Deep reinforcement learning for power system applications: An overview

Zidong Zhang; Dongxia Zhang; Robert C. Qiu

doi:10.17775/CSEEJPES.2019.00920

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Deep reinforcement learning for power system applications: An overview

Regular Papers | 更新时间：2025-12-19

- Deep reinforcement learning for power system applications: An overview
- Deep reinforcement learning for power system applications: An overview
- 中国电机工程学会电力与能源系统学报(英文) 2020年6卷第1期页码：213-225
- 作者机构：
  
  1. Research Center For Big Data Engineering And Technologies, Shanghai Jiao Tong University,Shanghai,China
  2. China Electric Power Research Institute,Beijing,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2019.00920
  中图分类号：
- 纸质出版：2020
- 稿件说明：
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Zidong Zhang, Dongxia Zhang, Robert C. Qiu. Deep reinforcement learning for power system applications: An overview[J]. 中国电机工程学会电力与能源系统学报(英文), 2020,6(1):213-225.

Zidong Zhang, Dongxia Zhang, Robert C. Qiu. Deep reinforcement learning for power system applications: An overview[J]. CSEE Journal of Power and Energy Systems, 2020, 6(1): 213-225.
Zidong Zhang, Dongxia Zhang, Robert C. Qiu. Deep reinforcement learning for power system applications: An overview[J]. 中国电机工程学会电力与能源系统学报(英文), 2020,6(1):213-225. DOI： 10.17775/CSEEJPES.2019.00920.

Zidong Zhang, Dongxia Zhang, Robert C. Qiu. Deep reinforcement learning for power system applications: An overview[J]. CSEE Journal of Power and Energy Systems, 2020, 6(1): 213-225. DOI： 10.17775/CSEEJPES.2019.00920.

摘要

Abstract

Due to increasing complexity

uncertainty and data dimensions in power systems

conventional methods often meet bottlenecks when attempting to solve decision and control problems. Therefore

data-driven methods toward solving such problems are being extensively studied. Deep reinforcement learning (DRL) is one of these data-driven methods and is regarded as real artificial intelligence (AI). DRL is a combination of deep learning (DL) and reinforcement learning (RL). This field of research has been applied to solve a wide range of complex sequential decision-making problems

including those in power systems. This paper firstly reviews the basic ideas

models

algorithms and techniques of DRL. Applications in power systems such as energy management

demand response

electricity market

operational control

and others are then considered. In addition

recent advances in DRL including the combination of RL with other classical methods

and the prospect and challenges of applications in power systems are also discussed.

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