摘要
针对目前充电汽车(Electric Vehicle,EV)负荷预测的研究实时预测充电汽车起讫点(Origin-Destination,OD)准确率不高并考虑道路信息对用户充电行为选择造成影响的问题,在数据驱动方面,针对充电汽车出行OD矩阵的时空特性,使用长短期记忆(LSTM)网络和图卷积网络(GCN)的组合预测方法分析已有的充电负荷数据,实现对充电汽车起讫点的预测;模型驱动方面,在综合考虑交通网构成、环境温度、实时车流量等方法的基础上,建立一种包括动态交通信息、城市各路段电动汽车里程能耗及用户路径规划在内的电动汽车用户驾驶行为模型,采用改进A^(*)算法为电动汽车起讫点规划符合用户选择的行驶路径,模拟电动汽车用户的驾驶行为。最终在不同应用场景下完成不同类型电动汽车的路径规划实验和充电需求预测实验。结果表明,所得充电需求时空分布特征与客观需求相符合。
In response to the current research on charging vehicle(EV)load prediction,the accuracy of real-time prediction of charging vehicle origin-destination(OD)is not high and considers the influence of road information on users’charging behavior choices.On the data-driven side,a combination of long short-term memory(LSTM)networks and graph convolutional networks(GCN)is used to analyze the existing charging load data and realize the prediction of charging vehicle origin-destination(OD),with respect to the spatial and temporal characteristics of the OD matrix of charging vehicle trips.On the model-driven side,a combination of road information is taken into consideration to predict the OD in real time.In terms of model driving,based on the comprehensive consideration of traffic network composition,ambient temperature,real-time traffic flow and other methods,a driving behavior model of electric vehicle users is established,including dynamic traffic information,mileage energy consumption of electric vehicles in various road segments in the city and user path planning,and the improved A^(*)algorithm is used to plan driving paths for the starting and ending points of electric vehicles in accordance with the users’choices,so as to simulate the driving behavior of electric vehicle users.The improved A^(*)algorithm is used to plan driving paths for the starting and ending points of EVs that meet the user’s choice,and simulate the driving behavior of EV users.Finally,the path planning test and charging demand prediction test for different types of EVs are completed under different application scenarios.The results show that the spatial and temporal distribution characteristics of charging demand are consistent with the objective demand.
作者
顾玮
段敬
张栋
郝晓伟
薛泓林
安毅
段婕
GU Wei;DUAN Jing;ZHANG Dong;HAO Xiaowei;XUE Honglin;AN Yi;DUAN Jie(State Grid Shanxi Electric Power Company Information and Communication Branch,Taiyuan 030000,China;State Grid Shanxi Electric Power Company,Taiyuan 030000,China)
出处
《计算机科学》
CSCD
北大核心
2024年第S02期1022-1027,共6页
Computer Science
基金
国网山西省电力公司科技项目(52051C230005)。
