Estimating charging demand by modelling EV drivers' parking patterns and habits

Abstract

The diffusion of battery electric vehicles (BEVs) requires a proper charging infrastructure to supply users the chance to charge their vehicles according to energy, time, and space needs. Thus, city planners and stakeholders need decision support tools to estimate the impacts of potential charging activities and compare alternative scenarios. The paper proposes a modelling approach to represent parking activities in urban areas and obtain key indicators of the electric energy required. The agent-based model reproduces the dynamics of user parking and assesses the impacts on the electricity grid during the day. Since the focus is on parking activities, no detailed data on vehicle trips are required to apply the standard demand modelling approach, which would require Origin-Destination matrices to simulate traffic flows on the road network. Preliminary results concerning the city of Turin are presented for simulated scenarios to identify zones where charging demand can be critical and peak events in electric power over the day. The model is designed to be scalable for all European cities because, as the case study shows, it uses available data. The results obtained can be used for the design of charging infrastructure (power and type) by zones.