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Optimized Integration of Electric Vehicles in Low Voltage Distribution Grids

Author

Listed:
  • Martin Spitzer

    (Energy Informatics, Friedrich-Alexander University Erlangen-Nuernberg (FAU), Martensstr. 3, 91058 Erlangen, Germany)

  • Jonas Schlund

    (Lab of Computer Networks and Communication Systems, Friedrich-Alexander University Erlangen-Nuernberg (FAU), Martensstr. 3, 91058 Erlangen, Germany)

  • Elpiniki Apostolaki-Iosifidou

    (Grid Integration, Systems, and Mobility (GISMo), SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA)

  • Marco Pruckner

    (Energy Informatics, Friedrich-Alexander University Erlangen-Nuernberg (FAU), Martensstr. 3, 91058 Erlangen, Germany
    Lab of Computer Networks and Communication Systems, Friedrich-Alexander University Erlangen-Nuernberg (FAU), Martensstr. 3, 91058 Erlangen, Germany)

Abstract

All over the world the reduction of greenhouse gas (GHG) emissions, especially in the transportation sector, becomes more and more important. Electric vehicles will be one of the key factors to mitigate GHG emissions due to their higher efficiency in contrast to internal combustion engine vehicles. On the other hand, uncoordinated charging will put more strain on electrical distribution grids and possible congestions in the grid become more likely. In this paper, we analyze the impact of uncoordinated charging, as well as optimization-based coordination strategies on the voltage stability and phase unbalances of a representative European semi-urban low voltage grid. Therefore, we model the low voltage grid as a three-phase system and take realistic arrival and departure times of the electric vehicle fleet into account. Subsequently, we compare different coordinated charging strategies with regard to their optimization objectives, e.g., cost reduction or GHG emissions reduction. Results show that possible congestion problems can be solved by coordinated charging. Additionally, depending on the objective, the costs can be reduced by more than 50% and the GHG emissions by around 40%.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:21:p:4059-:d:280116
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    References listed on IDEAS

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    8. Fretzen, Ulrich & Ansarin, Mohammad & Brandt, Tobias, 2021. "Temporal city-scale matching of solar photovoltaic generation and electric vehicle charging," Applied Energy, Elsevier, vol. 282(PA).
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    11. Wanhao Yang & Hong Wang & Zhijie Wang & Xiaolin Fu & Pengchi Ma & Zhengchen Deng & Zihao Yang, 2020. "Optimization Strategy of Electric Vehicles Charging Path Based on “Traffic-Price-Distribution” Mode," Energies, MDPI, vol. 13(12), pages 1-26, June.
    12. Je-Liang Liou & Pei-Ing Wu, 2021. "Monetary Health Co-Benefits and GHG Emissions Reduction Benefits: Contribution from Private On-the-Road Transport," IJERPH, MDPI, vol. 18(11), pages 1-19, May.
    13. Bruno Pinto & Filipe Barata & Constantino Soares & Carla Viveiros, 2020. "Fleet Transition from Combustion to Electric Vehicles: A Case Study in a Portuguese Business Campus," Energies, MDPI, vol. 13(5), pages 1-24, March.
    14. Soomin Woo & Zhe Fu & Elpiniki Apostolaki-Iosifidou & Timothy E. Lipman, 2021. "Economic and Environmental Benefits for Electricity Grids from Spatiotemporal Optimization of Electric Vehicle Charging," Energies, MDPI, vol. 14(24), pages 1-22, December.
    15. Tuchnitz, Felix & Ebell, Niklas & Schlund, Jonas & Pruckner, Marco, 2021. "Development and Evaluation of a Smart Charging Strategy for an Electric Vehicle Fleet Based on Reinforcement Learning," Applied Energy, Elsevier, vol. 285(C).
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