2017/09/30 by Yingrui Zhang, Alex Arenas, Àlex Arenas +1 · 1 citation
Decision Sciences · Engineering · Physics and Astronomy · #Cascading failure #Computer science #Electric power system #Fault Detection and Control Systems #Infrastructure Resilience and Vulnerability Analysis #Interdependence #Physics #Political science #Redistribution (election) #Simulation Techniques and Applications #physics.soc-ph
paper · pdf · doi:10.1103/physreve.97.022307
published as Phys. Rev. E 97, 022307 (2018)
arxiv created 2017/12/21 · openalex publication_date 2018/02/15 · arxiv updated 2018/02/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Robustness and cascading failures in interdependent systems has been an active research field in the past decade. However, most existing works use percolation-based models where only the largest component of each network remains functional throughout the cascade. Although suitable for communication networks, this assumption fails to capture the dependencies in systems carrying a flow (e.g., power systems, road transportation networks), where cascading failures are often triggered by redistribution of flows leading to overloading of lines. Here, we consider a model consisting of systems A and B with initial line loads and capacities given by LA,i,CA,ii=1n and LB,i,CB,ii=1n, respectively. When a line fails in system A, a fraction of its load is redistributed to alive lines in B, while remaining (1-a) fraction is redistributed equally among all functional lines in A; a line failure in B is treated similarly with b giving the fraction to be redistributed to A. We give a thorough analysis of cascading failures of this model initiated by a random attack targeting p1 fraction of lines in A and p2 fraction in B. We show that (i) the model captures the real-world phenomenon of unexpected large scale cascades and exhibits interesting transition behavior: the final collapse is always first order, but it can be preceded by a sequence of first- and second-order transitions; (ii) network robustness tightly depends on the coupling coefficients a and b, and robustness is maximized at non-trivial a,b values in general; (iii) unlike most existing models, interdependence has a multifaceted impact on system robustness in that interdependency can lead to an improved robustness for each individual network.