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

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.

爱尔朗排队系统的瞬态分析

对于一般分布的排队系统的瞬态解是一个相当复杂的问题，本文利用把福克－普朗克方程降阶去奇异性，转化为有外力作用的状态方程，利用Ｒｕｎｇｅ－Ｋｕｔｔａ法，求得系统的瞬态及稳态解，并给出不同参数时的曲线族。