Pareto optimal design of charging infrastructure within a region

Abstract:
An efficient deployment of different types of charging infrastructure across different types of locations, such as public, work or highway locations, can benefit the integration of electric vehicles into electricity systems. Therefore, within this work we identify the required number of Charging Points (CPs) within a region while considering the corresponding charging peak load and the daily available flexibility, measured in shiftable charging load, of a regional fleet of battery electric vehicles (BEVs). An Agent-based E-Mobility Model is used to consistently simulate the regional system consisting of BEVs and CPs across different locations. An extensive variation of the number of CPs per type and location is conducted to determine the pareto optimal set of scenarios within a region. The pareto optimal set simultaneously follows two main objectives - minimizing the charging peak load and maximizing the flexibility of charging processes. As a boundary, we introduce a minimum required level of Service Quality of deployed Charging Infrastructure to guarantee charging access and convenient charging times. The pareto-optimal scenarios on the frontier are compared in terms of the number of CPs, the temporal distribution of the resulting charging demand, temporal distribution of the available flexibility and the charging infrastructure costs. The results reveal different charging infrastructure deployment strategies for a region and address different electricity system needs in the context of E-Mobility integration. It can be shown that highway fast charging reduces the need for work and public charging significantly and simultaneously reduces the peak load within the region. The total charging infrastructure costs are derived to 2.6 million € and 4.69 million € per 1000 BEVs in two differ-ent scenarios within one exemplary urban region.