Faculty Candidate Seminar

Structured Control and Learning for Sustainable Energy Systems

Wenqi CuiPh.D. CandidateUniversity of Washington
3316 EECS BuildingMap

Abstract: With decarbonization efforts in renewable integration and electrification, the electric grid needs to adapt and serve a larger system that is becoming more distributed, having less inertia, and facing more uncertainties. These changes have reduced the safety margins of the grid and significantly increased the costs of risk management. Machine learning tools can potentially unlock design freedoms found in the increased controllability from inverter-interfaced resources (e.g., solar, wind, and electric vehicles), and reshape the landscape of energy systems for more efficient operations. However, such algorithms typically do not provide guarantees about safety-critical constraints, making them difficult to implement in practice.

This talk will describe how to bridge the gap between learning and safety-critical constraints through structured neural networks guided by control theory and the physics of energy systems. Using Lyapunov theory, I will show how we can extract stabilizing controller structures for transient stability problems, and show how to parameterize the structures by neural networks. Then I will further show how we can achieve provable guarantees on steady-state optimal resource allocation and adapt to time-varying loads and renewables. The extension of the framework to broader networked systems will also be discussed. I will conclude the talk with future directions towards sustainable energy systems that are safe, efficient, resilient, and equitable.

Bio: Wenqi Cui is a PhD candidate in Electrical and Computer Engineering at the University of Washington. She received the B.Eng. degree in Electrical Engineering and Automation from Southeast University in 2016 and M.S. degree in Electrical Engineering from Zhejiang University in 2019. Her research interests are in the power and energy systems, from the perspective of control, machine learning, and optimization. She was a recipient of Rushmer Innovator Fellowship, Sarala Vadari Award, and Clean Energy Institute Fellowship at the University of Washington. She has participated in the Rising Stars in EECS Workshop in 2022 and the Rising Stars in Cyber-Physical Systems Workshop in 2023.


Linda Scovel

Faculty Host

Johanna MathieuAssociate Professor, Electrical Engineering and Computer ScienceUniversity of Michigan