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Collaborative Operation Optimization Scheduling Strategy of Electric Vehicle and Steel Plant Considering V2G

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  • Weiqi Pan

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Bokang Zou

    (School of Electrical & Automation Engineering, Nanjing Normal University, Nanjing 210023, China)

  • Fengtao Li

    (School of Electrical & Automation Engineering, Nanjing Normal University, Nanjing 210023, China)

  • Yifu Luo

    (School of Electrical & Automation Engineering, Nanjing Normal University, Nanjing 210023, China)

  • Qirui Chen

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Yuanshi Zhang

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Yang Li

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

Abstract

With the shortage of fossil fuels and the increasingly serious problem of environmental pollution, low-carbon industrial production technology has become an effective way to reduce industrial carbon emissions. Electrified steel plants based on electronic arc furnaces (EAF) can reduce most carbon emissions compared with traditional steel production methods, but the production steps have fixed electricity consumption behavior, and impact loads are easily generated in the production process, which has an impact on the stability of the power system. EV has the characteristics of a mobile energy storage unit. When a large number of EVs are connected to the power grid, they can be regarded as distributed energy storage units with scheduling flexibility. Through the orderly scheduling of EVs, the spatial–temporal transfer of EV charging and discharging load can be realized. Therefore, the EV situated in the steel plant’s distribution network node has the capacity to be utilized by providing peak shaving and valley filling services for the steel production load. This study proposes an operation optimization scheduling method for EVs and steel plants. Taking the lowest overall operating cost as the objective, an optimal scheduling model considering EVs operation, steel plant, and distributed generator is established. Based on the IEEE-33 node distribution network model considering distributed generators, the proposed model is simulated and analyzed, and the effectiveness of the EV steel plant operation optimization scheduling strategy is investigated.

Suggested Citation

  • Weiqi Pan & Bokang Zou & Fengtao Li & Yifu Luo & Qirui Chen & Yuanshi Zhang & Yang Li, 2024. "Collaborative Operation Optimization Scheduling Strategy of Electric Vehicle and Steel Plant Considering V2G," Energies, MDPI, vol. 17(11), pages 1-14, May.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:11:p:2448-:d:1398667
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    References listed on IDEAS

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    1. He, Lifu & Yang, Jun & Yan, Jun & Tang, Yufei & He, Haibo, 2016. "A bi-layer optimization based temporal and spatial scheduling for large-scale electric vehicles," Applied Energy, Elsevier, vol. 168(C), pages 179-192.
    2. Mohamed Mokhtar & Mostafa F. Shaaban & Mahmoud H. Ismail & Hatem F. Sindi & Muhyaddin Rawa, 2022. "Reliability Assessment under High Penetration of EVs including V2G Strategy," Energies, MDPI, vol. 15(4), pages 1-17, February.
    3. Zhang, Yuanshi & Qian, Wenyan & Ye, Yujian & Li, Yang & Tang, Yi & Long, Yu & Duan, Meimei, 2023. "A novel non-intrusive load monitoring method based on ResNet-seq2seq networks for energy disaggregation of distributed energy resources integrated with residential houses," Applied Energy, Elsevier, vol. 349(C).
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    Cited by:

    1. Guo, Shiliang & He, Jianqi & Ma, Kai & Yang, Jie & Wang, Yaochen & Li, Pengpeng, 2025. "Robust economic dispatch for industrial microgrids with electric vehicle demand response," Renewable Energy, Elsevier, vol. 240(C).

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