A stochastic model for estimating electric vehicle arrival at multi-charger forecourts. (November 2022) - Aboshady, F.M.; Pisica, I.; Axon, C.J.
Abstract: Many countries are observing significant growth rates in electric vehicle (EV) uptake, often backed by financial incentives or regulation and legislation. The availability of large multi-charger sites for rapid EV charging with an experience similar to conventional refuelling stations lowers the barrier to acceptance for drivers considering the switch to using an EV. The question arises about how to size such a facility at the design and planning stage, as well as accommodating growth in the number of EVs in daily use. One of the important factors is the vehicle arrival rate and the corresponding power and energy demand. EV charging is a function of several parameters, all of which are stochastic in nature, such as the vehicle daily travelled distance, charging start time and the required energy. To account for uncertainty in the parameters, a stochastic model has been designed to simulate realistic vehicle arrival rates. The model accounts for EVs coming from the site catchment area and opportunistic charging from passing traffic travelling on the major roads adjacent to the site, the seasonality of parameters, and charging at places other than the site (competitive charging). The model produced plausible EV arrival patterns for both local and passing traffic, and reproduced the characteristic power demand at the case study site. All estimates incorporate uncertainty, reflecting realistic variability of the important parameters. The model in independent of location, uses open-source data, and is structured flexibly, making it adaptable to new sites as part of the technical and business planning process. Highlights: EV arrival rate affects the instantaneous power demand at large multi-charger sites. Understanding power and energy demand assists with optimal site operation. We use two EV populations: local owners and opportunistic passing traffic. Charging parameters are stochastic in nature, derived from real driving patterns. Probability density functions were derived from real-world open-source data.