Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review

被引：237

作者：

Cao, Di ^{[1
]}

Hu, Weihao ^{[1
]}

Zhao, Junbo ^{[2
]}

Zhang, Guozhou ^{[1
]}

Zhang, Bin ^{[1
]}

Liu, Zhou ^{[3
]}

Chen, Zhe ^{[3
]}

Blaabjerg, Frede ^{[3
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Wide Area Measurement & Control Sichuan Prov Key, Chengdu, Peoples R China

[2] Mississippi State Univ, Dept Elect & Comp Engn, Starkville, MS USA

[3] Aalborg Univ, Dept Energy Technol, Aalborg, Denmark

来源：

JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY | 2020年 / 8卷 / 06期

关键词：

Uncertainty; Systems operation; Reinforcement learning; Smart meters; Power systems; Distributed power generation; Optimization; deep reinforcement learning; power system operation and control; optimization; MANAGEMENT; STRATEGY; AUCTION;

D O I：

10.35833/MPCE.2020.000552

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：1029 / 1042

页数：14

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