In power system operation, characterizing the stochastic nature of wind poweris an important albeit challenging issue. It is well known that distributionsof wind power forecast errors often exhibit significant variability withrespect to different forecast values. Therefore, appropriate probabilisticmodels that can provide accurate information for conditional forecast errordistributions are of great need. On the basis of Gaussian mixture model, thispaper constructs analytical conditional distributions of forecast errors formultiple wind farms with respect to forecast values. The accuracy of theproposed probabilistic models is verified by using historical data. Thereafter,a fast sampling method is proposed to generate scenarios from the conditionaldistributions which are non-Gaussian and interdependent. The efficiency of theproposed sampling method is verified.
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