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Calculating Operational Patterns for Electric Vehicle Charging on a Real Distribution Network Based on Renewables’ Production

Author

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  • Stavros Lazarou

    (Department of Electrical and Electronic Engineering Educators, School of Pedagogical & Technological Education (ASPETE), Heraklion Attikis, 141 21 Athens, Greece)

  • Vasiliki Vita

    (Department of Electrical and Electronic Engineering Educators, School of Pedagogical & Technological Education (ASPETE), Heraklion Attikis, 141 21 Athens, Greece)

  • Christos Christodoulou

    (Department of Electrical and Electronic Engineering Educators, School of Pedagogical & Technological Education (ASPETE), Heraklion Attikis, 141 21 Athens, Greece)

  • Lambros Ekonomou

    (Department of Electrical and Electronic Engineering Educators, School of Pedagogical & Technological Education (ASPETE), Heraklion Attikis, 141 21 Athens, Greece)

Abstract

The connection of electric vehicles to distribution networks has been an emerging issue of paramount importance for power systems. On one hand, it provides new opportunities for climate change mitigation, if electric energy used for charging is produced from zero emission sources. On the other hand, it stresses networks that are now required to accommodate, in addition to the loads and production from distributed generation they are initially designed for, loads from electric vehicles charging. In order to achieve maximum use of the grid without substantially affecting its performance, these issues have to be addressed in a coordinated manner, which requires adequate knowledge of the system under consideration. It is advantageous that electric vehicle charging can be controlled to a certain degree. This research provides better understanding of real distribution networks’ operation, proposing specific operational points through minimizing electric vehicle charging effects. The probabilistic Monte Carlo method on high performance computers is used for the calculations.

Suggested Citation

  • Stavros Lazarou & Vasiliki Vita & Christos Christodoulou & Lambros Ekonomou, 2018. "Calculating Operational Patterns for Electric Vehicle Charging on a Real Distribution Network Based on Renewables’ Production," Energies, MDPI, vol. 11(9), pages 1-15, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2400-:d:169165
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    References listed on IDEAS

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    Cited by:

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    3. Tiago P. Abud & Andre A. Augusto & Marcio Z. Fortes & Renan S. Maciel & Bruno S. M. C. Borba, 2022. "State of the Art Monte Carlo Method Applied to Power System Analysis with Distributed Generation," Energies, MDPI, vol. 16(1), pages 1-24, December.
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    6. Mike F. Voss & Steven P. Haveman & Gerrit Maarten Bonnema, 2021. "In-Company Smart Charging: Development of a Simulation Model to Facilitate a Smart EV Charging System," Energies, MDPI, vol. 14(20), pages 1-34, October.
    7. Nimalsiri, Nanduni I. & Ratnam, Elizabeth L. & Mediwaththe, Chathurika P. & Smith, David B. & Halgamuge, Saman K., 2021. "Coordinated charging and discharging control of electric vehicles to manage supply voltages in distribution networks: Assessing the customer benefit," Applied Energy, Elsevier, vol. 291(C).
    8. Quan Li & Xin Wang & Shuaiang Rong, 2018. "Probabilistic Load Flow Method Based on Modified Latin Hypercube-Important Sampling," Energies, MDPI, vol. 11(11), pages 1-14, November.
    9. Stavros Lazarou & Vasiliki Vita & Lambros Ekonomou, 2018. "Protection Schemes of Meshed Distribution Networks for Smart Grids and Electric Vehicles," Energies, MDPI, vol. 11(11), pages 1-17, November.
    10. Martin Spitzer & Jonas Schlund & Elpiniki Apostolaki-Iosifidou & Marco Pruckner, 2019. "Optimized Integration of Electric Vehicles in Low Voltage Distribution Grids," Energies, MDPI, vol. 12(21), pages 1-19, October.

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