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Selecting Locations of Electric Vehicle Charging Stations Based on the Traffic Load Eliminating Method

Author

Listed:
  • Bong-Gi Choi

    (Department of Electronic and electrical Engineering, Keimyung University, 1095 Dalgubeol-daero, Daegu 42601, Korea)

  • Byeong-Chan Oh

    (Department of Electronic and electrical Engineering, Keimyung University, 1095 Dalgubeol-daero, Daegu 42601, Korea)

  • Sungyun Choi

    (School of Electrical Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Korea)

  • Sung-Yul Kim

    (Electrical Energy Engineering, Keimyung University, 1095 Dalgubeol-daero, Daegu 42601, Korea)

Abstract

Establishing electric vehicle supply equipment (EVSE) to keep up with the increasing number of electric vehicles (EVs) is the most realistic and direct means of promoting their spread. Using traffic data collected in one area; we estimated the EV charging demand and selected priority fast chargers; ranging from high to low charging demand. A queueing model was used to calculate the number of fast chargers required in the study area. Comparison of the existing distribution of fast chargers with that suggested by the traffic load eliminating method demonstrated the validity of our traffic-based location approach.

Suggested Citation

  • Bong-Gi Choi & Byeong-Chan Oh & Sungyun Choi & Sung-Yul Kim, 2020. "Selecting Locations of Electric Vehicle Charging Stations Based on the Traffic Load Eliminating Method," Energies, MDPI, vol. 13(7), pages 1-20, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:7:p:1650-:d:340553
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    References listed on IDEAS

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    Cited by:

    1. Hak-Ju Lee & Byeong-Chan Oh & Seok-Woong Kim & Sung-Yul Kim, 2020. "V2G Strategy for Improvement of Distribution Network Reliability Considering Time Space Network of EVs," Energies, MDPI, vol. 13(17), pages 1-19, August.
    2. 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.

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