Wednesday, April 6, 2022 - 11:00am to 12:00pm
Event Calendar Category
Other LIDS Events
Speaker Name
Dr. Junbo Zhao
Affiliation
University of Connecticut
Join Zoom meeting
https://mit.zoom.us/j/96557918021
Abstract
For the past decades, the integration of intermittent renewable sources, responsive loads and other new technologies significantly complicates today’s power systems. It becomes more and more challenging to build accurate models for power grid planning, operation and control. This leads to the fruitful development of machine learning-based power system applications. However, the existing machine learning methods are data-driven, and the underlying physical models are typically not considered. As a result, a large number of high-quality training data and complex neural network structures are required. There are also serious concerns of the physical interpretability of the machine learning results. Motivated by the advancements of constrained machine learning methods that consider some critical physical constraints, we develop a physics-informed deep reinforcement learning framework for power system optimization and control, such as optimal power flow, preventive stability control, and Volt-VAR optimization.
Biography
Junbo Zhao is an assistant professor of the Department of Electrical and Computer Engineering at the University of Connecticut. He was an assistant professor and research assistant professor at Mississippi State University and Virginia Tech from 2019-2021 and 2018-2019, respectively. He received the Ph.D. degree from Bradley Department of Electrical and Computer Engineering Virginia Tech, in 2018. He was a Research Assistant Professor at Virginia Tech from May 2018 to August 2019. He did the summer internship at Pacific Northwest National Laboratory from May to August 2017. He is currently the chair of the IEEE Task Force on Power System Dynamic State and Parameter Estimation, the IEEE Task Force on Cyber-Physical Interdependency for Power System Operation and Control, co-chair of the IEEE Working Group on Power System Static and Dynamic State Estimation, the Secretary of the IEEE PES Bulk Power System Operation Subcommittee and TCPC of the IEEE PES Renewable Systems Integration Coordinating Committee.
He has published three book chapters and more than 140 peer-reviewed journal and conference papers, where more than 70 appear in IEEE Transactions. His research interests are cyber-physical power system modeling, estimation, security, dynamics and stability, uncertainty quantification, renewable energy integration and control, robust statistical signal processing and machine learning. He serves as the editor of IEEE Transactions on Power Systems, IEEE Transactions on Smart Grid and IEEE Power and Engineering Letters, the Associate Editor of International Journal of Electrical Power & Energy Systems, and the subject editor of IET Generation, Transmission & Distribution. He is the receipt of the best paper awards of 2020 and 2021 IEEE PES General Meeting (3 papers) and 2019 IEEE PES ISGT Asia. He received the 2020 Top 3 Associate Editor Award from IEEE Transactions on Smart Grid, the 2020 IEEE PES Outstanding Engineer Award, and the 2021 IEEE PES Outstanding Volunteer Award. He has been listed as the 2020 and 2021 World’s Top 2% Scientists released by Stanford University in both Single-Year and Career tracks.
This talk is part of the EESG seminar series, Changing Electric Energy Systems: Challenges and Opportunities.