Bayesian assessment of electrical power transmission grid outage risk

Abstract

Lack of unified risk assessment approach to electrical power transmission grid outages when taking into account uncertain data is addressed in this paper. Authors take on Bayesian approach to analyse statistical data of electric grid outages – this enables to achieve a more coherent way to express uncertainty in data and to obtain reliability related measures of the grid. The considered methodology of how to properly manage the statistical inference process is demonstrated through real outage data collected from North American transmission grid. The different cases of electrical power lines unreliability as well as cascading outages are addressed on various levels of complexity – starting from simple Bayesian assessment and then building a more general hierarchical Bayesian model. As a result, geography and environment related variability level is found to be of significant influence suggesting that unreliability of grid lines should in general be analysed having in mind specificity of each line. In addition, such variability highly influences the reliability of the whole grid or any network, as demonstrated in the paper as well. Considering the case of cascading outages, we obtained a hierarchical model, built under the basis of Borel–Tanner distribution, and demonstrated the capability to simulate large blackouts, which has a non-negligible probability of occurrence, as the history of blackouts in the last decades has already demonstrated.