Enabling resilient cyber-physical microgrids

Abstract: The modernization of power systems can be effectively accomplished through the development of Distributed Energy Resources (DERs). However, the widespread integration of power-electronic interfaces in high-penetration DER scenarios poses challenges to the system's inertia, making the DER dominant power grid sensitive and susceptible to disturbances. To address these challenges, a comprehensive set of methods has been developed at both the cyber and physical layers of microgrids. In this presentation, we will discuss several key approaches that contribute to mitigating the aforementioned issues. Firstly, we will introduce reachability analysis as a means to assess microgrid stability efficiently. This analysis takes into account the presence of heterogeneous uncertainties resulting from the high penetration of DERs. By leveraging reachability analysis, we can gain valuable insights into microgrid stability and ensure reliable operation. Next, we will explore the application of learning-based DER control, utilizing advancements in machine learning. This approach aims to enhance computational capabilities for nonlinear microgrids. By harnessing the power of machine learning, we can improve control strategies, optimize DER integration, and enhance the overall performance of microgrids. Furthermore, we will delve into a programmable software-defined attack detection strategy designed to safeguard the microgrid system against cyberattacks and physical threats. This strategy combines software-defined networking principles with advanced attack detection mechanisms to proactively identify and mitigate potential security risks, ensuring the robustness and resilience of the system.
Collectively, these cutting-edge technologies and methodologies provide a suite of powerful tools for planning and operating future renewable energy systems. By incorporating these approaches into the design and management of DER-based power grids, we can effectively overcome challenges, ensure stability, and maximize the benefits of renewable energy integration.

Biography: Yan Li holds a Ph.D. degree in electrical engineering from the University of Connecticut, Storrs, CT, U.S., obtained in 2019. She also received a Ph.D. degree in electrical engineering from Tianjin University, Tianjin, China, in 2013. Currently, Dr. Li serves as the Charles H. Fetter Endowed Fellow Assistant Professor at the School of Electrical Engineering and Computer Science at Penn State. Dr. Li's research interests span various domains, including cyber-physical power systems, quantum computing, data-driven modeling and control, resilience analysis, cybersecurity, and software-defined networking. Her work contributes to advancing the understanding and development of these fields. Recognized for her exceptional contributions, Dr. Li has been honored with several prestigious awards, including the IEEE-PES Outstanding Engineer Award and the Connecticut Women of Innovation Award, among others.