Characterization of Electric Vehicle Fast Charging Forecourt Demand

Not all Electric Vehicle (EV) charging in future will take place at drivers' homes or on-street; at least some will take place at fast-charging `forecourts' analogous to today's petrol stations. This paper presents a Monte Carlo (MC)-based method for the characterization of the likely demand profile of EV fast charging forecourts based on activity profiles of existing petrol stations, derived from smartphone users' anonymised positional data captured in the `Popular Times' feature in Google Maps. Unlike most academic works on the subject to date which rely on vehicle users' responses to surveys, these data represent individuals' actual movement patterns rather than how they might recall or divulge them. Other inputs to the model are generated from probability distributions derived from EV statistics in the UK and existing academic work. A queuing model is developed to simulate busy periods at charging forecourts. The output from the model is a set of expected time series of electrical demand for an EV forecourt and statistical analysis of the variation in results. Finally, a method is presented for the probabilistic evaluation of the combined loading of an EV forecourt and existing demand; this could be used to assess the sufficiency of existing network capacity and the potential for innovative smart grid technologies to facilitate increasing penetration of EVs.