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

IF 2 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Iet Generation Transmission & Distribution Pub Date : 2024-12-13 DOI:10.1049/gtd2.13347
Leyan Ding, Jun Yang, Song Ke, Xingye Shi, Peixiao Fan, Hongli Wang
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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.

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随着充电站(CS)的建设和车联网(V2G)的发展,电动汽车(EV)已成为重要的负荷侧可控资源。因此,本文提出了一种基于 CS 实时频率调节(FR)能力预测、评估和修正的 V2G 功率响应模型。首先,提出了大规模电动汽车聚集参与电网调度/频率调节服务的分层控制框架。其次,针对 CSs 调频能力提出了极端梯度提升(XGBoost)-卷积神经网络(CNN)-双向长短期记忆(BiLSTM)-注意力预测模型,该模型结合了 CNN 和 BiLSTM 的优势,加强了对多维特征的挖掘。同时,提出了基于电动汽车 CC-CV 充电过程的 CSs FR 能力滚动评估修正模型,提高了评估的精细度和聚合适配性。此外,还建立了考虑电动汽车电池损耗的 V2G 功率响应模型。最后,仿真结果表明,与 LSTM、XGBoost-CNN-BiLSTM、支持向量机等预测模型相比,改进后的 XGBoost-CNN-BiLSTM-attention CSs FR 能力预测模型具有更好的预测精度。此外,V2G 功率响应模型可实现电动汽车用户充电需求与 FR 任务之间的协调。
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来源期刊
Iet Generation Transmission & Distribution
Iet Generation Transmission & Distribution 工程技术-工程:电子与电气
CiteScore
6.10
自引率
12.00%
发文量
301
审稿时长
5.4 months
期刊介绍: IET Generation, Transmission & Distribution is intended as a forum for the publication and discussion of current practice and future developments in electric power generation, transmission and distribution. Practical papers in which examples of good present practice can be described and disseminated are particularly sought. Papers of high technical merit relying on mathematical arguments and computation will be considered, but authors are asked to relegate, as far as possible, the details of analysis to an appendix. The scope of IET Generation, Transmission & Distribution includes the following: Design of transmission and distribution systems Operation and control of power generation Power system management, planning and economics Power system operation, protection and control Power system measurement and modelling Computer applications and computational intelligence in power flexible AC or DC transmission systems Special Issues. Current Call for papers: Next Generation of Synchrophasor-based Power System Monitoring, Operation and Control - https://digital-library.theiet.org/files/IET_GTD_CFP_NGSPSMOC.pdf
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