Reinforcement learning in deregulated energy market: A comprehensive review

Ziqing Zhu, Ze Hu, Ka Wing Chan, Siqi Bu, Bin Zhou and Shiwei Xia

Applied Energy, 2023, vol. 329, issue C, No S0306261922014696

Abstract: The increasing penetration of renewable generations, along with the deregulation and marketization of power industry, promotes the transformation of energy market operation paradigms. The optimal bidding strategy and dispatching methodologies under these new paradigms are prioritized concerns for both market participants and power system operators. In contrast with conventional solution methodologies, the Reinforcement Learning (RL), as an emerging machine learning technique that exhibits a more favorable computational performance, is playing an increasingly significant role in both academia and industry. This paper presents a comprehensive review of RL applications in deregulated energy market operation including bidding and dispatching strategy optimization, based on more than 150 carefully selected papers. For each application, apart from a paradigmatic summary of generalized methodology, in-depth discussions of applicability and obstacles while deploying RL techniques are also provided. Finally, some RL techniques that have great potentiality to be deployed in bidding and dispatching problems are recommended and discussed.

Keywords: Energy market; Reinforcement learning; Bidding strategy; Optimal dispatching (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (4)

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DOI: 10.1016/j.apenergy.2022.120212

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