• 대한전기학회
Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
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  • 한국과학기술단체총연합회
  • 한국학술지인용색인
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Title A Survey on Deep Reinforcement Learning Approaches for Power System Control and Optimization
Authors 장호천(Haotian Zhang) ; 왕천(Chen Wang) ; 이민주(Minju Lee) ; 이명훈(Myoung Hoon Lee) ; 문준(Jun Moon)
DOI https://doi.org/10.5370/KIEE.2025.74.6.1041
Page pp.1041-1057
ISSN 1975-8359
Keywords Deep reinforcement learning; energy dispatch; topology control; emergency load shedding
Abstract With the increasing complexity of modern power systems due to the access of large-scale renewable energy sources, minimizing operational costs while achieving stable grid operation has become a core challenge in power scheduling and optimization. Energy dispatch, topology control and emergency load shedding are key measures to improve power system stability and flexibility.
However, the outputs of their traditional control policies rely on predefined rules or mathematical optimization models, which are prone to computational bottlenecks and response lags in high-dimensional dynamic environments, making it difficult to meet the demands of smart grids. In recent years, deep reinforcement learning (DRL) has gradually become a cutting-edge technology for power system scheduling and control by virtue of its powerful adaptive learning and decision optimization capabilities. According to the existing research, DRL can improve the flexibility and anti-interference ability of the power grid by learning the optimal policies through autonomous interaction, surpassing the real-time decision-making ability of traditional optimization methods in high-dimensional state space. In this paper, we systematically review the applications of DRL in energy dispatch, topology control and emergency load shedding, focus on its optimization policies, technological breakthroughs and applicability, and analyze the current challenges and future research directions.