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

•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...

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Bibliographic Details
Published in:Transportation research. Part C, Emerging technologies Emerging technologies, 2016-04, Vol.65, p.128-143
Main Authors: Li, Shengyin, Huang, Yongxi, Mason, Scott J.
Format: Article
Language:English
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
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Summary:•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.
ISSN:0968-090X
1879-2359
DOI:10.1016/j.trc.2016.01.008