Modeling power loss during blackouts in China using non-stationary generalized extreme value distribution

Abstract

Power loss during blackouts shows an increasing complexity with the increase in the size of power grids. Traditional statistical methods based on the assumption of stationarity can lead to inappropriate assessments of blackouts. This study builds on the generalized extreme value (GEV) distribution by introducing time as a covariate to account for possible non-stationarities. The location, scale, and shape parameters of GEV are modeled step-by-step through the one-factor-at-a-time experiment. The results indicate that the GEV model of the annual maximum power loss in China exhibits a significant change of the scale parameter. By contrast, changes in location and shape parameters are not significant. Overall, the power loss during blackouts is tested to change with time, which reflects the sustained increase in the variability of power loss due to the growing size of power grids. The mean tends to be stable and the distribution type remains the same, which can be related to technological advances that improve the reliability of power grids. The non-stationary GEV model with time as a covariate can effectively analyze power loss during blackouts in China. It has the potential to be extended to describe non-stationary high-dimensional characteristics of power grids.