Autonomous Volt/Var Control in Active Distribution Networks via LLM-Driven Dynamic Reward Shaping

Abstract

To alleviate the severe voltage security and operational efficiency challenges brought about by the increasing penetration of distributed energy resources in active distribution networks, Volt/Var control (VVC) has become a key mechanism to stabilize node voltage and minimize power loss by coordinating reactive power injection. While multi-agent reinforcement learning (MARL) offers a promising decentralized control approach, its static reward functions are prone to creating harsh trade-offs between voltage constraint enforcement and cost-efficiency. In this paper, a hierarchical autonomous control framework featuring large language model-driven dynamic reward shaping (LLM-Driven DRS) is introduced to balance security and efficiency. The dynamic priority shifting (DPS) mechanism lies at the center of the framework and dynamically varies the reward weights through the detection of real-time grid bottlenecks. Under the LLM-Driven DRS framework, this mechanism successfully achieves a fluid transition between a Constraint-Dominant Phase for voltage stabilization and an Objective-Refinement Phase for economic optimization. Validation on a modified IEEE 33-bus system demonstrates that the proposed framework achieves Pareto superiority over conventional static weight strategies. Crucially, compared with the 1.250% static baseline, the absolute voltage violation rate is suppressed to 0.014%, mitigating long-tail risks of hardware degradation and inverter tripping, while active power losses are reduced by up to 33.76%. A robust safety margin is further confirmed by spatiotemporal analysis, which reveals an average minimum voltage margin increase of over 0.011 p.u. under severe stress conditions.