Electric vehicle routing problem with backhauls considering the location of charging stations and the operation of the electric power distribution system

Abstract

Logistics companies are largely encouraged to make greener their operations through an efficient solution with electric vehicles (EVs). However, the driving range is one of the limiting aspects for the introduction of EVs in logistics fleet, due to the low capacity provided by the batteries to perform the routes. In this regards, it is necessary to set up a framework to virtually increase this battery capacity by locating EV charging stations (EVCSs) along the transportation network for the completion of their routes. By the other side, the Distribution Network Operators (DNOs) express the concern associated with the inclusion of new power demands to be attended (installation of EVCSs) in the Distribution Network (DN), without reducing the optimal power supply management for the end-users. Under these circumstances, in this paper the Electric Vehicle Routing Problem with Backhauls and optimal operation of the Distribution Network (EVRPB-DN) is introduced and formulated as a mixed-integer linear programming model, considering the operation of the DN in conditions of maximum power demand. Different candidate points for the EVs charging are considered to recharge the battery at the end of the linehaul route or during the backhaul route. The problem is formulated as a multi-objective approach where the transportation and power distribution networks operation are modeled. The performance and effectiveness of the proposed formulation is tested in VRPB instance datasets and DN test systems from the literature. Pareto fronts for each instance are presented, using the ε-constraint methodology.

Prediction of Traffic Flow considering Electric Vehicle Market Share and Random Charging

“…Equation ( 13) is the minimization objective function, which has no intuitive economic signifcance. Equations ( 14) and (15) are the relationship between path fow and OD demand; equations ( 16) and ( 17) indicate the path trafc logit probability loading. Equation (18) represents the proportion of category m BEV in the total fow of BEV.…”

“…where π rs g and π rs e,m are Lagrange multipliers associated with constraints ( 14) and (15), respectively. Take the derivative of equation ( 25) and obtain its corresponding Kuhn-Tucker condition as follows:…”

“…In addition to classic route attributes and variables related to rapid charging (such as charging time and waiting time), the SOC at the start and end of the journey is also an important indicator that afects the driver's decision on route and charging behavior. EV routing problem with backhauls was solved considering the location of charging stations and the operation of the electric power distribution system [15]. Koç et al [16] presented an electric vehicle routing problem with a shared charging station (E-VRP-SCS), optimal charging station location, and delivery route planning based on ALNS and mixed-integer linear programming.…”

Modelos matemáticos para la programación óptima de rutas: formulación e implementación

“…Finalmente, las restricciones (5.40)-(5.46) definen las diferentes variables del modelo matemático. En este capitulo se presenta una nueva formulación de programación lineal entera mixta (MILP) para las versiones simétrica y asimétrica del VRP considerando entrega y recogida de mercancía usando vehículos eléctricos (EVRPB, Electric VRPB) (Cubides et al, 2019;Granada-Echeverri et al, 2020). Diferentes Estaciones de Recarga (ER) son consideradas para que la recarga de la batería de los Vehículos Eléctricos (VEs) ocurra al final de la ruta de entrega o durante el recorrido de la ruta de retorno.…”

The electric vehicle routing problem and its variations: A literature review

“…, Keskin, Akhavan-Tabatabaei, et al (2019),Lin et al (2016)). GUROBI, another commercial solver, was used for the EVRP byAksoy et al (2018),Chen et al (2016),Cubides et al (2019), Froger et al (2017), Froger et al (2019), Granada-Echeverri et al (2020,Moghaddam (2015),Montoya et al (2015),Walther (2017), andWang et al (2019).…”