考虑分布式光伏和电动汽车接入的配电网空间负荷预测方法
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摘要
针对大规模分布式光伏和电动汽车接入配电网对空间负荷预测影响的问题,提出一种考虑远景年屋顶分布式光伏饱和安装、大规模电动汽车参与V2G的城市配电网空间负荷预测方法。区分不同小区,依据相应的容积率和可利用率系数计算屋顶光伏饱和安装面积,结合历史辐射值数据计算光伏出力。基于改进型停车生成率模型预测停车需求,结合日行驶里程、停车特性和充放电策略,建立电动汽车V2G负荷预测模型,利用蒙特卡罗仿真得出V2G负荷时空分布情况。采用改进型负荷密度指标法,实现对考虑时序的配电网传统日负荷的预测。以某规划区为例,预测结果表明:屋顶分布式光伏和电动汽车V2G对配电网空间负荷预测结果影响较大,且对不同小区负荷影响的程度不同。
Aiming at the problem of large-scale distributed photovoltaic and electric vehicle access distribution network impacting spatial load forecasting, this paper proposes a spatial load forecasting method for urban distribution network with large-scale electric vehicles participating in V2G considering the long-term roof distributed photovoltaic saturation installation and large-scale electric vehicles. It differentiates different communities, calculates the saturated installation area of the roof photovoltaic according to the corresponding floor area ratio and availability coefficient, and calculates the output of the photovoltaic by combining the historical radiation value data. Based on the improved parking generation rate model to predict the parking demand, and combined with the daily driving mileage, parking characteristics and charging and discharging strategy, the V2G load forecasting model of electric vehicle is established, and the Monte Carlo simulation is used to obtain the spatial and temporal distribution of V2G load. The improved load density index method is used to predict the traditional daily load of the distribution network considering the timing. Taking a planning area as an example, the prediction results show that the roof distributed PV and electric vehicle V2G have a great influence on the spatial load forecasting results of the distribution network, and the degree of impact on the load of different communities is different.
Aiming at the problem of large-scale distributed photovoltaic and electric vehicle access distribution network impacting spatial load forecasting, this paper proposes a spatial load forecasting method for urban distribution network with large-scale electric vehicles participating in V2G considering the long-term roof distributed photovoltaic saturation installation and large-scale electric vehicles. It differentiates different communities, calculates the saturated installation area of the roof photovoltaic according to the corresponding floor area ratio and availability coefficient, and calculates the output of the photovoltaic by combining the historical radiation value data. Based on the improved parking generation rate model to predict the parking demand, and combined with the daily driving mileage, parking characteristics and charging and discharging strategy, the V2G load forecasting model of electric vehicle is established, and the Monte Carlo simulation is used to obtain the spatial and temporal distribution of V2G load. The improved load density index method is used to predict the traditional daily load of the distribution network considering the timing. Taking a planning area as an example, the prediction results show that the roof distributed PV and electric vehicle V2G have a great influence on the spatial load forecasting results of the distribution network, and the degree of impact on the load of different communities is different.
引文
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[20]王雨,谢炜.谈屋顶光伏设计及安装[J].山西建筑,2017,43(2):196-198.WANG Yu,XIE Wei.Discussion on photovoltaic design and installation of the roof[J].Shanxi Architecture,2017,43(2):196-198.
[21]CHARABI Y,RHOUMA M B H,GASTLI A.GIS-based estimation of roof-PV capacity&energy production for the Seeb region in Oman[C]//2010 IEEE International Energy Conference and Exhibition,December 18-22,Manama,Bahrain:41-44.
[22]徐福圆.基于遥感图像的屋顶面积识别及屋顶光伏容量估计[D].杭州:杭州电子科技大学,2016.XU Fuyuan.Roof area recognition and PV capacity estimation based on remote sensing image[D].Hangzhou:Hangzhou Dianzi University,2016.
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[2]刘自发,庞铖铖,王泽黎,等.基于云理论和元胞自动机理论的城市配电网空间负荷预测[J].中国电机工程学报,2013,33(10):98-105.LIU Zifa,PANG Chengcheng,WANG Zeli,et al.Spatial load forecasting for distribution network based on cloud theory and cellular automata[J].Proceedings of the CSEE,2013,33(10):98-105.
[3]周湶,邓景云,任海军,等.基于蚁群算法的配电网空间负荷预测方法研究[J].电力系统保护与控制,2010,38(24):99-104.ZHOU Quan,DENG Jingyun,REN Haijun,et al.Research on spatial load forecast of distribution networks based on ant colony algorithm[J].Power System Protection and Control,2010,38(24):99-104.
[4]李金超,牛东晓,李金颖,等.基于负荷细分与SVM技术的电力负荷空间分布预测[J].电工电能新技术,2008,27(1):40-43.LI Jinchao,NIU Dongxiao,LI Jinying,et al.Spatial load forecasting based on load decomposition and support vector machine[J].Advanced Technology of Electrical Engineering and Energy,2008,27(1):40-43.
[5]钟庆,吴捷,伍力,等.基于系统动力学的分区负荷预测[J].电网技术,2001,25(3):51-55.ZHONG Qing,WU Jie,WU Li,et al.Subarea load forecasting based on system dynamics[J].Power System Technology,2001,25(3):51-55.
[6]于容.采用非均匀区域法的空间负荷预测[J].国际电力,2001,5(2):40-42.YU Rong.Spatial load forecasting using non-uniform areas[J].International Electric Power for China,2001,5(2):40-42.
[7]刘自发,庞铖铖,魏建炜,等.基于IAHP和TOPSIS方法的负荷密度指标计算[J].电力系统自动化,2012,36(13):56-60.LIU Zifa,PANG Chengcheng,WEI Jianwei,et al.Index calculation of load density based on IAHP and TOPSISmethods[J].Automation of Electric Power Systems,2012,36(13):56-60.
[8]刘自发,陈艳旭,张杰超.基于AHP和TOPSIS法的空间负荷预测[J].电力科学与技术学报,2009,24(4):44-48,53.LIU Zifa,CHEN Yanxu,ZHANG Jiechao.AHP and TOPSIS method based spatial electric load forecasting[J].Journal of Electric Power Science and Technology,2009,24(4):44-48,53.
[9]符杨,曹家麟,谢楠,等.基于模糊综合评判的负荷密度指标选取新方法[J].电网技术,2007,31(18):19-22.FU Yang,CAO Jialin,XIE Nan,et al.A novel fuzzy comprehensive evaluation based method to select load density and index[J].Power System Technology,2007,31(18):19-22.
[10]李滨,覃芳璐,吴茵,等.基于模糊信息粒化与多策略灵敏度的短期日负荷曲线预测[J].电工技术学报,2017,32(9):149-159.LI Bin,QIN Fanglu,WU Yin,et al.Short-term daily load curve forecasting based on fuzzy information granulation and multi-strategy sensitivity[J].Transactions of China Electrotechnical Society,2017,32(9):149-159.
[11]陶文斌,张粒子,潘弘,等.基于双层贝叶斯分类的空间负荷预测[J].中国电机工程学报,2007,27(7):13-17.TAO Wenbin,ZHANG Lizi,PAN Hong,et al.Electric load forecasting based on double-level Bayesian classification[J].Proceedings of the CSEE,2007,27(7):13-17.
[12]国务院《能源发展战略行动计划(2014-2020年)》[EB/OL].[2014-12-03].http://www.nea.gov.cn/2014-12/03/c_133830458.htm.State Council"Energy Development Strategic Action Plan(2014-2020)".
[13]赵勇强.2050年我国高比例可再生能源情景的初步思考[J].中国能源,2013,35(5):5-11.ZHAO Yongqiang.Preliminary thinking about high proportion of renewable energy scenario in China in2050[J].Energy of China,2013,35(5):5-11.
[14]徐晨曦.新能源汽车:从130万辆到8000万辆60倍增长,15年完成[J].中国战略新兴产业,2016(25):64-68.XU Chenxi.60 times increase from 1.3 million to 80million,completed in 15 years[J].China Strategic Emerging Industry,2016(25):64-68.
[15]马钊,周孝信,尚宇炜,等.未来配电系统形态及发展趋势[J].中国电机工程学报,2015,35(6):1289-1298.MA Zhao,ZHOU Xiaoxin,SHANG Yuwei,et al.Form and development trend of future distribution system[J].Proceedings of the CSEE,2015,35(6):1289-1298.
[16]杨秋霞,刘同心,高辰,等.计及光伏电站功率预测的电力系统优化分析[J].电力系统保护与控制,2018,46(2):117-123.YANG Qiuxia,LIU Tongxin,GAO Chen,et al.Power system optimization analysis considering power prediction of PV power station[J].Power System Protection and Control,2018,46(2):117-123.
[17]张晨彧,丁明,张晶晶.基于交通出行矩阵的私家车充电负荷时空分布预测[J].电工技术学报,2017,32(1):78-87.ZHANG Chenyu,DING Ming,ZHANG Jingjing.Atemporal and spatial distribution forecasting of private car charging load based on origin-destination matrix[J].Transactions of China Electrotechnical Society,2017,32(1):78-87.
[18]黄伟,高怡芳,张勇军,等.计及不确定性的分布式光伏接入配电网极限容量评估[J].电力系统保护与控制,2018,46(14):9-16.HUANG Wei,GAO Yifang,ZHANG Yongjun,et al.Evaluation of ultimate capacity for distributed photovoltaic access distribution network considering uncertainty[J].Power System Protection and Control,2018,46(14):9-16.
[19]VILLAVICENCIO J,MELO J D,FELTRIN A P.Estimation of photovoltaic potential on residential rooftops using empirical Bayesian estimator[C]//2015 IEEE PESInnovative Smart Grid Technologies Latin America(ISGT LATAM),October 5-7,2015,Montevideo,Uruguay:242-247.
[20]王雨,谢炜.谈屋顶光伏设计及安装[J].山西建筑,2017,43(2):196-198.WANG Yu,XIE Wei.Discussion on photovoltaic design and installation of the roof[J].Shanxi Architecture,2017,43(2):196-198.
[21]CHARABI Y,RHOUMA M B H,GASTLI A.GIS-based estimation of roof-PV capacity&energy production for the Seeb region in Oman[C]//2010 IEEE International Energy Conference and Exhibition,December 18-22,Manama,Bahrain:41-44.
[22]徐福圆.基于遥感图像的屋顶面积识别及屋顶光伏容量估计[D].杭州:杭州电子科技大学,2016.XU Fuyuan.Roof area recognition and PV capacity estimation based on remote sensing image[D].Hangzhou:Hangzhou Dianzi University,2016.
[23]ASHTARI A,BIBEAU E,SHAHIDINEJAD S,et al.PEV charging profile prediction and analysis based on vehicle usage data[J].IEEE Transactions on Smart Grid,2012,3(1):341-350.
[24]DARABI Z,FERDOWSI M.Plug-in hybrid electric vehicles:charging load profile extraction based on transportation data[C]//2011 IEEE Power and Energy Society General Meeting,July 24-29,2011,San Diego,CA,USA:1-8.
[25]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.
[26]SANTOS A,MCGUCKIN N,NAKAMOTO H Y,et al.Summary of travel trends:2009 national household travel survey[J].Demographics,2011.
[27]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.
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