My cookie preferencesEvaluating hourly charging profiles for different electric vehicles and charging strategies
Rotondo G, Prina MG, Manzolini G, Sparber W
Project: PNRR iNEST - Interconnected Nord-Est Innovation Ecosystem
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.
DOI: 10.1016/j.est.2024.112388