For over 30 years we have been negotiating agreements that try to reduce greenhouse gas emissions. The aim is to stabilize their concentration in the atmosphere at a level that would prevent dangerous anthropogenic interference with the climate system. In 2007, the Intergovernmental Panel on Climate Change had stated that the 2-degree goal could be achieved if the electricity sector reduced its emissions to 80% below 1990 levels by 2050. Different countries have been pushing the frontier to reduce their emissions by deploying renewable energy sources (RES). Despite these efforts, we still have a long way to go. Research on how and when to install more RES, and how to model and operate power systems with more RES in real time needs to continue advancing as we aim to reach higher levels of penetration. In addition, academics need to translate and communicate these findings to policy makers. This seminar will go over some of my recent contributions in this field: (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) cost effectiveness of stronger RES targets in the U.S. by 2030 given the carbon reductions goals of 2050, (c) a new time-varying representation for power dynamics that reflects the presence of RES, (d) designing through machine learning a stable time-invariant frequency controller for the new time-varying power dynamics, and (e) the trade-off between information availability to the frequency control agents and their performance and stability. The talk will go in more depth on my work in control of power dynamics with variable and low inertia, i.e. bullets (c), (d), and (e).
Patricia Hidalgo-Gonzalez is a Ph.D. candidate at UC Berkeley co-advised by Claire Tomlin and Daniel Kammen, and she also collaborates with Duncan Callaway. She obtained an M.S. from the Energy and Resources Group, UC Berkeley (2016). She graduated with highest honors as an Industrial and Electrical engineer from Pontificia Universidad Católica of Chile.
She is an NSF GRFP fellow, Siebel Scholar in Energy, Rising Star in EECS, and has been awarded the UC Berkeley GOP, and the Outstanding Graduate Student Instructor Award (for teaching Convex Optimization with professors Laurent El Ghaoui and Alexandre Bayen).
Her work focuses on high penetration of renewable energy using optimization, control theory and machine learning. Patricia co-developed a stochastic power system expansion model to study the Western North America’s grid under climate change uncertainty. She also works on frequency regulation for new power dynamics with low and variable inertia due to renewable energy. Her collaborations have included national and international organizations such as: the California Energy Commission, Lawrence Berkeley National Laboratory (LBNL), National Renewable Energy Laboratory, Energy and Environmental Economics, Natural Resources Defense Council, Industrial Economics, State Grid Corporation of China, Chinese National Development and Reform Commission, AI Now Institute, ETH Zurich, Tufts University, UC Irvine, Tsinghua University and Chongqing University, as well as other new collaborations that are emerging (MIT, University of Wisconsin Madison, Pacific Gas and Electric Company).
She served as Best Paper Session Judge for the session “Power System Stability, Phasor Measurements, Protection, and Control” at the 2019 IEEE Power & Energy Society General Meeting (PESGM). Patricia also serves as an IEEE reviewer for the Transactions on Power Systems journal, Conference on Decision and Control, American Control Conference, and PESGM.