1.输配电装备及系统安全与新技术国家重点实验室(重庆大学),重庆市 400044;2.国网重庆市电力公司綦南供电分公司,重庆市 401420
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。
国家自然科学基金资助项目(52177073)。
刘志强(1998—),男,硕士研究生,主要研究方向:电动汽车与电网互动技术。E-mail:1119597419@qq.com
张谦(1980—),女,通信作者,博士,副教授,主要研究方向:电动汽车与能源交通系统融合、分布式能源入网。E-mail:zhangqian@cqu.edu.cn
朱熠(1994—),男,硕士,主要研究方向:电动汽车与电力系统可靠性评估技术。E-mail:779456328@qq.com
1.State Key Laboratory of Power Transmission Equipment & System Security and New Technology (Chongqing University), Chongqing 400044, China;2.Qinan Power Supply Company of State Grid Chongqing Electric Power Company, Chongqing 401420, China
In view of the problem of inaccurate prediction due to insufficient consideration of the interaction between vehicle-road-station-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.
[1] | 刘志强,张谦,朱熠,等.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. DOI:10.7500/AEPS20211227002. LIU Zhiqiang, ZHANG Qian, ZHU Yi, et al. 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. DOI:10.7500/AEPS20211227002. |