Advanced AI tools, especially large language models (LLMs), are opening new pathways for automating intelligent power system analysis and decision-making. In this talk, I will present GridAgent 1.0, an LLM-powered agentic AI system that performs professional power system studies directly from natural language requests, in a ChatGPT-like user interface. GridAgent can load power grid cases, retrieve system-specific information, and autonomously invoke the appropriate embedded tools for power flow calculation, contingency analysis, optimal power flow, short-circuit calculation, topology analysis, and more. Leveraging the reasoning capabilities of LLMs, GridAgent can generate an executable, step-by-step plan with clearly defined tasks to fulfill complex user queries. It also features coordinated long-term and short-term memory management and reveals its full chain of reasoning, planning, and actions, making the workflow interpretable and trustworthy. GridAgent has the potential to become a powerful AI assistant for grid operators, engineers, and researchers.
Date and Time: November 19, 2025, at 3:00 pm CST
Location: zoom
Speaker
Dr. Xin Chen is an Assistant Professor in the Department of Electrical and Computer Engineering at Texas A&M University (TAMU). He directs the Smart Power, Energy, and Decision-making (SPEED) Lab and co-directs the Consortium on AI and Large Flexible Load (CALL). His research focuses on the integration of control, optimization, and AI for smart power and energy systems. Dr. Chen received his Ph.D. in Electrical Engineering from Harvard University, and his M.S. in Electrical Engineering as well as dual B.S. degrees in Engineering and Economics from Tsinghua University. Before joining TAMU, he was a Postdoctoral Associate with the MIT Energy Initiative at the Massachusetts Institute of Technology. He is a recipient of the IEEE PES Outstanding Doctoral Dissertation Award, IEEE Transactions on Smart Grid Top 5 Papers, the Best Research Award at the 2023 IEEE PES Grid Edge Conference, and several best paper awards at leading power and control conferences.
More information about Dr. Chen’s research can be found here