A conditional model of wind power forecast errors and its application in scenario generation. (15th February 2018) - Wang, Zhiwen; Shen, Chen; Liu, Feng
Highlights: Conditional models of wind power forecast errors under different forecast values. The appealing properties of Gaussian mixture model are adequately utilized. The non-Gaussianity and temporal-spatial correlation are considered. A fast method for generating non-Gaussian interdependent wind power scenarios. Abstract: In power system operation, characterizing the stochastic nature of wind power is an important albeit challenging issue. It is well known that distributions of wind power forecast errors often exhibit significant variability with respect to different forecast values. Therefore, appropriate probabilistic models that can provide accurate information for conditional forecast error distributions are of great need. On the basis of Gaussian mixture model, this paper constructs analytical conditional distributions of forecast errors for multiple wind farms with respect to different forecast values. The accuracy of the proposed probabilistic models is verified by using historical data. Thereafter, a sampling method is proposed to generate scenarios from the conditional distributions which are non-Gaussian and interdependent. The efficiency of the proposed sampling method is verified.