As power grids integrate renewable energy sources and grow in complexity, efficiently solving AC Optimal Power Flow (AC-OPF) is essential for grid stability, operational efficiency, and market participation. This talk presents two complementary approaches to accelerate AC-OPF solutions while ensuring accuracy and reliability. First, we introduce two novel deep learning frameworks: (i) an unsupervised learning approach with dynamic Lagrange multiplier adaptation, and (ii) a physics-informed gradient estimation method augmented by semi-supervised learning. These methods achieve up to 35x speedup compared to conventional solvers, with optimality gaps below 1%. Second, we propose a constraint screening framework that exploits the mathematical structure of convex OPF formulations to eliminate non-binding constraints, significantly reducing computational complexity. Time permitting, we will also briefly discuss other AI-driven research in energy, including load forecasting and power system event detection.

Bio: Dr. Yu Zhang received his Ph.D. in Electrical and Computer Engineering from the University of Minnesota in 2015. He is currently an Assistant Professor in the Department of Electrical and Computer Engineering at the University of California, Santa Cruz (UCSC). Prior to joining UCSC, he held a postdoctoral position at the University of California, Berkeley, and the Lawrence Berkeley National Laboratory. His research interests include smart power grids, optimization theory, and artificial intelligence. Dr. Zhang was awarded the Hellman Fellowship in 2019 and co-recipient of the Early Career Best Paper Award from the Energy, Natural Resources, and the Environment (ENRE) Section of the Institute for Operations Research and the Management Sciences (INFORMS) in 2021.