Title: Control of power-electronics-dominated power systems: challenges, solutions, and future prospects
Abstract: Renewable energy is the key to achieving carbon neutrality. However, the large-scale integration of renewable energy sources poses tremendous challenges to the stable operation of modern power systems, as the systems become much more complex and are under lots of uncertainties. In particular, the grid interface of renewable generators (i.e., power electronics converters) generally has distinct dynamics compared with traditional fossil-fuel-based synchronous generators, resulting in new stability problems in practice. Moreover, it has been widely recognized that conventional control methods for power converters cannot support a stable power-electronics-dominated power system because they have no grid-forming capability. Hence, it is essential to develop advanced control strategies for power electronics. This talk will introduce the challenges in a power-electronics-dominated power system and discuss how we can understand the dynamics of such a system. I will present my research works in designing stabilizing, robust, and optimal controllers for power converters to handle uncertainties in the system and enable their grid-forming capabilities. In particular, I will demonstrate how data-driven control can equip power converters with adaptability and ensure robust and optimal performance under variable grid conditions. Moreover, I will discuss other unresolved problems in power-electronics-dominated power systems and envision future research directions.
Bio: Dr. Linbin Huang is a postdoctoral researcher at ETH Zurich since September 2020, working in the Automatic Control Laboratory. He received his Ph.D. degree in College of Electrical Engineering at Zhejiang University in 2020 and a B. Eng. degree from the same institution in 2015. From 2018 to 2019, he was a visiting scientist at ETH Zurich. His research interests include modeling, analysis, and control of power systems, focused on developing new theories and algorithms for data-driven control and robust control to stably integrate large-scale renewable energy generators into modern power grids