The vehicle charging problem with renewable energy sources in freight transport: Model and application

The transportation sector is responsible for more than 30 % of the total energy consumption in most countries, making it necessary to promote and develop green and sustainable methods in order to eliminate the air pollution and the climate change caused by it. Electromobility is developing rapidly, with the aim of reducing the environmental footprint in transport, given that the freight transport sector is responsible for the biggest portion of the CO 2 emissions. However, it should be investigated whether the electric vehicles (EVs) are truly low-carbon, especially during their charging process. To this end, this paper studies the integration of renewable energy sources (RES) in the charging of freight vehicles. It analyzes the penetration of RES, such as solar energy and wind energy in charging systems, with the aim of (a) reducing the supply of electricity for charging from the grid, and (b) maximizing the use of RES. Moreover, this study formulates the electric vehicle charging problem for freight transport to minimize the power derived from the grid for charging uses. It also strives to minimize the charging costs and maximize the satisfaction related to delivery services. The developed model is a bi-objective mixed-integer nonlinear model, which is reformulated as a mixed-integer linear model. Experimental tests are conducted, exploring the trade off between the increase in charging from RES and the reduction in vehicle running costs. • Mathematical formulation of the freight EV charging problem and its reformulation as a mixed-integer linear program which can be solved to global optimality. • Bi-objective modeling of the EV charging problem considering the trade-off between the energy used from non-renewable energy sources and the travel times of EVs for charging purposes. • Computational analysis of the proposed model and examination of its computational complexity. • Sensitivity analysis of the impact of exogenous factors, such as traffic congestion and energy consumption, to the optimal solution. • Implementation of the new model to a real-world network in Athens, Greece.