Modeling of Suppliers' Learning Behaviors in an Electricity Market Environment

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.