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Optimal Operation Strategy of ESS for EV Charging Infrastructure for Voltage Stabilization in a Secondary Feeder of a Distribution System

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  • Dae-Jin Kim

    (Jeju Global Research Center (JGRC), Korea Institute of Energy Research (KIER), Jeju 63357, Korea
    Department of Mechanical Design and Robot Engineering, Seoul National University of Science and Technology (SeoulTech), Seoul 01811, Korea)

  • Kyung-Sang Ryu

    (Jeju Global Research Center (JGRC), Korea Institute of Energy Research (KIER), Jeju 63357, Korea)

  • Hee-Sang Ko

    (Jeju Global Research Center (JGRC), Korea Institute of Energy Research (KIER), Jeju 63357, Korea)

  • Byungki Kim

    (Jeju Global Research Center (JGRC), Korea Institute of Energy Research (KIER), Jeju 63357, Korea)

Abstract

The introduction of electrical vehicle charging infrastructure including electric vehicle (EV) charger renewable energy resource at the secondary feeder in a distribution system has been increased as one of countermeasure for global environmental issues. However, the electric vehicle charging (EVC) infrastructure may act as the peak load in the distribution system, which can adversely impact on the voltage stability when the electric vehicle is quickly charged. Therefore, to keep within the limit capacity of a secondary feeder and allowable limit for the feeder voltage, this paper proposes a stabilization method by an energy storage system (ESS) control strategy at the secondary feeder to not exceed the upper limit or fall below the lower limit. In addition, this paper presents an estimation method to keep the proper standard value of the state of charge (SOC). From the simulation results, the voltage stabilization operation by the ESS should make the feeder voltages of the distribution system (secondary feeder) introduced EVC infrastructure keep better voltage conditions. In addition, the estimation method was able to keep the proper standard value confirming that the SOC of the ESS when it is in standby can be kept within the proper reference range. Therefore, it is confirmed that this strategy is an effective tool to solve the voltage problems by ESS.

Suggested Citation

  • Dae-Jin Kim & Kyung-Sang Ryu & Hee-Sang Ko & Byungki Kim, 2020. "Optimal Operation Strategy of ESS for EV Charging Infrastructure for Voltage Stabilization in a Secondary Feeder of a Distribution System," Energies, MDPI, vol. 13(1), pages 1-22, January.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:1:p:179-:d:303913
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    References listed on IDEAS

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    Cited by:

    1. Ahmad Almaghrebi & Fares Aljuheshi & Mostafa Rafaie & Kevin James & Mahmoud Alahmad, 2020. "Data-Driven Charging Demand Prediction at Public Charging Stations Using Supervised Machine Learning Regression Methods," Energies, MDPI, vol. 13(16), pages 1-21, August.
    2. Yi-Ying Zhang & Jing Shang & Xi Chen & Kun Liang, 2020. "A Self-Learning Detection Method of Sybil Attack Based on LSTM for Electric Vehicles," Energies, MDPI, vol. 13(6), pages 1-15, March.

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