Modeling of Suppliers’ Learning Behaviors in an Electricity Market EnvironmentYu, Nanpeng (2007) Modeling of Suppliers’ Learning Behaviors in an Electricity Market Environment. Masters thesis, Iowa State University. Full text available as:
AbstractIn order to study the strategic bidding behavior of electricity suppliers and test the electricity market design, 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. The profit maximizing objective of a supplier naturally requires the player to learn from its bidding experience and behave in an anticipatory way. With volatile Locational Marginal Prices (LMPs), ever-changing transmission grid conditions, and incomplete information about other market participants, decision making for a supplier is a complex task. A learning algorithm that does not require an analytical model of the complicated market but allows suppliers to learn from the past experience and act in an anticipatory way is a suitable approach to this problem. Q-Learning, an anticipatory reinforcement learning technique, has all these desired properties. Therefore, it is used in this research to model the learning behaviors of electricity suppliers in a Day-Ahead electricity market. Simulation of the market clearing results under the scenarios in which agents have learning capabilities is compared with the scenario where agents report true marginal costs. It is shown that, with Q-Learning and strategic gaming, electricity suppliers are making more profits compared to the scenario without learning. As a result, the LMP at each bus is substantially higher.
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