A Human-in-the-Loop Framework for Outage Scheduling of Power Grids via LLM-RL Coordination

As the operation modes and maintenance management requirements of power grids become increasingly complex, the optimization of monthly outage maintenance schedules faces challenges such as difficulty in multi-department coordination, high reliance on manual experience, and insufficient adaptability to dynamic demands. To address these issues, this paper proposes a collaborative optimization method for monthly outage schedules of power grids based on a large language model and reinforcement learning. The method uses the large language model to parse natural language requirements raised in balance meetings, converting requirements such as fixed maintenance dates, duration adjustments, and forbidden maintenance periods into structured constraint parameters. Subsequently, the small reinforcement learning model based on Dueling Deep Q-Network (Dueling DQN) re-optimizes the outage schedule according to the updated environment parameters, achieving a collaborative solution from meeting requirement parsing to schedule generation. Case study results on the IEEE 39-bus system show that the proposed method can integrate the dynamic requirements arising from multiple rounds of balance meetings and perform adaptive optimization of the outage schedule under different constraint conditions. Experimental results on the IEEE 39-bus system demonstrate that the proposed framework effectively optimizes monthly outage schedules. Compared with the initial schedule, the proposed method reduces monthly voltage violations by 31 times and decreases active power loss by 35.88 MWh.