.2.1 3.2.2 3.2.3 3.2.4 3.2.5 1.0 INTRODUCTION 2.0 OVERALL REVIEW CONCLUSIONS 2.1 General Conclusions 2.2 Specific Conclusions 3.0 DETAILED REVIEW OF DRAFT REPORT 3.1 Alternative Forecasting Methods Considered by ISER 3.2 Forecasting Methodology Economic Growth Scenarios MAP Statewide Econometric and Demographic Model Household Formation Model Regional Allocation Model General Comments Regarding Remaining Model Components 3.2.6 Appliance Saturation and Energy Utilization Model 3.2.7 Final Energy Demand Model 3.2.8 Housing and Appliance Stock Model 3.2.9 Energy Availability Scenarios 3.2.10 Mode Split Model 3.2.11 Energy Efficiency Model 3.2.12 Energy Requirements by Fuel Type 3.3 Quantity and Accuracy of Data 3.4 Methods Used by ISER to Consider Uncertainties 4.0 IMPLICATIONS OF OTHER WORK 5.0 STRENGTHS AND LIMITATIONS OF ISER WORK REFERENCES· 1 3 3 4 7 7 8 9 13 14 15 16 20 28 29 32 34 37 37 38 40 42 46 51 , j r 77/14 LIST OF TABLES Table 3.1.Components of Residential Use Per Customer Table 3.2.Population,Households,and Customers 21 22 I""" ! I r r""" I i l r I""'" I - Table 3.3.Appliance Saturations and Contributions to 23 Annual Average Residential Use Table 4.4.Comparison of ISER's Population Projections [ISER 1980,44 Table 0,P.3.6.]To the Alaska Power Administration's Population Projections [U.S.DOE,Alaska Power Admin- istration 1979,Table 8,P.34]For the Railbelt Area Table 4.5.Comparison of ISER's "Dry Run"Electric Power Demand 45 Projections rISER 1980,Table 00,p.3.9]and the Alaska Power Administration's Projections [U.S.DOE, Alaska Power Administration 1979,Table 12,p.46] for the Railbelt Area 77 /14 Figure 3.1.Major Exogenous Variables and Economic Scenarios Figure 3.2.Major Components of An Electric Demand Forecasting Model Figure 3.3.Detailed Components of an Electric Demand Forecasting Model Figure 3.4.Housing Unit Model LIST OF FIGURES 10 18 19 31 - I"""". I r r - -i 273/9 1.0 INTRODUCTION This document constitutes the written critique of the University of Alaska Institute of Social and Economic Research (ISER)draft re- port as required by Section 1.1.5 of the Scope of Work for agreement no.P5700.10.21 between Woodward-Clyde Consultants (WCC)and Acres American Incorporated (Acres). Under Subtask 1.01 of the abovementioned Scope of Work,WCC is to review the methods investigated by ISER for possible use in its forecasting,and to assess the strengths and weaknesses of the methods selected by ISER for its forecasting.This review and assessment is to include consideration of the techniques and methods investigated by ISER for use in: 1)Projecting economic development, 2)Selecting input scenarios for its economic development models, 3)Developing its econometric-end-use mode for forecasting electricity load requirements,and In addition,the review is to consider the quantity and accuracy of I""" I 4)Considering the uncertainties in its forecasts. the data used in the ISER forecasting methods.Furthermore,WCC 1 273/9 is to assess the implications for the ISER work of the work done by others in the area of energy and economic development in the Railbelt Region. These issues are addressed here to the degree possible given the information in ISER's draft report.It should be noted that ISER refers to their draft report as a "progress report."However,it is clear from discussions with them that this report is the draft report called for in Clause I of the contract between the State of Alaska Legislative Affairs Agency and ISER.Hence,it is this report that wce is to re- view under its agreement with Acres to critique the ISER draft report. This review is organized into the following sections: 1)a summary of the general conclusions of our review 1., 1 ,j 2)a detailed review of the draft report 3)a consideration of the implications for the ISER work of the work done by others,and 4)an assessment of the strengths and limitations of the ISER work. 2 273/9 2.0 OVERALL REVIEW CONCLUSIONS 2.1 GENERAL CONCLUSIONS ~ISER's basic approach to forecasting total electric energy demand [' rl """" 1 I""" I is state-of-the art.Because the approach requires substantial model development effort and an extensive data base,it has generally only been attempted by large utilities or other organizations with substan- tial resources.Althouth the basic approach that ISER has taken is sound,the specific methodology they have developed to implement the approach has serious technical deficiencies which substantially limit the defensibility of the results obtained.In addition,there are se- rious weaknesses in the data base that ISER is using to support their modeling work. Most of the methodological weaknesses could be corrected with sev- eral person-months of additional development work by knowledgeable anal- ysts.Some deficiencies in the data base could also be corrected with several additional person-months of data collection.Additional work would also be required to adequately document the methodology and data base. 3 273/9 However,even with this additional work,certain types of data that are important for defensible forecasting using ISER's approach could only be collected by a well-designed data-gathering program over a period of some years.This length of time is necessary to obtain information on variations in electric energy consumption patterns as weather conditions change with the seasons. With the additional model development,data collection and documen- tation effort,defensible forecasts could be produced for use in the Susitina Project power studies.However,the sophisticated methods that are being used by ISER will probably not produce forecasts that are, on the whole,necessarily more defensible than what could be obtained using considerably simpler methods.This is because there are substantial uncertainties about some of the major inputs needed by any model that forecasts future Railbelt development.The variations in the forecasts resulting from plausible variations in these uncertain input quantities will probably be greater than errors that may result from using a simpli- fied forecasting model. 2.2 SPECIFIC CONCLUSIONS Our specific conclusions regarding the work presented in ISER's draft report are summarized in this section.The results of our de- tailed review of the draft report (which serve as the basis for these conclusions)are presented in Section 3. 4 •-i I ~• ..... - ....., i r -I r- I, - - I""'" t I 273/9 We conclude the following: •ISER's overall approach t utilizing economic and population pro- jections coupled with an end-use model to forecast total elec- tric energy demand.is sound.However t their methodology for implementing this approach has numerous technical and proced- ural flaws.In addition t there are numerous deficiencies in the way they have implemented this methodology. Many of the methodological deficiencies could be reduced with moderate additional effort by knowledgeable analysts.Simi- larlYt substantial improvements in the implementation should be possible with moderate additional work. Some deficiencies in the current work are due to lack of some important data and the poor quality of other data.Some im- provement in data would be possible with a short-term data collection program.However t major improvements can only be achieved by an ongoing data collection program over a period of years. •End-use models.by their nature t require an extensive data base.Due to the current lack of quality data t the forecasts made using the ISER end-use model are not necessarily superior to those provided by a simpler analysis approach.Based on our review of other applications of end-use models,we expect that the defensibility of the end-use model results will im- prove over time as better data become available. •At present.ISER's end-use model is incomplete and poorly docu- mented.In particular.distinctions between the residential and commercial/industrial sectors are not well addressed.The treatment of the commercial/industrial sector is very weak t and within the residential sector not enough emphasis has been placed on analyzing various types of residential housing and their associated electric demands. •The documentation in the draft report is generally poor.Many important assumptions are not substantiated while others are not explicitly stated.A systematic documentation of all input assumptions.and the rationale for making them t is highly desirable. •The draft report does not indicate that any structured ap- proach was used to develop input scenarios regarding possible future developments in the Railbelt.In view of the dynamic political climate and great uncertainties about the future of Alaska.it is essential that input scenarios be carefully selected if the resulting forecasts are to be defensible. 5 273/9 •The draft report indicates an inadequate review of existing literature and data sources regarding modeling and forecasting demand for electricity.Some of ISER's model components could be substantially improved by adopting existing similar models or model components. Anyone of these deficiencies would compromise the defensibility of ISER's forecasts for the purposes of the Susitna Project.In our judgment, the combination of all the above deficiencies means that ISER's current forecasts would not be able to withstand critical review well enough to serve as a defensible basis for assessing the need for the power the Sustina Project would provide. We have provided specific suggestions for overcoming many of the de- ficiencies as part of our detailed critique in Section 3.Some of the deficiencies can be overcome without excessive delay or effort.Other deficiencies,particularly data inadequacies,would require more effort and time to improve. There are some important issues related to the Susitna Project power studies that are not directly addressed by the ISER work.Con- sideration of these issues goes beyond just a review of ISER's work,so further discussion of them will be deferred until Section 5 where there is an assessment of the strengths and limitations of ISER's work with regard to the Susitna Project power studies. 6 r r 273/9 3.0 DETAILED REVIEW OF DRAFT REPORT This section contains a detailed review of the ISER draft report. In keeping with the scope of work for our review,this section con- siders the following: I""'" I - 1) 2) 3) 4) Alternative forecasting methods considered by ISER, The forecasting methodology used, Quantity and accuracy of the data used,and Methods used to consider uncertainties. r l ~ I -, Because of serious editorial problems with the ISER draft report,it is often difficult to be certain exactly what was done. In what follows,page numbers in parentheses refer to pages in the ISER draft report unless otherwise noted.In numerous p~aces we have suggested further work that could be done or additional sources of infor- mation that we believe would be useful for ISER's work.Strictly speaking, these suggestions are beyond the immediate scope of our review.Ulti- mate responsibility for the total electric energy demand forecasting work rests with ISER,of course. 3.1 ALTERNATIVE FORECASTING METHODS CONSIDERED BY ISER The draft report contains no discussion of alternative forecasting methods considered by ISER before adopting their present methodology. 7 273/9 From our discussions with ISER it appears that they considered various alternative forecasting methods.It would be helpful to discuss what alternative methods were considered and the rationale for selecting the methodology used.ISER's contract with the Alaska Legislative Affairs Agency calls for a report on this topic in mid-January 1980;this has still not been delivered. Considerable research has been carried out elsewhere in the U.S. on forecasting electric power demand,and ISER appears to be unfamiliar with this literature.There are no references to this work in ISER's draft report.Several references and data sources are suggested in our discussion in the following section. 3.2 FORECASTING METHODOLOGY The forecasting methodology used by ISER is presented in Part II of their draft report.The methodology consists of eleven components: I.Economic Growth Scenarios II.MAP Statewide Econometric and Demographic Model II.A.Household Formation Model III.Regional Allocation Model IV.Appliance Saturation and Energy Utilization Model V.Final Energy Demand Model VI.Housing and Appliance Stock Model VII.Energy Availability Scenarios 8 ,J ,... 273/9 VIII.Mode Split Model IX.Energy Efficiency Model x.Energy Requirements by Fuel Type Model Each of these is separately discussed below. 3.2.1 Economic Growth Scenarios "..., I I l !""'" I This model component is critical as it influences every other as- pect of the model.At present,this component is inadequately defined, as well as poorly structured and presented.Although further discussion of economic scenarios is presented in Part III (pp.3.10-3.16),even with this added discussion,the scenarios are inadequate and poorly documented. Major problems are that relationships between endogenous and exo- genous variables are not well def"ined and that the sources and relative magnitudes of impacts for given scenarios are not discussed.This is a significant shortcoming since several major exogenous factors are the basic driving forces of the Alaskan economy.These exogenous variables influence three major sectors which,in turn,affect everything else in the economy (see Figure 3.1).To develop credible economic scenarios, one must start with a clear specification of these basic entities and their interrelationships.Particular attention,for example,should be given to state government policies.The role of the federal government must also be considered,particularly as it applies to energy policies. 9 273/9 Major Exogenous Variables •National/International Economy •World Market Fuel Prices •National Energy Policies •State Government Investment/Expenditures •Discovery of Major New Fuel Reserves in Alaska Basic Private Sector State Government I 11 Fed.eral Govern.ment; Activity Activity Activity Il-------.;t--1 Macro-Economic Scenario Figure 3.1.MAJOR EXOGENOUS VARIABLES AND ECONOMIC SCENARIOS 10 - -I I"'"" I ,... i r 273/9 The role of the private sector must be defined in the context of state and federal regulations and policies. A defensible scenario combines ~easonable and internally consistent assumptions about these basic sectors.ISER's scenarios are not well documented and presented.For example,their "High Scenario"(pp.3.14- 3.16)results in construction employments (Table 3,p.3.19)which are not only low but,in fact,incredible for the 1990-2000 period.Part of this problem (which is also present in the "low"and "moderate" scenarios)may be attributed to myopia.ISER only considers projects that are currently being considered and can be expected to be completed by 1990.This implicitly assumes that no additional projects will start in the 1990s.At the very least it seems appropriate to assume a con- tinuing,reasonably healthy level of construction activity under the "high"scenario. Another shortcom~ng of ISER's work is the absence of direct or induced state government investment/expenditure (although part of the indirect involvement may be implicit in ISER's "Industry Assumptions" [pp.3.11-3.16]regarding industries such as agriculture and fisheries). In view of Alaska's large expected budget surplus for the next couple of decades*and of the potential for further exploration,development, *According to a recent article in the Wall Street Journal,"The extra money will total $53 billion over the next 10 years and a further $44 billion in the succeeding decade"(Wall Street Journal,1980). 11 273/9 and export of oil and natural gas t particular attention should be de- voted to the role of state government.The high scenario t in particular t should consider a sizable and increasing state government surplus which can be used to accelerate economic development and growth. Furthermore t the effect of state and/or federal regulatory deci- sions t energy and conservation policies t and politically induced legislations are not considered.Any of these factors could have a significant impact on the Alaskan economy and demand for electricity in the Railbelt. To illustrate the importance of carefully considering input sce- narios t consider the possibility of a trans-Canada natural gas pipeline or an LNG facility on the Kenai Peninsula.Currently,all utilities in the Anchorage area use natural gas t at rates far below the world market price,to generate electricitYt and their customers enjoy some of the lowest electricity rates in the nation.As a result,many homes are electrically heated.If the natural gas were to be exported the local utilities might be forced to pay higher prices which would be passed on to their customers.Under these circumstances t electric space heating might no longer remain attractive.The long-term effect of this on future electricity consumption is likely to be sizable.There is cur- rently strong opposition by some of the gas burning utilities to such an eventuality.Hence,it may be politically unpopular to vote for the gas pipeline or LNG plant.On the other hand t federal regulations may make it progressively more difficult to use natural gas for power 12 r 273/9 generation when 'other alternatives are available.This example illus- trates the importance of well thought-out and consistent scenarios. ISER's "Current Status"(p.2.5)is clearly not adequate,and their proposed "Future Work"(p.2.5)does not appear adequate to provide de- fensible scenarios. 3.2.2 MAP Statewide Econometric and Demographic Model* The current MAP model appears to be a defensible method for providing overall population,employment,and income level forecasts for Alaska for the Susitna Project.In the long-run,however,MAP should be modified to better accommodate policy type variables and macro-economic scenarios. The link between the national and Alaskan economies should also be strengthened using a national macro-economic model (such as those avail- able from Data Resources,Inc.and Chase Econometrics).Variables other than wage differentials (e.g.,low mortgage rates,lower taxes,etc.) may attract people to Alaska in the future and should be considered and appropriately modeled.A particularly useful economic/demographic model which may be of value to ISER's subsequent work is the model jointly developed by New England Power Pool (NEPOOL)and Battelle Columbus Labo- ratories (1977). *Our comments on the MAP model are based on documentation provided by ISER dated May 31,1979:"Man-In-The-Arctic-Program,"compiled by Oliver Scott Goldsmith,Institute of Social &Economic Research, University of Alaska,Anchorage,Alaska. 13 273/9 3.2.3 Household Formation Model ISER has linked this model to the MAP model.In our judgment,it would be better to link this model to the Regional Allocation Model. It appears simpler to allocate total population to the Railbelt area first and then forecast household formation rates for the Railbelt. ISER's modeling component diagram does not show this subcomponent and its relationship to the housing and appliance stock model (p.2.1). Particular reasons for recommending the above modification are: (1)the Railbelt area comprises Alaska's most developed and populous region and better data (compared to the rest of Alaska)is available for this area,(2)the Railbelt has a relatively low percentage of na- tives (whose household formation and size patterns are not as well under- stood),and (3)the modification would simplify the link between the MAP and Regional Allocation Models. Since similar models have been developed previously and adjustments for Alaska's unique characteristics could have been readily made,we are not sure why ISER developed their own model.The present model is fairly crude and its forecasts depend on several key assumptions that are not adequately documented.ISER's claim that "In reality,the complexity of the household formation decision and the important recent structural changes make any 'Rtatistical estimates of this relation questionable" (p.2.9)is only partially true.While there are disagreements between demographers on future rates of household formation,certain qualitative trends are likely to continue and can provide useful forecasting bounds 14 , i - P'" " I - r r 273/9 (for example,see Slater 1980;NEPOOL and Battelle 1977;U.S.Department of Commerce,Projections of the Population of the US:1977-2050,1976). Despite these shortcomings the ISER model could provide adequate results which could be tested against more detailed models.This would provide an opportunity to fine tune their model and calibrate its para- meters.However,we were not able to verify if the model is appropri- ately formulated because ISER's intermediate results (such as the aver- age number of people per household,etc.)are not presented in the draft report.We recommend that such information be summarized in their final report and that they compare this information to national trends and trend forecasts.Furthermore,we recommend that ISER perform sensitivity analyses on the key assumptions used and present these results in summary form.Their statement,"The future household formation rates are assumed to follow the pattern of change projected at the national level,"(p.2-10) requires substantiation. 3.2.4 Regional Allocation Model This model component converts MAP's statewide projections to cor- responding projections for the Railbelt area,bypassing the development of a separate regional economic model.This is a reasonable approach because many development and construction activities are likely to take place outside the Railbelt which affect the residential and commercial activities in the Railbelt.This is true because of the central geo~ graphical location of the area and the fact that more than three quarters of the state's population lives within its boundaries. 15 273/9 ISER's present approach appears to be based on a continuation of historical trends and past relationships between various regions.While we do not recommend a detailed analysis of regional economics and growth patterns,it is suggested that analysis of historical regional growth trends be complemented by a study of their relative potential for future development and economic activity.For example,some regions of the state may be expected to prosper more than proportionately as a result of new discoveries of natural resources (e.g.,oil,natural gas,wood products)and subsequent development of these resources.Such possibi- lities should be considered in the context of the overall economic sce- narios to produce consistent and credible results. ISER's "Current Status"(p.2.14)indicates that this model com- ponent requires additional work.Their present documentation does not clearly indicate exactly which factors are assumed to determine each region's share of activity (p.2.13).Better documentation would be necessary to judge the validity of ISER's assumptions.It would be adviseable to perform sensitivity analysis to identify and better define the most influential parameters. 3.2.5 General Comments Regarding Remaining Model Components FollOWing a review of several other forecasting approaches (in particular NEPOOL and Battelle 1977;Pacific Gas and Electric Co., California Energy Commission,Burbank 1979;Comerford 1979;Thomas 1979; Torrence and Maxwell 1979;Fitzpatrick 1979;National Research Council 1978;and DRI 1976),taking into account Alaska's unique characteristics, 16 ..... - - -i -I -, r -i - I""" ! 273/9 data inadequacies.and ISERts time and resource constraints.we conclude that a reasonably sophisticated and defensible electric demand forecasting model of the type ISER is developing should include the following four major components (Figure 3.2): •Scenarios that provide the primary exogenous inputs of the model (as discussed in Section 3.2.1). •Economic projections that take various scenarios and other data as input and generate forecasts of population.employment.in- come.and so on using econometric models, •End-use models that convert the output of the economic projec- tions into forecasts of electric appliance ownership (purchasel replacement)and utilization taking into account factors such as price of alternative fuels,income of the household,regu- lations on average efficiency of electric appliances and·so on (Note that a direct link between the scenarios and the end-use models is required.).and •Electricity demand projections that simply sum total electricity consumption across individual consuming units using information generated in the previous two steps. These components should be linked so their interdependencies are technically correct and logically consistent.ISER's present model com- ponents (p.2.1)do not fully satisfy either qualification.Figure 3.3, a more detailed version of Figure 3.2.shows the subcomponents of a reasonably sophisticated and defensible model and their interdependence. A comparison of this figure and that shown in ISER's report (p.2.1) suggests how ISER's model components might be rearranged and what new components are necessary.(Some of these subcomponents may already be implicit in ISER's work but not specifically referred to or presented.) The suggested rearrangement should not involve substantial additional work and would result in a better structured and more defensible model. 17 273/9 I.Scenarios II.Economic Projections III.End Use Models IV.Electricity Demand Projections Figure 3.2.MAJOR COMPONENTS OF AN ELECTRIC DEMAND FORECASTING MODEL - ...... I"""' I ...,. I - I MACRO SCENARIOS ExoqenOU5 V;Y18b'f'~ •NiIIUOn;l.l/lrtl@'fnat'Onai Econom" •World Market Fue-~Prices- •N.llonal (i.e.,Feclenil £oet9V PDlicl9l!i •State/Local Irrwestment/~mentPOIICle'5 •OitcO'lo'lIty or New F-u~RIIMK'Ye&m ,l\,laka I Sta~e Go...en1nwrnt I ,F~r"GOIIrWIlm&nl I I B.H~Pnvate SectOr ~ ~1 II.ECONOMIC PROJECTIONS rata Inpu' ~ MAP model Nationail Economic ~ode, 1 R.i:lbltl:EoonomlC Modl!:l Slmil", r the Ret! •OernQ/Poif3Ulatlon 5, •Emplovment f-•Income •S~ate Re'II'enue/Expendltureos I11 L Household Formation ....odel Suppon Sector A.ctll,'I!V Model I 1 1 JKMprt....A,,,,,ao@y and P""\Resfdenti,1 Hou:tmg StOel<..Model Commerd.-t/lndu'S1r;al Stock Modej •Singie FalTlll,,·•Sma'J CommerCial •Wulti FamIly •Ulge Comrnen:::~alnndu!itrial •New ConstructJOn •New ConstruCtIOn •-Exlnrng Units •E)UHlngUOl'S ! III.ENO·USE MODE LS I ... Residential End·Us.e/StOCk Moclel Commercial/InduStrial End·U~iStock Model K Na[lona1/R~ional Trends \J Ap""onc.Satu,ar,on Rat"l IA",,"anc,Saru,"'on Rat" I 1I kEn,,""A.a,''''''''.nd ""CO '\Apptiollnc:t!Fuei Type I I Appliance fUEl T'fP-eCons-ervatlonrLQad Management ~~ K Efficienc'f StandaTds J A.PC>Iiance-ElectrJcity I I A.ppll.r1Ce Eh!ctrlcitv CORlU"'Ptialt Rates ConaumoptiOn Rates !1 1k""CO Eia".c,'Y App/;·••ne.·WtHizatIOf1 ,I APC>Iiilf1ce UtililS1l0nConsel"Yat~n/LoadManegemenl Roll.and Plltt....n Rates and PatternAvailabllavofSubs:titute~ 'v.ELECTRrCITY DEMAND PROJECT1ONS J R ...ident~Elec1ricJry Comm"",iolllndumi.1 1 -j o.m.nd ·Model Electncfty Demand Model ~ I 1 Total EJectnciw Demand Figure 3.3.DETAILED COMPONENTS OF AN ELECTRIC DEMAND FORECASTING MODEL 273/9 3.2.6 Appliance Saturation and Energy Utilization Model This section of ISER's model is poorly documented.The assumptions/ results presented in Part III are difficult to interpret and are generally shown in unconventional units and terms.The assumptions made about fu- ture saturation and utilization rates,when presented,are unsubstantiated. Substantial additional work and better documentation and presentation are required on this model component. It would aid the reader if a summary table were included,showing for residential and commercial customers: 1)Saturation rates for major appliances,both historical and projected (%), 2)Average consumption rates for appliances,both historical and projected (kWh!unit!yr),and 3)Average utilization rates per appliance,both historical and projected (kWh!household/yr). Since this type of data is available for many lower 48 utilities,(for example,see Table 3.1)a rough check on the validity of the projected rates could be made if this information was presented.The information in this proposed table,coupled with information on numbers of households, would allow a computation of total demand per household,which could also be compared to national data (for example,see Tables 3.2 and 3.3).Much of the desired information is in the report,but one has to sift through several tables and do additional calculations to convert it to the de- sired format. 20 '1 j r r Table 3.1.COMPONENTS OF RESIDENTIAL USE PER CUSTOHER+ Annual Kilowatthours -: I .... I Frost-frep.refrigerator Refrigerator Freezer COlor television B&W tel evi si on Water heater El ectri c range Clothes washer El ectri c dryer Di shwasher Air conditioner,window Air conditioner,central L i ghti ng Small appliances Heating plant kWh/Unit 1400 860 1400 500 235 4500 1200 103 993 363 390 3200 1000 300 560 1976 Saturation 0.578 0.493 0,271 1.048 0.924 0,067 0,474 0,837 0.455 0.517 1.020 0.115 1.000 1.000 0.975 kWh/Cust. 949 424 379 524 217 302 569 86 452 lel8 3Y8 36~ 1000 300 546 6702 kWh* - i""" I ..... I *6702 kWh/customer compares with an actual 1976 experience of 6689 kWh. **1.92 kW/customer compares with an actual 1976 experience of 1.90 kW. +b "Data a stracted from Peak Load Forecasting Methodology"by George L.Fitzpatrick,Long Island Lighting Company,Mineoia, New York.Presented in EPRI Symposium on Electric Load Forecasting (Fitzpatrick 1979). 21 .+Tahle 3.2.POPULATION.HOUSEHOLDS.AND CUSTOMERS Population Households perCalendarperResidential ResidentialYearPopulation*Household Households*Customer Customers*----- 1950 5059 3.80 1331 1.37 970 1955 5071 3.70 1371 1.12 1229 1960 .5349 3.61 1483 1.05 1418 1965 5630 3.44 1635 1.00 1635 1970 5882 3.23 1819 0.9]1868 19]5 6285 3.09 2033 0.92 2203 1980 6630 2.93 2265 0.90 2509 N 1985 6940 2."72 2550 0.89N 2852 1990 7252 2.61 2783 0.89 3143 *1970 TVA region (thousands). +Table reproduced from "Three Methods of ForecastIng Residential Loads"by James Torrence and Lynn C.Maxwell.T~nnessce Valley Autllority,Chattanooga. Tennessee.Presented in EPRI Symposium on Electric Load Forecasting (Torrence and Maxwell 1979). ~.,~I Uc".•O',J t'''~c."dJ &'".~.;',.l b~-.,J ,"",,,",.~J i,"~""••,,j ".J.,,,J 'L_~J ~£.,.J ,~.~~J ~~--I '~-J '_.~--]]----1 r~1 -1 1 "'--1 1 ~l --~1 ~"-'-l --1 Table 3.3.+APPLIANCE SATURATIONS AND CONTRIBUTIONS TO ANNUAL AVERAGE RESIDENTIAL USE Calendar Ye~r 1976 Calendar Year 1986 Average Use Contribution Average Use Contribution Annual of to Annual of to Annual Growth Saturat10n f,ppl1ance Average Use Saturation Appliance Average Use Rate (t)--.ill__(kWh)(kWh)('t )(kWh)(kWh)1976-86 E1 ectric heater 44 9300 4,092 55 8580 4,720 Range 80 1330 1,064 B6 1210 1,040 Wa ter heater 73 5000 3,650 84 5000 4,200 Air cond1tioner 63 2900 1,827 81 2650 2,145 Refrigerator 99 1220 1,208 100 1560 1,560 Freezer 47 1075 505 56 llHO 665 Washer 74 100 74 75 100 75 Dryer 45 1370 616 5 1350 760 N Dishwasher 24 350 84 40 330 130w Lighting,TV,other 1,797 3,210 Average use 14,917 18,505 2.2 Average customers (1000s)7.,251.0 2,918 2.6 Energy use (l06 kWh)33,577.4 53,99B 4.!I +See footnote for Table 3-2 273/9 Several important types of appliances (lighting,TV,refrigerators) are apparently combined together under the heading "non-substitute elec- tric"(p.3.27).We suggest that all "major"appliances be separately accounted for.The reason for this being that improvements inefficiency standards,price elasticity of demand,and numerous other variables are likely to affect these appliances in different ways,hence the need for separate record keeping.Other small electrical appliances can then be combined under one category.Electric cars should be considered for the period 1990-2010,since they may become commercially available during that time frame (Burbank 1979;EPRI Journal 1979). There is little documentation presented on per unit comsumption of various appliances,their saturation rates,average useful life,and expected improvements in efficiency.What little data is presented is fragmentary and divided between Parts I and III of the report.Ap- parently,ISER has not utilized the available information from several generally quoted sources such as: •Association of Home Appliance Manufacturers (AHAM)-Informa- tion on average size,consumption,and replacement of major appliances. •Federal Energy Administration (FEA)-Information on energy efficiency targets for major appliances. •Electrical Power Research Institute (EPRI)-Information on electrical load forecasting and modeling.In particular,the following four reports: (1)"How Electrical Utilities Forecast:EPRI Symposium Pro- ceedings,"EA-1035-SR,March 1979. 24 ""J j J.._~ - r f"'" ! L ,... t r ,.... i i" - - 273/9 (2)~Patterns of Energy Use by Electrical Appliances,"Report prepared by Midwest Research Institute (MRI),EA-682, January 1979. (3)"Analysis of Household Appliance Choice,"Report prepared by Charles River Associates,Inc.(CRA),EA-1100,June 1979. (4)"Electric Load Forecasting:Probing the Issues with Models -Final Report,"Report prepared by Stanford University EA-1075,April 1979. •Edison Electric Institute (EEl)-Various reports. •Bureau of the Census,Statistical Abstracts of the U.S., Electrical Appliances,various years. Other useful data sources include: •FEA Electric Pricing Experiments -Conducted on ten regions of the country and more underway in other areas.Questionnaire surveys were used for each pricing experiment and a complete documentation on all of these data sets should be available shortly.Detailed information on housing type,income,age, number and types of appliances,and utilization rates are included in these data sets. •"Models for Long Range forecasting of Electric Energy and Demand,"models and report jointly developed by the New England Power Pool (NEPOOL)and Battelle Columbus Labora- tories,June 30,1977 (revised and updated version forth- coming). •Washington Center for Metropolitan Studies (WCMS)-Conducted two national surveys on number and ages of household residents, household income,attitudes toward energy consumption,insul- ation type used and extensive information on appilance owner- ship and utilization. •San Diego Gas and Electric -Conducted extensive customer sur- veys on a number of household and appliance characteristics and use pattern. •A.C.Nielsen Co.-Conducted a survey for the State of Illinois which was restricted to single family dwellings. 25 273/9 Failure to consider the various available data sources is a significant deficiency of ISER's present work. ISER's definition of the saturation rate,defined as "the number of appliances divided by the number of consumers"(p.2.17)is unconven- tional.This makes it more difficult to interpret and compare their assumptions/results to other studies.The conventional definition of the saturation rate uses number of households (as opposed to number of consumers)and makes more intuitive sense since many appliances (e.g., black and white TVs)are approaching 100 percent saturation by this definition (U.S.Department of Commerce). The "logistic curve"(p.2.17)is not completely defined in the draft report.A simpler approach might be to extract useful information from the EPRI reports (i1)and (iii)mentioned above and to calibrate this model to fit Alaskan data.The above two studies identify several relevant at- tributes affecting the choice and use patterns of most common appliances and can provide the basis for better end-use modeling as well. While we agree with ISER's statement that "it does not appear cost- effective to construct detailed models for predicting changes in tsatur- ation and utilization]rates"(p.2.18),we believe that development of simple,common sense models based on results of similar studies elsewhere (e.g.,Thomas 1979)would be desirable. 26 "I ,~ J I""", !""" i """' 273/9 The following are a number of other specific comments/suggestions which may be useful in ISER's subsequent work on the appliance satura- tion and energy utilization model.Some of these comments/suggestions are applicable to other model components as well. •More emphasis should be placed on forecasting per capita and per customer electricity demand and cost.The relationship between cost of electicity and utilization rate (i.e.,price elasticity of demand)should be considered. •If possible,develop relative cost of labor ratios (Alaska vs. national average)for some major commercial/industrial sectors (Burbank 1979).This information would be useful in determining which commercial/industrial sectors may attract workers from the lower 48 states. •Consider the effect of new energy efficiency standards mandated by Federal Energy Administration (FEA)--now part of DOE (FEA 1977).For example,a 50 percent energy use reduction for cer- tain types of end-use by 1990 may not be unreasonable (Burbank 1979).Higher and lower energy efficiency improvements should be considered in the context of appropriate economic and regu- latory scenarios (see Figure 3.3). , •Consider the possibility of different rate structure for elec- tric space heating (as was once the case in the Anchorage area) and declining vs.inverted block rates. •Consider establishment of time-of-day-pricing in the 1990s and beyond,particularly for large users.Also consider the po- tential for heat pumps and heat storage systems in the same time frame. •Consider higher insulation standards in response to: (1)higher electricity rates (i.e.,voluntary action due to economic inducements), (2)regulations,either forced or through incentives. •Consider the implications of the following two events on demand for electricity: (1)price of natural gas (used for power generation)rising to world market price,and/or 27 273/9 (2)a federally imposed ban on use of natural gas for power generation. 3.2.7 Final Energy Demand Model ISER's current model forecasts total energy demand (in BTDs)fol- lowed by a second model component which breaks total energy demand into subcomponents (e.g.,electricity,gas,oil).For the purposes of the Susitna Project,this two-step approach is unnecessarily complicated. .., -~ It would be sufficient to directly forecast electric energy demand.The two-step approach is more involved and produces results which are not of immediate interest in the context of the Susitna Project.Furthermore, to obtain defensible results,the approach requires simultaneous analysis of mode splits (between various fuel types)and supply and demand (one for each fuel type).This,in turn,requires development of a series of internally consistent and plausible scenarios concerning the supply of, and demand for,each fuel type at various prices.To date,ISER has not undertaken this complete of an analysis. The approach we recommend,given ISER's limited resources,is to concentrate on electricity demand and derive it in a one-step process using an end-use model.The advantage of a well thought-out end-use model is that it can generate electricity demand projections directly taking as input data on number of households and housing units,income,assump- tions about relative fuel prices,and several other parameters.Similar studies have been carried out in the lower 48 (e.g.,Burbank 1979)and would be relevant to the present study. 28 "',,,,:J r- I i -: ""'"I I"""" , I I""'" I I I -t r I I""'" I I ..... 273/9 The "Sources of Variation"(p.2.19)listed in ISER's draft report leave out several important variables (e.g.,rise in price of energy rel- ative to labor and commodities).ISER's "Methodology"section suggests, "For the commercial-industrial sector,there is no data on average con- version efficiency,so no final demand model exists"(p.2.19).If this is true,then additional effort in this area is required.This is not mentioned in their "Future Work"section. Finally,ISER's implicit assumptions suggest that total energy de- mand is not sensitive to price.This is clearly implied by the figure on p.2.1 where energy availability and price scenarios and energy ef- ficiency models follow the final energy demand model.This asswnes per- fect price inelasticity in demand for energy.It is a strong assumption and requires further substantiation. 3.2.8 Housing &Appliance Stock Model The "Model Description"(p.2.22)for this model component indicates that ISER has projected future housing stock on the basis of local and national trends.An approach more in keeping with the rest of ISER's fore- casting methodology would be to develop a model incorporating demographic information,particularly household formation rates and income/employment data,into a demand for housing function which can then be broken down into single-family and multi-family components taking income,housing supply,and mortgage availability into account.The model need not be complicated,and the required effort need not be substantial.Models of 29 ~ ! I ..... ..... ,.... I t - r ""'"I - ..... i 273/9 this type have been developed (e.g.,NEPOOL and Battelle 1977)and can be readily modified for the present study.Since Alaskan mortgage rates are currently subsidized by the state government and may continue to be influ- enced by forces other than availability and cost of funds in financial markets,they should be accounted for as part of the state government scenarios. Single-family units should be separated from multi-family units.Sim- ilarly,new homes/businesses should be distinguished from older homes/busi- nesses since their energy consumption rates and patterns generally differ. A flow diagram similar to Figure 3.4 would be helpful. We do not fully agree with ISER's statement that estimation of hous- ing stock and mode split should be carried out "on the basis of housing demand without significant supply constraints"(p.2.21).Both housing demand and mode split decisions are sensitive to supply conditions although they tend to be sluggish.A supply crunch,for example,can increase the cost of housing and affect the ratio of single-family to multi-family units.A similar result can also occur if the supply of available funds for housing dries up or government subsidies on mortgage rates are removed. In the lower 48,housing vacancy rates,particularly for owner-occu- pied single-family dwellings,are quite low.(The U.S.national average for 1976 was 1.2 percent,u.s.Department of Commerce,Statistical Ab- stracts 1976).From ISERfs discussion of the topic it appears that this is not the case in Alaska.The same apparently applies to second 30 1 '~"~')"-1 '---1 ·····~-1 '-~-l --]1 )-'---1 ..__._-)---J To ....ldendll III..model Totelsing!__ femilv units Singl __ femily oonnructlon r--------r-------------, I -,I, I ~~~I ~~I ~~I I I I I Populltioo by. IIld leX From lIConomicl demOllr.phic model Totel clemend for hauling Net demend I I Tot.1 for hou.ill8 •connruction Tot.1 multifllnlly uni .. ,To midentle' se'"modelr.----.." MultifemllY construction Multiflmily demolitions I I II ' ~J ~ w..... -----.....FIo~ ---Uoued "ows 0-ExOQloous V3rl.b1.D-EndOllloous v.riebl. Figure 3.4.Housing Unit Hodel Source:See Table 3.2. "'""i !""" i -I 273/9 homes.If this is the case,better discussion,documentation and model- ing of vacancy rates and second homes is required. ISER's discussion on "preference"(as opposed to "affordability") for a housing type and "household head age"as explanatory variables (p.2.22)are not strictly correct.The two most important determinants of housing type are household income and size (U.S.Department of Com- merce,Statistical Abstracts).(Age of the household head is sometimes used as a surrogate for income and/or family size.) ISER proposes to project housing mode split on the basis of local national trends in Part II (p.2.22).What they have actually done,how- ever,is to take the present mode split percentages and assume that they remain constant over time (see p.3.7 and pp.3.21-3.23).In the absence of more documentation and better substantiation,this assumption is clearly unacceptable.Since housing mode split is an important parameter affecting all subsequent work,small variations in this split can lead to significant variations in electricity demand. 3.2.9 Energy Availability Scenarios This model component,which would more accurately be called "Energy Price and Availability Scenarios,"is treated as a separate model compo- nent.In our view,energy price and availability are more defensibly treated as outputs of the economic scenarios already discussed.This distinction is important because energy scenarios are a major component of any economic scenario developed and should be consistent with the 32 273/9 other scenario assumptions and implications.For this reason.energy price and availability scenarios should be carefully integrated with other endogenous and exogenous variables,such as state and federal government regulations or policies;relative price of alternative fuels in the world market over time;and critical energy related projects such as an LNG plant on Kenai Peninsula or the trans-Canadian gas pipe- line. A defensible scenario considers a consistent sequence of events/de- cisions over time and makes reasonable assumptions about its implications. According to their draft report (p.2.24).ISER has not developed such scenarios.This is a critical step in any forecasting model and particu- larly in energy forecasting.No cne can expect to accurately predict the future,and so scenario generation is designed to allow analysis of a num- ber of "what if"questions in order to show the sensitivity of the projec- tions to variations in important parameters.The results obtained allow a study of the implications of given decisions/policies under a variety of assumptions about the future. It is difficult to evalute ISER's work on this aspect of the problem since very little is presented about it in the draft report.In particular, it is not clear if ISER's energy price and availability scenarios are con- sistent with the more general macro-economic scenarios affecting the MAP model. 33 ,j , j - - - -I -I i r -I 273/9 3.2.10 Mode Split Model According to ISER's stated "Objective"this model component deter- mines "the proportion of consumers owning a particular appliance,type of housing>or type of commercial-industrial space that utilizes a par- ticular fuel type"(p.2.25).It describes the process by which a con- surning unit decides to purchase new>or replace existing>appliances. The single most important "appliance"under consideration is,of course> space heating.Other appliances are not as energy intensive individually> but they become significant collectively.For certain appliances (e.g.> lighting)the choice of fuel types is fairly limited>whereas in other cases several alternative fuel types may be available>each with its par- ticular attributes.The question addressed in this model component is which alternative fuel type will be chosen when several are available. This problem is a typical marketing problem and can be analyzed in two stages.The first stage deals with the question of whether to buy a new appliance and/or replace an existing one (Theil 1967;Charles River Associates 1979).The second stage considers the type of appliance (e.g.>size>fuel type>model)chosen once a decision has been reached to purchase a new appliance or replace an old one (McFadden 1974;Theil 1971;Domenich et ale 1976). ISER's initial approach (p.2.26»if used in a dynamic context> appears adequate.This approach is,however>considerably simplified before it is considered ready to apply through a number of unrealistic 34 273/9 assumptions (p.2.27)and reduced to the form presented on page 2.28. This simplified approach is crude,and many of its important parameters (e.g.,replacement and saturation rates)appear to be judgmentally set (e.g.,p.3.28). In addition,there is a discrepancy between what is proposed in Part II (pp.2.25-2.28)and what is actually carried out in Part III (e.g.,p.3.7 and pp.3.21-3.23).Percentages of residential units on electric space heating,for example,are assumed to remain constant (pp.3.21-3.23)at their present levels (p.1.8)over the next thirty years (also see,e.g.,pp.3.27-3.29).It is misleading to present a mode split model in Part II,since it is not actually used.* As already pointed out,an important component of the mode split model should be its sensitivity to economic and fuel price and avail- ability scenarios.Based on what is presented in Part III,ISER's cur- rent approach is inadequate.There is nothing in the draft report to indicate that these deficiencies will be addressed in future work. In modeling mode splits it is desirable to distinguish purchase/ replacements in three appliance markets that are known to differ on their choice of appliances (Burbank 1979): *Strictly speaking,ISER's report refers to what appear to be input as- sumptions/results as "electric power requirement worksheet"(pp.3.20 - 3.38).In the absence of better clarification,we assume that what is presented in these "worksheets"are indeed assumptions that are fed into the model by the modelers. 35 - - -i - ..... I ! 273/9 •the new housing (residential or commercial)market, •the re-placement market,and •the existing market. New homes or commercial establishments consider all available alter- natives and purchase appliances based on several important parameters such as: •perceived initial costs, •perceived operating and maintenance costs, •perceived availability and cost of fuel,and •perceived safety and convenience. New homes and commercial establishments have great flexibility in their choice and take long-run marketability of the home/establis~~ent into account (NEPOOL and Battelle 1977). The replacement market deals with existing homes or commercial es- tablishments with particular appliances that are wearing out,becoming obsolete,or becoming uneconomical to operate.This market does not have the flexibility of the new market (e.g.,lack of duct work may make it difficult/expensive to add central forced air heating systems • The third market consists of existing houses or commercial estab- lishments without particular appliances which are considering ?urchase of new appliances.It also includes households/establishments that are considerii:l.g duplicating appliances (e.g.)se~ond TV). A complete housing stock model would provide information on new and existing housing stocks which could be used to provide input regarding 36 273/9 the above markets.At a minimum the new housing market should be distin- guished from the other two and treated separately.Similarly,single-family dwellings should be distinguished from multi-family units,and small com- mercial (e.g.,small retail)from large commercial.Their use patterns and available choices are different enough to warrant separate treatment. 3.2.11 Energy Efficiency Model We recommend including this model as an integral part of the end-use model (Figure 3.3),since the decision to ?urchase a particular appliance and appliance fuel type is affected by its perceived operating and main- tenance costs which are dependent on its fuel conversion efficiency.As currently presented in ISER's work,a person 's purcl'.ase decison is unaf- fected by improvements in efficiency standards because the model component which considers this follows the mode split model. The current model's only function is specification of appliance fuel efficiency (p.2.30)which is,apparently,judgmentally set.BTU demand for various appliances and ·efficiencies of electric conversion are,for example,set wi~hout any supporting rationale (e.g.,pp.3.27-3.29).Speci- fication of appliance fuel efficiency,should be part of the input scenarios that are fed into the end-use model and should be co~sistent ficiency standard targets (FEA 1977). 3.2.12 Energy Requirements By Fuel Type No comments. 37 wi~h FEA 's ef- .... r, .... ,.... ..... i t 273/9 3.3 QUANTITY AND ACCURACY OF DATA A defensible forecast requires reasonably complete data in addition to a logical,well-structured,and technically correct model.Our comments in Section 3.2 were directed at ISER's model.In this section,we consider the accuracy and adequacy of the available data. End-use models are data intensive because many parameters have to be identified and specified over time.Based on our experience and revie',.; of other end-use models (e.g.,those of the California Energy Commission and Pacific Gas and Electric Company),these models generally take several years of data gathering and calibration before they can provide completely defensible forecasts.For this reason we believe that ISER's end-use model cannot be realistically expected to become fully operational in the short- run.ISER has undertaken an ambitious task in attempting to put together an end-use model for the Railbelt area~Their model should provide more defensible forecasts as additional data are collected and the remaining deficiencies of the model are rectified. The quantity and quality of Anchorage data could be improved.The data for the Fairbanks area is even less satisfactory_More specifically, the following comments are addressed at data and input assumptions pre- sented in Parts I and III • •The documentation and discussion of the end-use inventory should be presented separately from the data,which belong in an appendix • •Factual data should be distinguished from assumed data • 38 273/9 •Factual data should be appropriately referenced.Assumed or estimated data should be substantiated and discussed to the extent possible.Judgmental assumptions should be distin- guished from assumptions based on historical trends or similar studies done elsewhere. •Each data sheet and inventory table should co~e complete with its footnotes and references. •It appears that the key on p.1.7 contains much redundant in- formation (i.e.,only three pieces of information are neces- sary to complete the remaining five blank spaces).If so,a more compact format,with instructions on how to obtain addi- tional information could be presented. •Many important input assumptions (e.g.,percent of year-round housing units [po 1.15]and electric consumption for space heat- ing [po 1.14J)are crudely estimated.Substantial additional effort appears necessary to improve these rough estimates. •In cases where national (as opposed to Alaskan)data is used (e.g.,p.1.18),judgmental adjustments for Alaska,based on the available information,seem appropriate. •Estimates regarding all-electric homes and electric space heat- ing are crucial.ISER's rough estimates (p.1.20)should be better substantiated and double checked to the extent possible. •Appliance unit demands,energy efficiencies and annual con- sumption rates (pp.1.30-1.33)should be augmented with more recent and accurate data (e.g.,[Fitzpatrick 1979],[Charles River Associates 19791,[Mid~est Research Institute 1979], [Edison Electric Institute])and adjusted for Alaska. •The flow pattern presented in Figure 1 (p.3.2)is confusing. •Assumptions stated on p.3.7 are unsubstantiated.Each one of them is critical and requires careful consideration,evaluation and sensitivity analysis.As presently stated,they are clearly indefensible. •The heading for Table 00 (p.3.9)is incomplete. •The "Electric Power Requirement Worksheets"(pp.3.20-3.38)are undocumented and their assumptions are questionable and unsub- stantiated.Many of the stated assumptions are dependent on future economic and energy scenarios.It is not clear if a~d how these scenarios will be integrated into more reasonable input assumptions and if and how sensitivity analysis will ~e performed. 39 i~ -i J ""'"'1 ,) - -i - 273/9 •The Commercial/industrial sector requires a finer breakdown of the "General"category (p.3.33)while certain other categories (e.g.,Manufacturing and Warehouse)may be combined . •Fairbanks area data appears non-existent (e.g.,pp.3.36-3.38) or of poor quality (e.g.,p.3.45).Better documentation of assumptions such as housing size and heat requirements (e.g., p.3.40)is necessary. -3.4 METHODS USED BY ISER TO CONSIDER UNCERTAINTIES The accuracy of total electric energy demand forecasts produced by .... r - - I""", ISER's models is affected by three major factors: 1)The degree to which the models accurately capture the true structure of energy use in the Railbelt, 2)The accuracy of data about current conditions in the Railbelt, and 3)The degree to which the assumed input scenarios for future economic development and energy use are accurate. ISER's draft report,as well as our discussion elsewhere in this critique, shows that there are significant uncertainties about all three of these factors.It is important that these uncertainties be considered in the forecasting T..ork in a systematic and realistic manner if the results are to be defensible.The draft report does not indicate how ISER intends to address these uncertainties.The steps generally used to address them include the following: 1)Sensitivity analyses to identify which structural features and input data most significantly affect the forecasts, 2)Additional modeling work or data collection where the sensi- tivity analyses indicate it is warranted,and 40 273/9 3)Incorporation of a broad range of viewpoints ineo the pro- cedure for selecting input scenarios. ISER has apparently not yet estabished methods for carrying oue these steps.Although we foresee no particular difficulties in doing this, our experience indicates that carrying out the steps can be time-con- suming. 41 , - ..... t "... 273/10 !,"~.v IMPLICATIONS OF OTHER WORK A review of several other electricity demand forecasts for the Rail- belt region ([U.S.Department of Energy,Alaska Power Administration 1979], [u.s.Department of the Army,Corp of Engineers 1979],and [Battelle Pa- cHic ~orthwest 1978])indicates that past work has·used less sophisti- cated methods than those proposed by ISER.None of these studies make use of end-use models.Generally,the studies are based on crude esti- mates of per capita energy demand and demand growth rates based on his- torical trends.The Corps of Engineers Report,for example,uses per capita consumption projections for "comparable regions in the Pacific Northwest,"(U.S.Department of the Army,Corps of Engineers 1979,Appen- dix,Part I,p.C-32)as the basis for its projections.The population projec tions used are typically those provided by ISER I S previous ~.,-A.P fore- casts with a consideration of "low"and "high"growth scenarios. Overall,these forecasts have limited defensibility since there is little documentation and specification of their critical parameters and input assumptions.Furthermore,various assumptions are not well integrated (e.g.,population scenario,per capita consumption and energy price and availability scenarios are not necessarily consistent with one another). However,the assumptions are clearly stated and can be readily varied to produce alternative forecasts. 42 273/10 The two most recent power market analyses [U.S.Department of Energy, Alaska Power Administration 1979]and [U.S.Department of the Army,Corp of Engineers 1979]are based on ISER's ~1AP population projections (December 1978 revisions).These population projections,reproduced in Table 4.4 for easy reference,are higher than ISER's current projections (compare Table 8,p.34 of {U.S.Department of Energy,Alaska Power Administration 1979] to Table 0,p.3.6 of [ISER 1980]).In fact,ISER's previous "low"pro- jections for 1980,1990,and 2000 exceed their respective present "medium range.Similarly,the previous "low"forecasts of total annual energy demand for 1980,1990,and 2000,reproduced in Table 4.5 for easy refer- ence,exceed ISER's present "medium range"forecasts (compare Table 10, p.40 of [U.S.Department of Energy,Alaska Power Administration 19791 to Table 00,p.3.9 of [ISER 1980]).Most of the discrepancy between these two population forecasts appears to be the result of updating of the ~~model;the previous population forecasts were based on a ~model calibration using data up to 1973 where the more recent forecasts are ap- parently based on a recalibration of MAP which includes data up to 1978, including post Arab oil embargo data. 43 - .- 24/7 Table 4.4 COMPARISON OF ISER'S POPULATION PROJECTIONS [ISER 1980, TABLE 0,P.3.6]TO THE ALASKA PO\~~R ADMINISTRATION'S POPULATION PROJECTIONS [U.S.DOE,ALASK.~POHER ADMIN I5- TRATION 1979,TABLE 8,P.34]FOR THE ~~ILBELT AREA population in thousands,rounded to nearest full thousand ISER's "Medium Case Scenario"Projections APA's Projections -I t """"I - 1"'""I, i \ Anchorage Area+Fairbanks Area+ Anchorage Fairbanks Year Area*Area*"Low""High""Low""High" 1980 208 61 240 270 60 62 1990 286 78 299 407 75 95 2000 371 97 424 651 90 140 *ISER uses .~~chorage-Matanuska Susitna and Fairbanks-Southeast Fairbanks, respectively. +APA uses Anchorage-Cook Inlet and Fairbanks-Tanana Valley,respectively. 44 24/7 Table 4.5.COMPARISON OF ISER'S "DRY RUN"ELECTRIC POWER DE~:"-\ND PRO- JECTIONS (ISER 1980,TABLE 00,P.3.9]AND TF£AL~SKA POw~R ADMINISTRATION'S PROJECTIONS (U.S.DOE,ALASKA pm~ER Amll:i- ISTRATION 1979,TABLE 12,P.46]FOR THE RAILBELT AREA* Total Annual Electri::Demand in GWh,rounded ISER I S Medium Case Scenario Projections ,l..PA's Pro jec tions Year Low ~edium High 1980 2,200 3,400 3,700 3,900 1990 3,800 5,200 7,lCO 11,000 2000 5,700 7,900 12,700 21,000 *lncludes the entire Ra~lbelt area. 45 - , .! r -, - 273/10 5.0 STRENGTHS AND LIMITATIONS OF ISER WORK ISER's work has the ambitious objective of accomplishing the first integrated combination of economic and end-use models for the Railbelt region.This is a major undertaking.It requires a systematic inventory of current end-use devices,their replacement,and utilization rates, efficiency levels and use patterns over time.In addition,future pur- chase/replacement decisions have to be modeled and integrated with various possible assumptions about relative price and availability of alternative fuels,energy efficiency standards,as well as policies and regulations on conservation. A well integrated economic/end-use model can be a powerful planning tool for considering the possible implications of proposed actions or policy changes.However,attempts to develop such models by electric utilities and regulatory commissions in the lower 48 states have been carried out with substantially more resources than the work by ISER. Generally,it has taken two or more years of data collection,model specification and calibration to achieve reasonably defensible results. 46 273)10 Our review of ISER's present work indicates tha~substantial ad- ditional work will be required before their model becomes fully integrated and operational.This assessment is based on our conclusions (see Section 2)that serious deficiencies exist in ISER's work to date.(A detailed discussion of these deficiencies is presented in Section 3.)At the nre- sent state of development,the model's forecasts are not necessarily more accurate or defensible than forecasts from a less sophisticated approach. This is true because: •ISER's present model is not complete nor fully integrated, •The model is based on an incomplete and possibly inaccurate data base,and •The selection and specification of input scenarios is not adequately addressed. Additional work would substantially improve the defensibility of ISER's forecasts.However,there are other important issues related to the Susitna Project power studies that are not directly addressed by the ISER work,at least as it is presented in the draft report. These are not,strictly speaking,technical deficiencies in the work but rather limitations on the scope of what ISER is attempting to do. However,they may limit the ultimate usefulness for the Susitna Project of ISER's work. A variety of studies have been carried out during the last two de- cades to assess future electricity demand in the Railbelt.Several studies have assessed the desirability of building the Susitna Project.Generally, 47 , J, J "'"'! .- - - 273/10 these studies have concluded that there will be a need for substantial ad- ditional electric generating capacity and that the Susitna Project would be a reasonable way to meet this need. However,the Susitna Project continues to be highly controversial, with both support and opposition by substantial interest groups.There is no reason to believe that another forecasting study.regardless of how complex it is or how carefully it is carried out,will damp the controversy surrounding Susitna. Fundamentally,the assessment of the need for the Susitna Project is not an issue that can be "resolved"by analysis.In view of the many uncertainties that exist regarding the future of the Railbelt, there is no way to assure that any forecast of future demand for elec- tric energy is accurate.In view of this,the central focus of Susitna Project concerns with regard to the need for power might profitably be shifted from concern for forecasting by itself to the following question: "For what level of future demand is it prudent that the Alaska Power Authority plan in carrying out its responsibilities to the citizens of the Railbelt?" Addressing this question requires some forecasting work;however. it also requires careful consideration of a variety of other factors. Given the enormous influence that the state government will have un de- velopments over the next few decades,the question requires careful con- sideration of options open to the government during this period.Perhaps 48 273/10 most importantly,it requires careful consideration of the consequences of having over-or under-generation capacity relative to the demand. This last point warrants further discussion.An analysis of Table 4.5 shows that ISER's projected "medium case"total electric energy de- mand growth from 1980 to 2000 averages 4.9%per year while the Alaska Power Administration's "low","medium"and "high"projections average, respectively,4.3%,6.4%and 8.8%annual growth over this period.Thus there is a range of 8.8%-4.3%=4.5%in the Alaska Power Administration's projected growth rates. The differences between ISER's forecast and those of the Alaska Power Administration appear to be due mainly to updated initial conditions and differing estimates of future population growth.In both cases the MAP model was used to do the population estimation.Thus it seems likely that there is roughly the same level of uncertainly associated with ISER's forecasts as with the earlier forecasts by the Alaska Power Administration based on the MAP model.If we assume this then the growth rate might be as low as 4.9-4.5/2=2.7%or as high as 4.9+4.5/2=7.2%. Using these growth rates,starting form a base of 2200 GWh in 1980, results in the following projections of total electric energy demand in 2010: •For 2.7%/yr.growth:4900 GWh •For 4.9%/yr.growth:9200 GWh •For 7.2%/yr.growth:17,700 GWh 49 ,...., - - r - - F, r 273/10 Note that the range of these estimates is 12,800 ~Jh.The firm annual energy from the Susitna Project is estimated to be 6,100 GWh,so the un- certainty in the projections is as great as two Susitna Projects! An important issue relative to the power studies for the Susitna Project is which demand figures should be used for planning purposes in view of this uncertainty.For example,if the APA plans on the assump- tion of 2.7%/yr.growth in demand,and the actual growth is 7.2%/yr.the consequences for the citizens of the Railbelt are likely to be very dif- ferent than if plans are made for a 7.2%/yr.growth and the actual growth is 2.7%/yr. Questions of this type are not addressed in the ISER work,except in a very indirect manner.Thus,we believe that even if the technical de- ficiencies in ISER's work are corrected,it will not address some impor- tant needs of the Susitna Project. 50 77 /14 REFERENCES Abromaitis,Stanley C.,Jr."Integrated Methodology for Forecasting Electric System Energy and Demand Requirements."Economic Research and Planning Dept.,Gilbert Mangement Consultants.Reading,PA. In "How Electric Utilities Forecast:EPRI Symposium Proceedings," R:r.Crow (ed.)March 1979.EPRI EA-1035-SR,pp.18.1-18.30. Alaska Water Study Committee,Electric Power Work Plan Committee.Phase 1 Technical Memorandum:Electric Power Needs Assessment (Draft). March 1979. American Demographics.Various issues. Average Electric Bills by Company.Boston,MA:Electric Council of New England.Various years. Battelle Pacific Northwest Laboratories.Alaskan Electric Power:An Analysis of Future .Requirements and Supply Alternatives for the Railbelt Region.Final Report,Vols.I and II.March 1978. Baumgartner,Mark,and Samuel Skaggs.Energy Conservation Potential for Alaska's Railbelt.Draft prepared for Alaska Center for Policy Studies by the Alaska Federation for Community Self-Reliance,Inc. March 1980. Burbank,Donald H."Forecasting Residential Demand for Electric Energy." Northeast Utilities.Berlin,CT.In "How Electric Utilities Fore- cast:EPRI Symposium Proceedings,"R.T.Crow (ed.)March 1979. EPRI EA-1035-SR,pp.1.1-1.91. Charles River Associates Inc.Analysis of Household Appliance Choice. Final Report.Prepared for EPRI EA-1100.June 1979. Comerford,Ri.chard B."PSE&G Method for Forecasting Residential Kilo- watthour Consumption."Public Service Electric &Gas Company. Newark,N.J.In "Row Electric Utilities Forecast:EPRI Symposium Proceedings,"R.T.Crow (ed.)March 1979.EPRI EA-I035-SR,pp. 2.1-2.12. Carlton,Robert W. Consumption at of N.Y.,Inc. Proceedings," 9.1-9.10. "Methodology for Forecasting Commercial Kilowatthour Consolidated Edison."Consolidated Edison Company In "How Electric Utilities Forecast:EPRI Symposium R.T.Crow (ed.)March 1979.EPRI EA-1035-SR,pp. .",:,!, I J , Crow,Robert E.,James H.Mars,and Christopher J.Conway.A Preliminary Evaluation of the I.S.E.R.Electricity Demand Forecast.Working Paper #1.Prepared by Energy Probe,Toronto,Canada,for the House Power Alternatives Study Committee (HPASC).Undated. 51 i' I i7/14 Data Resources,Incorporated."The Data Resources Energy Model:Model Description."July 1976. Domencich,Thomas A.and Daniel McFadden.Urban Travel Demand. Amsterdam:North-Holland Publishing Co.1976. Edison Electric Institute.Economic Growth in the Future: Debate in National and Global Perspective.New York: 1976. The Growth McGraw-Hill. Edwards,J.M."Methodology for Forecasting Commercial and $mall Indus- trial Kilowatt/hour Sales."Houston Lighting &Power Company. Houston,IX.In "Row Electric Utilities Forecast:EPRI Sumposium Proceedings,"R:"T.Crow (ed.)EPRI EA-I035-SR,pp.10.1-10.8. Energy Modeling Forum."Electric Load Forecasting: with Models."Volume 1.Stanford University. April 1979. Probing the Issues Stanford,CA. .... - "U.S.Oil and Gas Supply,Summary Report"(Staff Draf t)• Stanford University.Stanford,CA.November 1979. Electric Power Research Institute."How Electric Utilities Forecast: EPRI Symposium Proceedings,"R.T.Crow (ed.)EPRI EA-1035-SR. March 1978. "Costs and Benefits of Over/Under Capacity in Electric Power System Planning."Prepared by Decision Focus,Inc.EA-927. October 1978 • "Piecing Together the Electric Vehicle."EPRI Journal. November 1979. Federal Energy Administration (FEA)."Energy Conservation Program for Appliances:Energy Efficiency Improvement Targets."Federal Reg- ister.July IS,1977.Washington,D.C.:U.S.Government Printing Office. "National Energy Outlook."Washington,D.C.:U.S.Gov- ernment Printing Office.1976. Federal Power Commission.Typical Electric Bills.Washington,D.C.: Government Printing Office.Various years. Fitzpatrick,George L.··Peak Load Forecasting Methodology."Long Island Lighting Company.Mineola,N.Y.In "How'Electric Utilities Fore- cast:EPRI Symposium Proceedings,"R.T.Crow (ed.)EPRI EA-I035-SR, pp.5.1-5.13. 52 77/14 Fuller,Keith."Forecasting Peak Demand and Load Shapes." State Electric &Gas Corp.Binghampton,N.Y.In "How Utilities Forecast:EPRI Symposium Proceedings;'R.T. EPRI EA-I035-SR,pp.7.1-7.24. New York Electric Crow (ed.) Goldsmith,Oliver S."Alaska Electric Power Requirements:A Review and Projection."Alaska Review of Business and Economic Conditions, Vol.XIV,No.2.June 1977. "Alaska's Revenue Forecasts and Expenditure Options." Alaska Review of Social and Economic Conditions,Vol.XV,No.2. June 1978. ,compiler.Man-In-The-Arctic-Program,Alaskan Population--""'":":-.,.......".-Model,Documentation.Institute of Social and Economic Research. ~rsity of Alaska.Anchorage,AK.May 31,1979. ,compiler.Man-In-The-Arctic-Program,Alaskan Ec~nomic---:---,.--Model,Documentation.Institute of Social and Economic Research. University of Alaska.Anchorage,AK.May 31,1979. Hudson,E.A.,and D.W.Jorgenson."U.S.Energy Policy and Economic Growth 1975-2000."Bell Journal of Economics and Management Sci- ence.Autumn 1974. Institute of Social and Economic Research."Electric Power Requirements for the Railbelt,"Progress Report.University of Alaska.Anchorage, AK.March 14,1980. Judd,Bruce R.,H.G.lJ..ike Jones,and Allen C.Miller III."Electricity Forecasting and Planning Report:Decision Analysis Framework for Future Electrical Planning,"Vol.1.California Energy Resources Conservation and Development Commission.November 1976. Kalscheur,Robert J."Forecasting Peak Demand and Load Shape for Wis- consin Electric Power Company."Wisconsin Electric Power Company. Milwaukee,WI.In "How Electric Utilities Forecast:EPRI Symposium Proceedings,"R.~Crow (ed.)March 1979.EPRI EA-I035-SR,pp. 6.1-6.20. l-1anne,A.S."ETA:A Model for Energy Technology Assessment."Bell Jour- nal of Economics.Autumn 1976. Marcuse,W.,L.Bodin,E.Cherniavsky,and Y.Sanborn."A Dynamic Time- Dependent Model for the Analysis of Alternative Energy Policies." Brookhaven National Laboratory.1975. 53 - ..... - 77 /14 McFadden,Daniel."Conditional Logit Analysis of Qualitative Choice Be- havior."Paul Zarembka (ed.)Frontiers of Econometrics.New York: Academic Press.1974. McMahon,J.A.and L.C.Maxwell.Load Forecasting in Today's Environment. Chattanooga,Tenn.:Tennessee Valley Authority.April 1977.-, -! Midwest Research Institute. ances,"Final Report. "Patterns of Energy Use by Electrical Appli- Prepared for EPRI,EA-682.January 1979. - - National Research Council."Energy Modeling for an Uncertain Future," Supporting Paper 2.Washington,D.C.:National Academy of Sciences. 1978. New England Power Pool (NEPOOL)and Battle Columbus Laboratories."Mod- els for Long Range Forecasting of Electric Energy and Demand." June 30,1977.(Available from New England Power Planning,174 Brush Hill Ave.,West Springfield,MA 01089;revised and updated version forthcoming)• Nordhaus,W.D."The Demand for Energy:An International Perspective." In W.D.Nordhaus (ed.),Proceedings of a Workshop on Energy Demand. May 22-23,1975.International Instituce for Applied Systems Analysis,Report CP-76-1.1976. Pacific Gas and Electric Company.Private Communications.1980 Slater,C.M."Pieces of the Puzzle:wnat Economists Can Learn From Demo- graphers."American Demographics.February 1980,Vol.2,No.2. Taylor,Lester D."The Demand for Electricity:A Survey."Bell Journal of Economics,pp.74-110.Spring 1975. Theil,H.Economics and Information Theory.Amsterdam:North-Holland Publishing Co.1967. Principals and Econometrics.New York:Wiley.1971. Thomas,Brian."A Common-Sense Approach to Forecasting."Puget Sound Power &Light Company..Bellevue,VIA.In "How Electric Utilities Forecast:EPRI Symposium Proceedings,"R.T.Crow (ed.)March 1979. EPRI EA-1035-SR,pp.3.1-3.6. Torrance,James M.,and Lynn C.Maxwell."Three Methods of Forecasting Residential Loads."Tennessee Valley Authority.Chattanooga,TN.• In "How Electric Utilities Forecast:EPRI Symposium Proceedings," R:"T.Crow (ed.)March 1979.EPRI EA-I035-SR,pp.4.1-4.21. 54 77/14 u.s.Department of the Army.Alaska District,Corps of Engineers. "Hydroelectric Power and related purposes.Southcentral Railbelt Area,Alaska,Upper Susitna River Basin,"Interim Feasibility Re- port.1975.Anchorage,Alaska. "Hydroelectric Power and related purposes.Southcentral Railbelt Area,Alaska,Upper Susitna River Basin,"Supplemental Feasibility Report.Appendix Part 2.Anchorage,AK.1979. U.S.Department of Commerce.Bureau of the Census.Household and Family Characteristics.Washington,D.C.:Government Printing Office. Various years. Population Estimates and Projections,Series P.2S.Wash- ington,D.C.:Government Printing Office.Various years. Statistical Abstracts of the U.S.Washington,D.C.:Gov- ernment Printing Office.Various years. 1972 Census of Manufacturers,Fuels,and Electric Energy Consumed,Nos.Me72 (SR-6)and MC72 (SR-65).Washington,D.C.: Government Printing Office.1974. Projections of the Population in the U.S"1977-2050. Series P.27.Washington,D.C.:Government Printing Office.1976. iIII>li -J -.j .j U.S.Department of Energy.Alaska Power Administration. River Project Power Market Analyses."Juneau,AlL "Upper Susitna March 1979. u.s.Department of the Interior.Alaska Po'Wer Administration."Alaska Electric Power Statistics 1960-1976."Fift:h Edition.Juneau,AK. July 1977. "Economic Analysis and Load Projections."1974 Alaska Power Survey.Juneau,AK.May 1974. The Wall Street Journal."Belated }1illions:As Oil Money Rolls In, Alaska Has to Decide w"hat to Do With It."Friday,April 11,1980. Weiss,Moshe."Integrated Approaches to Forecasting."National Economic Research Associates,Inc.New York,N.Y.In "How Electric Utili- ties Forecast:EPRI Symposium Proceedings,-"-R.T.Crow (ed.)March 1979.,EPRI EA-I035-SR,pp.19.1-19.19. 55 , .~ r- I -I - - -, -I ,.... i - - APPENDIX D CRITIQUE OF ISER REPORT BY ENERGY PROBE JUL Y 30,1980 Enargy Kobe!Enqu@te Energie I""'" I an evaluation !"""'::, '-J" I: i -,. -I oTthe ISER electricity demand forecast Ju1y 30,1980 ;~obert E.(ra", James H.f·1ars Christopher Conway l""I------------- I(S3 Queen Street, :Ottawa,Ontario,Canada, r-,KIA O€:4 (613)233-0260 ,. ",. ~: i I 43 Queen's Park Crescent East, Toronto,Ontario,Canada,' ~1:5S 2C3. (416)978-7014 t, - .1 ~'. In OC'u::;:i,~r 1979,[;iCr'~JY r!(JI;f~\';,1",l,;;~,rd(:d a contract by The House Po\,;er Altcl'ii<lLivc:s Stuoy COlil- mittee of The i~l<lskJ Str-dr:li:rJisldt~II'e to C'l:iliinc lInd evaluate cln c1cc~ricit..Y ,;u:.;rH.!~,I',',~il'0 "'Lj(~l being d.:.'volo[Jod by The Ulljvi~I':,iLy (if 1\1.:'~'il'S J::'.t- itute of Soc Ll1 Jnu [COIlOlilic f~(">{dlLil (l\!;.;). Energy Probe's \·jork,i}long \'/ith ,'(IShHCh carried out by several other COf1sult.Jnts !'C,t(~inc:d by The Po\yer Alternatives St.udy CJ::i1lliUr·c \-:llS iftLc.::l(k'd La provide a frame\'lol'k \·d thin '1,i!lich the proposr-d Susi Lna Hydroelectric Power OcvelojJlllent could be cvalueltcd. A working paper published in January 1980 presented an initial evaluation of the ISCR ill()<1el,pt-ill1uily on the basis of JSER's "[letJilcd \'!01'1;Plan". The fol1ol'/in9 ;s the final (C:~IQrt prerdrt:d under Energy Probe's contract.lt ,presents ,).1 c:vJ.luation of the l'SER demand fOl'C'casting IIJode1 in its pt-C':,c:nt form;tests the sensitivity of Railt\elt electxicity demand to changes ;.1 various policy and technolugical factors;and outlines ,·,hat the outhOl'S believe to be the a.r-,~,:-~...~:~i~l~.E lL:\-~·~,t·l.:.,-~~~...~:~ the f~:l":;:--u::.~·...··i~l·fi~,~1~:_~~.,:J.-~~.~'J"' energy policy develo~n~nt. ,.,. ..':-. ,..~,. II l 1 " n\ n The vic"/s and conclusions on!~,['nt'.:(1 herein dte those or Lh'-.\:-':j ~i~c.'~";~,il1l lf il1 ,nlid -.;(}I:;t :,:..c~:I"::!'j l J' reflect the position of The lIou~.c 1\)I','l:l-f\ltt'I'll\1Livcs Study COililll itt ee. ,, TABLE OF CO~TENTS 1.I NTRODUCi I ON 2.A USER'S GUIDE TO THE lSER ~ORECASTING ~OUrL . 2.1 Introduction .'2.2 Stage I CUii,jJonents 2.2.1 The !'lAP Econumetric ;'1odel 2.2.2 The Household Formation Model 2.2.3 The Regional Allocation Model 2.2.4 The Hous i 119 St (lC k ;·icdel 2.2.5 Stage 1 Sulilli1ary 2.3 Stage II:The Electrkity End U~~e ~'\udel 2.3.1 The Residential Sector 2.3.2 The Commel'cial-IndLJstrial-C;ov\;:':,iill~nt Sr'etar 2.3.3 Stage II Summary 3.A TECHNICAL REVIEIJ OF THE ISlR IO:<[Cfi~il;;G :YfiiOO . 3.1 Introduction 3.2 HAP 3.3 The Household Formation (,jadel 3.4 The Regional Allocation Model 3.5 The :-:C'~~s i rlC'·S~C1:-!:!';(\del 3.G 1"he E':':'=7.'\"~,:~i:~.-:I~:~US.2 ~':(Ijel 3 ,i The E1 ec \.r 1city E.old Us <=;;00 e 1 3.8 SU~~:lla ry 20 ., j 4.AN ANALYSIS OF 7!!ri,_28 4.1 Introduction 4.2 Case "A"End Use Scenario 4.2.1 Residential Space Heating R2quil'c:::cnts 4.2.2 Hajor Residential Appliance Enel'gy Requirements 4.2.3 Unspecified Residential Appliance Requirenents 4.2.4 Residential Summary 4.2.5 The Commercial-Industrial-Government Sector 4.2.6 Summary of Case "All Scenario 4.3 Case "B"End Use Scenario 4.3.1 Residential Space Heating Requirements 4.3.2 Major Residential Appliance Energy Requirffilents 4.3.3 Unspecified Residential Appliance Requirenents 4.3.4 Residential Summary 4.3.5 The Commercial-lndustrial-Gover~nent Sector 4.3.6 Summary of Case "B"Scenario 1 -, 1 1I- 1(.... I I.''i Ir'J I I J i J I ! ~I J\' - r i r- \ r -.,."-~"~..:,I :'',s ..~4 5.1 G),l'l'i'lll (DiH:~j(rl~dr"JI 5.2 r~lJllJing FOf LOi'ld FlJlt~CijSli:lg R(~~earch 5.3 l~,Er:r';()\~el t\ut~.:;i\t.ion 5.4 Fut.ure Use of the ISLR FC.ll'(..'(ast 5.5 Da t <J Col 1(>c t ion 5.6 St,itc":iidr.Eli':ll'iciLy ntl::,llld :i;I('ldSlillg 5.7'Pe.Jk [\..".i:llld l-(,,"",dt1:1g 5.S Ad d i t i Ct r;a 1 S~.(I tj e I Sec:I i J ('i 0 5.9 IndcfJl:IlJ.::nt [:qlCl-t /\dvice on t.he LOiid Fu:cc,;·~t ..Il.PPEND I X /4.-1 r •.•\ "11 1 / ,\ 1.·INTRODUCTiON...---------------_._-------_..------------------ The electricity demand forecasting trlodel developed by .the Institute for Social and Econoll:ic ~cShJr'ch (lSlR)is a major step fan'lard for Alaskan energy p1unning.The ISER model is of a quality which is orders of illLlgnitudc uhc:ad of previous forecasting models employed in the State. This report seeks to aecampl ish three tasks.The first of these in an introduction to the structure and logic of the ISER model aimed at a non-technical audi2nce.The second is a technical review of the lSER Illodel with a focus on the the effects of alternative energy pOlicy iJSSlllliptions on the model's outpu-t. 8y far the most important of these is the third.Since Alaska's electricity future is not fixed but rather subject to both fate and policy intervention it is important to appreciate that any forecast depends on assumptions concerning factors which can and'cannot be cantroll ed. I" - r \ i r- I f""" I r-, r- I ,. ,, On Uie fale side of the lcdu(~r <.Ire all those faclors \'/bich are beyond the control of Alaskans,Tht~,e include national econolllic po1icy to the extent that it sets the tone for stilte econolilic and social deve1c'l.J1i:ent and,1n00-e import.iint])'.tile future of resource discovery and exploitation in Alaska. ~lanageable factors include the ways in \·/hich Alaskans actually use the energy which is available to them -\,;,hether they lise is efficiently or inefficiently.A very clcar cXJll1ple of the "Illanagl,ability"of lhese fJet.or"s is lhc t"CCCl1t (;!1ergy conservation legislation which will undollhtedly influence energy us~in the State. contl-ollable are identified and managed to bl-ing about a desirab1e futul"e.In <.tddi:.icn,plc:nning seeks to idctlLify itl;filS sL;L;jccr. to fate to adequately prepare for the realization of a range of possible outcomes.A forecasting model is nothing other than an aid to clear thinking in this complex situation.~good forecasting model should be able to accommodate both controllable and non-controllable factors and progress logically to actual numeric forecasts.On this count the ISER model is exemplary. -": ~;iH<:~ ~i ~~g;~nt ;.:-;; "," .•.~~:.!:~ " _. ;ri,11i!;;lf .;~.. ..~..:~:;. In any forecasting envil'oill::l:nt llSSUlllplioilS life ::nJcial; to the extent that they ure hidden t.here is 110 cleur 1 ink L;(~t\"lc,=n policy and tlctual out.comes.To t.ile t-',Lellt t.lielt.liJC.·y rlf!?()Ji(~n this count as well,the ISER l1Iodel is excellent.Assulllptions are c1early stated und rcadi1y changed.\·Ihen Llie lIlodel is ultimately computerized the latter \·Jill DE:COillC even casier and the model even more useful, But what is,most important to real ize is that the ISER lrodel is only a tool.Alaskans do to a large extent have control over many aspects.of their energy fut.ure.In an appropriate planning of making that future more desirable.- 1;:t!j!U;[j ~!~;i~i~HJ -~:n~f(~~:F i~~~ii.mi~ .,' .:~1 \•;.:.:' ., J J , Jr- ·1".., I ir-,I J ~I: i ,I 2.1 Introduction The ISER electricity (k~l\;ind for('(.J~,l.if1'J iil()dcl.'.';lli1e seemingly complex,has il very straiC]iltfonidrd a/ld !u/Jical structure and flow of infonna t ion bet\'I(?cn COlnpO!lents,The output of the model is pl~ojected values of electricity consumption for each of the three geographical areas of the Railbelt classified by final use (i.e.heating,lighting,etc.) and consuming sector (commercial,residentia 1,l~tc,).In its current form the lSER model produces values for the years 1985,1990,1995,2000,2005 and 2010. allG technicul 6ssu::1ptions .:~nG stutt_'~~;I_~:~i:i:·'~'~-."I _. i z ed 5L:b-rnod e 1s 1 i il ked by ~:c,y Vi]1"i J t'..1 c ~,';n~jr i ':C:i;i'~,r~-1 ~.-'J t.hisToQCComp1ish flow diagl-ulll sho\-Jing the sub-models and tlle;l"linkinu ;:110 driving variables is given in Figure 2.1 below.Of the five sub-mOdels,only the ~1AP econometric model \-Ias in existence !I,prior to the Railbelt study~the remaining four were developed by ISER during the course of the study. 2.2 Stage I Components In our earlier working paper (contained as the appendix to this report)we argued that the electricity demand fore- casting process was essentially two-stage.In StJ9~I I bJ3ic tJIa1 ~~UliiltlfI ......,&Jill taaW IJB ...UJDI ~lJ8 __..aJII·~ I ..._---_.' Figure 2.1:A SCh(llliltic oi I:he ISER £1ectricity Demand Forecast ...-US economl c trend s ~ e ;'~J li S r~~j r rj secto,'over' t ilile • I 'i (.--.n ';-r,., ~"")Li.),~J:'•\.,I fuel market shares ,.---. utilization efficiency -i ~~II,' I housei-.olds , I--household ~headshi p -housing ISTOCK L stock spl it ~I I I _------<I 1 --- ;IOW;t:HGI i) FOFl·\A II {jj: L_,,~il)C:L incomes , 'r)4 nd S ta te ,rklilog raph i c tJends ' Ii ~__..i state populat ionl b:t age and sex;::--' r--state government decisions ,-resource extract ion scenarios MAP ECONOMETRIC MODEL survey on hous i no C 110 i .."-::011'",r-•I._j <1._"., ,....EI'-,'l; '---- _,._LJApOPUl cJ t ion I I ;R[LiOi.,'·,by region I employment bV:'>-i\LUU:1 :il:.~I,~"J~'.~'---~-_JI sector and L __I'I::~_,POPU'I at ion and l . population employment for 3 I cJ I'ea S I'----__~__- STAGE I Sj/;Gi::;I 'II 11 J~~rl:':J:',-A"(L:..-c'~';;'~'-",,;.~,~,,:.,': ",",,,,:,,).iii,,',,,;,,1 ",;,J ~c••,,,J """",~J ",,;,.J r r r-, - I ' I r- i, .- r I""" I - ,, an electricity den,and model ,</hich \','c CJ11r'd Si"Ue ]I.The final lSER model has this hasic ~t.l'u(tUt·e 1'liLh the r~/\P, household·formation,!lousing stock,dflc1 l'I'~Ji(I:llll fl1lrJC;l!.]on models perfol~lIIing the Stage I fUllction dllll Lhe pl(~Cll'ilily end use mod e1 sin the Stil gel 1 r 0 1 e . 2.2.1 The MAP Econometric Model The basis of the Stage I function in the I$£R model is HAP,a medium siz.e-econometric model which trunslates forecasted or assumed levels of national economic Ll-ends,state govcl'n- ment activity.and developments in the Alaska resource sector into forecasted levels of statewide population by age dnd sex, l\ladel is intenlally cOliljJ1ex,its Dasic lU(jic i:,i..iicll..tilt: State of Alaska ~...i11 tend to fo11o\'1 national trends in economic and state government activity.These l'lnl Ciluse the stu te to per-form somewhat better or worse tl1an the Outside.In pel~iods of plentY I Alaska will attract immigrants seeking employment opportunities;in periods of relatively poor economic perform- ance.people will tend to leave the Stat~to seek opportunities in the.lower-48. As a result of this basic lagle,MAP's output is quite sensitive to the national trends.resource activity,and state government actions assumed as input.Since ;.lAP input5 dil"cctly into the el~ctricity (:nd lise model.the final n:r,ults of U;e cssumpt ions. l'\AP'~Olitput,\'ihile technically quite rCilsonable,i.s not dPpl~opriate for direct input into the electricity 11lode1 for t~·,o reasons.The first of these is that r·\AP ;woduces forecasts for the entire state of which the Railbelt and its component areas are only a part,albeit an imrortant one.Secondly, electricity consumption is more closely related to households an·d the number of housing units than to the number of individuals in the market area;}1AP ptOociuces only t.he lilt~er.fhe hOl/sehold formation,housing stock,and regiona1 allocation models translate MAP output into final Stage r form. .J 2.2.2 -.I n E'r~ousctlollj frH~H~{lti\~n li~i.jt:'1 -~---"--------.'---.-~.-.-_..~-~.~._.._- The hou St:'f'(.'I d fOlil12 L iC.Hl mode1 gi·()U po.i !Ie i ':i due 1s i ilto household units on the basis of national and state dE:lIIographic tl-ends.The basic logic of this model is than an individual has a finite chance of being a household head;the probability of headship depends on the individual's age and sex. Applying these probabil ities to MAP's output yields the number of households,a critical input into the electricity end use model,and the'number of household heads by age and sex,an input into the housing stock model. ~ '""",.•-r I i -...... I,.' :~, l l I r1 r4\ rt·. r1 r1 -1i ~ r 2.2.3 ne ?,·(,ic,nal A11oCilt;(;!l 1·~ndQl----------~-- -'".-_.- The purf.lDSe of the regio/lal alioc<lLion lIiodel is to allocate l'lAP's stat2\'/ide fon:>cnst"S of pOj'ulilliun to the rr;:9ions of the Railbelt.The inherent logic of this Illodel is tpat regional population ~hiil-es al"e sensitive t.o ('mploy1llc:nt opl!ortunities in the variou~t·egions.These oprortunities in turn depend on which i nd us t d a1 s ec tor i 5 Pred 0111 ina ntin t he WI P for'ef il s t,il nd its likely 1ocation.The regional allocation model Ultimately disaggrega:.tes NAP's statewide forecasts of emplo.)1nent and population into l"egionai shal-es.This infonnation sel'ves as input into both the housing stock illociel rind the cl('ctricity end use model. Because heating of residences is an irnpol·tan~use of of diffel~ent types of hOllsing avai1ilble (single ftlfllily,duplex, apartments and mobi1e homes)it is necessary to forecast the numbers of each type of dwelling unit in each o~the Railbelt regions.This tasK is accomplished in the housing stock model which combines the household headship information from the household formation model.the regional populatian information from the regional allocation model.and the resu1 ts of an independent survey on housing choice.to produce the number of housing units by type tnd.region for each of the forecast years. I I The logic cf '.-he houslng stock model is quite siJniliJr La households per I"cgion over the ron:cil.st pr-rlod,the inFormation on housing choice is applied La as:,iUri c;leh household to a household head of a pol'ticulor-i1ge unci ~,cx I':il1 choose to live in either a single family,duplex,apartment or mobile housing unit.The housing stock Iilodel thus j\nJ(\ilces the last Cl"uc.ial item of Stage 1 info'rmation,namely,the number of housing units by type and region over the forecast period. 2,2.5 Sta Il~.?ulllrna2 In summary,.the Stage I portion of the electricity demand household fonHatian model,the regional al1ociltion model,2nd employrnent,population end income 011 the bi1si5 of national economic trends,acticity in the Alaska resource sector,and state government policy.The household formation mode,l groups individuals into household units on the basis of state and national demographic trends.The regional allocation model assigns a portion of statewide population and .~ployment to the regions of the Railb-elt.on the basis of the location of projected economic activity.The housing stock model produces forecasted counts of dwelling units by type on the basis of the• ou~tput of the household formation model,the regional allocation ~ t i rlI II I n 1 Q-!j model.and a survey of housing chGic~s, housing information is then passed fOl',.(i!-d t.o the electricity end use model for translation into ilf'oj(·l..tp.d r'('quircrl'lUlts for electricity in the Railbelt. Assumptions playa central role in determining the overall output of the Stage I part of lSLR's Iliudel.While the liIost important of these are national economic trends.resource sector activities,and state government decisions l'lhicn drive MAP.there are in addition nationa.l (Jlld ~,l.dt(?dcm()~rdphic trends and housing choice information l'lhich ultirnate1y influence electricity consumption forecllsts for space and \'Iilter heating.and for other residential uses,Critical among these as a I'/hole becomes some\·lhat suspect. 2.3 Stage II:The Electricity End Use Model The ISER electricity end use model translates the Stage I output into estimates,of the final demand for electricity for each region and consuming s.ector in the Railbelt.The basic logic of virtually a11 components of lSER's Stage II model is t ha t e.l ec,t ri city i sus ed ; n ide nt i f i ab1e act i v it i es sue has cooking.heating a building.etc.Each activity has an observed electricity ''In:'ensity'',that is,;;u;'.JIt.1ty of c1r:cL.riCJl energy iE'quired to fuel a single unit of :.lic '-lct.ivity in question.FurUlcr,these intensities arc subject to Ctlul1ge over time.Cnmbining this information with the oU'Crut of Stag~I,which project~the magnitude of srccific activities aver the fOfl"Cast period,yields projc!clions of clf'ctricity r~quirelllents for each activity in each rcgion.These !Ilay be summed to give final forecast estimates. Consider,for example.the activity of refrigeration.In 1980,a "typical"refrigerator in the f<i"lilbelt used <lbout 1250 rJoih.per year.Over tillle this averJge inLl:r1sity chllngc:s as older',smaller,manual-defl~ost models are t-epliJced by ne~"er, ~.\'0ical refd']!'rator in sE'r'vice in tho ':0,'11"19S5 w.r~lPOO k\·lh larger,forst-free units,Suppose,hypothetical,ly,that a .. I L~1tl\..~t>ILilt..ui ·.f~(rl .1• I J '- out units \·tith J1el'i large units and purchases of nel'i units by ne~J1y Torn.eo ~lou::'t:iIOids.If tilel"eo-an:,say,1 ::,000 i,ousi:holds forecasted to the located in the Fair-Lhlnks rC9ion in l~l95 then the total energy requirement for refrigeriltion in Fairbanks in 1995 ;·s 1800 kW~1ousehold x 15000 households,or 27,000,000 kWh,assuming that each household has a refrigerator. In-actual fact.the lSER method does not work this way mechanicallx.;however,logically and mathematically ISER's model foHows this basic procedure for nearly all activities. -..'B!!§, ,-.-, r • -I'II-i. I""'" 1_-4 r~ l ~ ~ ~j . ,. separate activit.ies for \)nul'y~;is.Th(·~.c ilrr.:. 1.hculing a sillgle fJlIIily !IOniC '2.hea t i ng a dup1 ex 1.heating a multi-family ullit 4.heating'a mobile home 5.powering a water heater for genrral hot water needs 6.powering a watel~heater for hot \'later input into a dis!H"asher 7.pO\'Jering a water heater for hot l'later input into a wash;119 mach;ne 8.powering an electric range ~.powering a clothes dryer 10.powering a refrigerator 11.powering a freezer 12.powering a television set 13.poweri n9 an a ir condit i oner 14.powering a dishwasher exclusive of hot ','later needs 15.pm"ering a \vashin9 machine exclusive of hothZilQI"IH:eds 16.,powel"ing lights 17.pO~'/eringsmall,unspecified appliances In the model,activities S throllgh 15 are tr'caLcd similarly as they relate to energy for large appliances.Activities 1 through 4 ate also similal"as they deal with space heJting. Activities 16 ~nd 17 are dealt with as spcciJl coscs. In space heating,the basic unit of analysis is the individual heating plant of the dwelling unit.For an elect- dc.ally heated dwelling unit this means either an electric furnace,a collection of baseboard or ceiling resistance units, or an el ectric hea t pump.I SER ha s assumed the 1a t ter to be insignificant over the forecast period.Heating plants are classified according to their "vintage",that is,their period of installation.There are seven vintages of heating units, ;2. I ·1 I I .1 I pre-1980.1981 -85,:986 -90,il.nd ~,o on . .reouirement which is based on the ~ile.C(1!\~;trllLL'ion,t!rld.. ")-etrofit"potential of the dl'Jel1ing unit into I..hich iL \'i<1~; original1y installed.For units buiH in \'130 and Lt:fo(e, average consumption is silnply the observod consuJIlptiCJfI of existing units with no conservation or retrofit over tirlle.For new units.average cqnsumption is the pruduct of four terms: a baseconswnption level,a housing unit size coefficient,a conservation coefficient.and a retrofit coefficient.The base level gives the consulllption of a ~yp~cal electr-ic unit currently being produced.-The size coefficient factors this up over time to account for increasing dwell ing unit sizes. The conservation coefficient factors the pr0duct down to a.ccount for impt-oved heating lechniqui;s;~I!C ~r,c:l'i";;"1 u:Ii. factor further reduces this product to account for illlptO'lf2ill~llts to the d~vel1ing unit's ~fficiency over the.:liff2 of ti.:;;ill...~Liny plant.The average consumption of an electric heating plant can.therefore.increase or decrease with newer vintages depending on theassumpt.ions made concerning base 1evel consump- tion and the relative strengths of conservation and retrofit as opposed.to increasing unit size. Heating plants in the ISER model wear out over time, according to an expected lifetime schedule.A heating plant has an explicit probability of usurvivingU from one forecast year to the next,which depends on the age of the heating unit. -...•..., i (1, ril f'".;&Iclli( r...Ill.tJIIt, r'til ,.... !. I·J .r""":, i'"'"t, r' ~, A ~.,., ~i~. \ ~; ,iIrs~ J ~.. ~ ~. Ii n For example,the probability t.hat J l,r·\iting plJnt installed in 1980 will s.till be in service in l':1gS is :Iluch higher than the probability that a heating pli1nt ~w,~i\1L~d in 19801.,,;11 be \ in set-vice in the year 2000. When a heJting plant "dies",lIle IlI11dcl JS:.UJII(~S tll,lt,1n effect,the housing unit dies with it.The heating unit is ....eplaced with either an electric or non-electric heating plant according to specified probabil Hies of "capture"which run on the order of 9:1 in favour of non-electric units.If an electric heating plant is chosen,it is of the average consumption appropriate to the vintage of the replacement period.This assumes for all intents and purposes that either the.dwelling unit itself is replaced with a new unit or that th~dNelHng undergoes.major Cllt<:rations to incn:nse its size to approxim<lte tha t of cun·~ni.1y \J1·OJUCl'J uni b. There is a·logic problem in tiyis c..ase I'lhich will be discussed in our technical revievL Basically,the problem is th~t the replacement of electric units by non-electric units is 1 ikely overstated as is'the alleged "growth"of units which switch from one electric heating plant to another in a partic- ular period.In terms of el ectricity requirements.these tend to offset one a.nother,to an unknown extent.We will assume that they offset one another exactl y for the purposes of our subsequent analysis;.however,we strongly recommend that the space heating section of the ISER model be reformulated in terms of d\-lel1in91 rather than heating plants to more accurately ',."...... ~,""""'-.~: I'·'r., i~ .~ .~ reflect reality. from previous jJcriodsconstitute r.C',,~housing starts.The numbel-of these 'dhich iire electr-ic is:lultiplii.;d by t.l:e Jvuage consumption of electric units of the nr.i-l vintage;tOt]f:ther \'iith the total consumption of previously built e1ectric units this given elcctr-ic sp,lce hc-aLins u'quir·(,i:;f~llt.s for the; forecast year. Assumptions again are critical at this stage in the model. to oil or gas heating is quite illlportant. For major appliances.the ISER electricity end usc:model follows a structure similar to that of the space heating segment.Each appliance is classified according to its vintage;for each vintage the average consumption is computed as the product of base level consumption,a size factor and a conservation factor.The appliances follow a survival schedule similar to that of heating plants;the number of appliances of a particular type in service at a point in time is the number 1 ,.il r-~ ~~it~ ~-Il,i ~r-,~- ~,....fi- 7~""'"l:. i.. I""" I i I r-"jj .;i!j r-~.... 7"l r-'~.2:. ~,-"..... -~ :1 I""'"~i ,~I -.J " "Ir,"JJ I i gro\'I over the fan',cast ~lc:r·iod. dishwdshing is adjusted dO\'Ii1'.~ar-ci to ,',ccount fot'c!iiLinishing household size.',nl!2rc tlltel"[lcltivc fuels exist,en explicit assumption is introduced to JC\.i;!·j;iin2 Lhe C12ctcical s!',ue . Operationally,the model dC'liO:l',!iir;('s t'r'qui/'cd additions to the appliance stock by SUU~l'(lCl.iilg ,'r.:,,;uircu SL0d;in J iQI'(,;Ci:st yea I'f I'om II s lJ rv i v in 9 II U 11 its f I'Olii PI'(:'J i n Ij S Pe ,-i 0 d s .f\sin the space heating model,the total energy consUll1ptian is the sum .....,- appropriate energy in<.ensit.y pc,'Ullit . The ,'emaining activities in Ue residential $l:CLUf 011'12 lighting and pO\'lc1'ing sr11illl appl i~nccs.The ISER ::1(..1.]01 C1S"U:;les a constant electricity requirement of 1000 kWh per unit annually for lighting.This level is assumed constant over the forecast period with increasing lighting requirements arising from increased dwelling size offset by conservation and technical improvements in the efficiency of lighting devices.Small appliances begin with a base requir~nent (in Anchorage,this is 1010 kWh per year per housing unit),and grow by a constant amount in each five year forecast period to accommodate expanded use of existing small devices as ~ell as the use of for ec as t ~:u i od . i r ::',..'.1 , -J;J I'IOl'ks with disccete vintages of C0I1::,uii;1.ng devices.It in~ro- duces explicit ilSSUiiiptions about the ('/I(:r9Y int::nsit/'lnd survival characteristics of ~ach device Jnd vintage and calcu- lates the numbers of each vintage in service on the basis of explicit assumptions about electricity's si,are and the proportion of households owning a particular energy using dey ice. :~-.".".~2.3.2 The COlnl11ercial-lndustr;'ill-Gove"fiJiient S(~ctOt' ~....'...~ (~.: jJ Gecause of datJ stlol-tilges tile ISER e1ccUicity,end use certainly as many specific activities using electricity in this sector as in the residential sector,they Me unknol'ln at the present time.Consequently,the lSER model takes a "second best"approach to modelling electricity requirell1ents for the elG sector. In the elG portion of the end use model there is effectively only one activity,providing all the electricity required for a CIG employee to carry out his or her jab.Included,or creation date \'ihich is in tUI-n I-elated to the estimiltes of t!l i s c 1 a ~,s i fie a.~"i (;n The ClG portion of the IPodel employs a structure silililar regions by the regional allocation model.The basic logic emplo}1nent originating in the r,!l\p !ilodel ,IIHj ullocdted to to ell1p 1oym en t. sector.Jobs dr-e of one of seven Vi:1LlgCS,dcpL:lldil19 an their father Sut~,l;::.i~d by ~.f!. ~~Jj. ~ 1"""'1 ~I I""'"i i'<r'"' -)~-~I I""'"~ r-~.. -j .A '..". r'"i,'''- .A,'. ~~.-.... is that the energy intensHy of a pJl"ticlJlar job depends on the technology in place at the time of its cl'C'iltion;ti,e:job maintains essentially the same energy intensity over the forecast period although conservation may be factored in 0',1 er tin e. Explicit assumptions about per job energy inter!sities ore a centr'al fe ....:'c.uri,;:U1 [he erG portion of LIl(:jilodl:l;III i~lj<,~; -I... forecast these Me projected to gro\·/[\(\1r\Y three-fold ovc.:r' the forecast period.Jobs created in the Z005 -2010 period requir~about 30,000 kWh per year in the Anchorage region as compared to about 10,000 kHh per year for jobs created before 1980. Operationally,the erG model is virtually identica1 to the residential model except that it is driven by employment rather -~ ~~~ 1 ~l than the number of households.For a given forecast year, em p 1 0 ym en t 9 r 0 ',','t his cal cu 1ate d by sub tl"Jet i n9 .requ i remen ts . Because of the aggregate nature of C1G activity in the model,it is v~rtual1y impossible to idelltify all th~,::ssu,l\ptions upon I..hich it is based.The actual ;JarJmeters used in the forecast indicate that ISr:R was quite cOlIscr-vative in 'dorf:ing with this portion of the l1Iodel;a large clmount of electricity growth per employee is fOl"eSeen.HOi-lever,it is not clear in \..hich of the specific activities of Cillpl11}1i:rnt t!ll~'J1"n\'lth ;, to occur. -~:", ,.•7_...;'\.... .'..,;j ~. projections of electricity l"N]uircmcnts for each r"(;,gion of the Railbelt.The electricity end use model develJped by rSER identified 18 electricity using activities,of which 17 are in the residential sector and 1 in the commercial- industrial-government sector.The model forecasts on the basis of the vintages of consuming devices.Explicit assumptiohs regarding numbers of devices in operation,energy intensity, and electricity's share of the fuel market are introduced where appropriate. .....~"" -r-~..." -, F"" , ......, ir-~'.. -; -= ~-I -,~I !; ,-~,,~j 1""".ir. r-~ r-'~l.::J -tl l~ 3J !""")1 ;1 ~)f,:..v. 3.!Introduction mOGel,both lSER and energy F'1'oLe "~in'i:d ;.Iiat the uea1 of ISCR's reseal'ch should be the development of all "('COllOllJetric end-use" (EEU)forecas t i n9 IJ'Ode 1.The name is deri ved (1-0111 eCOllolilet ri c methods,which employ statistical techl1iuu f ;s to est~lnate the effects of price,income,and other pel-tillent factors on demand, C:lI1ployment,ai'population change,and elid-~sp.met.hods,'.-Jr,icn seek to explain energy use according to its final use. The EEU approach is rapidly gaininq \'iide acceptance in the .,-~,.'...~. i !~f 0 rine.t ion 0 n fin ale 1ect t'i city tI S (10 (''.','i (.h e C0 11 om i c i n for'III ,1 t ion which governs consumer choice. In an ideal EEU model,not only would basic economic and demographic variables be modelled and forecasted econometrically,so too would information on devices which transform electricity into useful work.The number of appliances,for example,would depend on not only the number of households in a given period, .. ~J ,"1 C (::~,iIi C r:i l.t:S J - .'~""..'",:;,..;. and even sti1te fiscal ~)olicies. intensive.This provi'd ;i:OSt l..el1i:i~T 1ut'hLR's !"~.:',:r-cil;a i;i:sic combined an ecollolJletric !1Iodel,t'i:~P,\'il '..il fO~I-n(:\·:1I0f1-l::conC!':J(:vic 3.2 HAP appropriate for a large role in electricity forecas~ing be~wus~It Technically,MAP is quite good.as we argued in our earlier l1ol-king Paper.It produces stateiide forecasts of c:npbyrnent and population by age and sex on the basi5 of state and national trends and resource and state government activities.Unfortuna:ely, MAP's output is not directly applicable to electricity forecasting for the Railbelt and we made a number of recommendations on improving this situation,the majority of which have been irnple- - - - ...... I I ""'"f ..... -i - ~I i ! men ted by !SER in their 5uhsH!llent \·Iork. 3.3 The Household Fonnation I'lodel .'He tecOillmended Ulat the dC!11ogl'aphic diitc1 output of [.·,/l,P be expanded to include the nUlllLJer of 11OusellolJs by age of head to complement i'1AP's population by age and sex.This I'las cCli'fied out by the addition of the household fOrination Iliodel. The household fOI":!1Cition lnodel is an adc~qLJi1tely developed method of accounting for households but n:~lies only on dOiilO- graphic analysis for its aggregation of individuals;no economic activities modelled in MAP affect household formation. Since l1AP produces state\·,ide estill1at.es of economic:i1l1d demographic variables anothel~l-equil'ed chnnge I-ias to distl'jbute to Greater Anchor~ge,Fairbanks,and Glenallen-Valdez appropriate shares of statewide activity.The regional allocation model was developed to meet this requirement.This is extremely important because resource development projects used in projections of statewide activity could shift population and economic growth regionally within the state and even within the Railbelt. t~~J1;M .~.'.' ~ the possibility of a cClI10te oil JiSCDVtCr"y 1(~adin9 to the ex- pansion of COllililunilies outside the Railbelt YI":d,fOr"e:<a:ilple. Other sc.el",ci!·ios i11~9ht include pl'ojecLs \:l1ich r,ilve a Jiffe(cntial impact on the Lhn.:>e Railbel t 1'C'lJiorls . The ISER apPI'oach to this pt'(J!Jlern is "c:cciJtable.It appears to be a precise statistical allocation of regional Activity based on resource sector ernployment and other factors.HOI'lever, thel-e has been so l:ttle vctriation in U:c n:giollal ~n)pol-tiuns of activity in the years for which dAta is available that the l"egional allocaLion model has not been thOl'CJughly tested. all 0c.:t i r.n i!l C'd c 1 i S ,1 den I t.=1 t e i r the con t ext 0 f the l'r'C5 en t s t LI dy : ul1usually-loc2tcd n:SOUI"CC projects 01'to C;{!",nd the sEudy area to other l'egions of AlaSKa. 3.5 The Housino Stock Model >' The housing stock model is the final bridge between MAP and the electricity end use model,The most important aspect of this model is the projection of the relative proportions of single family,duplex,apartment,and mobile units. r- i r ! r - r- I '"""I i, r - 2 ~. Like the household fonnatiQIl !ilodel,l.!1t')HI(Jsjn~J <;(:IC': model is based only on deJl10svaphic f,lCL(JI'S .it,O net on .t,hr. housing data it is not possible to J'CLlt.e he'lsing stock La construction activity,inten?st '"ates,311d uU:"J infL:.:!:ticl1 variables which would clearly be desirable. While the necessary data is missing it is possible to recreate it in the future on the besis of clcl';al photogt',:phy, utility hookups,housing sales,and building pennit ac;tivity. 1·le strongly recommend this he done in futun?i;;lprOV2mrnts to the ISER model. on heatin'g plant,appliance m-me)"ship,housing heating efficiency, alld their changes over time to fOI"eCasts of households 311U li0usill';) units.ihe IlUlnel'OUS \;,ays in \':Ilich this <1110\,:5 the analyst to examine the impact of alternative policy options is admirable;the detailed calculation process allows for changes in virtually any aspect of residential electricity consumption patterns. A major problem ;n the·mode 1 is the apparent confusion be tvl(?en housing stock attrition,which is not in the model but should be, and heating plant attrition,which is in the model but overly emphasized.Essenti ally.the rate of heating plant attrition is !tl~ponents. The model should allol'"fOl'il VC:!"V 510'.'/luss of ricr.lJa]ch-;ell inC/s, plants to nel'/er and rnOJ"(~efficient dcsi'lilS.CUI1~J_',;lJQlltly,tile particular numerical values used by IS[R I"/hich Sii:luiL,l11C'ously understate attrition of buildings Clild o'Jet'statc r-Co't,"ciit 2r'e 'J;'l.:Il to question. ,J 3.7 The Electricity End Use Hod~~_-._CI_G Sc-ctor The commercial sector end use {ilodel is nuite undeveloped and .'....,-spal-se 1/1 CQl;:paj'"i$\...,r1 to r:.ne i-;';:)1-.:::"':,:,It.:,"- •~~.~.~j I ;'.:.:~:.c;i .~.'~ ISER had intended to build the !\lodel Oil the basis of flo01" space in cOI!:mer"cial.industrial,and aovel'nment buildings with a very modest bl-eakdOl"n .by type of ilctivity.In the Final Jlli:llysis, tions. This is adequate for the present study but is difficult to interpret as end use analysis as no physical efficiency changes can be directly related per employee energy use.Clearly,a model based on physical attributes of structures,such as floor- area,would be easier to relate directly to energy USE. !"""-r....'"""tt,~a ~ I"""\,:f,'·' I ~:'! r-§1~~,.., .....,-~" ,-"r.fi ";::::.;?... :~I !"""~?;.< ~~..-;.' -ll:J ~;..., 1"""~l - ,-~1~ ~ '"~.:.J... -: i ";J -L!= ,, i jr--;: ,., ~~,.. r •(!WS~A..- (G. The most ~il1ool-tant·pl-oblell1 in t.he CG fon~c(lst is that the per-emp1oyeC'cnet'g'y CO!1Su,n~~t i on fiS;l1n:s an~be.sed on 1973-78 changes in consumption per CIG cust.o:r:e)-,i.e.store,factory,'=:tc . \.;hile these t\-IO yeus avoid the highest iioint in the pipeline r.eve been p:Jshed up by the pt'actices of the boom year's (uninsulated .:O.'1".- I ~;'0 \..... o f n~s i ci ",11 t i "~,I II J C1G c u s t (iii Ie:-s \'1 ill 0 C l.d )Til:J u u t.I 11 c:aUG i t ~ offet-a pl'ill1E:o~)pol·tunity to build a bettel'data hase I'll1ic11 includes information on the physical characteristics of buildin;s. In the mean ti1,1e,a close scrutiny of actual erG electticity sales should offer c1 check on ISER's assumptions and should reveal \-Jhether the potential LJias(~s suggested illlQVC C1!'C in operation. 1.(. ilctivity, OV['~!-I~\"-'l'i!!ji(l '.:"'.~. 3 ,8 S U;::;:',(j I"V----_.-._.._-_.....:- ;;iod e1 (!·1;1.1' plus houseilOl:':fO!"TI1ation,I'?gional alioc<1tion,and housing stock ~;~!1 '1 .,i j •:.:i r:'.':::::,','i (.:.:~ i r~t r ~l'C', .-'''',:'l ~t ,. ~ ....~_"">......,..",__"r._•..,.".,.,.,...,""'..""''''''''",,~.' %t1. ~.AN ANALYSIS OF and consuming seclor,fa,"c(ich of the ;<iJilbr:lt's tilt'ce divisilJlIS, for the years 1935,1990,1995,...,in10,2nd fOt-eilch of -I qnge of economic development possibilities).Of the tllree economic sccnanos -low,Inadel"ate a!:c:hiiJh eCClnumic ell'PI'ilh sun:mary of aggregate Raiibelt elcctxicity gcm,ttl rOI"ciJer:of ~j._... , i, these three scenarios is presented in Figure 4.1 following: -------_._--_. I I1,\,)"1 Cl',~:c Hir:h-~--"'_.--,- 3171 'J r ,.r ,J .J LJ 1 3599 !':.')1.}') ~l.t.i L ~GOl 5739 5730 7192 6742 917i 7952 11736 4.18 6.00 4.76 5.32 3.33 5.02 (%) 4.09 5.45 3.08 4.66 1.94 2921 3236 3976 5101 5617 6179 (X) Fi ~u;e 4.1 : 3.22 Averaqe Annual Growth 1980-2010 1980-1990 1990-2000 2000-2010 1990 1995 2000 2005 2010 I~,nnua 1 Growth t •. ~ .-~ :- i ,- -t cOnstant thr"ough all its electricity ik;::dno p('ojC'c':iu!1s its electl-ici ty end (lse as:,UJllf,L ions,\"i t.h i.I:P ('xcc:pl ion of the end use assumptions,it is ,,,orth [\otin0 [joth the dSSU::iptions util ized in the lSER model,anc the n\(illnel"in \'ihich they ,-,ere i~corporated into the forecasts. In summary!the fol1o'l'Jing gcncrGl c~s~,')mptions have been utilized in ISER's end use calculations: 1.The el ectricity market is presently in relative ,-Ihere a shift JI'i(\j'fl'Olr.elecL)'ic und2r\'iilY· he J t.i ng is 2.Thi:;equilibrium is expected Lo l"cJl:ilin in C'ffect constant fuel price ratios. 3.The price of energy relative to othor goods and services wil~continue to rise. 4.Rising real incomes will act to increase the demand for e1 ectricity. 5.Federal policies will be effective in the iJrea of appliance energy conservation,but 'ilill have tl illuch heating dell1ilnd; coal .~ill not greatly affect ilggregate space \'/ill (Jct to reduce average electricity projected; sCt'apping rate; domestic elr::cuici:.y applicoLiufls. increase; (d)space heating alternatives such as oil,\-med or (c)natul-al gas availabi1ity win not significantly (b)average housing unit size continues to grOI-/; (a)a sliGht tt-end to\'i<ll"d Sil1<]10 lc:n:iiy nomes is (e)consull1ption is sensitive tG '.he appl~ance (a)s Cl t u \"a t i CJ r:rat e s \'1 iii t r <1 e k !,::t ion a 1 t I'c'fid s ; (b)for some app1iances,teduccd household size 1inpl e;~~ent.rd. 9.In tel"iHS of J"esidential appliances: 8.No ne\,i electricity tecilnDlugies \~i11 ~IC inll"uQUCf,d. ,, f ) r r f; \ ~ I t l""'" I I r I, r \ f", I, l' \ ), r l Case "A"-indicates what we believe l'/i11 12.l'iiscellanoous u~ility sules (street lighting ilnd be the result of the recent state conservation bill,combined fiOt stiift significantly. In order to denollstl'ate the effects of u:ilizing different overall uti~ity sales. bel ieve to be too hiSh.{( •• \'lith national trends in conservation policies and technologies. end use assumptions \'iitliin the model,we have taken lSER's end use sce~arios for the Anchorage sUb-region of the Railbelt. moderate economic growth case.and have developed two alternative The first of these of calculations,tiS Gocu::iented in <:he "User's Guide"secticn of r CDnlell1plill2G b,Y till'Seide'i l''Ji SL:tlJi C,:'.11.\'lllicli liul:!~,rJ:l esc; lies lye 1 1 within the l'r",1111 of tilt,i111 ".ihlc.. 11.2 Case "A"End Use Scci'luio 4.2.1 Residential f\cqu i l"Ciilcnt S ISER's calculations for future residential space heating requir"snents employ a number of specific assuill~tiDns.The applicable,aUl"modifications for the Cilse "A"end use scer.ol"io. dl..el1ing unit size (assumption 2)is (]reflection of J I"ecen! s:naller expected households. The impact of the conservation,retrofit and unit size modification assumptions documented in Figure 4.2 is a reduction in Anchorage space heating requirements gro_ith from ISER's expected 3.77%per annum to 1.98%per annum,a 47% reduction. no cL;u::Je1. 3.Ret )-0 fits c h c d u1c s for r rc- 1 9.0,0 s to ck : (a)single family and duplex: 3':'per 5-yr.period (b)mu 1t i -f (J In i 1y:2 c;per 5-yr. 2.[o,,'e)"gro\'Ith in unit size is iJ S S u me d,Sin 9 1e f a In i 1Y un d I1lJ bile llIi i t ~9 ra w 15 ~~1 ar 9 e r hy 1985 i)nd then stabilize; mu1tu-fi1l1lily i'lild dUjllex units (p'm-!?(1 ',:1;;1-~C'r hy }')C!0 <l n d <-lien stilDiiize 1S [I-:i.l nd Ci!s e~;'''--j:',:-,:;~~:-:;;~~~~:~--------l -----------------l I I I ! I I rigure 4.2:A CO::lpal-ison of 1.r,dditions to the housirg stock \,;i:1 require about lO~~mOre ele~tricity for space heating than the average pre-1980 unit.reflecting lal-ger unit size a t the marg in 2.Average dwelling unit size for al1 unit types (sil~gle family, cup1 ex I mul ti-farni 1y and mobile homes)\'Ii11 increase 5::per 5~yr.period through- out t.ile forecast pel-iod 3.Pre -19 80 s to ck will re ma ina t present levels of efficiency throughout the foreeas t period (i .e.no retrofi ts :,:sSt;;:-eci) 1.S.f::.R. Post-1980 units \'li11 be 5% more efficient per square foot than pre~1980 units for all housing types throughout the forecast period 5.Once bui It J new units wi 11 remain at constructed levels of efficiency throughout the forecast period.except for single family units,which wi 11 i mpro ve 2% ",ent,stobi 1 i 2i n9 at 10~:; (c)mobile 11011les:no retro- fi t s ass umf:d 4.!Ill po s t -19130 un its '~I i 11 be 5~: more efficient per square foot per 5-yr.period S.Once built.units conform to retro fi t s chedul e as in (3) above.effective beginning 5 years after the completion date of the unit r I""" I l r l. ISER's l11ec:hod of de21 ing I,;ith j],ajol",-:pp1 iances is sill:ila.r to their space hl'ilting technique,\·;il\;tid:['xccpt.io'n of Lhe retrofit paruiOeter,which does not exist for llppliililces.In Figure 4.3:ISER's t~ujor f,ppliance /\SSUii)Ption~-------uu-l k\~h Requirements .Eel'Ui].i t __2_e_~~e":.!:. Unit GrO\'lth per 5-VI".PCi-i nc ..--"'.---._--. Efficiency Jl1Iprv,'C:'- went over 1920 (\·,a t er )lOS (J - \~ater heater s tOY e clothes orser refr igera tor freezer c at hes \·;a sher r-c.<.~-.e~.,'.i;So rlt::( 3650 1250 1000 1560 1550 ';:-".,) 230 i ,".'-'I "1\..; 70 2 5~:, o o 5.m; 5 0% (1 o ')r.('>1 o a 14 ~: 3'1.. G';', 2 9~'; 21 ~:, UX ?~.' 07,; J 1, r I'. I""'" I ,.,... I Source:ISER \-IOI-Lsheets In terms of a moderute conservation effort,these values, and especially the estimated reduction in demand attributable to conservation,seem quite reasonable.pihile not a function of state policy,appliance efficiencies have been the focus of !Ducll federal study;indications are that federal appliance efficiency standards ,·lill be rea1ized.Therefore,for Out"Case period. r\nchorage district of 4,9%per annum thl-Ollghout tIle forecast 4.2.4 Residential Summa.!2 In general.\'ie find lSER's end use assumptions acceptable, its potential fOl'efficiency improvements,and the passinil ity unspecified appliance assumptions,\'Ihich 'yield u growth rate in lSER has disJ9SlI'egated unspecifir.:d ilppliZinCl::l'rleJ"gy dl'ifi2nd Thus,fOl'our Case "A"scenario,\'18 hilve i:\cceptcri lSER's it is necessary to treat it as an aggregate.ISER's allowance (l010 Uvl1/househ01d/ycar and 91"o\,,'iI19 at SO ~~\·[h/household/YC:ilr). number of uppliances \'iith extl'c,11ely 10','1 uti1iztJtion rutes and into 1 ighting (1000 kl·ih/l1otISChold/year)2nd (issor'ted appl iances of 50 kWh/household annual growth in assorted app1iance energy Because this classification net of lighting contains a very lurge Th2se assumptions yield a 91'c,;th rate ill I;,<or iJpplidncc l:n::rgy II I"\H f'\ dc::;~and in the !\l1chvrage district of 3.17:;~pcr i,llfdHr:. rl. r I ~, esce.::>t for Spece heating cl cc,...-icity c,'!;:and,"'Iiich l'lf;:)el -i(:ve are vn:-ealistica11y high.Utilizing (lU)'i:~,StI:lipti(Jns for ~'il,:ce heating '<'Iilile accept.ing lSCR'::,i1;'pliiJllcr ,l'.'.tlliij,tiun::"11:",u1t.o, in a growth forecast.for the flilCho:-ilge n.'':Jion of 3 .36::',pl'r tinnum,cOll1pared to USER's 3.75';:. 4.2.5 The COlllll1el'cial-Indust,'ial-GJvr:rllil!ent Sector use model is.because of serious data 1illlitations,:nuch less lSER's Comm8Y'cial-lr.dustl-ial-GoverI1lIlent sector-(CJG)end of elllployees yi [11 d s total c U[]:)Uj)~Pt.1 un !vI'l.rl(~~:YCJ r'.j lIt' .,;:,.' ,, USemOGel~ ---------..,-------------.1 ISER CIG Assumptions IFigure4.4: follo\'iing v21ues '''ere used in the JSER C1G ellC t:npioyment for each of the snlif)silot yc'(\,'s is ;::u1tiplie:c L;)CJn electricity use per emoloyee para:netel-,yielding totill chctricity '.":i 7 I:~~1 ~c!c:t !'"i .:i ~J.-r\.",..'".,,•.~.'--,.,.-r .,i 1 consumption for that "vintage"of employee.Summing this figure detailed than the residential end use 1110del.In general,C1G Date Base MWh/Employee Jl -Cs)(1 -ret)Actua 1 H\~h/t:mpl oyee 1980 10.675 1.0 1.0 10.675 1935 15.156 1.0 1.0 15.156 1990 18.180 .95 l.O 17.270 1995 21.200 .90 l.0 19.030 2000 24.220 .90 1.0 21.800 2005 27.240 .90 1 ;0 2~,.520 2010 30.26 .90 1.0 27.230 '------------------_._--_._------_..__._---- r-. it" """ r, ,.... ! r r- I .- I reT1ains constant at 1.0).Rising electricity prices seem to And thirdly,lSER assumes that no retrofits 'dill be conducted too low. be conservative,we believe the 10%conservation estimate is far While the lack of erG data certainly suggests the need to ,'ul··)'er lr:,e!)C I ...I .....1 ,(I .. Secondly,iSER's assumes only a 10';;rl,.j!J(~ion by l'J~;O in (the (1 -Cs)term above).Such a figure seems to underestimate \·Ie have serious l-eserviltion about i:cccptinO tht:,va1.!f:s used lSER's assumption that fuel price ratios \~ill J-Uiliiin ,'curly potential for the cevelopment of ne\~applic;jtions (notl'iithstJllcing on the buildings inhabited by eIG employees (the (l -ret)term \·,'orld's most energy efficient COl,imerc;al str"uctures.Since tile the efficiency with \'ihich each elG employee util izes electricity in the C1G end use model.It is difficult to imagine that ilctual unavailability of alternative fuels). electr-icity use per omployee can nelll-ly tx:plr:'>iil!:in t.he constant throughout the forecast period see!~IS to 1 i:nit the growth in per employee elect)"icity use Sf;l':i1S to 5lJ90eSt that confires of existing elG electricity appl icatiofiS.Such ri1pid "r it ;::~ r'" .<,;t1 I ¥J.{ {~ l "'"'~. ~ r , -=~~ ~~...; ,....~! .~;j. ~~ !'"""~",.,~ ,....~I ,....~ I ~ ~ !"""~~ ! l 1m,....~ 't~,...lii.t;:; "'" lf8.~,...".< ! ,....~ , ~·;'i r ~ in ,.....~ I ~ ,...,.~ ~ r-~ 'l5 ,....~ structures. as fol1ol'ls:- (2.)Per.E~np1oyee electl'icity d'cili,:iicion is il110',I2d to 91"0\\',as lSER 'has forecast,to 1995,but rriildins stable at the 1995 leve:chl-oughout the i[':::iJi,ldc~r'of the forecast period; (b)An stock is assumed to j"etrofit,resulting in i)2;; suvings per cmploj/cc per S-yt:(~~-period,~ffective fl'orn five year-S cfter the completion of the struccure wi thin Wllich the erG citlp1oyc:e is ilSSUIHCd to Vion:; Con s e ,'"V?t ion,~./r2 \'J ill (\CCcpt ~i t.for Cas 0 j.f\II (tJ u t \'J 11 1 i:n::1ys is). These 2.JJendlllents result in a reduction of the !Inchorage district eIG electrical growth rate from ISER's 4.2%per ~nnum to 3.6%per annum over the entire forecas~period. 4.2.6 Summary of (ilse "An Scenario ihe fo1lo\oJing figul-e $uil1l11arizes electrical dCIlli.1nd gl'oy/th in the Ancho:'2ge c[-gion accor-ding to our :-,uqgestiorIS ot:ove. effect of aUJ-revisions is to (educe ovel-all S,-ol-,th in the Figure 4.5: -_._--.-----_.__._._------~ Ca se II A"SUIII:::a r y I Space ~la jar ... j'"nor Da te Heat .6.PJ?l-:.;"\pEL.C-I-G Tota 1 ISER For eca s t Tota 1 ---~--_. 1985 492 c;64 203 1219 2378 2438 1990 556 523 255 1353 2637 27 82 1 Q a:::660 628 334 17-'~3399 .3 564____..J .'I / 2000 784 753 427 21 51 ,j 11 S [;451 2005 848 858 509 2450 [;665 5226 2010 903 975 604 27 97 527 9 6141 Avel-age::::,nual Gl-ol'v'th 1980 -2010: -:11--: ...J.-. I J 4.3 Case "S"End Use Scenol-io 4.3.1 Residential Space Heating Reguirements Figure 4.6 below indicates the residential space heating assumptions utilized in our Case "B"end use scenario (refer- ence should be made to Figure 4.2 for comparison with Case "A" and ISER assumptions). --,~~~ r .~ l'iE'. --- f rt~d1.'~.- !""""i!t ;0:.0 ,.."r iti~ Figure 4.6:Case "8"/j,~.Slirnptions (Rr:r,i:~,-,nt.iJl Spilce ilC:ilt) 1.Sing)e r':lIllily Owell i1!9S: (a)P:-e-19BO stock ilnd 1985 :,tock l-cLrofit so a~.to aChieve imp'"OV8iilcnt in efficiency of 4j:[,er 5-year 'period compurcd to base 1980 ll/lel hd~C 1%5 ,"cquit"C:- l11ents,stabililing at 80-;;of illl~,e rcquirc,::cnts (0)B.:lse Ilpalil1g ,"[;quin1fl1cnts fur"l~)~;O dr:~'Iic,c:.,sive stock vintilges decrcAse as folic"ls: 1990 -35000 k.·lh (based on 1(180 house size) 1995 30000 " 2000 25000 2005 20000 2010 ZOOOO (c)Ur1 its i ze i riC r Cil s est 0 1.1:;x ]980 v 0 lu e hy 1 995 , then reln2ins stable (d)1985 stock has a (l -Cs)of 0.95 (as per lSER);all other stock has a (l -Cs)of 1.0 (conse,-vation is accommodated th,-ough \"eductions to base \-cquil-ci',ents as per (b)above 2.Duplexes: (a)retrofit as per single family dwellings (b)Base heating requirements for 1990 and ~ucce~sive stock vintages decreases as follows: 1990 23600 kWh (hASc>d on 19::1,0 hou("e si::e) -.. .-'.','.;-j -'-".. 2005 1460C ............,,~ ~\J':"V -j-I\.,)00 (c)unit size increases to 1.2 x 1980 villue i~'.'2COO, (d)con S en a t l 0 n t rea t ed asp e t'sin IJ 1 e fciHI i 1y r,\,1(::1 i n9s 3."iu)t i -Fam i 1Y 0\·/ell i n9 s : {a)all ilssumptions as per Case "A" 4.Mobile Homes: (a)all assumptions as per Case "/\" These Case "B"modifications to the ISER tesidential space heat forecast result in a growth reduction from ISER's 3.77% per annum to 1.77%per annum throughout the forecast period,a 53%reduct iOf1. effects of both improved efficiency and growing saturation. per year and allowing other appl iance use to increase at a can present a reasonable estimate of the combined potential in this category,it is -impossible to deal explicitly ...,ith the Agc:;n,because of the lack of appliance disilggregation Although lSER'5 model incCi!-l'orc1tcs the effects of'fl:d")-ilily compound rate of 2%per annum per household,we believe we effects of increasing saturation and improved unit efficiencies. However,by holding lighting requirements constant at 1000 kWh be developed.Such irnpr-ovements ,..auld serve to l-ed,iCe l~iiljor by 2000,a revised estimate of major appl iJnce consumption can pl"esent federal standards are possible.Thus,by using ISER's appliance energy consumption grOI·/th from lSER's (iinG Cil:.(>"/""'5) aggregate majol'appliance consumption data,ilnd facturing in a S~;improvement by 1985;10~~by 1990;15~by 19CJ5;(lnd 20'" ances vary greatly,it seems 'reasonable to suggest that on Llrea.I~hile the potential illlpl'ovc;nents in iridiv"idual ,i~'pli- ... use 0 f growth of ~.9~·rer annlJm.II "',11 "Ca se ,J:inua I t c j S ERa no increased efficiency 01"as ilCColllilloc!ating the same (or,of course,as a combination of these two factors). (b)retrofitting on all v~ntages of stock is assumed to proceed at 5%per 5-year period over the base figure. The effect of these assumptions is to reduce C1G sector number of app1ications at current levels of efficiency after 1985 at the 1985 level of 15.156 I,n·ib.This (a)per cUlployee electricity utilizdt.lon remains constant value call be interpl"eted in 'L\~O 1-luyS,as either For our Case "B"SCCIl<11-io.Vie h,1VC as:.lIl1~ed CIG d,lt.a viilues 4.3.5 The CO;~1111ercial-lndustrial-G()"'C'I-nlllent Sector i h e 0 v u a 11 e ff C'c t 0 f the a pp 1 i cal ion 0 f Cas e "B" 4.3.4 Res i d ell'Li a1 511111.11"J'Y--------_._-------~.•----_.-----"'-- consumption growth from ISER's 4.18~to 2.71%per annum. residential sector gro\"th fJ'olli 3.75%pel'Mlnum to 2.7~;per assumptions 'Lo the 15ER end use model is to reduce [lllchorag'e as fol1ows: annum throughout the 1980 -2010 period . 4 ,26~compOJ"ed .-mI>1-:, l r T~iW,"I t ~ -it) I ~I i'Ok:: ir ~*~; I ~I i I f~} I"'"~ ifr-.~.... ~~ if) .".~.~~ """ , F""'~j f~~ ~,;~....'\ .5-'"~~~~'.i!~r ~ I ~C"~<.}~ ~'f'"-~,;..... I"'.ir~.? ~ ;~.,~ ~~..-~ WlS ~. r-...:J I I ~! -! 4.3.6 SUl'.::larv of Case "3"SCCJ',crio ________4 ._...JI -.-------.-_..----_.....-...-- in the Anchol"Jge d1st.rict accordins to Cil:,e "G"ilSSLJlr,ptions. Oveja11 grov,th is forecast at 2.7~;,per annllili CO;l!pur:o t.o lSER's 3.98 %. Figur-e 4.7:Case "13"SWililiary - Oa te 1985 1990 1995 2000 2005 2010 Spac e Hea t 489 548 641 722 740 764 ~jaj or .J:.jJoL 4'-\1 471 533 603 687 780 t'i i no r ApDl.-,,_1_- 191 230 291 363 427 503 C-I-G 1190 1267 1558 1798 1967 2152 Total 2311 2516 3023 3436 382: 4199 ISER Forecast Total 2(,3P, 2732 3 S6{~ ~..)51 S22G 6141 ,... 15th; E1';e t-Sy D:-0 [\e ....-,- ';"",'::JUt::' 2.7 o··~ 5.M.A.JOR CONClUSJQrlS AND RECO~lr'lEND.A,T10rjS ----------_._._--__-----__.•. Although \~e have outlined many rr:CUldll:r:f1clJtioIlS t.koughout the text of this l'eport,"'e believc SOIlIC {Ire suff'ic1('nLly important to be restated and explained in gl'cater detail. 5.1 General Commentary Although the ISER model in its present form is not EEU as bath ISER and Energy Probe has desired initially,it nonetheless represents an enormous C1dv,JJ1cC in the quality of electdcity del1\Cind fOI'ecasting in the State of r~lClska.lSEr; is to be cOliil,',cnded on the extent to \o,l1ich they h,lve incorporilted available data,on the manner in which they have laid out procedures and techniques.\4e bel ieve tn,,'c,()UI'~,uggestions notwithstanding.the ISH 1110del represents an excellent vehicle through which to view the State's electricity future. 5:2 Funding for Load Forecasting Research Although demand forecasting is considered to be the most important element of the planning process;it continues to receive a disp1oportionately small share of overall research funding.If demand forecasting is to provide a useful guide to energy pol icy developnent,and if energy projects are to be evaluated with the highest possible degree of confidence, additioria:f~ncs must be it'lade available so thc:t dt:tc:cen 02 --~~~~""::-~...._...~-~ ,~,,--, " coliected and an"lyzed and the model ~)\r-lJcture irnpl-uved, 5.3 lSER Hodel ,i\,'.!t.omation While the lSER MAP ll10del is fully Jutoli1ated,the r.nd use !:wdel at pre5.ent consists of several hundred \·lorl:~hC.'('ts, changes to \~hich must be made manually,In this form,the end use model is virtually inaccessible to analysts who might wiSh to test the effects of various end lise 2ssumptions:the development of a single alternative scenario for the entire Railbelt I"ould take lDany days.This sCI-ves to limit.the potential of the model as J pol iCj iin,liy"is tool. Ideally,the entire forecast model,tilat is,the Hi;P, effort should be made. 5.4 Future Use of the lSER Forecast Because the ISER model represe~ts such an advance over previous forecast methods,we believe that it should be utilized in the evaluation of future energy projects in the State.In othe~words,while specific assumptions can, of course,vary over time and among analysts,they should ,. be incorporated,and the results viewed,within the context of the lSER forecast.Efforts should be made to improve the '.Ieal:points of the fOl"'2CilSt,the )'cs',;lt of \·:hich ',:auld be a forecast stl-ucture which for:ns an excellent t.d:;i~for project evaluation and policy analysis. "5.5 Data Collection Data collection methods v,ithin the Stdte s)\ould be improved,in at least the follo\'ling \'iays: (a)the results of the 1980 Census should be incorporated into the forecast at the earl iest opportunity; (b)air photo intel"pretation should be ernployed to reconstruct the building stock for the Rc.:ilbelt; (c)infot-mation fl-om the energy i1uditing programs should be used to gain a fuller understanding of the CIS and Data should be collected,and the ISER f11oc:r.l revised and expanded,So that the model can be used to forecast electricity requirements for the entire State of Alaska.This will require several structural revisions to the model,especially with respect to the regional allocation component. 5.7 Peak Demand Forecasting A peak demand forecasting method should be developed to be ~pplicable to all Stage 1 cJnd Stage 1:sc~n2~"ios.Tr11S (1nal.y~)~~. should be conducted by est.imating ane slllii!ning the load characteristics of each individual (~nd use.The r-,(,tf:ntial for load management and the effects of till:c-of-day pricing .should be considered in the research.However,a·t the present time-,we do not believe that it \~ould be \·mrU\\·,hile to develop an integrated energyjdci:;and forecast. 5.8 Additional Stage Scenario ~~ ~~iJOl..L At present,all three Stage I scenarios developed by ISER assume a steadily increasing level of State ~conomic act iv ity. h0\';eve r,the po s sib i 1 i t Y 0 f J Si9n i fie d d slowdown in reSOUl-ce sector activity dUI'illg the 1930's hCls been conside:'cd by a number of individuals,resu1ting f)"om natural gas alld oil depOSits.'~i'./en the I-eal puss,iLiii,-j and significant consequences of such a scenario,we believe that it would be worthwhile to model this possibility in the same fashion as ISER has modelled the three m~jor scenarios to date. 5.9 Independent Expert Advice on the Load Forecast It has been argued that an appropriate way to review and evaluate the ISER model results would be to draw together a group of individuals familiar with State economic and energy affairs.This group would evaluate the likelihood and feasibiiity of the model's assum~tions,from \';hic!1 [;~ull:::- I"'"' t t r t r i t r r r t -I ! ~ t L ' - -r. _..-....-.::.?!'i~, appreciation of the range of possit)le electrical futu)'(:s could be obtained. '\~e believe that such an excl'cise might !WOVc fnJitful for two reasons.First1y,such a gl'oup llIi~lht <Jchieve a consensus with respect to probable electrical futures (or, failing consensus,might better understand the assumptions about which the group cannot agree).Secondly,the logic behind the ISER method could be spread over a wider range of parties,resulting in a deeper appreciation of the factors affecting electricity growth and the ra1e of State policy in these areas. He should qua1ify -:::he above,hO\-Iever,by stating that be seen not as a substitute for,but ratJ1er as a cOlilplement ;:0,continued ene1'91 policy l"esear"ch in the StaLe. 1_introduction The vie\>/s expressed herein are those of the autho)'s,and not necessarily The House Power Alternatives Study Co~nittee. Electricity demand forecasting,like all quantitative forecasting, is an effort to view the past and present in a systematic way with a view towards making reasonable statements about the future. The basic problem is that the future is not known,and indeed can- not be known.even in a probabilistic sense.{IS a matter of fact,pretending to forecast the ;;uture is an indictable offence under the iiew York State Criminal Code (1),Similar provisions, we are certain.are in effect elsewhere. However,analysts often find it necessary to fly in the face of strict legality when the viability of a large project hinges on ]SEI~:LEC'rR1CITY .~.-,\?RELlH1NARY EVALUflT10N OF TilE Preface January 2,1980 (c:uiiendedfor il\clusion) In October 1979.Energy Pr'obe \-lilS il~,ked by ike:lic!U~;e rel\·/or :\l~er nativ'es Study COlllmittee (HPASC)of The Alaska Stute Legislature to submit a proposal for c study thet would eVilluate the elect- ricity d~nand forecasting method developed by The University of Alaska's Institute of Socibl and Economic Research (ISER). This report presents an initial evc.luation of the ISER fOl-eCilsti:lg model and the Man in the Arctic P.:AP)model or,I-ihich,in pill-t, the electricity demand forecast is based. The'present report dral','s on information contained l'IiUlin the Detailed W:)rK Plan submitted NovCirbcr 14,1979,by 0:-,Scott Goldsmith of ISER;Hay 1979 r·jAP model COCUfi1Clltcll ion;V,II'10U$ pl"blications relevant to the future social and lCconull1ic activi:'y in the State of Alaska;and personal discussions I-lith ISER personnel. A further report will deal with the sensitivity of electricity growth in the Railbelt region of Alaska to policy and market induced chRnges in tl:e soci"l,eC0nn';1ic 2nd r,h'!c,iu.l f"ct'll-o; v/hich influence electricity gr{H·jth:ZInc \-:i""l.l)t~n '::~;~I~\::~.~~ c~pr~Gpf'iate tole of electricit,Y dCL'~dnc --0l~I~c..;)St.:·.;-.'-:LiJil'i...::........,,'l;~~1 context of State enenJy policy development. Cecause this report is a working dccument intended oniy for use ~;..:;P.~\SC Ilif:mbers t~nd cu:~sultant~"11.is ~':ri!~Lcn iIi rC::l[~-;:" tect,nical language.Our final repol-t I"ill detail the three areas mentioned above in less technical tenns. WORKING PAPER #1: DUiAND FORE CAST APPENDIX 2.Staqe II Modelling Approaches Attempts to deal seriously with this complexity became nece~sary in the eel"'y ~,'«1rs of the }9:Jt~,\".':~Cli ~~i~,tDr~i'::(';l l"Zlte:S CJ:£:jc-~- approach l'ioulG ill'gue that the dl'lIldllu {Ol'ilCJusili9 is lbilly c: composite cemi1nd for the services offered hy?stl"llctun~.lll,'I'gy and transportation are similar.Rarely ~r~they required for thei!"own sake:in t-eality they are crucial inputs into a number of satisfaction-yielding activities. In electricity deilland forecasting it was once possible to do a reasonable job of prediction by looking at a historical growth rate and simply plotting future levels of consumption against time,P,.draftsman with a French curve (or an engin-eer vlith semi- log paper)could make a reasonable guess at future demand by simple curve fitting and extrapolation.However,it is logically clear that the growth in electricity demand has little to do with the passa~..e of time ~g.Rather,it is related to individual decisions to engage in a growing number of electricity-using activities over time. i;u..JS~J'9J fCl~:""A~tilp;e,Li'li=luLt~I'~il...l..u~l~~~J'-=:"~'.L ',..-i.Ujii~JtL;!;U\..~JL budgets.pi-ices and so on_;1(\\'1("\'[>1-.",1\.!o!linCl nffF;I'~cf:,rvirp far beyond simple she1ter;amenity,pro>;imity ilnd ofiJJortunities for ~0(inl i::tC'I-:1(~·i(ir.r.r'r rl:t (-:fr ..;..t;~r:.....-r.!~r·'i(·(·.~}1r.-f(\',~"~r;' the need for it in the future,Hence,forecasting has hC:'come dr\ integral part of planning for investlllents in ener-gy,transportaticn, housing,and a myriad of other functional service delivery areas. Forecasting the demand for such services comprises a two stage process.In the first stage,agg:C'lj<lte social and economic activity is pl-ojected into tile future (using,for cxaillple,the ISER HAP model);the second stage trilnsliltf?S this 'ag~J1-egate activity into a detailed forecast of the dell1and for the product or service in·question. Stage I models tend to be rather ubiCjuitOllS,fincJ~n9 i1ppl iciltion in a number of functional at-eas,t'IAr,fat-eXi\!l1ple,has been ,-used in a variety of forecasting envir-on!Jlents including energy impact anal~vsis and fiscal forecasting.On the other hand,Stage II models are generally specialized and tailored to the problem at hand.In transportation planning,they are classified under the general heading of travel demand models.In energy demand forecasting,a number of different approaches have been developed, which have met with varying degrees of success.To the extent that a debate over appropriate forecasting ~ethod~exists,it is real1ya debate over the choice of a Stage II approach.In fact as we argue below,the choice of a Stage II approach essentially dictates the output and hence the stnJcture of the Stage I model to be used. The argument over Stage II models centers on the extent to \1hich the model should deal \'Iith two distinct but equally impodant aspects of the pl"obl em.Given an aggl-egate fOI-ecast from Stage I,should a Stage 11 model focus on the specific _~_c_tJ.v.i.!L ;r:\'(!l"'ed or sh0:~ld it.focus (p"tr'~C".-1r·,-~-i:,,~,,-:~~,:,.~'rl~·"-"~i'_~(" r-'S ~-~I i i-.! ~~~~ ~ ~"'t.-""'" ~r-~ @'I~ I"""~ II- t":":g.,-0 I (bo !f~-D I ~ i:~1 ~.... f I l t.:~,"It!-\ ...r tj l"""-~~.'--' [-il r ~ 1 ~ .-, -~I ~,< ......~ t,-icity growth ceased to be realized by'lilOSt rlf:'ctricill util~ties in North America,The formation of OPEC and the 1973 ~\rab oil embargo,with its subsequent incrri1ses in ilctrolcum prices,ended the era of cheap energy;and all fuels,including electricity, rose in price rather dl-u[Jliltically.Unfo,-tlJJl,ltely,the cconOinetric demand forecasting lIlodels in use at ttllS time (2)l'lcr,e incapable of dealing with such rapid changes and continued to ~oint to historic or near-historic rates of electricity growth.ISER's 1975 e1ectricity demand forecast for the State of Alaska (with which.we might add,lSER itsclf l';o1S not cOll1fol"tatlc)is ,:case in point.The most tell ing uiticism of its strict til:le-series econometric appJ-oach is that poten:ially ludiCl-ous E.ct_iyi!l forecasts result.In ISER's 1975 effort,for example,initial results indicated a demand for electricity vlnich implied 100% saturation of electric space heating in Fairbanks in the future, The point to be made here is that because individuill activity levels are not explicitly identified in aggr"egate economic models,such models run the risk of implying physically unrealistic activity 1 eve 1 s . End use forecasting models in their pur"e form take the opposite approach by relying almost exclusively on l\ctivjtie~,imicp:::ndent of the underlying economic conditions.The logic is silllple: consuming units engage in various activities requiring energy, Energy growth can result from (a)engaging in <Jdditional cncl-gy COllsLlliing activities; (b)engaging in the same activities more intensively; ,".- i.L ...-""-~...,I", ,~"!.I I The case of ol'a 1 :lv'1iene r,l"ovic1eS a humo:-r;us ('\:?m~le,1\household may s\~itch f1-orn "manual"to electric tootht.lI"ushing,an additional ener9'-'\lsinS ?cti~.ity Gi'/~n ;';1"'el;'):t,~ir +nr'~~!'I~L·~.h.rr"d··,c··~,rl~ tilE:ilOWSE:hoid iiidy \.,ish :'0 U(USii 11i0"",l'\:::Juiol-',);~'il:","11 LI,<:'-(.;ULil- brush \·:ears out it "lily be rerlaced \\'ith <1 morlrl \'.'hich dchven fewer brush strokes per unit of energy input.In any of these cases,electr"icity lise inueJses.in prillCi)Jle,it is possible to examine al1 electricity use in this manner,noting that ill1 energy is used in a final form such as heat,light,motion or sound.and that it is transformed from its input form to its final end use form by a "device". Again,in principle,electricity demand can be projected by fore- casting the characteristics of devices and activities.This has become known as the engineering or end use approach to demand forecasting.The most telling criticism of this method in its pure form is that it is not sensitive to changes in prices,incomes and preferences,i.e.the decision aspect of the process modelled in Stage II.This is a generally accurate criticism whose resol- ution requires an examination of policies affecting the decisions of the i nd iv i dual consumi ng unit.In further work for HPASC we will be discussing this problem. r r~. r L ..- I., r"".i . l ..- \ r L r i L._~ r !"'"' i I t.. - r For functional forecas·.ing purposes,an approach is emerg~ng \~hich seeks to overcome t~e inherent difficulties of both extremes of Stage II lllodell ing methods.The econometric-end usc approach (EEU)attempts to deal with electricity use at the le':el of the activity while recogniZing that the .ci.e.c_i~,~~to own and operate a d'evice,i.e.to engage in an "ctivity at some level of intensity, is inherently a problem of microeconomic choice and is therefore sensitive to priCes,incomes and the availability of lllternatives (3). In o~__E.Pj_~..L~!...,9,llj..~_,~J)P~"o~j ..~h,~_§__t ..he"on~y scn,Si ..bJ_e,,_\':'<'t.Y .t,0l..0_r:e,c a,s.,!... e 1 ec tr i c itt Oem"C',~_?_nj~iu_~..\..iJy"_a_h_u_9_e,S,xj1,~,I.2..d_i...~_r_~,gJ ,p,u,bl_i~f~~g,~. We are pleased that lSER agrees ~J2.r...in~2pJ~with th's general philosophy.The detailed wor.k schedule circulated by JSER lays out a rather impressive work plan.We anticipate problems arising because of the extensive data requirements of EEV,which w~l1 be intensified by the basic data problems of Alaska:short time series and srnal1 population.However,vie fully support ISER's desire to cast the net widely at first.while recognizing that data,and more important1y timE ilnd financial constraints ".,ill require the net to be drawn in somelvha t. f\t this P0111t 1'1(:I','ould like to cOlllii:cnl un l!IC dlllJcat.ion of !-CSOUI-C(;:" for independent demand forecasting relativc to the Il'agnitude of potential capital investments.Given the l1Iilgnitude of the stakes for II pl"oject such as Susitna,i.e.a potcnti,)l investment of bil1ions of dollars,Ive feel that far too littlel\oney 1S being spent on this crucial element of project feasibility.lSER ...lil1 likely CliQUe.i1nri .':llstifii1hly SO.that fin.tel is sil11l'lv not ~v(lilahlC' ~~",~C~j!~':..~u '.\.)l>'.:j..I;,.;.,::;-~;',d;~.n ~.,',("':1~·j~.. .~,..-~l~:;,:~::.~~.~C \::j'.~';.l~.iC;:~:::IL'i":::I.'~:·::":....'~...i:_" adcit.ional resources made available.it could be gather'2G and ·lilCUI'f.il.;(aLeG il,to 1.lii:'forecast :;looe1,'-C::SU'jlilig ill a lUI"eCdsL method Ivith l'ihiCn 211 could he re?son,'bly C(1;!1fortahle. in [he TollOl·!)llg pC':Jes vie wii1 I'ellie'v!tne [EU apPI"()Jch to Stuge 11 .:nc t.he requii"e;;H:i;:s of a Stllge I 'ilJdcl to pI'ovid€:,"equisi'ce inputs into EEU.Our goal is twofol~;first to aralyze ilnd suggest ap~l:-oaches to particular probiems for the benefit of ISCR,and secondly to layout the logic of lSER's forecasting proposal for the benefit of all consultants involved in HPASC studies.It is our hope that this will facilitate discussion and understanding of ISER's methods and in the longer term,identify avenues for potential policy intervention. ~.The Econometric-End Use Approach EEU begins with the simple proposition that all energy is used in capital items or devices,which pei"form a specific task,i.e.an end use.Each device,by virtue of its design,has (l specific energy input requirement for each unit of useful output,a conc€pt similar to "First Law ~fficiency".Devices are owned or rented and operated by consuming units.However,not all consuming units own all types of devices,nor do devices operate at all times. .oJhere (2) ( 1) M ~(N.x PO ..x PE ..X 1 1 • J .x R ..-S.)j=l 1 1J lJ 1J 1i;:;1 Thi sis an account i og frcl!'r:",:ork fOI-tiT.i 1 ity ('(':':::no (!l).0 operiltionalize it for forecasting ptil-pC):,cs,(1,1eh of th(~COliiPOi1cnts 1l1Ust be ["elated 7.0 krlO\'/fl of "knOI'/ilole"val-iuble~.Lrigineci-ing knowledge and economic theory suggest potential relationships. Econometric or other tecllniques "are used to estiil1ate their direction and strength. For operational purposes it is necessary to group consuming units into classes on the basis of predominant activity within the unit (i.e.residential,commercial,etc.),similarity in patterns of device ownership or energy requirenents.or some other appropriate criterion.Clearly,there are losses in precision due to this sort of aggregation.After grouping consuming units into classes,the demand for utility electricity is obtained by the following expression: TUD =l CUD.=l. 1 i=l St.=amOUr1t of self supplied elccL)'icity uy (OI,:.lJ;!lin~i uniL L (i;\·i.h) N ;:;total number of consuming units H n~.t·"~~f:r 0.((~i~.tin('"t r("'\'iC"['( There are,of course,many consuming units and li1ilny devices.We may translate from the device level at the consuming unit by simply summing over-devices and consuming units yielding the foilowing exptession for utility electric dl~mand over a period of one year: total utility demand (kW.h) ;:;1 (if consuming unit k has device j) o (if otherwise) ;:;1 (if device j is pOl'/ered by elcctl-icity ill consuiliing unit k) o (if otherwise) i:ltensity of use of device j by cons\!lilinC]unit k (hOU1"S) LJ Further,many devices may be po'tieced by mOil::'..hun one fuel.The decisions to Ol·m or 1 ease and opel-ate c device i)re economic decisions maGe by the consuming unit in 1 isht of p:-ices,incolnes. preferences and avai1able options.For a gh'en period,say a year,the total energy required by a conSLiini :19 unit to iJov:cr a given d.evice is by definition its hours of operation times its power requirement.If the device is electrici.llly powere<i,this energy demand will CDl1tritlllte to iln eleclricity ('{'lIlilnd (:~Lilllilt.C. Any portion of the electric pOloJer curl:,liillf'C!Ill'tll('('lullumic lillie wilictl it 'Jenerates itsclf,docs nut cunl.lih\JLc La lhis utility foreca s t. -. .,r r r r l r r l l r" \ .'l r l ""'"l r !'I l r f t. r , l'ihere CUD i :::the demand for e1 ectrici ty by class i (fJlh) N.:::the number of con$lJl11i ng units in class i 1 PO •.:::the PI"O pon ion o·f closs i c ()II SUlil (;1':.O\·m i ng d cv ice jlJ PE ..:::the pro POI"t i on of d cv icc j 1 n class i t IIi!t il.r e. 1 J e 1 ec t ric a 1 I Y pOl-lcred 1..:::the average intensity of use of device J lJy I1\Clllbcr 5 oflJclassi(hours) R•.:::the av C1'a ge pOI'lel'requirement of d cv ice j Ol-In cd by " 1 J members of c 1ass i (kIn .S.:::the amount of e1 ectticity self supplied by c I a ss i 1 members (kWh) Q :::the number of cor;suming classes The advantage of examining end use demand in this lllanner is obvious.Not only is it less data intensive than Equation (1), but also,key parameters become easier to pinpoint.For eXilrnple, in an analysis of a subclass comprised of mobile homes built before 1970,space heating requirements \'Iould be rether similar. Time,of course,is olso a Cl"ucial consid<2riltion "Ibidl must enter the model in a fOt'ecasting environment.The ildvantage of an end use model is that the factors developed ubove exhaust the realm of demand factors,and each 1"lill change over time.As time passes,classes of consuming units grol'l or decline,devices become more or less prevalent and more or less "elcctrical", self-stlDrl iect e1 ect:-icit'.·r~~,'\"~(,('('."'.-.,nf'!"",:·;rj,.,l \'l!~f'rl.r!C".:i(c·~ mc:.\··t"'e uS'2d iil0r-e or 10~,:.iTr~~~";~'"."j •••,.,;\(t·~;jci['~·<:'~':- \,."ij 1 ull~uuLtedlJI cl1un9:':~::;t.i~L~:...:.....rd.'.;".~.;i...l·~i ;".;'-J';.:.\''''~ since many devices l'Iill be reDlucd avel't{H'fCI1'('Cilst pprioc! and those "Ihich are not i:1ay be "1"etrofitl.eu'lCI iliq.n-ovc:tileir ,","rfn r m2 nc f'. While the passage of time is itself not the reason for change, the argument above suggestS that.it may pl"OVe fruitful to vie\'1 demand gro\'lth in a teil1j1ol"al sense.At a point in time l'le begin with a "stock"of consuming units equipped I-lith devices. Over the ensuing jear the consuming unit may disappear,change or modify its col:ection of devices or means of powering them. ]n addition,new consuming units may be formed complete with new devices.Presumably these new devices would have energy consumption characteristics different from "old"devices.At the end of the year we witness a revised stock of existing consuming units and devices comprised of the previous year's units plus net increases.This may be taken a year at a time over the entire forecast period yielding electricity require- ments for specific annual points and annual incrsnents in demand. 4.The ISER Model and Sugqested Ap.2roaches and ReviY.i..ons ]n the context of the Railbelt region,EEU makes a great deal of s e r.s e fa r the res i den t i a I and C omm er cia I S I?C to i S whi c h,t a ken together,account for about 86~~:of Alaska':;totel el ectric ity demand.Because indust:-ial developnent in i'llilska is largely of the major project variety,it is best to ~xamine these in a case by case manner.Further,\,ith the excortion of block heating in vehicles,the transportation sector currentiy uses an i.nsignificant amount of electricity.Again,this is best viewed as a special case.,. ISER's EEU model,Figure 1 in their "Detailc>d \~ork Phn", incorporates'most of the features of an ideal [EU discussed above.It is a stock/flow model v1hich $egre(ji.Jtes consuming units into "new"and "old"and deals with ~our residential subclasses,and segregates devices into six categories including an "other fi category for minor·appliances. The commercial sector should be divided into at least the following groups: {a)publ ie/institutional buildings; (b)large shopping plazas/office buildings (say larger than 100,000 or 250,000 square feet); (c)other commercial buildings. This \'lOuld be fl"uitful for two reasons;\'iiUJir eilch ~Jl"0up thl:r'e are sirlOi1ar requirements for electricity;and ro1 icies/progl"illlls may be specifically tailored,at a la:et date,to this partic- l.ilar pattern of consumption and occupancy/owr.ership. ;'';ssing in lSER's proposed model is a tel"m to account for el ectric i ty or energy suppl ied by the consuming unit and hence not required from a central system.This should be added to ,,.,,.... "_;.c..'1,\......~i2:i i2\l:":"i ~~:'._"~',~...il:2y n~,l oj:..t:'::t Jy (,i;!._'~.,1.::;~.(;:;.,,._, :..'i~-ti 112 I j~ort,,::_~~..~..:Jtr~~~)t.q'of ~~ens ider.::~_~C"i:..C,~-l'l:n~_i;,~ inclusion,not the least of which is the possibility of cD-gc-ntrotioll G7 r21~,,-:'l"icit..y and steatn for S~JLL'lleati!ls.i ~:;~iJj 9(;- cammet"cial establi~.hi1ien-::s,schools,hospitals and the like. The present 15th i()l'liJulation allows for the scrofj;Jing of (jl"illli119 units but not fOJ-the replcccment of appliullces '.:ithin existing units.II mIf;lbcl-of appliances lSER intends to cOTls,icJer have useful lives of substantially fewer years than either the forecast period or the structure.In lSER's model,this pl-oblem could be ~olYed by adjusting the average consumption of appliances en an an"ual basis.Jt is better,however,not to confound the efficiency measure with the effect of new appliance stocks, Given these structUl-al refinenents which we consider necessary, the ISER approach to residential and commercial electricity demand forecasting is methodologically sound.Since residential and commercial consumption in the Railbelt is quite important, i~is necessary to examine the components of the EEU model and to suggest possible approaches to modelling each component.In this case we refer initially to our formulation of EEU above, and explicitly to these elements pertaining to Stage 1l. In Equation (2),total utility demand was expressed as the sum of class demands.Class demand is a fur.ction of the number of """i, r I""" i, I"'" It rI r l r l rl r r r l units in the class,the proportion owning various devices,the proportion of these devices powered by electricity,the average intensity of each device's use,the average pOvler requirements of the various devices and the amount of self supplied electricity. The number of consuming units in each class is essentially a modified form of the output of State I which we discuss below. The I-enaining facto'"s are,however,Stage II concerns whjch \"ie deal with in turn. PDij,the proportion of class i units ol...niog device j,is obviously a variable whose value lies between a and 1.For certain end uses,i.e.space heating,its value equals unity 2nd will ~ontinue to do so over the forecast period.In other cases like clothes drying and refrigetdtion,its value is a matter of choice,and while perhaps initially close to unity,it is variable over the forecast period.In ari ideal world we would hope to estimate this proportion on the basis of income level una distribution within the Rail belt region,bear"ing in Illind that the decision to own a device also commits the Q',mer to operating expenses over its lifetime.Hence the general price level of all competing fuels may be-important. PEij,the PI"opol"tion of device j owned by class i \~hich are electrically po\·'e1-ed is also a variable I·,hose value I"i:lnges from o to 1.Again,for cel-tain end uses,especially refrigeration, its val ue i sCI 0 set0 u nit y a nd wi 11 1 ike 1y r em a ins 0 0 v e r the forecast period.Ho\~ever,a great deal of choice exists in this area ..n..useful \~ay to look at this pr-oblem has been proposed by Fuss who suggests the decision to engage in an activity with a specific fuel is essential1,Y sepal-able.In other 1·lords.~iven a ,..'. pI-ices. The question of the treatment of conservation arises in this .;r'~~z::~~~~~:f C ("I I~~0~'"'.I ,-•-i r :~!i ':"~::~t ("~~,'"~;-:t 0 2 v C'~-·l ~}P ('.n r--!'"'C1 y ,'CC;U;rE:iilents,then 110 ::'UI'e ni::E:U ut:stlio,;iol·lev21',if I'i<::viE;I·1 each or cny device ?os h2\'inS a "b~se-linc"ellen])'rC'quirc!!1cnt, then any effort to reduce it involves lin explicit tradeoff of electl'icity for conservation.In this sense,conscrviltion is self-supply,and has an average supply price equal to the amortized annual .cost of the conservation project divided by the number of kilowatt-hours displaced during a year.Marginal costs maybe calculated by assuming,ideally,various levels of conser- vation and calculating,presumably,a step function for the fuel equival ent value of various conservation schemes.The same logic may be applied to renewable energy projects as well. We feel it is useful to view conservation and reneylables in this way when considering existing activities at.a point in time.The major point is that given an existing activity,li·ke space and water heating (the major ones)the consuming unit can choose not only to switch from one conventional fuel to another but can also choose to supply a portion of its requirenents with conservation. In an oil heated home,for example,the household may switch to gas,electricity,or conservation for all or part of its heating ...--------,,~",---- I~-9 5.Stage I Approaches __,,[1 ij,,,, I l .'~I,•.-."J,.,•: -~I _ Considering conservation ,IS an modification of ii~:erfuel energy requirements for nevI considerations,one of which is ~;'I '.-'l·,..:~L ~~S II •SC 1"U ;~:'i :";~;()f t~"~""';:" deterioration but also economic i.rle device's flii;~requlrL,:,2i,i.". should increase with decreasing We not turn to the merits of the f~AP model of the Alaskiln economy as a Stage 1 mode1 for EEV forecasting.Regional economic forecasting can take a number of forms.Some approaches being on the basis of relative pricES. explicit fuel represents a u:.eful substitution analysis. Rij,the average rO\'ler requirement of device j in class i,becoliles ba sic a 11 y an e 119 inc e r i n9 des i 9n piP'aI net e r \';hen con s e r vat ion i s treated as a fuel,Consequently,it is a functioa mainly of vi-ntage.not confounded by!'etrofit,One item that should be examined is the trend in device efficiencies over time.This may i~ell be an -appropriate area for regulation. Iij,the average intensity of use of device j by class i members is also a consumer choice variable although to a lill1ited extent ~n the major consumption categories.Acliuns like reducing inside temperatures and the like are evidcr,ce of the economiz.ing behaviour of households und~r this category;how much further we can go in this area is certainly questionable.In this case. comfort and convenience bound choice from be1o\'1.To the extent that there is flexibi1ity it is likely price and income rr:lated. The final term in our formulation is Si,the amcunt of self- s~PDlied electricity by members of class i.In this instance we suggest that this term be ke~t pure in the sense that conser- vation not be \'ie\-Ied as self supply in this Urn!.l·ie include Si in the model for the :-easons s'liltr:d above:.Ther-e is iJ [,(ice at \'ihich self genet'ation or co-gene:-atian becoilles attractive ",hether by means of I·,ater po\~er,wind or convent ionol fuel _The model should be sensitive to this possibility. ihe above relates to our formulation and also to lSER's ;IJodel. ihe remaining terillS in lSER's model :"elate to nel'i hnusc1lold [:,~J21s of c p~r~~l:..:1al ',:j~·..IL:!....\[1~:'i ..·~_·.,t:;~'.)~(..]i:l:::i~""\.:;~·~iL.Ll~ to measure and project over time;Ilovlever,it is scmethlng to be ;<ept in mind. Generally speaking,\'1e arc illlp,"esscd i"ith lSER's proposed ii\ethod for handling the Stage 11 modelling of the residential and commercial sectors.With the modifications suggested above we can wholeheartedly endorse ISER's approach and we look for~ard to working with ISER on further questions of approach and sensitivity analysis.With respect to the ISER approach to non-residential and commercial use of electricity,we reserve judgement since the method has not yet been developed,We will, of course,comment at an appropriate time and we are confident that ISER will take a sound approach,based on their work to date. 5 U c h coSt 11 e U S 8 LJ i \..~L ~i 1i i (,:J ~(..;)~;i j l.i t..'j I ~.:..L:U I i U ,-.~J f i'U j (I U l..1j l:, studies -ilrp il1"n:>l-Clpri"te fClr ;j'n 1!1l!i.<1l?:1 r':?tr r'cnnmn':c;lIrh 2S that of Al aska. is useful \"hen the regional ecollolllY pivots on cleMly defined basic industries -iJt'e all-eady cont.{lined lilUlin tile f,j!;P iliOCl'·i. The simple econo:nic DJSe methods i!'-C too C:1CIIIC'I1Lwy;:5[[(is \"e11 beyond them already in its 1-lOrk.The SJllle uiticism holds for purely extrapolative methods.Just as ruler and graph paper are inappropriate for load forecasting.they are too simplistic for the economic part of econometric-enc use analysis. Curtis C.Harris developed a regional forecasting model at the detailed industry level based on short "ime series changes in output by industry and state and incorporating transportation costs estimated by optimization techniques.Alaska clearly is not likely to exhibit consistent locationa'cost patterns of industrial development necessary to take Harris'approach. Delphi,a technological and political forecasting teChnique developed first at the Rand Corporation is unlikely to yield the moderately detailed consuming unit forecasts needed here.However, it may always be considered for developing scenarios for energy projects,general economic growth levels,or energy policy 'I··:.:..··;.·.·.. ...";. ~; I~t.:.-~~' ",i' _.'."\.~"''.,:1 L 01"~..."J I,;.i !t f U f't !1:::...5 h,~~r't r;.':::.-::'::t f 'li ~~:~f<::.-,~;:-:]i ~,j •'.:';-t ~.~;-. considered in the "Detailed I'lork PlJn"ore input-output analysis, the economic bas(~appl-oach,CUt-tis iiar-ris'lociltione::lly efficient model.and the Delphi technique.These all have strJng and "'Ieak points but none is a sct-ious contendor to a moderately detailed econ am et ric 111 0 del 1 ike !,~AP. \~hat is required of the Stage J model?It must provid.e the number of consuming units in each class for the end use equation.That is,in the number of housing units of several types and the numher of firms,employpcs,square footage 0'-lJllS1neSS volume for cOlllll:e;-cial and in~titutional units.It Illust he ~,(cnsilive Lo the scenarios of fast,likely ond slow grC"'lth IIlCIll.iul1Cd in the "Oe.tai1ed I~ork Plan".Jt must resflond to chCinges in oil dnd ')as pricing,energy and other major investment projects,na~ional economic trends,and demographic realities including migration. While the current MAP model incorporates most of the latter functions,the restriction of dOllographic pt"ojections to persons (not households or families),the introduction of housing only through the dollar volume of construction,and the lack of other physical measures of economic activity closely related to the number and type of consuming units are major deficiencies.As noted in the "Detailed \~ork Plan",data-mus-t be gatherec and incorporated into new versions of MAP. What regional techniques must be added?In O,lI-opinion,none of the above mentioned techniques merit much effort. Input-output analysis is appcopriate \'Ihen a region has a large industrial base which relies to a great extent on inter-industry sales.Alaska does not have such an economy yet,ilnd the method's - - A-ll decisions.Hence it is nct a Stage I :lludel but a source:of exogenous and policy vat"iable value"lV"Jny forecasting method. Among general methods for forecasting I-csional econolilic activity, one not yet mentioned is shift-share analysis.This method is based on statislical.esti11lation of the contribution to a state's industrial gro\~th of industry fact.ors nnd I-egional factors.1t is an excellent basic method which is sufficiently incorporated in a MAP-style econome~ric approach.\·lhil e both input-output and shift-share methods are usually performed with il great deal of industry detail,such detail is not necded in our Stage I apprOach. Hhat is needed is mOre detail aimed at huusehold characteristics and building stock characteristics.Wl:iie<:(ata sourc end points for households are well known and trusted,a region such ~s Alaska can have rapid and crucial post-Censal fluctuations in households and household size.As for buildings,only dwelling units are enumerated in ~he Census.Building stock estimates for non-residential units are I-are above the city level (6). Land use surveys and Civi1 Defense surveys give spotty data sets, but the building stock is basically an unknown quan~ity for regions such as states.For the current reseal"ch,incn:Jsed in~ormation on the bui1ding stock is import.ant. As an expedil2nt is is suggested thQt ::pu:,ii,g be louked at in detail (so as to aliO\~better end use fon:casts for s[Joce and water heating,lighting and appliance loads);that largE coml1lerci~l and institutional uses be,examined through enumeration of structures;and that the rest be treated by the use of ernplO}1l1ent or sales estimates. :i,~~rQ~C !';es: (a)macrocconOfilic e::::onometric liiodcds such dS I-if,P; .(0)mi'-I"ut::~UI!0;lliL.~ii.jU~~J:.iu;j:'"u;~'-l...lil~\I'I.j~j'::'-'Jt;~jL ....;.•.<j:~~i..:~ to chilnges in price,income i1nd the aVililability of The former is npcessary ;:0 intt'ocluce Ilational illl9 mojor regional trends.lhe latler is used to discovcl-'.:iLll.the c..iistl'ilJutionai effects of :ie',1 p)'icing and suprly levels ,,:ill bc. A study commi ssioned by a numbe!"of iie\~York consumer groups and carried out at Cornell University was used in testimony before the New York State Energy Master Plan Meetings in SeptBnber 1979 (7).In this approach,Green,t~ount and Saltzman utilized a four-sector economic/demographic state econometric model with a part ia lly integrated energy sub-model.The four sectors were residential,indus~rial,commercial and transportation,All major energy types -electricity,oil,gas and coal -were forecasted simultaneously.This Cornell model as well as another model developed with end use detail by The New York State Energy Office,predicted significantly lower electricity requir6uents than has previous state plans.It should be noted that \·/hile the Cornell model is not extremely campl icated (57 economic equations, 150 demographic equations)it contains .b0usehold formation functions for each age-sex cohort.Unfortunately,the Cornell model does not give expl icit place in its structure to self-supply wood space he~ting or conservation. •J 1 - -i, r -b 11-12 Furthermore.in the Cornell approach,a microeconornic simuiation was linked to the macro model in Ot-dcr to r-elate income and price changes and restrictions on fuel supply to consumer dE:~nand for the d if fer e n t f u e1s (8).This,0 f co ur s e,,-C'qu ire san ext ens i ve data base of individual households studied by survey I-esearch methods.In this case a sample of 7000 households \';dS utilized. While suchmicrosimulation may be beyond current poss{bilities in evaluating Susitna (and lye are not convinced that such [ul-the, study should be c6nsidered extrilvagent)it suggests again the need to make the energy forecilsting vCI-sicHl of t·lAP lIlon::orient.ed to consuming units.llOus~tlOl_d2.,dlld tile \)i~E;r':;t devices of all, bu i 1d i n..9.2,• Looking in more detail at MAP,based on the may 31 1979 docuiiient- ation,we note that it has more than enough economic detail,but not enough demographic infonnation because of households not appearing explicitly.Finally,a housing anrl/or buildings component is lacking;this is a critical shortcoming. In the "Detailed \~ork Plan",we support most strongly items A7 -9 . on,electricity consumption;Item 10 on households.houses and appliances.These are more important,in au!"estimation,than the refinement of the ~IAP economic modelf!.c.c s~'They should receive top priority. Regional disaggregation (Task B)is impol-tant,but less so than getting on to EEU forecasting for the Railbelt region as a whole.Thus the items in "0"are crucial -interfuel substitution ~the addition of conservation. tile liiiliL.ed len91.1\of tiie ;\lasi-a Ji.J1..a SI.::I:,:::"Li,,-,1'<::.uL.i;:j equations are adeoli?,te hy cClnventi0nal ste1tistical benchmarks, at least for forecasting use.The detailed fiscal and native/ nnn-l1tltivr/rnilit,-.!..y r·~~,t:lt~,.nr n or--r"1 (..-.,)..(,..~\..!i('r ~~rli(·flt~0n~,are wen developed,but i:iay not be partici.Alul'j,Y 11cipiu·l in ti'l:;cutrell;: application. \~hat is needed,mOI'e than any othet-:nodificr,tion,is a housing stJb-model.Whethe,"the data can be guthcred fo!"such an addition remains to be seen.lacking a formal housing model,some inter- mediate step is required based on the housing stock data from the decennial censuses.A brief outline of each alternative is in order. A fu11-blown econometric sub model _for hous i n9 woul d f1 ow from the fo11owing modifications to MAP: (a)inclusion of household formation equations in the demographic sub-model; (b)a set of equations for the housing stock (or alternatively changes to that stock)by age and type of unit. Some of the crucial right hand variables would be from the construction and investment functions of the economic model as well as the household formation results. :0-.~. ....._-----,.----_.,_._-------- A-I3 If the time series data are lacking for the housinQ modifications to MAP,tnen the avai1Jble cC'n"us benCll1ilJd:s -nUil1l!L:r of d\·I(~lliflc units by age and type -should be combined \/ith J'C!cent data on . housing starts,mobile home sales,bui1ding nel-mits,etc.,to update the distribution of the housing stock.This results in the following structure: Stage J,..-A _Stage 1I ,--........,A......_ ~lAP -----;)=___ec 0 nom i c d erno gr apMy )-- including households \'----->Olo-rna teh of le----...J>-.....households to hov ses housing stock estimate of data )future stock, bu i1 ding da ta 5.Conclusions Er.ergy demand fOl"ec:asting,the most c)"ucial el cmcnt of energy , -'.~~.::.....-~ procedures,the analyst is fClceo "'ritn Lr'f;: than has traditionally been the case . i I eCG .~i_ t G ci t:v 1.::j (1 P 'oJ i\Cl t .1 r _.,_.'.r ~r- .pUl~e ec.:O!\Uiilt2L:iL:t.ill~jJur"i;:;'Llaj d~l:1 u!l.':"'{;~~',....._'1..1 Ii L'•11.__'I,-Jl..l\-I'':'~, heT'\ce,a blended arrroach combining the:he,.t elE'iTiC'nts of each is necessary.ThiS blended EEU ~pproach is difficult Lecause of its data l"equin::li1ents and because lnodifici'Jtiolis lilliSt be:mace to the structure of the underlying econol1letr"ic and end use rnode1s on which it is based. In the long run,an EEU forecasting system for Alaska can be developed with MAP,suitably modified,at its heart.lts data requirements are not yet attai nabl e ina sma 11 region such as Alaska with a short data history.Therefore,in the short term, ad hoc forecasting must be carried out with the outputs of the current version of MAP.These outputs must be obtained by using a very wide range of input scenarios. The most crucial shortcoming of the current version of MAP is the lack of a housing sector and this must be bridged by some reason- able.if imperfect method of estimating Alaskan housing stock and characteristics in recent years. .,,~ ~, "'"'", - """ """I ;\... 7.Footnotes 1.Joan Robinson,"What are the Questions?",Jour'nal of Economic Literature 15,December 1977,p.1322.------------------------- 2.These are extremely expensive and sophisticated versions of semi-log paper.See Hennon iDaly,"Energy Demand Forecasting: Pr ed i c t ion ar P1 ann i ng ?",J 0 urn a 1 0 f The Alil e ric an'Ins tit uteofPlanners,Ju nuary 1976.--------_..-------------.------ 3.Robert W.Sha\'1'Jr.,"New Factors in Utility Load Forecasting", Public Utilities Fortnightll.July 19,1979,pp.19 -23. 4.~1uch as Or.--C-oldsln--iTil-i s--is"a-stock/flo\'1 approach to accounting for delnand. '5.M.A.Fuss,"The Demand for Energy in Canadian l'lunufacturing; An·Example of the Estimation of Production Structures with Many Inputs",Journal of Econometrics 5,January 1977,pp. 89 -116. 6.B.Jones,D.Manson,J_Mulford,M.Chain,The Estimation of Building Stoc~s a~d their Characteristic~in Urban Areas, Pro 9 ram i n Ur ba nan d Reg ion a1 Stu die s,COl'n ell Un iv e-r s i~ 1976. 7.'W.Greene,T.;"1ount,and S.Saltzman,"Forecast of the Demand for Majol-Fuels in New York State )CJ80 -1994",Technical Report,Sept ember 4,1979. 8.S.Caldwel1,W.Greenej'T.Mount and S.Saltzman,"Forecasting Regional Energy Demand with Linked t~acro/l~iuo Models", ~~orkin~~r in Planning __}l,Department of City and Regional Planning,Cornell Univel'sity,January 1979,forthcoming in Papers of the Regional Science Association 45. r -i I ! ..... r A PRELIHINARY EVALUATION OF THE I.S.E.R.ELECTRICITY DD·~ND FORECAST (Working Paper #1) Roberl E.Crov Dr.James H.Mars Christopher J.Conyay Energy Probe, 43 Queen's Park Crescent East, Toronto,Canada,M5S 2C3. (416)978-7014 ... - -! !""'" ! - ,.... -..... - - In October 1979,Energy Probe ~as asked by The House Po~er Alternatives Study Committee (HPASC)of the Alaska State Legis- lature to submit a proposal for a study that \lould evaluate the electricity demand forecasting method developed by the University of Alaska's Institute for Social and Economic Research (ISER). This report is the first of three to be prepared by Energy Probe, and nresentsan initial evaluation of the ISER forecasting modei and the Man in the Arctic Project (MAP)model on \lhich,in part,the electricity dema."'l.d forecast is based. The present report dravs on information contained vi thin the Detailed Work Plan submitted November 14,1979 by tr.Scott Goldsmith or ISER;May 1979 MAP model documentation;various publications relevant to the fUture social and economic activity in the state of Alaska;and personal discussions vith rSER personnel. Further reports in this series vill deal vith the sensitivity of electricity grovth in the Railbelt region of Alaska to policy and market induced changes in the social~economic and physical factors ...hich influence electricity grovth;and vith an analysis of the appropriate role and interpretation of electricity demand forecasts vithin the broader context or state energy policy development. Because this report is a \lerking document intended only for use by HPASC members and consultants,it is vritten in relatively technical language.Our final report,to be prepared by May 1980,vill detail the three areas mentioned above in less technical language. The vievs expressed herein are those of the authors,and not necessarily the House Pover Alternatives Study Committee. .--"'...."'--:;.~."~~..""...__-,II':.........: ~,--.....",.-'.f I _,I. .. TABLE OF CONTENTS 1.Introducti on 1 I •.j 2·.Stage II Modelling Approaches 3 3.The Econometric -End Use Approach T 4.The ISER Model a..'ld Suggested Approaches and Revisions 11 5.Stage I Approaches 17 6.Conclusions 24 """i ') 1.':"~ r -i l-, 1.INTRODUCTION Electricity demand forecasting.like all quantitative forecasting.is an effort to viev the past and present in a systematic yay -.nth a viev tovards making reasonable state:llents about the future.The basic problem is that the future is not knovn,and indeed cannot be knolln,even in a probabilistic sense,.As a matter of fact,pretending to forecast the future 1. is an indictable offence under the Hev York State criminal cede. Similar provisions.ve are certain,are in effect elsevhere. Hovever,~~alysts often find it necessary to fly in the face of strict legality vhen the viability of a large proJ ect hinges on the need for it in the future.Hence,forecasting has become an integral part of planning for inyestments in energy, transportation,housing and a myriad of other tu."'1ctional service delivery areas.Forecasting the demand for such services ,comprises a.tva-stage process.In the first stage,aggregate social and economic activity is projected into the ,future (using, for example,the rSER MAP model);the second stage translates this aggregate activity into a detailed forecast of the demand for the product or service in question. stage I models tend to be rather ubiqUitous,finding application in a number of fUnctional areas.MAP,for exacple, has been used La a variety of forecasting environments including energy impact analysis and fiscal forecasting.On the other hand,Stage II models are generally specialized and tailored to the problem at hand.In transportation planning,they are classified under the general heading of travel demand models. In energy demand forecasting,a number of different approaches have been developed,vhieh have met vith varying degrees of success.To the extent that a debate over appropriate fore- casting methods exists,it is really a debate over.the choice of a Stage II approach.·In fact,as ve argue belov,the choice of a Stage II approach essentially dictates the output and hence the structure of the Stage I model to be used. The argument over Stage II models centers on the extent to vhich the model should deal >lith tvo distinct but equally important aspects of the problem.Gi ven an aggregate forecast from Stage I,should a Stage II model focus on the specific activity involved or should it focus en the decision of the consuming unit1 In forecasting vithin a policy environment concerned vith housing,for example,the latter dictates that ve examine household budgets,prices and so OIl..Hovever,a dvelling offers service far beyond simple shelter;amenity,proximity end opportunities for social interaction are but a fev of these.Hence,the former approach vauld argue that the demand for housing is really a composite demand for the services of~ered by a structure. Transportation and energy are similar.Rarely are they required for their 0'\0111 sake;in reaJ.i ty they are crucial inputs into a number of satisfaction yielding activities. In electricity demand forecasting it vas once possible to ... ".... [ r- I i !""'" i i J- \ I do a reasonable job of prediction by looking at a historical rate ot grovth and simply plotting future levels of consumption against time.A draftsman vith a French curve (or an engineer vi th semi-log paper)could roe.ke a reasonable guess at future demand by simple curve fitting and extrapolat:ton.Hovever, it is logically clear th~t the growth in the demand tor electricity has little to do vith the passage of time ~~.-Rather,it is related to individual-decisions to engage in a graving number of electricity using activities ever time. 2.STAGE II MODELLING APPROACHES Attempts to deal seriously vith this complexity became necessary in the early years of the 1970's vhen historical rates of electricity growth ceased to be realized by most electrical utilities in North America.The formation of OPEC and the 1973 Arab oil embargo,vi th its subsequent increases in petroleum prices,ended the era of cheap energy;and all fuels,including electricity,rose in price rather dramatically.Unfortunately, 2. the econometric demand forecasting models in use at this time vere incapable of dealing vith such rapid changes and continued to point to historic or near-historic rates of electricity gro·~h. ISERts 1975 electricity demand forecast for the State ot Alaska (vith vhich,ve might add,ISER itself vas not comfortable)is a case in point.The most telling criticism of its strict time- series econometric approach is that potentially ludicrous activity forecasts result.In ISER's 1975 effort.initial results indicated a demand for electricity which implied 100%saturation of electric space neating in Fairbanks in the fUture.The point to be made here is that because individual activity levels are not explicitly identified in aggregate economic models.such models run the risk of implying physically unrealistic activity-levels. End Use forecasting models in their pure form take the opposite approach by relying almost eXClusively on activities. independent of the underlying economic conditions.The logic is simple:consuming units engage in various activities requiring energy.Energy grovth can result trom (a)engaging in addi tionu·energy consuming activities; (b)engaging in the same activities more intensively; (c)engaging in the same activities less efficiently; (d)any·combination of the above. The case of oral hygiene provides a humorous example.A house- hold may svitch from "manual"to electric toothbrushing.an additional energy using activity.Given an electric toothbrush. members of the household may wish to brush more regularly.When the toothbrush years out it may be replaced vith a model vhich delivers fever brush strokes per unit of energy input.In any of these cases,electricity use increases.In principle,it is possible to examine all electricity use in this ma.."'1ner,noting .. - ,.... I r -I "'""i r""'" I ""'"I i, - r -;:- that all energy is used in a final form such ashee.t,light, motion or Bound,and that it is transformed from its input form to its fiDel end use form by a "devi ce"• Again,in principle,electricity demand can be projected by forecasting the characteristics of devices and activities.This has become knovn as the engineering or end use approach to demand forecasting.The most telling criticism of this method in its pure form is that it is not sensitive to changes in prices, incomes and preferences,i.e.the decision aspect of the process modelled in Stage II.This is a generally accurate criticism whose resolution requires an examination of policies affecting the decisions of the individual consuming unit.In turther work for liPASe,we will be discussing this problem. For functional forecasting purposes,an approach is emerging which seeks to overcome the inherent difficulties of both extremes of Stage II modelling methods.The econometric-end use approach (EEU)attempts to deal with electricity use at the level of the activity while recognizing that the decision to avo and operate a device,i.e.to engage in an activity at some level of intensity,is inherently a problem of microeconomic choice and therefore sensitive to prices,incomes and the availability of 3. alternatives. In our oninion,an EEU annroach is the only sensible ....ay to forecast electricity demand and to ,justify a hUlle exnenditure of "DubHe funds. ... -\.1- we are pleased that ISER agrees in urinciule with this general philosophy.The detailed work schedule circulated by ISER lays out a rather impressive plan.we anticipate problems arising because of the extensive data requirements of EEU t vhich will be intensified by the basic data problems of Alaska:short time- series and a small population.novever t ve fully support ISER's desire to cast the net v1dely at first t vhile recognizing that data t and ~ore importantly time and financial constraints vill require the net to be dravn in somevhat. At this point we would like to co~ent on the allocation of resources for independent demand forecasting relative to the magnitude of potential capital investments.Given the magnitude of the stakes for a project such as Sus1tna t i.e.a potential investment or billions of dollars,we fell that tar too little money is being spent on this crucial element ot project feasibility.·ISER will likely argue,and justifiably 50 t that data is simply not available to construct a 1'ull scale EEU model. The missing data,however,is not ot the variety vhich 1S impossible to collect.With additional resources made available, it could be gathered and incorporated into the forecast model, resulting in a forecast method ~ith vhich all could be reasonably comfortable. In the t'ollo......ing pages .....e vill review the EE"u approach to Stage II and the require~ents of a stage I model to provide requisite inputs into EEU.Our goal is tvo fold:first to .. - - - r r analyze and suggest approaches to particular problems for the benefit of ISER,and secondly to layout the logic of ISER's forecasting proposal for the benefit of all consultants involved in liPASC activities.It is our hope that t~s vill facilitate discussion and understanding of ISER's methods and in the longer term,identify avenues for potential policy intervention. 3.THE ECONOMETRIC -END USE APPROACH EEU begins vith the simple proposition that all energy is used in capital items or devices,vhieh perform a specific task,i.e. an end use.Each device ,by virtue of its design,bas a speeit'ic energy input requirement for each unit of useful output,a concept similar to "First Lav Efficiency".Devices are ovned or rented and operated by consuming units.Hovever,not all consuming units ~.m all tyPes of devices.nor do devices operate at all times. Further,many devices may be po....ered by more than one fuel.The decisions to OllIl or lease and operate a device are economic decisions made by the consuming unit in light of ~ices,incomes, preferences and available options.For a given period.say a year,the total energy required by a consuming unit to pa....er a given device is by definition its hours of operation times its energy demand ....ill contribute to an electricity demand estimate.-pover requirement.If the device is electrically po....ered,this -v·- Any portion o~the electric power consumed by the economic unit which it .generates itself.does not contribute to this utility forecast. There are.o-f course 9 many consuming units and many devices. We may translate from the device level at the consuming unit by simply summing over devices and consuming units yielding the folloving expression for utility electric de~and over a period o-f one year: , '-}:j.1. N M where [D kj • E kj . I kj • R kj - S ) k TUD =total utility demand (kW.h) D =1 ir consuming unit k has device j kj 0 if othervise E =1 it'device j is povered by electri"city in consuming unit k kJ 0 if othervise I =intensity of use of device J by consuming unit k (hours) kj R =pover requirement o-f device j by consuming unit k (kW) kj S •amount o-f selt'supplied electricity by consuming unit k (kW.h) k - N =total number of consuming units M =number or distinct devices 4. This is an accounting rramevorkror utility demand.To operationalize it for forecasting purposes,each of the components must be related to knovn or "knova.ble"variables.Engineering r- I l 'j knovledge and economic theory suggest potential relationships. Econometric or other techniques are used to estimate their direction and strength. For operational purposes it is necessary to group consuming units into classes on the basis of predoninant activity vi thin the unit (i.e.residential,commercial,etc.),similarity in patterns of device ovnership or energy requirements,or some other appropriate criterion.Clearly,there are losses in precision due to this sort of aggregation.After grouping consuming units into classes,the demand for utility electricity is obtained by the folloving expression: =~~ i=l j=l TUD = vhere Q t cun i=l i Q M [n i PD iJ PE ij I iJ R iJ s ] i CUD =demand for electricity by class i (kW.h) i N =number of consuming units in class i i PD =proportion of class i consumers o~~ing device j iJ """!PE ij =proportion of devices j in class i that are electrically pevered - r-, I =average intensity of use of device j by members of class i ij (hours) R =average pover requirement of device j ovned by members of ij class i (kW) s =amount of electricity self supplied by class i members (kW.h) i Q =number of consuming classes The advantage of examining end use demand in this manner is obvious.Not only is it less data intensive than Equation (1). but also.key parameters become easier to pinpoint.For example. in an analysis of a subclass comprised of mobile homes built before 1910.space heating requirements vould be rather similar. Time.of course.is also a crucial consideration yhich must enter the ::lodel in a forecasting environment.The advantage o-r an end use model 1s that the factors developed above exhau3~the rea.l.m of demand factors.and each vill change over time.As time passes.classes o-r consuming units groy or decline.devices become more or less prevalent and more or less "electr1cal".sel-r - supplied electricity may become more widely used.devices may be used more or less intensively.and device efficiencies vill undoubtedly change.The latter is particularly important since many devices vill be replaced over the forecast period and those vhich are not may be "retrofitted"to improve their performance. While the passage of time is itself not the reason for change. the argument above suggests that it may prove fruitf'ul to view demand grovth in a temporal sense.At a po.int in time ve begin vith a "stock"of consuming units equipped vith devices.Over the ensuing year the consuming unit may disappear.change or rnodit'y its collection of devi~es or means of povering them.In addition. new consuming units may be formed complete uith new devices. Presumably these ney devices would have energy consucption character- istics di.fferent from "old't devices.At the end of the year we , I_~ -I I r I - - ,..., -i -i ,...., 1 -~....- vitness a.revised "stock"of existing consuming units and devices comprised of the previous year's units plus net increases.This may be taken a year at a time for the entire forecast period yielding electricity requirements for specific annual points and annual increments in demand. 4.THE ISER MODEL AND SUGGESTED APPROACHES AND REVISIONS In the context of the Railbelt region,EEU makes a great deal of sense for the residential and commercial sectors vhich,taken together.account for about 86%of Alaska's total electricity demand.Because industrial development in Alaska is largely or the major project variety,it is best to examine these in a case by case manner.Further,vith the exception of block heating in vehicles,the transportation sector currently uses an insignificant aoount of electricity.Again,this is best vieved as a special case. ISER's EEU model,Figure 1 in their "Detailed Work Plan", incorporates most of the features of 8.'1 ideal EEU discussed above. It is a stock/flo."t:lodel vhich segregates consuming units into "ney"a.~ld "old"and deals vi th four residential subclasses.and segregates devices into six categories including an"other"category for minor appliances. The commercial sector should be divided into at least the follo~ing groups: 1)Public/Institutional Buildings; 2)Large shopping plazas/office buildings (say larger than 100,000 or 250,000 sq.ft.); 3)Other commerical buildings. This would be fruitful for two reasons:within each group there are similar requirements for electricity,and policies/programs may be specifically tailored,at a later date,to this particular pattern of consumption and occupancy/ow~ership. Missing in ISER's proposed model is a term to account for electricity or energy supplied by the consuming unit and hence not required from a central system.This should be added to the model even though it may not greatly affect the magnitude of the final forecast.A number of considerations warrant its inclusion,not the least of which is the possibility of co-generation of electricity and steam for s:pace heating in large commercial establishments,schools,hospitals,and the like. The present ISER formulation allowS for the scrapping of dwelling units but not for the replacement of appliances within existing units. A number of appliances ISER intends to consider have useful lives of substantially fever years than either the forecast period or the structure.In Ism I s,model,this problem could be solved by adjusting the average consumption of appliances on an annual basis.It is better, however,not to confound the effiency measure with the effect of new appliance stocks. -----------------IIIIIIIJ "'",r r ~~j ""'"'i i, l """I i ..... I ~ I I"""' I, r ..... .... ~ ! Given these structural refinements Yhich Ye consider necessary, the ISER approach to residential and commercial electricity demand forecasting is methodologically sound.Since residential and commercial consumption in the railbelt are quite important,it is necessary to examine the components of the EEg model and to suggest possible approaches to modelling each component.In this caSe ye refer initally to our formulation of EEU above,and explicitely to these elements pertaining to Stage II . In Equation (2),total utility demand yas expressed as the sum . of class demands.Class demand is a function of the number of units in the class,the proportion owning various devices,the proportion of these devices powered by electricity,the average intensity of each device's use,the average power requirements of the various devices and the amount of self-supplied electricity.The number of consuming units in each class is essentially a modified form of the output of Stage I which we discuss below.The reamining factors are however,Stage II concerns which we deal Yith in turn. lJ PD iJ ,the proportion of classVunits owning device J,is obviously a variable whose value lies between a and 1.For certain end uses,i.e.space heating,its value equals unity and will con- tinue to do so over the forecast period.In other cases like clothes drying and refrigeration,its value is a matter of choice,and while perhaps initially close to unity it is variable over the forecast period.In an ideal world we would hope to estimate this proportion on the basis of income level and distribution yithin the Railbelt negion,bearing in mind that the decision to own a "device"also corr~its the owner to operating expenses over its lifetime.Hence .. the general price level of all competing fuels may be important. PE ij ,the proportion of device j owned by class i which are electrically powered is also a variable whose value ranges between o and 1.Again,for certain end uses,especially refrigeration,its value is close to unity and will likely remain so over the forecast period.However,a great deal of choice exists in this area.A useful way to look at this problem has been proposed by Fuss 'Who suggest')the decision to engage in an activi ty 'W'i th a specific fuel is essentially separable.In ot~er words,given a decision to er~age in an activity,the choice of fuel is essentially a separate question~·made on the basis of relative prices. The question of the treatment of conservation arises in this instance.If conservation is factored into average energy require- ments,then no more need be said.However.if we view each or any device as having a "base-line"energy requirement,then any effort ... ., -, to reduce it involves an explicit tradeoff of electricity for con-~ ."In this sense,conserva~ion is self supply,and servat~on.nas an average supply pr~ce equal to the amortized annual cost of the conservation project divided by the number of kiln watt-hours displaced during a year.Marginal costs may be calculated by assuming.ideally,various levels of conservation and calculating, presumably,a step function for the fuel equivalent value of various conservation schemes.The same logic may be applied to renewable energy projects as well. We feel it is useful to view conservation and renewables in this way 'W'hen considering existing activities at a point in time. The major point is that given an existing activity,like space and r ,..... '"""i !L water heating (the major ones)the conslli~ing unit can choose not only to switch from one convential fuel to another but can also choose to supply.a port ion of its requirements '",i th conservat ion. In an oil heated home,for example,the household may switch to gas,electricity,or conservation for all o~part of its heating on the basis of relative prices.Considering conservation as an explicit fuel represents a useful modification of interfuel substitution analysis. R ..•the average power requirement of device j in class i,becomes ~J basically an engineering design param.eter when conservation is treated as a ~~el.Consequently it is a function mainly of vintage, not confounded by retrofit.One item that should be examined is the trend in device efficiencies over time.This may well be an appropriate area for regulation. I ij ,the.average intensity of use of device j by class i members is also a consumer choice variable although to a limited extent in the major consumption categories.Actions like reducing inside temperatures and the like are evidence of the economizing behavior of households under this category;how much farther we can go in this area is certainly questionable.In this case,comfort and convenience bound choice from below.To the extnet that there is flexibility it is likely price and income related. The final term in our formulation is 5.,the aoount of self- ~ supplied electricity by members of class i.In this instance we suggest that this term be kept pure in the sense that conservation .. not be viewed as self supply in this term.We include S.in the ~ model for the reasons stated above.There is a price at which self-generation or cogeneration becomes attractive whether by means of water power,wind,or conventional fuel.The model should be sensitive to this possibility. The above relates to our formulation and also to ISER's model. The remaining terms in ISER's model relate to new household formation which we discuss belo·...and the various "scr~pping rates".Scrapping of a device involves not only physical deterioration but also economic considerations,one of which is the device's fuel requirements. Logically,the scrapping rate should increase with decreasing energy requirements for new models a particular device.This is extraordin- arily difficult to measure and project over t~e;hovever,it is something to be kept in mind. Generally speaking,we are impressed with ISER's proposed method for handling the Stage II modelling of the residential and commercial sectors.With the modifications suggested above we can Wholeheartedly endorse ISER's approach and we look forward to working with ISER on further questions of approach and sensivity analysis.With respect to the ISER approach to non-residential and ccmmercial use of electri- ... ,, city,we reserve judgement since the method has not yet been developed. We will.of course.co~~ent at an appropriate time and we are confident that ISER will take a sound approach,based on their work to date. 5.STAGE I APPROACHES ~We nov turn to the merits of the MAP model of the Alaskan economy 1 fasaStageImodelforeconometricend-U3e ,';,-ecasting.Regional economic forecasting can take a variety of f~rms.Some approaches r, .... I -! ..- i I""" I [ being considered.in the "02.tailed Work Plan"are input-output analysis, the economic base approach,Curtis Harris'locationally efficient model, and the Delphi technique.These all have strong and veak points but none is a serious contender to a moderately detailed econometric model like MAP. What is required of the Stage I model?It must provide the number of consuming units in each class for the end use equation.That is. in the number of housing units of several types and the number of firms, employees,square footage,or business volume for commercial and institutional units.It must be sensitive to the scenarios of fast, likely,and sloW'grovth mentioned in the "Detailed Work Plan".It must respond to changes in oil and gas pricing,energy and other major investment projects,national economic trends,and demographic realities including migration.Whiel the current MAP models incorporate most of the latter functions,the restriction of demographic projections to persons (not households or families)~the introduction of housing only through the dollar Volume of construction,and the lack of other physical measures of economic activity closely related to the number and type of -consuming units ~ma,ior deficiencies.As noted in the "Detailed Work--- .... Plan",data must be gathered and incorporated into new versions of !.'LLU'. What regional techniques must be added?In our opinion none of the above oentioned techniques merit much effort. ... Input -output analysis is appropriate vhen a region has a large industrial base which relies to a great extent on inter-industry sales. Alaska does not have such an economy yet,and the method's veIl-know data-intensity suggests that it need not be considered further.Shortcuts to true regional input-output data gathering --such as the use of technical coefficients borrowed from other studies --are inappropriate for an unusual state economy such as that of Alaska. The strong points of economic base analysis --a technique which is useful when the regional economy pivots On clearly defined basic industries --are already contained within the MAP model.The simple economic base methods are too elementary;ISER is ..ell beyond them .... ~ already in its work.The s~e criticism holds for purely extrapolative methods.Just as ruler and graph paper are inappropriate for local forecasting,they are too simplistic for the economic part of econometric- end-use analysis. Curtis C.Harris developed a regional forecasting model at the detailed industry level based on short time series changes in output by industry and state and incorporating transportation costs estimated by optimization techniques.Alaska clearly is not likely to exhibit consistent locational cost patterns of industrial·development necessary to take Harris'approach. -Delphi,a technological and political forecasting technique developed first at the Rand Corporation is unlikely to yield the moderately detailed consuming unit forecasts needed here.Hovever,it may alvays be considered for developing scenarios for energy projects, general ecomic grmrth levels,or energy policy decisions.Hence it is ,","1 i ;1 ~ I),I I i 1 ,j i" I. .... r"" I ! r t - r I""'" t r, ..... ..... r - not a Stage I model but a source of exogenous and policy variable values for any forecasting method. ", Among general methods for forecasting regional economic activity, one not yet mentioned is shift-share analysis.This method is based on statistical estimation of the contribution to a state's industrial growth of industry factors and regional factors.It is an excellent basic method which is sufficiently incorporated in a MAP-type econometric approach.While both input-output and shift-share methods are usually performed with a great deal of industry detail,such detail is not needed in our Stage I approach. What is needed is more detail aimed at household characteristics and building stock characteristics.While data source end points for households are'.....ell-known and trusted,a region such as Alaska.can have rapid and crucial post-Censal fluctuations in households and household size.As for buildings,only d.....elling units are enumerated in the Census. Building stock estimates for non residential units are rare above the 6.city level.Land use surveys and Civil Defense surveys give spotty data sets,;~,it the building stock is basically an unknown quantity for regions such as states.For the current research,increased information on the building stock is important. As an expedient it is suggested that housing be looked at in detail (so as to allo.....better end use forecasts for space and water,heating, lighting,and appliance loads);that large commercial and institutional uses be examined through enumeration of structures;and that the rest be treated by the use of employment or sales estimates. In this Recent efforts by others in energy forecasting suggests two approches:1)r;":acroeconomic econometric models such as VlAP,and 2)microeconomic simulations of consuming unit responses to changes in price,income and the availability of alternatives.The former is necessary to introduc~national and major regional trends.The latter is used to discover what the distributional effects of new pricing and supply levels will be. A study commissioned by a number of New York consumer groups and carried out at Cornell University vas used in testimony before the New York State Energy Master Plan M~etings in September 1979.7 approach,Green,MollOg,and Salt~an utilized a four-sector economic/ demographic state econometric model with a partially integrated energy sub-model.Tne four sectors were residential,industrial,commercial and transportation.All major energy types -electricity,oil,gas and coal -were forecasted simultaneously.This Cornell model as vell as another model developed with end use detail by the New York State Energy Office,predicted significantly lower electricity requirements than had previous state plans.It should be noted that while the Cornell Model is not extremely complicated (57 economic equations, 150 demographic equations)it contains household formation functions for each age-sex cohort.Unfortunately the Cornell Model does not give explicit place in its structure to self-supply wood space heating, or conservation. Furthermore,in the Cornell approach,a microeconomic simulation was linked to the macro model in order to relate income and price changes and restrictions on fuel supply to consumer demand for the ... ,".. I I ~ ,1 ., \• F"",., r r r t ,-. i ..8d~fferen~!Uels.This,of course,requires an extensive da~a base of individual households studied by survey research methods.In this case a sample of 7000 households was utilized. While such microsimulation may be beyond current possibilities in evaluating Susitna (and we are not convinced that such further study should be considered extravagant)it suggests again the need to make the energy forecasting version of MAP more oriented to consuming units,households,and to the biggest devices of all, buildings. Looking in more detail at ~~,based on the May 31,1979 documentation~ we note that it has more than enough economic detail,but not enough demographic information because of households not appearing explicitly. Finally a housing and/or buildings component is lacking;this is a critical shortcoming. In the "Detailed Work Plan"W"e support most strongl¥Items A 7 - 9 on electricity consumption;Item 10 on households.houses,and appliances.These are more important,in our estimation,than the r I""'" ! r l -! refinecent of the ~~economic model per se.They should receive top priority. Regional disaggregation (Task B)is important,but less so than getting on to EEU forecasting for the Railbelt region as a vhole.~nus the Items in D are crucial --interfuel substitution ~the addition of conservation. •l -22- "I '.~~ • r A general evaluation of the MAP model;serves to reveal several strengths in addition to the above shortcorr.ings.First despite the limited length of the Alaska data series,the resulting equations are adequate by conventional statistical benchmarks.at least for forecasting use.The detailed fiscal and native/non-native/ military results,needed for earlier applications,are well develo!Jed. but may not be particularly helpful in the current application. What is needed.more than any other modification,is a housing sub model.wnether the data can be gathered for such an addition remains to be seen.Lacking a formal housing model.some intermediate _ step is required based on the housing stock data from the decennial censuses.A brief outline of each alternative is in order. A full-blown econometric sub model for housing would flow from the following modifications to MAP: I}inclusion of household formation equations in the demographic sub model 2}a set of equations for the housing stock (or alternatively changes to that stock)by age and type of unit. Some of the crucial right hand variables would be from the construction and investment functions of the economic model as well as the household formation results . ""'II1.···1 "~I J -23- ., ..... If the time series data are lacking for the ~ousing ~odifications to MAP,then ~he available census benc~arks n~~ber of d~elling units by age and t)~e --should be combined with recent data on ~ I housing starts,mobile home sales,building permits.etc .•to update ~ the distribution of the housing stock.This results in the following structure: ..... ..... Stage I r---------------.....,.;/o-------------.. StageII .pECONOMICD~~OGRAPHY INCLUDING HOUSEHOLDS i....,.....,MATCH OF .-;:-HOUSEHOLDS ---'.F» \TO HOUSES HOUSING STOCK DATA ------.~~ESTIMATE OF FUTURE ~BUILDING STOCK DATA r- I ,..,.. ~ I r ,. ." 6.CONCLUSIONS Energy demand forecasting,the most crucial element of energy policy development,is difficult in the face of growing uncertainties.In order to maintain confidence in forecasting procedures,the analyst is faced wi"th the need to develop what amount to relatively more sophisticated models and forecasts than has traditionally been the case. Pure econometric and pure end use forecasts suffer inadequacies;hence,a blended approach combining the best elements of each is necessary.This blended EEU approach is difficult because of its data requirements and because modifications must be made to the structure of the underlying econometric and end use models on which it is based. In the long run,an EEU forec!tSting system for Alaska can be developed with MAP,suitably Qodit1ed.at its heart.Its data requirements are not yet attainable in a small region sucb as Alaska vith a short data history.Therefore.in the short ter.:::t, ad hoc forecasting must be carried out vith the outputs of the current version at HAP.These outputs must be obtained by using a very vide range of input sceDarios. The most crucial shortcoming ot the current version ot HAP is the lack or a housing sec~or and this must be bridged by some reasonable it i~perrect ~ethod of estimati~g Alaskan housing stock aod characteristics in recent ye!U"~• .......-'., I'"'" I ..... i - r 7.FOOTNOTES 1.Joan Robinson,"What are the Questions?",Journal of Economic Literature 15.December,1911,p.1322. 2.These are an extremely expensive and sophisticated version o.'r. semi-log paper.See Hennen De.l.y,"Energy Demand Forecasting: Prediction of Planning?",Journal of the America..'1 Tnsti tute of Planners,January 1916. 3.Robert W.Sha....Jr.,"Nev Factors in Utility Load Forecasting". Public Utilities FortnilZht1:r,July 19,1919.pp.19 -23. 4.Much as Dr.Goldsmith's is a stock/f1ov approach to accounting for demand. 5.!of.A.Fuss,"The Demand for Energy in Canadian Manufacturing:An Example of the Estimation of P:-oduction Structures vi th Many Inputs". Jourhal of Econometrics 5.January 1911,pp.89 -116. 6.B.Jones,D.Manson,J.MUlford,M.Chain,The Estimation of Bui1din~Stocks and their Characteristics in Urban Areas, Program in Urban and Regional Studies,Cornell University,1916. 7.VI.Greene,T."Mount,and S.Saltzman,"Forecast of the Demand for Major Fuels in New York State 1980-1994",Technical Report,September 4,1979. 8.S.Cald.....ell,W.Greene,T.Mount and S.Saltzman,"Forecasting Regiogal Energy Demand vith Linked Macro/Micro Models",Working PaDer in P1anninl:!:#1,Department of City and Regional Planning, Cornell University,January 1919,forthcoming in PaDers of the Regional Science Association 45 . .. - l"""I i i i "'""i APPENDIX E CRITIQUES OF ISER AND TUSSING REPORTS BY ALASKA UTILITY MANAGERS -Golden Valley Electric Association (R.L.Hufman)-June 20,1980 Alaska Rural Electric Cooperative Association (D.Hutchens)-June 11,1980 -Anchorage Municipal Light &Power (T.R.Stahr)-April 24,1980 r I, 3CLOEN VALLEY ELeCTRIC ASSOCIATION INC.Box 1249,F,W'brtnks,Aiaska 99707,Phone 907-452 ·115' June 20,1980 RECEIVED Following are comments relative to the ISER energy forecast authored by Scott Goldsmith and Lee Huskey: 1.The study estimates kWh sales only.Therefore at least a 10%upward adjustment must be made to properly forecast gross generation requirements. ,... i Mr.Eric Yould Executive Director Alaska Power Authority 333 W.4th Avenue,Suite 31 Anchorage,AK 99501 Dear Mr.Yould: JUN 2 --:J980 AlASKA POWER AUTHORITY r- I I \ ,- I I 2.Estimates should include the Canrwell Summit areas of the railbelt. 3.The major problem we have with the ISER forecast involves our objection ~o what we consider an extreme ultra- conservative approach.All studies regardless of who the authors are will,to varying degrees,reflect certain philo- sophical leanings of those persons participating.As I read the report,it is my opinion that the authors'philosophies tend to favor conservative approaches resulting in forecasts that reflect those tendencies.Utilities are ever mindful of the inherent dangers of grossly underestimating demandl energy projections for planning purposes.There may be some economic penalty for overbuilding for a short period of time. Most likely that penalty would be promptly wiped clean by inflationary trends.However the penalty for underbuilding may be a severe economic penalty,browcouts,blackouts, inability to serve new customers and other inconveniences all related to the above items.Those persons with responsi- bility to forecast only and with no utility responsibility to keep the lights on and provide-Service for new customers can perheps afford to take the cons~rvative approach.Utility planners simply cannot. 4.The study fails to adequately provide for increased usage that Susitna would encourage due to a long term stable rate base and adequate supply.With Susitna I believe electric heat for Interior Alaska would be the best buy.Deregulated oil may not even be competitive.Wood will be in short s~pply. 30LOcN VALLEY ELECT!=l'C ASBOClATION INC. Mr.Eric Yould,Executive Director Alaska Power Authority June 20,1980 Page 2 Coal in all the homes would cause intolerable pollution levels. Gas mayor may not be available.Over 80%of the homes are now heated with oil most of which are hydronic baseboard systems. These are easy and economical conversions to electric boilers. If the price incentive is there,retrofitting will o~cur on a large scale.We know we have experienced such an occurrence fo~the past several years. 5.We also believe that a substantial number of electric cars will be in use by 1990;capturing 10-12%of the commuter market by 1995. 6.Further,the study anticipates substantial declines in petroleum royalties causing a severe drop in State spending. I believe thet Royalty Gas revenues will pick up that slack. In addition,this Nation simply cannot allow the extreme deficit balance of trade to continue.We must produce our way out of it.Alaska has the oil and gas resources and I predict our State will be opened up to exploration such as we have never seen.There is a good chance that discoveries will be made that in combination will make the Prudhoe Bay Field seem like small potatoes. 7.One other item.It is reasonable to assume that the military plants would purchase all electrical energy from Susitna beyond that provided by their steam heat/electric plant balance point. I admit to a rather radical philisophical departure from those that authored the ISER forecast.The main difference I suppose is that during the course of my 34 years in the electric utility business,I have had an opportunity to sec and experience problems related to inadequate base load capacity and insufficent reserves. Sincerely, wg~Y'-- R.L.Rufman General Manager cc:Tom Stahr Lee Wareha.m Bob Penny Dave l'iutchens Mike Kelly 1..~ 1.j -,~.,, .J 6000 C STREET·SUITE C'ANCHORAGE,ALASKA 99:502'(907)276-323:5 I"'" -{ / / ALASKA RURAL ELECTRIC COOPERATIVE ASSOCIATION,INC. r I ,... i i' i -I -i l """" r -I June 11,1980 Mr.Eric Yould Executive Director Alaska Power Authority 333 W.4th Ave. Suite 31 Anchorage,Ale 99501 Dear Eric: Today r have read through the document prepared by ISER titled "Electric Power Consumption for the Railbelt: A Projection of Requirements."It seems obvious that the methodology used and the projections made are extremely conservative. I will not attempt a point by point com~enca~y on this report at this time,but a basi:characteristic of this study does requir~comment.In the iection on Major Economic Assumptions (page viii of :he Executive Summary),the study assumes a highprobab ilit~: of a number of large construction projects i~the early 1980's.However,the second item stat=s, "In the subsequent decades,it becomes more difficult to identify specific projects or types of prcjects which may generate continued economic growth-There the range of economic projection widens," To me,this says that since the preparers of this report cannot predict with certainty exactly what the components of railbelt economic growth will be after the early 1980's,they feel free to make their projections anything they wish within a wide range of what is possible.When the projections in Table A are read in this light,it is apparent that this study consistently assumes a level of economic activity in the lower portion of that range of possibility. DEMOCRACY IN ACTION Mr.Eric Yould June 11,1980 Page 2 There is a very basic difference between an economist and a utility planner when both are looking into their crystal balls and attempting to project electric power requirements some distance into the future. The natural inclination of the economist is to be conservative in his growth projections.This is easy to understand because if he should err on the high side,he would be subject to ridicule for pulling numbers out of the air.If the economist understates the actual requirements,he can easily explain the growth in excess of his projections as the result of new economic activity which it was not safe to predict at the time of his study. For the economist to project low is to play it safe.If h~is ,vrong on the low side I his professional reputation is carefully preserved. A utility planner in projecting the future relies very heavily on the actual trends in load requirements contained in his experience.If he errs on the high side,the electric rates will be more than are really necessary.If he is wrong on the low side,people may freeze in the dark. Especially in a period.of rapid changes throughout the energy world I neither the econ'omis t nor the utility planner can be certain he is right.Ie is probable that actual power requirements will be somewhere between the projections made by these two.However,I would submit to you that the public safety requires that very great weight be given to the projection made by utility planners. Sincerely, David Hutchens Executive Director DH:ra I •...: Commen ts on Tuss ing Sus i tna l<.ev ie,." ll.pril 24 I 198C By Thomas R.Stahr Demand Studies Pl3-Pl6 RECEIVED AlASKA PCW::R AUTHORITY I"'"' i !""" [ l r ! f"" i I""'" ! l - ..... ! I ! I share Dr.Tussing's concern about the ISER study because an unproved methodology is being used.They are using the "end use" a9proach what is the latest tad in energy requirements projection techniques.Not only is this methodology unproven but even li\ore important .adequate end use data for Alaska's unique environment does not exist.At least two years of data collection and reduc- tion would be required to obtain meaningful input data.I strongly urge a more conservative approach where principal reliance is placed on proven traditional load projection techniques.Contrary to Tussing's assertions,past load projec- tions have proved reasonably accurate. Negative Growth Demand Scenario PIS and 16 If the conservationist dream should come true and the State reverts toward complete wilderness,the in-state demand Eor energy would drop.This would also mean Alaskan contribution to Lower 48 and world energy sU9Ply would drop thus intensifying the national energy problem.Under these conditions industrial use of surplus energy would not be difficult to find.A study of dismal sce- narios is not inappropriate if the broader social issues are incorporated.I would urge consideration of energy supply and demand in Alaska during the not too distant period when overall production of oil and gas enters the period of it-s inevitable decline. Peak Loads,load duration curves I reak respons ib il i ty pr ic i ng and load management Pl6-17 Load factor,the ratio of average to peak load,expresses the relationship between annual energy and peak power demands.This relationship is quite stable and varies only slightly over large changes in total load and has changed only slightly with time.In Alaska the annual load factor is sensitive to climatic variations (i.e.,warm winter vs.cold winter,etc.)but this variation can be factored out by relatively simple calculations. It has been proposed,mainly by economists and conser- v ationis ts that annual load factors can be signi f icantly chang ed by pr ic ing and load management techn iques .Analys is and experience indicates that load management and'peak load !.?ricing will noi have significant impact on Alaskan electric generation requirements.First it must be borne in mind that these tech- niques do not reduce the need for energy -they only switch around the time the energy is used.For many years ML&P has had a ~eak load pricing (time of day)rate which offers substantial savings for customers who transfer their energy use to off peak.At tje time of our rate restructuring we found only 15 out of a possible 2,000 COnsumers who had done enough in this direction to yield any say ings over the standard rate.Of even more importance is the fact that during extremely cold weather the night time valley in the load curve nearly disappears.Therefore,a strong effort to economically,or through load management,to force off peak usage would only result in changing the time of the peak -not eliminate it.I do not mean to say peak load pr ic ing and load management techniques are worthless but that the potential gai~s are easily measurable and small. Our main peak problem is seasonable and is due to increased lighting and heating needs in winter (irregardless of type of heat).Certainly by jacking 'up the winter rate high enough we could enforce extreme conservation or deprivation.Budget billing,where the customer pays a fixed amount each month IJould have to be prohibited to make this scheme work. You must bear in mind that the net income that the utility receives through the year must remain the same so the iimit is when the power is free in the summer and 1 ikely somewha t less than twice average in winter (assuming a two part seasonal rate with each period six months long).The net result is that the summer transient gets a Eree ride and the year round resident pays the amount he would have paid anyway throughout the year plus his share of the tra~sient's use. Even if we could eliminate the free ride problem,given our climatic conditions it is extremely questionable that we could levelize electric use throughout the year.And most importantly any decision to attempt to do so is a social matter ',.;hich should be dealt with straightforwardly through the appropriate pUblic processes. Finally it must be understood that the value of concepts such as peak load pricing,load management and seasonal rates is more relevant to systems which have large amounts of conventional steam and nuclear genera t ion and summer peaks.ror sy stems such as .Alaska's railbelt with a high percentage of gas turbines and winter peaks the advantages of levelizing load are not so great as the capacity of gas turbines,transmission and distribution equipment increases greatly with cold weather. Hydro systems tend to be energy limited rather than ?eak load limited.The cost of hydro capacity is relatively insensitive to reasonable changes in plant design load factor or to put it another way ,energy costs are not greatly dependent on load fac- tor.Thus the economic advantage of load levelizing are not large.Backup thermal capacity needed for critical water years will be in place at the time Susitna goes on line. r I - r Generation Alternatives P17-18 Study after study after study on generation alternatives have already been made.We are getting close to the time that construction must be started so it is too late to consider unproven resources or techniques.As previously discussed,the changes poss ible throug h pr icing or load manageme nt techn iques are very limited or of minor economic consequence so the proposed ACRES study is quite sufficient and appropriate for the task at hand. Financial Feasibility P18 and 19 The Susitna project is beyond the ability of any single electric utility in the State to finance.More than likely,this would also be the case if the coal alternative were pursued.We have determined this to be the case for Anchorage Municipal Light and Power.Therefore State assistance is necessary for the construction of base load generation sources allowed by the National Energy Act (excluding of course gas or oil burning plants permitted by temporary exemptions). Financial studies are required to determine the type and degree of direct State participation required. In regard to binding "Hell or high water"contracts,this in essence is no different than the obligations a utility takes on when it constructs its own projects through revenue bond sales. Marketability P20-21 Under the Fuel Use Act of 1978 the cost test is based on -electric generation using imported oil.Generation units under the Act must use fuels other than gas or oil unless the cost is 1.5 times or greater than using imported oil.It is extremely unlikely that Susitna power will be more expensive than that.By 'the time Susitna is on line most of the generation capacity pre- dating the Act wirl be too old to be used for base load generation. Therefore even if natural yas should remain relatively inex- pensive it will not be an option.The thrust of National Energy policy is clear -we must reduce our reliance on oil and gas. This will be the controlling factor,not conventional economics nor the desire of our utilities management. I certainly hope that development of Beluga coal proves econo- mically feasible.But if it does,it is still highly unlikely that new coal fired generation will be more attractive than Susitna.,; Study Findings and Credibility P2l-22 If the seismic,geological and environmental studies indicate no major unanticipated problems,Susitna is infact the best generation alternative for the railbelt.This has been establi~hed time and again. 1""" i - r, I r- I I -! r i APPENDIX F ISSUES RAISED DURING ENERGY REQUIREMENTS FORECAST WORKSHOPS JUNE 10 &11,1980 - ALASliA I-OlVER A UTIIOI:ITY ATTACHMENT 1 r i ,... r "'""r Partial List of Issues Raised during Energy Requirements Forecast Workshops,June 10 &11,1980 1.A sensitivity analysis of the forecasting model needs to be per- formed in order to determine the sensitive assumptions and the degree of variability in the outcome from changes in the assumptions.One par- ticular question is how sensitive is the forecast to the set of con- servation assumptions. 2.Scott Goldsmith stated that the limits of the forecast range represent 20-25%probability of exceedance;in other words,that there is a 40 to 50%chance that the actual energy requirements would fall outside the forecast range.This is a much narrower band than APA had assumed.Is it too narrow?Goldsmith should be asked to clarify the issue. 3.Do subjective probabilities have to be assigned over the forecast range to permit later risk analysis?Can it be done? 4.Consider a legislatively mandated shift away from electrical use, especially space heating. 5.There appears to be a downward bias in the econometric model due to the i nabn ity to identify di screet exogenous proj ects in the peri od after 1985 and before tre.nding takes over in the year 2000. 6.Should a high 1eve1 growth case with a mode switch toward electric (i.e.)an H-E case)be added?A L-E case would not be useful since the forecast range would not be enlarged. 7.Should supply side information be fed into the forecast at some future date to somewhat define the nature and timing of the gas-electric mode split? 8.The conversion response time in the electric space heat conversion r-scena ri 0 may be underestimated. - /. -1- /ITT/'";cli/'-1£"N -r 2 --:a B )Iv;=/1-1A}7I!C;;,q /,,/CJl,/I"5 STUDY FORECASTS SALES ONLY -NEED TO ADJUST 8 TO 10% TO COMPENSATE FOR LOSSES,Low FIGURES ARE ACCENTUATED BY PROJECTING SALES ONLY WHEN COMPARED TO PREVIOUS REPORTS ESTIMATING GROSS GENERATION, STUDY ASSUMES ALTERNATE ENERGY WILL BE uCHEApu ENERGY, PHOTOVOLTAIC CELL INSTALLATION JUST ON-LINE COSTS 3 t1ILLION FOR 100 KW.=41'3ooJooo ('..tJ.r'I.{v'./ STUDY FAILS TO ADDRESS THE PROBABILITY OF INCREASED USE DUE TO A STABLE RATE PROVIDED BY AMPLE HYDRO CAPACITY, IN FAIRBANKS,DEREGULATED OIL WILL EVENTUALLY NOT BE COMPETITIVE FOR SPACE HEATING.WOOD WILL BE DEPLETED,COAL WILL CAUSE ADDITIONAL AIR QUALITY PROBLEMS,NATURAL GAS MAY OR MAY NOT BE AVAILABLE.HOWEVER,ELECTRIC HEAT 'filTH A ~"T,~eLE RATE BASE WILL BE A TOP CONTENDER WHEN PRODUCED FROM HYDRO, THE STUDY DECLARES THAT ELECTRIC HEAT RETROFITTING DOES NOT OCCUR.IF THE PRICE INCENTIVE IS THERE IT WILL OCCUR. GVEA CAN ATTEST TO THAT FACT,..IN ADDITION,MANY OF THE -,(SYSTEMS HAVE HYDRONIC BASEBOARD WHICH ARE EASY INEXPENSIVE., CONVERSIONS TO ELECTRIC BOILERS, THE CANTWELL SUMMIT AREA SHOULD BE INCLUDED IN THE... FORECAST, THE PROBABILITY OF SEEING A SUBSTANTIAL NUMBER OF ELECTRIC CARS BY 1990 IS GREAT.EVEN THOUGH MOST WOULD HOPEFULLY RECHARGE OFF PEAK,THE TOTAL I<WHS MAY BE SUBSTANTIAL.We MAY SEE 10% ...-"",.......--,.. ..........., /. I' -I I i ...~. -2-'... ,.... ,.... I r r l ,- I,, r f 1""'"I I !I \ \ r r -i r r r- ! ! ..... WE EXPECT ELECTRIC HEAT CONVERSIONS TO BOTTOM OUT BY 1981,THOSE LEFT WILL STAY THERE REGARDLESS OF PRICE - PERHAPS 250 -350 ACCOUNTS, ,DECLINE OF REVENUE FROM PETRO~EUM ROYALTIES WILL BE COMPENSATED FOR FROM NATURAL GAS ROYALTIES AND NEW DISCOVERIES, WOULD PREFER TO HAVE BOB RICHARDS FROM ALASKA PACIFIC BANK DO AN ECONOMETRIC STUDY, MILITARY PLANTS WOULD PURCHASE ENERGY FROM HYDRO BEYOND THAT DERIVED FROM A STEAM/ELECTRIC OVERALL PLANT BALANCE, WAINWRIGHT)EIELSON)CLEAR AFB)ELMENDORF)FT,RICHARDSON, HEAT PUMPS MAY BE PRACTICAL IN SOUTHCENTRAL WITH HYDRO,' WHO IS DOING DEMAND FORECAST? THIS HAS A MAJOR BEARING ON INSTALLED CAPACITY, UTILTIES ARE COGNIZANT OF THE INHERE0T DANGERS OF GROSS~Y UNDERESTIMATING DEMANDlENERGY PROJECTION FOR ~~PURP~:"jE~;, .!.' IT IS QUITE EASY FOR THOSE WITH ZERO"R~SpbNSIBILITY T0 ,;. KEEP THE LIGHTS ON)TO USE THE ULTRACONSEI.,VAT!Y.:E"APPRO:l\CH;:,., MOST FAVORED BY OBSTRUCTIONISTS AND NO GRC,WTHER:S,TH:~'qg MAY BE SOME ECONm-U C PENALTY TO OVERBU I LDI NG BUT MOST L"~EL Y THE PENALTY WOULD BE WIPED CLEAN BY INFLATION WITHIN A SHORT PERIOD OF TIME,HOWEVER)THE PENALTY FOR UNDERBUILDING COULD BE A DISASTER BOTH ECONOMIC AND OTHERWISE,