Review on the research and practice of deep learning and reinforcement learning in smart grids

Dongxia Zhang; Xiaoqing Han; Chunyu Deng

doi:10.17775/CSEEJPES.2018.00520

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Review on the research and practice of deep learning and reinforcement learning in smart grids

更新时间：2025-12-18

- Review on the research and practice of deep learning and reinforcement learning in smart grids
- Review on the research and practice of deep learning and reinforcement learning in smart grids
- 中国电机工程学会电力与能源系统学报(英文) 2018年4卷第3期页码：362-370
- 作者机构：
  
  1. China Electric Power Research Institute,Beijing,China,100192
  2. Taiyuan University of Technology,,, CN,Shanxi,Taiyuan
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2018.00520
  中图分类号：
- 纸质出版：2018
- 稿件说明：
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Dongxia Zhang, Xiaoqing Han, Chunyu Deng. Review on the research and practice of deep learning and reinforcement learning in smart grids[J]. 中国电机工程学会电力与能源系统学报(英文), 2018,4(3):362-370.

Dongxia Zhang, Xiaoqing Han, Chunyu Deng. Review on the research and practice of deep learning and reinforcement learning in smart grids[J]. CSEE Journal of Power and Energy Systems, 2018, 4(3): 362-370.
Dongxia Zhang, Xiaoqing Han, Chunyu Deng. Review on the research and practice of deep learning and reinforcement learning in smart grids[J]. 中国电机工程学会电力与能源系统学报(英文), 2018,4(3):362-370. DOI： 10.17775/CSEEJPES.2018.00520.

Dongxia Zhang, Xiaoqing Han, Chunyu Deng. Review on the research and practice of deep learning and reinforcement learning in smart grids[J]. CSEE Journal of Power and Energy Systems, 2018, 4(3): 362-370. DOI： 10.17775/CSEEJPES.2018.00520.

摘要

Abstract

Smart grids are the developmental trend of power systems and they have attracted much attention all over the world. Due to their complexities

and the uncertainty of the smart grid and high volume of information being collected

artificial intelligence techniques represent some of the enabling technologies for its future development and success. Owing to the decreasing cost of computing power

the profusion of data

and better algorithms

AI has entered into its new developmental stage and AI 2.0 is developing rapidly. Deep learning (DL)

reinforcement learning (RL) and their combination-deep reinforcement learning (DRL) are representative methods and relatively mature methods in the family of AI 2.0. This article introduces the concept and status quo of the above three methods

summarizes their potential for application in smart grids

and provides an overview of the research work on their application in smart grids.

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