PFAgent: A Tractable and Self-Evolving Power-Flow Agent for Interactive Grid Analysis
For power system engineers, PFAgent reduces manual effort in simulation workflows, but the novelty is incremental as it applies existing LLM-based agent frameworks to a specific domain.
PFAgent automates power system simulation workflows by integrating intent parsing, knowledge retrieval, tool execution, and self-evolution, achieving automated case changes, voltage violation analysis, N-1 contingency analysis, and reproducible results on IEEE benchmarks.
Power system simulation workflows remain expert-intensive. Engineers must translate study intents into code or API calls, execute analyses, and interpret outputs. To automate this workflow, this paper presents PFAgent, a tractable and self-evolving power-flow agent for interactive grid analysis. PFAgent integrates four key capabilities: i) a tractable and interactive architecture for intent parsing, knowledge retrieval, tool execution, and structured reporting; ii) a self-evolution mechanism combining verification-driven refinement and human-in-the-loop feedback; iii) an AI-assisted evaluation and debugging loop that leverages conversational context, generated code, and execution errors for iterative fixing; and iv) an evaluation framework covering task success, convergence validity, numerical consistency, and explanation quality. Verification on IEEE benchmark systems shows that PFAgent can automate case change, analyze voltage violations, perform N-1 contingency analysis, generate plots and concise summaries, and return reproducible results with transparent execution logs. The proposed framework highlights a shift from conventional simulation tools to interactive, tractable, and self-evolving agents for power system analysis.