A Procedure for Estimating the Energy Demand of Electric Cars Recharging in Urban Areas

The paper describes a procedure for simulating the recharging activities of private electric cars in urban areas, thus enabling the reconstruction of daily electric power demand profiles on a zonal basis. The procedure relies on a behavioral model, which is calibrated using results from a large Stated Preference (SP) survey. The behavioral model, along with pre-processed Floating Car (FC) data, allows for simulating individual recharge operations through the integration of both behavioral insights and real-world travel patterns. The procedure compares, with a 'what - if' approach, different demand and supply scenarios, varying the distribution and technical characteristics of the electric cars fleet as well as the level of both private and public charging infrastructure in the study area. The goal is to identify charging supply networks that meet demand needs while minimizing installation costs and local power requirements. The procedure has been applied to an anonymous sample of approximately 10,000 cars circulating in the metropolitan area of Rome, which users' home was been approximately identified in the study area, accordingly to the stopover cluster analysis. Private mobility Charging Profiles (CPs) have been estimated for daily periods, by urban zone and by different scenario assumptions on electric cars penetration and private and public recharge infrastructure configuration, thus enabling decision makers at verifying the impacts of different strategies for electric mobility boosting. Moreover, estimation of Charging Profiles can facilitate smart charging operation, through V2X facilities