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Aggregated Electric Vehicle Fast-Charging Power Demand Analysis and Forecast Based on LSTM Neural Network

Author

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  • Munseok Chang

    (Department of Electrical Engineering, Hanyang University, Seoul 04763, Korea)

  • Sungwoo Bae

    (Department of Electrical Engineering, Hanyang University, Seoul 04763, Korea)

  • Gilhwan Cha

    (Algorigo Software Development Inc., Seoul 06103, Korea)

  • Jaehyun Yoo

    (Algorigo Software Development Inc., Seoul 06103, Korea)

Abstract

With the widespread use of electric vehicles, their charging power demand has increased and become a significant burden on power grids. The uncoordinated deployment of electric vehicle charging stations and the uncertainty surrounding charging behaviors can cause harmful impacts on power grids. The charging power demand during the fast charging process especially is severely fluctuating, because its charging duration is short and the rated power of the fast chargers is high. This paper presents a methodology to analyze and forecast the aggregated charging power demand from multiple fast-charging stations. Then, pattern of fast-charging power demand is analyzed to identify its irregular trend with the distribution of peak time and values. The forecasting model, based on long short-term memory neural network, is proposed in this paper to address the fluctuating of fast-charging power demand. The forecasting performance of the proposed model is validated in comparison with other deep learning approaches, using real-world datasets measured from fast-charging stations in Jeju Island, South Korea. The results show that the proposed model outperforms forecasting fast-charging power demand aggregated by multiple charging stations.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:24:p:13783-:d:701865
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    References listed on IDEAS

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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. Young-Eun Jeon & Suk-Bok Kang & Jung-In Seo, 2022. "Hybrid Predictive Modeling for Charging Demand Prediction of Electric Vehicles," Sustainability, MDPI, vol. 14(9), pages 1-15, April.
    3. Sabbir Ahmed & Sameera Mubarak & Jia Tina Du & Santoso Wibowo, 2022. "Forecasting the Status of Municipal Waste in Smart Bins Using Deep Learning," IJERPH, MDPI, vol. 19(24), pages 1-15, December.
    4. Sahar Koohfar & Wubeshet Woldemariam & Amit Kumar, 2023. "Performance Comparison of Deep Learning Approaches in Predicting EV Charging Demand," Sustainability, MDPI, vol. 15(5), pages 1-20, February.

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