Non-Stationary Random Process for Large-Scale Failure and Recovery of Power Distributions
For power distribution network operators, this provides a rigorous framework to quantify and improve resilience against large-scale disturbances like hurricanes.
This work models large-scale failure and recovery of power distribution networks using a non-stationary random process, defines a resilience metric, and validates the model with real data from Hurricane Ike, showing the ability to learn time-varying parameters and resilience metrics.
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.