CDRpy: Data-Driven Decision Support for the Rollout of Public Charging Infrastructure in Urban Areas

In this study, the data-driven simulation framework CDRpy is presented, validated, and applied using the example of the city of Munich to support decision makers in the rollout of public charging infrastructure in urban areas. The innovation of the study lies in how CDRpy can be used to evaluate different concepts for public charging infrastructure at a detailed level. Demand was mapped based on real charging events for different types of electric vehicles and pricing models, and the evaluations were broken down to the level of individual charging stations. The developed framework was validated using charge detail records from Munich’s largest public charging infrastructure operator for the years 2020, 2021, and 2022. The validation compared CDRpy simulation results with real usage data for time utilization, energy transferred per month, and energy transferred per subdistrict. In a further case study, the additional demand for public charging infrastructure in the city was estimated for the year 2030. The results indicate that mixed and time-based pricing models could reduce the required number of public charging points by around 10% compared with energy-based pricing models. Determining the expansion requirements for each subdistrict showed that the additional demand for public charging infrastructure varied greatly across the city. Since both technological and economic perspectives were considered, the study is relevant for all decision makers involved in the planning, operation, and scaling of public charging infrastructure.