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A novel real-time scheduling strategy with near-linear complexity for integrating large-scale electric vehicles into smart grid

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  • Zheng, Yanchong
  • Shang, Yitong
  • Shao, Ziyun
  • Jian, Linni

Abstract

Coordinated charging can utilize the properties of electric vehicles (EVs) to obtain various benefits. However, there are two major challenges, viz. the uncertainty of EV charging behaviors and the overlong solving time for the optimal solutions in the charging scheduling problem of large-scale EVs. It is almost infeasible to precisely predict the charging information of EVs due to the uncertainty of their mobility as transportation tools. In order to tackle this issue, the real-time charging scheduling method is employed in this paper. Even so, the computational complexity is crucially important in the real-time scheduling methods since the charging strategies of large-scale EVs must be acquired in a short time. Hence, a high efficient methodology is proposed for EV real-time scheduling based on the definition of capacity margin index and charging priority index. Finally, the simulation results show that the proposed scheduling method has a significant superiority over the uncoordinated charging in the regard of relieving the demand stress on the power system. Moreover, the complexity analysis demonstrates that the proposed method has near-linear complexity so that it can acquire the optimal real-time charging scheme in a rather shorter time than other methods.

Suggested Citation

  • Zheng, Yanchong & Shang, Yitong & Shao, Ziyun & Jian, Linni, 2018. "A novel real-time scheduling strategy with near-linear complexity for integrating large-scale electric vehicles into smart grid," Applied Energy, Elsevier, vol. 217(C), pages 1-13.
    • Handle: RePEc:eee:appene:v:217:y:2018:i:c:p:1-13
    DOI: 10.1016/j.apenergy.2018.02.084
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    12. Grzegorz Benysek & Bartosz Waśkowicz & Robert Dylewski & Marcin Jarnut, 2022. "Electric Vehicles Charging Algorithm with Peak Power Minimization, EVs Charging Power Minimization, Ability to Respond to DR Signals and V2G Functionality," Energies, MDPI, vol. 15(14), pages 1-16, July.
    13. Yan, Jie & Menghwar, Mohan & Asghar, Ehtisham & Kumar Panjwani, Manoj & Liu, Yongqian, 2019. "Real-time energy management for a smart-community microgrid with battery swapping and renewables," Applied Energy, Elsevier, vol. 238(C), pages 180-194.
    14. Md. Rayid Hasan Mojumder & Fahmida Ahmed Antara & Md. Hasanuzzaman & Basem Alamri & Mohammad Alsharef, 2022. "Electric Vehicle-to-Grid (V2G) Technologies: Impact on the Power Grid and Battery," Sustainability, MDPI, vol. 14(21), pages 1-53, October.
    15. Khaki, Behnam & Chu, Chicheng & Gadh, Rajit, 2019. "Hierarchical distributed framework for EV charging scheduling using exchange problem," Applied Energy, Elsevier, vol. 241(C), pages 461-471.
    16. González-Garrido, Amaia & González-Pérez, Mikel & Asensio, Francisco Javier & Cortes-Borray, Andrés Felipe & Santos-Mugica, Maider & Vicente-Figueirido, Ibon, 2024. "Hierarchical control for collaborative electric vehicle charging to alleviate network congestion and enhance EV hosting in constrained distribution networks," Renewable Energy, Elsevier, vol. 230(C).
    17. Wei Li & Jiekai Shi & Hanyun Zhou, 2024. "Coordinated Charging Scheduling Approach for Plug-In Hybrid Electric Vehicles Considering Multi-Objective Weighting Control in a Large-Scale Future Smart Grid," Energies, MDPI, vol. 17(13), pages 1-17, June.
    18. Li, Bin & Dong, Xujun & Wen, Jianghui, 2022. "Cooperative-driving control for mixed fleets at wireless charging sections for lane changing behaviour," Energy, Elsevier, vol. 243(C).
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    21. Huang, Tian-en & Guo, Qinglai & Sun, Hongbin & Tan, Chin-Woo & Hu, Tianyu, 2019. "A deep spatial-temporal data-driven approach considering microclimates for power system security assessment," Applied Energy, Elsevier, vol. 237(C), pages 36-48.

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