Study on Capacity Distribution of Optimal Configuration Ratio of Urban Replenishment Station of Electric Vehicles

Author(s): Bo Zhang, Bicheng Huang, Zhongxian Wang*.

Journal Name: Recent Advances in Electrical & Electronic Engineering
Formerly Recent Patents on Electrical & Electronic Engineering

Volume 13 , Issue 1 , 2020

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Graphical Abstract:


Background: This paper deeply studies the influence of the capacity distribution of urban charging and replacing power stations on the distribution network and gives the optimal solution.

Objective: The optimal configuration scheme of the charging and replacing station has been given to reduce the impact of the charging and replacing station on the distribution network.

Methods: First, the Monte Carlo simulation method is used to extract the influencing factors. The probability load models of the battery replacement station, the slow charging station and the fast charging station are established respectively. The capacity distribution is represented by three different types of charging and switching station configuration ratios. Furthermore, the impact of the charging and replacing power station on the economics and safety of the distribution network is analyzed. Different from other literature, the “peak-filling” model with “minimum peak load”, “maximum valley load” and “minimum peak-to-valley difference” is established, and then PSO is adopted.

Results: Finally, the actual configuration is used to compare and analyze the four configuration schemes to prove the superiority of the optimization scheme.

Conclusion: Three models have different key influencing factors, so that the electric vehicle load forecasting is more targeted and accurate. The results show that more battery replacement stations can reduce the impact of EV charging on the distribution network, and the construction of the supply replenishment should be based on the construction of the power station.

Keywords: Urban replenishment station, power grid, harmonic pollution, load model, peak load shift, particle swarm optimization.

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Article Details

Year: 2020
Page: [119 - 125]
Pages: 7
DOI: 10.2174/1874476105666190830111228
Price: $25

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