Evaluating hourly charging profiles for different electric vehicles and charging strategies

This study evaluates the potential increase in hourly electricity demand from the electrification of road transport in an Alpine region considering different charging strategies within a multi-node framework. The python-based emobpy tool generates hourly charging profiles for passenger cars, buses, light commercial vehicles and heavy trucks considering a tailored charging infrastructure for each vehicle type. Oemof framework, a linear programming model, is then used to analyse the regional energy system with a multi-node representation integrating smart charging and vehicle-to-grid options with a multi-node representation at the regional scale. Baseline results without demand management indicate peak load increases of 12–59 % by 2030–2050. Implementing smart charging for passenger cars in the high electrification 2050 scenario reduces this peak by 34 % by aligning loads to renewable generation surplus periods. Enabling vehicle-to-grid technology yields an additional 60 % reduction in imports from surrounding regions by utilizing electric vehicles as distributed energy resources. The study provides insights into fleet transitions and the role of flexible demand-side options in renewable power system integration, with a high level of detail in the modelling process.