Exploring Reinforcement Learning Method in Bidding Strategy Development for Day-Ahead Electricity Market
This paper introduces the detailed process of applying reinforcement learning to solve market participant bidding strategy problem. The process includes the setup of market clearing environment, reinforcement learning structure, and Q-learning algorithm. A comprehensive study on three specially designed problems demonstrates the Q-learning method can achieve significantly higher profit than the baseline method, which employs marginal cost as the offer price. The study provides insights to the learning process and the performance of Q-learning and demonstrates the performance varies with the changing condition of the environment, and tends to degrade with more complex patterns or random disturbances in the environment.
H. Chen et al., "Exploring Reinforcement Learning Method in Bidding Strategy Development for Day-Ahead Electricity Market," Proceedings of the Asia-Pacific Power and Energy Engineering Conference, APPEEC, pp. 1-5, Institute of Electrical and Electronics Engineers (IEEE), Oct 2020.
The definitive version is available at https://doi.org/10.1109/APPEEC48164.2020.9220677
12th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2020
Electrical and Computer Engineering
Center for High Performance Computing Research
Keywords and Phrases
Bidding Strategy; Electricity Market; Q-Learning; Reinforcement Learning
International Standard Book Number (ISBN)
International Standard Serial Number (ISSN)
Article - Conference proceedings
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13 Oct 2020