A multi-period optimization model for the deployment of public electric vehicle charging stations on network

• Published in: Transportation research. Part C, Emerging technologies Vol. 65; pp. 128 - 143
• Main Authors: Li, Shengyin, Huang, Yongxi, Mason, Scott J.
• Format: Journal Article
• Language: English
• Published: Elsevier India Pvt Ltd 01.04.2016
• Subjects: Charging; Charging station; Deviation; Electric vehicle; Electric vehicles; Genetic algorithm; Genetic algorithms; Heuristic; Markets; Mixed integer linear program; Multi-period location model; Networks; Stations; USA, South Dakota, Big Sioux R., Sioux Falls; ANW, USA, South Carolina; Multi-period location model; Electric vehicle; Mixed integer linear program; Genetic algorithm; Charging station
• ISSN: 0968-090X; 1879-2359
• DOI: 10.1016/j.trc.2016.01.008

•Develop a multi-period multipath location model for expanding a charging network.•The model captures the dynamics in the topological structure of the network.•Formulate the model as a mixed integer program and solve it by genetic algorithm.•Justify the model and heuristic using the benchmark Sioux Falls road network.•Implement it in a South Carolina case study for an optimal station rollout scheme. A multi-period multipath refueling location model is developed to expand public electric vehicle (EV) charging network to dynamically satisfy origin–destination (O–D) trips with the growth of EV market. The model captures the dynamics in the topological structure of network and determines the cost-effective station rollout scheme on both spatial and temporal dimensions. The multi-period location problem is formulated as a mixed integer linear program and solved by a heuristic based on genetic algorithm. The model and heuristic are justified using the benchmark Sioux Falls road network and implemented in a case study of South Carolina. The results indicate that the charging station rollout scheme is subject to a number of major factors, including geographic distributions of cities, vehicle range, and deviation choice, and is sensitive to the types of charging station sites.