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

(E-pub Ahead of Print)

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

Journal Name: Recent Advances in Electrical & Electronic Engineering

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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.

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. The algorithm optimization model gives the optimal configuration scheme of the charging and replacing station to reduce the impact of the charging and replacing station on the distribution network.

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

(E-pub Ahead of Print)
DOI: 10.2174/1874476105666190830111228
Price: $95