Title: Data-Driven Function Discovery and Quantum Computing Applications in Power System Resilience Under Extreme Weather Conditions
Abstract: The increasing frequency and intensity of heat waves pose significant challenges to power grid reliability and operational efficiency. In this talk, I will present our recent work on Data-Driven Function Discovery (DFD) for power systems under heatwave conditions. We leverage Gaussian Processes (GPs) to uncover hidden functional dependencies in power system operations, particularly under Thermal-ACOPF (T-ACOPF) and Thermal-DCOPF (T-DCOPF) models. By employing probabilistic inference, our framework enables adaptive, uncertainty-aware predictions that improve system resilience and decision-making.
Additionally, I will explore the role of quantum computing in solving power system problems, with a focus on Quantum Variational Linear Solvers (VQLS) and their potential applications in OPF, constraint screening, and security assessment. The discussion will highlight computational trade-offs between classical and quantum approaches, outlining future directions for integrating quantum algorithms into power grid optimization and anomaly detection.
This talk will provide insights into how advanced machine learning and quantum computing can address emerging challenges in power system resilience, offering new pathways for improving grid security, efficiency, and sustainability in the face of extreme weather events.
Bio: Dr. Masoud Barati is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Pittsburgh (Pitt), where he leads the PITT-LEADs Lab. His research focuses on power system optimization, quantum computing, and data-driven modeling, with applications in AC Optimal Power Flow (ACOPF), grid resilience, and climate-informed energy systems.
Dr. Barati’s expertise spans large-scale optimization, interregional transmission planning, and digital twins for power systems. He has successfully integrated machine learning, Gaussian processes, and topological data analysis into power system planning and anomaly detection. His recent work leverages quantum computing for power system optimization and explores hypergraph knowledge discovery for causality assessment in energy networks.
His research has been supported by the NSF, DOE, and industry partners, and he has collaborated with utilities, and national labs to develop advanced decision-making tools for grid modernization.
Dr. Barati received his Ph.D. in Electrical Engineering from the Illinois Institute of Technology, Chicago, and has held a Postdoc position at the University of Chicago, Booth School of Business. He actively mentors students and leads initiatives to integrate quantum computing and AI into power system education and research.
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