Orchestrating Power Grid Studies with Multi-Agent AI and MCP Servers
For TSOs and power system engineers, this work proposes a framework to make grid studies more interactive and auditable, but it is an early-stage position paper without empirical results.
This position paper explores how Agentic AI and Model Context Protocol (MCP) can support power-grid studies in a Transmission System Operator (TSO) context, introducing pypowsybl-mcp as an MCP-based interface for integrating LLMs with simulation tools. It identifies key industrial requirements and outlines an evaluation strategy, positioning MCP-based tool integration as a step toward more interactive and scalable grid-study environments.
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.