Wenqi Cui is a Ph.D. candidate in the Department of Electrical and Computer Engineering at the University of Washington, advised by Prof. Baosen Zhang. Previously, she received the B.Eng. degree and M.S.degree in electrical engineering from Southeast University and Zhejiang University in 2016 and 2019, respectively. Her research interests lie broadly in machine learning, control and optimization for cyber-physical energy systems. She is a recipient of Rushmer Innovator Fellowship and Clean Energy Institute (CEI) Fellowship.
Increasing the amount of renewable energy sources (RESs) in energy systems is of fundamental importance to achieving carbon neutrality. Due to the reduction in rotating mechanical inertia and larger uncertainties, electric grid with higher penetration of RESs are more prone to instabilities. My research focuses on control and machine learning methods that apply to large-scale energy systems. Most existing learning-based approaches rely on finite samples and soft penalties for hard constraints, which are not convincing enough to system operators. To overcome these challenges, I developed structured learning methods with provable guarantees on stability and optimal resource allocation for nonlinear and large-scale networked systems, especially cyber-physical energy systems. To realize efficient learning in large networks, I further proposed sample-efficient trajectory generation algorithms and decentralized safe learning algorithms that can scale to partially-observed large systems. Together these works provide theoretical guarantees and efficient algorithms for learning-based control in cyber-physical systems.