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Electric vehicle charging scheduling considering urgent demand under different charging modes

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  • Liu, Lu
  • Zhou, Kaile

Abstract

This study proposes a multi-objective electric vehicle (EV) charging scheduling model, aiming at minimizing both the peak-valley load difference of power grid and the total charging cost of EV users. Two modes with different charging power and tariff schemes are investigated for EVs with urgent and unurgent charging demand, respectively. A case study is carried out with 100 EVs under home and public charging mode to demonstrate the effectiveness of the proposed model. Moreover, 200, 300 and 500 EVs are considered to further investigate the influence of number of EVs. The proposed multi-objective charging scheduling model can benefit both the power grid and the EV users, since it can not only reduce the impact of EVs on the stable and safe operation of power grid but also reduce the charging cost of EV users. It also shows that the number of EVs has no significant effect on the reduction ratio of peak-valley load difference and total charging cost. But EV users’ charging behavior affect the effectiveness of coordinated charging scheduling model. The results of this study can better support the operation of power system with high penetration of EVs and the sustainable development of EV industry.

Suggested Citation

  • Liu, Lu & Zhou, Kaile, 2022. "Electric vehicle charging scheduling considering urgent demand under different charging modes," Energy, Elsevier, vol. 249(C).
    • Handle: RePEc:eee:energy:v:249:y:2022:i:c:s036054422200617x
    DOI: 10.1016/j.energy.2022.123714
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    4. Zhao, Zhonghao & Lee, Carman K.M. & Ren, Jingzheng, 2024. "A two-level charging scheduling method for public electric vehicle charging stations considering heterogeneous demand and nonlinear charging profile," Applied Energy, Elsevier, vol. 355(C).
    5. Pegah Alaee & Julius Bems & Amjad Anvari-Moghaddam, 2023. "A Review of the Latest Trends in Technical and Economic Aspects of EV Charging Management," Energies, MDPI, vol. 16(9), pages 1-28, April.
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    7. Zeinab Teimoori & Abdulsalam Yassine, 2022. "A Review on Intelligent Energy Management Systems for Future Electric Vehicle Transportation," Sustainability, MDPI, vol. 14(21), pages 1-23, October.

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