A Knowledge-Enhanced LLM Framework for Power Semantic Understanding and Multi-Agent Decision Support

Abstract

Power-system decision support increasingly requires the joint interpretation of heterogeneous information sources, including real-time measurements, topology states, alarm logs, operating rules, dispatch instructions, and historical restoration cases. Existing LLM-based approaches remain limited in such settings because their reasoning is often weakly grounded in power-domain knowledge and difficult to verify under physical and procedural constraints. To address this issue, we propose a knowledge-enhanced LLM framework for power semantic understanding and multi-agent decision learning. The framework first organizes heterogeneous grid information into a dynamic semantic graph, then performs graph-grounded semantic understanding, translates the inferred semantics into an executable task graph for hierarchical multi-agent collaboration, and finally applies safety-constrained verification with evidence tracing before action execution. Experiments on Grid2Op/L2RPN WCCI 2022, SMART-DS, RTS-GMLC, and PowerGym show that the proposed framework consistently improves semantic grounding accuracy, restoration quality, and feasible-decision rate over rule-based, text-centric RAG, and graph-retrieval multi-agent baselines, while exhibiting stronger robustness under partial observation. These results indicate that explicit semantic grounding and safety-aware collaborative reasoning are both necessary for reliable LLM-assisted power-system decision support.