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Smart Charging of Electric Vehicles Considering SOC-Dependent Maximum Charging Powers

Author

Listed:
  • Benjamin Schaden

    (Institute of Logic and Computation, TU Wien, 1040 Vienna, Austria)

  • Thomas Jatschka

    (Institute of Logic and Computation, TU Wien, 1040 Vienna, Austria)

  • Steffen Limmer

    (Honda Research Institute Europe GmbH, 63073 Offenbach, Germany)

  • Günther Robert Raidl

    (Institute of Logic and Computation, TU Wien, 1040 Vienna, Austria)

Abstract

The aim of this work is to schedule the charging of electric vehicles (EVs) at a single charging station such that the temporal availability of each EV as well as the maximum available power at the station are considered. The total costs for charging the vehicles should be minimized w.r.t. time-dependent electricity costs. A particular challenge investigated in this work is that the maximum power at which a vehicle can be charged is dependent on the current state of charge (SOC) of the vehicle. Such a consideration is particularly relevant in the case of fast charging. Considering this aspect for a discretized time horizon is not trivial, as the maximum charging power of an EV may also change in between time steps. To deal with this issue, we instead consider the energy by which an EV can be charged within a time step. For this purpose, we show how to derive the maximum charging energy in an exact as well as an approximate way. Moreover, we propose two methods for solving the scheduling problem. The first is a cutting plane method utilizing a convex hull of the, in general, nonconcave SOC–power curves. The second method is based on a piecewise linearization of the SOC–energy curve and is effectively solved by branch-and-cut. The proposed approaches are evaluated on benchmark instances, which are partly based on real-world data. To deal with EVs arriving at different times as well as charging costs changing over time, a model-based predictive control strategy is usually applied in such cases. Hence, we also experimentally evaluate the performance of our approaches for such a strategy. The results show that optimally solving problems with general piecewise linear maximum power functions requires high computation times. However, problems with concave, piecewise linear maximum charging power functions can efficiently be dealt with by means of linear programming. Approximating an EV’s maximum charging power with a concave function may result in practically infeasible solutions, due to vehicles potentially not reaching their specified target SOC. However, our results show that this error is negligible in practice.

Suggested Citation

  • Benjamin Schaden & Thomas Jatschka & Steffen Limmer & Günther Robert Raidl, 2021. "Smart Charging of Electric Vehicles Considering SOC-Dependent Maximum Charging Powers," Energies, MDPI, vol. 14(22), pages 1-33, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:22:p:7755-:d:682658
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    References listed on IDEAS

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    1. Sara Deilami & S. M. Muyeen, 2020. "An Insight into Practical Solutions for Electric Vehicle Charging in Smart Grid," Energies, MDPI, vol. 13(7), pages 1-13, March.
    2. Steffen Limmer, 2019. "Dynamic Pricing for Electric Vehicle Charging—A Literature Review," Energies, MDPI, vol. 12(18), pages 1-24, September.
    3. Jinil Han & Jongyoon Park & Kyungsik Lee, 2017. "Optimal Scheduling for Electric Vehicle Charging under Variable Maximum Charging Power," Energies, MDPI, vol. 10(7), pages 1-15, July.
    4. Nicolson, Moira L. & Fell, Michael J. & Huebner, Gesche M., 2018. "Consumer demand for time of use electricity tariffs: A systematized review of the empirical evidence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 97(C), pages 276-289.
    5. Ilham Naharudinsyah & Steffen Limmer, 2018. "Optimal Charging of Electric Vehicles with Trading on the Intraday Electricity Market," Energies, MDPI, vol. 11(6), pages 1-12, June.
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    Citations

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    Cited by:

    1. Francesco Lo Franco & Vincenzo Cirimele & Mattia Ricco & Vitor Monteiro & Joao L. Afonso & Gabriele Grandi, 2022. "Smart Charging for Electric Car-Sharing Fleets Based on Charging Duration Forecasting and Planning," Sustainability, MDPI, vol. 14(19), pages 1-19, September.
    2. Francesco Lo Franco & Mattia Ricco & Vincenzo Cirimele & Valerio Apicella & Benedetto Carambia & Gabriele Grandi, 2023. "Electric Vehicle Charging Hub Power Forecasting: A Statistical and Machine Learning Based Approach," Energies, MDPI, vol. 16(4), pages 1-27, February.
    3. Rafał Różycki & Joanna Józefowska & Krzysztof Kurowski & Tomasz Lemański & Tomasz Pecyna & Marek Subocz & Grzegorz Waligóra, 2022. "A Quantum Approach to the Problem of Charging Electric Cars on a Motorway," Energies, MDPI, vol. 16(1), pages 1-20, December.
    4. Steffen Limmer & Johannes Varga & Günther Robert Raidl, 2023. "Large Neighborhood Search for Electric Vehicle Fleet Scheduling," Energies, MDPI, vol. 16(12), pages 1-14, June.

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