[1]居佳琪,王 琦,唐小波,等.基于双重粒子群算法的电动汽车参与配网优化调度[J].南京师范大学学报(工程技术版),2018,18(01):011.[doi:10.3969/j.issn.1672-1292.2018.01.002]
 Ju Jiaqi,Wang Qi,Tang Xiaobo,et al.Optimization Algorithm for Electric Vehicle Participation in DistributionNetwork Scheduling Based on Multiparticle Swarm[J].Journal of Nanjing Normal University(Engineering and Technology),2018,18(01):011.[doi:10.3969/j.issn.1672-1292.2018.01.002]
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基于双重粒子群算法的电动汽车参与配网优化调度
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南京师范大学学报(工程技术版)[ISSN:1006-6977/CN:61-1281/TN]

卷:
18卷
期数:
2018年01期
页码:
011
栏目:
电气与电子工程
出版日期:
2018-03-31

文章信息/Info

Title:
Optimization Algorithm for Electric Vehicle Participation in DistributionNetwork Scheduling Based on Multiparticle Swarm
文章编号:
1672-1292(2018)01-0011-13
作者:
居佳琪王 琦唐小波姜宁秋陆 斌陈良耳薛 松
南京师范大学电气与自动化工程学院,江苏 南京 210042
Author(s):
Ju JiaqiWang QiTang XiaoboJiang NingqiuLu BinChen LiangerXue Song
School of Electrical and Automation Engineering,Nanjing Normal University,Nanjing 210042,China
关键词:
电动汽车分布式光伏配电网有序充电协同调度
Keywords:
electric vehicledistributed photovoltaicdistribution networkordered chargingcooperative scheduling
分类号:
TM731
DOI:
10.3969/j.issn.1672-1292.2018.01.002
文献标志码:
A
摘要:
研究电动汽车参与含分布式光伏电源的配电系统的协同调度问题. 首先,建立了分布式光伏和电动汽车充电负荷的数学模型,给出了两种协同调度策略:电动汽车充电位置优化和电动汽车充电时段优化. 优化模型以配电系统网损最小为目标,分别就充电地址不固定和固定两种情况进行优化. 然后,使用蒙特卡洛法对电动汽车充电负荷情况进行模拟,使用双重粒子群算法对优化模型进行最优求解. 最后,用一个33节点系统作为算例系统对所提出的调度策略进行仿真验证,证明了优化策略能平抑电网波动,减少配电系统网损.
Abstract:
In this paper,we use the double particle swarm optimization algorithm to solve the two models for the coordinated operation of electric vehicle in distribution system with distributed photovoltaic power supply. This paper firstly establishes the mathematical model of the distributed load of photovoltaic and electric vehicle. Then two kinds of cooperative scheduling strategies are given:optimizing the charging position of electric vehicle and optimizing the dispatching strategy of electric vehicle charging time. The optimal model is based on the minimum loss of the distribution system,and the two cases are optimized for the fixed charging address and the fixed address,Monte Carlo method is used to simulate the charging load of electric vehicle. The optimal solution is optimized by using the double particle swarm optimization algorithm. At last,a 33-node system is used as an example to simulate the proposed scheduling strategy,and it is proved that the optimization strategy can stabilize the fluctuation of the power grid and reduce the network loss of the distribution system.

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相似文献/References:

[1]谈丽娟,赵彩虹,刘 笠,等.计及电动汽车自由充电的负荷需求[J].南京师范大学学报(工程技术版),2014,14(01):018.
 Tan Lijuan,Zhao Caihong,Liu Li,et al.Load Demand by Considering the Free Charge of Electric Vehicles[J].Journal of Nanjing Normal University(Engineering and Technology),2014,14(01):018.
[2]唐小波,赵彩虹,吴薛红,等.智能电网中电动汽车快速有序充电实时电价优化方法[J].南京师范大学学报(工程技术版),2013,13(04):013.
 Tang Xiaobo,Zhao Caihong,Wu Xuehong,et al.Optimization of Spot Power Price in Coordinated Fast Charging Model of Electric Vehicles[J].Journal of Nanjing Normal University(Engineering and Technology),2013,13(01):013.
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备注/Memo

备注/Memo:
收稿日期:2017-08-15.
基金项目:江苏省研究生科研创新计划项目(KYCX17_1079).
通讯联系人:王琦,博士,副教授,研究方向:新能源发电,电力系统,电动汽车,电力电子技术. E-mail:wangqi@njnu.edu.cn
更新日期/Last Update: 1900-01-01