Resilience assessment of mobile emergency generator-assisted distribution networks: A stochastic geometry approach

Abstract

Escalation of extreme weather events represents substantial threat to power system infrastructure. Mobile emergency generators (MEGs) can form part of a flexible restoration strategy against such destructive events. However, with continued expansion of distribution networks, quantification of the impact of MEGs has become increasingly challenging owing to extreme-weather-event-induced uncertainties. In this paper, we propose a stochastic geometry-based method for assessing the impact of MEG deployment on distribution networks affected by extreme weather events through investigation of structural features. First, we propose a distance measure to represent the electrical connection between power grid components. Subsequently, we adopt the point process and Voronoi tessellation to describe the spatial distribution of power grid components and the service coverage provided by MEGs under different scenarios. Then, we propose a set of assessment metrics to evaluate the survivability of power grid components and the resilience of the entire distribution network under extreme weather events. Finally, we derive accurate analytical expressions for the distance distribution and resilience metrics, such as coverage probability and load shedding, enabling us to explore the relationship between MEG deployment decisions, structural features, and power grid resilience. The proposed method enables analytic assessment of the impact of MEG deployment on the resilience of distribution networks, and provides beneficial insights to help formulate efficient measures for enhancing resilience. Case studies demonstrated that the proposed method is accurate and efficient in dealing with network analysis and assessment problems for distribution networks under massive potential failure scenarios.