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Data-Driven Evaluation for Demand Flexibility of Segmented Electric Vehicle Chargers in the Korean Residential Sector

Author

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  • Keon Baek

    (Gwangju Institute of Science and Technology, School of Integrated Technology, Gwangju 61005, Korea)

  • Sehyun Kim

    (Korea Electric Power Corporation, Naju 58322, Jeollanam-do, Korea)

  • Eunjung Lee

    (Gwangju Institute of Science and Technology, School of Integrated Technology, Gwangju 61005, Korea)

  • Yongjun Cho

    (Gwangju Institute of Science and Technology, School of Integrated Technology, Gwangju 61005, Korea)

  • Jinho Kim

    (Gwangju Institute of Science and Technology, School of Integrated Technology, Gwangju 61005, Korea)

Abstract

The rapid spread of renewable energy resources has increased need for demand flexibility as one of the solutions to power system imbalance. However, to properly estimate the demand flexibility, demand characteristics must be analyzed first and the corresponding flexibility measures must be validated. Thus, in this study, a novel approach is proposed to evaluate the demand flexibility provided by Electric Vehicle Chargers (EVC) in the residential sector based upon a new process of electric charging demand characteristic data analysis. The proposed model estimates the frequency, consistency, and operation scores of the flexible demand resource (FDR) during identified ramp-up/down intervals presented in our previous work. The scores are included in the components that calculate the flexibility score referring that the closer it is to 1, the higher utilization as an FDR. A case study was conducted by considering EV user segmentation based on their demand characteristic analysis. The results confirm that flexibility scores of segmented EVC groups are about 0.0273 in ramp-up and ramp-down intervals. Based on the experimental results, the flexibility score can be utilized for multi-dimensional analysis and verification in perspectives of seasonality, participation time interval, customer group classification, and evaluation. Thus, the proposed method can be used as an indicator to determine how a segmented EVC group is adequate to participate as an FDR while suggesting meaningful implications through EVC demand data analysis.

Suggested Citation

  • Keon Baek & Sehyun Kim & Eunjung Lee & Yongjun Cho & Jinho Kim, 2021. "Data-Driven Evaluation for Demand Flexibility of Segmented Electric Vehicle Chargers in the Korean Residential Sector," Energies, MDPI, vol. 14(4), pages 1-10, February.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:866-:d:495215
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    References listed on IDEAS

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    1. Heggarty, Thomas & Bourmaud, Jean-Yves & Girard, Robin & Kariniotakis, Georges, 2020. "Quantifying power system flexibility provision," Applied Energy, Elsevier, vol. 279(C).
    2. Schuller, Alexander & Flath, Christoph M. & Gottwalt, Sebastian, 2015. "Quantifying load flexibility of electric vehicles for renewable energy integration," Applied Energy, Elsevier, vol. 151(C), pages 335-344.
    3. Huber, Matthias & Dimkova, Desislava & Hamacher, Thomas, 2014. "Integration of wind and solar power in Europe: Assessment of flexibility requirements," Energy, Elsevier, vol. 69(C), pages 236-246.
    4. Eunjung Lee & Keon Baek & Jinho Kim, 2020. "Evaluation of Demand Response Potential Flexibility in the Industry Based on a Data-Driven Approach," Energies, MDPI, vol. 13(23), pages 1-12, December.
    5. Strbac, Goran, 2008. "Demand side management: Benefits and challenges," Energy Policy, Elsevier, vol. 36(12), pages 4419-4426, December.
    6. Lizana, Jesus & Friedrich, Daniel & Renaldi, Renaldi & Chacartegui, Ricardo, 2018. "Energy flexible building through smart demand-side management and latent heat storage," Applied Energy, Elsevier, vol. 230(C), pages 471-485.
    7. Tanaka, Makoto, 2006. "Real-time pricing with ramping costs: A new approach to managing a steep change in electricity demand," Energy Policy, Elsevier, vol. 34(18), pages 3634-3643, December.
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    Cited by:

    1. Naoui Mohamed & Flah Aymen & Abdullah Altamimi & Zafar A. Khan & Sbita Lassaad, 2022. "Power Management and Control of a Hybrid Electric Vehicle Based on Photovoltaic, Fuel Cells, and Battery Energy Sources," Sustainability, MDPI, vol. 14(5), pages 1-20, February.

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