In this article, the Latin hypercube sampling (LHS) based probabilistic optimal power flow (P‐OPF) technique is employed to assess the performance of power systems under large wind power penetration. In the case where only wind speed samples are available, the kernel tuned polynomial expansion series are developed to recover marginal distributions of wind speeds. Because the dependence among wind speeds has a considerable impact on P‐OPF solutions, 13 Archimedean copulae are derived to represent the dependence structure of correlated wind speeds at multiple sites. The rejection sampling method and Metropolis‐Hastings algorithm are deployed to generate samples of copula models, and a midpoint LHS technique is proposed to produce correlated low discrepancy sequences for P‐OPF computation. Finally, a case study is performed on an IEEE 118‐bus system to illustrate the proposed methods.

中文翻译：

---

包含相关风源的概率最优潮流分析

在本文中，基于拉丁文超立方采样（LHS）的概率最优潮流（P-OPF）技术用于评估大风电渗透下的电力系统性能。在只有风速样本可用的情况下，开发了内核调整的多项式展开级数以恢复风速的边际分布。由于风速之间的依存关系对P-OPF解决方案有相当大的影响，因此导出了13个阿基米德系算子来表示多个站点上相关风速的依存关系结构。部署了拒绝采样方法和Metropolis-Hastings算法来生成copula模型样本，并提出了中点LHS技术来生成相关的低差异序列以进行P-OPF计算。最后，