RePower: An LLM-driven autonomous platform for power system data-guided research
Yuxiao Liu, Mengshuo Jia, Yongxin Zhang, Jianxiao Wang, Guannan He, Shao‐Long Zhong, Zhi‐Min Dang
Abstract
Large language models (LLMs) have shown strong capabilities across disciplines such as chemistry, mathematics, and medicine, yet their application in power system research remains limited, and most studies still focus on supporting specific tasks under human supervision. Here, we introduce Revive Power Systems (RePower), an autonomous LLM-driven research platform that uses a reflection-evolution strategy to independently conduct complex research in power systems. RePower assists researchers by controlling devices, acquiring data, designing methods, and evolving algorithms to address problems that are difficult to solve but easy to evaluate. Validated on three critical data-driven tasks in power systems-parameter prediction, power optimization, and state estimation-RePower outperformed traditional methods. Consistent performance improvements were observed across multiple tasks, with an average error reduction of 29.07%. For example, in the power optimization task, the error decreased from 0.00137 to 0.000825, a reduction of 39.78%. This framework facilitates autonomous discoveries, promoting innovation in power systems research.