A critical step in stochastic optimization models of power system analysis is to select a set of appropriate scenarios and significant numbers 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 sites 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 for day-ahead markets. Our results show that the generated scenarios based on clustered wind sites outperform existing approaches in terms of reliability and sharpness and can reduce the total computational time for scenario generation and reduction significantly. The clustering-based framework can therefore provide a better support for real-world market simulations with high wind penetration.

中文翻译：

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基于聚类的风电市场模拟场景生成框架

电力系统分析随机优化模型的一个关键步骤是选择一组合适的场景，并且文献中存在大量场景生成方法。本文开发了一种基于聚类的场景生成方法，旨在通过将一组相关的风场根据它们的互相关性分组到集群中来提高现有场景生成技术的性能。基于 Copula 的模型用于对时空相关性进行建模，然后使用 Gibbs 采样来生成日前市场的情景。我们的结果表明，基于集群风站点生成的场景在可靠性和清晰度方面优于现有方法，并且可以显着减少场景生成和缩减的总计算时间。