集群风电场出力统计指标建模与应用

Modeling and application of statistical indicators for wind farm cluster

基于概率统计理论,建立单个风电场与集群风电场出力的中心矩关系模型。从风电出力概率分布特性的角度,引入描述风电出力"分布形状"的2个统计学指标——偏度和峰度。基于集群风电出力均值、标准差、偏度和峰度4个统计性指标,构建表征集群风电出力概率分布的皮尔逊族模型,模拟集群风电场出力时序数据,进而得到集群风电场出力曲线。基于对区域典型风电集群历史出力数据的分析,根据经验建立风电场间相关系数与风电场间距离的指数关系模型,并给出区域风电场各阶标准差与平均出力之间的近似多项式关系模型,降低了计算核心指标所需数据维度。对福建省集群风电场进行实例应用分析,结果验证了所提集群风电场模型的准确性和实用性。

The model of central moment relationship between the outputs of single wind farm and wind farm cluster is established based on the probability theory. According to the probability distribution characteristic of wind power output,two statistical indicators are introduced to describe the wind power distribution shape：skewness and kurtosis. Based on four statistical indicators,i.e. mean,standard deviation,skewness and kurtosis,a Pearson family model is established to represent the power output of wind farm cluster,and the sequential data of its power output are simulated to obtain its output curve. The historical data of typical regional wind farm cluster are analyzed,the model of exponential relation between the distance and the correlation coefficient among wind farms is empirically established,and the model of approximate polynomial relation between the standard deviations of different orders and the average output of regional wind farms is established to reduce the data dimension of core index calculation. A wind farm cluster of Fujian province is analyzed as a case and the results show the proposed model is correct and practical.