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As society’s reliance on electricity deepens, artificial intelligence (AI) is reshaping how we manage power grids and optimize energy sources. A recent workshop hosted by the U.S. Department of Energy’s Argonne National Laboratory brought together leading experts from national labs, universities, government agencies, and industry to explore the transformative potential of AI foundation models for electric grids.
The three-day Foundational Models for Electric Grid workshop—organized by Argonne researchers Kibaek Kim, Emil M. Constantinescu, and Adrian Maldonado—was the third event in an evolving series. In partnership with IBM, Hydro-Québec, and the National Rural Electric Cooperative Association (NRECA), the event reinforced collaboration for smarter, more adaptive grids.
Kim noted the field’s momentum. “We've seen attendance grow from about 25 participants at our first workshop to well over 100 at this latest session,” he said. “This surge in interest reflects the field’s rapid advancement and the urgent need for AI solutions in electric grid management.”
The workshop emphasized real-world application. Through technical sessions, panels, live demos, and structured networking, participants shared insights and best practices. Industry leaders showcased AI innovations from advanced forecasting to automated distribution systems that boost performance and resilience.
Argonne’s Valerie Taylor and Henry Huang delivered keynotes underscoring the lab’s leadership. Huang emphasized that advanced analytics and AI are essential to modernizing power systems and making them more resilient and efficient.
A major focus was the use of foundation models—AI systems pre-trained on vast datasets and tailored to grid challenges. Maldonado explained, “Our foundation model is an AI engine trained on extensive datasets covering various power grid functions. It’s designed to handle everything from forecasting to operations, making it a comprehensive solution for modern grid management.”
“These models can detect subtle signals that traditional methods often miss, helping us predict and prevent outages before they cause significant disruptions,” said Constantinescu.
Privacy-preserving federated learning (PPFL) was also highlighted. This method trains models on sensitive energy data without compromising privacy. Kim said, “As we introduce more distributed energy resources to the grid — such as natural gas generators and geothermal — these AI systems will help us manage increasingly complex operations with greater precision.”
“Our goal is not just theoretical,” added Constantinescu. “We are actively working on integrating foundation models into operational workflows, ensuring they can be used effectively in real-world power systems.”
Argonne remains at the forefront of advancing AI-driven energy resilience. Insights from this workshop will drive future research initiatives and strengthen industry collaborations, ensuring that tomorrow’s power systems are secure, efficient, and adaptive.
"There are two major challenges we are addressing—technological limitations in grid modeling, and broader resilience issues facing modern power systems," concluded Constantinescu. "This is only the beginning. We are laying the foundation for a future where AI-driven models will be an integral part of how we manage and optimize our power grids."
