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SEMINAR:Towards Reliable Hybrid Data-Driven and Physics-Based...

Guest: Kyri Baker,  University of Colorado Boulder

Title: Towards Reliable Hybrid Data-Driven and Physics-Based Optimal Power Flow

Date/Time: 8 May 2024, 17.40


Abstract: Electric power grids are growing increasingly more complex to operate with the introduction of renewable energy. In particular, the optimal power flow (OPF) problem, a large-scale, nonconvex optimization problem, remains challenging to solve for real-time operation. Recent advancements in machine learning (ML) based solutions for solving optimal power flow show outstanding results – solution times orders of magnitude faster than conventional approaches. However, it is challenging to practically use these solutions, as many models are only trained on data corresponding to typical operating conditions, and existing operator workflows are typically not incorporated. In this talk, we discuss how machine learning can be used to learn the solution of the OPF problem in order to solve it reliably on fast timescales appropriate for real-time operation.

Bio: Dr. Kyri Baker received her B.S., M.S., and Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University in 2009, 2010, and 2014, respectively. From 2015 to 2017, she worked at the U.S. National Renewable Energy Laboratory. Since Fall 2017, she has been an Assistant Professor at the University of Colorado Boulder and is a Fellow of the Renewable and Sustainable Energy Institute (RASEI). She develops computationally efficient optimization and learning algorithms for energy systems ranging from building-level assets to transmission grids.



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