基于动态Copula的风光联合出力建模及动态相关性分析
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  • 英文篇名:Modeling and dynamic correlation analysis of wind/solar power joint output based on dynamic Copula
  • 作者:段偲默 ; 苗世洪 ; 霍雪松 ; 李力行 ; 韩佶 ; 晁凯云
  • 英文作者:DUAN Simo;MIAO Shihong;HUO Xuesong;LI Lixing;HAN Ji;CHAO Kaiyun;State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Hubei Electric Power Security and High Efficiency Key Laboratory, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology;Jiangsu Electric Power Dispatch Center;
  • 关键词:Copula理论 ; 动态Copula函数 ; 风光互补 ; 相关系数 ; 拟合优度
  • 英文关键词:Copula method;;dynamic Copula function;;wind/solar hybrid;;correlation coefficient;;goodness of fit
  • 中文刊名:JDQW
  • 英文刊名:Power System Protection and Control
  • 机构:华中科技大学电气与电子工程学院强电磁工程与新技术国家重点实验室电力安全与高效湖北省重点实验室;江苏电力调度控制中心;
  • 出版日期:2019-03-07 09:24
  • 出版单位:电力系统保护与控制
  • 年:2019
  • 期:v.47;No.527
  • 基金:国家重点研发计划项目(2017YFB0902600);; 国家电网公司科技项目(SGJS0000DKJS1700840);; 国家重点基础研究发展计划项目(973计划)(2015CB251301)~~
  • 语种:中文;
  • 页:JDQW201905005
  • 页数:8
  • CN:05
  • ISSN:41-1401/TM
  • 分类号:43-50
摘要
准确描述风力发电和光伏发电的动态相关性及联合出力的波动性,对风光互补系统的出力预测和经济调度具有重要意义。针对现行静态相关系数无法准确描述风光出力相依关系的问题,研究了风光出力的动态相关性,提出了基于动态Copula函数的风光联合出力模型构建方法。结合实测数据建立了8组动态与静态的风光联合出力Copula模型,用动态相关系数描述风光出力的相关性。运用拟合优度检验方法验证了动态Copula模型对比其静态模型的优越性,选出最优模型。最后将该模型应用在数据驱动的风光联合系统中,验证了其合理性与正确性。
        Describing the dynamic correlation of wind/solar power output and the fluctuation of joint output accurately are of great significance to the output forecasting and economic dispatch of the wind solar hybrid system. According to the problem that the existing static correlation coefficient cannot accurately describe the relationship between the wind power and PV output, this paper studies dynamic correlation of wind/solar power output and proposes a wind/solar power joint output model construction method by using dynamic Copula function. Combining with the measured data, it establishes 8 groups of wind/solar power joint output model, applies dynamic correlation coefficient to describe dynamic correlation of wind/solar power output and uses goodness of fit method to prove the superiority of the dynamic Copula model comparing to the static model to select the optimal model. Finally, the model is applied to the data driven wind/solar joint system, and the rationality and correctness of the model are verified.
引文
[1]舒印彪,张智刚,郭剑波,等.新能源消纳关键因素分析及解决措施研究[J].中国电机工程学报,2017,37(1):1-8.SHU Yinbiao,ZHANG Zhigang,GUO Jianbo,et al.Study on key factors and solution of renewable energy accommodation[J].Proceedings of the CSEE,2017,37(1):1-8.
    [2]郭永明,李仲昌,尤小虎,等.计及备用容量优化配置的风火联合随机经济调度模型[J].电力系统保护与控制,2016,44(24):31-36.GUO Yongming,LI Zhongchang,YOU Xiaohu,et al.Stochastic economic dispatch model for joint delivery of wind power and thermal power generation system considering optimal scheduling of reserve capacity[J].Power System Protection and Control,2016,44(24):31-36.
    [3]刘阳,杨正瓴,张泽,等.采用变系数模型改进空间相关性风速预测[J].广东电力,2017,30(6):50-54.LIU Yang,YANG Zhengling,ZHANG Ze,et al.Improvement of spatial correlation wind speed prediction based on variable coefficient model[J].Guangdong Electric Power,2017,30(6):50-54.
    [4]周强,汪宁渤,何世恩,等.高弃风弃光背景下中国新能源发展总结及前景探究[J].电力系统保护与控制,2017,45(10):146-154.ZHOU Qiang,WANG Ningbo,HE Shien,et al.Summary and prospect of China's new energy development under the background of high abandoned new energy power[J].Power System Protection and Control,2017,45(10):146-154.
    [5]初壮,窦孝祥,于群英.考虑风电随机性的多场景配电网重构[J].电力系统保护与控制,2017,45(1):132-138.CHU Zhuang,DOU Xiaoxiang,YU Qunying.Multi scene distribution network reconfiguration considering the randomness of wind power[J].Power System Protection and Control,2017,45(1):132-138.
    [6]董朕,殷豪,孟安波.基于混合算法优化神经网络的风电预测模型[J].广东电力,2017,30(2):29-33.DONG Zhen,YIN Hao,MENG Anbo.Wind power forecasting model based on optimized neural network of hybrid algorithm[J].Guangdong Electric Power,2017,30(2):29-33.
    [7]叶燕飞,王琦,陈宁,等.考虑时空分布特性的风速预测模型[J].电力系统保护与控制,2017,45(4):114-120.YE Yanfei,WANG Qi,CHEN Ning,et al.Wind forecast model considering the characteristics of temporal and spatial distribution[J].Power System Protection and Control,2017,45(4):114-120.
    [8]唐波,葛光祖,张建功,等.特高压直流线路对调幅广播台无源干扰防护间距的求解[J].信阳师范学院学报(自然科学版),2013,26(4):577-580.TANG Bo,GE Guangzu,ZHANG Jiangong,et al.Protecting distance of reradiation interference between UHVDC power line and receiving station of am broadcasting[J].Journal of Xinyang Normal University(Natural Science Edition),2013,26(4):577-580.
    [9]许童羽,马艺铭,曹英丽,等.基于主成分分析和遗传优化BP神经网络的光伏输出功率短期预测[J].电力系统保护与控制,2016,44(22):90-95.XU Tongyu,MA Yiming,CAO Yingli,et al.Short term forecasting of photovoltaic output power based on principal component analysis and genetic optimization of BP neural network[J].Power System Protection and Control,2016,44(22):90-95.
    [10]舒印彪,汤涌,孙华东.电力系统安全稳定标准研究[J].中国电机工程学报,2013,33(25):1-9.SHU Yinbiao,TANG Yong,SUN Huadong.Research on power system security and stability standards[J].Proceedings of the CSEE,2013,33(25):1-9.
    [11]PAPAEFTHYMIOU G,KUROWICKA D.Using Copulas for modeling stochastic dependence in power system uncertainty analysis[J].IEEE Transactions on Power Systems,2009,24(1):40-49.
    [12]黄静,刘江峰,赵志强,等.组串式并网逆变器运行性能研究[J].信阳师范学院学报(自然科学版),2018,31(4):650-656.HUANG Jing,LIU Jiangfeng,ZHAO Zhiqiang,et al.Operational performance analysisof grid-connected PVstring-type inverter[J].Journal of Xinyang Normal University(Natural Science Edition),2018,31(4):650-656.
    [13]徐玉琴,张林浩.考虑风速相关性的风电接入能力分析[J].可再生能源,2014,32(2):201-206.XU Yuqin,ZHANG Linhao.Analysis on wind power penetration limit considering wind speed correlation[J].Renewable Energy,2014,32(2):201-206.
    [14]HAGHI H V.Using Copulas for analysis of large datasets in renewable distributed generation:PV and wind power integration in Iran[J].Renewable Energy,2010,35(9):1991-2000.
    [15]杨洪明,王爽,易德鑫,等.考虑多风电场出力相关性的电力系统随机优化调度[J].电力自动化设备,2013,33(1):114-120.YANG Hongming,WANG Shuang,YI Dexin,et al.Stochastic optimal dispatch of power system considering multi-wind power correlation[J].Electric Power Automation Equipment,2013,33(1):114-120.
    [16]蔡菲,严正,赵静波,等.基于Copula理论的风电场间风速及输出功率相依结构建模[J].电力系统自动化,2013,37(17):9-16.CAI Fei,YAN Zheng,ZHAO Jingbo,et al.Dependence structure models for wind speed and wind power among different wind farms based on Copula theory[J].Automation of Electric Power Systems,2013,37(17):9-16.
    [17]王小红,周步祥,张乐,等.基于时变Copula函数的风电出力相关性分析[J].电力系统及其自动化学报,2015,27(1):43-48.WANG Xiaohong,ZHOU Buxiang,ZHANG Le,et al.Wind power correlation analysis based on time-variant Copula function[J].Proceedings of the CSU-EPSA,2015,27(1):43-48.
    [18]赵继超,袁越,傅质馨,等.基于Copula理论的风光互补发电系统可靠性评估[J].电力自动化设备,2013,33(1):124-129.ZHAO Jichao,YUAN Yue,FU Zhixin,et al.Reliability assessment of wind-PV hybrid generation system based on Copula theory[J].Electric Power Automation Equipment,2013,33(1):124-129.
    [19]张盼盼,熊炜.基于Copula方法的风光互补发电系统相关性模型研究[J].电测与仪表,2014,51(17):93-98.ZHANG Panpan,XIONG Wei.Correlation model research on Copula function based wind/solar complementary generation system[J].Electrical Measurement&Instrumentation,2014,51(17):93-98.
    [20]钟嘉庆.基于Copula理论的风/光出力预测误差分析方法的研究[J].电工电能新技术,2017,36(6):39-46.ZHONG Jiaqing.Method of wind/solar output forecast error analysis based on Copula theory[J].Advanced Technology of Electrical Engineering and Energy,2017,36(6):39-46.
    [21]NELSEN R B.An introduction to Copulas[M].Springer,2006.
    [22]XU Haidong,JIANG Mingyan,XU Kun.Archimedean copula estimation of distribution algorithm based on artificial bee colony algorithm[J].Journal of Systems Engineering and Electronics,2015,26(2):388-396.
    [23]PATTON A J.Modelling asymmetric exchange rate dependence[J].International Economic Review,2006,47(2):527-556.
    [24]PATTON A J.Estimation of multivariate models for time series of possibly different lengths[J].Journal of Applied Econometrics,2006,21(2):147-173.
    [25]ENGLE R.Dynamic conditional correlation[J].Journal of Business&Economic Statistics,2002,20(3):339-350.
    [26]任仙玲.基于Copula理论的金融市场相依结构研究[D].天津:天津大学,2008.REN Xianling.Research on dependence structure among financial markets based on the Copula theory[D].Tianjin:Tianjin University,2008.
    [27]AKAIKE H.Factor analysis and AIC[J].Psychometrika,1987,52(3):317-332.
    [28]MILLER F P,VANDOME A F,MCBREWSTER J.Bayesian information criterion[M].Alphascript Publishing,2010.
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