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.
Key Industrial Requirements
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 set up simulations, execute analyses, retrieve results, and interact with power-system simulators through standardized tool calls.
Human-in-the-Loop Principles
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.
Blogger's Review: This paper introduces a compelling integration of multi-agent AI and MCP for power grid studies, showcasing the potential for automating and enhancing research capabilities. The standardized tool calls could significantly improve efficiency and effectiveness in practical applications, making it a noteworthy area of exploration.