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The SOC Based Dynamic Charging Coordination of EVs in the PV-Penetrated Distribution Network Using Real-World Data

Author

Listed:
  • Murat Akil

    (Department of Electronics and Automation, Aksaray Technical Science Vocational School, Aksaray University, Aksaray 68100, Turkey)

  • Emrah Dokur

    (Department of Electrical-Electronics Engineering, Engineering Faculty, Bilecik Seyh Edebali University, Bilecik 11200, Turkey
    Marine and Renewable Energy Center, University of College Cork, P43 C573 Cork, Ireland)

  • Ramazan Bayindir

    (Department of Electrical-Electronics Engineering, Technology Faculty, Gazi University, Ankara 06500, Turkey)

Abstract

A successful distribution network can continue to operate despite the uncertainties at the charging station, with appropriate equipment retrofits and upgrades. However, these new investments in the grid can become complex in terms of time and space. In this paper, we propose a dynamic charge coordination (DCC) method based on the battery state of charge (SOC) of electric vehicles (EVs) in line with this purpose. The collective uncoordinated charging profiles of EVs charged at maximum power were investigated based on statistical data for distances of EVs and a real dataset for charging characteristics in the existing grid infrastructure. The proposed strategy was investigated using the modified Roy Billinton Test System (RBTS) performed by DIgSILENT Powerfactory simulation software for a total 50 EVs in 30 different models. Then, the load balancing situations were analyzed with the integration of the photovoltaic (PV) generation and battery energy storage system (BESS) into the bus bars where the EVs were fed into the grid. According to the simulation results, the proposed method dramatically reduces the effects on the grid compared to the uncoordinated charging method. Furthermore, the integration of PV and BESS system, load balancing for EVs was successfully achieved with the proposed approach.

Suggested Citation

  • Murat Akil & Emrah Dokur & Ramazan Bayindir, 2021. "The SOC Based Dynamic Charging Coordination of EVs in the PV-Penetrated Distribution Network Using Real-World Data," Energies, MDPI, vol. 14(24), pages 1-19, December.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:24:p:8508-:d:704526
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    References listed on IDEAS

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    1. Xydas, Erotokritos & Marmaras, Charalampos & Cipcigan, Liana M. & Jenkins, Nick & Carroll, Steve & Barker, Myles, 2016. "A data-driven approach for characterising the charging demand of electric vehicles: A UK case study," Applied Energy, Elsevier, vol. 162(C), pages 763-771.
    2. Farmann, Alexander & Sauer, Dirk Uwe, 2018. "Comparative study of reduced order equivalent circuit models for on-board state-of-available-power prediction of lithium-ion batteries in electric vehicles," Applied Energy, Elsevier, vol. 225(C), pages 1102-1122.
    3. Manu Lahariya & Dries F. Benoit & Chris Develder, 2020. "Synthetic Data Generator for Electric Vehicle Charging Sessions: Modeling and Evaluation Using Real-World Data," Energies, MDPI, vol. 13(16), pages 1-18, August.
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    Cited by:

    1. Swapna Ganapaneni & Srinivasa Varma Pinni & Ch. Rami Reddy & Flah Aymen & Mohammed Alqarni & Basem Alamri & Habib Kraiem, 2022. "Distribution System Service Restoration Using Electric Vehicles," Energies, MDPI, vol. 15(9), pages 1-15, April.
    2. Vitor Fernão Pires & Ilhami Colak & Fujio Kurokawa, 2022. "Smart Grid as a Key Tool for the Future of Electrical Distribution Networks," Energies, MDPI, vol. 15(9), pages 1-3, April.
    3. Carlos Henrique Valério de Moraes & Jonas Lopes de Vilas Boas & Germano Lambert-Torres & Gilberto Capistrano Cunha de Andrade & Claudio Inácio de Almeida Costa, 2022. "Intelligent Power Distribution Restoration Based on a Multi-Objective Bacterial Foraging Optimization Algorithm," Energies, MDPI, vol. 15(4), pages 1-23, February.

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