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Report Contents Report#:EIA/DOE-0607(99)
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[1] Energy Information Administration, Annual Energy Outlook 1999, DOE/EIA-0383(99) (Washington, DC, December 1998), reference case AEO99B.D100198A. [2] For example, see the Electric Power Research Institute and U.S. Department of Energy report, Renewable Energy Technology Characterizations, EPRI TR-109496 (December 1997). [3] In general, capital costs in NEMS are assumed to decline by 10 percent for every doubling of U.S. capacity from the first through the fifth commercial unit, 5 percent for every doubling from the sixth through the fortieth unit, and 2.5 percent for every doubling thereafter. [4] For more information on cost and performance characteristics and sources, see Energy Information Administration, Assumptions to the Annual Energy Outlook 1999 (AEO99), DOE/EIA-0554(98) (Washington, DC, December 1998), pp. 59-71, web site www.eia.doe.gov/oiaf/archive/aeo99/assumptions/electricity.html. [5] Pacific Northwest Laboratory, An Assessment of the Available Windy Land Area and Wind Energy Potential in the Contiguous United States, PNL-7789 (August 1991), with updates, as available in National Renewable Energy Laboratory, U.S. Wind Reserves Accessible to Transmission Lines, draft for the Energy Information Administration (1994). [6] PNL offers two levels of restriction, “moderate” and “severe.” The severe restriction eliminates 100 percent of wind resources in each category. [7] Figure 1 presents wind class effects in terms of capital costs. In the National Energy Modeling System (NEMS), large increases in wind resource use, along with passage of time, spur increased capacity factors and large learning effects, thereby markedly lowering capital and levelized electricity costs from wind power. [8] Estimated at 32 percent capacity factor. [9] For additional definition and description of resource classes, see U.S. Department of Energy, Assistant Secretary for Conservation and Renewable Energy, Characterization of U.S. Energy Resources and Reserves, DOE/CE-0279 (Washington, DC, December 1989). [10] Because NEMS employs a linear programming (LP) model, discrete steps are required rather than a continuous supply function. [11] Figures 3 and 4 isolate wind class and cost adjustment factor effects separately from learning and other cost effects applied in NEMS. In NEMS, large increases in wind resource use, along with passage of time, also spur increased capacity factors and large learning effects, thereby markedly lowering capital and levelized electricity costs from wind power. EIA test runs utilizing step 5 ($3,000 per kilowatt) wind resources, for example, yield net wind capital costs under $2,000 per kilowatt when learning effects are included. [12] EIA treats these costs solely as capital costs. In fact, some, such as weather-related costs and maintenance of transmission and distribution links, also increase O&M costs. The result of incorporating these effects as capital rather than O&M costs is likely to understate their overall impact on wind technology costs. [13] Energy Information Administration, Office of Integrated Analysis and Forecasting, derived from conversation with Northern States Power wind resources staff, February 16, 1999. [14] For estimates of expected costs, Electric Power Research Institute and Office of Energy Efficiency and Renewable Energy, U.S. Department of Energy, Renewable Energy Technology Characterizations, EPRI TR-109496 (Washington, DC, December 1997), pp. 6-13. For the Big Spring, Alta, and Foote Creek projects, Financial Times, Renewable Energy Report (March 1999), p. 4. For the Buffalo Ridge project, Energy Information Administration, Office of Integrated Analysis and Forecasting, derived from conversation with Northern Alternative Energy, June 18, 1999. [15] Appel Consultants, Evaluation of the Current Energy System in Minnesota, Final Report (Valencia, CA, June 28, 1996), p. 23. [16] California Energy Commission, Technical Potential of Alternative Technologies, Final Report, Contract 500-89-001 (Regional Economic Research, December 1991). [17] California Energy Commission, page XIII-3. [18] California Energy Commission, page XIII-2. [19] Northwest Power Planning Council, Northwest Power in Transition: Opportunities and Risks, Draft Fourth Northwest Conservation and Electric Power Plan (March 1996). [20] U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Characterization of U.S. Energy Resources and Reserves, DOE/CE-0279 (Washington, DC, December 1989). [21] For example, see Characterization of U.S. Energy Resources and Reserves, pp. 19, 25, and 31. [22] Energy Information Administration, Annual Energy Outlook 1999, DOE/EIA-0383(99) (Washington, DC, December 1998), and Impacts of the Kyoto Protocol on U.S. Energy Markets and Economic Activity, SR/OIAF-98-03 (Washington, DC, October 1998). [23] For more information on the high renewables case, see Energy Information Administration, Annual Energy Outlook 1999, DOE/EIA-0383(99) (Washington, DC, December 1998), pp. 209 and 227, or Assumptions to the Annual Energy Outlook 1999, DOE/EIA-0554(98), pp. 68-70. Figure 1. U.S. Wind Supply. Source: Energy Information Administration, Office of Integrated Analysis and Forecasting. Figure 2. Electricity Market Model Regions. Source: Energy Information Administration, Office of Integrated Analysis and Forecasting. Figure 3. Comparing U.S. Wind Supply Estimates. Source: Energy Information Administration, Office of Integrated Analysis and Forecasting. Figure 4. Electricity Market Model Regions. *Northwest Power Planning Council (NWPPC) values, converted to capital costs by EIA, were doubled to account for the larger NWP region represented in NEMS. Sources: Energy Information Administration, Office of Integrated Analysis and Forecasting; and Northwest Power Planning Council, Northwest Power in Transition: Opportunities and Risks, Draft Fourth Northwest Conservation and Electric Power Plan (March 1996). |
