Abstract
The increasing penetration of new variable generations in power systems necessitates the modeling of the stochastic and variable characteristics, especially the modeling of wind power forecast error. A conditional probabilistic dependent method of modeling wind power forecast error of one single wind farm and multiple wind farms is presented in this paper, providing a range of potential forecast error for a given confidence level. Furthermore, a distribution generation algorithm of power forecast error based on Monte Carlo algorithm and genetic algorithm is proposed by applying the historical data and rank correlation of forecast error in multiple wind farms. Finally, the proposed method is verified in the case study by comparison of the conditional probabilistic model and the unconditional probabilistic model.
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