Coincidence Factors for Domestic EV Charging From Driving and Plug-In Behavior

This study models the coincidence factor (CF) of electric vehicle (EV) charging given driving and plug-in behaviors by combining data sources from travel surveys and recorded EV charging data. From these, we generate travel and plug-in behaviors by using a Monte Carlo approach to derive CFs. By varying the EV battery size, the rated charging power, and the plug-in behavior, their influence on the CF is examined. The key results show that the CF decreases to less than 25% when considering more than 50 EVs with a charging level of 11 kW, with the CF strongly depending on the number of EVs considered. By contrast, the driving behavior and the battery size have a minor influence on the CF. Furthermore, when mixing the parameters, such as EV battery size and rated charging power, then, especially, the active power drawn by the feeder does not change linearly. Ultimately, the study aims to add to the state-of-the-art by solely and systematically focusing on the CF and its sensitivity to a number of key factors. For planning and design, distribution system operators may use this study as a part of their planning for the integration of electric vehicles in the electrical grid.

Automated summary

This study models the coincidence factor (CF) of electric vehicle (EV) charging based on driving and plug-in behaviors. The results show that the CF decreases with an increase in the number of EVs considered, while driving behavior and battery size have minimal influence. Additionally, mixing parameters such as EV battery size and charging power leads to non-linear changes in the active power drawn by the feeder.