Non-Stationary Random Process for Large-Scale Failure and Recovery of Power Distributions

TL;DR

This paper introduces a non-stationary random process model to analyze and quantify the resilience of power distribution networks during large-scale failures caused by external disturbances like hurricanes, supported by real-world data.

Contribution

It develops a novel non-stationary stochastic model for failure and recovery, providing analytical resilience metrics and applying them to real hurricane data.

Findings

Derived a non-stationary failure-recovery process

Provided analytical resilience expressions for specific scenarios

Validated model with Hurricane Ike data

Abstract

A key objective of the smart grid is to improve reliability of utility services to end users. This requires strengthening resilience of distribution networks that lie at the edge of the grid. However, distribution networks are exposed to external disturbances such as hurricanes and snow storms where electricity service to customers is disrupted repeatedly. External disturbances cause large-scale power failures that are neither well-understood, nor formulated rigorously, nor studied systematically. This work studies resilience of power distribution networks to large-scale disturbances in three aspects. First, a non-stationary random process is derived to characterize an entire life cycle of large-scale failure and recovery. Second, resilience is defined based on the non-stationary random process. Close form analytical expressions are derived under specific large-scale failure scenarios. Third, the non-stationary model and the resilience metric are applied to a real life example of large-scale disruptions due to Hurricane Ike. Real data on large-scale failures from an operational network is used to learn time-varying model parameters and resilience metrics.