ORC Seminar: A Foundation Model for Power System Operations

Baosen Zhang Associate Professor University of Washington Abstract: Electric grids are becoming more dynamic and uncertain as we integrate renewable resources and increasingly large loads. Modern AI and machine learning tools have the potential to overcome the computational bottlenecks and maintain efficiency and reliability of the grid. However, such algorithms typically do not provide guarantees about stability or safety, making them difficult to implement in practice. In addition, they may require compute resources that are not available to grid operators. In this talk, I will describe how to bridge these gaps by introducing a physics-driven foundation model that can be applied to all grid topologies and load conditions. I will demonstrate how the construction of the model allows us to provide guarantees on hard constraints. I will show how it allows us to solve some difficult optimization problems and illustrate that more efficient operations can substantially reduce the high price of electricity many customers are currently facing. Bio: Baosen Zhang received his Bachelor degree in Engineering Science from the University of Toronto and his PhD degree in Electrical Engineering and Computer Sciences from University of California, Berkeley. He was a Postdoctoral Scholar at Stanford University. He is currently an Associate Professor in the Department of Electrical and Computer Engineering at the University of Washington, Seattle, and holds the Keith and Nancy Endowed Professorship. His research interests are in control, optimization and AI for power and energy systems. He received the NSF CAREER award as well as several best paper awards.