刘志强, 张谦, 朱熠, 吴佳琦, 黄耀宇, 李春燕. 计及车-路-站-网融合的电动汽车充电负荷时空分布预测[J]. 电力系统自动化, 2022, 46(12): 36-45.
引用本文: 刘志强, 张谦, 朱熠, 吴佳琦, 黄耀宇, 李春燕. 计及车-路-站-网融合的电动汽车充电负荷时空分布预测[J]. 电力系统自动化, 2022, 46(12): 36-45.
LIU Zhiqiang, ZHANG Qian, ZHU Yi, WU Jiaqi, HUANG Yaoyu, LI Chunyan. Spatial-Temporal Distribution Prediction of Charging Loads for Electric Vehicles Considering Vehicle-Road-Station-Grid Integration[J]. Automation of Electric Power Systems, 2022, 46(12): 36-45.
Citation: LIU Zhiqiang, ZHANG Qian, ZHU Yi, WU Jiaqi, HUANG Yaoyu, LI Chunyan. Spatial-Temporal Distribution Prediction of Charging Loads for Electric Vehicles Considering Vehicle-Road-Station-Grid Integration[J]. Automation of Electric Power Systems, 2022, 46(12): 36-45.

计及车-路-站-网融合的电动汽车充电负荷时空分布预测

Spatial-Temporal Distribution Prediction of Charging Loads for Electric Vehicles Considering Vehicle-Road-Station-Grid Integration

  • 摘要: 针对目前对车-路-站-网相互影响考虑不足,导致电动汽车充电负荷时空分布预测不准确的问题,提出了基于万有引力模型的电动汽车充电负荷时空分布预测模型。首先,计及路网交通流和环境温度,分析外部环境与电动汽车能耗之间的关系。其次,考虑了温度、湿度、辐射等外部环境因素对用户出行的影响,建立基于出行意愿修正的出行链模型。最后,计及多方信息融合,建立基于万有引力模型的电动汽车充电站选择模型。算例结果表明,所提出的模型能够计及电动汽车、路网、充电站和电网的相互影响,并准确计算电动汽车充电负荷的时空分布,分析多场景、多区域下的电动汽车充电需求负荷特性。

     

    Abstract: In view of the problem of inaccurate prediction due to insufficient consideration of the interaction between vehicle-roadstation-grid and other parties in the study of spatial-temporal distribution of charging loads for electric vehicles(EVs), a prediction model of spatial-temporal distribution of charging loads for EVs based on the universal gravity model is proposed. Firstly, the relationship between the external environment and the energy consumption of EVs is explored, taking into account the road network traffic flow and ambient temperature. Secondly, considering the influence of external environmental factors such as temperature, humidity and radiation on users’ trips, a trip chain model modified by travel intention is obtained. Finally, considering the multi-information fusion, a charging station selection model for EVs based on the universal gravity model is proposed. The results show that the proposed model can take into account the mutual influence of EVs, road networks, charging stations and power grids, accurately calculate the spatial-temporal distribution of charging loads for EVs, and analyze the characteristics of charging loads for EVs in multiple scenarios and regions.

     

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