Future electric vehicle fleets pose both challenges and opportunities for power systems. While increased power demand from transport electrification necessitates expansion of power supply, vehicle batteries can to a certain degree shift their charging load to times of high availability of power. The model-adequate description of power demand from future plug-in electric vehicle fleets is a pre-requisite for modelling sector-coupled energy systems and drawing respective policy-relevant conclusions. Vehicle Energy Consumption in Python (VencoPy) is a tool that provides boundary conditions for load shifting and vehicle-to-grid potentials based on transport demand data and techno-economic assumptions (Figure 1). It has so far been applied to the German travel survey (Mobilität in Deutschland) on a national scale to derive hourly load-shifting constraining profiles for the energy system optimization model REMix.
Figure 1: Overview of the VencoPy model. Graphic: DLR
VencoPy follows an object-oriented approach containing four main classes in the data pipeline from mobility data to estimated load shifting potentials of electric vehicle fleets (see Figure 2). These classes provide a read-in of mobility data sets, the composition of trip and park diaries, the modeling of charging infrastructure at specific parking categories (e.g. home or shopping) as well as the modeling of uncontrolled charging and load shifting.
Figure 2: Modelling procedure of the VencoPy model. Grafik: DLR (CC-By 4.0)
Current development work focusses on charging behavior across multiple days, the integration of other data sets (e.g. commercial transport) and the validation of the tool with empirical charging data.