High-resolution modelling of electric vehicle demand profiles in private passenger transport under future societal change scenarios

Kurzfassung

The electrification of private passenger transport is a key strategy for reducing greenhouse gas emissions and has significant implications for future energy systems. The increasing adoption of battery electric vehicles can highly affect future electricity demand and power system operation. Therefore, capturing these demand patterns is essential for reliable energy system modelling and the assessment of future decarbonization pathways. This paper presents a methodology to generate scenario-specific electricity demand for private mobility at high temporal resolution. The approach explicitly accounts for long-term societal change and is suitable for coupling with energy system and electricity market models. Societal change scenarios are derived using a Cross-Impact Balance analysis and then translated into quantitative changes in socio-economic indicators and travel demand. The future projections are used to determine user-specific weighting factors that scale and recombine individual mobility profiles obtained through the clustering of large-scale German mobility survey data. The resulting scenario-specific travel demand profiles are converted into 15-minute EV electricity demand. Results for five contrasting scenarios indicate that societal and behavioural assumptions substantially influence electricity demand profiles, with pronounced impacts on peak demand timing. The findings highlight the importance of integrating societal-science insights into energy demand modelling, since neglecting societal and future behavioural dynamics may result in the misrepresentation of peak loads and flexibility potentials in future decarbonised electricity systems.