Modeling of Suppliers Learning Behaviors in an Electricity Market Environment
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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|Date of creation:||19 Aug 2008|
|Date of revision:|
|Publication status:||Published in International Journal of Engineering Intelligent Systems 2007, vol. 15 no. 2, pp. 115-121|
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Web page: http://www.econ.iastate.edu
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- 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.
- Sun, Junjie & Tesfatsion, Leigh S., 2006.
"Dynamic Testing of Wholesale Power Market Designs: An Open-Source Agent-Based Framework,"
Staff General Research Papers
12649, Iowa State University, Department of Economics.
- 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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