基于车辆行为模拟的加氢站氢负荷概率建模方法OA北大核心CSTPCD
Probabilistic Modeling Method of Hydrogen Load of Hydrogen Refueling Station Based on Vehicle Behavior Simulation
加氢站的氢负荷受氢燃料电池汽车出行行为影响,其时空分布具有高度的不确定性.为了研究加氢站氢负荷的分布特性,并准确描述车辆用户出行规律,提出一种基于车辆行为模拟的加氢站氢负荷概率建模方法.首先,基于美国家庭出行调查数据集对车辆出行过程中的行为特征量(如出行时刻、行驶时间、目的地、行驶距离等)概率分布进行拟合;其次,基于蒙特卡洛模拟的方法多次抽取样本,形成由车辆出行行为所组成的出行链;最后,以出行目的地类型为划分标准,分别模拟出一天内不同区域加氢站的氢负荷特性.通过合理利用实际出行数据和蒙特卡洛模拟方法,能够准确描述车辆行为规律,并根据出行目的地类型推断出不同区域加氢站的氢负荷特性.
The hydrogen load of hydrogen refueling station is affected by the travel behavior of hydrogen fuel cell vehicles,and its spatio-temporal distribution is highly uncertain.In order to study the distribution characteristics of hydrogen load of hydrogen refueling station and accurately describe the travel law of vehicle users,a probabilistic modeling method of hydrogen load of hydrogen refueling station based on vehicle behavior simulation is proposed in this paper.Firstly,the probability distribution of behavioral features(such as travel time,travel time,destination,travel distance,etc.)during vehicle travel is fitted based on the American household travel survey data set.Secondly,based on Monte Carlo simulation method,samples are extracted several times to form a travel chain composed of vehicle travel behavior.Finally,based on the type of travel destination,the hydrogen load characteristics of different regional hydrogen stations in one day were simulated.Through rational use of actual travel data and Monte Carlo simulation method,this paper can accurately describe the vehicle behavior law,and infer the hydrogen load characteristics of different regional hydrogen refueling stations according to the type of travel destination.
卢纯颢;周春丽;林溪桥;陈志君
广西电网有限责任公司电网规划研究中心,广西南宁 530023
加氢站不确定性车辆行为模拟氢负荷概率建模方法蒙特卡洛模拟
hydrogen refueling stationuncertaintyvehicle behavior simulationhydrogen load probability modeling methodMonte Carlo simulation
《中国电力》 2024 (008)
46-54,66 / 10
广西电网公司科技项目(GXKJXM20222123).This work is supported by the Guangxi Power Grid Company Science and Technology Project Funding(No.GXKJXM20222123).
评论