Abstract: The trend in electric power systems is the displacement of traditional synchronous generation (e.g., coal, natural gas) with renewable energy resources (e.g., wind, solar photovoltaic) and battery energy storage. These energy resources require power electronic converters (PECs) to interconnect to the grid and have different response characteristics and dynamic voltage and frequency stability issues compared to conventional synchronous generators. As a result, there is a need for next-generation methods to characterize and mitigate PEC-based dynamic stability issues, especially for converter-dominated power systems (e.g., island power systems, remote microgrids). This talk will discuss recent advancements in dynamic state estimation and control of battery energy storage systems. A framework will be introduced to provide fast frequency dynamic voltage support for converter-dominated power systems using both model-based and model-free state estimation and control approaches. Model-based methods will first be introduced using reduced-order power system dynamics equations. Specifically, a moving horizon state and model parameter estimator provides dynamic state inputs to a model-predictive controller. These classic model-based methods are then compared to state-of-the-art model-free methods from machine learning; a neural ordinary differential equations (NODEs)-based framework will be described to infer critical state information of the power system frequency dynamics. The state information is used by a soft-actor-critic (SAC) reinforcement learning-based controller. The model-based and model-free methods are compared for performance and computational efficiency. The topics presented will have broad applicability to both undergraduate and graduate electrical and computer engineering students, including: - How is the global energy transition impacting electric power grid operations? - What is the interaction and future role of power electronics with the electric power system? and - What are the tradeoffs between complexity, accuracy, and computational tractability of traditional model-based and model-free machine learning approaches? Speaker Bio: Timothy M. Hansen (IEEE Senior Member 2020) received the B.S. degree in computer engineering with high honors from the Milwaukee School of Engineering, Milwaukee, WI, USA, in 2011, and the Ph.D. degree in electrical engineering from Colorado State University, Fort Collins, CO, USA, in 2015. In 2014-2015, he held a graduate research position in the Distributed Energy Systems Integration group at the National Renewable Energy Laboratory, Golden, CO, USA. He is currently the Harold C. Hohbach Endowed Associate Professor with the Electrical Engineering and Computer Science Department, South Dakota State University, Brookings, SD, USA. His research interests are in the application of optimization, high-performance/edge/quantum computing, and distributed stochastic control to the areas of sustainable power and energy systems, smart cities, robotics, and cyber-physical-social systems. Speaker(s): Timothy M. Hansen, Room: 336, Bldg: ITE Building, 371 Fairfield Way, Storrs, Connecticut, United States