TITLE:
Non-Stationary Random Process for Large-Scale Failure and Recovery of Power Distribution
AUTHORS:
Yun Wei, Chuanyi Ji, Floyd Galvan, Stephen Couvillon, George Orellana, James Momoh
KEYWORDS:
Resilience, Non-Stationary Random Process, Power Distribution, Dynamic Queue, Transient Little’s Law, Real Data
JOURNAL NAME:
Applied Mathematics,
Vol.7 No.3,
February
29,
2016
ABSTRACT: This work applies non-stationary random processes to resilience of power distribution under severe weather. Power distribution, the edge of the energy infrastructure, is susceptible to external hazards from severe weather. Large-scale power failures often occur, resulting in millions of people without electricity for days. However, the problem of large-scale power failure, recovery and resilience has not been formulated rigorously nor studied systematically. This work studies the resilience of power distribution from three aspects. First, we derive non-stationary random processes to model large-scale failures and recoveries. Transient Little’s Law then provides a simple approximation of the entire life cycle of failure and recovery through a queue at the network-level. Second, we define time-varying resilience based on the non-stationary model. The resilience metric characterizes the ability of power distribution to remain operational and recover rapidly upon failures. Third, we apply the non-stationary model and the resilience metric to large-scale power failures caused by Hurricane Ike. We use the real data from the electric grid to learn time-varying model parameters and the resilience metric. Our results show non-stationary evolution of failure rates and recovery times, and how the network resilience deviates from that of normal operation during the hurricane.