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.

翻译：可再生能源的日益渗透,加上电力工业的放松管制和市场化,促进了电力市场运作模式的转变;在这些新模式下,最佳投标战略和发送方法是市场参与者和电力系统操作者优先关注的问题,存在着不确定特点、计算效率以及超高操作决策要求等障碍;为解决这些问题,强化学习作为一种新兴机械学习技术,具有与常规优化工具相比的优势,正在学术界和工业界发挥日益重要的作用;本文件根据150多份仔细选定的文献,全面审查了在放松管制的电力市场业务中应用RL的应用,包括招标和发送战略优化;除了对通用方法进行典型总结外,还就应用和运用RL技术时的障碍进行了深入讨论;最后,建议和讨论在招标和发送问题中具有巨大潜力的一些RL技术。