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Stationary Energy Storage System for Fast EV Charging Stations: Optimality Analysis and Results Validation

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  • Akhtar Hussain

    (Department of Electrical Engineering, Incheon National University, 12-1 Songdo-dong, Yeonsu-gu, Incheon 406840, Korea
    Research Institute for Northeast Asian Super Grid, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon 22012, Korea)

  • Van-Hai Bui

    (Department of Electrical Engineering, Incheon National University, 12-1 Songdo-dong, Yeonsu-gu, Incheon 406840, Korea)

  • Ju-Won Baek

    (Division of Smart Grid, Korea Electrotechnology Research Institute, Changwon 51543, Korea)

  • Hak-Man Kim

    (Department of Electrical Engineering, Incheon National University, 12-1 Songdo-dong, Yeonsu-gu, Incheon 406840, Korea
    Research Institute for Northeast Asian Super Grid, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon 22012, Korea)

Abstract

In order to minimize the peak load of electric vehicles (EVs) and enhance the resilience of fast EV charging stations, several sizing methods for deployment of the stationary energy storage system (ESS) have been proposed. However, methods for assessing the optimality of the obtained results and performance of the determined sizes under different conditions are missing. In order to address these issues, a two-step approach is proposed in this study, which comprises of optimality analysis and performance evaluation steps. In the case of optimality analysis, random sizes of battery and converter (scenarios) are generated using Monte Carlo simulations and their results are compared with the results of sizes obtained from sizing methods. In order to carry out this analysis, two performance analysis indices are proposed in this study, which are named the cost index and the power index. These indices respectively determine the performance of the determined sizes in terms of total network cost and performance ratio of power bought during peak intervals and investment cost of the ESS. During performance evaluation, the performance of the determined sizes (battery and converter) are analyzed for different seasons of the year and typical public holidays. Typical working days and holidays have been analyzed for each season of the year and suitability of the determined sizes is analyzed. Simulation results have proved that the proposed method is suitable for determining the optimality of results obtained by different sizing methods.

Suggested Citation

  • Akhtar Hussain & Van-Hai Bui & Ju-Won Baek & Hak-Man Kim, 2020. "Stationary Energy Storage System for Fast EV Charging Stations: Optimality Analysis and Results Validation," Energies, MDPI, vol. 13(1), pages 1-18, January.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:1:p:230-:d:304654
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    References listed on IDEAS

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    1. Ding, Huajie & Hu, Zechun & Song, Yonghua, 2015. "Value of the energy storage system in an electric bus fast charging station," Applied Energy, Elsevier, vol. 157(C), pages 630-639.
    2. Akhtar Hussain & Van-Hai Bui & Ju-Won Baek & Hak-Man Kim, 2019. "Stationary Energy Storage System for Fast EV Charging Stations: Simultaneous Sizing of Battery and Converter," Energies, MDPI, vol. 12(23), pages 1-17, November.
    3. Yorick Ligen & Heron Vrubel & Hubert Girault, 2019. "Local Energy Storage and Stochastic Modeling for Ultrafast Charging Stations," Energies, MDPI, vol. 12(10), pages 1-14, May.
    4. Yian Yan & Huang Wang & Jiuchun Jiang & Weige Zhang & Yan Bao & Mei Huang, 2019. "Research on Configuration Methods of Battery Energy Storage System for Pure Electric Bus Fast Charging Station," Energies, MDPI, vol. 12(3), pages 1-17, February.
    5. Yan Bao & Yu Luo & Weige Zhang & Mei Huang & Le Yi Wang & Jiuchun Jiang, 2018. "A Bi-Level Optimization Approach to Charging Load Regulation of Electric Vehicle Fast Charging Stations Based on a Battery Energy Storage System," Energies, MDPI, vol. 11(1), pages 1-21, January.
    6. Akhtar Hussain & Van-Hai Bui & Hak-Man Kim, 2017. "Impact Analysis of Demand Response Intensity and Energy Storage Size on Operation of Networked Microgrids," Energies, MDPI, vol. 10(7), pages 1-19, June.
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    Cited by:

    1. Zhouquan Wu & Pradeep Krishna Bhat & Bo Chen, 2023. "Optimal Configuration of Extreme Fast Charging Stations Integrated with Energy Storage System and Photovoltaic Panels in Distribution Networks," Energies, MDPI, vol. 16(5), pages 1-20, March.
    2. Graham Town & Seyedfoad Taghizadeh & Sara Deilami, 2022. "Review of Fast Charging for Electrified Transport: Demand, Technology, Systems, and Planning," Energies, MDPI, vol. 15(4), pages 1-30, February.
    3. 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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