发表机构
Faculty of Electrical Engineering University of Sarajevo; Oak Ridge National Laboratory（萨拉热窝大学电气工程学院; 橡树岭国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出AgentiGrid框架，结合LLM智能与HPC，可自主完成多场景潮流研究，能在20次迭代内近乎完美地收敛传输约束交流最优潮流
AI 中文摘要
我们提出AgentiGrid，这是一款将大语言模型（LLM）智能与高性能计算（HPC）相结合的智能体人工智能（AI）框架，用于简化和加速多场景潮流研究。AgentiGrid是一款自主决策智能体，可提出参数修改方案、通过HPC分析工具包ExaGO调用分析、解读结果并确定后续行动。ExaGO提供多种潮流应用，可执行确定性、随机性及安全约束最优潮流分析。AgentiGrid为多个LLM（OpenAI、Anthropic、Ollama及Ollama云）提供后端，辅以特定上下文和特定任务的提示。其关键特性包括搜索过程中的交互式引导、目标类型感知的搜索后分析，以及潮流优化的并发变体探索。基于Streamlit的图形启动器可提供迭代进度的实时可视化，并生成自然语言报告。AgentiGrid能够在20次迭代内自主收敛考虑传输约束的交流最优潮流，具备近乎完美的可靠性
英文摘要
We present AgentiGrid, an agentic artificial intelligence (AI) framework that integrates large language models (LLMs) intelligence and high-performance computing (HPC) to streamline and accelerate the multi-scenario power flow studies. AgentiGrid is an autonomous decision-making agent that proposes parameter modifications, invokes analyses through HPC analysis toolkit ExaGO, interprets results, and determines subsequent actions. ExaGO provides multiple power flow applications that can perform deterministic, stochastic and security constrained optimal power flow analyses. AgentiGrid provides backends to multiple LLMs (OpenAI, Anthropic, Ollama, and Ollama cloud) augmented with context specific and task specific prompts. Key features include interactive mid-search steering, goal-type-aware post-search analysis, and concurrent variant exploration for power flow optimization. A Streamlit-based graphical launcher provides real-time visualization of iteration progress and generates reports in natural language. AgentiGrid is capable of autonomously converging transmission constrained alternating current optimal power flow in under 20 iterations, with near-perfect reliability
Comments6 pages, 3 figures, 3 tables. To appear in proceedings of EnergyCon 2026
