ADA: Resolving Attribution Ambiguity in End-to-End Power System Dispatch via Two-Time-Scale Stochastic Approximation

Large Language Models (LLMs) present a novel paradigm for power system dispatch (PSD), enabling operators to derive optimal dispatch strategies from multi-objective requirements and grid dynamics. However, isolated paradigms focusing on modeling or solving often yield decision bias or physical infeasibility. Meanwhile, end-to-end paradigms couple modeling-solving with reflection-feedback loops, obscuring whether failures stem from inaccurate modeling, invalid solutions, or insufficient feedback, thereby causing ineffective iterations. To address these challenges, we propose Agile Dispatch Agent (ADA), an agent based on two-time-scale evolution that decouples the optimization process into two asynchronous loops. On the fast time scale, the Planner actively acquires information to clarify complex requirements, while the Solver selects algorithms to adapt to the problem mathematical characteristics. On the slow time scale, the Summarizer module updates the modeling feasible region based on evaluations from the Judger. Under a finite-horizon Planner and standard two-time-scale stochastic approximation assumptions, our analysis provides local tracking guarantees for the fast decision process and the slow knowledge evolution. Experiments on L2RPN benchmarks with ambiguous requirements and renewable dynamics show that ADA outperforms state-of-the-art baselines.