用大模型驱动多智能体系统，自动识别并修复电网异常
Grid-Agent: An LLM-Powered Multi-Agent System for Power Grid Control
- 通过多智能体架构融合语义推理与数值计算，实现精准决策
- 在多个标准电网测试集上，故障缓解成功率超95%
- 适合需要快速响应的智能电网运维人员使用
现代电网因分布式能源、电动汽车和极端天气而面临前所未有的复杂性，同时日益暴露于可能引发电网违规的网络攻击。本文提出Grid-Agent，一个基于大语言模型的自主式AI框架，利用多智能体系统实现违规检测与修复。该框架通过模块化设计，将规划智能体与验证智能体协同工作：前者使用潮流求解器生成协调动作序列，后者在沙箱环境中执行并具备回滚机制以确保系统稳定与安全。为提升可扩展性，框架采用自适应多尺度网络表征，根据系统规模和复杂度动态调整编码策略。违规修复通过优化开关配置、电池部署和负荷削减实现。在IEEE与CIGRE基准网络（包括IEEE 69-bus、CIGRE MV、IEEE 30-bus测试系统）上的实验表明，该方法具备优异的缓解性能，验证了其在需快速、自适应响应的现代智能电网中的适用性。
原文摘要 · Abstract (English)
Modern power grids face unprecedented complexity from Distributed Energy Resources (DERs), Electric Vehicles (EVs), and extreme weather, while also being increasingly exposed to cyberattacks that can trigger grid violations. This paper introduces Grid-Agent, an autonomous AI-driven framework that leverages Large Language Models (LLMs) within a multi-agent system to detect and remediate violations. Grid-Agent integrates semantic reasoning with numerical precision through modular agents: a planning agent generates coordinated action sequences using power flow solvers, while a validation agent ensures stability and safety through sandboxed execution with rollback mechanisms. To enhance scalability, the framework employs an adaptive multi-scale network representation that dynamically adjusts encoding schemes based on system size and complexity. Violation resolution is achieved through optimizing switch configurations, battery deployment, and load curtailment. Our experiments on IEEE and CIGRE benchmark networks, including the IEEE 69-bus, CIGRE MV, IEEE 30-bus test systems, demonstrate superior mitigation performance, highlighting Grid-Agent's suitability for modern smart grids requiring rapid, adaptive response.
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