The transport sector's extensive electrification and the growing adoption of electric vehicles pose significant challenges to distribution grids. To successfully integrate electric mobility and charging infrastructure into the power system while maintaining grid stability, detailed simulations are required. These simulations should consider the charging demand of electric mobility and charging infrastructure on a large-scale basis, as they are important for grid planning and operation.

In order to achieve this level of detail, an approach was developed that is capable of simulating a substantial number of electric vehicles, whilst also accounting for diverse driving patterns and locations. This approach is expected to ensure that the results obtained align with real-world driving behaviours on a stochastic basis. As main contribution, we present the computational model of a mobility simulator to simulate stochastic, scenario-specific, temporally and spatially resolved charging demands within a particular grid area. The first part of the modelling involves mapping driving behaviour in Germany. A Monte Carlo simulation is used to generate vehicle journeys, and regionalisation is used to expand the local component of the model. With the help of an assignment of destinations to locations, a region-specific evaluation of the energy demand time series is possible. A suitable procedure is presented and analysed for this purpose. This evolves the modelling of the individual driving behaviour to the usage of statistic input data. The energy system simulation framework SIMONA has been integrated in a seamless manner, thus facilitating a detailed assessment of charging demands for distribution grids. Grid integration can be supported by smart charging concepts that exploit the flexibility of charging sessions for load management. Optimising the operation of charging infrastructure has the potential to mitigate negative grid impacts, avoid the need for costly grid reinforcement, and support grid stability and the expansion of renewable energy sources.

The paper aims to demonstrate the capabilities of our approach by performing simulations on different grid models.