PFAgent: A Tractable and Self-Evolving Power-Flow Agent for Interactive Grid Analysis

TL;DR

PFAgent is an interactive, self-evolving AI-powered agent designed to automate and improve power system analysis workflows through intent understanding, iterative refinement, and transparent reporting.

Contribution

The paper introduces PFAgent, a novel self-evolving agent that automates power-flow analysis with interactive architecture, verification-driven refinement, and AI-assisted debugging.

Findings

Successfully automates case change and voltage violation analysis.

Performs N-1 contingency analysis with reproducible results.

Provides transparent execution logs and concise summaries.

Abstract

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.