Monthly Power Outage Maintenance Scheduling for Power Grids Based on Interpretable Reinforcement Learning

Wei Tang, Xun Mao, Kai Lv, Zhichen Cai and Zhenhuan Ding ()
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Wei Tang: State Grid Anhui Electric Power Co., Ltd., Hefei 230001, China
Xun Mao: State Grid Anhui Electric Power Co., Ltd., Hefei 230001, China
Kai Lv: State Grid Anhui Electric Power Co., Ltd., Hefei 230001, China
Zhichen Cai: Department of Electrical Engineering, Anhui University, Hefei 230601, China
Zhenhuan Ding: Department of Electrical Engineering, Anhui University, Hefei 230601, China

Energies, 2025, vol. 18, issue 20, 1-18

Abstract: This paper proposes an interpretable optimization method for power grid outage scheduling based on reinforcement learning. An outage scheduling optimization model is proposed, considering the convergence of power flow calculation, voltage violations, and operational economic behavior as objectives, while considering constraints such as simultaneous outage constraints, mutually exclusive constraints, and maintenance windows. Key features of the outage schedule are selected based on Shapley values to construct a Markov optimization model for outage scheduling. A deep reinforcement learning agent is established to optimize the outage schedule. The proposed method is applied to the IEEE-39 and IEEE-118 bus system for validation. Experimental results show that the proposed method outperforms existing algorithms in terms of voltage violation, total power losses, and computational time. The proposed method eliminates all voltage violations and reduces active power losses up to 5.7% and computation time by 6.8 h compared to conventional heuristic algorithms.

Keywords: power outage maintenance scheduling; reinforcement learning; interpretability; optimization (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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