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Applications of Reinforcement Learning in Deregulated Power Market: A Comprehensive Review

arXiv 2205.08369 ↗First posted 7 May 2022cs.LG cs.GT cs.SYcited by 0

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Abstract (arXiv)

The increasing penetration of renewable generations, along with the deregulation and marketization of power industry, promotes the transformation of power market operation paradigms. The optimal bidding strategy and dispatching methodology under these new paradigms are prioritized concerns for both market participants and power system operators, with obstacles of uncertain characteristics, computational efficiency, as well as requirements of hyperopic decision-making. To tackle these problems, the Reinforcement Learning (RL), as an emerging machine learning technique with advantages compared with conventional optimization tools, is playing an increasingly significant role in both academia and industry. This paper presents a comprehensive review of RL applications in deregulated power market operation including bidding and dispatching strategy optimization, based on more than 150 carefully selected literatures. 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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 160 references that are in the map. First posted on arXiv 7 May 2022.

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19 of 160 references.

References · 19

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7 more
  1. 2020Dynamic Energy Dispatch Based on Deep Reinforcement Learning in IoT-Driven Smart Isolated Microgridscited by 13
  2. 2020Option Hedging with Risk Averse Reinforcement Learningcited by 13
  3. 2014Monte Carlo Sampling Methods for Approximating Interactive POMDPscited by 12
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  6. 2021Robust Risk-Sensitive Reinforcement Learning Agents for Trading Marketscited by 3
  7. 2019A Deep Reinforcement Learning Based Approach for Optimal Active Power Dispatchcited by 2

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