Model-based estimation of private charging demand at public charging stations

In recent years, many concepts have been developed on how to build a sufficient charging infrastructure to satisfy the demand of Battery Electric Vehicle (BEV) users. However, the focus of these approaches often lies on the spatial distribution of charging stations and the amount of charging demand is often given beforehand. In this paper, we describe a model to estimate the future private charging demand at public charging stations for different regions. Several aspects that influence the needed amount of charging stations are considered, e.g. a growing range of BEVs and the behavior of different user groups. For example, we distinguish between BEV users with or without a home charging possibility. The spatial distribution of these user groups is modeled using an agent-based approach, respecting sociodemographic properties. Forecasting the spread of BEVs strongly depends on the assumptions made regarding these influencing factors, where different current studies obtain deviant results. Therefore, in a case study for the city of Munich, we consider three different scenarios assuming a pessimistic, a realistic and an optimistic spread of BEVs in the year 2020. Additionally, we present a sensitivity analysis of the influencing factors and identify the ones that have the highest impact on the future charging demand: the overall adoption rate of BEVs is the parameter that influences the output the most. In fact, an adoption rate that is 10% higher than expected leads to an increase in charging demand of about 16%. This means, that our model strongly depends on reliable input data. The output of our model is the expected number of charging events requested in a certain region on an average day. Together with the average parking time and the temporal distribution of car arrivals at public charging stations, it is possible to obtain the necessary size of the charging infrastructure such that the demand can be satisfied even during peak hours. These results can be used as an input to existing optimization algorithms for the allocation of charging stations.