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Electric Vehicle Charging Capacity of Distribution Network Considering Conventional Load Composition

Pengwei Yang1, Yuqi Cao2, Jie Tan2, Junfa Chen1, Chao Zhang1, Yan Wang1, Haifeng Liang2,*

1 Zhangjiakou Power Supply Company, State Grid Jibei Power Co., Ltd., Zhangjiakou, 075000, China
2 Department of Electrical Engineering, North China Electric Power University, Baoding, 071003, China

* Corresponding Author: Haifeng Liang. Email:

Energy Engineering 2023, 120(3), 743-762.


At present, the large-scale access to electric vehicles (EVs) is exerting considerable pressure on the distribution network. Hence, it is particularly important to analyze the capacity of the distribution network to accommodate EVs. To this end, we propose a method for analyzing the EV capacity of the distribution network by considering the composition of the conventional load. First, the analysis and pretreatment methods for the distribution network architecture and conventional load are proposed. Second, the charging behavior of an EV is simulated by combining the Monte Carlo method and the trip chain theory. After obtaining the temporal and spatial distribution of the EV charging load, the method of distribution according to the proportion of the same type of conventional load among the nodes is adopted to integrate the EV charging load with the conventional load of the distribution network. By adjusting the EV ownership, the EV capacity in the distribution network is analyzed and solved on the basis of the following indices: node voltage, branch current, and transformer capacity. Finally, by considering the 10-kV distribution network in some areas of an actual city as an example, we show that the proposed analysis method can obtain a more reasonable number of EVs to be accommodated in the distribution network.


Cite This Article

Yang, P., Cao, Y., Tan, J., Chen, J., Zhang, C. et al. (2023). Electric Vehicle Charging Capacity of Distribution Network Considering Conventional Load Composition. Energy Engineering, 120(3), 743–762.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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