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HomeMy WebLinkAboutPower Requirements Study for Golden Valley Electric Association, April 1985POWER REQUIREMENTS STUDY Prepared for GOLDEN VALLEY ELECTRIC ASSOCIATION GY CRMHILL April 1985 Engineers ieee some Planners Coll = Economists ae Scientists April 4, 1985 K18174.A0 Mr. Steve Haagenson Golden Valley Electric Association 758 Illinois Street Fairbanks, Alaska Dear Steve: We are pleased to present this final power requirements study for the Golden Valley Electric Association. The report is consistent with guidelines for power require- ments studies outlined by the U.S. Rural Electrification Administration in its Bulletin 120-1. Included in the report is a review of historic and projected economic development in the Fairbanks area, analysis of GVEA load development and characteristics, evaluation of other energy forecasts for the Fairbanks area, and a description of the methodology used to develop the GVEA forecast. We have enjoyed working with you, John Huber, and other GVEA personnel and look forward to being of continued service to you. Si Laps [oe A. Vai Manager, Econ. ics Department nb/seS5618HH CH2M HILL, INC. Seattle Office 1500- 114th Ave. S.E., Bellevue, Washington 206.453.5000 P.O. Box 91500, Bellevue, Washington 98009-2050 FAL OV POWER REQUIREMENTS STUDY Prepared for GOLDEN VALLEY ELECTRIC ASSOCIATION GY CHMHILL April 1985 K18174.A0 EMC PREFACE This report was prepared in completion of Task 1, Power Requirements Study, of a two-task project being conducted by CH2M HILL for the Golden Valley Electric Association of Fairbanks, Alaska, pursuant to a November 15, 1984, agree- ment. The results of Task 2, Power Supply Study, will be presented in a separate document at the conclusion of the project. Die CONTENTS Page Preface aka bo Acknowledgements el 1 Service Area a 2 Economic Base and Development of the Fairbanks North Star Borough 3 Historical Development 3 Population Growth 6 3 Projected Economic Development 9 Economic Base 9 Population Forecasts for the FNSB 11 4 GVEA Electric Load Growth and Customer Characteristics 19 Historical Customer and Load Growth 19 Residential/Commercial Customer Ratio 23) System Load Factor 2 Weather-Normalized 1984 System Peak Demand 25 Electric End Use 25 Conservation and Load Management az Econometric Analysis 33 5 Projected GVEA Load Growth 37 Alaska Energy Forecasting Models ae RED Model Forecast for the Fairbanks-Tanana Valley Area 37 Econometric Inference 38 GVEA Forecast Results 40 GVEA Forecast Methodology 40 References 49 Bibliography Su Appendix A. REA Forms Appendix B. Historic Customer and Load Curves Appendix C. Residential-to-Commercial Customer Equations Appendix D. Econometric Analysis Appendix E. Railbelt Energy Forecasts TABLES Page 1 Fairbanks North Star Borough, Nonagricultural Wage and Salary Employment, 1970, 1980, and 1984 5 Z Fairbanks and Fairbanks North Star Borough Population 1970 to 1984 7 3 Population Forecasts for the Fairbanks North Star Borough 11 4 Adopted Population Forecast for FNSB and GVEA Service Area 16 5 GVEA Number of Customers, Usage per Customer, Energy Requirements, and Revenues 20 6 GVEA Energy Requirements and Peak Loads, 1980-84 25 7 GVEA Peak Loads Versus Minimum Fairbanks Temperatures, January 1984 26 8 Residential Electric End-Use Data for the Fairbanks-Tanana Valley Area, by Housing Type, 1980 29 9 Estimated Residential Electric End-Use Data for the Fairbanks-Tanana Valley Area, 1980 30 10 Commercial Electricity Consumption, Fairbanks (1973 to 1980) and Comparison of Fairbanks, Anchorage, and United States Average, 1980 44 11 Railbelt Electric Demand Model Projected Growth Rates, Fairbanks-Tanana Valley Area 38 12 GVEA Electric Load Forecast 41 vii FIGURES Page 1 Golden Valley Electric Association Service Area 2 2) Fairbanks Area Population Growth, 1970 to 1984 8 3 Fairbanks North Star Borough Population Forecasts 12 4 Fairbanks North Star Borough Projected Population Growth Rates, 1984 to 1995 aS) 5 Number of GVEA Customers, 1970 to 1984 a. 6 GVEA Annual Sales and Own Use, 1970 to 1984 22 u Ratio of GVEA Residential to Commercial Customers, 1970 to 1984 24 8 Average Use per GVEA Residential Customer, 1970 to 1984 28 9 GVEA Electric Load Forecast, 1984 to 1994 42 10 REA Trend Analysis Versus Power Requirements Study Forecasts 43 yA! Number of GVEA Customers, Actual 1984 and Projected 1989 and 1994 44 12 GVEA Electric Energy Sales, by Class, Actual 1984 and Projected 1989 and 1994 45 ix ACKNOWLEDGMENTS We wish to express our appreciation to the Golden Valley Electric Association personnel for their guidance and as- sistance during the conduct of this project. In particular, we would like to acknowledge the direction provided by Mike Kelly, John Huber, and Steve Haagenson. In addition, much of the information developed in this report is owed to assistance and insights provided by members of planning agencies and the business community in Fairbanks. The CH2M HILL project team included: Principal-In-Charge Marty Bushue Project Manager David Gray Project Economists Mike Matichich Clint Stanovsky Lloyd Pernela Bob Brooks Research Assistants Paula Wellen Heather Horton Secretarial Jean Horton xi Chapter 1 SERVICE AREA The Fairbanks-Tanana Valley area of Alaska is provided with electric service primarily by two utilities: Alaska 6, Golden Valley Electric Association, Inc. (GVEA), and the Fairbanks Municipal Utilities System (MUS). In addition, the University of Alaska and three military installations have their own electric generation and distribution systems. The military installations include Fort Wainwright, Eielson Air Force Base, and Fort Greely. The MUS service area includes most of downtown Fairbanks. The Chena River flows through the northern part of the ser- vice area with Fort Wainwright providing a border to the east. The industrial area in the south part of Fairbanks is served by both MUS and GVEA. The GVEA service area covers 2,200 square miles (Figure 1), and includes most of the remaining populated portions of the Fairbanks North Star Borough (FNSB) including those portions of the City of Fairbanks not served by MUS; the City of North Pole; and the communities of Ester and Fox. In addition, GVEA serves areas outside the FNSB. To the southwest, GVEA's service area extends along the Nenana River and George Parks Highway through the communities of Nenana, Clear, Rex, Ferry, Healy, Carlo, Windy, and Cantwell; to the southeast, along the Delta River and Richardson Highway to include the commu- nities of Big Delta and Delta Junction. The FNSB is located near the geographic center of Alaska and contains the major trade, transportation, government, fi- nance, education, and service area for the interior and northern Alaska. The City of Fairbanks is situated on the banks of the winding Chena River, with rolling hills to the north, east, and west of the urban area. The Tanana River is located to the south with the Alaska range on the southern horizon. Mt. McKinley, the highest peak in North America, is visible on clear days. The Fairbanks North Star Borough has a continental climate. The sun is above the horizon from 18 to 21 hours each day during June and July, when daily average maximum tempera- tures reach the lower 70's. Temperatures of 80°F or higher occur on about 10 days each summer. Between November and March, daylight ranges from 10 to less than 4 hours per day. During December and January, maximum average temperatures are usually below zero. Extremely cold temperatures below -40°F occur, on average, 14 days each winter. The coldest temperature ever recorded in Fairbanks was -66° (January 1934). Precipitation in Fairbanks averages about 11 inches annually. August typically has the most precipitation and April the least. Snowfalls of 4 inches or more in a day occur about three times during the average winter, and bliz- zard conditions are almost never experienced. The highest total winter snowfall ever recorded was 145.7 inches in the winter of 1970-1971, and the lowest was 22.9 inches in the winter of 1952-1953. FAIRBANKS NORTH STAR BOROUGH BOUNDARY —_ . ; ---+- ‘ a ene t - : e a = aoe eo ee a Fairbanks Delta Junction GOLDEN VALLEY ELECTRIC ASSOCIATION SERVICE AREA SCALE: 1": 48 MILES Figure 1 Golden Valley Electric Association Service Area Chapter 2 ECONOMIC BASE AND DEVELOPMENT OF THE FAIRBANKS NORTH STAR BOROUGH The Fairbanks economy is based largely on the natural re- sources of Alaska's interior and northern slope and its strategic location for national defense. Over the years, this has resulted in economic "boom and bust" cycles as nat- ural resources have been exploited in staged development and as national military requirements have shifted upward and downward. These shifts have been reflected in production and employment fluctuations in the construction, mining, transportation, and military industries. In addition, busi- nesses associated with tourism and recreation development have been important to the development of the Fairbanks re- gional economy. Goods and services produced by these industries brought in- come to the region from outside sources, and have been the regional economy's "prime movers." This is because most of the income brought into the region by these industries was respent locally and became the basis for income to other local industries and to trade and services firms. (Produc- tion for export from a region is referred to as "basic"--or the economic "base" of a region--while other support activ- ity is defined as "local.") HISTORICAL DEVELOPMENT Fairbanks was originally established as a gold mining town. Although the first prospectors came into the Tanana Valley before the turn of the century, the major gold strike did not come until the summer of 1902 on a creek 16 miles north of Fairbanks. Gold mining was the single economic base in Fairbanks until its decline during the 1940's. During and after World War II, there was a significant expansion of military and federal government activity in the Fairbanks region. The construction of the Trans-Alaska Oil Pipeline in the mid-1970's created a major economic expansion for the Fair- banks North Star Borough. While total employment was gener- ally decreasing at a rate of about 2 to 3 percent per year during the early 1970's, it increased by two-thirds during the first 2 years of the pipeline boom, growing from 21,500 in 1973 to 35,900 in 1975. After the peak construction year in 1975, employment generally decreased an average of 6 per- cent per year for the remainder of the 1970's. During the 1980's, employment has grown rapidly, increasing an average of 7.3 percent per year. During the 1970's and early 1980's, the FNSB economy matured in two major ways: it developed a much stronger "local" or "support" sector, and its basic sector diversified. The fundamental change in the support sector is described in the Fairbanks North Star Borough Comprehensive Plan (Ref. 1): Despite the pipeline's intervening boom-bust cycle, Fairbanks' private sector employment grew from 8,093 in 1970 to 15,303 in 1982... However, less than 5 percent of this growth could be attributed to the two new industries--pipeline transportation and petroleum refining--that were made possible by the pipeline project. The Fairbanks economy's ability to sustain much of the growth during the pipeline boom is due to the fact that this expan- sion responded to service needs and retail demands that existed prior to the pipeline, but had not been met. As a result, the pipeline did not just increase private sector employment, it also brought about significant structural changes. Prior to the pipeline boom, regional income brought to the FNSB economy by its basic sectors quickly "leaked" from the economy as the income was largely respent for goods and ser- vices outside the region in Anchorage and the "lower 48." The structural change resulting from the pipeline boom was simply that the service and trade industry grew such that a much greater share of regional income was respent locally. In effect, the regional economic multiplier (the ratio of total regional income and jobs for the basic and local sec- tor combined to those for the basic sector alone) has in- creased and is continuing to do so. As shown in Table l, the FNSB's trade and service industries grew significantly in the 1970's and are continuing to do so in the 1980's. Wholesale and retail trade grew an average of 4.1 percent during the 1970's and 10.4 percent per year during the first 4 years of the 1980's. Employment in the finance, insur- ance, and real estate industry experienced a similar growth pattern. Employment in the service industry grew an average of 9.0 percent per year during the 1970's and 6.4 percent per year during the first part of the 1980's. Fairbanks' basic sector is now much more diversified than it was 15 years ago. In addition to mining, military, and other government activities, Fairbanks' basic industries now also include petroleum refining, petroleum transportation, tourism, staging, warehousing and construction for North Slope-related activity, and mineral exploration and develop- ment in Alaska's interior. Table 1 FAIRBANKS NORTH STAR BOROUGH NONAGRICULTURAL WAGE AND SALARY EMPLOYMENT, 1970, 1980, AND 1984 Average Annual Increase or Decrease HI (8) Industry 1970 1980 1984 1970-80 1980-84 Private Sector Mining 86 47 250 (5.9)% 51.9% Construction L255, 17620 2,900 ZP10) SO, Manufacturing 248 580 550 8.9 (1.3) Transportation, Communi- cation and Utilities 1,646 2,620 2,800 4.8 ey Trade 2,614 3,899 5,800 aT! 10.4 Finance, Insurance and Real Estate 518 674 1,000 Zi, 10.4 Service and Miscellaneous eS 4103) 5250 9.0 6.4 Subtotal--Private Sector 8,093 13),543 S550 Sys) Sai: Public Sector Federal Government 2), 533 27307 27,050 (0.9) Zi) State and Local 37625 yp} 6,600 29) 6.6 Subtotal--Public Sector 6,358 7,420 977250 1.6 HY) TOTAL 14,451 20,963 27,800 Bo) Tae Estimated. Sources: Fairbanks North Star Borough Comprehensive Plan, Working Paper 1, Socio- economic Trends and Forecasts, August 1983, and Alaska Department of Labor, Alaska Economic Trends, December 1984. POPULATION GROWTH The FNSB population has generally followed employment trends since 1970. As shown in Table 2 and Figure 2, population grew rapidly during the pipeline boom, increasing by 60 per- cent (from 44,415 in 1971 to 72,037 in 1976). Following the pipeline boom, population decreased by 17 percent to 57,432 in 1980. With the economic revival, population is again growing rapidly (at an average annual rate of about 5 per- cent between 1980 and 1984). FNSB population has grown at a somewhat lower rate than em- ployment. During the 1970's, employment grew at an average annual rate of 3.8 percent compared to a rate of 2.3 percent for population. Likewise, between 1980 and 1984 employment grew at 7.3 percent per year compared to an annual popula- tion growth rate of 4.9 percent. The primary reasons for these differences are increasing numbers of households with two or more wage and salary workers, and continued decrease in the size of the average household. Table 2 and Figure 2 also show that population in GVEA's service area (Borough population outside of Fairbanks) is growing at a more rapid rate than the population in the MUS service area (Fairbanks). Prior to the pipeline boom, most growth occurred within the City of Fairbanks, which nearly doubled in population between 1970 and 1976. However, since 1980 nearly 90 percent of the Borough's population growth has occurred outside of the Fairbanks city limits. Table 2 FAIRBANKS AND FAIRBANKS NORTH STAR BOROUGH POPULATION 1970 TO 1984 Remainder Total Fairbanks Fairbanks of Borough North Star Borough 1970 18,053 27,811 45,864 1971 18,739 25,676 44,415 1972 19,451 26,607 46,058 1973 26,033 24,507 50,540 1974 30,010 27,997 58,007 1975 34,475 27,880 62,355 1976 34,554 37,483 72,037 1977 29,263 40,315 69,578 1978 27,116 33.729 60,845 1979 24,641 31,163 55,804 1980 25,641 31,791 57,432 1981 25,999 34,190 60,266 1982 26,362 36,771 63,239 1983 26,730 39,546 66,359 1984 27,103 42,530 69,633 Annual Growth Rate 1970-1980 3.57% 1.35% 2.27% Annual Growth Rate 1980-1984 1.40% 7.55% 4.93% Source: FNSB Planning Department. &0 70 60 50 ale. ee a ma ares ao o£ Gis. 1970 1972 1974 1976 1978 YEAR'S Oo FAIRBANKS + REST OF BOR 1980 Figure 2 > 1982 TOTAL 1984 Fairbanks Area Population Growth, 1970 to 1984 Chapter 3 PROJECTED ECONOMIC DEVELOPMENT ECONOMIC BASE Generally, Alaska economists forecast "steady and sustain- able growth" for the state's economy during the foreseeable future. Fairbanks, as the commercial center for Alaska's interior and North Slope, is therefore expected to see con- tinued economic growth. As discussed in Chapter 2, over the past 10 years the FNSB economy has diversified in terms of basic sector industries and has developed a much stronger economic support sector. Continued economic growth is pro- jected for both the basic and support sectors of the FNSB economy. Petroleum Exploration and Development Continued expansion of oil exploration activities in Alaska is likely to translate into continued expansion of staging, warehousing, and construction-related activity in Fairbanks. Although there have been decreases in world oil prices and the future price outlook is uncertain, oil industry activity in Alaska is expected to remain strong. According to Alaska Pacific Bancorporation (Ref. 2): To maintain profitability, multinational petroleum companies must find ways to lower their per-barrel finding and production costs. Alaska's highly productive existing fields and its vast potential for yielding more of them are attracting growing industry interest. According to Alaska Pacific statistics, $5 billion were com- mitted to exploration and development prospects in Alaska in 1984, and 3 percent growth in petroleum industry employment is anticipated for 1985. Fairbanks is expected to play an increasingly important role in supporting petroleum industry development during the next decade. According to the FNSB Comprehensive Plan, "The in- creased skills and oil industry experience of local resi- dents has decreased the need to import skilled workers and is expected to continue to do so," and oil industry offi- cials have indicated that coating and insulating pipe work could be shifted from the lower 48 to Fairbanks (Ref. 3). Petroleum Refining In Fairbanks, major expansion of the Mapco Refinery at North Pole is planned during 1985. This will include a new asphalt plant, scheduled for operation in April 1985, anda gasoline plant and crude refining system scheduled to begin operation about September 1985. In addition, Petro Star has planned for a new topping plant for fall 1985 and has long- term plans for constructing an aviation gasoline plant in 1986 or 1987. Mining The recent revival of mining activity in Alaska's interior is expected to accelerate over the next two decades. Ac- cording to Alaska Pacific, Alaska's mining industry is "slowly making progress toward realizing its vast poten- tial...and...during the next 5 years, several world-class operations will be either producing or under construction" (Ref. 4). The Usibelli Mine at Healy in GVEA's service area is doubling its annual production by exporting coal to Korea. Export began in late 1984. Military Military presence in the FNSB is expected to expand signifi- cantly in 1985 and 1986. The United States Army has an- nounced that 2,600 additional light infantry division troops will be stationed in Alaska. It is expected that 2,000 or more of these troops will be stationed at Fort Wainwright. In addition to the military personnel and an estimated 2,500 dependents, it is estimated by the Fort Wainwright Popula- tion Impact Transportation Subcommittee that civilian popu- lation will increase by another 1,100 to 1,300 as a result of the new military activity. State Government State government employment grew rapidly in recent years because of large increases in state revenues flowing from petroleum development on Alaska's North Slope. Because of declining oil prices, state employment is not expected to be a growth factor in the foreseeable future. However, as the state population continues to grow, there will be expansion at the University of Alaska, which has its administrative seat in Fairbanks. Currently, enrollment is about 5,600 students. Tourism It is projected that tourism will continue to grow as a basic sector of the Fairbanks economy. It is estimated that 15 to 20 percent of the state's tourists come to Fairbanks. In recent years, the number of visitors to Alaska has been growing at annual rates of 4 to 5 percent. Alaska Pacific Bancorporation projects growth of 7 to 8 percent in 1985. 10 Fairbanks share of Alaska's tourism is expected to grow with the completion of a new, first-class, high-rise hotel planned for the area. POPULATION FORECASTS FOR THE FNSB Several population forecasts have been developed for the FNSB. These are summarized and compared in Table 3 and Fig- ure 3. The forecasts contained in the original Fairbanks Metropolitan Area Transportation Study (FMATS), the FNSB Comprehensive Plan, and the Man in the Arctic Program (MAP) were developed before the decision was made to deploy a new light infantry division to Alaska. Approximately 2,000 of these troops will be stationed at Fort Wainwright. Table 3 POPULATION FORECASTS FOR THE FAIRBANKS NORTH STAR BOROUGH Annual Rate of 1984 1995 Growth FMATS A Original study 58,436 71,000 1.8% Revised 75), 100 121,828 4.5% Adjusted 69,633 121,828 5.2% Comprehensive plan (Moderate growth b b scenario) 597,793 73,069 1.8% Map 70,523 87,997 2.08% This study 69,633 94,466 2.8% *Interpolated between 1980 and 1987 data. Pinterpolated based on forecast figures for 1980, 1990, and 2000. “Includes the area defined as the Fairbanks-Tanana Valley. The revised FMATS population forecast and that adopted for the GVEA forecast include consideration of the light infan- try division population impacts. Fairbanks Metropolitan Area Transportation Study (FMATS) A set of population forecasts was prepared in mid-1983 as part of the FMATS study sponsored by the Alaska Department me eT it eee | ae sey a 4 ie Lee 11) oe TEL een —_— yo oe Oot uaa | a ae a a | Aw eau EL ee gtr dd ( ahd POPULATION (Thousands) A Ti roi TTT TTT Titi T Tei Hanne 1954 1986 S88 oO YEARS a FMATS + COMPRE- 2 FPMATS fa hc * THIS STUDY ORIGINAL, HENSIVE REVISED PLAN Figure 3 Fairbanks North Star Borough Population Forecasts of Transportation and Public Facilities. The forecasts were prepared for traffic analysis zones (TAZ's) that were then aggregated into community areas. By overlaying FMATS commu- nity areas on utility service areas, FMATS population fore- casts for the GVEA service area, Fort Wainwright, and the MUS service area were calculated. The FMATS forecasts were prepared for an area that covers approximately 75 percent of the Borough's total population. The original FMATS study projected that the FNSB population would increase from the baseline population of 54,000 in 1980 to 62,000 in 1987, and 71,000 in 1995. The FMATS forecast was revised in December of 1984 to re- flect several major changes in the understanding of the area's economy. Two changes were most important. First, economic growth during the early 1980's was faster than had been anticipated. Second, deployment of the major portion of Alaska's new light infantry division to Fort Wainwright was announced. As mentioned above, direct and indirect pop- ulation effects on the FNSB were projected at approximately 6,000. Since early plans called for rapid deployment of the division, the revised FMATS data for 1984 included some of the effects of the light infantry division's deployment. As shown in Table 3, the revised FMATS study projected a 1984 population of 75,100 and a 1995 population of 121,828. Since the light infantry division deployment will actually occur somewhat later than anticipated in the revised FMATS forecast, the FMATS 1984 forecast was too high. A special FNSB census for 1984 showed the population to be 69,633 rather than the 75,100 assumed in the revised FMATS report. However, since the development of the light infantry divi- sion is still planned for the next year or two, the Depart- ment of Transportation is continuing to use the FMATS 1995 population forecast for planning purposes. The adjusted FMATS forecast shown in Table 3 presents the implicit 5.2 percent annual growth rate between the 1984 census and the 1995 revised FMATS forecast. FNSB Comprehensive Plan The August 1983 FNSB Comprehensive Plan included the prepa- ration of a set of socioeconomic trends and forecasts for the Borough. The comprehensive plan is based on a forecast of likely growth industries in the Borough. As mentioned above, it was prepared before the announcement regarding the light infantry division; the latest data available for inclu- sion in the report were economic and demographic statistics for 1982. The moderate growth scenario from that study fore- cast a compound annual growth rate of 1.8 percent per year. 13 Man _ in the Arctic Program (MAP) A set of population and employment forecasts in the Fairbanks area were generated as part of the Man in the Arctic Program (MAP) analysis prepared by the University of Alaska's Insti- tute of Social and Economic Research. The MAP forecasts show population in the greater Fairbanks area growing from 70,523 in 1984 to 87,997 by 1995, a growth rate of 2.0 per- cent per year. The economic model, prepared as part of the MAP, is a computer-based system that simulates the behavior of the economy and population of the state of Alaska and 20 regions of the state. The model includes submodels that define sce- narios of development, produce statewide projections of eco- nomic and demographic factors, and disaggregate the statewide data to the 20 subregions of the state. Adopted Population Forecast The population forecast adopted for input to the GVEA load forecast was derived from the existing forecasts described above. The growth rates implicit in each of these studies are compared in Figure 4. The FMATS-revised and FMATS- adjusted forecasts appeared too high as a long-term rate of growth in light of the other plans and forecasts and in light of the boom-bust history of the Fairbanks economy. Growth rates from the FMATS-original forecast, the FNSB com- prehensive plan, and MAP forecasts averaged 1.9 percent per year for 1984 to 1995. This is substantially lower than the 4.5 to 5.2 percent annual growth rates reflected in the re- vised and adjusted FMATS forecasts. As shown in Table 3 and Figures 3 and 4, a midrange rate of growth was developed for the GVEA load forecast. The base Borough population is forecast to grow at 2.2 percent per year. In addition, 6,000 people are added to reflect the direct and indirect population effects of the light infantry division deployment at Fort Wainwright. The base growth rate of 2.2 percent per year is somewhat higher than the average of the FMATS-original comprehensive plan and MAP forecasts. This reflects the fact that the FNSB population is growing at a much faster rate than forecast in these studies. With the addition of the light infantry division, the overall rate of growth for the 1ll-year period averages 2.8 percent per year. As shown in Table 4, the annual growth rate will be 4.7 to 5.1 percent during the next few years as the light infantry division is deployed. As a basis for forecasting growth in the GVEA service area, a population growth-rate forecast was also developed for the Borough area outside of the City of Fairbanks and Fort Wain- wright. As mentioned above, the FMATS forecasts included 14 ST 5.00% 4.00% 0,000 %% ale —. COMPRE- ! THIS FMATS ORIGINAL HENSIVE Study STUDY PEVISED ADJUSTED PLAN ee Figure 4 Fairbanks North Star Borough Projected Population Growth Rates, 1984 to 1995 9T Table 4 ADOPTED POPULATION FORECAST FOR FNSB AND GVEA SERVICE AREA 1984 1985 1986 1987 1988 1989 1998 1991 1992 1993 1994 1995 1. Base Pooulation Growth at 2.2 Percent Per Year 69.633 71.165 72.731 74.331 75,966 77.637 79.345 81,091 82.875 64,698 86,561 68,466 II. Lioht Infantry Division 2,000 4,008 = 6.008 =, 0006, MP6, OAR 6, ROR 6,008 6, 00M AOR. ROR II]. Forecast FNSB Pooulation 69.633 73.165 76,731 80.331 81.966 83.637 85.345 87,091 68.875 98,698 92.561 94,466 IV, Borouch Growth Rate 9.07% 4.87% 4.69% 94% O42 R4X ASK OSH USA Re. 6X V. GVEA Growth Rate TAQ% 5.85% 16% 24k 4k 2k ASR eok OBA BX 2.6% detailed analysis and forecasts of development for zones within the Borough. This analysis showed that the GVEA ser- vice area within the FNSB is expected to grow at a rate 1.1 times as fast as the Borough as a whole. For most years, the FNSB forecast growth rate adopted for this study was multiplied by 1.1 to determine the forecast growth rate for the GVEA service area population. However, for 1985 and 1986, multipliers of 1.4 and 1.2, respectively, were used to reflect the recent short-term trend of rapid growth in the GVEA service area. As Table 4 shows population is forecast to increase an aver- age of 4.5 percent per year for 1984 to 1989, and 2.2 per- cent per year for 1989 to 1994. L7 Chapter 4 GVEA ELECTRIC LOAD GROWTH AND CUSTOMER CHARACTERISTICS HISTORICAL CUSTOMER AND LOAD GROWTH The total number of GVEA customers has grown every year since 1970 and does not reflect the dramatic changes in pop- ulation shown in Figure 2. Overall, the number of GVEA cus- tomers grew at an average rate of about 9 percent per year between 1970 and 1984. The number of customers grew rela- tively more rapidly during the mid-1970's and has started to grow relatively more yapidly again during the 1980's (almost 10 percent per year). Historical GVEA customer and load growth are detailed in Rural Electrification Administration (REA) forms included in Appendix A. GVEA customer and kWh use trends during the period 1970 to 1984 are summarized in Table 5 and Figures 5 and 6. Growth in customers? for the commercial class was more heav- ily influenced by the pipeline construction than growth in the residential class (Table 5). These figures show that the residential and small commercial classes have experi- enced slightly faster customer growth during the 1980's than during the 1970's. The large commercial class experienced rapid growth during the peak period of the oil pipeline con- struction (1975 and 1976). After a sharp decline in 1977, the number of large commercial customers remained essen- tially constant until 1980, when the current trend of grad- ual growth began. Total system electric usage (sales plus GVEA's own use) has grown an average of about 7.5 percent per year between 1970 and 1984. Table 5 and Figure 6 show that sales increased dramatically to 1977, and then decreased through 1980. The system has again started to experience rapid growth during the 1980's. The figures in Appendix B show that sales to the residential class followed essentially the same pattern as total system sales during the 1970's and 1980's. Small commercial sales increased through 1975 and then decreased to essentially a constant level through the remainder of the decade. Since 1980 sales have increased consistently from year to year. Sales to the large commercial class grew very rapidly during the mid-1970's, reached a stable level during the late 1970's, and have grown at a modest rate since 1980. Data on growth in customers by class of service will be found in Appendix B. LS) 1, MUNBER OF CUSTORERS A, RESIDENTIAL B. COWERCIAL 1. SMALL COMMERCIAL 2. LARGE COMRERCI AL C. RESALE TO OTHERS D, STREET LIGHTING . OWN USE TOTAL CUSTOMERS TL. ANMURL Kilt USE AL RESIDENTIAL B. COMERCIAL 1, SAALL COMMERCIAL 2. LARGE COMPERCIAL C, RESALE TO OTHERS D. STREET LIGHTING ‘E. OWN USE TOTAL PAOLA Kit USED IIL, AMERAGE USE /CUSTORER AL RESIDENTIAL, B. COMMERCIAL 1. SARL COMERCIAL 2. LARGE COMMERCIAL C. RESALE TO OTHERS D. STREET LIGHTING €. Ow USE N oO IV. TOTAL REVENUES: A. RESIDENTIAL B. COMMERCIAL 1. SAUL COMMERCIAL 2. URRGE COMMERCIAL C. RESALE 10 OTHERS D. STREET LIGHTING E. Oww USE TOTAL REVEMES AVERAGE REVENUE /KiH AL RESIDENT Ae. B. COMERCIAL : 1, SARL COMMERCIAL 2. LARGE COMMERCIAL C. RESALE 10 OTHERS D, STREET LIGHTING €. Oem USE Table 5 GVEA NUMBER OF CUSTOMERS, USAGE PER CUSTOMER, ENERGY REQUIREMENTS, AND REVENUES AVERAGE AMMURL PERCENT CHANGE YEAR 1978 m1 1972 1973 1974 1975 1976 1977 1978 1979 1968 1981 1982 1983 10 1 S66 6A 6 SAT 1,32 082,203 18,688 11,885 13,08 13,58 1G IATHR 16176 TSE BL 1 168 816 656 m1 87 1,063 1,064 1,338 1,468 1,553 1,614 1,715 he rr 37 a a re 6 126 mm ate a Fe 6 218 233 a ast 1 1 1 1 1 1 1 1 i 8 8 9 9 3 3 9 9 9 3 ' 1 1 1 1 1 1 1 1 1 1 at ST — —— | 6,364 Q16h 10,462 12,856 13,386 A716 15,364 15,751 16,687) = 18,279 19,886 22, 788 | 1 ! 67,123,445 04,718,325 96,701,717 186,881,754 127,872,049 168,208,086 162, 369,687 168,274,885 150,804, 123 142,968, 368 135,958,270 133,871,644 158, 486,931 158,645, 387 172,658, 822 | ' 25,568,019 37,682,081 40,396,044 39,318,206 44,262,910 51,021,038 27,409,260 37,008,367 36,941,187 37,284,778 36,198, 322 38,651,262 43,195,497 45, 747,326 53.748, 118 | 43,495,686 51,591, 798 52, 358,516 59,425,731 38,079,477 82, 158, 175 111, 326,097 117,618,078 128, 261, 385 118, 158,624 115,988, 195 128, 889, 095 129, 394,538 138, 751,900 139, 962, 928 1 9,191 939,687 4,168,500 4,058,900 8,332,000 127,308 3,042,293 3,520,268 3,025,000 9,534, 708 8,382,500 17, 132,200 | 298,668 487,639 383,728 48,0252, 6B 74,78 354,54 414,645 393,735 316,074 318,825 22,891 7,51 ' 1,573,796 1,639,781 1,725,166 1,503,387 1,431,098 1,468,476 1,441,491 1,465,953 1,338,456 1,386,239 1,470,876 1,284,149 1,372,726 1,317,008 1,465,822 | ee a ee gies et, Sere | se = 7 eas, | ey 134,059, 534 176, 688, 807 191,567, 969 287,611, 104 212,989, 189 380, 183,645 386,951,939 333, 914, 728 316, 458, 186 383, 140, 376 293,837, 954 297,964,041 334, 311, 958 344, 985,047 384, 959, 882 1 ' 1 1,682 13,169 13,920 144791582217, 1S ABT 142 STA 8,519,787 9,089,983 i BLGA2 4617947195 43.638 4 BMG 47858 25,761 8257251648088 22,428,537 23,236 22,78 IS 763,061 (659, 063 872,642 625, 357 41, 651, 986 (9, BS ‘S382, 268 388, 973 365, 314 363,011 3,178 ‘5,41 5, 008 ‘S28, 162 | ‘ ‘ ‘ . ‘ @ 4,050,900 8,332,000. 727,388 3,042,293 3,928,268 3,025,008 9,534, 708 8 382,500 17,132,200 | UL 64, 955 47,966 (4,253 4, 708 41,628 33, 69 %, 072 43,798 Sy (3, 306 5a %, 336 28, 226 er 1,573,796 1,639,781 1,725,164 1,583,307 1,431,898 1,468,476 1,441,491 1,465,953 1,338,456 1,386,239 1,478,874 1,284,149 1,372,726 1,317,600 1,465,822 | ! 2,597,069 3,098,247 3,508,039 3,985,459 4,587,516 6,888,681 8,693,771 9,272,358 10,082,414 10,734,467 12,257,077 14,067,173 16,374,858 16,821,075 16,791,119 | ' 1,412,756 1,521,698 1,545,035 1,734,698 2,481,788 1,871,578 2,231,378 2,622,117 2,922,378 3,400,059 4,127,819 4,907,146 5,295,013 | 1,198,633 1,382,547 1,988,063 1,672,361 3,101,994 5,306,474 5,686,629 6,718,924 7,138,179 6,700,611 11,578, 775 11, 122, 888 1 26,501 152,491 198,987 408,158 24,711 148, 168185, Bb 701,534 098,661 | 19,785 23,662 32,352 37,438 27,688 23,7985] 18,912 15,4552 AeB 1,2 36,471 1 ' ees ces pete ss ee — ches, Stee! eects, ees Secs St | 4,522,335 5,725,298 6,516,628 7,076,795 8,048,756 12,688, 712 16,097,798 17,536,827 19,467,621 28,965,612 24,575,058 29,089,829 33,598,782 33,772,667 34,016,681 | ' ' 90.039 90.037 08.037 8.037 0.035 OSG OS HO]. ' O42 0.037008). 07] 8B TT IS 88. 90.028 0.023 ATH 888.079. meee ma Ce ee SC Ce SS Sn SC St ERR I 90.000 OU) 88800 1970-4 1978-88 1988-84 fpeeees Genememrn,| ons = 9.28 9.068 9.678 0 7,624 O58 11. 6e8 1378 65a ” a toe 108. 0 1.188 -108. 005 om om Lid aim 6.97% 9.678 6.968 Las » 168 5.450 Lat 10, 398 oe 10. sm 4.618 ” Lt AS 108. oes 0.63% -108. 08% O58 0.678 +. 7.6% 1.08 6.99% “tt 1.618 3. 2.3 -Lon 178 eed 158 ” 4.5 a ed on +7 14.268 16, 788 6.1% 12.008 12.128 7s 17.0% 21.6% 6.3 m ” wan ” 4.68% a o m La 14.818 1s oun 6.008 6.828 1.98 b.2i8 8am 12m was 10.338 1 « Ls m « 4.028 «a ” a ” GS sands) Cc 1 i te = _— a (Th 4. fh o —_— ra 1972 apap 1a74 _— T ———— eae —— 1980 1982 1984 Figure 5 Number of GVEA Customers, 1970 to 1984 p86 O} O26} ‘eSM UMO pue sales jenuUy WIAD 9 ainBy4 Si oO mm bal: esél Oss eee eee ee le 4. Bfél = 1t ADSI. S/ 61 1 vi6l éLél Ss he ses = OL61 Os ool OSl OSE OOF OSfe OOF (SUO!||1)4) HAM TY ONN'Y 22 Use per customer has declined for the residential and com- mercial classes since the mid-1970s. (These trends are also summarized graphically in Appendix B.) Residential use per customer grew in the early 1970's and then declined rapidly during the late 1970's. Use per customer has been roughly stable during the 1980's. Small commercial use per customer increased during the early 1970's and then declined signifi- cantly in the mid-1970's to a level where usage has stabi- lized. A similar pattern occurred in use per customer for the large commercial class. RESIDENTIAL/COMMERCIAL CUSTOMER RATIO Since 1970, the historical ratio of residential to commercial customers has been fairly constant, ranging between 7 and 8 residential customers per commercial customer (Figure 7). Simple least-square regression analyses were made to express the relationship between residential and commercial customers. The analyses, detailed in Appendix C, showed a strong his- torical relationship between the total number of commercial customers and the number of residential customers as well as between the number of small commercial customers and the number of residential customers. The regression equations show that for every new residential customer, the total num- ber of commercial customers has increased by a factor of 0.126 and the number of small commercial customers has in- creased by a factor of 0.107. This historical relationship of commercial to residential customers is expected to con- tinue in the future, and is therefore incorporated into the GVEA load forecast. SYSTEM LOAD FACTOR From 1980 to 1981, GVEA system annual load factor grew slightly; there was a marked increase in 1982, followed in 1983 by a slight reduction and then in 1984 by another sig- nificant increase (Table 6). The increase in load factor in 1982 appears to have resulted from a combination of several factors. First, GVEA sold more than three times as much energy to MUS in 1982 than in 1981. (After a 12 percent decline in 1983, GVEA doubled the energy sold to MUS in 1984.) Since the energy was sold off-peak, these increased sales for resale account for a portion of the increase in load factor. It is expected that future sales to MUS will continue to be off-peak, and will therefore not contribute to peak-load requirements. Second, beginning in 1982, GVEA altered its generation dispatch methods to take advantage of economy energy purchase and sales opportunities. This ap- proach resulted in an improved system load factor. Besides sales to MUS, the GVEA is selling economy energy to the Uni- versity of Alaska. Third, GVEA engineering staff reports that load factors for individual customers and customer groups have improved over the past several years. This could not be verified, however, because of the lack of spe- cific metered data for various customer classes. seS5617AA2 a3 vz ed 10 a, o 7 0 VA os, “ aa 9 fo a | -_— “ ieee 71h een Eee ee ag hi Wey To i e eh UN o 7 ae 8g 4 —_2a— 94 CS cee ae €t —o——— b 7 E E eg m or ii 5 o = re 4 2 1: 3 r ” 1 o eT i aT aetiT i Tait T Tiny: 7 pit 7 in| = T T nt 1970 1972 1974 1976 1978 1980 1982 1984 Year Oo Tatal Carnimercial + Small Cornmercial Figure 7 Ratio of GVEA Residential to Commercial Customers, 1970 to 1984 Table 6 GVEA ENERGY REQUIREMENTS AND PEAK LOADS, 1980 TO 1984 Net System Annual kWh Purchased Peak Demand Load Year and Generated (kW) Factor 1980 318,588,660 70,000 52.0% 1981 319,947,853 68,700 Sseze. 1982 359,884,612 69,700 60.5% 1983 370,416,761 72,200 58.6% 1984 416,278,640 74,700 63.6% GVEA engineering staff expect that these conditions will continue in the future. For this reason, we have assumed that the GVEA load factor will be in the 60 percent range throughout the forecast period. WEATHER-NORMALIZED 1984 SYSTEM PEAK DEMAND Weather normalization analysis indicated that the GVEA an- nual peak was very close to what it would be calculated to be under normal temperature conditions. This normalization analysis was based on a statistical analysis of daily mini- mum temperatures for the month of January 1984. A linear regression was used to plot daily peak demands on the GVEA system against minimum daily temperatures in January of 1984. Temperature and peak-demand data were provided by GVEA. Table 7 shows the January 1984 temperature and peak data used in the analysis. Based on an average 1964-to-1984 minimum January temperature of -43.7°, the actual January 1984 system peak demand of 74,700 kW was adjusted by 630 kW to a weather-normalized peak of 74,070 kW. This adjustment was made by applying the temperature coefficient (-273.73) to the difference between the actual minimum recorded tem- perature and the 20-year-average-minimum temperature for January. ELECTRIC END USE Residential The major factor affecting electricity consumption patterns in the GVEA service area over the past 20 years has been electric heat. In the late 1960's, the cost of electricity continued to decline with the startup of the 25-MW coal- fired Healy Plant. To market available capacity from this plant, a promotional rate structure was established to pro- mote electricity for water heating, space heating, and other uses. In 1967, the Chena River flood inundated 50 percent 25) of the homes in the GVEA service area. many oil furnaces were replaced with electric furnaces or baseboard heating systems. Date Jan. 1 Jan. 2 Jan. 3 Jan. 4 Jan. 5 Jan. 6 Jan. 7 Jan. 8 Jan. 9 Jan. 10 Jan. 11 Jan. 12 Jan. 13 Jan. 14 Jan. 15 Jan. 16 Regression equation: minimum daily temperature. R-squared: Standard deviation: F-statistic: Durbin-Watson stat: Minimum Daily Temperature (Farenheit) - 3 -4 -4 -20 -26 -15 -14 -20 -20 -7 0 27 10 7 17 - 3 0.8838. 0.8838. 1.685. Table 7 GVEA PEAK LOADS VERSUS MINIMUM FAIRBANKS TEMPERATURES, JANUARY 1984 System Peak (kW) 58,400 62,900 61,000 62,600 63,800 62,100 61,100 65,400 64,100 60,200 59,100 56,300 53,700 55,300 56,800 59,000 5,774.4, Date Jan. Jan. Jan. Jan. Jan. Jan. Jan. Jan. Jan. Jan. Jan. Jan. Jan. Jan. Jan. Peak = 59,264 - 273.73 7; 18 Lo) 20 2A: 22 23 24 25 16 Pt 28 29 30 Su During the cleanup, Minimum Daily Temperature (Farenheit) = 4 - 4 -22 -26 -30 -43 -46 -46 -46 -40 -46 -47 -20 12 =10 System Peak (kW) 61,600 60,800 62,200 63,400 66,500 71,100 73,700 74,300 74,700 69,500 71,500 74,400 65,300 64,600 63,200 (TEMP) were TEMP equal Although the marketing of electric heat was discontinued in the early 1970's, electric heat continued to grow. estimated that during the mid-1970's, struction in Fairbanks had electric heat. 2c i8 70 percent of new con- Much of the new housing stimulated by the pipeline activity was poorly built GVEA's rates were beginning to increase primarily because of increases in fuel costs following the Arab oil embargo. and poorly insulated. At the same time, Although electricity was no longer competitive with fuel oil for heating, contractors continued to install electric heat in new construction because of the relatively lower equipment cost. Faced with the prospects of even higher electric rates to meet the generating cost for new electric heating loads, GVEA instituted an electric heat moratorium and began an energy conservation program. In the late 1970's and early 1980's, GVEA electricity prices continued to increase more rapidly than general inflation. As a result, a large number of consumers converted from electric to oil heat, both for space heating and for water heating. In addition, numerous buildings were retrofitted to be more energy efficient. The growth of the electric heat market in the early 1970's, followed by the heating moratorium and conversion away from electric heat, resulted in dramatic shifts in the average use per residential customer on the GVEA system. As shown in Figure 8, average usage per customer increased by about 50 percent in the first half of the 1970's and then de- creased to about half the usage of 1975 over the following 10 years. GVEA's engineering staff estimates that only 5 to 7 percent of GVEA residential customers are still electrically heated. This is consistent with the results of an electric end-use survey conducted for the Alaska Power Authority (APA) as input to the Railbelt Electric Demand (RED) model, also sponsored by the APA (Ref. 5). For each of the four types of housing--single family, mobile home, duplex, and multi- family--market saturation for various household appliances and the percentage of these appliances using electricity was estimated from the survey. Results are shown in Table 8 for the Fairbanks-Tanana Valley area (including the service area of both GVEA and MUS). From these data, the market saturation of electric appliances was estimated (Table 8). Given the following distribution of households served in the Fairbanks-Tanana Valley area in 1980, the overall appliance saturation was estimated to be as shown in the first column of Table 9: Single family 47.1% Mobile home 34.5% Duplex 7.8% Multifamily 10.6% Through this analysis, it is estimated that 7.1 percent of the households in the area use electricity as the primary heating source. It is estimated that nearly five times as many residences use electricity for water heating. None- theless, at 34 percent, this is only a moderate share of the 27 82 hf Year byt! (Th nds GUST 0 Mee ee en 1970 1972 1974 1976 1978 1980 1982 1984 Year Figure 8 Average Use per GVEA Residential Customer, 1970 to 1984 6z Soace Heat Water Heat Clothes Washer Clothes Dryer Cooking Ranoe Refrioerators Freezers Dishwashers Saunas, Jacuzzis RESIDENTIAL ELECTRIC Market Saturation Electric 1 SOURCE : 00.2 86.9 84.9 81.4 99.5 99.8 84.9 53.8 SINGLE FAMILY Percent 9.7 33.1 108.8 %.2 79.8 108.8 108.8 108.@ Electric Market Market Table 8 Saturation Saturation Electric 9.7 28.8 84.9 78.3 78.6 99.8 84.9 53.8 108.8 9.0 3 9.3 98.6 99.0 73.8 48.6 END-USE DATA FOR THE FAIRBANKS-TANANA VALLEY AREA, BY HOUSING TYPE, 1980 MOBILE HOME DUPLEX MULTI FAMILY Electric Electric Electric Percent Market Market Percent Market Market Percent Narket Saturation Saturation Electric Saturation Saturation Electric Saturation 0.8 0.8 108.8 11.7 11.7 108.0 14.8 14.8 42.8 42,4 100.8 43.1 43.1 108.8 26.2 26.2 108.8 2.3 85.5 108.8 65.5 63.8 108.2 63.8 94.6 67.3 65.5 94.4 68.7 61.8 100.0 61.8 48.2 47.5 108.8 9.8 95.8 108.8 97.1 97.1 100.8 99.0 9.8 108.0 99.8 99.8 100.8 99.8 108.8 73.8 75.2 108.8 75.2 57.2 108.0 37.2 100.8 48.6 57.4 108.8 7.4 23.3 108.8 23.3 108. @ 25 8.2 68.8 5.8 5.7 100.8 a7 1g 61.8 4.9 Alaska Power Authority, RED Mocel Technical Documentation Reoort 2.5 Table 9 ESTIMATED RESIDENTIAL ELECTRIC END-USE DATA FOR THE FAIRBANKS-TANANA VALLEY AREA, 1980 Weighted Weinhted Electric Annual Average Appliance Appliance Household APPLIANCE Saturation Usage Usage (Percent) (kWh) (kih) Space Heat 71 3158 2567 Water Heat 4.3 3300 1132 Clothes Washer 85.3 ) 7 Clothes Dryer 79.8 1838 Bee Cooking Range 71.4 852 604 Refrigerators 99.2 1636 1620 Freezers TA 1342 1035 Dishwashers 49.1 28 123 Saunas, Jacuzzis 4,2 2002 84 Lights and Other 1704 Total 9767 30 water-heating market. There is substantial market penetra- tion of electric refrigerators, freezers, washers, dryers, and ranges. On the other hand, there is still room for greater market penetration for these appliances as well as for dishwashers, saunas, and smaller electric appliances. Documentation for the RED model (Ref. 6) also includes esti- mates of the average annual kWh requirements for appliances in the Fairbanks area. These estimates are shown in the second column of Table 9. Given these factors, end use com- ponents of the average GVEA residential customer's kWh con- sumption in 1980 were estimated as shown in the third column of Table 9. Although electric heat has the lowest market penetration of the major appliances shown in Table 9, it makes the most significant contribution to the weighted average usage per household: 2,567 kWh out of the 9,767 kWh total in 1980. As Table 9 shows, this is because of the substantial annual energy requirement for heating the average residence in Fairbanks: 36,150 kWh according to the APA study (Ref. 6). This underscores the pronounced impact that changes in heat- ing sources have had on GVEA's average-usage-per-customer statistics and on the system's overall energy requirements. Since there is now relatively little electric space heating in the GVEA service area, it is apparent that the signifi- cant decreases in average use per customer will likely not continue. However, now that the heating moratorium has been lifted, if the price of oil were to increase sharply rela- tive to GVEA's electric rates, the potential would exist for significant increases in average usage per customer. In our load forecast, it is assumed that this scenario does not occur. However, market saturation of other electric appli- ances is expected to increase gradually in the future as incomes increase and new products are brought to the market. Commercial Relatively little data are available on electric end-use by commercial customers in the Fairbanks area. These data are more difficult to develop for commercial customers versus residential customers because of the more heterogeneous nature of the commercial class. End-use surveys have not been conducted for businesses in the Fairbanks area; how- ever, documentation to the RED model (Ref. 7) contains data on commercial employment, floor space, and kWh consumption (Table 10). Two separate sets of data are shown. The first shows the change in figures for Fairbanks between 1973 and 1980. The second data set compares data for Fairbanks, Anchorage, and the remainder of the United States in 1980. Ss Table 10 COMMERCIAL ELECTRICITY CONSUMPTION, FAIRBANKS (1973 TO 1980) AND COMPARISON OF FAIRBANKS, ANCHORAGE, AND THE UNITED STATES AVERAGE, 1980 Square Footage kWh Per kWh Per Fairbanks Per Employee Employee Square Foot 1973 217.8 6,631 2iu7 1974 201.1 5,399 26.8 1975 169.1 5,368 31,7 1976 185.2 5,641 20.5 1977 224.1 6,922 30.8 1878 25964 7,550 29.6 1979 292.4 6,858 23.58 1980 318.3 6,913 21.7 1981 NA NA 21.5 1980 Comparison Fairbanks 360.0 7,496 20.8 Anchorage 429.0 8,407 19.6 U.S. 531.0 7,303 13.8 Analysis supporting the RED model showed that floor space was "theoretically superior and a slightly more stable pre- dictor of electricity consumption" (Ref. 8). Data in Table 10 show that since the pipeline boom, square footage per employee has trended upward but is still well below the national average and somewhat lower than the Anchorage aver- age. Conversely, the kWh usage per square foot in Fairbanks is trending downward but is above averages for Anchorage and the nation as a whole. CONSERVATION AND LOAD MANAGEMENT As mentioned above, there has been substantial development in electric energy conservation in the GVEA service area since the mid-1970's. Average use per residental customer decreased from 17,300 kWh in 1975 to 8,500 kWh in 1984, During the same period, average annual use per commercial customer decreased by 30 percent, from 110,000 kWh in 1975 to 77,000 kWh in 1984. Much of this reduction in usage can be attributed to cus- tomer shifts from electricity to fuel oil and wood for heat- ing purposes. In addition, more efficient electricity usage resulted from increases in the real price of electricity, elimination of promotional rates, and government- and utility-sponsored conservation programs. 32 GVEA's energy conservation program is based on REA regula- tions. The utility has an energy use advisor who performs energy audits, generally disseminates conservation data, and makes conservation recommendations. In addition, GVEA has integrated its conservation program with that of the State of Alaska. The State of Alaska conservation programs have included grants and low-interest loans for insulation and weatherization. The Alaska Power Authority (APA) indicated in its FERC Susitna Hydro Project License Application that there are now few inexpensive opportunities to save large amounts of power in Fairbanks and the remainder of the Railbelt region (Ref. 9). In Fairbanks, this is supported by the fact that there is relatively little remaining use of electricity for heating. To date, GVEA has had little need to expand its load manage- ment program so as to control loads at the time of the sys- tem peak. There are three reasons for this. First, as average kWh usage per customer has decreased, peak demand per customer has also decreased. Second, there is an excess of generating capacity available to GVEA, so the utility is not facing supply constraints during peak periods. Third, as mentioned above, the utility's load factor has improved without customer load management. GVEA management has indi- cated that load management will continue to be included in its planning process. ECONOMETRIC ANALYSIS Econometric equations for historic electric consumption per customer were estimated for the residential and small com- mercial customer classes. The goal of the econometric anal- ysis was to achieve insight into consumption behavior during the 1970's and early 1980's, and to attempt to develop a basis for forecasting future usage patterns. Explanatory variables tested for significance included electric consump- tion, wages and salaries per employee, total employment, electric energy and fuel oil prices, and annual heating de- gree days. Descriptions of these time series analyses will be found in Appendix D. Residential Equation The major findings of the residential consumption analysis are consistent with the historical development of service to the residential customer class, and with the end-use analy- sis discussed above. Residential consumption in a given year was found to be strongly associated with consumption in the previous year, evidence of the fact that electric appli- ances are replaced slowly over time. 35) Nonetheless, real electric prices exerted a significant in- fluence on current consumption, showing an estimated elas- ticity of approximately -43 percent during the period of analysis. This coefficient is quite high compared to short- run price elasticities estimated in more refined studies of other service areas, which range from zero to 30 percent. It is likely that the estimated price coefficient reflects other factors (including housing stock changes such as floor- space per customer) for which data were not readily avail- able for inclusion in the analysis. Further, as discussed above, the late 1970's was a period of fuel-switching and conservation investment in Fairbanks, when many one-time investments were made in response to electricity prices. Because most of these investments are not repeatable, it is reasonable to expect lower price elasticity in the future. Lagged real wages and salaries per employee and lagged oil prices figure significantly in the historical model, con- sistent with a plausible theory of fuel switching by resi- dential customers in which the decision to switch to fuel oil is related to the previous year's price of oil and to the ability of the consumer to pay the necessary capital costs. Finally, a dummy variable related to the pipeline construction boom of the mid-1970's was significantly re- lated to residential consumption. Consistent with an in- creasing penetration of oil heat, annual heating degree days were not found to be significantly related to consumption. Small Commercial Equation Historical data show a dramatic transformation of small com- mercial consumption behavior during 1975. During the 1970- 1974 period, small commercial customers consumed an average of 43,100 kWh per customer annually, and consumption was strongly related to heating degree-days. Consumption fell sharply during 1975 and averaged only 24,400 kWh annually during the 1975 through 1984 periods, when electric use was not strongly related to heating degree-days. Consistent with a less temperature-sensitive load, small commercial consumption was much less variable in the later period. To minimize the influence of the period of apparent fuel switching and of the pipeline construction boom, an esti- mation equation was developed for the 1975 to 1984 period. This equation indicates that, similar to the residential equation, current consumption is strongly related to con- sumption in the preceding year, but that the elasticity of lagged consumption is only about one-half of the residential value. The short-run price elasticity of small commercial consumption was estimated to be 56 percent. Though the dis- cussion regarding the estimated residential price elasticity applies to the small commercial equation as well, the small commercial equation provides evidence that small commercial 34 consumption is more price-sensitive and less constrained by the appliance and equipment stock than residential consumption. Current real wages and salaries per employee make a positive contribution to the equation, probably as an indicator of overall business activity in Fairbanks. Lagged oil prices also contribute to the equation, consistent with the theory that fuel switching has continued since 1975. Because of the period selected for the estimation equation, the effects of the pipeline boom and of most fuel switching are less strongly represented than in the residental equation. 35 Chapter 5 PROJECTED GVEA LOAD GROWTH The GVEA load forecast was developed on the basis of pro- jected economic development in the Fairbanks area, discussed in Chapter 3; analysis of customer kWh usage characteris- tics, discussed in Chapter 4 and below; and review of other electric load forecasting studies, also discussed below. ALASKA ENERGY FORECASTING MODELS Over the past several years, a number of models have been developed to forecast electric energy requirements in Alaska and the Alaska Railbelt Region. The most recent of these analyses is the Railbelt Electric Demand (RED) model forecast as included in the Alaska Power Authority's application for license for major project--Susitna Hydroelectric Project submitted to FERC in July 1983. RED MODEL FORECAST FOR THE FAIRBANKS-TANANA VALLEY AREA The RED model is an end-use econometric model that forecasts energy requirements and peak loads in the Railbelt region. It provides breakdown for the Anchorage-Cook Inlet and Fair- banks-Tanana Valley load centers. The model has seven inter- related modules including uncertainty, housing, residential consumption, business consumption, program-induced conserva- tion, miscellaneous consumption, and peak-demand modules. A primary driver of the RED model is the MAP model, described in Chapter 3, which provides forecasts of the following in- dependent variables as input to the RED model: population, households, employment, demographics, and real energy prices. RED model output in terms of growth rates for key forecast parameters in the Fairbanks-Tanana Valley area is shown in Table 11. The number of residential customers in the region is projected to grow 3.6 percent annually between 1985 and 1990 and 3.0 percent annually between 1990 and 1995. This projection is based on the assumption that the number of persons per household in the area will continue to decline. The number of employees in the area is forecast to increase at a significantly lower annual rate of 2.0 percent during 1985-90 and 1.5 percent during 1990 to 1995. This follows the historical trend discussed in Chapter 2. 1lohese studies are reviewed in Appendix E. 37 For both usage is over the is based does not next 10 years. the residential and business categories, average projected to increase at about 0.9 percent per year As mentioned above, partly on the conclusion that the Railbelt region have any inexpensive opportunities to save large this forecast amounts of power, and partly on the projection that the real price of electricity will, over the next 10 years, be lower than the current price. Table 11 RAILBELT ELECTRIC DEMAND MODEL PROJECTED GROWTH RATES, FAIRBANKS-TANANA VALLEY AREA Average Change Per Year (%) Description 1985-90 1990-95 Residential Number of Customers 3.6 3.0 kWh Usage per Customer 1.0 0.9 Total kWh Requirements 4.6 3.8 Business Number of Employees 2.0 1.5 kWh Usage per Employee 0.9 0.9 Total kWh Requirements 3.0 2.4 Other kWh Requirements® 52.1 0.3 Total Requirements Sad 3.0 Peak Requirements 63 3.0 @harge industrial, military, and miscellaneous (high growth rate in initial years is due to relatively small initial kWh requirements--7 million kWh in 1985). Overall, the area's electric energy requirements are pro- jected to grow at an average annual rate of 5.3 percent dur- ing the 1985 to 1990 period and 3.0 percent during the 1990 to 1995 period. A constant average annual load factor of 50 percent is projected throughout the forecast period. ECONOMETRIC INFERENCE Trial forecasts of future residential and small commercial consumption were prepared on the basis of the econometric models discussed in Chapter 4 and in Appendix D. The re- sults were considered along with end-use data and the RED forecasts to arrive at the final GVEA forecast. 38 Residential Because the equation estimated to provide the best explana- tion of past residential consumption reflects a period of widespread, unrepeatable fuel switching and conservation investment, it does not provide a plausible forecast of fu- ture residential consumption. Assuming one percent annual growth in real wages and salaries and assuming electricity and oil price forecasts generated in the RED model, the his- torical equation would project an average decrease in resi- dential consumption of approximately one percent annually through 1995. The increase in wages and salaries adopted from the RED model drives down electric consumption as though large numbers of residential customers could continue to switch to oil-fired heat. However, as discussed in Chap- ter 4, only about 7 percent of GVEA's residential customers currently heat with electricity, so relatively little addi- tional potential for shifting away from electric heat exists. To arrive at a more satisfactory equation, a new estimation equation was developed from the historical data by specify- ing only lagged consumption and current residential electric rates. A dummy variable was included that accounts for the pipeline construction boom and for much of the fuel switch- ing that occurred shortly thereafter. The result is a fore- casting equation that is not directly affected by wages and salaries or by oil price. The estimated price elasticity is about the same as in the original equation, but the estimated coefficient of lagged consumption rises significantly. As- suming electric rates decrease as forecast by the RED model, the new equation projects approximately 1.5 percent average annual growth in residential consumption per customer. As described below, these results, coupled with those from the RED forecast, formed the basis for adopting 1.0 percent as our forecast growth rate in GVEA's average annual usage per residential customer. Small Commercial Unlike the residential model, the small commercial equation was estimated only for the 1975-1984 period, following the major customer transition from electric to oil heat. Ac- cordingly, it yields a more plausible forecast of future consumption. A forecast of consumption per customer based on the small commercial demand equation projects an average annual in- crease of approximately 1.7 percent through 1995, assuming one percent annual growth in real wages and salaries and assuming electricity and oil price forecasts generated by the RED model. However, there is an unusually large dis- crepancy between the fitted and actual values for 1984, which results in a forecast of 5 percent growth in 1985 above the 39 actual 1984 value. Adjusting the 1985 forecast to growth of only 2.2 percent results in an average increase of 1.4 per- cent annually through 1995. This analysis together with the results of the RED forecast were the basis for the adoption of a 1.0 percent projected growth rate in GVEA's average annual usage per small commercial customer. GVEA FORECAST RESULTS The GVEA system load forecast projects numbers of customers by class, annual kWh use per customer per class, and total annual kWh use by class through 1994. It also projected system load factor and system peak load. The forecast is detailed in Table 12; key aspects are graphically presented in Figures 9 through 12. The number of electric customers served by GVEA is forecast to grow from about 23,000 in 1984 to about 32,000 by 1994, almost 3.5 percent per year. Customer growth during the first 5 years of the forecast period will average about 4.5 percent per year, as the direct and indirect effects of the deployment of the light infantry division are felt. In the first few years, the effect will be even more pronounced. For example, customer growth is forecast to be 7.1 percent in 1985. Total electric sales are forecast to grow at an average annual rate of 4.3 percent over the 10-year forecast period. During the same period, the system peak load is forecast to grow at an average annual rate of about 5.0 percent, given an assumed annual load factor of 60 percent for the forecast years. Both system sales and peak load are forecast to grow at a faster rate during the first 5 years of the forecast period, reflecting the effects of relatively rapid customer growth from the deployment of the light infantry division and the increase in large commercial loads during this pe- riod. For the period 1984 to 1989, kWh sales are forecast to increase an average of 6.0 percent per year. The system peak load is forecast to grow to about 107 MW by 1989. Figure 10 presents the load forecast on semi-log graph paper, as required for an REA power requirements study. This figure also includes a comparison of the forecast developed in this study (1985 study) with GVEA load forecasts prepared in 1976 and 1978. As the graph shows, the 1985 forecast is somewhat lower than the forecast made in 1978 and substantially lower than the 1976 forecast. GVEA FORECAST METHODOLOGY This section describes the method used to forecast the four principal components of the GVEA system load forecast: 40 1, NUMBES OF CUSTOMERS A. RESIDENTIAL 8, COMMERCIAL i. SMALL COMMERCIAL 2. LARGE COMMERCIAL A. OVER 258 KVA B. UNDER 35@ KVA 3. TOTAL COMMERCIAL RESALE TO OTHERS }. STREET LIGHTING » OWN USE moo TOTAL TI, ANNUAL KWH USE/CUSTOMER A, RESIDENTIAL B, COMMERCIAL 1. SMALL COMMERCIAL 2. LARGE COMMERCIAL A. OVER 35@ KVA B. UNDER 350 KVA C, RESALE TO OTHERS D. STREET LIGHTING E. OWN USE TI]. ANNUAL KWH USE (882) A, RESIDENTIAL B. COMMERCIAL 1, SMALL COMMERCIAL 2. LARBE COMMERCIAL A. OVER 358 KVA B. UNDER 358 KVA C, RESALE 70 OTHERS D. STREET LIGHTING &. OWN USE SYSTEM TOTAL SYSTEM AT INPUT LEVEL 1V, SYSTEM LOAD FACTOR V. SYSTEM PEAK LOAD Table 12 GVEA ELECTRIC LOAD FORECAST ANNUAL, % CHANGE 1984 1989 1984-89 1994 22, 281 25,274 4.50% 2B, 252 2, 268 2,773 4, Bn 3,091 265 368 6, 31% 416 26 2 2.21% ® 239 Hy 6, 72% 386 2, 585 3,133 4.58% 3,588 1 1 0. eax 1 1 1 @, Ot 1 1 1 0. 0x 1 22, 789 28, 418 4.51% 31, 763 8,513 6, 947 1.08% 9,404 23, 995 25,219 1.08% 26, 585 3,178,196 4, 166,379 5.62% 4,284,633 248, 744 248, 744 0.00% = 248, 744 17,132,288 17, 132, 208 @. 08% 17, 132, 208 28, 226 35, 175 4, 5x 39, 328 1,465,822 1, 826, 684 4.58% 2,041,965 172, 652 226, 132 5.55% 265,676 53, 749 69, 938 5.41% 81,938 82, 425 128, 825 7.95% 128,539 57,538 79, 654 6.72% 93, 037 17, 132 17,132 0. 08x 17,132 28 3s 4, Sex 39 1,466 1,827 4. 5e% 2,042 384, 998 515,542 6.01% 588, 404 415, 989 562, 206 6.21% 641, 662 64, 11% 60. 0% 1.32% 68. 06% 74, 078 106, 965 7.63% 122, 082 Note: The Systes Peak Load for 1984 is temperature normalized. 41 % CHANGE 1989-94 2. 25% 2. 20% 2. 96% 6. 68% 3, 16% 2.2% 6. 0x 0. Can 0. 0e% 2.26% 1.08% 1, 0@% 8. 56% @. 08% 0. 0% 2.25% 2.25% 3. 28% 3.22% 1.25% 3. 16% @. 0% 2.25% 2. 25% 2, 68% 2. 68% 0. box 2. 68% 1, 08% 1.08% 3. 06% 6. 0e% 0. 08x 3.37% 3.37% 4.408 4.31% 4.54% 4.92% 8. 0a 3.37% 3.37% 4,334 4.43% @. 66% 5. 12% eV Electric Lead Forecast Tatal Svetern Hl a a are en = er £ a ~ © a E ar iid — i a =. a aa _ Ce a re a f a oo a 40 - - et aca oe eee T ee a er ——— 1 1a84 1986 1988 Son 1932 1994 “~ecar Figure 9 GVEA Electric Load Forecast, 1984 to 1994 — TEMI-* 77> WITHIN > ~ 7 YCLE7 ** 7-9 DIV ‘ K Eure SER na , 4 50 MW PEAK : 3238 8 8 vt Customers (The usands) w Cod ao Oo WN ra mM mM N NM bh a a NN ‘ ‘. ~“, ~~ ‘\, SS ok oe “ O S, vo vo vA 7 7 o Af “ Oe YS. S S SS WS Yee SSE EN RS EU NS = ‘, ON a % oS wo AS OS NS Figure 11 Number of GVEA Customers, Actual 1984 and Projected 1989 and 1994 St 500 REE Ws 400 ‘ 300 Se OB £8 =o <c& 200 100 0 ie Sales [77] Res. 5.Camm., L.Coarnm, far eee Figure 12 GVEA Electric Energy Sales, by Class, Actual 1984 and Projected 1989 and 1994 number of customers, annual kWh usage per customer, system losses, and load factor. Number of Customers Residential. The number of GVEA residential customers is projected to increase at the same rate as that forecast for the population in the service area. As described in Chap- ter 3, population in GVEA's service area is projected to grow an average of 4.5 percent per year between 1984 and 1989 and 2.2 percent per year between 1989 and 1994. By forecasting that the number of residential customers will grow at the same rate as the population, we have assumed that population per household will remain essentially un- changed from its 1984 level. This is the "middle ground" for other persons-per-household forecasts for Fairbanks: the MAP model projects continued decreases in persons per household while the FMATS study forecasts that household size will increase. Commercial. The number of commercial customers was forecast on the basis of the simple regressions developed to express the historical relationship between residential and commer- cial customers. As described in Chapter 4, the regression equations showed that for every new residential customer, the total number of commercial customers increases by 0.126 and the number of small commercial customers increases by 0.107. Thus, the total number of commercial customers and the number of small commercial customers were both forecast by applying the given coefficient to the projected number of new residential customers for each year. The number of large commercial customers was forecast by subtracting the projected small commercial customers from the projected total commercial customers. Resale and Street Lighting. No growth is forecast in the number of resale or street lighting customers. Average Annual Use Per Customer Residential. As shown in Table 12, average annual kWh use per residential customer is forecast to increase an average of 1.0 percent per year. This projection is supported by the RED model forecast and the econometric analysis (docu- mented in Appendix D). Small Commercial. Average annual kWh usage per small com- mercial customer is also forecast to increase an average of 1.0 percent per year. The bases for this projection are the same as for residential usage. 46 Large Commercial. Overall annual usage per large commercial customer is projected to grow an average of 5.3 percent per year between 1984 and 1989 and about 0.5 percent between 1989 and 1994. The major reasons for the relatively rapid growth in the first 5 years are major expansions in oil re- fining and coal mining over the next several years. Average usage for large commercial customers with demand less than 350 kVa is projected to remain constant throughout the fore- cast period. Forecasts for customers over 350 kVa are de- tailed in REA Form 4a (Appendix A). These forecasts are based on historical trends and interviews with customer representatives. The largest single increase in industrial energy require- ments is expected at the Mapco oil refinery at North Pole. As indicated, Mapco is planning two major expansions. The asphalt plant is to begin operations in April 1985 and will operate between April and October each year. This plant is expected to have a peak demand of over 250 kW and an annual load factor of about 55 percent. Mapco's new gasoline and crude-refining operation is scheduled to begin in September 1985. These new facilities will more than double Mapco's energy requirements. As shown in REA Form 4a (Appendix A), Mapco's overall energy requirements are expected to increase from 17 million kWh in 1984 to over 38 million kWh in 1989. This single increase is equal to 15 percent of the total large commercial energy requirements in 1984. Petro Star's new topping plant is to come on-line with a peak demand of between 350 and 400 kW in Fall 1985. Annual energy requirements are projected to be 2.5 million kWh. The aviation gasoline facility planned for 1986 or 1987 would have a peak demand of 2 MW and annual energy require- ments of about 13 million kWh. Given the uncertainty asso- ciated with the aviation gasoline facility, a 50 percent probability was assigned to the new facility for forecasting purposes. Therefore, for 1989 and 1994, Petro Star's energy requirements are forecast at about 7.8 million kWh rather than the 15.6 million kWh potential requirement. In the next few years, the Usibelli coal mine is planning to add a new reclaimer system with a rating of 250 to 300 horse- power and a new crusher with a rating of approximately 100 horsepower. The reclaimer system is expected to have about a 25 percent annual load factor and the new crusher is to operate with about a 50 percent annual load factor. Asa result, Usibelli's electric energy requirements are expected to grow from 9 million kWh per year in 1984 to 10 million kWh per year by 1989. In addition, the mining operation may also purchase a new electric shovel. Energy requirements for this equipment would be 50 to 70 percent of the existing dragline load. Again because of the uncertainty of this particular expansion, a 50 percent probability was assigned 47 to new energy requirements for the electric shovel. On the basis of this analysis, Usibelli's energy requirements were projected to increase by 2.7 million kWh per year between 1989 and 1994. Sales For Resale. The GVEA system has experienced substan- tial growth in sales for resale since 1982. However, GVEA's load dispatcher expects that additional annual sales of economy energy in this market are not likely in the foresee- able future. Therefore, sales for resale are projected to remain at their 1984 level throughout the forecast period. Street Lighting and Own Use. Street lighting and GVEA use are largely a function of the size of the overall service area. Therefore, usage by these service categories is fore- cast to grow at the same rate as the number of residential customers, which is considered to be a good indicator of the size of the overall service area. System Losses Annual system energy losses are projected to be 8.3 percent of annual energy requirements. This loss rate is based on projected system sales of slightly less than 250,000 kWh per mile of line. This loss estimate was made in accordance with methods prescribed by the REA in REA Form 344, REA Form 802, and REA Bulletin 45-4. (REA Form 344 is included in Appendix A.) Annual Load Factor GVEA's annual system load factor is projected to remain at 60 percent, which is about its average level for the last few years. No changes in load factor are expected, primar- ily because there are no foreseen changes in GVEA's economy sales opportunities. 48 REFERENCES Fairbanks North Star Borough. Comprehensive Plan: Working Paper No. 1--Socioeconomic Trends in Forecast. August 1983. Pp. 4 and 5. Alaska Pacific Bancorporation. Alaska Business Trends-- 1985 Economic Forecast. P. 3. Fairbanks North Star Borough, op cit. P. 34. Alaska Pacific Bancorporation, op cit. P. 53. Alaska Power Authority. Application for License for Major Project-Susitna Hydroelectric Project. Volume 2C. Poses Ibid. Ibid. Pp. 6.2-6.13. Ibid. P. 6.8. Alaska Power Authority. Application for License for Major Project-Susitna Hydroelectric Project. Volume 2A. P. B-5-65. 49 BIBLIOGRAPHY Acres American, Inc. and Woodward-Clyde Consultants. Forecasting Peak Electrical Demand for Alaskas Rail Belt. December 1980. Alaska Department of Labor. Alaska Economic Trends. Decem- ber 1984. Alaska Department of Labor. Employment Statistics. 1984. (Unpublished.) Alaska Pacific Bancorporation. Alaska Business Trends--1985 Economic Forecast. 1985. Alaska Power Authority. Application for License for Major Project-Susitna Hydroelectric Project. Submitted to the Federal Energy Regulatory Commission. Volumes 1, 2A, 2B, and 2C. July 1983. Alaska Power Authority. Sitka Hydroelectric Project Economic and Financial Update. February 1984. Alaska Power Authority. Susitna Hydroelectric Project Eco- nomic and Financial Update. Draft Report. February 27, 1984. Arthur D. Little. 1983 Long-Term Energy Plan. Department of Commerce and Economic Development, Division of Energy and Power Development. No date. Battelle Pacific Northwest, Inc. Alaskan Electric Power: An Analysis of Future Requirements and Supply Alternatives for the Rail Belt Region. 1978. Battelle Northwest, Inc. The Rail Belt Electric Power Alternative Study. September 1982. Burns and McDonald. Power Requirement Study. Chugiach Electric Association. 1983. CH2M HILL. Wastewater Facilities Plan for the City of Fair- banks. December 1984. DeLeuw Cather and Company. Fairbanks Metropolitan Area Transportation Plan. Alaska Department of Transportation and Public Facilities. April 1983 and December 1984. Fairbanks North Star Borough. Comprehensive Plan. 1983 and 1984. Fairbanks North Star Borough. The Fairbanks Factbook 1983. 51 Fairbanks North Star Borough Community Research Center. Community Research Quarterly. Various issues, 1981 through 1984. Fairbanks North Star Borough Community Research Center. The Energy Report. Various issues, 1981 through 1983. Fairbanks North Star Borough Community Research Center. 1983 Community Survey: Fairbanks North Star Borough. Janu- ary 1984. Fairbanks North Star Borough Community Research Center. 1984 Sample Census. October 1984. Fairbanks North Star Borough Community Research Center. Special Report No. 10, 1980 Census, Volume 1: Population and Housing Characteristics. September 1983. Fort Wainwright Population Impact Review-Transportation Sub-Committee Report. January 1985. Gilbert Commonwealth. Anchorage-Fairbanks Transmission Intertie. Alaska Power Authority. 1980. Golden Valley Electric Association. Alaska 6, Golden V- alley. 1983 Power Requirement Study. 1983. Golden Valley Electric Association. Alaska 6, Golden Valley 1979 Power Requirements Study. 1979. Goldsmith, Oliver Scott. Electric Power in Alaska. Univer- sity of Alaska. March 1976. Goldsmith, Scott and Lee Huskey. Electric Power Consumption for the Rail Belt: A Projection of Requirements. State of Alaska. June 1980. Goldsmith and Porter. Alaska Economic Projections for Estimating Electricity Requirements for the Rail Belt. University of Alaska, Institute of Social and Economic Research. 1981. Greater Fairbanks Chamber of Commerce. Fairbanks. 25th Anniversary Statehood Commemorative Edition. Volume 11, No. 1, 1984. Institute of Social and Economic Research. Electric Power in Alaska, 1976-1995. 1976. Institute for Social and Economic Research. Electric Power Consumption for the Rail Belt: A Projection of Requirements. May 16, 1980. 52 Kent and Company. Electric Power Generation for the Alaska Rail Belt Region. January 1984. Stanley Consultants. Power Supply Study 1978. Golden Valley Electric Association, Inc. 1978. U.S. Corps of Engineers, Mobile District. Stationing of A New Army Light Infantry Division Draft Environmental Impact Statement. May 1984. Wilbur Smith & Associates. Richardson Highway Corridor Study-Executive Summary. 1983. 53 FORM APPROVED OMB NO. 40-R3881 USDA - REA SUMMARY OF CONSUMERS AND kwh ESTIMATES CLASS OF CONSUMER .« RURAL RESIDENTIAL SYSTEM DESIGNATION Alaska _b Gelden Valley NAME OF SYSTEM Goldom Us tley Elechie DATE NO. OF CONSUMERS March 25/9 kwh ESTIM ATES* 19 £4 19 fy 252% 224,132,000 | . SEASONAL (Ann.) (we) . TOWN RESIDENTIAL Cue) | C va) . IRRIGATION (Ann.) ( . SECURITY LIGHTS (Ann.) Cu) . SMALL COMMERCIAL 61.936 46 | +» PUBLIC STREET & HIGHWAY LIGHTING (Ann.) 39000 Wr) . PUBLIC BUILDINGS . OIL WELLS (Ann) ( hp.) (way 7ORROWER’S OWN USE (NON-REVENUE PRODUCING) Le | 4, 827000 242,100 ( yp) 11. LARGE COMMERCIAL (Ann.) (Under 350 kw) 300 4% TOTAL POWER REQUIREMENTS ITEM | T 19 pyc. 20°47 19 bi «2/576 00d 19 af . ANNUAL kwh REQUIREMENTS YI 836 722. S562, 2056p OW COLI, . INCLUDING LOSSES@ . ANNUAL LOAD FACTOR (Based on non-coincident monthly system peak demand) 8.3 x | 63,957 83 «% 60,00 &% + 6.3 % Go. 00 % . NON-COINCIDENT MONTHLY SYSTEM PEAK DEMAND (kw) 74% Tol 4S 4g (20 Fo 3 . SOURCE(S) OF SUPPLY Self -Genevaton * REVISION OF kwh ESTIMATES DATED ESTABLISHED BY REA Form 34] 7-73 EWED BY wonne flasta @ Golden Valley —_ LARGE COMMERCIAL Golden Va [le Electr sso cating avy 25 1785~ i} i mete nces mer number of consumers involved and include all large power consumers, sales for resale, wheeling NAME OF LARGE COMMERCIAL eee me snare _ kwh USAGE sa t Alueska. Ramp 9 ais “is |_ 2,500, ood) 2,500,000 | | Alyeska Pump 6 peel) 7es| 4,500,000) 4,500,000 | Mapen Petro |_ 7,200 | 7,200 | 36,300,C00| 38,200,000 | | FBKS NSBSD North Tole High School 500) 500 | _ 1,750,000! 1,750,000 | | FaKS NSBSD North Pole MidSchol 430 | __ 430 1,530,000 1,530,000) | EBKS S£Q Red tMix BtSiif ridir-AOOs| anit POLOOOs miei GHOr GOO | Genera) Teamsters Union Complex S20 | ___ S40| 2,460,000! 2,810,000 akeview Tr! Ct Mobile Home G00! G00 | 2,900,000! 2,900,000! . FBKS No Star Trash Baler 735 | BEC 993 COD! | 180,000): GSA Finance Federal Bldg | 560 £60 765,000 785,000) St of AK Hiway Dent Laie SOs 3,500,000) 8,500,000 caf 450 | 450 | 2,600,000! _ 2,600,000). St of AK Div of Avn Airpord Five| 395 | 470 | 1,565,000! 1,878,000). AK Intl Aiv_ Hangar ZaTe Mi Ween ZeiS Hl MN AED 00 | 1,450,000 St of AK Div of Ayn Airport Eve 030 | 1165 | &,508,000| 7,357,000) ENSB Hamilton Sr Hi Scnool a35 | 335 | 1,237,000| 1,237,000 NASA Track Sta Satellite. 890 | 890 | _5,859000/| 5,869,000 FBKS NSBSD Adwii Dev Center | 370 370 | |, 400,000" _ 1,400,000 FBKS NSBSD W) Val Hi Sch/ reenttes iba = \, 8B, 000 | \, BB, 000 FBKS NSBSD Weodriver Sch] 250 Z10 920.000 | 1,000,000 | of A Yak Estates tos | 05 | 2759,000| 2,758,000 Usibelli Elec Dragin 3170 | 4,000 | 10, COO OLO! 12, 700.000)! . NW AK Pipe Frost Heave. B40 SES MEN OOOOnmaI aT Sy esrnvariretenrnrererPrtrdstrrernrerPrPererere|FePTPPE IPE E? | eFPPPPPNSOPET SEPFPOO TET Te Me eT “EA FORM3410 7-73 FORM APPROVED OMB NO. 40 R388) USDA - REA | arrangement S, etc. NAME OF LARGE COMMERCIAL Dutdr World Chalet MUS. Water Tyeadment Plant | Lof A Power Plant — + NA ESTIMATED kw BILLING DEMAND tigska b Celden Va lieu ME OF SYSTEM Loldty a I( kwh USAGE ey Electric Asseciatiyn | LARGE COMMERCIAL DATE Mave y 19 FE ; INSTRUCTIONS - Show number of consumers involved and include all large power consumers, sales for resale, wheeling + 989 | 225 + 19 aE Geo eet 200,006 5,U22, 4,400 COD 39g 900,000 | | 6,123,000 | Se | att Letvo Stay “71, 800,000! 7,800,000 | T ae | | . wh i. | + Thr oS = | le T | | | | [ Th, | FORM3410 7-73 46 5250 fo KEUFFEL & ESSER CO. MADE IN USA c™ KeE SEMI-LOGARITHMIC 2 CYCLES X 200 DIVISIONS. ono n © = We mona Uw & n N (ny PU vo) SEmernday yong DEMAND Hoe Wh hd DEMAND CURVE SUM OF MONTHLY NON—-COINCIDENT PEAKS id oof = VL 5 SS . —* J =a ae : ; . a ee ae i oe ee = es A are, al _ MONTHS o 1982 + 1983 & 1984 FORM APPROVED OMB NO. 40-R3681 USDA - REA ENTIRE SYSTEM LOAD ESTIMATES NUMBER OF CONSUMERS TYPE OF CONSUMER (REA Form 736) SYSTE GNATION NAME OF SYSTEM Calis Uo i Efecty< pissy DATE 5 Mavch DS, 19 S_ MONTHLY ANNUAL ANNUAL kwh REQUIREMENTS PROJECTION PROJECTION (No. of consumers X annual projection) 19 ey YEAR NO. (REA Form 736) | PER CONSUMER 9e¥ 19 er 9 9¢ 19 #7 25 aly 24b RAGA 1. RURAL RESIDENTIAL 20, z¢l [19 44 28,252 78 9 toy /7L S200 226 /Z2000 265 ¢ % 008 19 2. SEASONAL (Ann.) 19 19 3. TOWN RESIDENTIAL 19 | 19 4. IRRIGATION (Ann.) | 19 | 19 5. SECURITY LIGHTS (Ann.) {19 } 19 87 2,773 6. SMALL_ COMMERCIAL 2,240 119 it 2097 67728 aoe &/, 736,920 19 / 7 Pustin stnee AND HIGHWAY 7 a o4 ; 35400 3 7000 19 8. PUBLIC BUILDINGS | 19 | SALES Uae 9. QUL_WELLS (Ann.) / 1947 it 77 132 000 17,432 400 72 IZ 400 19 #9 / 10. BORROWER'S OWN USE (Ann.) / \9 4 A L¢C6 coo | WAG 7000 0 Zis09) 19 89 260 11, LARGE COMMERCIAL (Ann.) 265 [19 9 #16 /3}, %oJ 000 200 Lif 000 | Kal, S 7¢_000 19 12. SUBTOTAL 19 IEEE ELS 384. 7 90000 SUS SVY2Z000 | 58 5, Yo¥ ooo 13, PLUS SYSTEM LOSSES (Estimate-per REA Bulletin 45-4) C 8.3%) 37 °4%~ Y2 6 663 oh 53 257 IY 14. TOTAL we 83642 562, 206 06 | ol. o6Lgn 15. NON-COINCIDENT PEAK DEMAND 16. ANNUAL LOAD FACTOR COMMENTS REA Form 4 (REV. 7-73 63.57 % Gi 6 000% 006% Tsv FORM APPROVED OMB NO. 40-R3861 USDA-REA ENTIRE SYSTEM LOADS ESTIMATES LARGE COMMERCIAL OVER 350 kvA NUMBER OF CONSUMERS BILLING DEMAND 9 ey 19 § 19 rf | 97 ¥ 9 bY 19 Ty 9 BY 9 of c™ ESIGNATION NAME OF SYSTEM Nlasku & ede “ alley Electrc Assecis ch Golden Vall and 25, 178S- AVERAGE kW BILLING DEMAND ANNUAL kwh REQUIREMENTS sang | i muyeska. Pump 4 | 004] US| Us 2,272,800 |2,500,000 {2,500,000 Alyeska. Pump 8 4773 | 7a5]} 785 |4,999,200 |4, 500,000 |4,500,000 Mapto Petro 3,244 | 1200 | 7,200 I7,215,343 [28,300,000 [38,300,CC¢ FBKS NSBSD North Pole High Schoo 35 | 500} 500] 154,800} 1,750,000 | 1,750,000 FRKS NSBoD North Pok Mid School 430 | 430| 430]1,434,000] 1,530,000 1,530,000 564 ked §Mi 247 | 415 400 | 554,100 | 710,000} 900,CLO General Teamsters Union Compky 483 | 520| 50/2 4v2,400 | 2,150,000} 2,F0,000 Lakeview Tr | Ct Mobile Home 631 | GO] oo|3,022,200] 2,900,000] 2,900,000 FBKS No Stay Wash Baler 58lo | 735 | 880} TA6720} 983,000] 1, 1FC,c0O ns f 5141 se0| 580| 1,879800| 765,000] 765,000 St of AK Hiway Dept 62L | 830| 830] 3,454,800] 3500,000| 3,500,000 Safeway Parport 44 | 450 | 450] 2,592, 00) 2,600,000} 2,400,000 of AK Di of Avn Airport Fir 4) 95 470|1,252,200) 1,546,000] _1,27E,C00 AY Intl Air Hanga | TT eel ns REA Form 4e REV. 7-73 zine 1,450,000] |,4500L0 roms arenoves a came at aver USDA-REA OATE ENTIRE SYSTEM LOADS ESTIMATES i -ARGE COMMERCIAL OVER 380 AVA Kas Sanu | eee mena — et oe - otot_AK Divot Ayn Arpt Term GS | 1030] 6S |5,059,200| 6,508,000) 7,357,000 FNSB Hamilton Jr HiSehop! | 27 | __335| 33S] 1,004,100) 1,237,000 | 1,237,000 NASA Track Sta Sadelite | 119 | B90} B90 [4 515,40 5 859,000) 5,859,000. FBKS NSBSD Aduit Dev Center 310| 370| 370| 1,344,000} 1,400,000| 1,400,000 FBKS NSBSD Wval HiSsen! | 40d 475 | 475 ||, 687200] 1,886,000, 188,000 FBKS _NSBSD Woatriver Schl { 232 | 250 270| 930,000} \,0CO,C00 Liof A Yak Estates 569 |_705 705 | | 2,759,000 usibelli Elec Dragin | 2,695 | 3170 | 4000] 9,030,600) 10,000,000 |\2,700,000 Nw AK Pipe Frast Heave. [ | 297| 240] 385 2,375,000 Cutdr Warld Chatet 224! 225 | 225 | 900,000 Mus Woder Treatment Plant | 730| 805] e75 6 ,133,0CO Uo A Power Plant | © © O| 4,400,060 Petro Stay | | O} 1400] 1400 D | 7,800,000 | 7,800,000 fannamed Peterrtiad C 2{| 3 830| 1360 © | 3,700,000] &,!00,000 »420,009 [120,800,000 1128, 500,000 suBTOTAL 26 | 29 20 | 17,005] 25,055] 27,020 |62,425,103 [120,825,000 ||28,539,0CO so 5 LS eC cn ay | | 63] 83] 63 B83 8.3 | 8.3 TOTAL Zo | 29 30 10] 27,323] 29,4tdol89, 900, 000 |132,000,000)'40,000,000 NON-COINCIDENT PEAK DEMAND l 29,500 ANNUAL LOAD FACTOR REA Form 4a REV. 7-73 coe bate 54.25% GOLDEN VALLEY ELECTRIC ASSN., INC. MILES OF LINE ENERGIZED CONSUMER DENSITY TOTAL COST OF PURCHASED POWER ($) AVERAGE COST IN MILLS/KWH PURCHASED 1@-Feb-85 FORM 5 TOTAL SYSTEM PEAK KWH % ‘ KW DEMANDS YEAR = PURCHASED. «=I KH IN- KH 1 ON ALL AND GENERATED CREASE SOLD CREASE = -LOSS.-«—sLOSS SUBSTATIONS 1974 249, 083, 353 232, 989, 389 16,813,964 6.78 64, 498 1975 332,038,671 32.9% 308,183,745 28.8% 31,846,325 «9.681, 888 1976 343,638,306 3.5K 312,669,349 4.2% 38,958,957 «Lt 76, 428 1977 361,689,637 5.2% 334,581,255 7.0% 27,188,382 7.5% 89, 980 1978 342,231,739 5.4% 310,458,106 © -7. 2% 31,773,633 9.3% 71,788 1979 328,621,763 -4.@% 383,140,376 2.4% 25,481,387 7.8% 75, 780 1988 318,588,668 -3.1% 293,837,954 -3.1% 24,758,786 7.8 78, 088 1981 319,947,853 @.4% 297,964,061 «1.44 21,983,012 6.68, 708 1982 359,084,612 12.5% 334,311,958 12.2% 25,572,654 7.1% 67,988 1983 378,416,761 2.9% 344,985,847 «3.2% 25,438,914 «6.9K 72,288 1904 416,278,648 12.4% 384,959,882 11.6% 31,318,758 7.5% 74, 708 TOTAL 3, 743,043,395 3, 458, oe, 782 293, 048, 693 813,518 AVERAGE ANNUAL PERCENT INCREASE 5.2% 5.1% 6.4% g JPN JPM 1.3% 45.9% St 49.6% Se. en 53.2 68. 5% 36. 6% 63.6% 1,333 1,409 1,347 1,413 1,537 1,565 1,613 1,669 1,715 1,736 1,007 6.9 74 6.9 9.5 9.6 9.8 9.8 16.8 10.7 11.4 12.6 4,019, 815 8, 237, 236 11,527, 0%5 12,061, 445 9, 8S2, 861 10,677, 83 12,974, 485 15, 787, 746 17, 292, 751 17,437, 698 18, 613, 378 138, 461, 445 16. 6% 40.7 49.1 48.1 47.1 4.7 10.8% oT-W GOLDEN VALLEY ELECTRIC ASSN., INC. (6-Feb-85 FORM 7/PART R - 1964 AVERAGE CLASSIFICATION JANUARY FEBRUARY WARCH APRIL wy (JUNE JULY AUGUST SEPTEMBER OCTOBER NOVEMBER = DECEMBER «11 = TOTAL WONTHLY —i = ——— San Sneeenane ne 2 - es = amen | [aaa | i 1 1, RESIDENTIAL SALES it ' a. No.Consumers Served 16, 875, 19, 068 19, 45 19,345 19, 708 19, 639 20, 467 28, 978 21,287 21,595 21, 482 21,408 11 243,373 1 28, 281 b. kwh Sold 20,389,017 17,523,437 13,795,945 12,175,369 11,347,488 11,069,172 10,891,183 11,618,114 11,787,966 14,908,968 18,696,179 16,536,088 11172,658,822 | 14, 387,569 c. Revenue 2,066,347 1,847,237 1,485,129 1,359,845 1,276,186 1,271,@38@ 1,259,657 1,327,815 1,336,278 1,615,846 1,946,557 11 16,791,119 | 1,399,268 i 1 2, RESIDENTIAL SALES - SEASONAL i | a. No.Consusers Served ul el a b. kWh Sold it el a c. Revenue " eo! 8 i 1 3. IRRIGATION SALES it} ' a. No.Consumers Served i el 8 b. kh Sold it el a c. Revenue i} el a i 1 4. COMM AND IND S@ kVA OR LESS it} ' a. No.Consusers Served 2,137 2,118 2, 125 2, 142 2, 152 2,137 2,204 2,258 2,292 2, 432 2,445 2,458 11 26, 876 | 2,268 b. kh Sold 5,546,777 4,528,614 4,872, 188 3,738,688 6,587,828 3,281,438 3,351,628 3,504,782 3,724,925 4,573,833 5,334,313 5,688,080 11 53,748,118 | 4,479,018 c. Revenue 617,836 See, 571 476, 004 442,698 420,714 408,675 486, 758 425,155 444,912 538,677 687,021 11 5,295,@13 | 441,251 i 1 ‘5. COMM AND IND OVER 58 kVA ' a. No.Consumers Served 27 2S 261 262 268 263 262 268 265 266 278 e751 3,164 1 O64 b. kWh Sold 14,29, 878 11,463,193 11,242,083 10,035,876 10,316,252 9,542,549 9,505,328 9,836,008 11,389,498 12,251,369 13,689,982 13,608,008 11136, 822,928 | 11,401,918 c. Revenue 1,195,572 1,@55,@78 995, 824 927,622 926, 815 884, B42 894, 963 912, 987 985,493 1,069,868 1,175,878 1H 11,@24,082 | 918,674 W ' 6. PUBLIC STREET & HIGHWAY LIGHTING it} ' a. No.Consumers Served it} el e b. kWh Sold W el a c. Revenue i} er @ it ! 7. OTHER SALES TO PUBLIC AUTH i ! a. No.Consumers Served 1 1 1 1 1 1 1 1 1 1 1 il 11 1 b. kwh Sold 125, 208 218, 008 ‘544, 008 125, 608 300, 006 147,208 e 144,408 145, 208 470, 408 584, 008 336,088 1! 3,148,008! = 261,667 c. Revenue 4,548 7,985 17, 085 5,688 9,938 4,601 e 4,514 5,265 17,258 22, 172 Ht 98,806 1 8,234 W ' 8. SALES FOR RESALES - REA BORROWERS Nl | a. No.Consumers Served i el 8 b. kWh Sold Ww el 8 c. Revenue i} el e it | 9. SALES FOR RESALES - OTHERS i ' a. No.Consumers Served 1 1 1 1 1 1 1 1 1 1 1 itt 21 1 b. kWh Sold 436, 808 403, 908 109,908 4, 137,708 1, 408 2, 108 38, 5380 575, 408 704,288 © 2,003,408 6,998,988 = 1,728,088 1! 17,132,088 | 1,427,683 c. Revenue 16, 238 14,645 3,555 178, 654 2, 744 a 5, 188 54, 166 33,939 78, 318 422, 881 1) 809,661 | 67,472 W ' 1@. TOTAL NO. CONSUMERS (LINES 1A-9A) i, 271 21,447 21, 793 et, 751 22, 122 22,241 22,935 23, 482 23, 846 24,295 24, 127 24,127 11 273,437 1 22, 786 TI-W GOLDEN VALLEY ELECTRIC ASSN. , INC. (06-Feb-85 FORM 7/PART R - 1984 CLASSIFICATION JANUARY = FEBRUARY = WARCH APRIL my JUNE oy AUGUST SEPTEMBER OCTOBER © NOVEMBER DECEMBER 11 TOTAL fear is i CT i 1 ae a a 11. TOTAL kWh SOLD (LINES 1B-98) 40,447,672 34,129,144 29,763,956 38,285,233 28,472,968 24,042,451 23, 7B7,631 25,678,696 27,671,781 34,199,162 45,295,374 39, 888,008 11383, 494,068 | 31,957,638 12. TOTAL REVENUE RECEIVED FROM SALES i | OF ELECTRIC ENERGY (LINES 1C-9C) 3,988,533 3,447,436 2,977,497 2,913,819 2,636,397 2,561,189 2,566,578 2,723,757 2,885,879 3,311,967 4, 173,629 @ 11 34,018,681 | 2,834,898 13, OTHER ELECTRIC REVEME 6,492 73,127 3,443 79,559 39,613 134, 088 7,474 52,082 96,863 73,598 67,613, e ‘ 713, 886 59,491 14, kh - OWN USE 179,182 143,942 138,159 108,208 86,534 75,336 144,837 = 25,372 = 79,243 = 121,142 184,867 179,088 i 1,465, 822 122, 152 1S. TOTAL kh PURCHASED 1,183,988 78,588 2,057,480 1,281,338 2,315,608 2,081,478 1,213,008 1,081,408 1,473,988 1,391,008 195,388 494, 408 i 14,847, 208 1,237,267 16. TOTAL kh GENERATED 39,569, 408 48,361,800 31,424, 100 33,627,988 26,658,280 23,227,388 24,667,388 27,572,708 28,324,148 34,814,208 47,267,708 43,916, 700 114,31, 48 33, 452, 620 17. COST OF PURCHASES AND GENERATION 1,782,499 1,745,379 1,506,868 1,804,249 1,477,323 1,257,573 1,316,224 1,622,863 1,768,683 2,075,877 2,263, 928 e i 18,613, 378 | 1,551,114 18, INTERCHANGE - khh - NET 280,031 (377,442) (131,481) (588,425) (989,754) (4,588) (4,483) 565,648 8,824 (143,081) © (344,742) 48, 318 i (1,529, rm (127,431) 19, PEAK - SUN ALL kW INPUT (METERED) 74.7 72.7 61.8 52.2 48.6 41.6 45.6 49.6 52.8 65.5 3.8 MA " bab. 53.7 cT-W USDA - REA ‘orm Approved OMB No. 40-n3881 OPERATING DATA - CONSUMERS AND USAGE FOR YEAR 19 87 SYSTEM DESIGNATION WAME OF SYSTEM Go.vkW VALLEY DATE Nov 14,198 RES1 OR TIAL SMALL ComvriTRci A LAKCK Corset A ciate =| LIgn7s. MONTH NO. TOTAL NO. TOTAL TOTAL cons. twh cons. awh san. | 18875 | 20304017 Rist F516,727 1/4, 024,878 40, 447, 672 __ ree. 114 ogo | 17,523,437 | 2y/0 | 4 520,614 14,465, 19S l 103, 900 34,129,044 man. 119, 40S | 13,795, 99F 4,072,108 1,242, 004 104, 900 29, 763, 956 apr. |) HF 1 12,17$, 364 tat 3, 730, 682 zg2 10,035, B76 J 4,137,700 30, 205,233 Mav 114,708 | 347, 480 | 2152 | 3,507,628 260 2,516 L§2 25, 172, 960 sume 1/9, 834 | 11,069,172 | 3,28), 430 Gi F4UzF4 14,042, “IS) suey | 20, 2, B91,/8 262 4, 506 328 23, 787,63) aus. 120970 | 3,504,782 | 260 | G, 836,000 25,678,696 seer.|2/,287 2292 | 3,724,925 | 265 111,305, 490 27,674,781 ocr. [21,5 4,573,033 | 2uy | 12,251,349 21,400 18,536,000] 2450 43,373] 172,052), 8221208 Te 5,374,313 5,600,000 13,689,982 13,600,000 F199 162 45,295,374 ror [53,4818 |31U4 |13u,622,920 12 _|17,182,200| 12 |S 40,000 £73,437] 383,494, O00 ave, 120,261 [\4,361)509 12240 | 4,479,010 | 264 | 11,401,910 | |1,427,683) | | 201,667 |22,766] 31,957,838 yro ~ LVS bea ‘ OFFICE USE? ........ ee TOTAL kWh SALES «00.000. 6 9E 7 GBA *Non-revenue producing office or system use should be added totomsl sales and included in annual kWh sales on REA Form 5S, Annual Operating Data. COMMENTS: REA FORM IS6 REV 10-73 FORM APPROVED BET Oe Oe eee OMB NO. 40-R3881 Alaska 6 USDA —- REA ESTIMATE OF SYSTEM LOSSES Sate (Per REA Bulletin 45-4) Yark ; 73 KWH SALES PER MILE 3 SYSTEM Loss % SYSTEM LOSS STANDARD CUR aa SR at] ERE ' 2 3 ee : : soo.esus | a | 215.08 wei frawueouee | wu | 177,70 né2 DIFFERENCE (Plus or minus) 6. ESTIMATE (REA Form S$): (Consumer Density of Per Mile)* PROJECTED SYSTEM LOSS ON PROJECTED 7 8 9 Cc Ww wey | 55 542,000 247,506 iw 9¢ | s08vte00 | 2700 | svcceo | 83 | 83 * BASED OW AVERAGE BILLED CONSUMERS - NOT CONNECTED CONSUMERS. ee “AL NUMBER CONSUMERS - REA FORM GOR do + ITEM 6. Tem & beged on hislrican tre 19901994 REA FORM 344 7-73 A-13 iW GOLDEN VALLEY ELECTRIC ASSN., INC, 22-Feb-85 REA FORM 736 CLASSIFICATION: YEAR: 1974 1975 1976 1977 1978 1979 1988 1981 1982 1983 1964 AVG -YRLY GROWTH: 18 YR PERIOD 5 YR PERIOD 2 YR PERIOD COMPOUNDED RATE PERCENT INCREASE FORECAST : 1989 1994 AVERAGE YEARLY INCREASE 1984-89 1989-94 RESIDENTIAL SPPLL COMMERCIAL LARGE COMMERCIAL STREET LIGHTS RESALES OWN USE NO CONS KWH/MO/CU NO CONS —KWH/MO/CU NO CONS KWH/MO/CU NO CONS KWH/MO/CU ND CONS KAH/MO/CU NO CONS KWKH/O/CU 8, 082 1,318 987 3,737 85 447,994 944,708 11,431,898 9,243 1,444 1,083 3,988 126 651,966 941,628 1 1,468,476 10, 688 1,267 1,064 2,147 381-369, 854 9 33,389 14,858, 988 1 1,441,491 11, 886 1,188 1,338 2,355 202 982,268 9 46,072 1 8,332, 828 1 1,465,953 13,030 41,468 2,097 287 588,973 943,748 1 727,388 1 1,338, 456 13,591 671,553 2, 001 209 565,314 9 35,119 1 3,042,293 1 1,386,239 13,92 Bis 1,614 1,869 206 563,811 9 35,336 1 3,928,268 1 1,478,874 14,743 T1715 1,878 218 = 554,178 9-877 1 3,025, 828 1 1,284,149 16, 176 77 ‘1,859 1,936 233 555,341 39 -34,3% 1 9,534, 788 1 1,372,726 17,551 753 (2,088 1,899 248 544, 88 5 28,226 1 8, 382, 508 1 1,317, 64 20,281 7932 2h8 2, 9¢@ 265 528, 162 117,132, 288 1 1,465, B22 1,220 (61) 125 (am) 18 8,817 (4,471) @ 1,713,228 8 3,392 1,338 (Hs) 137 (@) W (7, 438) (2) (7,824) © = 2,817,981 e 15, 917 2,053 (3) 191 R 16 (13,598) (5) (18,198) @ 3,798, 758 8 465A 4.0% 1% 1.7% 108. 8% 19.8 8.2 25,274 ™% 2,773 2, 182 38367, 268 1 2,931 1 1,427, 683 152, 224 28, 252 74 (3,091 2,289 M6 377,115 1 3,277 1 1,427,683 170, 164 99 7 187 Fe) 196,649 8 116 e 8 e 6,014 5% 8 64 a u 1,971 8 69 8 8 e 3,588 SYSTEM DESIGNATION: GOLDEN VALLEY ELECTRIC ASSN.. INC. 25-Feo-85 REA LARGE COMMERCIAL WORKSHEET i982 1983 I. Total Larce Commercial (From Fors 7) kin 129, 394,538 138, 751. 908 Number of Consumers 233 248 kwn/Consumer 555, 341 544, 800 Il. Larce Commercial Over 358 KVA kWh 77,8%, 728 NA Numper of Consumers 24 NA xbh/Consuner 3.245, 697 Ps) III. Laroe Commercial Under 358 KVA keh 51.497, 18 NA Number of Consumers 209 NA xiih/Consumer 246, 481 NA IV, Prosection: Larce Commercial Under 358 KVA 1989 19% «ih 79,654,008 = 93,037. 208 Number of Consumers 331 386 xwh/Consumer 248, 744 248, 744 V. Projection: Laroe Coamercial Over 358 KVA 1989 1994 Wh 120.825.2008 128,539, 208 Number of Consumers “4 2 kWh /Consumer 4,166,379 4. 284.633 VI. Prosection: Total Larse Comercial 1989 1994 bib 208,479,008 221, 576. 208 Number of Consumers x 416 xwh/Consumer 557, 093 532. 051 A-15 1984 139, 962, 928 265 528, 162 82, 425, 183 26 3,178, 196 97.537, 817 239 248, 744 2/26/83 GOLDEN VALLEY ELECTRIC ASSNey INC. REA FORM 345 - NAME ALASKA INTOL AIR Cacasin Aracnes WArs8An) ACCT NO 35-1750 350175000 MON 9 --=-= 1980 ----- ----- 1981 ----- ----- 1982 ----- KWH KW KWH KW KWH KW JAN 164,100 366 129,900 249 185,400 339 FEB 117,0C0 252 121,500 330 122, 40C 354 MAR 125,700 258 120,600 240 120,000 300 APR 110,700 228 108,990 240 103,80C 240 MAY 108,900 222 106,800 246 115,50C 219 JUN 103,5C0 240 100,200 222 90,60C 225 JUL 103,500 222 103,200 240 90,900 240 ALG 102,6CO 222 1234300 252 105,90C 225 SEP 125,100 240 197,190 258 114,000 255 OCT 75 4600 240 115,500 240 137,1CC 285 NOV 141,000 330 135,300 300 120,300 300 DEC 155,100 333 134,100 375 158,10C 300 TOTAL 15432,8C0 3153 144064400 3183 1,464,COC 3282 AVERAGE 119,400 262 117,200 265 122,000 273 LOAD FACTOR 62642 60.58 61.21 A-16 NAME ALASKA INT’L AIR — ALASKA AIRLINES HANGAR ACCT NO 3521752aa-12 MON een 1984------- KWH KW JAN 143.702 348 FER 114, 60a 324 MAR 166, 20a 330 APR 126. 202 32a MAY 125, 20a 27a JUN 115, 202 27a JUL 88. aaa 240 AUG 95. 10a zee SEP 123. 52a 189 ocT 99, 22a ese NOV 127. 50a 272 DEC 122. 72 s2a TOTAL 1,407,300 3,315. AVERAGE 117,275 276 LOAD FACTOR 58. 2% Ba 17. » »d » ) ») 2/26/83 NAME ACCT NO JAN FEB MAR APR MAY JUN JUL ALG SEP oct NOV OEC TOTAL ly AVERAGE LOAD FACT ALASKA RAILROAD 61-0300 @\co 30000 --- 1980 ----- KWH Kh 174,800 352 120,0C0 320 124,400 268 120,000 252 83,600 252 80,000 192 7742C0 216 90,4C0 236 95,600 248 122,800 268 154,800 324 155,200 304 398,800 3232 116,566 269 oR 59.36 GOLDEN VALLEY ELECTRIC ASSNey» REA FORM 345 RAILROAD $7A7790) Te aah 1981 <-<<- KWH KW 124,800 ai2 152 4400 296 119,200 272 113,600 280 94,000 232 84,000 180 100,890 229 117,200 252 198,800 292 129,600 296 162,400 332 189,400 464 1,487,200 3428 123,933 285 59.56 A-18 INC. 153,200 182,80C 138, 80C 122,0C0 138,006 95,20C 81,20C 94-80C 118, 80C 166,00C 146, 80C 166, 8CC 1,604,40C 133,700 360 344 316 320 212 256 300 292 300 312 3432 286 64.03 2/26/83 GOLDEN VALLEY ELECTRIC ASSNey INC. REA FORM 345 NAME ST OF AK — DIV OF AVN (Aieporr ree-asAc) ACCT NO 35-1950 2501945000 MON 9 --=-- 1980 —---- === 1981 ----- -=--- 1982 ----- KWH Kh KWH KW KWH KW JAN 561,600 1020 516.000 888 652, 80C 1044 FEB 400,800 804 490,800 1044 474,090C 1044 MAR 410+4C0 780 387,600 840 402,00C 816 APR 319,800 660 350,000 696 378,0CC 744 MAY 316,800 600 332400 732 384,00C 696 JUN 320,400 636 381,600 720 390,00C 720 JUL 343,200 612 421,200 768 342,C0C 696 ALG 321,600 612 398,400 756 351,6CC 672 SEP 412,800 672 3681400 720 356,40C 792 oct 302,400 888 454,800 864 602,40C 984 NOV 607,200 1200 534,000 948 534,00C 1068 OEC 664,800 1368 655,200 1236 795,60C 1020 TOTAL 4,972,800 9852 543901400 10212 5,572,80C 10296 AVERAGE 414,400 821 441,700 851 464,40C 858 LOAD FACTOR 69.14 71.10 74.14 A-19 NAME ST OF AK — DIV OF AVN AIRPORT TERMINAL ACCT NO 35@195a02-12 MON 2 ween 1984------- KWH KW JAN 624, B22 1292 FEB 742, aaa 1152 MAR 469. 222 1256 APR 451. 200 936 MAY 338, 422 756 JUN . 366, Baa 564 JUL 382, 422 900 AUG 373. 200 72a SEP 274, 402 696 OCT 354, 222 720 NOV 621,622 1032 DEC 583, 220 1116 TOTAL "5,659,200 10,740 AVERAGE 471.622 895 LOAD FACTOR 7z. 2% A-20 — —/Z/ 2/26/83 NAME ACCT NO MON =) JAN FEB MAR APR MAY JUN JUL ALG SEP OcT NOV DEC TOTAL 3% AVERAGE LOAD FACT STATE OF ALASKA GOLCEN VALLEY ELECTRIC ASSNey 31-3850 31138500 | --- 1980 ----- KWH KW 547,200 1284 313,200 924 336,000 864 202,800 600 1224400 384 116,400 324 112,800 312 90,000 468 177,600 492 230,409 708 471,600 1068 7034200 1428 423,600 8856 285,300 738 OR 52.95 REA FORM 388,890 4464400 252,000 256,800 115,200 99,000 109,200 114,900 162,009 276,090 412,800 688,800 3¥312,000 276,000 A-21 345 WibsA, PDNPT DOTS. 480 456 489 594 516 876 1104 1428 9876 823 45.93 INC. 540,000 501,60C 321, 60C 277,200 156,00C 111, 60C 104,400 157,200 239, 400 428,400 474,000 530,400 3,832,80C 319,40C 1332 1380 996 732 600 444 444 552 768 1020 1236 1356 19860 905 48.34 GOLDEN VALLEY ELECTRIC ASSN.. INC. REA FORM 345 NAME STATE OF ALASKA HIWAY DEPT HEADQUARTERS ACCT NO 311385aa1-12a MON enn 1984------- KWH KW JAN 517.220 1248 FEB 573. 622 1320 MAR 408. 222 12308 APR 276, 2B B42 MAY Z36, 422 76a JUN 166, Baa 620 JUL 132, aaa 348 AUG 92. 400 sen SEP 148, Baa 5a4 oct 152, aaa 540 NOV 326. 420 936 DEC 428, 402 1128 TOTAL 3,454,800 9,912, AVERAGE 287.900 826 LOAD FACTOR 47.7% 4-22 2/26/83 GOLDEN VALLEY ELECTRIC ASSNee INC. REA FORM 345 i NAME ST OF AK - DIV OF AVN (Arerer7 Fiz zrare~) ACCT NO 35-1050 2 3501905000 i MON 9 -===-- 1980 ----- wee 1981 --2--= 2 o---- 1982 ----- 2 KWH Kh KWH KW KWH KW w JAN 135,000 414 84,009 306 147,000 420 FEB 754600 258 934600 378 108,00C 414 MAR 734800 306 70800 294 76, 20C 324 ‘ APR 48,000 222 63 600 246 59,40C 252 MAY 33,600 108 404200 210 56,40C 234 JUN 32%4C0 138 39,600 246 40, 20C 126 ‘ JUL. 34,200 120 39,000 180 39,00C 120 ALG 40+2C0 210 52,200 192 51,6CC 192 f SEP 67,800 270 644200 282 65440C 318 e Oct 51,6C0 318 84,000 312 147,000 366 NOV 110,400 390 198 5600 378 123,60C 390 , DEC 139,800 450 159,600 432 162, 60C 432 ° TOTAL 842,400 3204 899,400 3456 1,076,400 3588 I AVERAGE 70,200 267 - 74,959 288 89,70C 299 (+ LOAD FACTOR 36-01 35 264 41.09 ( f 4 a r » A-23 NAME ST OF AK — DIV OF AVN AIRPORT FIRE STATION ACCT NO S5a1a52Vaz-ia MON 1984------- KWH KW JAN 147,622 420 FEB 134, 422 438 MAR 109, 202 396 APR 36, D22 294 MAY 73. 622 312 JUN - 69,600 272 JUL 5a. 422 168 AUG 59, 422 192 SEP 63, 22 240 OCT 72, 622 288 NOV 152, 622 366 DEC 159, 62a 378 TOTAL 1,252,200 3, 762° AVERAGE 104, 352 314 LOAD FACTOR 45. 6% A-24 2/26/83 GOLDEN VALLEY ELECTRIC ASSNey INC. REA FORM 345 NAME U OF A ACCT NO 87-0450 (yaw Frrarrs) 271045001 MON = = -=--- 1980) —-==-) =-=== D8 19e2e————— KWH KW KWH KW KWH KW JAN 4254400 924 2644000 732 370,350 1229 FEB 267,600 780 337,200 936 493,200 963 MAR 216,0CO 678 258,600 972 317, 70C 963 APR 168,6CO 480 222,600 672 232,20C 639 MAY 63,600 288 115,200 468 178,200 639 JUN 51,000 402 924400 378 97,65C 306 JUL 534400 246 141,000 378 84, 60C 279 ALG 109,2C0 564 197,400 936 101, 25C 446 SEP 151,200 570 165,605 1422 164,25C 464 OCT 22C,800 630 260,000 836 322,65C 743 NOV 303,000 1914 3624250 729 340,200 1233 DEC 538,200 1830 428,859 846 356,85C 1338 TOTAL 2,568,000 8406 24755109 9305 34059,100 9242 AVERAGE 214,000 700 229,591 775 254,925 770 LOAD FACTOR 41.87 40.58 45035 A-25 NAME U OF A YAK ESTATES ACCT NO 871845a21-12 MON 0000 wee 1984----—-- KWH KW JAN 396, 450 1017 FEB 427,500 992 MAR 286.200 774 APR 229, 50a 620 MAY 158. 40a 524 JUN 121, 050 356 JUL 85. 95a 275 AUG 91,800 279 SEP 172, 8a2 504 oct 245, 250 63a Nov 437, 400 864 DEC TOTAL 2,652,300 6, 823 AVERAGE 221,025 569 LOAD FACTOR 53. 3% A-26 Form Approved OMB No. 40-R388) U S DEPARTMENT OF AGRICULTURE RURAL ELECTRIFICATION ADMINISTRATION REQUEST FOR INDIVIDUAL CONSUMER DATA 2 NAME OF CONSUMER AWvers:y OF AlpsheA INSTRUCTIONS « Sve reverse side of this form. 3. TYPE OF BUSINESS (Give aetatis/ 4. NAME OF SUPPLY SUBSTATION 5. 1S CONSUMER BILLEO ON A OEMANO 4 ENERGY | 6. RATE APPLICABLE TYPE RATE? __ i ' ives NO 7. MONTHLY kWh CONSUMPTION ANO DEMAND PRIOR YEARS THIS YEAR 19 19 19 80 noMeu wF2 BILLED lei. LED! aicced tLLED BILLED kWh IDE MANO: kWh E MANO, kwh DE MAN kwh De Man kWh IDE MAND 1 kW kW kW kw kW | | | TAIL C78 000 = 43 b00 ! | \ ull Go 000) | | S575 600» | ; \ Ifo 4ov> 156 ¥oo> | j | | | | fo400> /144 200 | suey | \0%¥ 00> $2. 400 nue. | a ae at | 3 t (C06 000, 1678 200 | | Serr \ S25 S00> JO7 200 ocr. | | | 1626 00 1297 200 NOV. | | 1 (RYG boo So 400 DEC. | | C336 Pov oF Yoo TOTA 4393 200 65 6080> AVERAGE BILLING DEMAND (kW) 8. CONNECTED LOAD BREAKDOWN @. LIGHTING LOADS (kW) b. HEAT LOADS (kW) MOTOR LOADS HP MOTOR LOADS MOTOR LOAOS MOTOR LOADS 9. TOTAL CONNECTED LOAD THiS CONSUMER 10. TRANSFORMER SIZE (kVo) 11. MINIMUM BILL s (Per mo. [rer vr 12. REMARKS iL se reverse side if mure space is needed) A-27 Sea fnew Tae EP aa 2/26/83 GOLDEN VALLEY ELECTRIC ASSNey INC. REA FORM 345 Oo NAME ALYESKA PIPELINE CO (Pum 4) ACCT NO 1-4500 10450000 MON = ====- 1980 ----- =---- 1981 ----- ----- 1982 ----- KWH KW KWH KW KWH KW JAN 283,200 874 2034040 672 249,600 672 FEB 202,560 682 2624560 672 234,720 672 MAR 216,480 682 2244640 643 218,400 662 APR 274,080 634 238,080 624 261, 60C 643 MAY 226,560 586 203 520 566 200,16C 634 JUN 189,6CO 590 202,560 566 123,84C 528 JUL 182,880 518 135,360 538 170,400 547 AUG 157,920 523 186 +240 528 178,08C 538 SEP 245,280 634 1934440 624 172, 8CC 566 OCT 269,280 643 208,320 566 210,240 629 NOV 200,640 624 2734120 634 2464 24C 667 DEC 301,440 682 2824240 662 245, 760 672 TOTAL 2,749,920 7672 2,613,120 7295 25511,84C 7430 AVERAGE 229,160 639 217,760 607 209,320 619 LOAD FACTOR 49212 49.14 46.32 A-28 SOLDEN VALLEY ELECTRIC ASSN.. REA FORM 345 NAME ALYESKA PIPELINE CO (PUMP 9) ACCT NO 10452222 MON nnn 1984------- KWH KW JAN 213. 622 643 FEB 219. 360 634 MAR 236.642 653 APR 236. 642 658 MAY 204, 22a 628 JUN - 156. 962 595 JUL 158, 42a 547 AUG 154. 28a 54 SEP 148, 322 5z8 oct 142, 562 S76 Nov 178. 562 6a5 DEC TOTAL AVERAGE 189. 40a 604 LOAD FACTOR 42. 9% A-29 INC. a 2/26/83 GOLDEN VALLEY ELECTRIC ASSNey INCe REA FORM 345 NAME ALYESKA SERVICE CO (Pump g) ACCT NO 8-2250 061225000 MON = =--=- 1980 ----- -==-- 1981 ----- 0 ene 1982 ----- KWH KW KWH KW KWH KW JAN 562,800 858 271,800 1026 291,600 606 FEB 4764400 864 525 4000 906 315,006 720 MAR 477,600 972 4184200 846 339,600 846 APR 464,400 846 4174600 966 472, 80C 864 MAY 418,200 984 332,400 786 457,200 1032 JUN 4134400 738 3234400 786 241, 200 750 “JUL 405,000 750 183,000 540 322,800 720 AUG 331,800 744 1754800 540 328,200 822 SEP 406,209 852 387 4600 726 235, 80C 504 oct 315,600 792 235,800 696 314,400 822 NOV 216,600 732 3544600 726 394, 20C 846 DEC 350,400 846 3404200 726 289, 800 786 TOTAL 4,838,400 9978 349654400 9180 4,002,60C 9318 AVERAGE 403,200 831 3304450 765 333,550 776 LOAD FACTOR 66446 59.17 58.88 A- 30 GOLDEN VALLEY ELECTRIC ASSN.. INC. REA FORM 345 NAME ALYESKA SERVICE CO (PUMP 8) ACCT NO Bleze5aaa MON nen 1984------- KWH KW JAN 379, B22 782 FEB 354, 600 762 MAR 414, 222 7a2 APR 476. 400 834 MAY 384, 622 768 JUN - 475, 822 73 JUL 365. 400 348 AUG 296. 400 732 S=P 513. 622 762 oct 367. 2002 91 NOV 276, 220 eae DEC 695. 402 546 TOTAL 4,999,200 9,276 AVERAGE 416.620 773 LOAD FACTOR 73. 8% A-31 ‘s 2/26/83 GOLDEN VALLEY ELECTRIC ASSNe, INC. REA FORM 345 NAME FBKS NO STAR BORCUGH (77.7. ACCT NO 27-4575 271457500 MON = --=-- 1980 ----- ----- 1981 ----- ----- 1982 ----- KWH KW KWH KW KWH KW JAN 0. _0 51,360 576 -51,84C = 624 FEB 47,040 600 53,280 586 46,560 576 MAR 50,880 566 - 43,680 576 45,600 576 APR 52,320 576 50.400 576: (604960 = 576 MAY 474520 586 545240 566 56,640 581 JUN 41,760 566 564640 576 58,560 581 JUL 58,080 576 44,640 538 54,240 494 AUG 39,840 538 52,320 576 65,760 499 SEP 52,800 576 52,320 538 644800 494 OCT 514360 _ 576 52,800 538 64,800 ~538 NOV 58,080 586 54,720 576 561640 586 DEC 50,880 586 491440 576 70,56C 581 TOTAL 550,560 6332 6155840 6798 6961960 6706 AVERAGE 50,050 575 51,320 566 58,08C 558 LOAD FACTOR 10.93 12.42 14.25 A-32 GOLDEN VALLEY ELECTRIC ASSN., INC. REA FORM 345 NAME FBKS NO STAR BOROUGH TRASH BALER ACCT NO 2714575a2-12 Ce 1984------- KWH KW JAN 62, 88a 586 FEB 67. 200 581 MAR 54.240 59a APR 62. 400 S81 MAY 39. 360 5a6 JUN - 91.200 581 JUL 72. 28a 576 AUG 72. 28a S76 sep 71.520 608 oct 61.440 SEE NOV 74,400 624 DEC 61.920 581 TOTAL 786,720 7,036 AVERAGE 65. 56 586 LOAD FACTOR 15. 3% A- 33 2/26/83 GOLDEN VALLEY ELECTRIC ASSNey INC. REA FORM 345 NAME FBKS NSBSD wre- 44-47 wien rerroue. ACCT NO 74-8885 742680000 MON = ----- 1980 ----- ----- 1981 ----- 0 ----- 19@2 ----- KWH KW KWH KW KWH KW JAN 186,000 540 1534200 564 163, 2CC 624 FEB 291,600 540 168,000 588 240,000 492 MAR 168,000 516 1644400 576 165,60C 480 APR 214,800 552 189,000 576 160, 80C 432 MAY 178,800 552 145,290 540 162,09C 420 JUN 141,600 492 128,400 516 128,40C 408 JUL 109,800 384 110,400 384 90,00C 408 AUG 130,8CO 360 634600 432 99,60C 288 SEP 135,600 528 1824400 564 178,8CC 420 Oct 198,000 552 1754209 564 186,00C 408 NOV 187,200 552 184,800 564 222,000 456 DEC 213,600 612 179 +400 624 169,20C 456 TOTAL 2,C56,800 6180 1,836,009 6432 1,965,600 5292 AVERAGE 171,+400 515 153,000 541 163,800 441 LOAD FACTOR 45.59 38.74 50.88 NAME FBKS NSBSD WEST VALLEY HIGH SCHOOL ACCT NO 742888S522-12 MON 2 nnn 1984------- KWH KW JAN 133, 222 456 FEB 169, 202 444 MAR 138, aaa 468 APR 172, aaa 420 MAY 152, 402 420 JUN - 115, 20a 396 JUL Bz, B22 228 AUG 87, 622 saa SEP 142, 82a 428 oct 134, 422 420 NOV ZQz, 82a 482 DEC 156, aaa 456 TOTAL 1,687,200 4,896 AVERAGE 142, 622 428 LOAD FACTOR 47.2% A-35 ww ww 2/26/83 GOLCEN VALLEY ELECTRIC ASSNey INC. REA FORM 345 NAME FBKS NSBSD thar CYRER OM eR RN Sp 4s7F ACCT NO 74-0080 T4100G00 \ MON = -=--- 1980 ----- ==--- 1981 ----- 0 ----- 1962 ----- KWH KW KWH KW KWH KW JAN 134,400 384 127,200 444 115, 200 408 F£B 144,000 396 132,000 429 162,00C 420 MAR 126,0C0 408 132,000 432 122,40C 432 APR 248,800 384 139,800 384 111,600 396 MAY 124,800 396 123,600 384 122,40C 396 JUN 84,CCO 312 99,690 360 109, 20C ine JUL 70,800 228 94,800 252 797 20C 264 ALG 94,800 216 99,009 276 85,25C 240 SEP 104,4C0 360 123,600 336 111,60C 408 OcT 129,600 sii2. 121,200 498 116,40C 432 NOV 128,4CO 396 139,200 408 147,60C 420 DEC 140,490 420 117,690 408 111, 60C 420 TOTAL) 14430+4C0 4272 14431,600 4512 1, 394, 40C 4608 AVERAGE 119,200 356 119,300 376 116, 20C 384 LOAD FACTOR 45.86 43.46 41245 A-36 NAME FBKS NSBSD ADULT CAREER DEVELOPMENT CENTER ACCT NO 741a28221-12 MON 2 nnn 1984------- KWH KW JAN 115, 22a 428 FEE 148, Baa 428 MAR 116. 422 420 APR 127, 2a 428 MAY 121,200 384 JUN - 105, 622 372 JUL 78. 222 242 AUG 92, 22a z5e SEP 112, 422 372 oct 117,622 372 NOV 114, aaa 396 DEC 99, 622 428 TOTAL 1,344,000 4, 440° AVERAGE 112, 222 372 LOAD FACTOR 41.5% A-37 @ e @ € seal 2/26/83 GOLDEN VALLEY ELECTRIC ASSNey INC. REA FORM 345 NAME FBKS NSBSD) Wor Poe Junie /EF.sior Hib Feiooe. ACCT NO 16-8180 (620180082 MON = -==== 1980 ----- ss ----- 1981 ----- =----- 1982 ----- KWH KW KWH KW KWH KW JAN 135,600 468 136,200 378 138,60C 408 FEB 150,000 408 138 600 402 205200 456 MAR 120,000 384 126,000 372 132,60C 366 APR 161,400 384 139,800 369 140,400 360 MAY 131,400 oie 139,800 348 141,000 390 JUN 89,400 216 88,200 294 91,20C 222 JUL 85,200 198 91,800 216 77,40C Ave ALG 152,809 342 196,800 342 104,400 366 SEP 124,200 360 130,800 342 113,400 390 OCT 127,800 372 112,200 354 161,40C 414 NOV 148,200 384 170,400 354 165,0CC 462 DEC 156,600 462 124,200 396 148,800 738 TOTAL 1,581,600 4350 15495 ,800 4158 1,619,400 4764 AVERAGE 131,800 362 124,650 346 134,95C 397 LOAD FACTOR 49.87 49.35 46.56 1S00MA xFays72, FORTARE CLASS 200K + sit wend Apne 4 1982 A-38 GOLDEN VALLEY ELECTRIC ASSN.. INC. REA FORM 345 NAME FEKS NSBSD NORTH POLE MIDDLE SCHOOL ACCT NO 162818%@2-12 MON nm 1984------- KWH KW JAN 151.200 482 FER 159. 602 834 MAR 141, 020 666 APR 144, aaa 602 MAY 126. 222 426 JUN - 118, 82a 414 JUL 45. 622 152 AUG 61.220 168 SEP B=. 82a 326 OCT 122. 400 324 Nov 133.202 348 DEC 148. 222 384 TOTAL 4,434,000 5, 160. AVERAGE 119.502 43a LOAD FACTGR 38. 1% A-39 GOLDEN VALLEY ELECTRIC ASSN.. INC. REA FORM 345 NAME FBKS NSBSD NORTH POLE HIGH SCHOOL ACCT NO 15%@269501-12 MON nn 1984-----—- JAN FEB MAR APR MAY JUN JUL AUG SEP ocT NOV DEC 154. 822 423 TOTAL 15480 423 AVERAGE 12.922 35 LOAD FACTOR s2.1% A-40 NAME FEKS NSBSD WOODRIVER SCHOOL ACCT NO 861449521-18 MON 2 wen 1984------- KWH KW JAN 67. Baa 276 FER 76. 522 452 MAR 88. 82a 26a APR 84, aaa 33a MAY 78, 622 E46 JUN " g1, 32a eee JUL 65. 40a 93 AUG 53, 420 75 SEP 84,900 255 OCI 95,122 24a NOV az, a2a z4a DEC TOTAL 58,600 2, 787° AVERAGE 71.550 232 LOAD FACTOR 42. 2% 2/26/83 GOLDEN VALLEY ELECTRIC ASSNe,y INC. REA FORM 345 NAME FBKS SAND & GRAVEL (Rio--1« 427 ACCT NO 26-6550 ZwLESSOCOU MON = ----- 1980 ----- ----- 1981 ----- 0 ==---- 1982 ----- KWH KW KWH KW KWH KW JAN 11,000 39 9,150 25 10, 8CC 75 FEB 9,700 50 9,000 30 10,950 Sil; MAR 12,500 100 6,300 25 9,000 68 APR 11,300 60 11,600 80 17,10C 228 MAY 21,3C0 250 42,900 470 44,700 380 JUN 37,100 365 33,200 262 69,60C 452 JUL 50,500 392 67,500 525 61,95C 528 ALG 454100 472 76,050 540 79,50C 528 SEP 68,700 450 80,850 570 91,35C 602 OCT 35,100 450 20,850 "330 34,20C S21 NOV 454200 210 14,550 180 10,950 77 DEC 20400 50 10,500 30 12,00C _ 180 TOTAL 367,900 2888 382 5450 3067 452,100 3702 AVERAGE 30,658 240 31,870 255 37,675 308 LOAD FACTOR 17.49 Lee. 16.75 A-42 GOLDEN VALLEY ELECTRIC ASSN.. INC. REA FORM 345 NAME FEKS SAND & GRAVEL REDI-MIX PLANT ACCT nO z6ze5Seaa-10 MON nm 1984------- KWH KW JAN 3. 300 15 FEB 11,720 5 MAR 8.850 3a APR 11.650 77 MAY 33. B22 202 JUN . 69,150 28a JUL 72. 352 378 aus 77. 722 428 SEP 88.250 435 oct 92.150 435 NOV 55. 200 33a DEC 28.500 165 TOTAL 554.1@@ 2, 960 AVERAGE 46.175 247 LOAD FACTOR 25. 6% A-43 2/26/83 GOLDEN VALLEY ELECTRIC ASSNey INC. REA FORM 345 NAME FNSB HWArqnTI~ gue WIE SC HODe ACCT NO 44-5650 440565007 MON 9 -=--- 1980 ----- ----- 1981 ----- ----- 1982 ----- KWH Kh KWH KW KWH KW JAN. 164,100 375 89,700 399 177,000 405 FEB 180,300 372 118,500 399 183,90C 399 MAR 138,600 381 864400 393 135,30C 402 APR 154,500 381 114,600 375 121, 20C 372 "MAY 103,509 375 111,600 363 125,700 363 JUN 83,1C0 372 66 4900 351 58,80C 315 JUL 51,9CO 201 43,800 222 50,10C 180 ALG 41,4C0 195 46,800 234 39,00C 150 SEP 754900 372 971500 351 92,10C 306 OCT 1334800 372 113,100 381 124,200 321 NOV 118,800 378 127,800 384 147,300 330 DEC 169,2C0 372 153,000 399 105,00C 318 TOTAL 1,415,100 4146 1,169,700 4242 1,359,60C 3861 AVERAGE 117,925 345 979475 353 113,30C 321 LOAD FACTOR 46.82 37-82 48.35 A-44 NAME FNSB HAMILTON JUNIOR HIGH SCHOOL ACCT NO 448565002-18 MON 1984------- KWH KW JAN 94, 200 285 FEB 117.300 320 MAR 87. 320 330 APR 32, 720 34e MAY az, aaa saa JUN - 48,900 zE7 JUL 55. 82a 162 AUG 63. 220 462 SEP 71. 722 324 act 93. 902 327 NOV 36, 922 339 DEC 33, 220 32a TOTAL 1,004,10@ 3,738 AVERAGE 83.675 317 LOAD FACTOR 36.2% A-45 2/26/83 GOLDEN VALLEY ELECTRIC ASSN.» INC. REA FORM 345 NAME GENERAL TEAMSTERS e100 Gompure) ACCT NO 26-8450 : 26264500 | MON = --==-- 1980 ----- ----- 1981 ----- ----- 1982 ----- KWH KW KWH KW KWH KW JAN 172,800 462 171,600 444 200,400 486 FEB 186,000 468 196,200 456 217, 80C 486 MAR 180,600 444 151,200 414 181,800 450 APR 189,000 432 184,200 420 216,600 480 MAY 178,200 462 175200 450 182,400 498 JUN 178,800 498 220,800 444 219,600 498 JUL 237,000 474 183,000 462 222,600 504 AUG 171,000 444 1 207,000 468 190,800 492 SEP 181,800 450 202 4800 462 208, 80C 498 OCT 172,2C0 438 1994200 438 213,000 456 * NOV 205,800 438 223,200 450 206,400 480 DEC 200,400 468 199,800 468 252,600 492 TOTAL 2,253,600 5478 243054200 5376 24512,80C 5820 AVERAGE 187,800 456 192,100 448 209,400 485 LOAD FACTOR 56.41 58.73 59.14 A-46 BOLDEN VALLEY ELECTRIC ASSN.. INC. REA FORM 345 NAME GENERAL TEAMSTERS UNION COMPLEX ACCT NO z628450R1-12 MON nn 1984------- KWH KW JAN 191,400 498 FEB 251.400 524 MAR 181.8aa 432 APR 199.200 444 MAY 192. 602 452 JUN 187. 822 498 JUL 234, 200 512 AUG 214, Baa 438 SEP 185.400 480 oct 214,200 486 NOV 205. 200 452 DEC 204, 602 482 TOTAL 2.462.400 5,790 AVERAGE E25. 200 483 LOAD FACTOR 58. 3% A-47 2/26/83 GOLCEN VALLEY ELECTRIC ASSNee INC. REA FORM 345 NAME GEOPHYSICAL INST U-A (Rinse vetesueie srariow ) ACCT NO 51-8000 5i2¢00c0 MON = ----- 1980 ----- ----- 1981 ----- ----- 1982 ----- KWH KW KWH KW KWH KW JAN 118,8C0 630 804100 576 120,600 612 FEB 119,700 603 1724800 576 188, 10C 594 MAR 106,200 594 99,000 558 52520 135 APR 681400 603 704200 558 27,000 99 MAY 49,500 558 51,300 4B6 11,70C 72 JUN 79,200 531 66 »600 486 14,4CC 63 JUL 64,800 504 73,200 531 7, 20C 45 ALG 5i3C0 477 18,900 108 6,390 36 SEP 90,600 sak 39,600 oan 14,400 63 OCT 144,900 531 125,100 540 26,00C 60 NOV 95,400 549 1444090 558 27,000 66 DEC 99,906 576 1114600 531 30,000 90 TOTAL 15088,1CO 6687 110401400 6039 524,906 1935 AVERAGE 90,675 557 86,700 503 43741 161 LOAD FACTOR 22.30 23.61 37.21 A-48 2/26/83 GOLDEN VALLEY ELECTRIC ASSNey INC. REA FORM 345 NAME GSA FINANCE DIVISION (orem 7006. ACCT NO 28-5650 23256500 | MON = ==--- 1980 ----- —_ ----- 1981 ----- ----- 1982 ----- KWH KW KWH KW KWH KW JAN 229,200 690 144,000 579 180,000 672 FEB 170400 624 139,800 564 164,400 666 MAR 154,800 450 994600 354 135,600 582 APR 132,000 546 112,290 330 185,400 588 MAY 138,000 378 118,200 366 103,800 384 JUN 144,600 384 118,200 354 126,000 444 JUL 168,6CO 366 195,000 36 Onin 132,00C 438 ALG 102,6CO0 360 129,090 390 115,800 420 SEP 1514209 558 198,090 330 124,200 444 oct 153,600 558 144,600 546 156,600 606 NOV 1709400 | 576 ; 155.400 582 204,000 —- 666 DEC 162,000 678 186,000 636 205,800 696 TOTAL 158774400 6168 13542,000 5382 1,833, 60C 6606 AVERAGE 1569450 514 128,500 448 152,80C 550 LOAD FACTOR 41.69 39.29 38.05 A-49 GOLDEN VALLEY ELECTRIC ASSN.. INC. REA FORM 345 NAME GSA FINANCE DIVISION FEDERAL BUILDING ACCT KO F825E5N21-108 MON nm 1984------- KWH KW JAN 137.422 636 FER 217, 200 636 MAR 134.400 666 APR 162, aaa 576 mAY 130.200 372 JUN - 141.600 428 JUL 152. 000 432 aus 139.200 420 SEP 124.820 362 ocT 116, 82a 372 NOV 168, 20a S76 DEC 188. 400 654 TOTAL "1,870,802 6,168 AVERAGE 155.920 514 LOAD FACTOR 41.5% 2/26/83 GOLDEN VALLEY ELECTRIC ASSNey INCe REA FORM 345 NAME LAKEVIEW TRL CT roniue womnc Crarrcn merce) ACCT NO 26-9150 2624 1Ss000 MON = =-=-- 1980 ----- -=---- 1981 ----- 0 ----- 1982 ----- KWH KW KWH KW KWH KW JAN 450,600 744 283 4800 696 288,000 630 FEB 135,600 816 262,200 660 282,000 630 MAR 2934400 750 163,200 492 195,00C 642 APR 120,600 576 199,800 498 213,000 480 MAY 156,000 426 154,200 420 150,000 414 JUN 132,000 420 165,000 414 142, 80C 396 JUL 156,0C0 360 139,200 396 157,200 390 ALG 125,400 384 159,600 432 136, 80C 420 SEP 159,000 414 168 4600 516 177,000 444 oct 165,600 450 187,800 498 245, 40C 648 NOV 2534200 '552 264,000 564 271,80C 648 DEC 304,800 696 285 4600 900 ‘ 373,800 714 TOTAL 25452,200 6588 24424,000 6396 2,632,800 6456 AVERAGE 204,350 549 202000 533 219, 40C 538 LOAD FACTOR 59.98 51.91 55.86 A-51 NAME ACCT NO MON JAN FEB MAR APR MAY JUN JUL AUG SEP ocT NOV DEC TOTAL AVERAGE GOLDEN VALLEY ELECTRIC ASSN.. REA FORM 345 LAKEVIEW TRL CT MOBILE HOME (MASTER METER) F629150a2-12 S21, 2A 421.220 Ea2 Ter w ra fe + aa fre an - 233. 42a 196. 822 194,420 174, aaa 174, Qa LOAD FACTOR A-52 626 662 INC. GOLDEN VALLEY ELECTR REA FORM 34 NAME MAPCO PETROLEUM INC ACCT NO 1710@@12@-12 MON ene 1984 KWH JAN 1.474, 200 FEB 1,197. 200 MAR 1,222, 120 APR 1,379, 700 MAY 1,087, B22 JUN 1,295, 722 JUL 1,344, 22 AUG 1.457.420 SEP 1,659. 20a oct 1.632. 600 NCW 1,644, 300 DEC 1.763.543 TOTAL “17,215, 343 AVERAGE 1.434.612 LOAD FACTOR A-53 IC ASSN... cs oa INC. 2/26/83 GOLDEN VALLEY ELECTRIC ASSNey INC. REA FORM 345 NAME NASA TRACKING STA = Sarcehita Faced £87720 ACCT NO 51-4800 12480000 MON 9 -=--- 1980 ----- — ----- 1981 ----- wa -=- 1982 ----- KWH KW KWH KW KWH KW JAN 685,200 1206 506 +400 924 678,0CC 1140 FEB 552,000 1104 501,600 1032 565,20C 1116 MAR 582,000 1032 488 +400 804 556, 80C 1020 APR 494,400 912 444,000 852 537,60C 996 MAY 46312C0 828 4574290 816 518, 40C 840 JUN 490,800 840 432,000 816 478, 80C 900 JUL 499,200 864 4644400 816 500,400 888 ALG 468,000 876 504,006 e838 537, 60C 888 SEP 517200 876 484,800 864 432,00C 816 OCT 504,000 864 514,800 936 540,C0C 948 NOV 540,000 888 5724400 1008 552,00C 1044 DEC 656400 1308 616,800 1056 584,40C 1008 TOTAL 6,452,4C0 11592 51986,800 10812 6481,200 11604 AVERAGE 537,700 966 498,900 991 540,10C 967 LOAD FACTOR 76.25 75.85 76.51 A-54 NAME NASA TRACKING STA SATELLITE MON JAN FEB MAR APR MAY JUN JUL AUG SEP OocT NOV DEC TOTAL AVERAGE LOAD FACTOR TRACKING STATION ACCT NO 512482022-12 595, aa 985. 622 487,222 466, 822 » 384, B22 351, 622 298, 822 355, 202 426, 222 1228 1116 1142 948 A-55 515.622 376, 322 W193 7i. 7% Form Approved OMB No. 40-R3881 US. DEPARTMENT OF AGRICULTURE RURAL ELECTRIFICATION ADMINISTRATION REQUEST FOR INDIVIDUAL CONSUMER DATA 2 NAME OF CONSUMER 17-O000-0 NOM PLE FES/-ICPY Acer ee 1 718 e100 S. 1S CONSUMER BILLED ON A OEMANC & ENERGY | 6. RATE APPLICABLE INSTRUCTIONS « See reverse side of this form. 3. TYPE OF BUSINESS (Give details) PER FISCRY. 4, NAME OF SUPPLY SUBSTATION TYPE RATE? - F20°TH POL /PODKR 1240-7. tives NO 7. MONTHLY kWh TONSUMPTION ANO DEMAND PRIOR YEARS THIS YEAR 19 19°75 19i0 19 <7 19 22 MONTH BILLED BILLED aicced Bic BILLED kWh IDE MANO! kWh MANO) kWh DEMAND kWh ce kWh ID E MAND kw kw kW | kw kw GANS ft 121,600 2tiov | 1.276 You 2956] 1,839,600 |275, FEB 146 } g < 301 | 1 NGI, oo Ziow 12,190 b00 2$20| 2.501, 102 i ; ; | | MARCH! ' Ls El, Qov trov 11,726. 200 2egy | 1,644,200 ZOKS | TT rN —T APRI t | - 2 t i | | Lygt.bov 2iov |2,/44 200 12226 |Z, 256 200 43) i ! | may | ! $7600 |zr00 | L274 fou —Izzop 12194 590 (12919 ! | ! JUNE: | | 11,222,290 l2i00 |) S44 £59 L720 |' 706 300 235 | gucy | | | 1.2727, v0 too |i pe: 220 2319 |' 627 £29 29 i AUG. | 5 5 2:9 LIE .7 > 1 97) Boe | if £55299 2 1$..729 2772 722 509 26 Bert: | LIES, 600 Zieo |Z oF, 720 27s fi oct. | DSL £00 (12268 |) 1 ££. 000 2e264!,228 foo 2940 Nov. L720 ¢20 2262 |2 022200 2772 |/ 241,029 - 34 DEC. | 2./21 090 22s7 |i Aue woo 1275/1) 919,900 72p? TOTAY 13,496 ,*/09 16!74,290 |zk70% | 2 41,905 200 AVERAGE BILLING DEMAND (kW) Z:0 749.2 [632% 26.4% 83.82 1% . 8. CONNECTED LOAO BREAKOOWN 0. LIGHTING LOADS (kW) b. HEAT LOADS (kW) MOTOR LOADS | HP MOTOR LOADS | HP MOTOR LOADS HP MOTOR LOADS HP ©. 4. e. f, t } 9 h. i. i. 9. TOTAL CONNECTED LOAD THIS 10. TRANSFORMER SIZE (kVo) 11. MINIMUM BILL CONSUMER (“Per mo.[_}PER YR 12. REMARKS (Lse reverse side if mure space 1s needed) : : i827 1642, THE Ria hy ag perraccies fir ftw %IG0 3 400 “20 G50 Me og. , - ' ° geventire Ser A-56 Hull 20,953,920 21,714, 800 V?CAls phrasing ual baeligyg fo ‘ : - os : 7 0.3? °.8 Bap hinedeies: REA FORM 545 | 2/26/83 GOLCEN VALLEY ELECTRIC ASSNey INC. REA FORM 345 NAME NORTH STAR INC 9 (rvsK0m srt rtetemter) ACCT NO 27-7600 2737160005 MON = ==--- 1980 ----- ----- 1981 ----- ----- 1982 ----- KWH Kw KWH KW KWH KW JAN 30,000 84 922400 312 166,800 432 FEB 19,200 60 . 914200 240 168,000 516 MAR 18,000 60 99 5600 324 109, 20C 300 APR 15,6C0 36 114,000 324 144,000 312 MAY 14,400 120 195,600 672 109, 200 552 JUN 24400 0 135 4600 276 46, B0C 204 JUL 0 Oo 106,800 264 26,40C 180 ALG Oo 0 116,400 252 25,200 144 SEP 16,800 72 , 1114600 336 28, 80C 72 ocT 254200 216 104,400 240 494.200 360 NOV 121,2C0 276 120,000 252 186,0CC 384 DEC 97,200 636 141,600 348 69, 60C 264 TOTAL 360,000 1560 1,339,200 3840 1,129,200 3720 AVERAGE 36,000 156 111,600 320 94, 100 310 LOAD FACTOR 26034 47.77 41.58 A-57 2/2€/83 GOLDEN VALLEY ELECTRIC ASSNey INC. REA FORM 345 NAME NORTHWEST ALASKAN PIPE (ft0- heoe per nt ACCT NO 58-1690 &a1164000 MON 9 ----- 1980 ----- ==---- 1981 ----- -=--- 1982 ----- KWH Kh KWH KW KWH KW JAN 133,280 240 1164090 240 158,160 320 FEB 119,520 240 119,440 264 187,12€ 336 MAR 116,000 272 1184400 272 167,840 360 APR 112,080 248 105,280 272 157, 760 304 MAY 122,800 272 1194040 264 129, 36C 400 JUN 104,800 272 1134840 264 175, 60C 392 JUL 128,240 344 135 4440 264 168,08C 368 ALG 141,040 344 142,160 264 141, 44C 336 SEP 1024560 208 115840 288 133,200 288 OCT 107,200 272 130,400 336 153,20 336 NOV 130,880 272 1394040 344 148, 16C 288 DEC 109,040 280 ~ 188,320 368 193, 2CC 328 TOTAL 194271440 3264 145434200 3440 1,913,12C 4056 AVERAGE 118,953 272 128,600 286 1595426 338 LOAD FAC7OR 59.90 61.59 64.61 A-58 NAME NORTHWEST ALASKAN PIPE FROST HEAVE TEST SITE ACCT NO 5S811éEg9aa2-1a MON Senne 1984------- KWH KW JAN 188.562 320 FEE 146, 280 328 MAR 139.360 272 APR 141, 222 288 MAY 126, B22 288 JUN - 158,640 296 JUL 150,640 296 AUG 171, 28a 288 SEP 164,562 28a oct 129.040 288 NOV 143, 362 368 DEC 168.242 256 TOTAL 1,826,960 3,568 AVERAGE 152, 247 297 LOAD FACTOR 72. 1% 7 A-59 NAME QUTDOOR WORLD LTD CHALET ACCT NO 952616522-12 MON JAN FEB MAR APR MAY JUN JUL AUG SEP OcT NOV DEC TOTAL AVERAGE LOAD FACTOR 141, 202 157.122 136, 242 211, 36a 96, 642 3899, 242 74, 922 A-60 te w tf o fo Ss GW fa 2/26/83 GOLCEN VALLEY ELECTRIC ASSNey INC. REA FORM 345 NAME SAFEWAY STORES (Ai77027 tarresy) ACCT NO 33-4350 330435000 MON = ----- 1980 ----- ----- 1981 ----- ----- 1982 ----- KWH Kh KWH KW KWH KW JAN 226,200 426 2114200 432 319,80C 528 FEB "1994200 376 220,200 444 234,00C 600 MAR 220,200 378 213,000 408 222,00C 438 APR 181,800 366 294,600 438 246,000 420 MAY 175,800 390 198,600 420 190, 80C 408 JUN 267,0CO 348 163,800 396 186,600 426 JUL 213,0C0 360 188 4490 384 155,40C 354 ALG 159,000 366 184,800 354 216,60C 390 SEP 202,200 360 193,800 432 214,80C 432 OCT 182,400 432 2014600 - 438 283,200 432 NOV 261,600 456 2694400 426 * 229, 20C 486 DEC 2584600 462 2774200 558 312,60C 486 TOTAL 24487,000 4722 29526600 5130 2+811,00C 5400 AVERAGE 207,250 393 210,550 427 234, 25C 450 LOAD FACTOR 72224 67.54 71.30 A-61 GOLDEN VALLEY ELECTRIC ASSN.. REA FORM 345 NAME SAFEWAY STORES AIRPORT SAFEWAY ACCT NO S304S5200-12 MON JAN FER MAR APR MAY JUN JUL AUG SEP OCT NOV DEC TOTAL AVERAGE LOAD FACTOR A-62 272, 622 £88, 622 197. 42a Ea, 420 183, aaa 187,222 211,222 fy 156. 62a 171, 222 19é, aaa INC. 2/26/83 NAME ACCT NO MON CO JAN FE8 MAR APR MAY JUN JUL ALG SEP ocT NOV DEC TOTAL 3, AVERASE LOAD FACT GOLDEN VALLEY ELECTRIC ASSNey REA FORM 345 USIBELLI COAL MINE ertere 6 AGEs 96-4200 460420000 --- 1980 ----- -=--- 1981 ----- KWH KW KWe KW 262,809 396 399 4600 396 2214400 414 318,600 1584 210,6C0 396 5181400 1836 241,200 378 559,800 1890 223,200 1674 531,000 1899 408,600 1908 72,009 1710 228,600 1980 52,290 360 293,400 1998 72,990 414 293,4C0 1872 794290 414 23242C0 1638 354,690 1836 484,209 1638 419,400 2070 106,200 1494 415,890 1566 205,800 15786 3,693,600 15876 2671150 1315 397,800 1323 OR 27.82 31.87 A-63 INC. 612,000 1,035,000 720,00C 583,2CC 331,29C 180,00C 149, 40C 106,29C 124,29C 446,406 567,000 682, 2CC 52536, 80C 461,400 2160 2160 2160 1944 1872 684 648 1764 2196 2322 19134 1594 39.65 NAME USIBELLI COAL MINE ELECTRIC DRAGLINE ACCT NO 96242AR022-12 MON 2 een 1984------- KWH KW JAN 828, 200 2718 FEB 811, 822 Z2826 MAR 568. B22 326 APR 745, 200 3132 MAY 725, 422 2736 JUN 613, 82a 2772 JUL 529, 222 4122 AUG 399, 622 3528 SEP 975, 622 2304 OCT 824, 400 2448 NOV 1,081,822 z612 DEC 946, B22 2484 TOTAL "9,030,600 34,740. AVERAGE 752, 550 2, 895 LOAD FACTOR 35. 6% A-64 Appendix B HISTORIC CUSTOMER AND LOAD CURVES 4 Historical Customer Residential Class (5 nee rowth: Number of Customers (Thousands) 1970 Historical Customer Growth: Small Commercial Class 1972 1974 1976 1978 1980 1982 Year 1984 Number of Customers storica| — Large Cammercial Class 200 4 Customer Growth: ANNUAL KMH (Millions) 180 170 160 1450 140 130 120 110 100 1970 ANNUAL KWH USE RESIDENTIAL CLASS Al , N. _—— tr a ao PO ss a / om _ f Be a “ 1972 1974 1976 1978 1980 1982 “Year 1884 ANNUAL KyvH (Millions) 1972 ANNUAL KWH USE SMALL COMMERCIAL CLASS 1974 1976 1978 Year 1980 1982 ANNUAL KWH (Millions) 150 140 130 120 110 100 90 80 70 60 50 40 30 20 10 1970 ANNUAL KWH USE LARGE COMMERCIAL CLASS 1972 TT 1974 1976 ‘Year —~T 19 cd 8 1980 19682 1 c oT am | ae a | Ca | 84 ANNWAL KiWlH (Millions) N i) se ee eee ieee ceed fee cee ae eae O-NWN Foo w oO ANNUAL KWH USE RESALE TO OTHERS aoe f tf a O-NWFUDAOO Oo FT 1970 19 T S72 1974 1g eae TT T ot le T TT TT T iam 7G 1978 1980 19682 1984 ‘Year ANNUAL KWH (Theusa nds) 500 400 300 200 100 O 1970 / V v4 ee fo a ANNUAL KWH USE STREET LIGHTING es | 1972 —— r 1974 1976 1978 1980 1982 1984 ‘Year ANNUAL KH (Millions) o-e+-+-+A77+++-+ =) “u~aooo oo+-hNwW PhO 1972 ANNUAL KWH USE OWN USE mp at 1974 1976 1978 1980 Year 7 al 1962 1984 Histerical Electric lJse Residential Class Per Customer at It-d Historical Electric Use Per Customer Small Commercial Class 50 40 / mom a0 DE - £ $3 & 20 10 1970 1972 1974 1976 1978 1980 1982 1984 Year Histerical Electric Use Per Customer: Large Carnmercial Class ti-3 Historical Electric Use Per Customer Owr Use - ee Pl a7 on Ge oe & al a fh ——] ————eee en eee 1370 1972 Tord jaye 1275 1980 1982 {ae4 3 oa Appendix C RESIDENTIAL-TO-COMMERCIAL CUSTOMER EQUATIONS SMPL 1970 - 1983 14 Observations LS // Dependent Variable is TCOMNO COEFFICIENT STANDARD ERROR T-STATISTIC BeSsssecesesseseSSSSeSSeSSeeSSeSSeees sss essssssssssssesessesess=eees Cc 50.033075 18.774647 2.6649276 RESCUS 0.1258042 0.0016048 78.390144 SSS SSS SSS SS SS S SS SS SS SS SS SS SS SS SS SS SS SS SSS SS SS SSS SS SS SS SS SS SS SSS SS STE R-squared 0.998051 Mean of dependent var 1445. 857 Adjusted R-squared 0.997889 S.D. of dependent var 484.6859 S.E. of regression 22e2IAos Sum of squared resid 5952.168 Durbin-Watson stat 1.156119 F-statistic 6145.015 Log likelihood -62. 23231 Serre SSS SS SS SSS SSS SS SS SS SS SS SSS SSS SS SS SS SS SSS SS SS SS SS SS SS SSS SSS SS TSS = t 3 ' 5 ' 1970 31.5158 817.000 785.484 t : ‘ % 8 + 1971 16.6688 876.000 859.331 ‘ 3 * 3 3 + 1972 -7.99460 916.000 923.995 ' 3 * 5 : ' 1973 -5.71935 973.000 978.719 ‘ 3 | * : § 1974 5.21774 1072.00 1066.78 io 8 ' : $ 1975 -46.7637 1154.00 1200.76 ‘ * 3 ' : ' 1976 -28.6215 1365.00 1393.62 ‘ : ed 8 + 1977 4.84887 1540.00 1535.15 ' : * ' 3 + 1978 -14.2612 1675.00 1689.26 ' 8 i* : $$ 1979 2.16265 1762.00 1759.84 ‘ : ‘ *s ' 1980 18.7731 1820.00 1801.23 : : ' 2: * ' 1981 28.2363 1933.00 1904.76 t : i * 3 ' 1982 6.95891 2092.00 2085.04 ' 5 * ' : } 1983 -11.0218 2247.00 2258.02 obs RESCUS SCOMNO LCOMNO TCOMNO 1970 5846.000 760.0000 57.00000 817.0000 1971 6433.000 816.0000 60.00000 876.0000 1972 6947.900 856.0000 60.00000 916.0000 1973 7382.000 901.0000 72.00000 973.0000 1974 8082.000 987.0000 85.00000 1072.000 1975 9147.000 1026.000 128.0000 1154.000 1976 190680.00 1064.000 301.0000 1365.000 1977 11805.00 1338.000 202.0000 1540.000 19738 13030.00 1468.000 207.0000 1675.000 1979 13591.00 1553.000 209.0000 1762.000 1980 13920.00 1614.000 206.0000 1820.000 1981 14743.00 1715.000 218.0000 1933.000 1982 16176.00 1859.000 233.0000 2092.000 1983 17551.00 2008.000 239.0000 2247.000 Cor SMFL 1970 - 1983 14 Observations LS // Dependent Variable is SCOMNO c 115.08365 19.716361 S.82369723 RESCUS 0.1067952 0.00168 63.367039 SreSeSeeeSeeeeSeeee eee eee S SSeS SSS S55 SS S55 SS 55555 = et R-squared 0.997020 Mean of dependent var 1300.000 Adjusted R-squared 0.996772 S.D. of dependent var 411.6629 S.E. of regression 23.38848 Sum of squared resid 6564.251 Durbin-Watson stat 0.633016 F-statistic 4015.382 Log likelihood -62.91749 Serres eSe Sees eeei SSS SSS SS SS SS SS SSS SSS SS SSS SSS SSS SSS SS SS SS SS SS SS SSS SS SE Baeescsaseeseseaesesssessesessasesesseeeseesseseeeee=ee= Residual Flot obs RESIDUAL ACTUAL FITTED ' 8 : *: $1970 20.5911 760.000 739.409 ‘ 2 : * 8 + 1971 3.9023 816.000 802.098 : 8 * g $ 1972 -0.99048 856.000 856.990 : & *} . 8 { 1973 -2.44636 901.000 903.446 : & io 8 ' 1974 8.79697 987.000 978.203 : 8 : * 8 $ 1975 12.0600 1104.00 1091.94 : * 8 : 8 $ 1976 -34.6571 1221.00 1255.66 rs : : 8 $ 1977 -37.8017 1338.00 1375.80 io 8 : 8 { 1978 -38.6259 1468.00 1506.63 : 8 * ‘ 2 | 1979 -13.5380 1553.00 1566.54 : 8 ; * 8 ' 1980 12.3263 1614.00 1601.67 ' : ' 2% + 1981 25.4338 1715.00 1689.57 : & : * 3 ' 1982 16.3962 1859.00 1842.60 : 2 ' * 3 ' 1983 18.5528 2008.00 1989.45 BreesssssseeeaseesssseeeseesssessesseeseesssssessssseeeSeeeeee555==5 obs - RESCUS SCOMNO Seeeeesseescsssssesseeeseee= 1970 5846.000 760.0000 1971 64323.000 816.0000 1972 6947.000 856.0000 1973 7382.000 901.0000 1974 8082.000 - 987.0000 1975 9147.000 1104.000 * 1976 10680.00 1221.000 * O77, 11805.00 1338.000 1978 13030.00 1468.000 1979 13591.00 1553.000 1980 13920.00 1614.000 1981 14743.00 1715.000 1982 16176.00 1859.000 1983 17551.00 2008.000 SSeSSessSe Sees S>=> *Note: For purposes of this estimate, the actual 1975 and 1976 values of Number of | Small Commercial Customers (SCOMNO) have been replaced by linear interpolation between 1974 and 1977. c-2 Appendix D ECONOMETRIC ANALYSIS OF GVEA RESIDENTIAL AND SMALL COMMERCIAL ENERGY CONSUMPTION PER CUSTOMER This appendix provides details of the econometric analysis of GVEA residential and small commercial electric consump- tion per customer, discussed in the body of the report. The analysis has three objectives: We To provide insight into residential and small com- mercial electric consumption behavior during the 1970's and early 1980's by modelling historic res- idential electric use patterns. ae To generate and test possible equations for fore- casting residential electric use through 1995, Sis To arrive at conclusions applicable to the prepa- ration of a 10-year residential load forecast for the GVEA service area. The final load forecast is based on an end-use analysis and other system planning considerations as well as on this econo- metric analysis. DATA The analysis considers time series data for 1970 through 1984 available in January 1985. Table D-1 summarizes the data source and variable name given to each time series. Table D-2 presents each time series tested for inclusion in the model of past use per customer. In some instances data gaps or 1984 values have been filled by estimates, as noted in Tables D-1 and D-2. Table D-3 provides the mean, stan- dard deviation, and extreme values of each time series. Figures D-1 through D-7 are plots of the basic time series considered for inclusion in the model. The last 14 years have been a period of dramatic economic change in the GVEA service area, with corresponding changes in residential and small commercial electric consumption per customer (RESCON and COMCON, respectively). Real wages and salaries per employee (RWSPE) rose sharply during construc- tion of the trans-Alaska oil pipeline, beginning to rise in 1973, peaking in 1975, and falling rapidly during 1976 and 1977 (Figure D-3). The real residential price of electricity (RRPRIC) reached a minimum in 1974 and then rose to a peak in 1981 (Figure D-4). The real price of No. 2 fuel oil (ROPRIC) also peaked in 1981 after declining gradually through most of the 1970's, reflecting generally sufficient Tail figures are grouped at the end of this appendix. Table D-1 RESIDENTIAL AND SMALL COMMERCIAL CONSUMPTION ANALYSIS TIME SERIES SUMMARY Variable Name Description Units Source RESCON Residential Electric Consumption Per Customer kWh/Customer GVEA COMCON Small Commercial Electric Consumption per Customer kWh/Customer GVEA RWSPE Real Wages and Salaries Per Employee 1967 $/Employee 1970-1983: State of Alaska Department of Labor RRPRIC Real Residential Electric Energy Price 1967 $/kWh GVEA RCPRIC Real Small Commercial Electric Energy Price 1967 $/kWh GVEA ROPRIC Real Residential Price of No. 2 Fuel Oil 1967 $/kWh 1970-1974: Estimated by Tesoro Alaska petroleum 1974-1983: Fairbanks North Star Borough Community Research Center 1984: Petroleum Sales, Fairbanks, Alaska RPRATIO Ratio of Residential Electric Energy Price to the - Calculated Price of No. 2 Fuel Oil CPRATIO Ratio of Small Commercial Electric Energy Price tad Calculated to the Price of No. 2 Fuel Oil ADDAY Annual Heating Degree Days for Fairbanks, Alaska Heating Degree U.S. National Oceanic and Days/Year Atmospheric Administration Year 1970 197d 1972 1973 1974 1975 1976 L977 1978 1979 1980 1981 1982 1983 1984 TIME SERIES USED IN RESIDENTIAL AND COMMERCIAL ECONOMETRIC ANALYSES? RESCON (kWh) 11482.00 13169.00 13920.00 14479.00 15822.00 17514.00 15203.00 14255.00 11574.00 10519.00 9767.00 9080.00 9303.00 9039.00 8513.00 COMCON (kWh) 33642.00 46179.00 47195.00 43638.00 44846.00 26067.00 25761.00 28257.00 25164.00 24008.00 22428.00 22537.00 23236.00 22783.00 23995.00 RWSPE (dollars 10039.01 9675.641 9800.544 10125.63 11836.07 15301.96 15792.60 13650.53 11573.42 10914.66 10750.17 10752.71 11001.54 1183259 11295.43° ) Table D-2 RRPRIC (cents per kWh) 3.56 3.28 3.19 3.06 2.69 3.02 3.14 3257 3.62 3.94 4.26 4.19 4.00 See RCPRIC ROPRIC (cents (cents per per kWh) _gal) ADDAY RPRATIO CPRATIO 3.83 42.61 L256 SS. 0.084 0.090 3.28 41.36 1254.58 0.079 0.079 3.28 39.43 1238.17 0.081 0.083 Sez 37.83 LUST 7 0.081 0.085 2.91 35.62 1205.03 0.075 0.082 Seo 34.99 1194.17 0.086 0.090 4.14 34.86 1132550 0.094 0.119 3.37 34.86 1132.83 0.090 0.097 Ba) B2e11 1052).:25) Oe 11: 0.118 3.77 Seek) 1135 617, 0.097 0.101 4.12 45.35 1103.00 0.087 0.091 4.34 50239 994.75 0.085 0.086 4.38 44.02 1166.25 0.095 0.100 4.34 39.61 1131.75 0.101 0.110 3.60 39.40 NA 0.089 0.091 @constant dollar amounts deflated using Bureau of Labor Statistics: Urban Consumers. PEstimated assuming one percent growth over 1983. October 1967 = 100. Anchorage Consumer Price Index, All Items, All Table D-3 TIME SERIES DESCRIPTIVE STATISTICS 1970 - 1984 Standard Series Mean Deviation Maximum Minimum RESCON 12242.600 2866.4070 17514.000 8513.0000 COMCON (1970-1984) 30649.067 9686.0233 47195.000 22428.000 (1975-1984) 24423.600 1877.7236 28257.000 22428.000 RWSPE 11579.567 1882.6846 15792.600 9675.6410 RRPRIC 3.4899564 0.4568724 4.2596350 2.6885740 RCPRIC (1970-1984) 3.7021089 0.4862914 4.3829290 2.9126220 (1975-1984) 3.9002494 0.4338193 4.3829290 3.1516740 ROPRIC 39.322756 4.8272449 50.385400 32.106670 RPRATIO 0.0890678 0.0097771 0.1112957 0.0754717 CPRATIO 0.0949967 0.0129236 0.1188811 0.0792291 ADDAY® 1144.0712 68.555690 1254.5830 994.75000 *poes not include 1984. Note: For years 1970-1984 unless otherwise noted. supplies and federal price controls in the early in the dec- ade (Figure D-6). Annual heating degree-days (ADDAY) fluc- tuated sharply throughout the study period (Figure D-7). Visual inspection indicates that residential energy use is strongly linked to both prices and the economic boom (Fig- ure D-1). In addition to these influences, a moratorium on the addition of new electric heating customers began in 1975. As Figure D-2 shows, small commercial consumption underwent a dramatic transformation during 1974. During the period from 1970 through 1974, small commercial consumption aver- aged 43,100 kWh per customer. Since 1975, consumption has averaged only 24,400 kWh per customer (with a standard devi- ation of 1,900 kWh). The correlation coefficient of COMCON with ADDAY is 0.83 in the early period but only 0.34 in later years, suggesting that fuel switching is an important cause of the sudden change in the pattern of commercial de- mand. Consistent with a hypothetical shift to a less D-4 temperature-sensitive load, small commercial consumption is much less varied after 1974: the ratio of mean consumption to its standard deviation equals 7.9 for 1970 to 1974, and rises to 13.0 for 1975 to 1984. This shift is more sudden and pronounced for COMCON than for RESCON, making it practi- cal to estimate an equation for only the post-shift period. Correlation coefficients for COMCON with other variables for only the 1975 through 1984 period are presented in Table D-5. Dummy variables for the moratorium (MORA) and the pipeline construction boom (BOOM) were included in the analyses of both residential and small commercial consumption. Figures D-8 through D-12 are scatter diagrams suggesting the strength of relationships between RESCON and each of the other time series. Table D-4 is a matrix of correlation coefficients for RESCON and each of the candidate explana- tory variables. Figures D-13 through D-17 and Table D-5 provide similar information for COMCON. RESIDENTIAL CONSUMPTION Model of Past Residential Consumption Various linear and logarithmic specifications for an econo- metric model of past residential consumption behavior were tested, using combinations of the time series discussed above, heat moratorium and economic boom dummy variables, and lagged values of each variable. The most satisfactory equation is presented in Table D-6, along with the relevant statistics. A one-year lag in the value of a variable is denoted throughout this appendix by the suffix (-1). According to the model, the largest variable contribution to current-year consumption per customer (RESCON) is last year's consumption, RESCON(-1). This result may best be explained by the persistent effects of most electric appli- ances: though patterns of appliance use may vary over time, once a household acquires a major electric appliance, the appliance is likely to contribute to household consumption for many years. In any single year, only a small amount of consumption is likely to arise from new or replacement ap- pliances. The inclusion of lagged consumption in the re- gression equation reflects this gradual change in the appli- ance stock of the GVEA service area. Current real residential electricity price (RRPRIC) is a large contributor to consumption. As Figure D-9 suggests, consumption and price are closely correlated (r=0.93). The t-statistic of RRPRIC is large. However, the estimated -43 percent price elasticity of demand is subject to at least two problems that limit the application of the esti- mated elasticity beyond the purposes of this model. Table D-4 RESIDENTIAL TIME SERIES CORRELATION COEFFICIENTS 1970 - 1984 RESCON RWSPE RRPRIC ROPRIC PRATIO appay® RESCON 1.000 0.532 -0.873 -0.613 -0.417 0.542 RWSPE - 1.000 -0.314 -0.533 0.227 -0.042 RRPRIC - - 1.000 0.702 0.452 -0.604 ROPRIC = = = 1.000 -0.313 -0.290 PRATIO _ _ _ _ 1.000 -0.483 ADDAY - - - - “ 1.000 "Does not include 1984. Table D-5 SMALL COMMERCIAL TIME SERIES CORRELATION COEFFICIENTS 1975-1984 COMCON RWSPE RCPRIC ROPRIC PRATIO ADDAY® COMCON 1.000 0.748 -0.736 -0.762 0.213 0.346 RWSPE 7 1.000 -0.484 -0.594 0.253 0.471 RCPRIC = td 1.000 0.633 0.224 -0.391 ROPRIC = = - 1.000 -0.606 -0.427 PRATIO - i - - 1.000 0.049 ADDAY - - - - - 1.000 *poes not include 1984. Table D-6 ECONOMETRIC MODEL OF RESIDENTIAL CONSUMPTION PER CUSTOMER 1971 - 1984 Dependent variable log RESCON R? 2 0.983596 Adjusted R - 0.973343 Standard error of regression 0.039803 Durbin-h statistic 0.462062 F-statistic 95.93599 Independent Standard Variable Coefficient Error t-Statistic Constant 6.0705867 1.8641451 3.2564990 log RESCON(-1) 0.8622173 0.1136389 7eS873457 log RRPRIC -0.4345698 0.1622497 -2.6784012 log ROPRIC(-1) 0.2438676 0.1383345 1.7628831 log RWSPE(-1) -0.5547526 0.1459398 -3.8012426 BOOM 0.1639276 0.0488787 3.3537640 First, the equation does not specify certain variables that, in theory, should be closely related to consumption and that underwent major change during the period of rapid electric rate increases. For example, since the beginning of the pipeline boom, the housing stock in the GVEA service area has changed significantly: floor space per residential cus- tomer has decreased, and average levels of dwelling thermal efficiency may have fallen during the rapid housing con- struction beginning in the middle 1970's and then probably rose again later in the decade. Data directly measuring these changes were not available for this analysis. Because these changes are contemporaneous with increasing electric- ity rates, their effects may be added to those of the price variable, increasing the estimated coefficient. Thus, though the price variable is adequate to explain overall historic variation in consumption, the estimated coefficient probably also reflects the effects of nonprice influences. It should not be interpreted as a pure measure of the re- sponse of residential consumption to electric rates. More sophisticated estimates of short-run electric price elas- ticity commonly range from 0 to 30 percent for other service areas. A second limitation on the usefulness of the RRPRIC coeffi- cient also affects its use in forecasting. During the late 1970's and early 1980's, the GVEA residential heating load fell in response to the electric heat moratorium and wide- spread switching from electric to oil heat. During the pe- riod, oil heat saturation rose to an estimated 93 percent. In addition, insulation and other energy conservation mea- sures probably changed the nature of the remaining electric heat load. The overall efficiency of the appliance stock also probably increased during the late 1970's and early 1980's. Many of these changes will not be repeated and may be nearly complete. Thus, the estimated elasticity reflects a period of rapid structural change in the residential elec- tric demand function which is unlikely to continue much longer, if at all. Without some form of correction a fore- cast based strongly on the price coefficient is likely to overemphasize the influence of price on consumption. Lagged values of real fuel oil price (ROPRIC) and real wages and salaries per employee (RWSPE) both make significant con- tributions to estimated residential consumption. Together they may be interpreted as modelling the fuel-switching phe- nomenon discussed above: this year's oil prices influence the decision to switch fuel before the next heating season; and last year's income is related to a customer's ability to pay the capital cost of conversion. Finally, the dummy variable BOOM (equal to 1 for 1976 to 1978 and to zero otherwise) captures the complex and unusual influence of the pipeline construction boom on residential consumption. Taken together, the variables listed in Table D-6 account for about 97 percent of the variation observed in residen- tial consumption per customer during the period from 1971 through 1984. Residential Forecasting Model The forecasts of RESCON considered in this analysis assume electric price and fuel-oil price forecasts for Fairbanks generated in the reference case of the RED model. Also con- sistent with the RED model forecast, RWSPE is assumed to increase one percent annually through 1995. Figure D-18 presents the result of a forecast using the equation of Table D-6. As the figure shows, a forecast based on the historical model would project an average annual decrease of about one percent due to the effects of fuel switching driven by forecasts of increasing wages and salaries. How- ever, it is not reasonable to expect fuel switching to con- tinue as in the recent past because almost all residential customers already use oil heat. To arrive at a more satisfactory model, a new equation was estimated from the historical data specifying only RESCON(-1), RRPRIC, and BOOM (-1). The results are pre- sented in Table D-7. The major difference between the new equation and the best historical model is the absence of RWSPE and ROPRIC, which formerly reflected fuel switching by residential consumers. Compared to the best historical model, the coefficient of RESCON(-1) increases significantly and the coefficient of RRPRIC decreases slightly. The co- efficient of the BOOM dummy variable changes dramatically, from 0.16 in the first equation to -0.16 in its lagged form in the second equation. Because of its close relationship to RWSPE, the BOOM variable now probably reflects much of the fuel switching during the 1977 to 1979 period (and thus, for forecasting purposes, filters it out). Table D-7 FORECASTING MODEL OF RESIDENTIAL CONSUMPTION PER CUSTOMER 1971 - 1984 Dependent Variable log RESCON R? 5 0.949587 Adjusted R 0.934463 Standard error of regression 0.062409 Durbin-h statistic 1.596739 F-statistic 62.78735 Independent Standard Variables Coefficient Error t-Statistic Constant 0.5653382 1.9372390 0.2918268 Log RESCON (-1) 0.9944249 0.1793021 5.5460856 Log PRPRIC -0.4022483 0.2412297 -1.6674905 BOOM (-1) -0.1655889 0.0617777 -2.6803996 Figure D-18 compares the forecast of residential electric consumption per customer based on the new equation with the forecast based on the original historical model. The re- vised forecast assumes the RED reference forecast of future residential electricity prices in Fairbanks. The new equa- tion projects consumption to grow less than one percent in 1985, averaging approximately 1.5 percent growth annually during the 1984 to 1995 period. Though the equation is still subject to some of the flaws inherent in the original historical model, it eliminates much of the influence of past fuel switching. This forecast supports a conservative projection of at least 1.0 percent annual growth during the forecast period. Conclusions of Residential Analysis The econometric modelling exercise leads to several useful conclusions regarding a forecast of future residential energy consumption: 1. The existing stock of electric appliances, whose effects are embodied in the lagged value of res- idential consumption, exerts a very large influ- ence on current consumption. Thus, changes in response to other variables will be moderated by the existing appliance stock. Subject to the characteristics of the existing appliance stock, real electric price appears to exert a strong short-run influenced on consumption. Residential consumption is not strongly related to heating degree-days relative to other influences. Fuel switching has been a major influence on con- sumption in the recent past. The decision to switch fuels has probably been related to both the price of fuel oil and to the ability of customer to pay the cost of switching. There is no strong evidence that the fuel switching is related to the ratio of electricity price to oil price. An equation based strictly on past behavior does not appear to provide a reasonable forecast of future residential consumption because fuel switching cannot continue as in the recent past. A forecasting equation estimated to exclude much of the fuel switching from the model projects av- erage annual growth of 1.5 percent from 1984 through 1995. Because some fuel switching and conservation is still possible, growth in consumption during the next few years could be affected by continued, if slowing, change in the structure of residential demand. Thus, it seems reasonable to expect res- idential consumption to increase relatively slowly in the near future, and to increase somewhat more rapidly during the later years of the forecast period. D-10 SMALL COMMERCIAL CONSUMPTION Model of Past Small Commercial Consumption Linear and logarithmic specifications for an econometric model of past small commercial consumption were tested by using current and lagged combinations of the time series listed in Table D-2. Because of the rapid shift in consump- tion per customer during 1974, the small commercial model was estimated using only data from the 1975 to 1984 period. The most satisfactory equation is presented in Table D-8. Table D-8 ECONOMETRIC MODEL OF SMALL COMMERCIAL CONSUMPTION PER CUSTOMER 1975 - 1984 Dependent Variable log COMCON R? 2 02955365 Adjusted R 0.910730 Standard error of regression 0.022589 Durbin-h statistic -0.656476 F-Statistic 21.40394 Independent Standard Variables Coefficient Error t-Statistic Constant Se 203 7/57) T.9282755 1.6614626 Log COMCON (-1) 023:793257, 0.1732763 2.1891380 Log RWSPE 0.3221847 0.0700900 4.5967285 Log RCPRI -0.5646269 0.1159143 -4.8710727 Log ROPRI (-1) OR 2227151 0.1039554 2.1424100 According to the estimated model, previous consumption (COMCON[-1]) makes a significant contribution to current consumption. This variable, which is theoretically associ- ated with the persistent effect of appliance and equipment stocks, is less influential here than in the residential demand equation. The current real commercial electricity price (RCPRIC) has an estimated coefficient of -0.56. As in the previous dis- cussion of residential price elasticity, the estimated price coefficient should not be interpreted as pure price elastic- ity because (1) the effects of unspecified variables that are correlated with prices during the estimation period are likely to be reflected by the price coefficient and (2) the estimated elasticity reflects a period of structural change in the market for electricity, when customers switching to oil heat or investing in conservation measures caused a de- cline in consumption that cannot be repeated in the future. The latter problem is much less serious for the small com- mercial than for the residential model because the small commercial equation was estimated for a period after the majority of fuel switching was complete. Real wages and salaries per employee (RWSPE) enters the small commercial equation as a broad indicator of commercial activity. Lagged real oil price (ROPRI[-1]) contributes to consumption much as it did in the residential model, and is related to the relatively low level of fuel switching that continued after 1974. The estimated equation explains about 91 percent of the variation in small commercial consumption from 1975 through 1984. Small Commercial Forecast A forecast of consumption per customer based on the small commercial demand equation projects an average annual in- crease of approximately 1.7 percent through 1995, assuming one percent annual growth in real wages and salaries and assuming electricity and oil price forecasts generated by the RED model. However, the equation has a particularly large negative residual of almost 3 percent for 1984. Though the equation forecasts only 2.2 percent growth from the fitted 1984 value to 1985, with the residual it effec- tively projects 5 percent growth over actual 1984 consump- tion. Adjusting the 1985 forecast to allow only 2.2 percent growth above actual 1984 results in an average annual in- crease of 1.4 percent through 1995. Figure D-19 presents historical and forecast small commercial consumption. The results are plausible and support a conservative estimate of one percent annual growth through 1995. Conclusions of Small Commercial Analysis bes Existing appliance and equipment stocks exert an influ- ence on commercial consumption, though their effect is much smaller than for the residential customer class. 2 The price coefficient is larger than estimated for the residential customer class. This suggests that small commercial demand is more sensitive to price than res- idential demand. However, the price coefficient is subject to error arising from the specification of the equation, which was limited by the availability of data. Changes in real wages and salaries per employee, prob- ably as an indicator of general economic activity, have been strongly associated with small commercial consump- tion since 1975. Fuel switching apparently occurred very rapidly in 1974; since then fuel switching has been relatively gradual. Because the model was estimated only for the 1975 to 1984 period, it places less emphasis on fuel switching than the residential model, making it a more reliable predictor of future consumption than the resi- dential equation. A forecast based on the small commercial equation pro- duced a plausible projection of 1.4 percent annual growth through 1995, after correcting for a large 1984 residual. r= 18888 17088 16888 15008 14008 13888 12888 11888 18888 9888 8888 1978 RESIDENTIAL kWh PER CUSTOMER 1975 i980 1984 Figure D-1 == 38888 45888 40888 39888 38888 25888 28808 1978 SMALL COMMERCIAL kWh PER CUSTOMER 1975 1988 1984 Figure D-2 REAL HAGES AND 96? ALARIES PER EMPLOYEE § 1 dollars 16688 15808 14888 13888 12088 11888 18888 9000 , 1978 1975 i988 1984 Figure D-3 45 4.8 3.5 3.8 2.9 i978 REAL Rett 1975 re IAL ELECT cents per Balt? PRICE 1988 1984 Figure D-4 4.9 4.8 3.9 3.8 ee 1978 REAL SMALL. COMMERCIAL ELECTRICITY PRICE 1967 cents per kHh 1975 1988 1984 Figure D-5 99 38 45 48 35 38 1978 REAL ( PRI 196? 1975 CE c OF N0.2 FUEL 0] ents per gallon) L 1988 1984 Figure D-6 ANNUAL HEATING DEGREE DAYS 1978 - 1983 HE 978 8 1388 1258 1288 1158 1188 1858 1888 958 1978 1975 1988 1983 Figure D-7 D-20 RESIDENTIAL kWh PER CUSTOMER yofEMe HRGES AND SALARIES PER EMPLOYEE 18888 =zoorwerm=a BB88 9888 16808 RHSPE Figure D-8 D-21 RESIDENTIAL kWh PER CUSTOMER vs REAL RESIDENTIAL ELECTRICITY PRICE 1975 - 1984 18888 Zorn ri=a aid 45 RRPRIC Figure D-9 D-22 RESIDEHTIAL kWh PER CUSTOMER vs REAL PRICE OF HO.2 FUEL OIL 1978 - 1984 18888 zoorra 38 . 55 ROPRIC Figure D-10 D-23 RESIDENTIAL kWh PER CUSTOMER vs ANNUAL DEGREE DAYS 1978 - 1983 ip808 R E S ¢ 0 H page 958 1380 ADDAY Figure D-11 RESIDENTIAL kWh PER are Rare OF ELECTRICITY PRICE 10 OIL PRICE 18888 zorweri=a @.878 @.415 RPRATIO Figure D-12 SMALL COMMERCIAL kHh PER CusTOWE ws ae WAGES AND SALARIES PER EMPLOYEE 29888 C 0 M ¢ 0 H ec8e8 18888 16888 RHSPE Figure D-13 SMALL COMMERCIAL kWh PER CUSTOMER vs REAL PRICE OF ELECTRICITY 29888 ZOOEZTOO 22888 ; 4,58 ce RCPRIC Figure D-14 D-27 SMALL COMMERCIAL kHh PER CUSTOMER vs REAL NO.2 FUEL OIL PRICE 1975 - 1984 29888 Borszon 22888 Q 30 : ROPRIC Figure D-15 SMALL COMMERCIAL kWh PER CUSTONER vs ANNUAL HEATING DEGREE DAYS 1975 - 1983 29888 Zomzoss 22888 958 1288 ADDAY Figure D-16 D-29 SMALL COMMERCIAL kHh PER CUSTORER ys RATIO OF ELECTRICITY PRICE TO OIL PRICE 29888 ZOorszom ecBee 8.885 8.128 CPRATIO Figure D-17 c= 28888 17588 15888 12588 19888 7588 3888 HISTORIC AND FORECAST RESIDENTIAL kWh PER CUSTOMER ACTUAL FORECAST Forecast Best Historical Nodel 1978 1975 1988 1985 1998 1995 Figure D-18 > HISTORIC AND FORECAST SMALL COMMERCIAL kWh PER CUSTOMER 98888 40888 38888 20808 10888 ACTUAL FORECAST 1978 1975 1988 1985 1998 1995 Figure D-19 Appendix E RAILBELT ENERGY FORECASTS Appendix E REVIEW OF RAILBELT ENERGY FORECASTS This appendix contains a review of a number of studies and reports that have addressed electrical demand and supply for the State of Alaska, the Railbelt region, and the Fairbanks- Tanana Valley region. There are three general groups of studies that address future electric energy requirements for the state, the Railbelt, and, to some extent, the Fairbanks area: ls The Alaska Department of Natural Resources annual determination that royalty oil is surplus to state needs and may be exported on a statewide basis ae The Department of Commerce and Economic Develop- ment's Annual Alaska Longterm Energy Plan that addresses demand, supply, conservation, emergency requirements, and research for all energy needs including electricity 34 Susitna-related electrical forecasts DEPARTMENT OF NATURAL RESOURCES The Department of Natural Resources is mandated to use roy- alty oil and gas to satisfy present and projected intrastate domestic and industrial needs before being sold for export from the state. The department's annual report to the leg- islature is titled Historical and Projected Oil and Gas Con- sumption. The report summarizes petroleum and natural gas consumption and forecasts future needs. Some of the reports by consultants have addressed the Railbelt region's oil and gas electric generation. However, in recent reports they have typically used existing forecasts. ALASKA LONGTERM ENERGY PLANS State statutes require that an annual report on the long-term energy requirements of the state be presented to the legis- lature by the Department of Commerce and Economic Development (originally the Division of Energy and Power Development, now the Office of Enersy). The first three long-term energy plans were done by consultants with DEPD staff review. The 1984 Longterm Plan was prepared by staff and followed the A. D. Little Model described below. The 1985 Longterm Plan was also by staff and was primarily a review of the state's various energy programs. The first plan, the 1981 Longterm Energy Plan, was conducted by Applied Economics Associates. They used regional energy balances and forecast energy needs by means of econometric models. Regional consumption was based on population, heat- ing degree-days, real total income, and price. They used their own models for electricity assumptions outside the Railbelt. Within the Railbelt region, they used the 1980 ISER Railbelt forecasts described below. The second plan, the 1982 Longterm Energy Plan, was by Booz, Allen, and Hamilton and DEPD staff. The study incorporated the assumptions that were present in the Battelle Railbelt Alternative for variables that drive Alaska's economy. The plan adopted the Battelle Railbelt electricity forecast in- dicating an average 3.5 percent per year increase between 1980 and 2000. The forecast was a 5.3 percent Railbelt electricity growth in demand for the next ten years followed by 1.7 percent per annum thereafter. The 1982 plan raised the question of the timing of the Susitna project "when fu- ture electricity needs are clearer and the state revenue situation more stable," and pointed out that "a growing group of analysts now predict that the real price of crude oil could decrease over the next two years," based on the then current $34 per barrel. The third plan, the 1983 Longterm Energy Plan, was prepared by A. D. Little. This work was unique compared to other Railbelt studies because A. D. Little constructed an inde- pendent regional econometric economic model and end-use model that were used together to forecast energy demand for 14 demand categories. The A. D. Little regional economic model, a U. S. Bureau of Economic Analysis-adapted baseline model of employment, labor, and income by industry sector, forecasts economic ac- tivity, population, households, earnings, and employment. Baseline growth rates were used to define population, em- ployment, and income. From the text, it is not clear whether these rates apply uniformly to all regions. Baseline Growth Rates (%) 1980-1990 1990-2000 2000-2005 Population 350) 4.0 4.2 Employment 4.0 2.2 23 Earnings 6.2 2.3 Oe) To the baseline, the impact of new industry was added. Three growth scenarios were developed from a review of 14 large projects. A much-publicized delphi technique was used to determine the likelihood and timing of project development. These scenarios used national industry ratios to define such factors as future employment for input into the economic de- velopment model. The most likely "moderate-growth" scenario project assump- tions were: 1982 Upper Cook Inlet, midrange production 1990 NPR and National Wildlife Reserve Exploration 1983 OCS development 1990 Tidewater ANS Gas Pipeline of ANGTS 1992 Tidewater LNG facility oo00°0 These development data, together with demand parameters and energy prices by fuel type by region, are used in the energy demand model to define the total energy demand requirements by fuel type by sector for each region. The end-use models consisted of a system of constant- elasticity-demand equations that forecast demand based on own-price and cross-price elasticities of demand and exogen- ous variables (e.g., number of households, household income, sector employment). The models' calibration of elasticity was not documented in the report, nor were any regional var- iances in elasticities identified. Under the moderate-growth scenario, demand for electricity in Fairbanks was forecast as follows: 1985 1990 1995 2000 2005 1985-95 1995-2005 Gwh 597 615 700 718 742 1.6% 0.6% The A. D. Little model combined baseline growth rates with growth expected from the new projects. The model was crit- icized because it did not reflect the effect crude oil prices have had upon the state's economy both in the private and public sectors. Also, neither the regional models nor the BEA baseline model were documented. SUSITNA-RELATED ELECTRIC FORECASTS A number of forecasts have been conducted to define the Railbelt power needs as part of the feasibility and final FERC application for construction of the Susitna project. In the following summary, it is recognized that each succes- sive study or report has built upon a former study and that in reality two entities, the Institute of Social and Eco- nomic Research (ISER) and Battelle Northwest, Inc., have been responsible, directly or indirectly, for most of the Susitna Railbelt electric-demand forecasts. This arises primarily from the complexity of modeling the regional de- mand for electric power and the practical notion of taking advantage of and refining what has been done before. The Man in the Arctic Program (MAP) Economic Model In the early 1970's, the Institute of Social, Economic, and Government Research [later the Institute of Social, Economic Research (ISER)], was awarded a sizable grant to construct a model of the Alaska economy. This effort, which encompassed over four years of development and calibration work, was originally documented in 1979. The model has continued to evolve. The current model has been updated and improved and is generally recognized as the primary forecast model for Alaska and its regions. The MAP model is a disaggregate economic-base model in which economic activity is classified as either endogenous or exo- genous. Exogenous activity determines the level of endogen- ous activity, and the specific relationships between the two components of economic activity are what make up the system of equations that form the econometric model. Typical exo- genous sectors are forestry, fisheries, federal government, agriculture, and other manufacturing. Exogenous sectors, state and local governments, petroleum (a primary exogenous factor), construction, and the support sectors define indus- trial production, which defines employment, which defines wages and salaries, which defines personal income, which de- fines disposable income, which, when taxes are removed, de- fines real disposable income. Disposable income, in turn, affects construction and the support sector. Taxes affect state and local government. The model is a large set of simultaneous equations that are particularly sensitive to many factors (e.g., wage rates, level of petroleum industrial activity, oil prices, state government expenditure policy, and assumptions about exogen- ous development activity). The MAP model functions have a set of three submodels: the scenario generator, the economic model, and the regionaliza- tion model. The scenario generator model allows the user to input development activities that are basic to the economy (e.g., development of OCS petroleum resources). The eco- nomic model produces statewide projections of economic and demographic data based on relationships among employment, state revenues and spending, wages and salaries, gross prod- uct, and state population. The regionalization model pre- sents forecasts of various subregions. "Electric Power In Alaska, 1976-1995" (ISER, 1976) This study was performed for the state legislature and rec- ommended the formation of what became the Alaska Power Authority (APA). The study was the first use of the MAP economic model results for electric forecasting. Two sets of economic growth assumptions and four sets of electricity intensity and saturation were employed. The military sector was assumed constant. For moderate residen- tial electrification and limited economic growth, Fairbanks energy requirements were forecast as: Average Annual Growth 1980 1985 1990 1995 1980-85 1986-90 GWh 598 833 1,090 1,410 6.8% 5.4% "Alaskan Electric Power: An Analysis of Future Requirements and Supply Alternatives for the Railbelt Region" (Battelle Pacific Northwest, Inc., 1978) This study based its load forecasts upon the work of ISER, the APA, and railbelt utilities, and focused on energy sup- ply options including Susitna, coal, and gas alternatives. "Electric Power Consumption For Railbelt: A Projection of Requirements" (ISER, May 16, 1980) Under contract to the Alaska Power Authority and the House Power Alternatives Committee, ISER prepared an energy-growth forecast for the Railbelt region that was to be incorporated in the Susitna Feasibility Study conducted by Acres American, ENC The study constructed an end-use model, a departure from the previous trend models. The end-use model calculated esti- mates of electricity for specific uses (e.g., household con- sumption, commercial and industrial consumption, and other consumption per entity or unit). The second part of the study forecast future electric power requirements by using the MAP economic model output plus new submodels that forecast households and housing stock. The MAP model provided regional estimates of population, employ- ment, income, household formation, and household stocks. All of these, in turn, were used as input to the calibration of the end-use model. Particular emphasis was placed on household consumption, which was further disaggregated into space heating, major appliances, small electric appliances, and lighting. The level of detail required significant data that were not available (e.g., appliance saturation rates). Further, the commercial end-use model was based on electric energy consumption per square footage and electricity use per employee. The forecast for Fairbanks electric energy requirements was as follows: Average Annual Growth 1980 1985 1990 11995) 2000 1980-85 1985-95 GWh 446 619 666 813 6.8% 2.8% "Susitna Hydroelectric Project, Task 1 Power Studies" (Acres American, Inc., and Woodward-Clyde Consultants, December 1980) and "Forecasting Peak Electrical Demand for Alaska's Railbelt" (Woodward-Clyde Consultants, December 1980, for Acres American, Inc.) The Susitna Feasibility Study contract was awarded by the Alaska Power Authority to Acres American, Inc., (Acres). Acres' subcontractor, Woodward-Clyde Consultants (WCC), was to select an energy-growth forecast for the Alaska Railbelt Region. The work by WCC involved reviewing the May 1980 ISER energy-growth forecasts and forecasting the peak elec- trical demand for the Railbelt. They also provided a de- tailed critique of the ISER modeling approach. Acres completed the Feasibility Report in April 1982. The work of Acres-WCC and Battelle was input into the applica- tion for licensing before FERC, February 28, 1983. "The Railbelt Electric Power Alternatives Study" (Battelle Northwest, Inc., September 1982) In 1980, the legislature deemed it appropriate that an in- dependent consultant should prepare a review of railbelt electrical power alternatives. The Office of the Governor contracted with Battelle to perform the study. Battelle prepared 17 separate reports on the alternatives study. The Railbelt Electricity Demand (RED) model was con- structed by Battelle to forecast railbelt electric demand. It is a partial end-use econometric model that forecasts annual consumption for residential, commercial, small indus- trial, government, large industrial, and miscellaneous end- use sectors for the two load centers (Anchorage-Cook Inlet and Fairbanks-Tanana Valley). The model is composed of interrelated modules: housing, residential consumption, business consumption, program-induced conservation, miscel- laneous consumption, and peak demand and uncertainty. The RED model used as input the MAP model forecasts of August 1981. It was different from the previous ISER end-use model in its sophistication and extensive detailed development. The emphasis was to have an independent evaluation of demand to check Acres' work, but using the same economic scenarios. The MAP economic development scenarios were enlarged: low, high, moderate, industrialization, and super-high cases. The primary scenario differences dealt with rates of OCS, NPR, petrochemical development and production, and state government spending. Fairbanks-Tanana Valley Region Medium Economic Scenario, Plan 1B (with Upper Susitna) Average Annual Growth 1980 1985 1990 1995 1980-85 1985-95 GWh 487 662 1,136 Lt 33) 6.3% 5.5% The price-of-oil forecast in the MAP models used by both Acres and Battelle in the initial FERC license application assumed a 2 percent growth until 2010 and zero percent thereafter. In September 1982, Battelle issued a prologue statement in- dicating that the results presented in their study were no longer valid because of the 1982 drop in oil prices of $4 to $5 per barrel. "Susitna Hydroelectric Project, FERC Application for License Volume 2A, 2B, and 2C" (Harza-Ebasco, July 1983) In February 1983, the Alaska Power Authority submitted the Susitna license application to FERC for approval. Noting the change in oil pricing, FERC directed the APA to update its application and, among other things, reflect up-to-date crude oil prices and document the models used. Oil prices were analyzed in-depth in the new application and extensive use was made of Sherman H. Clark Associates' Oil Price Forecasts. Oil prices used in the application (1983 dollars with no supply disruption) were: Average Annual Growth 1983 1993 2010 2020 1983-93 1993-2010 $/BBL 28.95 30.49 50.39 64.48 0.5% 2.0% The MAP and Battelle RED models were used exclusively for the revised application. They were rerun with expanded and better definition of scenarios. The reference-case forecast for the Fairbanks-Tanana Valley area was: Average Annual Growth Rate 1985 1990 1995 1985-1990 1990-1995 GWh 535 691 800 5.2% 3.0% "Susitna Hydroelectric Project Economic and Financial Up- date" (APA Staff, February 1984) In response to an APA board request that was concerned with the 1983 drop in world oil prices, the staff updated the forecasts and reviewed the application. It would appear that they used the same oil prices as the July 1983 FERC ap- plication. However, it was a further chance to review the workings of the models. The only significant changes in in- put variables per year were inflation, changed from 7.0 per- cent to 6.5 percent per year, an increase in real interest rates from 3.0 percent to 3.5 percent, and a change in dis- count rates from 3.1 percent to 3.5 percent. Also, the Cook Inlet gas price was significantly increased from $6.39 per MMBtu in 2020 to $8.92 and, similarly, in 2010 from $5.00 to $6.97. The overall effect was to increase the projected railbelt electrical generation. GWh 1983 1993 2010 2020 1983 FERC Application SOS, 4,321 6,280 8,039 Update 3,088 4,397 6,444 Bonz For the Fairbanks-Tanana Valley Region, the estimates re- mained the same. "Electric Power Generation for the Alaska Railbelt Region" (Kent and Company, January 1984) This report is basically a summary of all the previous re- ports exclusive of the February 1984 update.