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Surrogate DC Microgrid Models for Optimization of Charging Electric Vehicles under Partial Observability

Author

Listed:
  • Grigorii Veviurko

    (Faculty of Electrical Engineering, Mathematics and Computer Sciences, Delft University of Technology, Van Mourik Broekmanweg 6, 2628 XE Delft, The Netherlands)

  • Wendelin Böhmer

    (Faculty of Electrical Engineering, Mathematics and Computer Sciences, Delft University of Technology, Van Mourik Broekmanweg 6, 2628 XE Delft, The Netherlands)

  • Laurens Mackay

    (DC Opportunities R&D B.V., Molengraaffsingel 12, 2629 JD Delft, The Netherlands)

  • Mathijs de Weerdt

    (Faculty of Electrical Engineering, Mathematics and Computer Sciences, Delft University of Technology, Van Mourik Broekmanweg 6, 2628 XE Delft, The Netherlands)

Abstract

Many electric vehicles (EVs) are using today’s distribution grids, and their flexibility can be highly beneficial for the grid operators. This flexibility can be best exploited by DC power networks, as they allow charging and discharging without extra power electronics and transformation losses. From the grid control perspective, algorithms for planning EV charging are necessary. This paper studies the problem of EV charging planning under limited grid capacity and extends it to the partially observable case. We demonstrate how limited information about the EV locations in a grid may disrupt the operation planning in DC grids with tight constraints. We introduce two methods to change the grid topology such that partial observability of the EV locations is resolved. The suggested models are evaluated on the IEEE 16 bus system and multiple randomly generated grids with varying capacities. The experiments show that these methods efficiently solve the partially observable EV charging planning problem and offer a trade-off between computational time and performance.

Suggested Citation

  • Grigorii Veviurko & Wendelin Böhmer & Laurens Mackay & Mathijs de Weerdt, 2022. "Surrogate DC Microgrid Models for Optimization of Charging Electric Vehicles under Partial Observability," Energies, MDPI, vol. 15(4), pages 1-17, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1389-:d:749280
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    References listed on IDEAS

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    1. Wu, Fei & Sioshansi, Ramteen, 2017. "A two-stage stochastic optimization model for scheduling electric vehicle charging loads to relieve distribution-system constraints," Transportation Research Part B: Methodological, Elsevier, vol. 102(C), pages 55-82.
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