Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework

• URL: http://arxiv.org/abs/2411.16707v3
• Date: Mon, 19 May 2025 15:51:40 GMT
• Title: Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework
• Authors: Mengshuo Jia, Zeyu Cui, Gabriela Hug,
• Abstract summary: This paper proposes a feedback-driven, multi-agent framework for managing simulations in power systems.<n>It incorporates three proposed modules: an enhanced retrieval-augmented generation (RAG) module, an improved reasoning module, and a dynamic environmental acting module with an error-feedback mechanism.<n>It significantly outperforms ChatGPT 4o, o1-preview, and the fine-tuned GPT-4o, which all achieved a success rate lower than 30% on complex tasks.
• Score: 1.4255659581428337
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The integration of experimental technologies with large language models (LLMs) is transforming scientific research. It positions AI as a versatile research assistant rather than a mere problem-solving tool. In the field of power systems, however, managing simulations -- one of the essential experimental technologies -- remains a challenge for LLMs due to their limited domain-specific knowledge, restricted reasoning capabilities, and imprecise handling of simulation parameters. To address these limitations, this paper proposes a feedback-driven, multi-agent framework. It incorporates three proposed modules: an enhanced retrieval-augmented generation (RAG) module, an improved reasoning module, and a dynamic environmental acting module with an error-feedback mechanism. Validated on 69 diverse tasks from Daline and MATPOWER, this framework achieves success rates of 93.13% and 96.85%, respectively. It significantly outperforms ChatGPT 4o, o1-preview, and the fine-tuned GPT-4o, which all achieved a success rate lower than 30% on complex tasks. Additionally, the proposed framework also supports rapid, cost-effective task execution, completing each simulation in approximately 30 seconds at an average cost of 0.014 USD for tokens. Overall, this adaptable framework lays a foundation for developing intelligent LLM-based assistants for human researchers, facilitating power system research and beyond.

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