Kankar Bhattacharya – University of Waterloo, Canada
With increasing environmental concerns, the penetration of electric vehicles (EVs) is expected to increase in the future. Such electrification of the transportation sector will impact the distribution grid adversely; however, EV smart charging strategies can help mitigate the impacts. In this presentation a mathematical model will be presented that represents the total charging load at an EV charging station (EVCS) in terms of controllable parameters. A queuing model is used to construct a data set of EV charging parameters which are input to a neural network (NN) to determine the controllable EVCS load model as a function of the number of EVs charging simultaneously, total charging current, arrival rate, and time; and various class of EVs. The load model is integrated within a distribution operations framework to determine the optimal operation and smart charging schedules of the EVCS. In the second part of the presentation, a smart charging approach is proposed where the charging loads are controlled and incentivized by the local distribution company (LDC) for every unit of energy controlled. A framework is proposed, that captures the relationship between EV customers’ participation and incentives offered by LDC, to determine the optimal participation of EVs in smart charging program and optimal incentives paid by the LDC, such that both parties are economically benefited. The relationship between the expected investment deferral and hence the economic benefits from smart charging participation are considered as well.
15h30-15h45: Come meet the speaker and other researchers over drinks and snacks
Do not forget to confirm your attendance: https://doodle.com/poll/g2h27bqsq7c9sbwm
Campus de l'Université de Montréal
Chaire de recherche industrielle CRSNG-Hydro-Québec-Schneider Electric en optimisation des réseaux électriques intelligents
Poster_Seminar_oct_25.pdf (310 Ko)