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Comparative Impact of Three Practical Electric Vehicle Charging Scheduling Schemes on Low Voltage Distribution Grids

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  • Yunhe Yu

    (DCES Group, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands)

  • Aditya Shekhar

    (DCES Group, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands)

  • Gautham Ram Chandra Mouli

    (DCES Group, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands)

  • Pavol Bauer

    (DCES Group, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands)

Abstract

This paper benchmarks the performance of three practical electric vehicle (EV) charging scheduling methods relative to uncontrolled charging (UNC) in low-voltage (LV) distribution grids. The charging methods compared are the voltage droop method (VDM), price-signal-based method (PSM) and average rate method (ARM). Trade-offs associated with the grid performance, charging demand fulfilment and economic benefits are explored for three different grid types and four increasing levels of EV penetration for summer and winter. This study was carried out using grid simulations of six existing Dutch distribution grids, and the EV charging demand was generated based on 1.5 M EV charging sessions; therefore, the findings of this research are relevant for actual case studies. The results suggest that the PSM can be a preferred strategy for achieving a charging cost reduction of 6–11% when the grid performance is not a bottleneck for the given EV penetration. However, it can lead to an increased peak loading of the grid under certain operational conditions, resulting in a charging energy deficiency ratio of 4–8%. The VDM should be preferred if user information on the parking time and energy demand is not consistently available, and if the mitigation of grid congestion is critical. However, both unfinished charging events and charging costs increase with the VDM. The ARM provides the best balance in the trade-offs associated with the mitigation of grid congestion and price reduction, as well as charging completion. This research provides a perception of how to select the most appropriate practical charging strategy based on the given system requirements. The outcome of this study can also serve as a benchmark for advanced smart charging algorithm evaluation in the future.

Suggested Citation

  • Yunhe Yu & Aditya Shekhar & Gautham Ram Chandra Mouli & Pavol Bauer, 2022. "Comparative Impact of Three Practical Electric Vehicle Charging Scheduling Schemes on Low Voltage Distribution Grids," Energies, MDPI, vol. 15(22), pages 1-24, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8722-:d:978451
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    References listed on IDEAS

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    1. Chandra Mouli, G.R. & Bauer, P. & Zeman, M., 2016. "System design for a solar powered electric vehicle charging station for workplaces," Applied Energy, Elsevier, vol. 168(C), pages 434-443.
    2. Crozier, Constance & Morstyn, Thomas & McCulloch, Malcolm, 2020. "The opportunity for smart charging to mitigate the impact of electric vehicles on transmission and distribution systems," Applied Energy, Elsevier, vol. 268(C).
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    1. Nico Brinkel & Thijs Wijk & Anoeska Buijze & Nanda Kishor Panda & Jelle Meersmans & Peter Markotić & Bart Ree & Henk Fidder & Baerte Brey & Simon Tindemans & Tarek AlSkaif & Wilfried Sark, 2024. "Enhancing smart charging in electric vehicles by addressing paused and delayed charging problems," Nature Communications, Nature, vol. 15(1), pages 1-10, December.

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