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Electric Vehicle Charging Scheduling Considering Different Charging Demands

In: Smart Energy Management

Author

Listed:
  • Kaile Zhou

    (Hefei University of Technology)

  • Lulu Wen

    (Hefei University of Technology)

Abstract

The uncoordinated charging of large amounts of electric vehicles (EVs) can lead to a substantial surge of peak loads, which could be harmful to the operation of the power system. In this chapter, a coordinated charging scheduling method is provided to achieve peak shaving and valley filling of the microgrid load when EVs are connected. In the method, the charging mode of EVs is selected based on a charging urgency indicator, which is used to measure the charging demand. Then a coordinated charging scheduling optimization model that aims to minimize the overall peak-valley difference of the microgrid load is presented. The optimization model is subject to a series of constraints set for slow charging EVs, fast-charging EVs and microgrid operation. Furthermore, Monte Carlo simulation (MCS) is used to measure the randomness of EVs. The results have shed light on both the selection of charging modes by EV owners and peak shaving and valley filling for microgrid operation. As a result, this model can support a more friendly power supply–demand interaction to accommodate the increasing access of EVs and the rapid development of the flexible microgrid.

Suggested Citation

  • Kaile Zhou & Lulu Wen, 2022. "Electric Vehicle Charging Scheduling Considering Different Charging Demands," Springer Books, in: Smart Energy Management, chapter 0, pages 223-249, Springer.
    • Handle: RePEc:spr:sprchp:978-981-16-9360-1_10
    DOI: 10.1007/978-981-16-9360-1_10
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    Citations

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    Cited by:

    1. Liu, Shuohan & Cao, Yue & Ni, Qiang & Xu, Lexi & Zhu, Yongdong & Zhang, Xin, 2023. "Towards reservation-based E-mobility service via hybrid of V2V and G2V charging modes," Energy, Elsevier, vol. 268(C).
    2. 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.
    3. Hussain, Shahid & Irshad, Reyazur Rashid & Pallonetto, Fabiano & Hussain, Ihtisham & Hussain, Zakir & Tahir, Muhammad & Abimannan, Satheesh & Shukla, Saurabh & Yousif, Adil & Kim, Yun-Su & El-Sayed, H, 2023. "Hybrid coordination scheme based on fuzzy inference mechanism for residential charging of electric vehicles," Applied Energy, Elsevier, vol. 352(C).
    4. Hany Habbak & Mohamed Baza & Mohamed M. E. A. Mahmoud & Khaled Metwally & Ahmed Mattar & Gouda I. Salama, 2022. "Privacy-Preserving Charging Coordination Scheme for Smart Power Grids Using a Blockchain," Energies, MDPI, vol. 15(23), pages 1-23, November.
    5. 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).

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