Resilience of Interdependent Communication and Power Distribution Networks against Cascading Failures

The operations of many modern cyber-physical systems, such as smart grids, are based on increasingly interdependent networks. The impact of cascading failures on such networks has recently received significant attention due to the corresponding effect of these failures on the society. In this paper, we conduct an empirical study on the robustness of interdependent systems formed by the coupling of power grids and communication networks by putting real distribution power grids to the test. We focus on the assessment of the robustness of a large set of medium-voltage (MV) distribution grids, currently operating live in the Netherlands, against cascading failures initiated by different types of faults / attacks. We consider both unintentional random failures and malicious targeted attacks which gradually degrade the capability of the entire system and we evaluate their respective consequences. Our study shows that current MV grids are highly vulnerable to such cascades of failures. Furthermore, we discover that a small-world communication network structure lends itself to the robustness of the interdependent system. Also interestingly enough, we discover that the formation of hub hierarchies, which is known to enhance independent network robustness, actually has detrimental effects against cascading failures. Based on real MV grid topologies, our study yields realistic insights which can be employed as a set of practical guidelines for distribution system operators (DSOs) to design effective grid protection schemes

Analyzing Cascading Failures in Smart Grids under Random and Targeted Attacks

We model smart grids as complex interdependent networks, and study targeted attacks on smart grids for the first time. A smart grid consists of two networks: the power network and the communication network, interconnected by edges. Occurrence of failures (attacks) in one network triggers failures in the other network, and propagates in cascades across the networks. Such cascading failures can result in disintegration of either (or both) of the networks. Earlier works considered only random failures. In practical situations, an attacker is more likely to compromise nodes selectively. We study cascading failures in smart grids, where an attacker selectively compromises the nodes with probabilities proportional to their degrees; high degree nodes are compromised with higher probability. We mathematically analyze the sizes of the giant components of the networks under targeted attacks, and compare the results with the corresponding sizes under random attacks. We show that networks disintegrate faster for targeted attacks compared to random attacks. A targeted attack on a small fraction of high degree nodes disintegrates one or both of the networks, whereas both the networks contain giant components for random attack on the same fraction of nodes.Comment: Accepted for publication in 28th IEEE International Conference on Advanced Information Networking and Applications (AINA) 201

Towards a Realistic Model for Failure Propagation in Interdependent Networks

Modern networks are becoming increasingly interdependent. As a prominent example, the smart grid is an electrical grid controlled through a communications network, which in turn is powered by the electrical grid. Such interdependencies create new vulnerabilities and make these networks more susceptible to failures. In particular, failures can easily spread across these networks due to their interdependencies, possibly causing cascade effects with a devastating impact on their functionalities. In this paper we focus on the interdependence between the power grid and the communications network, and propose a novel realistic model, HINT (Heterogeneous Interdependent NeTworks), to study the evolution of cascading failures. Our model takes into account the heterogeneity of such networks as well as their complex interdependencies. We compare HINT with previously proposed models both on synthetic and real network topologies. Experimental results show that existing models oversimplify the failure evolution and network functionality requirements, resulting in severe underestimations of the cascading failures.Comment: 7 pages, 6 figures, to be published in conference proceedings of IEEE International Conference on Computing, Networking and Communications (ICNC 2016), Kauai, US