Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework
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| Format: | Preprint |
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2024
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| author | Jia, Mengshuo Cui, Zeyu Hug, Gabriela |
| author_facet | Jia, Mengshuo Cui, Zeyu Hug, Gabriela |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_16707 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework Jia, Mengshuo Cui, Zeyu Hug, Gabriela Computation and Language Artificial Intelligence Multiagent Systems Systems and Control 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. |
| title | Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework |
| topic | Computation and Language Artificial Intelligence Multiagent Systems Systems and Control |
| url | https://arxiv.org/abs/2411.16707 |