风电功率波动特性定量刻画及应用研究
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摘要
为能够准确地描述风电功率的波动特性,该文利用勒贝格积分构建简单有效的采样损失率作为衡量风电功率时间序列波动性的指标,采样损失率越大,风电功率波动越剧烈;采样损失率越小,风电功率波动越平缓,并通过风电功率预测结果证实了该指标的有效性。该文还利用该指标对风电场内部平滑效应进行研究,重点研究风速对平滑效应的影响以及平滑效应的季节性。得出结论:风速是影响风电功率平滑效应的重要因素,风速越大,平滑效应越显著。平滑效应具有季节性,春季和冬季的平滑效应较夏季和秋季的平滑效应更显著。
In order to describe the wind power fluctuations accurately,a fluctuation index of wind power named sampling loss,which can be regarded as a simple and effective quantitative characterization of wind power fluctuations is established by using the Lebesgue's integral. The larger rate of sampling loss indicates the more drastic of the wind power fluctuation,and the smaller the sampling loss rate demonstrates the gentler of the wind power fluctuation. The validity of the index is verified by wind power prediction. In addition,the index is applied to the study of smoothing effect of the outputs of aggregated wind farms,focusing on the influence of wind speed on smoothing effect and the seasonality of smoothing effect. Then,we draw the conclusion that wind speed is the major factor to smoothing effect,the smoothing effect is more pronounced when wind becomes stronger,and smoothing effect is seasonal,the rule of seasonal is that smoothing effect in the spring and winter is more pronounced than in summer and autumn.
In order to describe the wind power fluctuations accurately,a fluctuation index of wind power named sampling loss,which can be regarded as a simple and effective quantitative characterization of wind power fluctuations is established by using the Lebesgue's integral. The larger rate of sampling loss indicates the more drastic of the wind power fluctuation,and the smaller the sampling loss rate demonstrates the gentler of the wind power fluctuation. The validity of the index is verified by wind power prediction. In addition,the index is applied to the study of smoothing effect of the outputs of aggregated wind farms,focusing on the influence of wind speed on smoothing effect and the seasonality of smoothing effect. Then,we draw the conclusion that wind speed is the major factor to smoothing effect,the smoothing effect is more pronounced when wind becomes stronger,and smoothing effect is seasonal,the rule of seasonal is that smoothing effect in the spring and winter is more pronounced than in summer and autumn.
引文
[1]王乾坤.国内外风电弃风现状及经验分析[J].华东电力,2012,40(3):378—381.[1]Wang Qiankun. Update and empirical analysis of domestic and foreign wind energy curtailment[J]. East China Electric Power,2012,40(3):378—381.
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[8]申颖,赵千川,李明扬.多时空尺度下风电平滑效应的分析[J].电网技术,2015,39(2):400—405.[8]Shen Ying,Zhao Qianchuan,Li Mingyang. Analysis onwind power smoothing effect in multiple temporal and spatial scales[J]. Power System Technology,2015,39(2):400—405.
[9]张良勇,董晓芳.浅谈从黎曼积分到勒贝格积分的演变[J].高等函授学报:自然科学版,2006,19(4):19—23.[9]Zhang Liangyong, Dong Xiaofang. Discussion ofRiemann integral evolved to Lebesgue’integral[J].Journal of Higher Correspondence Education:Natural Sciences,2006,19(4):19—23.
[10]顾滕.黎曼积分和勒贝格积分的比较[J].科教文汇,2015,(18):57—59.[10]Gu Teng. The comparison of Riemann integral and Lebesgue integral[J]. The Science Education Article Collects,2015,(18):57—59.
[11]Holttinen H. Hourly wind power variations in the nordic countries[J]. Wind energy,2005,8(2):173—195.
[12]林卫星,文劲宇,艾小猛,等.风电功率波动特性的概率分布研究[J].中国电机工程学报,2012,32(1):38—46.[12]Lin Weixing, Wen Jinyu, Ai Xiaomeng, et al.Probability density function of wind power variation[J].Proceedings of the CSEE,2012,32(1):38—46.
[13]肖荣.近50年东北地区近地层风速的时空变化特征研究[D].长春:东北师范大学,2012.[13]Xiao Rong. Study on the temporal-spatial change characteristics of near-surface wind speed in the northeast of China in recent 50 years[D]. Changchun:East China Normal University,2012.
[2]杨茂,孙涌,孙兆键,等.风电场大规模数据管理系统设计与研发[J].东北电力大学学报,2014,34(2):27—31.[2]Yang Mao,Sun Yong,Sun Zhaojian,et al. Design and development of large-scale data management system of wind farm[J]. Journal of Northeast Dianli University,2013,34(2):27—31.
[3]刘舒,孟建.样本序列的可预测性研究[J].预测,1997,(2):47—49.[3]Liu Shu,Meng Jian. Study on predictability for sampling sequence[J]. Forecast,1997,(2):47—49.
[4]王爽心,赵欣,李涛,等.基于小世界优化的风电功率变权组合预测模型[J].太阳能学报,2015,36(12):2867—2873.[4]Wang Shuangxin,Zhao Xin,Li Tao,et al. Variable weight combination forecasting model of wind power based on small-world optimization[J]. Acta Energiae Solaris Sinica,2015,36(12):2867—2873.
[5]王聪,杨建斌,邓颖.数学建模方法在风电功率预测中的应用[J].太阳能学报,2015,36(5):1081—1087.[5]Wang Cong,Yang Jianbin,Deng Ying. Application of mathematical modeling methods in wind-power prediction[J]. Acta Energiae Solaris Sinica,2015,36(5):1081—1087.
[6]尹佳楠,葛延峰,高凯.风电场群出力的汇聚效应分析[J].电测与仪表,2015,52(5):104—108.[6]Yin Jianan, Ge Yanfeng, Gao Kai. Analysis on clustering effect of wind generations[J]. Electrical Measurement&Instrumentation,2015,52(5):104—108.
[7]刘燕华,田茹,张东英,等.风电出力平滑效应的分析与应用[J].电网技术,2013,37(4):987—991.[7]Liu Yanhua,Tian Ru,Zhang Dongying,et al. Analysis and application of wind farm output smoothing effect[J].Power System Technology,2013,37(4):987—991.
[8]申颖,赵千川,李明扬.多时空尺度下风电平滑效应的分析[J].电网技术,2015,39(2):400—405.[8]Shen Ying,Zhao Qianchuan,Li Mingyang. Analysis onwind power smoothing effect in multiple temporal and spatial scales[J]. Power System Technology,2015,39(2):400—405.
[9]张良勇,董晓芳.浅谈从黎曼积分到勒贝格积分的演变[J].高等函授学报:自然科学版,2006,19(4):19—23.[9]Zhang Liangyong, Dong Xiaofang. Discussion ofRiemann integral evolved to Lebesgue’integral[J].Journal of Higher Correspondence Education:Natural Sciences,2006,19(4):19—23.
[10]顾滕.黎曼积分和勒贝格积分的比较[J].科教文汇,2015,(18):57—59.[10]Gu Teng. The comparison of Riemann integral and Lebesgue integral[J]. The Science Education Article Collects,2015,(18):57—59.
[11]Holttinen H. Hourly wind power variations in the nordic countries[J]. Wind energy,2005,8(2):173—195.
[12]林卫星,文劲宇,艾小猛,等.风电功率波动特性的概率分布研究[J].中国电机工程学报,2012,32(1):38—46.[12]Lin Weixing, Wen Jinyu, Ai Xiaomeng, et al.Probability density function of wind power variation[J].Proceedings of the CSEE,2012,32(1):38—46.
[13]肖荣.近50年东北地区近地层风速的时空变化特征研究[D].长春:东北师范大学,2012.[13]Xiao Rong. Study on the temporal-spatial change characteristics of near-surface wind speed in the northeast of China in recent 50 years[D]. Changchun:East China Normal University,2012.