ZOOM LINK
Meeting ID: 913 9744 0830
Passcode: 645291

The increasing penetration of stochastic and uncertain inverter-based resources (IBRs), such as wind and solar PVs, has a considerable influence on the power system dynamics, operation, and optimization, causing reliability and resiliency concerns. On the other hand, the power industry is transforming itself from a hierarchical, passive, and sparsely sensed engineering system into a flat, active, and ubiquitously sensed cyber physical system. The emerging multi-scale data from phasor measurement units, SCADA, smart meters, weather, and electricity markets offers tremendous opportunities and challenges for the industry to dynamically learn and adaptively control the smart grid.

This talk will present a new data-model convergence framework to fully unlock the potentials from data while respecting physical constraints, including physics-informed estimation, inference and learning frameworks for power system modeling, optimization, security assessment, control, and uncertainty quantification in presence of different types of uncertain resources. Various transmission and distribution system applications will be presented to highlight the benefits of the data-model convergence framework.

Hosted by Professor Laura Ray.

About the Speaker(s)

Junbo Zhao
Assistant Professor of Electrical and Computer Engineering, UConn

Junbo Zhao is the director of DOE Northeast University Cybersecurity Center for Advanced and Resilient Energy Delivery (CyberCARED), an associate director of the Eversource Energy Center, and an assistant professor of electrical and computer engineering at the University of Connecticut. He is also a research scientist at the National Renewable Energy Laboratory. He received his PhD from Virginia Tech and is currently the officer of multiple IEEE PES committees and subcommittees, such as the Renewable Systems Integration Coordinating Committee and Distribution System Analysis Subcommittee.

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 has published three book chapters and more than 200 peer-reviewed journal and conference papers, serves as an associate editor of IEEE Transactions, and is the recipient of multiple awards.

Contact

For more information, contact Ashley Parker at ashley.l.parker@dartmouth.edu.