Yu, Nanpeng; Liu, Chen-Ching; Tesfatsion, Leigh

International Journal of Engineering Intelligent Systems Vol. 15 no. 2 (2007): 115-121.

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

JEL Classification: C6, D4, D6, L1, L3, L94, Q4

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