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Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review
Journal of Modern Power Systems and Clean Energy ( IF 6.3 ) Pub Date : 2020-12-02 , DOI: 10.35833/mpce.2020.000552
Di Cao , Weihao Hu , Junbo Zhao , Guozhou Zhang , Bin Zhang , Zhou Liu , Zhe Chen , Frede Blaabjerg

With the growing integration of distributed energy resources (DERs), flexible loads, and other emerging technologies, there are increasing complexities and uncertainties for modern power and energy systems. This brings great challenges to the operation and control. Besides, with the deployment of advanced sensor and smart meters, a large number of data are generated, which brings opportunities for novel data-driven methods to deal with complicated operation and control issues. Among them, reinforcement learning (RL) is one of the most widely promoted methods for control and optimization problems. This paper provides a comprehensive literature review of RL in terms of basic ideas, various types of algorithms, and their applications in power and energy systems. The challenges and further works are also discussed.

中文翻译:

强化学习及其在现代电力和能源系统中的应用

随着分布式能源(DER),灵活负载和其他新兴技术的日益集成,现代电力和能源系统的复杂性和不确定性日益增加。这给操作和控制带来了巨大挑战。此外,随着先进传感器和智能电表的部署,产生了大量数据,这为处理复杂操作和控制问题的新型数据驱动方法带来了机遇。其中,强化学习(RL)是用于控制和优化问题的最广泛推广的方法之一。本文就基本概念,各种算法及其在电力和能源系统中的应用提供了有关RL的全面文献综述。还讨论了挑战和进一步的工作。
更新日期:2020-12-04
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