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Charge Pricing Optimization Model for Private Charging Piles in Beijing

Author

Listed:
  • Xingping Zhang

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Research Center for Beijing Energy Development, Beijing 102206, China)

  • Yanni Liang

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Yakun Zhang

    (Ministry of Industry and Information Technology, Beijing 100804, China)

  • Yinhe Bu

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Hongyang Zhang

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

Abstract

This paper develops a charge pricing model for private charging piles (PCPs) by considering the environmental and economic effects of private electric vehicle (PEV) charging energy sources and the impact of PCP charging load on the total load. This model simulates users’ responses to different combinations of peak-valley prices based on the charging power of PCPs and user charging transfer rate. According to the regional power structure, it calculates the real-time coal consumption, carbon dioxide emissions reduction, and power generation costs of PEVs on the power generation side. The empirical results demonstrate that the proposed peak-valley time-of-use charging price can not only minimize the peak-valley difference of the total load but also improve the environmental effects of PEVs and the economic income of the power system. The sensitivity analysis shows that the load-shifting effect of PCPs will be more obvious when magnifying the number of PEVs by using the proposed charging price. The case study indicates that the proposed peak, average, and valley price in Beijing should be 1.8, 1, and 0.4 yuan/kWh, which can promote the large-scale adoption of PEVs.

Suggested Citation

  • Xingping Zhang & Yanni Liang & Yakun Zhang & Yinhe Bu & Hongyang Zhang, 2017. "Charge Pricing Optimization Model for Private Charging Piles in Beijing," Sustainability, MDPI, vol. 9(11), pages 1-15, November.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:11:p:2075-:d:118455
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    References listed on IDEAS

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    Cited by:

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    2. Jianan Liu & Hao Yu & Haoran Ji & Kunpeng Zhao & Chaoxian Lv & Peng Li, 2020. "Optimal Operation Strategy of a Community Integrated Energy System Constrained by the Seasonal Balance of Ground Source Heat Pumps," Sustainability, MDPI, vol. 12(11), pages 1-24, June.
    3. Zhang, Meijuan & Yan, Qingyou & Guan, Yajuan & Ni, Da & Agundis Tinajero, Gibran David, 2024. "Joint planning of residential electric vehicle charging station integrated with photovoltaic and energy storage considering demand response and uncertainties," Energy, Elsevier, vol. 298(C).
    4. Lee, Yerim & Hur, Jin, 2019. "A simultaneous approach implementing wind-powered electric vehicle charging stations for charging demand dispersion," Renewable Energy, Elsevier, vol. 144(C), pages 172-179.
    5. Adil Amin & Wajahat Ullah Khan Tareen & Muhammad Usman & Haider Ali & Inam Bari & Ben Horan & Saad Mekhilef & Muhammad Asif & Saeed Ahmed & Anzar Mahmood, 2020. "A Review of Optimal Charging Strategy for Electric Vehicles under Dynamic Pricing Schemes in the Distribution Charging Network," Sustainability, MDPI, vol. 12(23), pages 1-28, December.
    6. Li, Yanbin & Wang, Jiani & Wang, Weiye & Liu, Chang & Li, Yun, 2023. "Dynamic pricing based electric vehicle charging station location strategy using reinforcement learning," Energy, Elsevier, vol. 281(C).

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