AI 中文总结
探讨智能体人工智能和MCP在TSO环境下对电网研究的支持，结合大语言模型等要素，介绍pypowsybl - mcp接口搭建试验台，研究智能体交互方式，讨论人工参与原则与评估策略，推动电网研究环境更具交互性、可审计性和扩展性。
AI 中文摘要
本立场文件探讨了智能体人工智能和模型上下文协议（MCP）如何在输电系统运营商（TSO）环境中支持电网研究。重点是将大语言模型与数值模拟工具、结构化工作流程和人工监督相结合。确定了智能体辅助电网研究的关键行业需求，并引入了pypowsybl - mcp，一个基于MCP的接口，向人工智能智能体展示模拟工具pypowsybl的选定功能。这一步提供了一个试验台，用于研究智能体如何通过标准化工具调用设置模拟、执行分析、检索结果并与电力系统模拟器交互。还讨论了人工参与的多智能体工作流程原则，并概述了结合技术指标和从业者反馈的评估策略。该文件将基于MCP的工具集成定位为迈向更具交互性、可审计性和可扩展性的电网研究环境的一步。
英文摘要
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.
CommentsAccepted to IJCAI AISE 2026 workshop
