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MV and LV Residential Grid Impact of Combined Slow and Fast Charging of Electric Vehicles

Author

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  • Niels Leemput

    (Faculty of Engineering, Department of Electrical Engineering, Division Electrical Energy & Computer Architectures, KU Leuven, Kasteelpark Arenberg 10, Box 2445, 3001 Leuven, Belgium)

  • Frederik Geth

    (Faculty of Engineering, Department of Electrical Engineering, Division Electrical Energy & Computer Architectures, KU Leuven, Kasteelpark Arenberg 10, Box 2445, 3001 Leuven, Belgium)

  • Juan Van Roy

    (Faculty of Engineering, Department of Electrical Engineering, Division Electrical Energy & Computer Architectures, KU Leuven, Kasteelpark Arenberg 10, Box 2445, 3001 Leuven, Belgium)

  • Pol Olivella-Rosell

    (Centre of Technological Innovation in Static Converters and Drives, Department of Electrical Engineering, College of Industrial Engineering of Barcelona, Universitat Politècnica de Catalunya-BarcelonaTech, Carrer Comte d'Urgell, 187-08036 Barcelona, Spain)

  • Johan Driesen

    (Faculty of Engineering, Department of Electrical Engineering, Division Electrical Energy & Computer Architectures, KU Leuven, Kasteelpark Arenberg 10, Box 2445, 3001 Leuven, Belgium)

  • Andreas Sumper

    (Centre of Technological Innovation in Static Converters and Drives, Department of Electrical Engineering, College of Industrial Engineering of Barcelona, Universitat Politècnica de Catalunya-BarcelonaTech, Carrer Comte d'Urgell, 187-08036 Barcelona, Spain)

Abstract

This article investigates the combined low voltage (LV) and medium voltage (MV) residential grid impact for slow and fast electric vehicle (EV) charging, for an increasing local penetration rate and for different residential slow charging strategies. A realistic case study for a Flemish urban distribution grid is used, for which three residential slow charging strategies are modeled: uncoordinated charging, residential off-peak charging, and EV-based peak shaving. For each slow charging strategy, the EV hosting capacity is determined, with and without the possibility of fast charging, while keeping the grid within its operating limits. The results show that the distribution grid impact is much less sensitive to the presence of fast charging compared to the slow charging strategy. EV-based peak shaving results in the lowest grid impact, allowing for the highest EV hosting capacity. Residential off-peak charging has the highest grid impact, due the load synchronization effect that occurs, resulting in the lowest EV hosting capacity. Therefore, the EV users should be incentivized to charge their EVs in a more grid-friendly manner when the local EV penetration rate becomes significant, as this increases the EV hosting capacity much more than the presence of fast charging decreases it.

Suggested Citation

  • Niels Leemput & Frederik Geth & Juan Van Roy & Pol Olivella-Rosell & Johan Driesen & Andreas Sumper, 2015. "MV and LV Residential Grid Impact of Combined Slow and Fast Charging of Electric Vehicles," Energies, MDPI, vol. 8(3), pages 1-24, March.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:3:p:1760-1783:d:46351
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    References listed on IDEAS

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    1. Lyon, Thomas P. & Michelin, Mark & Jongejan, Arie & Leahy, Thomas, 2012. "Is “smart charging” policy for electric vehicles worthwhile?," Energy Policy, Elsevier, vol. 41(C), pages 259-268.
    2. Schroeder, Andreas & Traber, Thure, 2012. "The economics of fast charging infrastructure for electric vehicles," Energy Policy, Elsevier, vol. 43(C), pages 136-144.
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    Cited by:

    1. Su Su & Yong Hu & Tiantian Yang & Shidan Wang & Ziqi Liu & Xiangxiang Wei & Mingchao Xia & Yutaka Ota & Koji Yamashita, 2018. "Research on an Electric Vehicle Owner-Friendly Charging Strategy Using Photovoltaic Generation at Office Sites in Major Chinese Cities," Energies, MDPI, vol. 11(2), pages 1-19, February.
    2. Viktor Slednev & Patrick Jochem & Wolf Fichtner, 2022. "Impacts of electric vehicles on the European high and extra high voltage power grid," Journal of Industrial Ecology, Yale University, vol. 26(3), pages 824-837, June.
    3. Mingchao Xia & Qingying Lai & Yajiao Zhong & Canbing Li & Hsiao-Dong Chiang, 2016. "Aggregator-Based Interactive Charging Management System for Electric Vehicle Charging," Energies, MDPI, vol. 9(3), pages 1-14, March.
    4. Sandström, Maria & Huang, Pei & Bales, Chris & Dotzauer, Erik, 2023. "Evaluation of hosting capacity of the power grid for electric vehicles – A case study in a Swedish residential area," Energy, Elsevier, vol. 284(C).
    5. Julia Vopava & Ulrich Bergmann & Thomas Kienberger, 2020. "Synergies between e-Mobility and Photovoltaic Potentials—A Case Study on an Urban Medium Voltage Grid," Energies, MDPI, vol. 13(15), pages 1-29, July.
    6. Resch, Matthias & Bühler, Jochen & Klausen, Mira & Sumper, Andreas, 2017. "Impact of operation strategies of large scale battery systems on distribution grid planning in Germany," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 1042-1063.
    7. Sylvester Johansson & Jonas Persson & Stavros Lazarou & Andreas Theocharis, 2019. "Investigation of the Impact of Large-Scale Integration of Electric Vehicles for a Swedish Distribution Network," Energies, MDPI, vol. 12(24), pages 1-22, December.
    8. Bishnu P. Bhattarai & Kurt S. Myers & Birgitte Bak-Jensen & Sumit Paudyal, 2017. "Multi-Time Scale Control of Demand Flexibility in Smart Distribution Networks," Energies, MDPI, vol. 10(1), pages 1-18, January.
    9. Paul Stewart & Chris Bingham, 2016. "Electrical Power and Energy Systems for Transportation Applications," Energies, MDPI, vol. 9(7), pages 1-3, July.
    10. Beaufils, Timothé & Pineau, Pierre-Olivier, 2019. "Assessing the impact of residential load profile changes on electricity distribution utility revenues under alternative rate structures," Utilities Policy, Elsevier, vol. 61(C).
    11. Math H. J. Bollen & Sarah K. Rönnberg, 2017. "Hosting Capacity of the Power Grid for Renewable Electricity Production and New Large Consumption Equipment," Energies, MDPI, vol. 10(9), pages 1-28, September.

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