Scenario Generation of Wind Power Based on Statistical Uncertainty and Variability

The short-term wind power scenarios have a significant impact on the operation cost and power system reliability due to the stochastic generation scheduling of wind-integrated power systems. In order to obtain the scenarios containing the information of forecast error distribution and fluctuation distribution for short-term wind power, a scenario generation method is proposed. This paper characterizes forecast error via empirical distributions of a set of forecast bins and assumes that wind power fluctuations over unit interval follow t location-scale distribution. An inverse transform sampling from a multivariate normal distribution is adopted to generate a large number of wind power scenarios. The covariance matrix of the multivariate normal distribution is estimated to fit the distribution of historical wind power fluctuations. The proposed scenario generation method is applied to the actual aggregate wind power data in the whole regions of Ireland's Power System. The results indicate that the variability of wind power scenarios can be adjusted by estimating the key range parameter in the exponential covariance structure of a multivariate normal distribution.