A V2G power response model based on the prediction, evaluation, and correction of charging stations real‐time frequency regulation capability

Abstract With the charging stations (CSs) construction and the vehicle‐to‐grid (V2G) development, electric vehicles (EVs) have become an important load‐side controllable resource. Therefore, a V2G power response model based on the prediction, evaluation, and correction of CSs real‐time frequency regulation (FR) capability is proposed in this paper. Firstly, a hierarchical control framework for large‐scale EVs aggregated to participate in power grid dispatching/FR service is proposed. Secondly, an extreme gradient boosting (XGBoost)‐convolutional neural network (CNN)‐bidirectional long‐term and short‐term memory (BiLSTM)‐attention prediction model for CSs FR capability is proposed, which combines the advantages of CNN and BiLSTM to strengthen the mining of multi‐dimensional features. Meanwhile, a rolling evaluation‐correction model for CSs FR capability based on the EV CC–CV charging process is proposed, which improves the evaluation fineness and aggregation fitness. Furthermore, a V2G power response model considering the EV battery loss is established. Finally, the simulation results show that compared with LSTM, XGBoost‐CNN‐BiLSTM, support vector machine, and other prediction models, the proposed XGBoost‐CNN‐BiLSTM‐attention CSs FR capability prediction model with improvement has a better prediction accuracy. In addition, the V2G power response model can achieve the coordination between the EV users’ charging demands and FR tasks.