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Electric vehicles (EVs) add significant load on the power grid as they becomewidespread. The characteristics of this extra load follow the patterns ofpeople's driving behaviours. In particular, random parameters such as arrivaltime and charging time of the vehicles determine their expected charging demandprofile from the power grid. In this paper, we first present a model foruncoordinated charging power demand of EVs based on a stochastic process andaccordingly we characterize an EV's expected daily power demand profile. Next,we illustrate it for different charging time distributions through simulations.This gives us useful insights into the long-term planning for upgrading powersystems' infrastructure to accommodate EVs. Then, we incorporate departure timeas another random variable into this modelling and introduce an autonomousdemand response (DR) technique to manage the EVs' charging demand. Our resultsshow that, it is possible to accommodate a large number of EVs and achieve thesame peak-to-average ratio (PAR) in daily aggregated power consumption of thegrid as when there is no EV in the system. This peak value can be decreasedfurther significantly when we add vehicle-to-grid (V2G) capability in thesystem.

A Statistical Modelling and Analysis of Residential Electric Vehicles' Charging Demand in Smart Grids