基于多尺度聚类分析的光伏功率特性建模及预测应用
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摘要
光伏功率曲线的建模和预测是电网运行调度和规划建设普遍关注的问题。对大量光伏电站录波数据的挖掘和利用为波动性光伏功率的建模提供了新的方向。提出了一种多尺度分析策略来实现对光伏功率时间序列的固有和随机特性的挖掘和建模。结合大时间尺度的多子集聚类分析和小时间尺度的标准化变换和概率分布建模方法,实现了对光伏功率波动性和规律性的挖掘,并与气象领域的理论模型进行了交互验证。最后通过实际光伏电站的日前功率预测实例验证了所述方法和模型的有效性。
The modeling and prediction of photovoltaic(PV)power curve are a widely concerned issue in the power grid operation and planning.Mining and utilization of the big data recorded by the PV stations provide a new and promising direction for the modeling of the fluctuating PV power.A multi-scale analysis scheme is presented to realize the digging and modeling for the fixed and stochastic characteristics of the PV power time sequence.By combining the multiple cluster analysis at large time-scale,the standardized transformations at small time-scale,and the probability distribution modeling,the digging to the fluctuated and regular characteristics of PV power is realized.The clustering results are also verified by cross validation with the theoretical models in the meteorology field.The effectiveness of the proposed method and model is verified by an example of day-ahead PV power forecasting.
The modeling and prediction of photovoltaic(PV)power curve are a widely concerned issue in the power grid operation and planning.Mining and utilization of the big data recorded by the PV stations provide a new and promising direction for the modeling of the fluctuating PV power.A multi-scale analysis scheme is presented to realize the digging and modeling for the fixed and stochastic characteristics of the PV power time sequence.By combining the multiple cluster analysis at large time-scale,the standardized transformations at small time-scale,and the probability distribution modeling,the digging to the fluctuated and regular characteristics of PV power is realized.The clustering results are also verified by cross validation with the theoretical models in the meteorology field.The effectiveness of the proposed method and model is verified by an example of day-ahead PV power forecasting.
引文
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[18]管霖,陈旭,吕耀棠,等.适用于电网规划的光伏发电概率模型及其应用[J].电力自动化设备,2017,37(11):1-7.GUAN Lin,CHEN Xu,LYU Yaotang,et al.Probability model of PV generation for power system planning and its application[J].Electric Power Automatic Equipment,2017,37(11):1-7.
[2]SALAMEH Z M,BOROWY B S,AMIN A R A.Photovoltaic module-site matching based on the capacity factors[J].IEEETransactions on Energy Conversion,1995,10(2):326-332.
[3]任洲洋,颜伟,项波,等.考虑光伏和负荷相关性的概率潮流计算[J].电工技术学报,2015,30(24):181-187.REN Zhouyang,YAN Wei,XIANG Bo,et al.Probabilistic power flow analysis incorporating the correlations between PVpower outputs and loads[J].Transactions of China Electrotechnical Society,2015,30(24):181-187.
[4]LIU Z,WEN F,LEDWICH G.Optimal siting and sizing of distributed generators in distribution systems considering uncertainties[J].IEEE Transactions on Power Delivery,2011,26(4):2541-2551.
[5]OMRAN W A,KAZERANI M,SALAMA M M A.Aclustering-based method for quantifying the effects of large ongrid PV systems[J].IEEE Transactions on Power Delivery,2010,25(4):2617-2625.
[6]郭一飞,高厚磊,田佳.引入聚类分析的光伏出力建模及其在可靠性评估中的应用[J].电力系统自动化,2016,40(23):93-100.DOI:10.7500/AEPS20160301008.GUO Yifei,GAO Houlei,TIAN Jia.Photovoltaic output modeling by introducing clustering analysis and its application in reliability evaluation[J].Automation of Electric Power Systems,2016,40(23):93-100.DOI:10.7500/AEPS20160301008.
[7]PULIPAKA S,KUMAR R.Comparison of SOM and conventional neural network data division for PV reliability power prediction[C]//2017IEEE International Conference on Environment and Electrical Engineering and 2017 IEEEIndustrial and Commercial Power Systems Europe(EEEIC/I&CPS Europe),June 6-9,2017,Milan,Italy.
[8]张曦,康重庆,张宁,等.太阳能光伏发电的中长期随机特性分析[J].电力系统自动化,2014,38(6):6-13.DOI:10.7500/AEPS20131009012.ZHANG Xi,KANG Chongqing,ZHANG Ning,et al.Analysis of mid/long term random characteristics of photovoltaic power generation[J].Automation of Electric Power Systems,2014,38(6):6-13.DOI:10.7500/AEPS20131009012.
[9]DEV S,LEE Y H,WINKLER S,et al.Color-based segmentation of sky/cloud images from ground-based cameras[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2016,10(1):231-242.
[10]陈志宝,李秋水,程序,等.基于地基云图的光伏功率超短期预测模型[J].电力系统自动化,2013,37(19):20-25.CHEN Zhibao,LI Qiushui,CHENG Xu,et al.A very shortterm prediction model for photovoltaic power based on groundbased cloud images[J].Automation of Electric Power Systems,2013,37(19):20-25.
[11]HOLMGREN W F,LORENZO A T,LEUTHOLD M,et al.An operational,real-time forecasting system for 250 MW of PV power using NWP,satellite,and DG production data[C]//2014IEEE 40th Photovoltaic Specialist Conference(PVSC),June 8-13,2014,Denver,USA:972-975.
[12]SUN Xinpei,ZHANG Tao.Solar power prediction in smart grid based on NWP data and an improved boosting method[C]//2017 IEEE International Conference on Energy Internet,April 17-21,2017,Beijing,China:89-94.
[13]DOLARA A,LEVA S,MANZOLINI G.Comparison of different physical models for PV power output prediction[J].Solar Energy,2015,119:83-99.
[14]高相铭,杨世凤,潘三博.基于EMD和ABC-SVM的光伏并网系统输出功率预测研究[J].电力系统保护与控制,2015,43(21):86-92.GAO Xiangming,YANG Shifeng,PAN Sanbo.A forecasting model for output power of grid-connected photovoltaic generation system based on EMD and ABC-SVM[J].Power System Protection and Control,2015,43(21):86-92.
[15]SILVA T V D,MONTEIRO R V A,MOURA F A M,et al.Performance analysis of neural network training algorithms and support vector machine for power generation forecast of photovoltaic panel[J].IEEE Latin America Transactions,2017,15(6):1091-1100.
[16]FEUILLARD T,ABILLON J M,BONHOMME R.Relationship between global solar irradiation and sunshine duration in Guadeloupe[J].Solar Energy,1989,43(6):359-361.
[17]KUMAR R,UMANAND L.Estimation of global radiation using clearness index model for sizing photovoltaic system[J].Renewable Energy,2005,30(15):2221-2233.
[18]管霖,陈旭,吕耀棠,等.适用于电网规划的光伏发电概率模型及其应用[J].电力自动化设备,2017,37(11):1-7.GUAN Lin,CHEN Xu,LYU Yaotang,et al.Probability model of PV generation for power system planning and its application[J].Electric Power Automatic Equipment,2017,37(11):1-7.