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Modeling of Suppliers Learning Behaviors in an Electricity Market Environment

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Author Info

  • Yu, Nanpeng
  • Liu, Chen-Ching
  • Tesfatsion, Leigh S.

Abstract

The day-ahead electricity market is modeled as a multi-agent system with interacting agents including supplier agents, load-serving entities, and a market operator. Simulation of the market clearing results under the scenario in which agents have learning capabilities is compared with the scenario where agents report true marginal costs. It is shown that, with Q-learning, electricity suppliers are making more profits compared to the scenario without learning due to strategic gaming. As a result, the LMP at each bus is substantially higher. Related work can be accessed at: http://www.econ.iastate.edu/tesfatsi/AMESMarketHome.htm Annotated pointers to related work can be accessed here: http://www.econ.iastate.edu/tesfatsi/aelect.htm

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Bibliographic Info

Paper provided by Iowa State University, Department of Economics in its series Staff General Research Papers with number 12976.

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Date of creation: 19 Aug 2008
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Publication status: Published in International Journal of Engineering Intelligent Systems 2007, vol. 15 no. 2, pp. 115-121
Handle: RePEc:isu:genres:12976

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Postal: Iowa State University, Dept. of Economics, 260 Heady Hall, Ames, IA 50011-1070
Phone: +1 515.294.6741
Fax: +1 515.294.0221
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Web page: http://www.econ.iastate.edu
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Related research

Keywords: Electricity market; Supplier modeling; Competitive Markov decision process; Q-learning;

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  1. Severin Borenstein & James B. Bushnell & Frank A. Wolak, 2002. "Measuring Market Inefficiencies in California's Restructured Wholesale Electricity Market," American Economic Review, American Economic Association, vol. 92(5), pages 1376-1405, December.
  2. Junjie Sun & Leigh Tesfatsion, 2007. "Dynamic Testing of Wholesale Power Market Designs: An Open-Source Agent-Based Framework," Computational Economics, Society for Computational Economics, vol. 30(3), pages 291-327, October.
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