Vehicle - to - grid regulation based on a dynamic simulation of mobility behavior

This study establishes a new approach to analyzing the economic impacts of vehicle-to-grid (V2G) regulation by simulating the restrictions arising from unpredictable mobility requests by vehicle users. A case study for Germany using average daily values (in the following also called the „static‟ approach) and a dynamic simulation including different mobility use patterns are presented. Comparing the dynamic approach with the static approach reveals a significant difference in the power a vehicle can offer for regulation and provides insights into the necessary size of vehicle pools and the possible adaptations required in the regulation market to render V2G feasible. In a first step, the regulation of primary, secondary and tertiary control is analyzed based on previous static methods used to investigate V2G and data from the four German regulation areas. It is shown that negative secondary control is economically the most beneficial for electric vehicles because it offers the highest potential for charging with “low-priced” energy from negative regulation. In a second step, a new method based on a Monte Carlo simulation using stochastic mobility behavior is applied to look at the negative secondary control market in more detail. Our simulation indicates that taking dynamic driving behavior into account results in a 40% reduction of the power available for regulation. Because of the high value of power in the regulation market this finding has a strong impact on the resulting revenues. Further, we demonstrate that, for the data used, a pool size of 10,000 vehicles seems reasonable to balance the variation in driving behavior of each individual. In the case of the German regulation market, which uses monthly bids, a daily or hourly bid period is recommended. This adaptation would be necessary to provide individual regulation assuming that the vehicles are primarily used for mobility reasons and cannot deliver the same amount of power every hour of the week.