提高风电功率预测精度的方法Research & Application of Raising Wind Power Prediction Accuracy
乔颖;鲁宗相;闵勇;
摘要(Abstract):
风电功率预测技术既是交叉应用学科,又是众多学科的共性基础性学科。目前国际最先进水平的风电预测误差甚至低于3%,我国还处于追赶期。从发展趋势来看,当前数据积累带来的精度提升效果日渐式微,数值天气预报短期内出现突破不可预期。风功率预测在未来一段时间内仍需要有限的天气预报水平下提高各环节的预报技巧,筛选和组合预测方法,争取误差学习曲线最末端2%~3%。若干实践表明,综合应用多项精细化预测技术,可显著提高原有水平较差的风场精度。这些技术包括优化数值天气预报参数化方案、提高功率转换环节精度与自适应能力、采用复合数据源的组合法、考虑空间相关性的非线性误差修正等。
关键词(KeyWords): 风功率预测;数值天气预报;物理法;统计法;精度提升
基金项目(Foundation): 国家重点研发计划(2016YFB0900100);; 国家自然科学基金重大项目(51190101)~~
作者(Author): 乔颖;鲁宗相;闵勇;
Email:
DOI: 10.13335/j.1000-3673.pst.2017.1581
参考文献(References):
- [1]Costa A,Crespo A,Navarrp J,et al.A review on the young history of the wind power short-term prediction[J].Renewable and Sustainable Energy Reviews,2008,12(6):1725-1744.
- [2]Giebel G,Kariniotakis G.Wind power forecasting-a review of the state of the art[C]//Renewable Energy Forecasting:From Models to Applications,Kariniotakis G(ed):Woodhead Publishing,1st ed,2017:59-109.
- [3]Giebel G,Brownsword R,Kariniotakis G,et al.The state-of-the-art in short-term prediction of wind power:a literature overview[R].ANEMOS.plus Project Report 2011.
- [4]Sideratos G,Hatziargyriou N D.An advanced statistical method for wind power forecasting[J].IEEE Trans on Power Systems,2007,22(1):258-265.
- [5]Alessandrini S,Sperati S.Characterization of forecast errors and benchmarking of renewable energy forecast[C]//Renewable Energy Forecasting:From Models to Applications.Kariniotakis G(ed):Woodhead Publishing,1st ed,2017:235-255.
- [6]Ernst B,Oakleaf B,Ahlstrom M L,et al.Predicting the wind[J].IEEE Power and Energy Magazine,2007,5(6):78-89.
- [7]Lange M,Focken U.New developments in wind energy forecasting[C]//IEEE Power and Energy Society General Meeting,Pittsburgh,PA,USA,2008:1-8.
- [8]Cabez?n D,Rub?n S,Lainez I.Benchmarking of forecasting models:Reviewing and improving the state of the art[C]//Wind Power Forecasting 2015 Meeting,Leuven Belgium,2015:1-6.
- [9]Madsen H,Pinson P,Peder B.State-of-art in forecasting of wind and solar power generation[C]//Wind and Solar Power Forecasting,Energy System Integration 101 Course,Denver,USA.2014.
- [10]Giebel G,Cline J,Frank H,et al.Wind power forecasting:IEA wind task 36&future research issues[C]//The Science of Making Torque from Wind,Journal of Physics:Conference Series,Munich,Germany,2016,753:1-10.
- [11]薛禹胜,郁琛,赵俊华,等.关于短期及超短期风电功率预测的评述[J].电力系统自动化,2015,39(6):141-151.Xue Yusheng,Yu Chen,Zhao Junhua,et al.A review on short-term and ultra-short-term wind power prediction[J].Automation of Electric Power Systems,2015,39(6):141-151(in Chinese).
- [12]Paiva L T,Veiga Rodrigues C,Palma J M L M.Determining wind turbine power curves based on operating conditions[J].Wind Energy,2014,17(10):1563-1575.
- [13]徐曼.动力学和统计学方法综合协调的风电功率精细化预测研究[D].北京:清华大学,2016.
- [14]徐曼,乔颖,鲁宗相.短期风电功率预测误差综合评价方法[J].电力系统自动化,2011,35(12):20-26.Xu Man,Qiao Ying,Lu Zongxiang.A comprehensive error evaluation method for short-term wind power prediction[J].Automation of Electric Power Systems,2011,35(12):20-26(in Chinese).
- [15]陈树勇,戴慧珠,白晓明,等.尾流效应对风电场输出功率的影响[J].中国电力,1998,31(11):28-31.Chen Shuyong,Dai Huizhu,Bai Xiaoming,et al.Impact of wind turbine wake on wind power output[J].Electric Power,1998,31(11):28-31(in Chinese).
- [16]谷兴凯,范高锋,王晓蓉,等.风电功率预测技术综述[J].电网技术,2007,31(2):335-338.Gu Xingkai,Fan Gaofeng,Wang Xiaorong,et al.Summarization of wind power prediction technology[J].Power System Technology,2007,31(2):335-338(in Chinese).
- [17]范高峰,裴哲义,辛耀中.风电功率预测的发展现状与展望[J].中国电力,2011,44(6):38-41.Fan Gaofeng,Pei Zheyi,Xin Yaozhong.Wind power prediction achievement and prospect[J].Electric Power,2011,44(6):38-41(in Chinese).
- [18]李莉,刘永前,杨勇平,等.基于CFD流场预计算的短期风速预测方法[J].中国电机工程学报,2013,33(7):27-32.Li Li,Liu Yongqian,Yang Yongping,et al.Short-term wind speed forecasting based on CFD pre-calculated flow fields[J].Proceedings of the CSEE,2013,33(7):27-32(in Chinese).
- [19]冯双磊,王伟胜,刘纯,等.风电场功率预测物理方法研究[J].中国电机工程学报,2010,30(2):1-6.Feng Shuanglei,Wang Weisheng,Liu Chun,et al.Study on the physical approach to wind power prediction[J].Proceedings of the CSEE,2010,30(2):1-6(in Chinese).
- [20]Bauer P,Thorpe A,Brunet G.The quiet revolution of numerical weather prediction[J].Nature,2015(525):47-55.
- [21]寿绍文.中尺度大气动力学[M].北京:高等教育出版社,2009.
- [22]Kalnay E.Atmospheric modeling,data assimilation and predictability[M].Cambridge:Cambridge University Press,2003.
- [23]Lorenz E N.The predictability of a flow which possesses many scales of motion[J].Tellus,1969,21(3):289-307.
- [24]Dalcher A,Kalnay E.Error growth and predictability in operational ECMWF forecasts[J].Tellus A,1987,39(5):474-491.
- [25]Giebel G,Landberg L,Nielsen T,et al.The Zephyr-Project—the next generation prediction system[C]//Proceedings of European Wind Energy Conference,Copenhagen,2001:1-8.
- [26]Foley A M,Leahy P G,Marvuglia A,et al.Current methods and advances in forecasting of wind power generation[J].Renewable Energy,2012,37(1):1-8.
- [27]Brown B G,Katz R W,Murphy A H.Time series models to simulate and forecast wind speed and wind power[J].Journal of Climate and Applied Meteorology,1984,23(8):1184-1195.
- [28]Louka P,Galanis G,Siebert N,et al.Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering[J].Journal of Wind Engineering and Industrial Aerodynamics,2008,96(12):2348-2362.
- [29]范高锋,王伟胜,刘纯,等.基于人工神经网络的风电功率预测[J].中国电机工程学报,2008,28(34):118-123.Fan Gaofeng,Wang Weisheng,Liu Chun,et al.Wind power prediction based on artificial neural network[J].Proceedings of the CSEE,2008,28(34):118-123(in Chinese).
- [30]Kariniotakis G N,Stavrakakis G S,Nogaret E F.Wind power forecasting using advanced neural networks models[J].IEEE Transactions on Energy Conversion,1996,11(4):762-767.
- [31]Mc Kinsey Global Institute.Big data:the next frontier for innovation,competition,and productivity[R].San Francisco:Mc Kinsey Global Institute,2011.
- [32]Colak I,Sagiroglu S,Yesilbudak M.Data mining and wind power prediction:A literature review[J].Renewable Energy,2012(46):241-247.
- [33]鲁宗相,徐曼,乔颖,等.风电功率预测的新型互联网运营模式设计[J].电网技术,2016,40(1):125-131.Lu Zongxiang,Xu Man,Qiao Ying,et al.New internet based operation pattern design of wind power forecasting system[J].Power System Technology,2016,40(1):125-131(in Chinese).
- [34]史坤鹏,乔颖,赵伟,等.计及历史数据熵关联信息挖掘的短期风电功率预测[J].电力系统自动化,2017,41(3):13-18.Shi Kunpeng,Qiao Ying,Zhao Wei,et al.Short-term wind power prediction based on entropy association information mining of historical data[J].Automation of Electric Power Systems,2017,41(3):13-18(in Chinese).
- [35]China Renewable Energy Scale-up Program.Project report as a part of capability building and studies on wind power electrical engineering[R].Beijing:Center of Renewable Energy Development,2009.
- [36]Pinson P.Wind energy:forecasting challenges for its operational management[J].Statistical Science,2013,28(4):564-585.
- [37]段青云.基于WRF物理参数化方案扰动的集合预报与WRF模型参数优化的研究与实例[C]//第33届中国气象学会年会S10城市、降水与雾霾——第五届城市气象论坛,西安,2016:1-6.
- [38]孙逸涵,程兴宏,柳艳香,等.不同参数化方案对风预报效果影响个例研究[J].气象科技,2013,41(5):870-877.Sun Yinhan,Cheng Xinghong,Liu Yanxiang,et al.Impacts of different parameterization combination schemes on wind forecast[J].Meterological Science and Technology,2013,41(5):870-877(in Chinese).
- [39]Zhang T,Li L,Lin Y,et al.An automatic and effective parameter optimization method for model tuning[J].Geoscientific Model Development,2015,8(5):3791-3822.
- [40]Zou Y,Xue W,Liu S S.A case study of large-scale parallel I/O analysis optimization for numerical weather prediction system[J].Future Generation Computer Systems,2014(37):378-389.
- [41]Xue W,Yang C,Fu H,et al.Ultra-scalable CPU-MIC acceleration of mesoscale atmospheric modeling on Tianhe-2[J].IEEE Transactions on Computers,2015,64(8):2382-2393.
- [42]张涛,谢丰,薛巍,等.格点大气环流模式GAMIL 2参数不确定性的量化分析与优化[J].地球物理学报,2016,59(2):465-475.Zhang Tao,Xie Feng,Xue Wei,et al.Quantification and optimization of parameter uncertainty in the grid-point atmospheric model GAMIL 2[J].Chinese Journal of Geophysics,2016,59(2):465-475(in Chinese).
- [43]Lydia M,Kumar S S,Selvakumar A I,et al.A comprehensive review on wind turbine power curve modeling techniques[J].Renewable and Sustainable Energy Reviews,2014(30):452-460.
- [44]Carrillo C,Obando Monta?o A F,Cidrás J,et al.Review of power curve modelling for wind turbines[J].Renewable and Sustainable Energy Reviews,2013(21):572-581.
- [45]Marvuglia A,Messineo A.Monitoring of wind farms'power curves using machine learning techniques[J].Applied Energy,2012(98):574-583.
- [46]Yampikulsakul N,Byon E,Huang S,et al.Condition monitoring of wind power system with nonparametric regression analysis[J].IEEE Transactions on Energy Conversion,2014,29(2):288-299.
- [47]Tetko I V,Livingstone D J,Luik A I.Neural network studies 1:Comparison of overfitting and overtraining[J].Journal of Chemical Information and Computer Sciences,1995,35(5):826-833.
- [48]Xu M,Pinson P,Lu Z X,et al.Adaptive robust polynomial regression for power curve modeling with application to wind power forecasting[J].Wind Energy,2016,19(12):2321-2336.
- [49]Tascikaraoglu A,Uzunoglu M.A review of combined approaches for prediction of short-term wind speed and power[J].Renewable&Sustainable Energy Reviews,2014(34):243-254.
- [50]Srivastava N,Hinton G,Krizhevsky A.Dropout:a simple way to prevent neural networks from overfitting[J].Journal of Machine Learning Research,2014(15):1929-1958.
- [51]Breiman L.Random forests[J].Machine Learning,2001,45(1):5-32.
- [52]杨红英,冯双磊,王勃,等.基于线性回归的风电功率预测误差修正方法[J].电力系统及其自动化学报,2013,24(4):14-17.Yang Hongying,Feng Shuanglei,Wang Bo,et al.Study of the MOS method based on linear regression for wind power prediction[J].Proceedings of the CSU-EPSA.2013,24(4):14-17(in Chinese).
- [53]Xu Man,Lu Zongxiang,Qiao Ying,et al.Wind power forecasting error modelling based on kernel recursive least-squares[EB/OL].[2017-01-05].Journal of Modern Power Systems and Clean Energy,,published online.
- [54]茆美琴,曹雨,周松林.基于误差叠加修正的改进短期风电功率预测方法[J].电力系统自动化,2013,37(23):34-38.Mao Meiqin,Cao Yu,Zhou Songlin.Improved short-term wind power forecasting method based on accumulative error correction[J].Automation of Electric Power Systems,2013,37(23):34-38(in Chinese).
- [55]吴昊,李霄,王昕,等.基于改进最小二乘支持向量机和预测误差校正的短期风电负荷预测[J].电力系统保护与控制,2015,43(11):63-69.Wu Hao,Li Xiao,Wang Xin,et al.Short-term wind load forecasting based on improved LSSVM and error forecasting correction[J].Power System Protection and Control,2015,43(11):63-69(in Chinese).
- [56]叶林,赵永宁.基于空间相关性的风电功率预测研究综述[J].电力系统自动化,2014,38(14):126-135.Ye Lin,Zhao Yongning.A review on wind power prediction based on spatial correlation approach[J],Automation of Electric Power Systems,2014,38(14):126-135(in Chinese).