“车–路–网”模式下电动汽车充电负荷时空预测及其对配电网潮流的影响
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摘要
该文提出一种"车–路–网"模式下电动汽车充电负荷时空预测模型。首先通过建立单体电动汽车充电模型和计及交通网络拓扑特性、速度–流量实用关系模型及区域属性特征的道路交通模型,来模拟城市区域路网约束下电动汽车的交通行驶特性;进而运用交通起止点分析法来精确模拟电动汽车行驶路径,采用蒙特卡洛方法对各类电动汽车的充电负荷进行时空预测;最后将城市路网及充电负荷归算至对应配电网节点,通过序列化潮流算法来评估大规模电动汽车接入后对配电网的影响。以某市主城区部分实际道路情况为例,预测充电负荷时空分布,分析城市区域电动汽车充电负荷整体分布特性及其对33节点配电网潮流的影响,结果表明该文所提方法现实有效。
A method for the forecast of charging load of electric vehicles(EVs) under "EVs-Traffic-Distribution"(ETD) system was developed to precisely manifest the spatial-temporal characteristics of large scale EV charging load in urban area and to evaluate the impact of the load on urban distribution network. An EV model with charging characteristics, a traffic network model with urban road topology and a classic velocity-capacity model were introduced to provide the spatial-temporal driving routes and velocity. With the above information, origin-destination(OD) analysis was used to simulate the mobility of each EV. And, Monte Carlo simulation was conducted to estimate the EVs' spatial-temporal charging load characteristics over a day. By allocating the charging load of each EV to the nearest node in the distribution network, sequential power flow was conducted to evaluate the impact of charging load on the distribution network. A 29-node urban area combined with the geographic information and a 33-node distribution system was selected to validate the proposed method. Simulation results demonstrate its effectiveness.
A method for the forecast of charging load of electric vehicles(EVs) under "EVs-Traffic-Distribution"(ETD) system was developed to precisely manifest the spatial-temporal characteristics of large scale EV charging load in urban area and to evaluate the impact of the load on urban distribution network. An EV model with charging characteristics, a traffic network model with urban road topology and a classic velocity-capacity model were introduced to provide the spatial-temporal driving routes and velocity. With the above information, origin-destination(OD) analysis was used to simulate the mobility of each EV. And, Monte Carlo simulation was conducted to estimate the EVs' spatial-temporal charging load characteristics over a day. By allocating the charging load of each EV to the nearest node in the distribution network, sequential power flow was conducted to evaluate the impact of charging load on the distribution network. A 29-node urban area combined with the geographic information and a 33-node distribution system was selected to validate the proposed method. Simulation results demonstrate its effectiveness.
引文
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[2]欧阳明高.我国节能与新能源汽车发展战略与对策[J].汽车工程,2006,28(4):317-321.Ouyang Minggao.Chinese strategies and countermeasures for energy saving and vehicles with new types energy[J].Automotive Engineering,2006,28(4):317-321(in Chinese).
[3]Guo Ping,Liu Peng.Research on development of electric vehicles in China[C]//Proceedings of 2010 International Conference on Future Information Technology and Management Engineering(FITME 2010).Changzhou,China:IEEE,2010:94-96.
[4]Guo Qinglai,Wang Yao,Sun Hongbin,et al.Research on architecture of ITS based smart charging guide system[C]//Proceedings of 2011 IEEE Power and Energy Society.San Diego,CA:IEEE,2011:1-5.
[5]王锡凡,邵成成,王秀丽,等.电动汽车充电负荷与调度控制策略综述[J].中国电机工程学报,2013,33(1):1-10.Wang Xifan,Shao Chengcheng,Wang Xiuli,et al.Survey of electric vehicle charging load and dispatch control strategies[J].Proceedings of the CSEE,2013,33(1):1-10(in Chinese).
[6]田立亭,史双龙,贾卓.电动汽车充电功率需求的统计学建模方法[J].电网技术,2010,34(11):126-130.Tian Liting,Shi Shuanglong,Jia Zhuo.A statistical model for charging power demand of electric vehicles[J].Power System Technology,2010,34(11):126-130(in Chinese).
[7]罗卓伟,胡泽春,宋永华,等.电动汽车充电负荷计算方法[J].电力系统自动化,2011,35(14):36-42.Luo Zhuowei,Hu Zechun,Song Yonghua,et al.Study on plug-in electric vehicles charging load calculating[J].Automation of Electric Power Systems,2011,35(14):36-42(in Chinese).
[8]Steen D,Tuan L A,Carlson O,et al.Assessment of electric vehicle charging scenarios based on demographical data[J].IEEE Transactions on Smart Grid,2012,3(3):1457-1468.
[9]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.Zhang Hongcai,Hu Zechun,Song Yonghua,et al.Aprediction method for electric vehicle charging load considering spatial and temporal distribution[J].Automation of Electric Power Systems,2014,38(1):13-20(in Chinese).
[10]高赐威,张亮.电动汽车充电对电网影响的综述[J].电网技术,2011,35(2):127-131.Gao Ciwei,Zhang Liang.A survey of influence of electrics vehicle charging on power grid[J].Power System Technology,2011,35(2):127-131(in Chinese).
[11]Qian Kejun,Zhou Chengke,Allan M,et al.Modeling of load demand due to EV battery charging in distribution systems[J].IEEE Transactions on Power Systems,2011,26(2):802-810.
[12]Clement-Nyns K,Haesen E,Driesen J.The impact of charging plug-in hybrid electric vehicles on a residential distribution grid[J].IEEE Transactions on Power Systems,2012,25(1):371-380.
[13]Soares F J,Lopes J A P,Almeida P M R.A Monte Carlo method to evaluate electric vehicles impacts in distribution networks[C]//Proceedings of 2010 IEEEConference on Innovative Technologies for an Efficient and Reliable Electricity Supply(CITRES).Waltham,U.S.A.:IEEE,2010.
[14]Mu Yunfei,Wu Jianzhong,Jenkins Nick,et al.Aspatial-temporal model for grid impact analysis of plug-in electric vehicles[J].Applied Energy,2012,94:395-405.
[15]严弈遥,罗禹贡,朱陶,等.融合电网和交通网信息的电动车辆最优充电路径推荐策略[J].中国电机工程学报,2015,35(2):310-318.Yan Yiyao,Luo Yugong,Zhu Tao,et al.Optimal charging route recommendation method based on transportation and distribution information[J].Proceedings of the CSEE,2015,35(2):310-318(in Chinese).
[16]黄小庆,陈颉,陈永新,等.大数据背景下的充电站负荷预测方法[J].电力系统自动化,2016,40(12):68-74.Huang Xiaoqing,Chen Jie,Chen Yongxin,et al.Load forecasting method for electric vehicle charging station based on big data[J].Automation of Electric Power Systems,2016,40(2):68-74(in Chinese).
[17]Wang Guibin,Xu Zhao,Wen Fushuan,et al.Trafficconstrained multiobjective planning of electric-vehicle charging stations[J].IEEE Transactions on Power Delivery,2013,28(4):2363-2372.
[18]Xiang Yue,Liu Junyong,Li Ran,et al.Economic planning of electric vehicle charging stations considering traffic constraints and load profile templates[J].Applied Energy,2016,178:647-659.
[19]EU Merge Project.Deliverable 2.1:modelling electric storage devices for electric vehicles[R].2010.http://www.ev-merge.eu/.
[20]Ma Jingsong,Yu Xiaoyan,Chen Gang,et al.Research on urban accessibility distribution areal model by Floyd algorithm and Kriging interpolation[C]//Proceedings of2010 18th International Conference on Geoinformatics.Beijing:IEEE,2010:1-4.
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[23]William AM,Martin AM.Travel estimation techniques for urban planning[R].Transport Research Board National Research Council,U.S.NCHRP 365 Rep:1998.https://ntl.bts.gov/lib/21000/21500/21563/PB99126724.pdf.
[24]罗霞,杜进有,陈应文.混合车流交通流特性分析[J].西南交通大学学报,2000,35(3):297-300.Luo Xia,Du Jinyou,Chen Yingwen.Analyses on multi-traffic flow’s characteristics[J].Journal of Southwest Jiaotong University,2000,35(3):297-300(in Chinese).
[25]陈盛.城市道路交通流速度流量实用关系模型研究[D].南京:东南大学,2004.Chen Sheng.Research on practical velocity-volume model of urban street[D].Nanjing:Southeast University,2004.
[26]S Beak,H Kim,Y Lim.Multiple-vehicle origindestination matrix estimation from traffic counts using genetic algorithm[J].Journal of Transportation Engineering,2004,130(3):339-347.
[27]Hu Shouren,Wang Changming.Vehicle detector deployment strategies for the estimation of network Origin-destination demands using partial link traffic counts[J].IEEE Transactions on Intelligent Transportation Systems,2008,9(2):288-300.
[28]肖志国.区域OD矩阵反推技术评价及其理论研究[D].武汉:华中科技大学,2006.Xiao Zhiguo.Study on evaluation and theory of regional O-D matrix estimation[D].Wuhan:Huazhong University of Science and Technology,2006(in Chinese).
[29]李艳红,袁振洲,谢海红,等.基于出租车OD数据的出租车出行特征分析[J].交通运输系统工程与信息,2007,7(5):85-89.Li Yanhong,Yuan Zhenzhou,Xie Haihong,et al.Analysis on trips characteristics of taxi in Suzhou Based on ODdata[J].Journal of Transportation Systems Engineering and Information Technology,2007,7(5):85-89(in Chinese).