Complex network approaches for vulnerability assessment and resilience enhancement in power systems

Abstract

The growing scale and complexity of modern power systems have created an urgent need for new frameworks capable of characterizing their structural, functional, and dynamic behaviors. Complex network theory offers such a paradigm by viewing the grid as an interconnected system where topology, physics, and dynamics collectively determine performance and resilience. This review summarizes the evolution of complex network approaches in power system analysis, emphasizing topology-based modeling, cascading-failure mechanisms, vulnerability assessment, and critical-component identification. It highlights how small-world and scale-free properties influence fault propagation and robustness, while betweenness- and flow-based indicators provide tools to identify vulnerable nodes and transmission corridors. These insights have advanced the understanding of cascading failures and vulnerability quantification through connectivity, load-loss, and efficiency metrics. However, challenges persist, including static assumptions, high computational demands, and limited integration with real-time data. Future progress will require coupling multilayer network models with dynamic simulations, high-performance computation, and artificial intelligence to enable predictive diagnostics and adaptive control. Complex network theory is poised to evolve into a unified analytical and engineering framework supporting the design of resilient, intelligent, and self-adaptive renewable power systems.