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Method for Planning, Optimizing, and Regulating EV Charging Infrastructure

Author

Listed:
  • Amor Chowdhury

    (Faculty of Energy Technology, University of Maribor, Hočevarjev trg 1, 8270 Krško, Slovenia
    Faculty of Mechanical Engineering, University of Ljubljana, Aškerčeva cesta 6, 1000 Ljubljana, Slovenia)

  • Saša Klampfer

    (Margento R&D d.o.o., Gosposvetska 84, 2000 Maribor, Slovenia)

  • Klemen Sredenšek

    (Faculty of Energy Technology, University of Maribor, Hočevarjev trg 1, 8270 Krško, Slovenia)

  • Sebastijan Seme

    (Faculty of Energy Technology, University of Maribor, Hočevarjev trg 1, 8270 Krško, Slovenia
    Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000 Maribor, Slovenia)

  • Miralem Hadžiselimović

    (Faculty of Energy Technology, University of Maribor, Hočevarjev trg 1, 8270 Krško, Slovenia
    Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000 Maribor, Slovenia)

  • Bojan Štumberger

    (Faculty of Energy Technology, University of Maribor, Hočevarjev trg 1, 8270 Krško, Slovenia
    Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000 Maribor, Slovenia)

Abstract

The paper presents and solves the problems of modeling and designing the required EV charging service capacity for systems with a slow dynamic component. This includes possible bursts within a peak hour interval. A simulation tool with a newly implemented capacity planning method has been developed and implemented for these needs. The method can be used for different system simulations and simultaneously for systems with high, medium, and low service dynamics. The proposed method is based on a normal distribution, a primary mechanism that describes events within a daily interval (24 h) or a peak hour interval (rush hour). The goal of the presented approach, including the proposed method, is to increase the level and quality of the EV charging service system. The near-optimal solution with the presented method can be found manually by changing the service capacity parameter concerning the criterion function. Manual settings limit the number of rejected events, the time spent in the queue, and other service system performance parameters. In addition to manual search for near-optimal solutions, the method also provides automatic search by using the automation procedure of simulation runs and increasing/decreasing the service capacity parameter by a specifically calculated amount.

Suggested Citation

  • Amor Chowdhury & Saša Klampfer & Klemen Sredenšek & Sebastijan Seme & Miralem Hadžiselimović & Bojan Štumberger, 2022. "Method for Planning, Optimizing, and Regulating EV Charging Infrastructure," Energies, MDPI, vol. 15(13), pages 1-32, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4756-:d:850988
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    References listed on IDEAS

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    3. Giansoldati, Marco & Monte, Adriana & Scorrano, Mariangela, 2020. "Barriers to the adoption of electric cars: Evidence from an Italian survey," Energy Policy, Elsevier, vol. 146(C).
    4. R. E. Odeh & J. O. Evans, 1974. "The Percentage Points of the Normal Distribution," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 23(1), pages 96-97, March.
    5. Kenné, Jean-Pierre & Dejax, Pierre & Gharbi, Ali, 2012. "Production planning of a hybrid manufacturing–remanufacturing system under uncertainty within a closed-loop supply chain," International Journal of Production Economics, Elsevier, vol. 135(1), pages 81-93.
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    Cited by:

    1. Verónica Anadón Martínez & Andreas Sumper, 2023. "Planning and Operation Objectives of Public Electric Vehicle Charging Infrastructures: A Review," Energies, MDPI, vol. 16(14), pages 1-41, July.

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