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ESE Seminar - Patricia Hidalgo-Gonzalez: Learning, control and optimization for the integration of renewable energy into grids of the future

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This meeting is in Room 104 and can also be viewed in Room 014

Event Details:

This seminar will go over some of our contributions in the field of renewable energy sources (RES) integration: (a) the value of long-duration storage and its interaction with a zero emissions grid, (b) a new time-varying representation for power dynamics that reflects the presence of RES, (c) designing through learning a stable time-invariant frequency controller and (d) the trade-off between information availability to the frequency control agents and their performance. I will also discuss ongoing work with the California Energy Commission, the Sloan Foundation and the IEEE Task Force "Data-Driven Controls for Distributed Systems" and near-term future work.

Professor Hidalgo-Gonzalez is an Assistant Professor at the University of California San Diego. She is an NSF GRFP fellow, Siebel Scholar in Energy, Rising Star in Electrical Engineering and Computer Science, and has been awarded Best paper at the Power Systems Computation Conference 2020, the UC Berkeley Graduate Opportunity Program Award, and the Outstanding Graduate Student Instructor Award. At UC San Diego, she directs the Renewable Energy and Advanced Mathematics (REAM) lab which focuses on high penetration of renewable energy using optimization, control theory and machine learning. She is currently working with the California Energy Commission, the U.S. Department of Energy, the Alfred P. Sloan Foundation, the Environmental Defense Fund and GridLab. In addition to capacity expansion modeling for the U.S. and other regions, she works on power dynamics with low and variable inertia, and controller design using machine learning and safety guarantees. She is generally interested in power dynamics, electricity market redesign to aid the integration of renewable energy, environmental justice, microgrids for wildfire risk mitigation, distributed control, and learning for dynamical systems with safety guarantees. Dr. Hidalgo-Gonzalez is part of the IEEE Power & Energy Society Task Force titled “Data-Driven Controls for Distributed Systems.” She holds two M.S. and a Ph.D. from UC Berkeley.

References/Related Papers
Hidalgo-Gonzalez, P., Henriquez-Auba, R., Callaway, D.S. and Tomlin, C.J., 2019, December. Frequency regulation using sparse learned controllers in power grids with variable inertia due to renewable energy. In 2019 IEEE 58th Conference on Decision and Control (CDC) (pp. 3253-3259). IEEE.

Dobbe, R., Hidalgo-Gonzalez, P., Karagiannopoulos, S., Henriquez-Auba, R., Hug, G., Callaway, D.S. and Tomlin, C.J., 2020. Learning to control in power systems: Design and analysis guidelines for concrete safety problems. Electric Power Systems Research189, p.106615.

Staadecker, M., Sánchez-Pérez, P., Szinai, J., Kurtz, S., Hidalgo-Gonzalez, P. The value of long-duration storage and its interaction with a zero emissions electricity grid (under review).