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A simplified control algorithm for utilities to utilize plug-in electric vehicles to reduce distribution transformer overloading

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  • Shokrzadeh, Shahab
  • Ribberink, Hajo
  • Rishmawi, Issa
  • Entchev, Evgueniy

Abstract

The introduction of electric vehicles on the power grid translates to operational challenges and opportunities of using these vehicles as distributed energy resources. This paper presents an algorithm for utilities to identify optimal scenarios for controlling electric load of plug-in electric vehicles on a neighborhood transformer, and their potential contribution to reducing overloading through controlled charging and feeding power back to the grid. A dataset of real-world driving data of 2800 vehicles in Canada is used to emulate realistic driving patterns for the simulations. The electric load of vehicles charging is estimated for different charging scenarios, battery capacities, and numbers of electric vehicle. Operational energy consumption data of single-family homes are used to predict realistic power profiles of a residential neighborhood. Different scenarios of controlling the electric load on a pole transformer are investigated for two seasons to account for ambient temperatures. By simulating the charging impacts of electric vehicles on a neighborhood transformer, the research presents a simplified control algorithm of optimal strategies to control and reduce transformer overloading. Results show that vehicle-to-grid is an effective measure to reduce peak loads, while climate condition, vehicle penetration rate, and driving profile are the key factors to the control strategies.

Suggested Citation

  • Shokrzadeh, Shahab & Ribberink, Hajo & Rishmawi, Issa & Entchev, Evgueniy, 2017. "A simplified control algorithm for utilities to utilize plug-in electric vehicles to reduce distribution transformer overloading," Energy, Elsevier, vol. 133(C), pages 1121-1131.
    • Handle: RePEc:eee:energy:v:133:y:2017:i:c:p:1121-1131
    DOI: 10.1016/j.energy.2017.04.152
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    Citations

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    Cited by:

    1. Bryden, Thomas S. & Hilton, George & Cruden, Andrew & Holton, Tim, 2018. "Electric vehicle fast charging station usage and power requirements," Energy, Elsevier, vol. 152(C), pages 322-332.
    2. Hamid Mirshekali & Athila Q. Santos & Hamid Reza Shaker, 2023. "A Survey of Time-Series Prediction for Digitally Enabled Maintenance of Electrical Grids," Energies, MDPI, vol. 16(17), pages 1-29, August.
    3. Charilaos Latinopoulos & Aruna Sivakumar & John W. Polak, 2021. "Optimal Pricing of Vehicle-to-Grid Services Using Disaggregate Demand Models," Energies, MDPI, vol. 14(4), pages 1-27, February.
    4. Li, Pengfei & Hu, Weihao & Xu, Xiao & Huang, Qi & Liu, Zhou & Chen, Zhe, 2019. "A frequency control strategy of electric vehicles in microgrid using virtual synchronous generator control," Energy, Elsevier, vol. 189(C).
    5. Zou, Wenke & Sun, Yongjun & Gao, Dian-ce & Zhang, Xu & Liu, Junyao, 2023. "A review on integration of surging plug-in electric vehicles charging in energy-flexible buildings: Impacts analysis, collaborative management technologies, and future perspective," Applied Energy, Elsevier, vol. 331(C).
    6. Shin-Ki Hong & Sung Gu Lee & Myungchin Kim, 2020. "Assessment and Mitigation of Electric Vehicle Charging Demand Impact to Transformer Aging for an Apartment Complex," Energies, MDPI, vol. 13(10), pages 1-23, May.
    7. Mohammadi Landi, Meysam & Mohammadi, Mohammad & Rastegar, Mohammad, 2018. "Simultaneous determination of optimal capacity and charging profile of plug-in electric vehicle parking lots in distribution systems," Energy, Elsevier, vol. 158(C), pages 504-511.
    8. Li, Yipu & Su, Hao & Zhou, Yun & Chen, Lixia & Shi, Yiwei & Li, Hengjie & Feng, Donghan, 2023. "Two-stage real-time optimal electricity dispatch strategy for urban residential quarter with electric vehicles’ charging load," Energy, Elsevier, vol. 268(C).
    9. Tohid Harighi & Sanjeevikumar Padmanaban & Ramazan Bayindir & Eklas Hossain & Jens Bo Holm-Nielsen, 2019. "Electric Vehicle Charge Stations Location Analysis and Determination—Ankara (Turkey) Case Study," Energies, MDPI, vol. 12(18), pages 1-22, September.
    10. Ahmadian, Ali & Sedghi, Mahdi & Fgaier, Hedia & Mohammadi-ivatloo, Behnam & Golkar, Masoud Aliakbar & Elkamel, Ali, 2019. "PEVs data mining based on factor analysis method for energy storage and DG planning in active distribution network: Introducing S2S effect," Energy, Elsevier, vol. 175(C), pages 265-277.
    11. Qiu, Chengqun & Wang, Guolin & Meng, Mingyu & Shen, Yujie, 2018. "A novel control strategy of regenerative braking system for electric vehicles under safety critical driving situations," Energy, Elsevier, vol. 149(C), pages 329-340.

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