  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 forecast values. The accuracy of the
proposed probabilistic models is verified by using historical data. Thereafter,
a fast 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.
