文章摘要
张洪财,胡泽春,宋永华,等.考虑时空分布的电动汽车充电负荷预测方法[J].电力系统自动化,2014,38(1):13-20. DOI: 10.7500/AEPS20130613009.
ZHANG Hongcai,HU Zechun,SONG Yonghua, et al.A Prediction Method for Electric Vehicle Charging Load Considering Spatial and Temporal Distribution[J].Automation of Electric Power Systems,2014,38(1):13-20. DOI: 10.7500/AEPS20130613009.
考虑时空分布的电动汽车充电负荷预测方法
A Prediction Method for Electric Vehicle Charging Load Considering Spatial and Temporal Distribution
DOI:10.7500/AEPS20130613009
关键词: 电动汽车  停车生成率模型  蒙特卡洛模拟  充电负荷  时空分布
KeyWords: electric vehicle(EV)  parking generation rate model  Monte Carlo simulation  charging load  spatial and temporal distribution
上网日期:2014-01-01
基金项目:国家重点基础研究发展计划(973计划)资助项目(2013CB228202)
作者单位E-mail
张洪财 清华大学电机工程与应用电子技术系北京市100084  
胡泽春 清华大学电机工程与应用电子技术系北京市100084 zechhu@tsinghua.edu.cn 
宋永华 清华大学电机工程与应用电子技术系北京市100084  
徐智威 清华大学电机工程与应用电子技术系北京市100084  
贾龙 清华大学电机工程与应用电子技术系北京市100084  
摘要:
      提出了一种基于电动汽车驾驶、停放特性的考虑时空分布的电动汽车充电负荷预测方法。采用停车生成率模型预测停车需求,结合不同类型汽车、不同停放目的地的停车特性,建立电动汽车停车需求时空分布模型。从电动汽车日行驶里程、日停放需求时空分布特性入手,分析充电需求。采用蒙特卡洛模拟方法,仿真大规模电动汽车不同时间、不同空间的停放、驾驶以及充电行为,预测电动汽车充电负荷的时空分布特性。以深圳市为例,预测结果表明:电动汽车用户充电行为选择以及公共停车场充电设施配建比例不同,充电负荷也将有不同的分布;居民区、工作单位配建充电设施可满足大部分电动汽车的充电需求;同一城市不同区域建设用地类型不同,充电负荷具有明显差异。
Abstract:
      A new method of predicting the electric vehicle(EV)charging load considering the spatial and temporal distribution is proposed based on driving and parking characteristics of private cars.The parking demand is predicted with the parking generation rate model and the spatial and temporal distribution model of EV parking demand is developed by integrating various parking demands and characteristics in different types of areas.Then,EV charging demands are analyzed based on the daily driving mileages and the spatial and temporal distribution characteristics of daily parking demands.The Monte Carlo simulation method is adopted to simulate EV parking,driving and charging behavior sat different time and different places for the prediction of the spatial and temporal distribution characteristics of EV charging load.The predicted outcomes of Shenzhen in2020show that EV charging load changes with different charging behaviors and charging facilities available;charging demands can be mostly satisfied with charging facilities at residential quarters and workplaces;charging loads in different parts of a city with different pieces of land for construction are markedly different.
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