 Study on orderly charging strategy of EV with load forecastingWanjun Yin, Jianbo Ji, Tao Wen and Chao Zhang Energy, 2023, vol. 278, issue C Abstract: The development and popularization of electric vehicles (EVs) is of great significance to environmental protection, energy saving and emission reduction. With the wide popularization of EV, the EV's disorderly charging brings the security hidden trouble to the grid. Firstly, according to the safe operation of power grid and the charging requirements of EVs, an optimal scheduling model based on grid loss is established, then, the optimal scheduling model is transformed by second-order cone relaxation technology. Secondly, because the orderly charging schedule of EV is based on accurate charging load forecasting, this paper based on LSTM-XGBoost dynamic combination forecasting, the dynamic combination model of LSTM and XGBoost is optimized by using Bayesian optimization method, and more accurate charging load forecasting results are obtained. Finally, the accuracy of the prediction method and the effectiveness of the optimal scheduling strategy are verified by the charging data of the EV in the actual area. Keywords: EVs; Load forecasting; Orderly charging; Optimal scheduling (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:278:y:2023:i:c:s0360544223012124 DOI: 10.1016/j.energy.2023.127818 Access Statistics for this article Energy is currently edited by Henrik Lund and Mark J. Kaiser More articles in Energy from Elsevier |
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