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Data-Model Convergence for Reliable and Resilient Cyber-Physical Power Systems

Research seminar with Junbo Zhao, assistant professor of electrical and computer engineering, University of Connecticut

4/8/2024
3:45 pm – 4:45 pm
Online
Intended Audience(s): Public
Categories: Lectures & Seminars

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.

For more information, contact:
Ashley Parker
ashley.l.parker@dartmouth.edu
Permanent URL to this event: https://irving.dartmouth.edu/events/event?event=73494

Events are free and open to the public unless otherwise noted.