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Electric Vehicle Charging Station Planning Considering Users′ Dynamic Charging Demand
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[1]ZHU Yongsheng,YANG Zhentao,DING Tongkui,et al.Electric Vehicle Charging Station Planning Considering Users′ Dynamic Charging Demand[J].Journal of Zhengzhou University (Engineering Science),2023,44(02):82-91.[doi:10.13705/j.issn.1671-6833.2023.02.001]
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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 44卷 Number of periods: 2023 02 Page number: 82-91 Column: Public date: 2023-02-27
Title:
Electric Vehicle Charging Station Planning Considering Users′ Dynamic Charging Demand
Author(s):
ZHU Yongsheng1;  YANG Zhentao1;  DING Tongkui2;  XU Qiying1;  WU Fuzhuan1;  NIE Caijing1
1.Central Plains College of Electronic Information, Henan Zhengzhou 450007, 2.State Grid Zhengzhou Power Supply Company, Henan Zhengzhou 450052

Keywords:
electric vehicles;  traffic network;  Dijkstra algorithm;  dynamic charging needs;  capacity configuration;  charging station
CLC:
TM715
DOI:
10.13705/j.issn.1671-6833.2023.02.001
Abstract:
In order to improve the rationality of the planning and layout of electric vehicle (EV) charging stations and avoid the situation of high investment and low efficiency, a planning method of EV charging station that considered users′ dynamic charging demand was proposed. Firstly, the starting and ending points of user were obtained by using the travel theory, start and end point (origin-destination,OD) matrix method; a dynamic traffic road network model with time-varying traffic congestion was constructed. The Dijkstra algorithm was improved to plan the EV travel path, considering the real-time changes of ambient temperature and vehicle speed. Based on the influence of mileage and power consumption, a charging station selection model considering the dynamic charging needs of users was established; then, the M/M/c queuing theory method was used to configure the capacity of charging stations. The cost of construction, operation and maintenance of charging stations and the economic losses of EV users (including the sum of time loss and power loss) was minimized as the objective function, and a charging station planning model was established. Finally, taking the actual road conditions in the main urban area of a city as the planning area, the model was solved by iterative arrangement optimization combined with particle swarm algorithm. The results showed that the locations of the six planned charging stations in the area were evenly distributed, which could reduce the cost of users′ charging journeys. And the optimal configuration number of charging piles could ensure charging satisfaction while minimizing the total economic cost of charging stations. The proposed planning method was reasonable and effective.
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Last Update: 2023-02-25
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