In April 2003 the U.S. Federal Energy Regulatory Commission proposed a complicated market design - the Wholesale Power Market Platform (WPMP) – for common adoption by all U.S. wholesale power markets. Versions of the WPMP have been implemented in New England, New York, the mid-Atlantic states, the Midwest, and the Southwest, and California. Strong opposition to the WPMP persists among some industry stakeholders, however, due largely to a perceived lack of adequate performance testing. This study reports on the model development and open-source implementation (in Java) of a computational wholesale power market organized in accordance with core WPMP features and operating over a realistically rendered transmission grid subject to congestion effects. The traders within this market model are strategic profit-seeking agents whose learning behaviors are based on data from human-subject experiments. Our key experimental focus is the complex interplay among structural conditions, market protocols, and learning behaviors in relation to short-term and longer-term market performance. Findings for a dynamic 5-node transmission grid test case are presented for concrete illustration. Annotated pointers to related work can be accessed here: http://www.econ.iastate.edu/tesfatsi/AMESMarketHome.htm
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Publisher Info
Paper provided by Iowa State University, Department of Economics in its series Staff General Research Papers with number
12649.
Length: 37 pages Date of creation: 27 Jul 2006 Date of revision: Publication status: Published in Computational Economics, October 2007, Vol. 30, No. 3, pp. 291-327. Handle: RePEc:isu:genres:12649
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