Modularised framework for power network disaster resilience assessment under natural hazards

Assessing the holistic performance of power networks under hazardous events is critical to ensuring a reliable and resilient power supply. While various research efforts have been made to achieve this objective, significant gaps remain. First, much of the existing literature emphasises either component-level or system-level performance, with relatively fewer studies explicitly integrating both perspectives in a unified framework. Second, given the computational intensity of resilience assessment, the development of efficient algorithms is essential to enable timely and effective post-disruption recovery strategies. Third, few studies provide detailed methodologies for developing GIS-based frameworks with graphical user interfaces that support practical decision-making. To address these research gaps, this study makes the following contributions. First, we introduce a novel component importance measure, termed the Conditional Probability Deaggregation Measure (CPDM), which facilitates comprehensive assessment at both the component and system levels. Second, we propose the ‘keepx’ algorithm, designed to identify optimal recovery sequences efficiently, thereby enabling effective evaluation of the resilience performance of power grids during the post-disruption recovery process. Third, we incorporate a GIS-based platform to operationalise a modular framework for resilience assessment of power grid networks. While not intended as a standalone novelty, the GIS integration enhances the practical applicability of the framework by enabling spatial visualisation and supporting decision-making for both researchers and practitioners. Application of the framework to the IEEE 9-bus system subject to seismic hazards demonstrates the framework’s practical utility, offering stakeholders proactive insights to enhance the power grid’s resilience.