Optimization model for simultaneous controlled charging of electric vehicles in distribution grids in rural, suburban and urban areas

The energy demand of future electromobility poses new challenges for distribution grids due to critical load peaks. Integrating electric vehicles (EVs) into the energy system by controlled charging could offer additional flexibility for the overall system yet may increase the peak loading in the distribution grid. We therefore developed and implemented a modular optimization model representing the simultaneous controlled charging of multiple EVs in one defined branch of a distribution grid from a combined aggregator and grid perspective. The objective consists of minimizing the costs to provide electrical energy to fully recharge all EVs connected to that grid branch during a full year, either in perfect foresight or by applying a rolling horizon with a 36h/24h scheme. The time intervals of connection to the power grid are explicitly modelled as well as the grid limitations. Using Monte Carlo simulations, a multiplicity of different single grid branches is evaluated. The number of households connected to these grid branches with a defined probability of EV ownership is applied as predictor for the load burden of the future German EV fleet. In the model, a classification of grid branches into fifteen distinct types is used, each defined by specific parameter settings, allowing to capture the diversity of grid branch configurations, e.g. regarding the degree of urbanization. Randomization is employed to create a representative sample of grid configurations based on the predefined clusters and corresponding ranges of grid parameters. This enhances the model's adaptability to reflect the variability of grid configurations. Compared to uncontrolled charging, which is considered as a reference in the model, “smart” optimised controlled charging of multiple EVs is concentrated during periods of the lowest spot prices, which do not coincide with the existing peak load hours in the evening. The impact of grid constraints on the controlled charging patterns is found to depend both on the degree of urbanization and the specificities of the considered grid branches.