A Review of Smart Grid Evolution and Reinforcement Learning: Applications, Challenges and Future Directions

Na Xu, Zhuo Tang, Chenyi Si (), Jinshan Bian and Chaoxu Mu
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Na Xu: School of Electrical and Information Engineering, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin 300072, China
Zhuo Tang: School of Electrical and Information Engineering, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin 300072, China
Chenyi Si: School of Electrical and Information Engineering, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin 300072, China
Jinshan Bian: School of Artificial Intelligence, Anhui University, Qingyuan Campus, 111 Jiulong Road, Hefei 230093, China
Chaoxu Mu: School of Electrical and Information Engineering, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin 300072, China

Energies, 2025, vol. 18, issue 7, 1-19

Abstract: In the face of the rapid development of smart grid technologies, it is increasingly difficult for traditional power system management methods to support the increasingly complex operation of modern power grids. This study systematically reviews new challenges and research trends in the field of smart grid optimization, focusing on key issues such as power flow optimization, load scheduling, and reactive power compensation. By analyzing the application of reinforcement learning in the smart grid, the impact of distributed new energy’s high penetration on the stability of the system is thoroughly discussed, and the advantages and disadvantages of the existing control strategies are systematically reviewed. This study compares the applicability, advantages, and limitations of different reinforcement learning algorithms in practical scenarios, and reveals core challenges such as state space complexity, learning stability, and computational efficiency. On this basis, a multi-agent cooperation optimization direction based on the two-layer reinforcement learning framework is proposed to improve the dynamic coordination ability of the power grid. This study provides a theoretical reference for smart grid optimization through multi-dimensional analysis and research, advancing the application of deep reinforcement learning technology in this field.

Keywords: smart grid; reinforcement learning; optimal power dispatch; reactive power management; distributed control (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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