用聊天方式做电网分析，又准又快。
GridMind: LLMs-Powered Agents for Power System Analysis and Operations
- 用大模型+工程求解器协作，自然语言对话完成电网计算。
- 在IEEE测试集上所有模型都算对，小模型更快更省资源。
- 适合电力工程师快速验证方案，降低技术门槛。
传统电网分析流程复杂，制约现代电网决策效率。本文提出GridMind，一种融合大语言模型（LLMs）与确定性工程求解器的多智能体AI系统，支持通过自然语言接口进行对话式科学计算。该系统通过专用智能体协调交流潮流最优功率分配（AC Optimal Power Flow）和N-1故障分析，在保持数值精度的同时实现工作流集成、知识可及性、上下文延续与专家决策辅助。在IEEE测试案例上的实验表明，所提代理框架在所有测试大模型下均能稳定输出正确结果，且较小规模模型在降低计算延迟的同时达到相当的分析精度。本研究确立了代理式AI在科学计算中的可行性，证明对话界面可在保障关键工程应用所需数值严谨性的前提下提升可访问性。
原文摘要 · Abstract (English)
The complexity of traditional power system analysis workflows presents significant barriers to efficient decision-making in modern electric grids. This paper presents GridMind, a multi-agent AI system that integrates Large Language Models (LLMs) with deterministic engineering solvers to enable conversational scientific computing for power system analysis. The system employs specialized agents coordinating AC Optimal Power Flow and N-1 contingency analysis through natural language interfaces while maintaining numerical precision via function calls. GridMind addresses workflow integration, knowledge accessibility, context preservation, and expert decision-support augmentation. Experimental evaluation on IEEE test cases demonstrates that the proposed agentic framework consistently delivers correct solutions across all tested language models, with smaller LLMs achieving comparable analytical accuracy with reduced computational latency. This work establishes agentic AI as a viable paradigm for scientific computing, demonstrating how conversational interfaces can enhance accessibility while preserving numerical rigor essential for critical engineering applications.
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