From Transportation Patterns to Power Demand: Stochastic Modeling of Uncontrolled Domestic Charging of Electric Vehicles

This paper presents a Monte Carlo simulation approach for the modeling of the power demand of electric vehicles under the scenario of uncontrolled domestic charging. A detailed transportation dataset for the Netherlands is used to derive the stochastic characteristics of the behavior of vehicles. The stochastic variables are the start/end-time of each trip and the respective travelled distance while the battery state of charge at the beginning of charging is derived by the consideration of the distance traveled since the last charging and the charging history. The stochastic variables are modeled using normal copula function based on the respective correlations and marginal distributions. The total load due to electric vehicles is computed based on the combination of the simulated commuting pattern with the charging profile of a typical electric vehicle battery. The results show that the EV power demand reaches the highest value during the evening peak hours for the residential load, however the peak is significantly lower than maximum which is mainly caused by the low charging time due to a generally low mean traveled distance.