Integrating Mobility and Energy Systems: Economic and Environmental Assessment of Vehicle-to-Grid Technologies

This study develops a Mixed-Integer Linear Programming optimization model, implemented in Python with the Gurobi solver, to quantify the economic benefits of integrating Electric Vehicles into Renewable Energy Communities. The research investigates how coordinated Vehicle-to-Grid and Grid-to-Vehicle operations provide Ancillary Services while maximizing revenues from market incentives and local selfconsumption. By accounting for battery degradation and diverse driving cycles, the model evaluates the impact of these revenues on the Discounted Payback Period of the initial vehicle purchase cost. Results demonstrate that the vehicle fleet effectively balances grid requests and community energy sharing, significantly enhancing financial viability compared to standard mobility use. This integration reduces the payback period for all analyzed models, reaching a return on investment in less than seven years for affordable vehicles. The findings confirm that smart charging ensures grid stability and provides critical flexibility, accelerating the transition toward sustainable, community-based energy systems.