共享电动汽车可调度容量时空预测  

Spatio-temporal Forecasting of Schedulable Capacity of Shared Electric Vehicles

作　　者：任惠[1] 陈萍 韩璐[1,3] 付文杰 王飞 REN Hui;CHEN Ping;HAN Lu;FU Wenjie;WANG Fei(School of Electrical and Electronic Engineering,North China Electric Power University,Baoding 071003,Hebei Province,China;Marketing Service Center,State Grid Hebei Electric Power Co.,Ltd.,Shijiazhuang 050000,Hebei Province,China;State Grid Hebei Electric Power Construction Co.,Ltd.,Shijiazhuang 050000,Hebei Province,China;State Grid Hebei Electric Power Co.,Ltd.,Shijiazhuang 050022,Hebei Province,China)

机构地区：[1]华北电力大学电气与电子工程学院,河北省保定市071003 [2]国网河北省电力有限公司营销服务中心,河北省石家庄市050000 [3]国网河北省电力有限公司建设公司,河北省石家庄市050000 [4]国网河北省电力有限公司,河北省石家庄市050022

出　　处：《电网技术》2023年第7期2732-2742,共11页Power System Technology

基　　金：国家重点研发计划项目(2018YFE0122200)。

摘　　要：针对共享电动汽车通过需求响应参与电力系统备用服务的可调度容量预测问题,基于历史轨迹数据提出一种基于模型无关的元学习(model-agnostic meta-learning,MAML)、卷积神经网络(convolutional neural network,CNN)、长短期记忆网络(long short term memory network,LSTM)和注意力机制(attention mechanism)的可调度容量评估模型,采用LSTM对CNN从历史数据中提取有效的特征向量动态变化进行建模学习,并用MAML对CNN-LSTM网络的初始化参数进行训练,在解决传统神经网络难以有效提取历史序列中潜在高维特征且当时序过长时重要信息易丢失的问题的同时,通过多任务训练对元预测网络进行微调以快速适应新预测任务,从而提高模型的预测精度及泛化能力;加入注意力机制突出对预测结果起关键性作用的时序信息,进一步提高预测精度。仿真结果表明所提模型可以有效预测不同日期类型和不同功能区域共享电动汽车的可调度容量,也为后续共享电动汽车通过需求响应参与电网备用服务的风险评估研究提供参考。Aiming at the schedulable capacity forecasting of shared electric vehicles participating in power system reserve service through demand response(DR),based on the historical trajectory data,this paper proposes a schedulable capacity evaluation model combining with the model-agnostic meta-learning,the convolutional neural network,the long-and short-term neural network and the attention mechanism(MAML-CNN-LSTM-Attention).Specifically,the LSTM is used to model and learn the dynamic changes of the effective feature vectors extracted by the CNN from the historical data,and the MAML is adopted to train the initialization parameters of the CNN-LSTM network.While solving that it is difficult for the traditional neural networks to effectively extract the potential high-dimensional features in the historical sequences and the important information is apt to be lost when the time series is too long,the meta-prediction network is fine-tuned by multi-task training to adapt to the new prediction tasks quickly,in order to improve the prediction accuracy and generalization ability of the model.The attention mechanism is added to highlight the timing sequence information which plays a key role in the results to further improve the forecasting accuracy.Simulation results show that the model is able to effectively forecast the schedulable capacity of the shared electric vehicles in different date types and functional areas,and provides a reference for the risk assessment study of the shared electric vehicles participating in the reserve services through DR.

关 键 词：共享电动汽车 需求响应 卷积神经网络 长短期记忆网络 注意力机制 模型无关的元学习 

分 类 号：TM721[电气工程—电力系统及自动化]