Generation of stochastic load profiles for mobile energy storages

The rapid growth of the power demand in the transport sector results in a dramatic increase of carbon dioxide emissions. This is only one of many reasons why alternative drive concepts, especially electric mobility, have never been more in focus than today.A future change from combustion engines to electrical drives leads to additional loads, which gives rise to the necessity to take corrective measures in the energy supply sector. In order to be able to scrutinize the impacts on the power grid, a model has been developed with the help of the software MATLAB to generate stochastic load profiles for electric vehicles. The model exclusively deals with electric vehicles with lithium ion batteries. All parameters originate from the transport statistics of the motorized individual traffic in Austria. In the end the calculated outcome of a driving profile mix for 100 battery powered electric vehicles is consolidated into a total load profile. The resulting implications of some uncontrolled and controlled charging scenarios are discussed. As a consequence of the assumption that most charging processes take place in residential areas, a comparison of the overall load profiles with standardized load profiles for regular households is carried out.If the charging processes are started immediately after the respective arrivals at home without being controlled, this will result in a rise of the evening load peak. It is shown that by means of several load cycles per day - provided the expansion of the charging infrastructure - this negative effect is limited. Anyway, the overall goal is a well controlled consumer-oriented charging process in order to shift the energy consumption to the night load depression. For this purpose several scenarios are elaborated and its advantages and drawbacks are described. Finally various approaches for the implementation of the highlighted scenarios are presented