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An Electric Power Consumption Analysis System for the Installation of Electric Vehicle Charging Stations

Author

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  • Seongpil Cheon

    (Department of Electronic Engineering, Sogang University, Seoul 04107, Korea)

  • Suk-Ju Kang

    (Department of Electronic Engineering, Sogang University, Seoul 04107, Korea)

Abstract

With the rising demand for electric vehicles, the number of electric vehicle charging stations is increasing. Therefore, real-time monitoring of how the power consumption by charging stations affects the load on the peripheral power grid is important. However, related organizations generally do not provide actual power consumption data in real time, and only limited information, such as the charging time, is provided. Therefore, it is difficult to calculate and predict the power load in real time. In this paper, we propose a new model for estimating the electric power consumption from the supplied information, i.e., the charging time and the number of charging involved. The experimental results show that by displaying this information on a map, it is possible to visually monitor the electric power consumption of the charging stations with an accuracy rate of about 86%. Finally, the proposed system can be used to relocate and select the location of vehicle charging stations.

Suggested Citation

  • Seongpil Cheon & Suk-Ju Kang, 2017. "An Electric Power Consumption Analysis System for the Installation of Electric Vehicle Charging Stations," Energies, MDPI, vol. 10(10), pages 1-13, October.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1534-:d:114003
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    References listed on IDEAS

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    1. Cedric De Cauwer & Joeri Van Mierlo & Thierry Coosemans, 2015. "Energy Consumption Prediction for Electric Vehicles Based on Real-World Data," Energies, MDPI, vol. 8(8), pages 1-21, August.
    2. Rob J. Hyndman, 2006. "Another Look at Forecast Accuracy Metrics for Intermittent Demand," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 4, pages 43-46, June.
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    Cited by:

    1. Namhyun Ahn & So Yeon Jo & Suk-Ju Kang, 2019. "Constraint-Aware Electricity Consumption Estimation for Prevention of Overload by Electric Vehicle Charging Station," Energies, MDPI, vol. 12(6), pages 1-18, March.
    2. Natascia Andrenacci & Roberto Ragona & Antonino Genovese, 2020. "Evaluation of the Instantaneous Power Demand of an Electric Charging Station in an Urban Scenario," Energies, MDPI, vol. 13(11), pages 1-19, May.
    3. 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.
    4. Munseok Chang & Sungwoo Bae & Gilhwan Cha & Jaehyun Yoo, 2021. "Aggregated Electric Vehicle Fast-Charging Power Demand Analysis and Forecast Based on LSTM Neural Network," Sustainability, MDPI, vol. 13(24), pages 1-17, December.
    5. Yunsun Kim & Sahm Kim, 2021. "Forecasting Charging Demand of Electric Vehicles Using Time-Series Models," Energies, MDPI, vol. 14(5), pages 1-16, March.
    6. Golsefidi, Atefeh Hemmati & Hüttel, Frederik Boe & Peled, Inon & Samaranayake, Samitha & Pereira, Francisco Câmara, 2023. "A joint machine learning and optimization approach for incremental expansion of electric vehicle charging infrastructure," Transportation Research Part A: Policy and Practice, Elsevier, vol. 178(C).

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