Deploying Public Charging Stations for Electric Taxis: A Charging Demand Simulation Embedded Approach

Authored by Aiqiang Pan, Teng Zhao, Haidong Yu, Yan Zhang

Date Published: 2019

DOI: 10.1109/access.2019.2894780

Sponsors: No sponsors listed

Platforms: MATLAB

Model Documentation: Mathematical description Flow charts Other Narrative

Model Code URLs: Model code not found


Properly deployed public charging stations are important foundations for the large-scale operation of electric taxis. This paper proposes a novel framework for the deployment of public charging stations, which takes into consideration the effects of passengers, taxi drivers, electricity retailers, transportation network, distribution network, and power consumers. First, on the premise that public charging stations have already been deployed, an agent-based model is constructed to simulate the charging demands of each station, considering passengers' travel demands and retailers' mutual competition. Second, to obtain candidate sites for public charging stations, the critical node index is put forward based on the massive trajectory data of taxis. Finally, a multi-objective optimizing model for public charging station deployment is proposed with charging demand simulation embedded. By traversing candidate sites and quantities of charging spots at each station using a modified genetic algorithm, the optimal deployment results are obtained. The framework and models are demonstrated and verified by a test case. The results indicate that the proposed framework could minimize the costs of charging stations, electric utilities, electric taxi drivers, and passengers while lowering the load heterogeneity in the distribution network at the same time.
Assignment Vehicles Optimal locations Trajectory data mining Public charging station planning Pricing strategy Multi-agent simulation Load heterogeneity Electric taxi