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Professor Hidalgo-Gonzalez’s work focuses on high penetration of renewable energy using optimization, control theory and machine learning. She co-developed a stochastic power system expansion model to study the Western North America’s grid under climate change uncertainty. She also 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, domestic and international energy policy, electricity market redesign to aid the integration of renewable energy, microgrids for wildfire risk mitigation, transmission and distribution systems, and machine learning for dynamical systems with safety guarantees.

Capsule Bio:

Professor Hidalgo-Gonzalez is an NSF GRFP fellow, Siebel Scholar in Energy, Rising Star in Electrical Engineering and Computer Science, and has been awarded the UC Berkeley Graduate Opportunity Program Award, and the Outstanding Graduate Student Instructor Award (for teaching Convex Optimization). She has served as Best Paper Session Judge and co-Chair, and Paper Forum Chair at the 2019 and 2020 IEEE Power & Energy Society General Meeting (PESGM). She is an academic co-lead of the IEEE Power & Energy Society Task Force titled “Data-Driven Controls for Distributed Systems.” She is the director of the lab at UCSD.

Professor Hidalgo-Gonzalez holds a Ph.D. and two M.Sc. from the University of California, Berkeley (Energy and Resources Group and Electrical Engineering and Computer Science). She graduated as an Industrial and Electrical engineer from Pontificia Universidad Católica of Chile (with highest honors).