Physically Grounded, Experience-Guided LLM Agents for Decentralized Grid Control

Power grids are safety-critical infrastructure that must maintain reliable operation under changing demand and increasing shares of variable renewable generation. Topology control can relieve transmission congestion by reconfiguring substation busbars, but decentralized operation requires coordinating interventions across regions with only partial network information. Existing approaches lack a mechanism that combines assessment of the current physical state, coordination of interdependent regional actions, and evidence from past interventions within a single decentralized decision process. We propose GridRefine, a physically grounded, experience-guided framework for decentralized topology control using large language models as regional agents. Each agent uses a graph representation of its local network, physical knowledge, and a memory of past intervention outcomes from offline simulations to select and explain actions. Through counterfactual reasoning, agents consider how differences between past and current grid states may change an intervention’s effects, then refine their assessment of promising candidates using feedback from a local power-flow tool. Because these interventions can redistribute power flows across regional boundaries, neighbouring agents coordinate their proposals before execution. We evaluate GridRefine on two power grids of different sizes and include planned line disconnections for maintenance on the larger grid. With two of the three evaluated LLM backbones, GridRefine improves mean grid survival over MARL baseline across all three settings. Code will be released upon publication.