Year-end Sale % OFF 
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.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
