Title: Optimal Power Flow and Smart EV Charging
Speaker: Steven H. Low
Date and Time: 11/4/2021 4:10PM ET
Location: Phillips 233 and Zoom
Optimal power flow problems are fundamental because they underly numerous power system operations. OPF is nonconvex and NP-hard. It is usually solved using local algorithms such as Newton-Raphson or convex relaxation, but neither guarantees globally optimal solutions. Even though OPF is hard in theory, it seems “easy’’ in practice in the sense that, empirically, both methods often yield global solutions. In the first half of the talk, we present necessary or sufficient conditions for an OPF problem to both have exact relaxation and no spurious local optimal.
In the second half, I’d describe a smart electric vehicle (EV) charging infrastructure and an open-source testbed at Caltech. We formulate the problem of optimally coordinating a network of EV chargers as a Quadratic Program, present some data from the deployment of our adaptive charging network. Finally, we describe the open-source ACN (Adaptive Charging Network) Research Portal that consists of large-scale EV charging data (ACN-Data), a realistic simulator (ACN-Sim), and the potential development of a real testbed (ACN-Live).
(Joint work with Fengyu Zhao (Caltech), Zach Lee (Caltech/PowerFlex), Sunash Sharma (Caltech/Berkeley))
Bio: Steven H. Low is the F. J. Gilloon Professor of the Department of Computing & Mathematical Sciences and the Department of Electrical Engineering at Caltech and Honorary Professor of the University of Melbourne, Australia. He is an awardee of the IEEE INFOCOM Achievement Award and the ACM SIGMETRICS Test of Time Award, and is a Fellow of the IEEE, ACM, and CSEE. He received his B.S. from Cornell and PhD from Berkeley, both in EE.