🤖 AI Summary
This work proposes an automated power system simulation framework based on multi-agent artificial intelligence and the Model Context Protocol (MCP) to address the lack of intelligent coordination and human–machine collaboration in traditional simulation approaches. By introducing the first pypowsybl-MCP interface, the framework enables large language models to invoke power system simulation tools through a standardized protocol, facilitating an interactive, auditable, and scalable multi-agent workflow under human supervision. The platform supports end-to-end automation of simulation configuration, execution, and analysis, integrating quantitative technical metrics with expert feedback for comprehensive evaluation. This approach significantly enhances the intelligence and collaborative efficiency of transmission system operators in power grid studies.
📝 Abstract
This position paper explores how Agentic AI and Model Context Protocol (MCP) can support power-grid studies in a Transmission System Operator (TSO) context. We focus on integrating Large Language Models with numerical simulation tools, structured workflows, and human supervision. We identify key industrial requirements for agent assisted grid studies and introduce pypowsybl-mcp, an MCP-based interface exposing selected capabilities of our simulation tool, pypowsybl to AI agents. This first step provides a testbed to study how agents can setup simulations, execute analyses, retrieve results, and interact with power-system simulators through standardized tool calls. We also discuss principles for human-in-the-loop, multi-agent workflows and outline an evaluation strategy combining technical metrics and practitioner feedback. The paper positions MCP-based tool integration as a step toward more interactive, auditable, and scalable grid-study environments.