A Survey on Large Language Models Enhanced Reinforcement Learning for Smart Grid

Xiaoyu Zhang; Qiuye Sun; Tianyi Li; Yumeng Song; Zhongming Yao; Yushuai Li

doi:10.35833/MPCE.2025.000685

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A Survey on Large Language Models Enhanced Reinforcement Learning for Smart Grid

Review | 更新时间：2026-09-28

- A Survey on Large Language Models Enhanced Reinforcement Learning for Smart Grid
- Journal of Modern Power Systems and Clean Energy Vol. 14, Issue 5, Pages: 1549-1567(2026)
- 作者机构：
  
  1.School of Information Science and Engineering, Northeastern University, Shenyang 110819, China
  2.State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China
  3.Department of Computer Science, Aalborg University, 9220 Aalborg, Denmark
- 作者简介：
  
  Xiaoyu Zhang received the B.S. degree in automation from North China Electric Power University, Beijing, China, in 2018, and the M.S. degree in electrical engineering from Northeastern University, Shenyang, China, in 2024. She is currently pursuing the Ph.D. degree in control science and engineering at Northeastern University. Her research interests include machine learning, integrated energy system, and optimal scheduling. Email: 2410400@stu.neu.edu.cn
  email: sunqiuye@ise.neu.edu.cn
  Tianyi Li received the B.S. and M.S. degrees in computer science from Northeastern University, Shenyang, China, in 2014 and 2017, respectively, and the Ph.D. degree in computer science from Aalborg University, Aalborg, Denmark, in 2022. She is currently an Associate Professor with Aalborg University. Her research interests include trajectory analysis, time series analysis, machine learning, and digital twin. email: tianyi@cs.aau.dk
  Yumeng Song received the B.S. degree in computer science and technology, the M.S. degree in computer software and theory, and the Ph.D. degree in computer science and technology from Northeastern University, Shenyang, China, in 2017, 2019, and 2024, respectively. She is currently an Assistant Professor with Aalborg University, Aalborg, Denmark. Her research interests include machine learning for spatio-temporal data management and analytics, graph neural network, and hypergraph neural network. Email:yumengs@cs.aau.dk;
  Zhongming Yao received the B.S. and Ph.D. degrees in computer science and technology from Northeastern University, Shenyang, China, in 2017 and 2025, respectively. He is currently a Postdoctoral Researcher with Aalborg University, Aalborg, Denmark. His research interests include trajectory data management and analysis, energy management, and blockchain technology. Email:zyao@cs.aau.dk
  Yushuai Li received the B.S. degree in electrical engineering and automation and the Ph.D. degree in control theory and control engineering from Northeastern University, Shenyang, China, in 2014 and 2019, respectively. He is currently an Assistant Professor with Aalborg University, Aalborg, Denmark. His research interests include machine learning, digital twin, and integrated energy and transportation system. Email:yushuaili@ieee.org
- 基金信息：
- DOI：10.35833/MPCE.2025.000685
  CLC：
- Received：27 July 2025，
  
  Revised：2025-11-18，
  
  Accepted：03 March 2026，
  
  Online First：30 March 2026，
  
  Published：15 September 2026
- 稿件说明：
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Zhang Xiaoyu,Sun Qiuye,Li Tianyi,et al.A Survey on Large Language Models Enhanced Reinforcement Learning for Smart Grid[J].Journal of Modern Power Systems and Clean Energy,2026,14(05):1549-1567.
DOI：

Zhang Xiaoyu,Sun Qiuye,Li Tianyi,et al.A Survey on Large Language Models Enhanced Reinforcement Learning for Smart Grid[J].Journal of Modern Power Systems and Clean Energy,2026,14(05):1549-1567. DOI： 10.35833/MPCE.2025.000685.

摘要

Abstract

The smart grid (SG) is a modern power system that leverages digital technologies to enhance the generation

delivery

and consumption of electric power. Reinforcement learning (RL) plays an important role in SG by helping make smart decisions. However

RL faces challenges such as sparse rewards

poor generalization

and difficulty in representing and interpreting regulatory constraints. Large language models (LLMs) offer new opportunities to address these challenges by understanding natural language

leveraging external knowledge

and enhancing reasoning. This paper presents a comprehensive review of LLM-enhanced RL for SG. It first analyzes the key challenges of RL and introduces how LLMs help enhance the performance

including a taxonomy based on the integration patterns and functions. It then reviews applications of LLM-enhanced RL for SG

with a focus on energy management

operational control

electricity market

and hardware design. Finally

it discusses research challenges and future directions of LLM-enhanced RL for SG. This paper aims to provide a clear framework for researchers to apply LLMs in RL and to promote the application of RL for SG.

关键词

Keywords

references

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