Grid-Agent: An LLM-Powered Multi-Agent System for Power Grid Control
This addresses the challenge of managing power grid violations for grid operators, though it appears incremental as it builds on existing multi-agent and LLM approaches.
The paper tackles the problem of controlling modern power grids under complexity and cyberattacks by introducing Grid-Agent, an autonomous AI framework that uses LLMs in a multi-agent system to detect and remediate violations, achieving superior mitigation performance on benchmark networks like IEEE and CIGRE test systems.
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.