The rapid electrification of the transportation fleet imposes unprecedented demands on the electric grid. If controlled, however, these electric vehicles (EVs) provide an immense opportunity for smart grid services that enable renewable penetration and increased reliability. In this talk we discuss paradigms for aggregating and optimally controlling EV charging. Specifically, we discuss (i) aggregate modeling via partial differential equations, (ii) distributed optimization of large-scale EV fleets, (iii) and plug-and-play model predictive control in distribution networks. The talk closes with future perspectives for EVs in the Smart Grid.
Scott Moura is an Assistant Professor at the University of California, Berkeley in Civil & Environmental Engineering and Director of eCAL. He received the Ph.D. degree from the University of Michigan in 2011, the M.S. degree from the University of Michigan in 2008, and the B.S. degree from the UC Berkeley, in 2006 - all in Mechanical Engineering. He was a postdoctoral scholar at UC San Diego in the Cymer Center for Control Systems and Dynamics, and a visiting researcher in the Centre Automatique et Systèmes at MINES ParisTech in Paris, France. He is a recipient of the O. Hugo Shuck Best Paper Award, Carol D. Soc Distinguished Graduate Student Mentoring Award, Hellman Faculty Fellows Award, UC Presidential Postdoctoral Fellowship, National Science Foundation Graduate Research Fellowship, University of Michigan Distinguished ProQuest Dissertation Honorable Mention, University of Michigan Rackham Merit Fellowship, and Distinguished Leadership Award. He has received multiple conference best paper awards, as an advisor & student. His research interests include control & estimation theory for PDEs, optimization, machine learning, batteries, electric vehicles, and the smart grid.
