An efficient Nataf transformation based probabilistic power flow for high-dimensional correlated uncertainty sources in operation

Abstract

This paper proposes and introduces a novel approach for probabilistic power flow (PPF) analysis on large-scale power systems with such Pearson correlated high-dimensional uncertainty sources existing in normal operation. The proposed method is holistic since it covers the whole procedure of PPF analysis and improves its efficiency from each aspect. As one key step, the combination of Gauss-Hermite quadrature and simplified Newton-Raphson method based Nataf transformation is presented to accelerate the process of probabilistic modeling for wind speed, solar radiation, tidal current speed and load consumption, in particular the correlation relationship amongst these uncertainty sources. Besides, dimension adaptive sparse grid interpolation (DASGI) method and Monte Carlo simulation (MCS) method have been put to joint application to further boost the efficiency of PPF calculation, with the Pearson correlation issue handled in this procedure as the first time. In order to highlight the superiority of the proposed method, several existing advanced PPF analysis methods are used for comparison test, in which simple random samples based MCS method i.e. the crude MCS is assigned as the reference. The performance of the proposed approach is verified in IEEE 118-bus system involving with 114 random variables.