Abstract:

Rising electric loads necessitate energy system models that integrate their unique demand and potential flexibility as electric car fleets expand. A modest number of tools for estimating electric car demand and flexibility have recently been made available under open source licences. These usually sample discrete trips based on aggregate mobility statistics. However, the full range of variables of travel surveys cannot be accessed in this way and sub-national mobility patterns cannot be modeled. Therefore, a tool is proposed to estimate future electric vehicle fleet charging flexibility while being able to directly access detailed survey results. The framework is applied in a case study involving two recent German national travel surveys (from the years 2008 and 2017) to exemplify the implications of different mobility patterns of motorized individual vehicles on load shifting potential of electric vehicle fleets. The results show that different mobility patterns have a significant impact on the resulting load flexibilities. Most obviously, an increased daily mileage results in higher electricity demand. A reduced number of trips per day, on the other hand, lead to correspondingly higher grid connectivity of the vehicle fleet. Vehicle Energy Consumption in Python (VencoPy) is an open source, well-documented and maintained tool, capable of assessing electric vehicle fleet scenarios based on national travel surveys. To scrutinize the tool, a validation of the simulated charging by empirically observed electric vehicle fleet charging is advised.