# Grid-Agent: An LLM-Powered Multi-Agent System for Power Grid Control

arXiv: https://arxiv.org/abs/2508.05702 · HTML: https://mlsanity.com/paper/2508.05702/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use.
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 7 Aug 2025.

## Placed in

- LLM agents and tool use / [Financial trading and forecasting agents](https://mlsanity.com/agents/financial-trading-and-forecasting-agents/)

## Builds on (most cited first, 3 of 3)

- [pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systems](https://mlsanity.com/paper/1709.06743/) (2017)
- [Multi-Agent Systems Powered by Large Language Models: Applications in Swarm Intelligence](https://mlsanity.com/paper/2503.03800/) (2025)
- [Harnessing Multi-Agent LLMs for Complex Engineering Problem-Solving: A Framework for Senior Design Projects](https://mlsanity.com/paper/2501.01205/) (2025)

## Built on by (most cited first, 15 of 15)

- [X-GridAgent: An LLM-Powered Agentic AI System for Assisting Power Grid Analysis](https://mlsanity.com/paper/2512.20789/) (2025)
- [A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data](https://mlsanity.com/paper/2601.17717/) (2026)
- [Competition and Cooperation of LLM Agents in Games](https://mlsanity.com/paper/2604.00487/) (2026)
- [Cost and Accuracy of Long-Term Memory in Distributed Multi-Agent Systems Based on Large Language Models](https://mlsanity.com/paper/2601.07978/) (2026)
- [Multimodal Large Language Model Framework for Safe and Interpretable Grid-Integrated EVs](https://mlsanity.com/paper/2510.02592/) (2025)
- [LLM Agents for Time-Series: A Survey](https://mlsanity.com/paper/2608.26226/) (2026)
- [SiLR: Structure-Preserving Admission and Process Reward for LLM Tool Agents](https://mlsanity.com/paper/2609.04629/) (2026)
- [LLM-Enabled Multi-Agent Systems: Empirical Evaluation and Insights into Emerging Design Patterns & Paradigms](https://mlsanity.com/paper/2601.03328/) (2026)
- [Grid-Orch: An LLM-Powered Orchestrator for Distribution Grid Simulation and Analytics](https://mlsanity.com/paper/2605.12728/) (2026)
- [Multi-Agentic Approach for History Matching of Oil Reservoirs](https://mlsanity.com/paper/2605.15028/) (2026)
- [How Do Tool-Augmented LLM Agents Perform on Real-World Energy Analytics Tasks?](https://mlsanity.com/paper/2606.26346/) (2026)
- [LLMs and Agentic AI Systems for Smart Grids: A Tutorial on Architectures and Applications](https://mlsanity.com/paper/2607.18147/) (2026)
- [TwinGridShield: Consequence-Aware Runtime Authorization for LLM Grid-Agent Actions](https://mlsanity.com/paper/2608.15391/) (2026)
- [A-CPES: A Reference Framework for Agentic AI in Cyber-Physical Energy Systems](https://mlsanity.com/paper/2608.22672/) (2026)
- [Large Language Models in the Loop: A Stability- and Network-Aware Survey in Networked Control, Cyber-Physical, and Multi-Agent Systems](https://mlsanity.com/paper/2609.16599/) (2026)

## Abstract (arXiv)

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.