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：可再生能源渗透率的不断提高，以及电力工业的放松管制与市场化进程，推动了电力市场运行范式的深刻变革。在此新范式下，市场参与者与电力系统运营商优先关注最优投标策略与调度方法，这些方法面临不确定性特征、计算效率问题以及前瞻性决策需求的挑战。为解决上述问题，强化学习作为一种新兴的机器学习技术，凭借其相较于传统优化工具的显著优势，在学术界和工业界发挥着日益重要的作用。本文基于150余篇精选文献，对强化学习在放松管制的电力市场运行中的应用进行了全面综述，涵盖投标与调度策略优化。针对每项应用，除对通用方法进行范式化总结外，还深入探讨了部署强化学习技术时的适用性与障碍。最后，本文推荐并讨论了在投标与调度问题中具有巨大应用潜力的强化学习技术。