Exploring Reinforcement Learning Method in Bidding Strategy Development for Day-Ahead Electricity Market
Abstract
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.
Recommended Citation
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
Meeting Name
12th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2020
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
Research Center/Lab(s)
Center for High Performance Computing Research
Keywords and Phrases
Bidding Strategy; Electricity Market; Q-Learning; Reinforcement Learning
International Standard Book Number (ISBN)
978-172815748-1
International Standard Serial Number (ISSN)
2157-4839; 2157-4847
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2020 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
13 Oct 2020
Comments
Defense Advanced Research Projects Agency, Grant D18AP00054