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

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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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    1. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2018. "Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †," Energies, MDPI, vol. 11(7), pages 1-20, June.
    2. Qing Deng & Changsen Feng & Fushuan Wen & Chung-Li Tseng & Lei Wang & Bo Zou & Xizhu Zhang, 2019. "Evaluation of Accommodation Capability for Electric Vehicles of a Distribution System Considering Coordinated Charging Strategies," Energies, MDPI, vol. 12(16), pages 1-20, August.
    3. Arias, Mariz B. & Kim, Myungchin & Bae, Sungwoo, 2017. "Prediction of electric vehicle charging-power demand in realistic urban traffic networks," Applied Energy, Elsevier, vol. 195(C), pages 738-753.
    4. Sanchari Deb & Kari Tammi & Karuna Kalita & Pinakeshwar Mahanta, 2018. "Impact of Electric Vehicle Charging Station Load on Distribution Network," Energies, MDPI, vol. 11(1), pages 1-25, January.
    5. Ke Yan & Xudong Wang & Yang Du & Ning Jin & Haichao Huang & Hangxia Zhou, 2018. "Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy," Energies, MDPI, vol. 11(11), pages 1-15, November.
    6. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
    7. Arias, Mariz B. & Bae, Sungwoo, 2016. "Electric vehicle charging demand forecasting model based on big data technologies," Applied Energy, Elsevier, vol. 183(C), pages 327-339.
    8. Juncheng Zhu & Zhile Yang & Monjur Mourshed & Yuanjun Guo & Yimin Zhou & Yan Chang & Yanjie Wei & Shengzhong Feng, 2019. "Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches," Energies, MDPI, vol. 12(14), pages 1-19, July.
    9. 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.
    10. Ziwen Ling & Christopher R. Cherry & Yi Wen, 2021. "Determining the Factors That Influence Electric Vehicle Adoption: A Stated Preference Survey Study in Beijing, China," Sustainability, MDPI, vol. 13(21), pages 1-22, October.
    11. 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.
    12. Majidpour, Mostafa & Qiu, Charlie & Chu, Peter & Pota, Hemanshu R. & Gadh, Rajit, 2016. "Forecasting the EV charging load based on customer profile or station measurement?," Applied Energy, Elsevier, vol. 163(C), pages 134-141.
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.

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