Efficient heuristic algorithms for location of charging stations in electric vehicle routing problems

Indexación: Scopus.This work has been partially supported by CONICYT FONDECYT by grant 11150370, FONDEF IT17M10012 and the “Grupo de Logística y Transporte” at the Universidad del Bío-Bío.. This support is gratefully acknowledged.Eco-responsible transportation contributes at making a difference for companies devoted to product delivery operations. Two specific problems related to operations are the location of charging stations and the routing of electric vehicles. The first one involves locating new facilities on potential sites to minimise an objective function related to fixed and operational opening costs. The other one, electric vehicle routing problem, involves the consolidation of an electric-type fleet in order to meet a particular demand and some guidelines to optimise costs. It is determined by the distance travelled, considering the limited autonomy of the fleet, and can be restored by recharging its battery. The literature provides several solutions for locating and routing problems and contemplates restrictions that are closer to reality. However, there is an evident lack of techniques that addresses both issues simultaneously. The present article offers four solution strategies for the location of charging stations and a heuristic solution for fleet routing. The best results were obtained by applying the location strategy at the site of the client (relaxation of the VRP) to address the routing problem, but it must be considered that there are no displacements towards the recharges. Of all the other three proposals, K-means showed the best performance when locating the charging stations at the centroid of the cluster. © 2012-2018. National Institute for R and D in Informatics.https://sic.ici.ro/wp-content/uploads/2018/03/Art.-8-Issue-1-2018-SIC.pd

Electric vehicles charging infrastructure demand and deployment : challenges and solutions

Present trends indicate that electrical vehicles (EVs) are favourable technology for road network transportation. The lack of easily accessible charging stations will be a negative growth driver for EV adoption. Consequently, the charging station placement and scheduling of charging activity have gained momentum among researchers all over the world. Different planning and scheduling models have been proposed in the literature. Each model is unique and has both advantages and disadvantages. Moreover, the performance of the models also varies and is location specific. A model suitable for a developing country may not be appropriate for a developed country and vice versa. This paper provides a classification and overview of charging station placement and charging activity scheduling as well as the global scenario of charging infrastructure planning. Further, this work provides the challenges and solutions to the EV charging infrastructure demand and deployment. The recommendations and future scope of EV charging infrastructure are also highlighted in this paper

Performance of gradient-based optimizer on charging station placement problem

The electrification of transportation is necessary due to the expanded fuel cost and change in climate. The management of charging stations and their easy accessibility are the main concerns for receipting and accepting Electric Vehicles (EVs). The distribution network reliability, voltage stability and power loss are the main factors in designing the optimum placement and management strategy of a charging station. The planning of a charging stations is a complicated problem involving roads and power grids. The Gradient-based optimizer (GBO) used for solving the charger placement problem is tested in this work. A good balance between exploitation and exploration is achieved by the GBO. Furthermore, the likelihood of becoming stuck in premature convergence and local optima is rare in a GBO. Simulation results establish the efficacy and robustness of the GBO in solving the charger placement problem as compared to other metaheuristics such as a genetic algorithm, differential evaluation and practical swarm optimizer

Optimal location of battery swap stations for electric vehicles

In this paper, a methodology that allows to determine the location and optimal sizing of battery swap stations for electric vehicles in distribution networks is proposed, which objective is to minimize investment costs and technical losses of the network. The set of constraints is associated with technical and operational characteristics of the system. To solve the mathematical model, an evolutionary algorithm is used. To verify the efficiency of the methodology, a Colombian distribution system is used, where the obtained results validate what is proposed in this work

Design and development of adaptive EV charging management for urban traffic environments

Due to the world’s shortage of fossil fuels, increasing energy demand, oil prices, environmental concerns such as climate change and air pollution, seeking for alternative energy has emerged as a critical study area. Transportation systems is one of the main contributors to air pollution and consumers of energy. Electric Vehicles (EVs) is considered as a highly desirable solution for a new sustainable transportation for many powerful advantages, such as energy efficient, environmentally friendly and may benefit from increased renewable energy technologies in the future. Despite all the acknowledged advantages and recent developments in terms of reducing the environmental impact, noise reduction and energy efficiency, the electric mobility market is still below the expectations. Among the most challenges that limit the market penetration of EVs as well as achieving a sustainable mobility system are the efficient distribution of adequate Charging Stations (CSs) and also determining the best CSs for EVs in metropolitan environments. This thesis is concerned in determining the optimal placement of EVCSs and the efficient assignment of EVs to CSs. To accomplish this, we thoroughly examine the interactions between EVs, CSs, and Electrical Grids (EGs). First, a novel energy efficient scheme to find the optimal placement of EVCSs are presented, based on minimizing the energy consumption of EVs to reach CSs. We then propose a comprehensive approach to find the optimal assignment of EVs to CSs based on optimization of EV users’ QoE. Finally, we proposed a reinforcement learning-based assignment scheme for EVs to CSs in urban areas, aiming at minimizing the total cost of charging EVs and reduce the overload on EGs. By comparing the obtained results of the proposed approaches with different scenarios and algorithms, it was concluded that the presented approaches in this thesis are effective in solving the problems of EVCS placement and EVs assignment

Planning the Charging Infrastructure for Electric Vehicles in Cities and Regions

Planning the charging infrastructure for electric vehicles (EVs) is a new challenging task. This book treats all involved aspects: charging technologies and norms, interactions with the electricity system, electrical installation, demand for charging infrastructure, economics of public infrastructure provision, policies in Germany and the EU, external effects, stakeholder cooperation, spatial planning on the regional and street level, operation and maintenance, and long term spatial planning

Planning the Charging Infrastructure for Electric Vehicles in Cities and Regions

Planning the charging infrastructure for electric vehicles (EVs) is a new challenging task. This book treats all involved aspects: charging technologies and norms, interactions with the electricity system, electrical installation, demand for charging infrastructure, economics of public infrastructure provision, policies in Germany and the EU, external effects, stakeholder cooperation, spatial planning on the regional and street level, operation and maintenance, and long term spatial planning