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
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Platforms:
MATLAB
Model Documentation:
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Abstract
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.
Tags
Multi-agent simulation
Vehicles
Assignment
Electric taxi
Load heterogeneity
Pricing
strategy
Public charging station planning
Trajectory data mining
Optimal locations