RESEARCH ON CALCULATION METHOD OF REPRESENTATIVE YEAR WIND SPEED BASED ON COPULA FUNCTION

Wang Yuankun; Feng Yudong; Ma Huiqun

doi:10.19912/j.0254-0096.tynxb.2023-1415

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (1) : 151-157. DOI: 10.19912/j.0254-0096.tynxb.2023-1415

RESEARCH ON CALCULATION METHOD OF REPRESENTATIVE YEAR WIND SPEED BASED ON COPULA FUNCTION

Wang Yuankun¹, Feng Yudong², Ma Huiqun²

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Abstract

This paper presents a method for estimating representative year wind speeds based on copula functions using a long series of reanalysis data. The probability marginal distribution of wind speeds for anemometer towers and meteorological stations are calculated using the Weibull distribution. The Gumbel-Hougaard Copula function is used to model the dependence between the wind speed distributions from the towers and stations. The difference between the conditional distribution of the wind speed from the towers and the representative year wind speed is calculated to obtain the representative year wind speed. The findings show that regardless of the quality of the correlation between the wind speeds from the meteorological station and the wind measurement tower, the Copula method achieves higher accuracy in calculating the annual wind speeds compared to the standard method. This study provides a new approach for estimating representative the annual wind speed in wind power resource assessment.

Key words

wind speed / Weibull distribution / wind power / Copula / conditional distribution / representative year

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Wang Yuankun, Feng Yudong, Ma Huiqun. RESEARCH ON CALCULATION METHOD OF REPRESENTATIVE YEAR WIND SPEED BASED ON COPULA FUNCTION[J]. Acta Energiae Solaris Sinica. 2025, 46(1): 151-157 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1415

References

[1] 邹才能, 陈艳鹏, 熊波, 等. 碳中和目标下中国新能源使命[J]. 中国科学院院刊, 2023, 38(1): 48-58.
ZOU C N, CHEN Y P, XIONG B, et al.Mission of new energy under carbon neutrality goal in China[J]. Bulletin of Chinese Academy of Sciences, 2023, 38(1): 48-58.
[2] 杜燕军, 冯长青. 风电场代表年风速计算方法的分析[J]. 可再生能源, 2010, 28(1): 105-108.
DU Y J, FENG C Q.Analysis on different computing method of wind speed of the representative year in wind farm[J]. Renewable energy resources, 2010, 28(1): 105-108.
[3] GB/T 18710—2002, 风电场风能资源评估方法》[S]. 2002.
GB/T 18710—2002, Methodology of wind energy resource assessment for wind farm[S]. 2002.
[4] 谢今范, 雷杨娜, 孙娴. 风电场代表年数据订正方法的不确定性分析[J]. 太阳能, 2015, (4): 48-55.
XIE J F, LEI Y N, SUN X.Uncertainty analysis of revision method of representative annual data of wind farm[J]. Solar energy, 2015, (4): 48-55.
[5] 陈振华, 李云涛, 唐杰, 等. 风电场设计中数值模拟数据的应用研究[J]. 电力学报, 2018, 33(6): 498-504.
CHEN Z H, LI Y T, TANG J, et al.Application of numerical simulation data in wind farm design[J]. Journal of electric power, 2018, 33(6): 498-504.
[6] 旷芳芳, 张友权, 张俊鹏, 等. 3种海面风场资料在台湾海峡的比较和评估[J]. 海洋学报, 2015, 37(5):44-53.
KUANG F F, ZHANG Y Q, ZHANG J P, et al.Comparison and evaluation of the three sea surface wind products in Taiwan strait[J]. Acta oceanologica sinica, 2015, 37(5): 44-53.
[7] 陈卓, 郭军红, 亢朋朋, 等. 5套再分析资料在阿勒泰地区风资源评估中的适用性研究[J]. 太阳能学报, 2023, 44(4): 60-66.
CHEN Z, GUO J H, KANG P P, et al.Applicability of five sets of reanalysis data in wind resource assessment in Altay prefecture[J]. Acta energiae solaris sinica, 2023, 44(4): 60-66.
[8] 王守峰, 戚振亚, 许卫东. 风电场代表年订正方法研究[J]. 绿色科技, 2022, 24(18): 237-240.
WANG S F, QI Z Y, XU W D.Research on the revision method of wind farm representative year[J]. Journal of green science and technology, 2022, 24(18): 237-240.
[9] WANG Y K, MA H Q, WANG D, et al.A new method for wind speed forecasting based on Copula theory[J]. Environmental research, 2018, 160: 365-371.
[10] 宋松柏, 王小军. 基于Copula函数的水文随机变量和概率分布计算[J]. 水利学报, 2018, 49(6): 687-693.
SONG S B, WANG X J.Probability distribution calculation of the sum of hydrological random variables based on Copula function approach[J]. Journal of hydraulic engineering, 2018, 49(6): 687-693.
[11] XU P C, WANG D, SINGH V P, et al.Copula-based seasonal rainfall simulation considering nonstationarity[J]. Journal of hydrology, 2020, 590: 125439.
[12] TOOTOONCHI F, SADEGH M, HAERTER J O, et al.Copulas for hydroclimatic analysis: a practice-oriented overview[J]. Wiley interdisciplinary reviews-water, 2022, 9(2): e1579.
[13] SEIFI A, EHTERAM M, SOROUSH F, et al.Multi-model ensemble prediction of pan evaporation based on the Copula Bayesian model averaging approach[J]. Engineering applications of artificial intelligence, 2022, 114: 105124.
[14] 张翔, 冉啟香, 夏军, 等. 基于Copula函数的水量水质联合分布函数[J]. 水利学报, 2011, 42(4): 483-489.
ZHANG X, RAN Q X, XIA J, et al.Jointed distribution function of water quality and water quantity based on Copula[J]. Journal of hydraulic engineering, 2011, 42(4): 483-489.