Benchmarking electric vehicle charging control strategies

Abstract

Electric vehicles (EVs) are expected to become an important part of individual mobility. In order to reduce CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions and release the full potential for sustainable mobility, EVs need to be charged with energy from renewable energy sources (RES). We employ a deterministic linear optimization approach with different coordination objectives for each simulation scenario. The objectives are to minimize the individual average charging costs, maximize the average use of wind power or minimize the average load factor for the charging times of each EV customer. Customers have real life driving profiles from the German mobility panel and are distinguished in employees and retired with their respective driving behavior. We find that the wind power share used for charging can be nearly doubled for both groups under the respective strategy. Average costs are increased in comparison to the cost oriented strategy but are considerably lower as in the uncoordinated charging case.