计及预测误差动态相关性的多风电场联合出力不确定性模型

为实现对多风电场联合出力不确定性的精细化建模,提出了计及预测误差动态相关性的多风电场联合出力不确定性建模方法。首先,分析了同区域风电场的出力及出力预测误差动态相关性特征。进一步,针对此特征,引入高维动态藤Copula理论,建立了多风电场预测出力及预测误差的联合分布模型。最终,将以上模型与基于Copula函数的离散卷积法相结合,建立了计及预测误差动态相关性的多风电场联合出力不确定性模型,并以置信区间对多风电场联合出力不确定性进行了离散化表征。仿真结果表明,对比其他模型,所提模型拟合精度更高,拟合过程与预测方法解耦,灵活性更强。

Uncertainty Model of Combined Output for Multiple Wind Farms Considering Dynamic Correlation of Prediction Errors

In order to realize the refinement modeling of the combined output for multiple wind farms, this paper proposes an uncertainty model of combined output for multiple wind farms considering dynamic correlation of prediction errors. Firstly, the dynamic correlation characteristics of the wind power output and output prediction errors of the wind farms in the same region are analyzed. Furthermore, the high-dimensional dynamic vine Copula theory is introduced to establish a joint distribution model of prediction output and prediction error for multiple wind farms. Combining the above model with the discrete convolution method based on Copula function, the uncertainty model of combined output for multiple wind farms considering the dynamic correlation of prediction error is established. And the uncertainty of combined output for multiple wind farms is discretely represented by confidence intervals. The simulation results show that, compared with other models, the proposed model has higher fitting precision, and the fitting process is decoupled from the prediction method, which is more flexible. This work is supported by National Key R&D Program of China(No. 2017YFB0902600)and State Grid Corporation of China(No. SGJS0000DKJS1700840).