基于时空自回归移动平均模型的风电出力序列模拟
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摘要
多风电场出力序列间的时空耦合相关性对风电并网下的电力系统运行具有重要影响。时空自回归移动平均(ST-ARMA)模型以较为简洁的形式对多维序列时空耦合相关性进行统计建模。针对多风电场出力时空序列的模拟问题,首先从时空序列的角度对风电场实测功率数据进行了统计分析,着重探讨了多风电场出力的时空耦合相关性。在此基础上,采用空间关系矩阵对风电场位置进行描述,并将其嵌入ST-ARMA模型的自回归过程建立多风电场出力序列的时空耦合相关性模型。该模型有效地模拟了实测风电场出力序列的时间相关性、空间相关性以及二者之间的耦合特性,可用于产生大量与实际风电出力统计特性相同的模拟数据,为风电并网下的电力系统运行与规划研究提供数据基础。
The coupled space-time correlation among output series in multiple wind farms has great effects on the operation of power systems.Space-time auto regressive moving average(ST-ARMA)model can restore statistical model of coupled spacetime correlation of multi-dimensional sequences in a relatively simple form.In order to build an effective model for multidimensional wind power series,the measured wind power data from the perspective of space-time series are statistically analyzed,especially for the coupled space-time correlation.Then,a spatial relation matrix is used to describe the location of each wind farm,and embedded into the auto regression process of ST-ARMA model in order to simulate the coupled space-time correlation of wind power series.The ST-ARMA model has restored not only the temporal and spatial correlation of original wind power series,but also the coupled characteristic between the two kinds of correlation.This model can be used to produce huge amount of simulation data with the same characteristics of actual wind power output,which is able to provide data basics for the planning and operation research of wind power integration.
The coupled space-time correlation among output series in multiple wind farms has great effects on the operation of power systems.Space-time auto regressive moving average(ST-ARMA)model can restore statistical model of coupled spacetime correlation of multi-dimensional sequences in a relatively simple form.In order to build an effective model for multidimensional wind power series,the measured wind power data from the perspective of space-time series are statistically analyzed,especially for the coupled space-time correlation.Then,a spatial relation matrix is used to describe the location of each wind farm,and embedded into the auto regression process of ST-ARMA model in order to simulate the coupled space-time correlation of wind power series.The ST-ARMA model has restored not only the temporal and spatial correlation of original wind power series,but also the coupled characteristic between the two kinds of correlation.This model can be used to produce huge amount of simulation data with the same characteristics of actual wind power output,which is able to provide data basics for the planning and operation research of wind power integration.
引文
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[2]张宁,周天睿,段长刚,等.大规模风电场接入对电力系统调峰的影响[J].电网技术,2010,34(1):152-158.ZHANG Ning,ZHOU Tianrui,DUAN Changgang,et al.Impact of large-scale wind farm connecting with power grid on peak load regulation demand[J].Power System Technology,2010,34(1):152-158.
[3]BILLINTON R,LI Wenyuan.Reliability assessment of electric power systems using Monte Carlo methods[M].New York:Plenum Press,1994.
[4]VILLANUEVA D,LUIS P J,FEIJOO A.Probabilistic load flow including wind power generation[J].IEEE Transactions on Power Systems,2011,26(3):1659-1667.
[5]CHEN Peiyuan,SIANO P,BAK-JENSEN B,et al.Stochastic optimization of wind turbine power factor using stochastic model of wind power[J].IEEE Transactions on Sustainable Energy,2010,1(1):19-29.
[6]李剑楠,乔颖,鲁宗相,等.多时空尺度风电统计特性评价指标体系及其应用[J].中国电机工程学报,2013,33(13):53-61.LI Jiannan,QIAO Ying,LU Zongxiang,et al.An evaluation index system for wind power statistical characteristics in multiple spatial and temporal scales and its application[J].Proceedings of the CSEE,2013,33(13):53-61.
[7]MA Xiyuan,SUN Yuanzhang,FANG Hualiang.Scenario generation of wind power based on statistical uncertainty and variability[J].IEEE Transactions on Sustainable Energy,2013,4(4):894-904.
[8]PAPAEFTHYMIOU G.Integration of stochastic generation in power systems[D].Delft:Delft University of Technology,2007.
[9]KLOECKL B,PAPAEFTHYMIOU G.Multivariate time series models for studies on stochastic generators in power systems[J].Electric Power Systems Research,2010,80(3):265-276.
[10]JIN Lin,SUN Yuanzhang,SORENSEN P,et al.Method for assessing grid frequency deviation due to wind power fluctuation based on“time-frequency transformation”[J].IEEE Transactions on Sustainable Energy,2012,3(1):65-73.
[11]LEE D,BALDICK R.Future wind power scenario synthesis through power spectral density analysis[J].IEEE Transactions on Smart Grid,2014,5(1):490-500.
[12]CHEN Peiyuan,PEDERSEN T,BAK-JENSEN B,et al.ARIMA-based time series model of stochastic wind power generation[J].IEEE Transactions on Power Systems,2010,25(2):667-676.
[13]ZHANG Ning,KANG Chongqing,XU Qianyao,et al.Modelling and simulating the spatio-temporal correlations of clustered wind power using copula[J].Journal of Electrical Engineering&Technology,2013,8(6):1615-1625.
[14]陈宁,薛禹胜,丁杰,等.利用空间相关性的超短期风速预测[J].电力系统自动化,2017,41(12):124-130.DOI:10.7500/AEPS20170109004.CHEN Ning,XUE Yusheng,DING Jie,et al.Ultra-short term wind speed prediction using spatial correlation[J].Automation of Electric Power Systems,2017,41(12):124-130.DOI:10.7500/AEPS20170109004.
[15]薛禹胜,陈宁,王树民,等.关于利用空间相关性预测风速的评述[J].电力系统自动化,2017,41(10):161-169.DOI:10.7500/AEPS20170109002.XUE Yusheng,CHEN Ning,WANG Shumin,et al.Review on wind speed prediction based on spatial correlation[J].Automation of Electric Power Systems,2017,41(10):161-169.DOI:10.7500/AEPS20170109002.
[16]MORALES J M,MINGUEZ R,CONEJO A J.Amethodology to generate statistically dependent wind speed scenarios[J].Applied Energy,2010,87(3):843-855.
[17]李玉敦,谢开贵,胡博.基于Copula函数的多维时序风速相依模型及其在可靠性评估中的应用[J].电网技术,2013,37(3):840-846.LI Yudun,XIE Kaigui,HU Bo.A Copula function-based dependent model for multivariate wind speed time series and its application in reliability assessment[J].Power System Technology,2013,37(3):840-846.
[18]LE D D,GROSS G,BERIZZI A.Probabilistic modeling of multisite wind farm production for scenario based applications[J].IEEE Transactions on Sustainable Energy,2015,6(3):748-758.
[19]MARTIN R L,OEPPEN J E.The identification of regional forecasting models using space-time correlation functions[J].Transactions of the Institute of British Geographers,1975,66:95-118.
[20]GLASBEY C A,ALLCROFT D J.A spatio-temporal autoregressive moving average model for solar radiation[J].Journal of the Royal Statistical Society Series C-Applied Statistics,2008,57(3):343-355.
[21]CHENG Tao,WANG Jiaqiu,LI Xia.A hybrid framework for space-time modeling of environmental data[J].Geographical Analysis,2011,43(2):188-210.
[22]常刚,张毅,姚丹亚.基于时空依赖性的区域路网短时交通流预测模型[J].清华大学学报(自然科学版),2013,53(2):215-221.CHANG Gang,ZHANG Yi,YAO Danya.Short-term traffic flow forecasting model for regional road network based on spatial-temporal dependency[J].Journal of Tsinghua University(Science and Technology),2013,53(2):215-221.
[23]CLIFF A D,ORD J K.Spatial processes:models and applications[M].London:Pion,1981.
[24]邹金,赖旭,汪宁渤.风电随机出力的时间序列模型[J].电网技术,2014,38(9):2416-2421.ZOU Jin,LAI Xu,WANG Ningbo.Time series model of stochastic wind power generation[J].Power System Technology,2014,38(9):2416-2421.
[25]魏武雄.时间序列分析:单变量与多变量方法[M].北京:中国人民大学出版社,2009.WEI Wuxiong.Time series analysis:univariate and multivariate methods[M].Beijing:China Renmin University Press,2009.
[26]王远飞,何洪林.空间数据分析方法[M].北京:科学出版社,2007.WANG Yuanfei,HE Honglin.Spatial data analysis method[M].Beijing:Science Press,2007.
[27]王佳璆.时空序列数据分析和建模[D].广州:中山大学,2008.WANG Jiaqiu.Space-time series data analysis and modelling[D].Guangzhou:Sun Yat-sen University,2008.
[28]PFEIFER E,DEUTSCH S J.A three-stage iterative procedure for space-time modeling[J].Technometrics,1980,22(1):35-47.
[29]王新洲.非线性模型参数估计理论与应用[M].武汉:武汉大学出版社,2002.WANG Xinzhou.Theory and application for parameter estimation of nonlinear model[M].Wuhan:Wuhan University Press,2002.