基于Copula函数与等概率逆变换的风电出力场景生成方法

Wind power output scenario generation method based on Copula function and equal probability inverse transformation

随着风电利用率的大规模提高,应用典型场景法应对风电机组出力的不确定性具有重要意义。针对多风电场出力之间的时空相关性提出一种改进的场景生成与缩减方法,并提出评价方法来检验生成场景的质量。该方法应用指数函数法构建体现风电时间相关性的多元变量协方差矩阵,应用Copula函数建立多风电场空间相关性模型,通过对随机数与历史数据的累积概率分布函数进行时空相关非线性变换与等概率逆变换生成大量初始场景。改进K-means聚类方法,通过手肘法与聚类有效性指标综合确定最优聚类数目后,缩减得到代表性时空相关风电场景。最后通过4项评价指标,对生成场景的波动性、相关性、可靠性等进行质量检验。算例分析表明,与其他方法相比,所提方法生成场景的波动性、爬坡情况和时空相关性均与历史数据更贴合,具有更高的实测值覆盖率。

Under the background of the large-scale increase in wind power utilization,the application of typical scenarios to deal with the uncertainty of wind output is of great significance.Aiming at the spatial-temporal correlation among the output of multiple wind farms,an improved scenario generation and reduction method is proposed,and an evaluation method is introduced to test the quality of the generated scenarios.The exponential function is used to construct a multivariate covariance matrix that reflects the temporal correlation of wind power,and the Copula function is used to build a multi-wind farm spatial correlation model.A large number of initial scenes are generated by performing spatio-temporal correlation non-linear transformation and equal probability inverse transformation on the cumulative probability distribution function of random numbers and historical data.The K-means clustering method is improved,and the optimal number of clusters is determined by the elbow method and the clustering effectiveness index,and then the representative spatial-temporal correlation wind scenarios are reduced.Finally,four evaluation indicators are proposed to test the quality of the scenarios.The calculation results show that the volatility,climbing situation and spatial-temporal correlation of the scenarios generated by the proposed method are more consistent with historical data.The proposed method has a higher coverage of actual measured wind power values than another method does.