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PATRICIA HIDALGO-GONZALEZ – Learning and control systems for the integration of renewable energy into grids of the future
October 28, 2020 @ 4:00 pm - 5:30 pm PDT
THE ENERGY AND RESOURCES GROUP PRESENTS: TITLE: Learning and control systems for the integration of renewable energy into grids of the future SPEAKER: Patricia Hidalgo-Gonzalez DATE: October 28, 2020 TIME: 4:00-5:30 PM LOCATION: Zoom Meeting MEETING ID: 923 2872 9058 PASSCODE: 535589 NOTE: All participants and hosts are now required to sign into a Zoom account prior to joining meetings hosted by UC Berkeley. Participants who are not eligible for a UC Berkeley-provided Zoom account can use a Zoom account provided by their institution, can create a free, consumer Zoom account (at https://zoom.us/freesignup/), or can dial in via phone. DESCRIPTION: This seminar will go over some of my contributions in the field of renewable energy sources (RES) integration: (a) modeling climate change uncertainty through a stochastic formulation of the capacity expansion of power systems in the U.S. with high penetration of RES, (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 on the role of long-duration storage, and near-term future work. BIOGRAPHY: Dr. Hidalgo-Gonzalez is an Assistant Professor in Mechanical and Aerospace Engineering and in the Center for Energy Research at UC San Diego. She is the director of the Renewable Energy and Advanced Mathematics lab. She holds a Ph.D. and two M.Sc. from the UC Berkeley in Energy and Resources and Electrical Engineering and Computer Sciences. She is an NSF GRFP fellow, Siebel Scholar, Rising Star in EECS, and an Outstanding GSI awardee. She serves as one of the academic co-leads of the IEEE Task Force “Data-Driven Controls for Distributed Systems.” Her work focuses on high penetration of renewable energy using optimization, control theory and machine learning.