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A simultaneous approach implementing wind-powered electric vehicle charging stations for charging demand dispersion

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  • Lee, Yerim
  • Hur, Jin

Abstract

Electric vehicle charging demand is highly variable depending on the charging pattern of consumers. If electric vehicle charging time is converged, the charging demand connected to power grids will increase and system instability may be induced. Wind Generating Sources (WGRs) can provide as much of the charging energy as possible. In this paper, we propose a simultaneous approach implementing wind-powered electric vehicle charging stations in order to distribute the charging demand of the electric vehicle with wind generating resources. The Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model is applied to predict hourly wind power outputs. The augmented ARIMAX model is extended from Autoregressive Integrated Moving Average (ARIMA) model by adding exogenous such as a wind speed including grid integration analysis simulation processes. To validate the proposed approach for electric vehicle charging dispersion, we use the empirical data from the Jeju Island's wind farms in South Korea.

Suggested Citation

  • Lee, Yerim & Hur, Jin, 2019. "A simultaneous approach implementing wind-powered electric vehicle charging stations for charging demand dispersion," Renewable Energy, Elsevier, vol. 144(C), pages 172-179.
    • Handle: RePEc:eee:renene:v:144:y:2019:i:c:p:172-179
    DOI: 10.1016/j.renene.2018.11.023
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    References listed on IDEAS

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    1. Kim, Deockho & Hur, Jin, 2018. "Short-term probabilistic forecasting of wind energy resources using the enhanced ensemble method," Energy, Elsevier, vol. 157(C), pages 211-226.
    2. 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.
    3. Xingping Zhang & Yanni Liang & Yakun Zhang & Yinhe Bu & Hongyang Zhang, 2017. "Charge Pricing Optimization Model for Private Charging Piles in Beijing," Sustainability, MDPI, vol. 9(11), pages 1-15, November.
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    Citations

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    Cited by:

    1. Sehyeon Kim & Markus Holz & Soojin Park & Yongbeum Yoon & Eunchel Cho & Junsin Yi, 2021. "Future Options for Lightweight Photovoltaic Modules in Electrical Passenger Cars," Sustainability, MDPI, vol. 13(5), pages 1-7, February.
    2. Hur, Jin, 2021. "Potential capacity factor estimates of wind generating resources for transmission planning," Renewable Energy, Elsevier, vol. 179(C), pages 1742-1750.
    3. Zeynali, Saeed & Nasiri, Nima & Marzband, Mousa & Ravadanegh, Sajad Najafi, 2021. "A hybrid robust-stochastic framework for strategic scheduling of integrated wind farm and plug-in hybrid electric vehicle fleets," Applied Energy, Elsevier, vol. 300(C).
    4. Heba M. Abdullah & Rashad M. Kamel & Anas Tahir & Azzam Sleit & Adel Gastli, 2020. "The Simultaneous Impact of EV Charging and PV Inverter Reactive Power on the Hosting Distribution System’s Performance: A Case Study in Kuwait," Energies, MDPI, vol. 13(17), pages 1-22, August.
    5. Julio Barzola-Monteses & Mónica Mite-León & Mayken Espinoza-Andaluz & Juan Gómez-Romero & Waldo Fajardo, 2019. "Time Series Analysis for Predicting Hydroelectric Power Production: The Ecuador Case," Sustainability, MDPI, vol. 11(23), pages 1-19, November.
    6. Zhang, Jinhua & Meng, Hang & Gu, Bo & Li, Pin, 2020. "Research on short-term wind power combined forecasting and its Gaussian cloud uncertainty to support the integration of renewables and EVs," Renewable Energy, Elsevier, vol. 153(C), pages 884-899.
    7. Wang, Han & Yan, Jie & Han, Shuang & Liu, Yongqian, 2020. "Switching strategy of the low wind speed wind turbine based on real-time wind process prediction for the integration of wind power and EVs," Renewable Energy, Elsevier, vol. 157(C), pages 256-272.

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