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Charging demand forecasting of electric vehicles considering uncertainties in a microgrid

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  • Wu, Chuanshen
  • Jiang, Sufan
  • Gao, Shan
  • Liu, Yu
  • Han, Haiteng

Abstract

The currently increasing integration of electric vehicles (EVs) in microgrids (MGs) has gained significant attention. However, affected by the high uncertainties of weather, traffic, and driver behavior, the charging demand of EVs is difficult to forecast accurately. In this study, an optimal parameter forecasting method is presented to improve the forecasting accuracy of charging demand of EVs in an MG. For the methods of forecasting of EV status by sampling from probability distributions, this study modifies the optimal parameter values of probability distributions within fuzzy sets based on the feedback of EVs that have arrived in an MG. Fuzzy sets are utilized to limit the modification ranges of parameter values for the consideration of robustness. Moreover, the average values of multiple sampling results are calculated to improve the stability of forecasting results. Combined with the forecasted results, this study is executed over a rolling time horizon for energy management of EVs, ensuring that acceptable charge levels are reached at the disconnection times. Simulation results show that, compared with other state-of-the-art forecasting methods, the proposed forecasting method is highly effective in reducing forecasting errors of EVs and, hence, has better performance in regulating the charging of EVs in an MG.

Suggested Citation

  • Wu, Chuanshen & Jiang, Sufan & Gao, Shan & Liu, Yu & Han, Haiteng, 2022. "Charging demand forecasting of electric vehicles considering uncertainties in a microgrid," Energy, Elsevier, vol. 247(C).
    • Handle: RePEc:eee:energy:v:247:y:2022:i:c:s0360544222003784
    DOI: 10.1016/j.energy.2022.123475
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    References listed on IDEAS

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    Cited by:

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    2. Zhang, Lei & Huang, Zhijia & Wang, Zhenpo & Li, Xiaohui & Sun, Fengchun, 2024. "An urban charging load forecasting model based on trip chain model for private passenger electric vehicles: A case study in Beijing," Energy, Elsevier, vol. 299(C).
    3. Jaikumar Shanmuganathan & Aruldoss Albert Victoire & Gobu Balraj & Amalraj Victoire, 2022. "Deep Learning LSTM Recurrent Neural Network Model for Prediction of Electric Vehicle Charging Demand," Sustainability, MDPI, vol. 14(16), pages 1-28, August.
    4. Zhang, Fan & Lv, Huitao & Xing, Qiang & Ji, Yanjie, 2024. "Deployment of battery-swapping stations: Integrating travel chain simulation and multi-objective optimization for delivery electric micromobility vehicles," Energy, Elsevier, vol. 290(C).
    5. Tostado-Véliz, Marcos & Jordehi, Ahmad Rezaee & Mansouri, Seyed Amir & Jurado, Francisco, 2023. "A two-stage IGDT-stochastic model for optimal scheduling of energy communities with intelligent parking lots," Energy, Elsevier, vol. 263(PD).

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