SCOPE: Sibling-Contrast Procedural Evolution for Self-Evolving Power Grid Planning

Large Language Model (LLM) agents can use external simulators to verify proposed actions in power grid planning, but turning the resulting search experience into reusable planning knowledge remains a challenge. We introduce Sibling-Contrast Procedural Evolution (SCOPE), a self-evolving framework for secure power grid reinforcement planning that learns reusable planning strategies from verified search experience. Within each episode, SCOPE generates complementary candidate plans guided by diagnostic evidence, investment cost, and security requirements, and evaluates their physical consequences from the same grid state. It then performs plan fusion by selecting and recombining actions from the candidates before selecting an improved plan. Across episodes, SCOPE maintains two external memory banks: an episodic bank that stores concrete precedents from previously solved scenarios, and a procedural bank that consolidates verified planning trajectories into reusable planning strategies that guide future tasks. We evaluate SCOPE on 100 operating scenarios of an augmented IEEE 118-bus system using 24-hour AC power flow checks and peak hour N-1 contingency analysis. Results show that SCOPE can continuously improve its power planning strategies across episodes and provide better plans for future scenarios.