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Treatment of Wind Energy in NEMS
Each year, the U.S. Department of Energy's Energy Information Administration (EIA) publishes a forecast of the domestic energy-economy in the Annual Energy Outlook (AEO). During the forecast period of the AEO (currently through 2020), renewable energy technologies have typically not achieved significant growth. The contribution of renewable technologies as electric generators becomes more important, however, in scenarios analyzing greenhouse gas emissions reductions or significant technological advancements. We examined the assumptions about wind power used for producing forecasts with the National Energy Modeling System (NEMS) to determine their influence on the projected capacity expansion of this technology. This analysis should help illustrate to policymakers what types of issues may affect wind development, and to understand more fully the NEMS model itself. Figure 1 illustrates the model structure and factors relevant to wind deployment.
We found that NEMS uses various cost multipliers and constraints to represent potential physical and economic limitations to growth in wind capacity, such as resource depletion, costs associated with rapid manufacturing expansion, and grid stability with high levels of capacity from intermittent resources. The model's flexibility allows the user to make alternative assumptions about the magnitude of these factors. While these assumptions have little effect on the Reference Case forecast for the 1999 edition of the AEO, they can make a dramatic difference when wind is more attractive, such as under a carbon permit trading system. With $100/ton carbon permits, the wind capacity projection for 2020 ranges from 15 GW in the unaltered model (AEO99 Reference Case) to 168 GW in the extreme case when all the multipliers and constraints examined in this study are removed. Furthermore, if modifications are made to the model allowing inter-regional transmission of electricity, wind capacity is forecast to reach 214 GW when all limitations are removed.
The upper end of these ranges are not intended to be viewed as reasonable projections, but their magnitude illustrates the importance of the parameters governing the growth of wind capacity and resource availability in forecasts using NEMS. In addition, many uncertainties exist regarding these assumptions that potentially constrain the growth of wind power. We suggest several areas in which to focus future research in order to better model the potential development of this resource. Because many of the assumptions related to wind in the model are also used for other renewable technologies, these suggestions could be applied to other renewable resources as well.
Julie Osborn, Lawrence Berkeley National Laboratory
Frances Wood, On Location, Inc.
R. Cooper Richey, formerly Lawrence Berkeley National Laboratory, now Enron
Sandy Sanders, On Location, Inc.
Walter Short, National Renewable Energy Laboratory
Jonathan Koomey, Lawrence Berkeley National Laboratory
The sensitivity analysis includes results for the "Reference Case and Sensitivities" and "Reduced Capital Cost Scenarios (Case 8)." The results of these runs are shown in Tables 14 and 15 of the paper. The installed capacity values are taken from Table 16 of the NEMS output file (fort.20), "Renewable Energy Generating Capability and Generation." This table for each of the cases discussed in these two sections is available for download here. Also available for download are expanded versions of Tables 14 and 15 that show capacity for every year (in the paper, only 5 year increments are given). These tables also include the results for two additional cases: a reduced capacity credit scaling factor (case 4b) and a reduced intermittent generation limit case (case 5b); these were omitted from the paper but may be of interest because they demonstrate the effect of tightening these constraints under reference case conditions.
(all files are Excel 98 workbooks)
A HREF="../SharedData/NEMSwind/key_data_t14and15.xls" TITLE="Total Installed Wind Capacity through Time (GW)">Table 14 and 15 - Total Installed Wind Capacity through Time (GW)
TABLE16_case0.xls - Reference case minus planned additions
TABLE16_case1.xls - Negated long-term supply curves
TABLE16_case2.xls - Negated short-term supply curves
TABLE16_case12.xls - Negated long and sort-term supply curves
TABLE16_case3.xls - Negated regional deployment limit
TABLE16_case4a.xls - Raised capacity credit scaling factor (1.0)
TABLE16_case4b.xls - Lowered capacity credit scaling factor (0.0)
TABLE16_case4c.xls - Raised capacity credit scaling factor (1.25)
TABLE16_case5a.xls - Raised intermittent generation limit (1.0)
TABLE16_case5b.xls - Lowered intermittent generation limit (0.0)
TABLE16_case7.xls - Negated learning by doing
TABLE16_case12345.xls - Combined 1, 2, 3, 4c, 5a
TABLE16_case8.xls - Lowered capital cost by 50%
TABLE16_case81.xls - Case 8 and 1 (long-term supply curves)
TABLE16_case82.xls - Case 8 and 2 (short-term supply curve)
TABLE16_case812.xls - Case 8 and 12 (long and short-term supply curves)
TABLE16_case83.xls - Case 8 and 3 (regional deployment limit)
TABLE16_case84a.xls - Case 8 and 4a (capacity credit scaling factor)
TABLE16_case84b.xls - Case 8 and 4b (capacity credit scaling factor)
TABLE16_case84c.xls - Case 8 and 4c (capacity credit scaling factor)
TABLE16_case85.xls - Case 8 and 5 (intermittent generation limit)
TABLE16_case87.xls - Case 8 and 7 (learning by doing)
TABLE16_123458.xls - Combined 1, 2, 3, 4c, 5a, 8
Osborn, Julie, Frances Wood, Cooper Richey, Sandy Sanders, Walter Short and Jonathan Koomey. 2000. A Sensitivity Analysis of the Treatment of Wind Energy in NEMS. Lawrence Berkeley Laboratory, LBNL-44070. February. 215K PDF
Marnay, Chris, R. Cooper Richey, Susan A. Mahler, Sarah E. Bretz and Robert J. Markel. 1997. Estimating the Environmental and Economic Effects of Widespread Residential PV Adoption Using GIS and NEMS. Lawrence Berkeley Laboratory, LBNL-41030. October. Abstract | Download the report.
The U.S. Department of Energy's National Renewable Energy Laboratory: This site has information on renewable energy and energy efficiency research, development and deployment.
On Location, Inc.: A management consulting firm that provides technical and financial counsel to clients in the energy business.
The U.S. Department of Energy's Energy Information Administration produces the National Energy Modeling System to forecast energy supply, demand and prices through 2020 as published in the Annual Energy Outlook.
This study was funded by Eric Petersen and Joe Galdo, Office of Energy Efficiency and Renewable Energy, U.S. Department of Energy.
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