A Clustering Based Scenario Generation Method for Stochastic Power System Analysis

A critical step in stochastic optimization models of power system analysis is to select a set of appropriate scenarios and significant amounts of scenario generation methods exist in the literature. This paper develops a clustering based scenario generation method, which aims to improve the performance of existing scenario generation techniques by grouping a set of correlated wind farms into clusters according to their cross-correlations. Copula based models are utilized to model spatiotemporal correlations and the Gibbs sampling is then used to generate scenarios. Our results show that the generated scenarios based on clustered wind farms outperform existing approaches and can provide a better characterization of wind power uncertainties.