New article on behavioral aspects of the spatial–temporal flexibility of electric vehicle charging

Electric vehicle (EV) charging can cause high peaks in electricity demand, assuming many EV users charge at the same time, particularly during the evening when they arrive at home and when baseline electricity demand is already high. To support the balancing of electricity supply and demand, it is crucial to shift EV charging load to other times of the day.

Christine Gschwendtner, Christof Knoeri and Annegret Stephan apply an innovative approach by explicitly dividing charging behavior into the plug-in behavior and the charging process. The plug-in behavior refers to connecting the EV with a charging station at the beginning of a dwell-time, followed by the charging process within that specific dwell-time. Consequently, the plug-in behavior affects not only the temporal component but also the spatial component. Additionally, the plug-in behavior depends on social behavior, i.e., where and when an EV user plugs in their car, whereas the charging process, once it is adopted by the EV user, is mostly automated, with minimum interaction of EV users. Distinguishing these two elements therefore allows to investigate how different plug-in behaviors can affect (future) EV charging load profiles and their spatial–temporal flexibility.

This article (1) reveals the effect of diverse plug-in behaviors on EV load profiles, particularly the flexibility potential resulting from different plug-in behaviors; (2) presents the (future) charging load in different spatial structures, i.e., urban, rural, or suburban, and home, work, or public charging locations; and (3) demonstrates the effect of detailed driving profiles in high spatial and temporal resolution. The authors develop an agent-based model (ABM) to investigate interactions between EV users and charging infrastructure with detailed temporal (15min) and spatial (postcode-level) resolutions. They implement three future scenarios regarding EV and charging infrastructure diffusion to account for EV mass adoption and technology developments.

One key finding is that the impact of potential changes in plug-in behavior on the spatial-temporal EV charging load increases as charging infrastructure becomes more spatially diversified. Plug-in behavior becomes particularly relevant for peak reduction and load shifting in scenarios with high EV and high charging-infrastructure diffusion due to the increasing spatial diversification of charging infrastructure. Regarding spatial structures, the impact of plug-in behavior is highest for urban areas. These differences in loads between urban, suburban, and rural areas likely result from the different driving-profile mixes in these areas.

Decision-makers in policy and industry can use these insights to evaluate the impact of EV charging in different distribution grid contexts. As diversifying charging locations and times provides load shifting potential, policymakers and charging-station providers should consider fostering the deployment of spatially diverse charging infrastructure. These findings can also support the design of incentives to leverage the flexibility potential of EVs.

Link to the full paper (open access): Download https://doi.org/10.1016/j.scs.2022.104263