*Note: This talk will be presented remotely via Zoom and on the Energy Institute's YouTube channel. See access details after the bio.
Bio: Dr. Hao Zhu is currently an Assistant Professor of Electrical and Computer Engineering (ECE) at The University of Texas at Austin. She received the B.S. degree from Tsinghua University in 2006, and the M.Sc. and Ph.D. degrees from the University of Minnesota in 2009 and 2012, all in electrical engineering. From 2012 to 2017, she was a Postdoctoral Research Associate and then an Assistant Professor of ECE at the University of Illinois at Urbana-Champaign. Her research focus is on developing innovative algorithmic solutions for problems related to learning and optimization for future energy systems. Her current interests include physics-aware and risk-aware learning for power systems, and the design of energy management system to account for the cyber-physical coupling. She is a recipient of the NSF CAREER Award and the faculty advisor for three Best Student Papers awarded at the North American Power Symposium. She is currently a member of the IEEE Power & Energy Society (PES) Long Range Planning (LRP) Committee.
Abstract: The electricity delivery systems are witnessing pressing needs to transform the operational paradigms and integrate new energy resources. At the bulk interconnection scale, the decreasing level of inertia makes it high important to analyze the grid’s dynamic responses. At the local distribution level, the integration of variable distributed energy resources (DERs) is challenged by the lack of observability in residential loads. To address these problems, this talk will present spatio-temporal learning approaches that harness the high-rate, high-accuracy synchrophasor data available for monitoring power grids. First, a statistical cross-correlation approach is developed to study the propagation of oscillations in the interconnected wide-area grids. Second, a subspace learning approach is introduced to reveal the characteristics of DERs such as rooftop solar and electric vehicles at the customer side. These learning approaches can enhance the situational awareness of physical grid resources for improving the security and reliability of energy infrastructure.
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