IEEE PES Lecture: Empowering Power Engineers using LLM and Electrical AI Copilot

[] Large Language Models (LLMs) are emerging as transformative tools for the next generation of power system engineering and operations. The Electrical AI Copilot concept envisions an intelligent assistant that leverages LLMs to support grid engineers and operators in real time—enhancing decision-making, situational awareness, and automation. By integrating domain-specific data, operational procedures, and simulation tools, an Electrical AI Copilot can interpret technical queries, generate code for grid analysis, summarize reports, and even suggest optimal control or contingency actions. It serves as a bridge between human expertise and complex system intelligence, providing natural language interaction for tasks such as outage diagnosis, renewable dispatch planning, and protection coordination. Ultimately, this LLM-driven assistant aims to improve reliability, efficiency, and safety of power systems while enabling a new era of human–AI collaboration in the electric grid ecosystem. Finally, an ETAP electrical copilot demonstration will be presented. About the Speakers: Ahmed Saber received his Ph.D. from the University of the Ryukyus, Japan, in 2007. He is currently the Vice President of Optimization and AI at ETAP R&D, USA, where he contributions to AI-driven methods, products, and systems for power system prediction, optimization, efficiency, sustainability, and operator assistance through large language models (LLMs). His pioneering research led to a novel deep learning-based model that improved load forecasting accuracy, CO2 estimation, efficiency, and operator support for power system optimization and sustainability. Dr. Saber’s research has received national and international funding, including support from the U.S. Department of Energy (DoE). With over 100 technical publications and three patents on AI applications for power systems, his expertise spans AI/ML for power systems, smart grids, energy storage, renewables, power system forecasting and optimization, cybersecurity, real-time systems, and operations research. TANUJ KHANDELWAL (Senior Member, IEEE) received the bachelor’s degree in electronics and telecommunications engineering from the University of Bombay, in 1999, and the master’s degree in electrical engineering from California State University Long Beach, in 2001. Before joining ETAP, he was an Associate Engineer with PricewaterhouseCoopers. He has been working as an Electrical Engineer with the Engineering Consulting Services Department, ETAP, since 2001. His duties involve algorithm design, testing, engineering and software support, training, and application engineering for ETAP family of products. He is a Group Member of the IEEE Std. 739 (Bronze Book) and IEEE Std. 551 (Brown Book) and a member of the IEEE Rail Transit Vehicle Interface Standards Committee. Virtual: https://events.vtools.ieee.org/m/523538