A Sensitivity Analysis of the Treatment of Wind Energy in the AEO99 Version of NEMS

TitleA Sensitivity Analysis of the Treatment of Wind Energy in the AEO99 Version of NEMS
Publication TypeReport
LBNL Report NumberLBNL-44070
Year of Publication2001
AuthorsOsborn, Julie G., Frances Wood, Cooper R. Richey, Sandy Sanders, Walter Short, and Jonathan G. Koomey
Date Published01/2001
PublisherErnest Orlando Lawrence Berkeley National Laboratory and the National Renewable Energy Laboratory
CityBerkeley, CA
ISBN NumberLBNL-44070, TP-28529
KeywordsAEO99, Enduse, Energy End-Use Forecasting, EUF, nems, wind energy
Abstract

This study investigates the effect of modeling assumptions about levelized costs and market penetration on the U.S. Department of Energy's Annual Energy Outlook (AEO) forecast for wind technologies. The AEO's annual report of energy supply, demand, and prices through 2020 is based on results from the Energy Information Administration (EIA) National Energy Modeling System (NEMS). NEMS predicts the market penetration of individual energy technologies based on a variety of inputs and assumed changes in these base values over time. The NEMS forecast of technology adoption and use is influenced most strongly by the model's assumptions about the levelized cost of energy for the various technologies. For each year, NEMS allocates a share of the energy market to least-cost technologies; this allocation affects forecasts for future years. NEMS uses cost multipliers and constraints to represent potential physical and economic limitations on growth in capacity; these limitations include depletion of resources, costs of rapid manufacturing expansion, and the stability or instability of the power grid when high levels of generation come from intermittent resources. In the AEO99 Reference Case version of NEMS, the electric generation supply mix remains fairly steady, and renewable energy technologies such as wind do not achieve significant market share during the forecast period. However, NEMS is also increasingly being used to analyze alternative scenarios (such as low-carbon futures) in which the role of renewables is likely to be enhanced. In these alternative scenarios, the way in which renewable energy technologies are modeled becomes critical.

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