Abstract:         In this talk, we first present a new rapid estimation method (REM) to perform stochastic impact analysis of grid-edge technologies (GETs) to the power distribution networks. The evolution of network states' probability density functions (PDFs) in terms of GET penetration levels are characterized by the Fokker-Planck equation. The approach is illustrated on a large-scale, realistic distribution network using a modified version of the IEEE 8500 feeder, where electric vehicles (EVs) or photovoltaic systems are installed at various penetration rates. Second, we design an incentive-based mitigation strategy for equipment overloading leveraging the REM. The objective is to shift the EV charging during the peak hours when the equipment is overloaded to the off-peak hours and maintain equipment service lifetime. The incentive level and corresponding contributions from customers to alter their EV charging habits are determined by a search algorithm with provable convergence guarantee. The strategy is illustrated on the same IEEE 8500 feeder with a high EV penetration to mitigate overloads on the substation transformer.

Bio: Antoine Lesage-Landry is an Assistant Professor in the Department of Electrical Engineering at Polytechnique Montréal, QC, Canada, a member of the Group for Research in Decisions Analysis (GERAD), and an associate academic member of Mila — Quebec’s AI Institute. He received the B.Eng. degree in Engineering Physics from Polytechnique Montréal, QC, Canada, in 2015, and the Ph.D. degree in Electrical Engineering from the University of Toronto, ON, Canada, in 2019. From 2019 to 2020, he was a Postdoctoral Scholar in the Energy & Resources Group at the University of California, Berkeley, CA, USA. His research interests include optimization, online learning, machine learning, and their application to power systems with renewable generation.

This talk is an Applied Math Guest Lecture and Special Data and Dynamics Group meeting.