电网技术2025，Vol.49Issue(7)：2671-2679,中插2-中插4,12.DOI:10.13335/j.1000-3673.pst.2024.0899
高维变量下电力系统不确定性分析的快速Nataf变换方法
A Fast Nataf Transformation Method for Power System Uncertainty Analysis With High-dimensional Uncertain Variables
摘要
Abstract
Correlation pervasively exists among uncertain elements in the power system,encompassing renewable generations and power loads.The Nataf transformation emerges as a ubiquitous framework for addressing the complexities associated with correlations.However,its computational burden is formidable when applied in huge electricity networks,which usually contain high-dimension uncertain variables.To mitigate this challenge,this study introduces a fast Nataf transformation strategy with the application of data-driven technologies.The realization is based on improving the initial point guessing and the acceleration of the integral computations based on data-driven models.Concurrently,for the possible heterogeneous nature of the marginal distributions,statistical moments are employed to encapsulate density distribution attributes,thereby facilitating a harmonized representation of heterogeneous distribution functions.Furthermore,the computational burdens associated with the generation of training samples in data-driven paradigms are leased by adopting a computationally efficient dataset transition from the standard Gaussian space to the original variable space.The proposed methodology signifies a computational efficiency enhancement exceeding 95％relative to conventional numerical methods,whilst maintaining computational precision.This advancement holds profound implications for the swift analysis of huge electricity networks,heralding a significant stride toward optimizing computational resource allocation in uncertainty analysis.关键词
不确定性分析/Nataf/电力系统/新能源/负荷/数据驱动Key words
uncertainty analysis/Nataf/power system/renewable resources/power load/data-driven分类
信息技术与安全科学引用本文复制引用
汤奕,王洪儒..高维变量下电力系统不确定性分析的快速Nataf变换方法[J].电网技术,2025,49(7):2671-2679,中插2-中插4,12.基金项目
国家自然科学基金国际(地区)合作与交流项目(5226-1145704) （地区）
国家自然科学基金面上基金项目(52377085).Project Supported by National Natural Science Foundation of China(under Grant 52261145704 and 52377085). （52377085）
