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Electric Vehicles Charging Algorithm with Peak Power Minimization, EVs Charging Power Minimization, Ability to Respond to DR Signals and V2G Functionality

Author

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  • Grzegorz Benysek

    (Ekoenergetyka Polska S.A., ul. Nowy-Kisielin-Wysockiego 8, 66-002 Zielona Góra, Poland)

  • Bartosz Waśkowicz

    (Ekoenergetyka Polska S.A., ul. Nowy-Kisielin-Wysockiego 8, 66-002 Zielona Góra, Poland)

  • Robert Dylewski

    (Institute of Mathematics, University of Zielona Góra, ul. Licealna 9, 65-417 Zielona Góra, Poland)

  • Marcin Jarnut

    (Ekoenergetyka Polska S.A., ul. Nowy-Kisielin-Wysockiego 8, 66-002 Zielona Góra, Poland)

Abstract

The number of electric vehicles (EV) on the roads, as well as the share of EVs in use, will inevitably increase in coming decades. This creates a number of problems. A large EV fleet is a significant additional load in the power system that is impossible to accurately predict. Another related problem is the limited distribution network capacity, which is not ready for the additional load from the widespread EV infrastructure. There is a need for an EV charging coordination algorithm capable of fulfilling the charging EV needs, while using as low demanded power as possible and using the lowest power values in each EV charging profile. We propose an EV coordinating algorithm that is capable of ensuring that all connected EVs in the considered parking lot will be charged at the user-defined departure time. The algorithm also controls the charging/discharging power of every connected EV in such a way that the parking lot as a whole will use minimal possible peak power while minimizing the charging power of every EV. The proposed algorithm is also capable of responding to demand response (DR) signals. The paper also includes the results of simulation with a statistical summary of the proposed algorithm effectiveness.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:14:p:5195-:d:865154
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    References listed on IDEAS

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    1. Xydas, Erotokritos & Marmaras, Charalampos & Cipcigan, Liana M., 2016. "A multi-agent based scheduling algorithm for adaptive electric vehicles charging," Applied Energy, Elsevier, vol. 177(C), pages 354-365.
    2. 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.
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    Cited by:

    1. Bibi Ibrahim & Luis Rabelo & Alfonso T. Sarmiento & Edgar Gutierrez-Franco, 2023. "A Holistic Approach to Power Systems Using Innovative Machine Learning and System Dynamics," Energies, MDPI, vol. 16(13), pages 1-29, July.

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