This paper reviews existing works on (deep) reinforcement learning considerations in electric power system control. The works are reviewed as they relate to electric power system operating states (normal, preventive, emergency, restorative) and control levels (local, household, microgrid, subsystem, wide-area). Due attention is paid to the control-related problems considerations (cyber-security, big data analysis, short-term load forecast, and composite load modelling). Observations from reviewed literature are drawn and perspectives discussed. In order to make the text compact and as easy as possible to read, the focus is only on the works published (or “in press”) in journals and books while conference publications are not included. Exceptions are several work available in open repositories likely to become journal publications in near future. Hopefully this paper could serve as a good source of information for all those interested in solving similar problems.

本文回顾了有关电力系统控制中（深度）强化学习注意事项的现有工作。由于与电力系统的运行状态（正常，预防，紧急，恢复）和控制级别（本地，家庭，微电网，子系统，广域）有关，因此对工作进行了审查。应适当注意与控制相关的问题（网络安全，大数据分析，短期负荷预测和复合负荷建模）。从回顾的文献中得出观察结果，并讨论观点。为了使文本紧凑并尽可能易于阅读，重点仅放在期刊和书籍中已出版（或“印刷中”）的作品上，而未包括会议出版物。例外是开放存储库中的几项工作，这些工作有可能在不久的将来成为期刊出版物。