Modelling and Simulation of a fully Electrified Urban Private Transport - a case Study for Berlin, Germany

In order to identify congestions in the electric grid due to the electrification of private internal combustion engine vehicles (ICEVs), a method is developed that allows forecasting the spatial and temporal distribution of charging energy demand resulting from the electrification of private ICEV s with high spatial and temporal resolution. The main database of the method is OpenStreetMap geodata which is freely available worldwide. The demonstration example of this work is Berlin, Germany, assuming that all private cars are replaced by battery electric vehicles (BEVs). The results show that more than 6000 MWh of charging energy is required on an average workday, with 62% of this energy being charged at residences. The results obtained serve as a basis for further investigations. It is found that through load shifting, vehicle charging times can be shifted to reduce peak power demand in the districts by up to 28%, without changes in the mobility behaviour. A full vehicle-to-grid (V2G) capability, where excess renewable energy is stored in BEV s and fed back into the grid as needed, is also analyzed. If V2G is deployed, more than 99% of household and BEV energy demand can be met with renewable energy if 30% of vehicles participate in V2G.