Abstract: In the power grid optimization literature, one often finds clean problem formulations with continuous decision variables and deterministic data. Reality is different. Specifically, this talk focuses on two tough problems my research lab has recently faced: (i) large-scale mixed integer programs, and (ii) power pricing and scheduling in the context of human choices. Specifically, large-scale mixed integer programs arise when managing large-populations of distributed energy resources with binary (on/off) control. We present a novel (yet historic) heuristic solution known as Hopfield methods. The problem of human choices in-the-loop is fundamental to our current Smart LeaRning Pilot for EV charging stations (SlrpEV). Specifically, we present a menu of differentiated charging service options to EV drivers, and optimize the pricing and charge scheduled based on their preferences, to maximize the operator's net profit. I close the talk with perspectives on tough problems that deserve increased attention for realizing sustainable and resilient power grids of the future.

Short Bio: Scott Moura is an Associate Professor in Civil & Environmental Engineering and Director of the Energy, Controls, & Applications Lab (eCAL) at the University of California, Berkeley. He is also a faculty member at the Tsinghua-Berkeley Shenzhen Institute. He received the B.S. degree from the University of California, Berkeley, CA, USA, and the M.S. and Ph.D. degrees from the University of Michigan, Ann Arbor, in 2006, 2008, and 2011, respectively, all in mechanical engineering.