5,122 research outputs found
Cascade effects of load shedding in coupled networks
Intricate webs of interlinked critical infrastructures such as electrical grid, telecommunication, and transportation are essential for the minimal functioning of contemporary societies and economies. Advances in Information and Communication Technology (ICT) underpin the increasing interconnectivity of these systems which created new vulnerabilities that can be seriously affected by hardware failure, link cut, human error, natural disaster, physical-attacks and cyber-attacks. Failure of a fraction on nodes may lead to failure of dependent nodes in the other networks. Therefore, the main objective of this paper is to investigate the cascades phenomena caused by load shedding between two interconnected networks using Bak-Tang-Wiesenfeld sandpile modeling. We have found that, large avalanche occurred when node degree and/interconnectivity link become dense. In addition, the coupled random-regular networks have been found to be more robust than the coupled Erdos-Renyi networks. However, coupled random-regular networks are vulnerable to random attack and coupled Erdos-Renyi networks are vulnerable to target attack due to the degree distribution
Reducing Cascading Failure Risk by Increasing Infrastructure Network Interdependency
Increased coupling between critical infrastructure networks, such as power
and communication systems, will have important implications for the reliability
and security of these systems. To understand the effects of power-communication
coupling, several have studied interdependent network models and reported that
increased coupling can increase system vulnerability. However, these results
come from models that have substantially different mechanisms of cascading,
relative to those found in actual power and communication networks. This paper
reports on two sets of experiments that compare the network vulnerability
implications resulting from simple topological models and models that more
accurately capture the dynamics of cascading in power systems. First, we
compare a simple model of topological contagion to a model of cascading in
power systems and find that the power grid shows a much higher level of
vulnerability, relative to the contagion model. Second, we compare a model of
topological cascades in coupled networks to three different physics-based
models of power grids coupled to communication networks. Again, the more
accurate models suggest very different conclusions. In all but the most extreme
case, the physics-based power grid models indicate that increased
power-communication coupling decreases vulnerability. This is opposite from
what one would conclude from the coupled topological model, in which zero
coupling is optimal. Finally, an extreme case in which communication failures
immediately cause grid failures, suggests that if systems are poorly designed,
increased coupling can be harmful. Together these results suggest design
strategies for reducing the risk of cascades in interdependent infrastructure
systems
Suppressing cascades of load in interdependent networks
Understanding how interdependence among systems affects cascading behaviors
is increasingly important across many fields of science and
engineering.Inspired by cascades of load shedding in coupled electric grids and
other infrastructure, we study the Bak-Tang-Wiesenfeld sandpile model on
modular random graphs and on graphs based on actual, interdependent power
grids. Starting from two isolated networks, adding some connectivity between
them is beneficial, for it suppresses the largest cascades in each system. Too
much interconnectivity, however, becomes detrimental for two reasons. First,
interconnections open pathways for neighboring networks to inflict large
cascades. Second, as in real infrastructure, new interconnections increase
capacity and total possible load, which fuels even larger cascades. Using a
multitype branching process and simulations we show these effects and estimate
the optimal level of interconnectivity that balances their tradeoffs. Such
equilibria could allow, for example, power grid owners to minimize the largest
cascades in their grid. We also show that asymmetric capacity among
interdependent networks affects the optimal connectivity that each prefers and
may lead to an arms race for greater capacity. Our multitype branching process
framework provides building blocks for better prediction of cascading processes
on modular random graphs and on multi-type networks in general.Comment: Accepted to PNAS Plus. Author summary: 2 pages, 1 figure. Main paper:
13 pages, 7 figures. Supporting information: 7 pages, 10 figure
Stochastic Dynamics of Cascading Failures in Electric-Cyber Infrastructures
Emerging smart grids consist of tightly-coupled systems, namely a power grid and a communication system. While today\u27s power grids are highly reliable and modern control and communication systems have been deployed to further enhance their reliability, historical data suggest that they are yet vulnerable to large failures. A small set of initial disturbances in power grids in conjunction with lack of effective, corrective actions in a timely manner can trigger a sequence of dependent component failures, called cascading failures. The main thrust of this dissertation is to build a probabilistic framework for modeling cascading failures in power grids while capturing their interactions with the coupled communication systems so that the risk of cascading failures in the composite complex electric-cyber infrastructures can be examined, analyzed and predicted. A scalable and analytically tractable continuous-time Markov chain model for stochastic dynamics of cascading failures in power grids is constructed while retaining key physical attributes and operating characteristics of the power grid. The key idea of the proposed framework is to simplify the state space of the complex power system while capturing the effects of the omitted variables through the transition probabilities and their parametric dependence on physical attributes and operating characteristics of the system. In particular, the effects of the interdependencies between the power grid and the communication system have been captured by a parametric formulation of the transition probabilities using Monte-Carlo simulations of cascading failures. The cascading failures are simulated with a coupled power-system simulation framework, which is also developed in this dissertation. Specifically, the probabilistic model enables the prediction of the evolution of the blackout probability in time. Furthermore, the asymptotic analysis of the blackout probability as time tends to infinity enables the calculation of the probability mass function of the blackout size, which has been shown to have a heavy tail, e.g., power-law distribution, specifically when the grid is operating under stress scenarios. A key benefit of the model is that it enables the characterization of the severity of cascading failures in terms of a set of operating characteristics of the power grid. As a generalization to the Markov chain model, a regeneration-based model for cascading failures is also developed. The regeneration-based framework is capable of modeling cascading failures in a more general setting where the probability distribution of events in the system follows an arbitrarily specified distribution with non-Markovian characteristics. Further, a novel interdependent Markov chain model is developed, which provides a general probabilistic framework for capturing the effects of interactions among interdependent infrastructures on cascading failures. A key insight obtained from this model is that interdependencies between two systems can make two individually reliable systems behave unreliably. In particular, we show that due to the interdependencies two chains with non-heavy tail asymptotic failure distribution can result in a heavy tail distribution when coupled. Lastly, another aspect of future smart grids is studied by characterizing the fundamental bounds on the information rate in the sensor network that monitors the power grid. Specifically, a distributed source coding framework is presented that enables an improved estimate of the lower bound for the minimum required communication capacity to accurately describe the state of components in the information-centric power grid. The models developed in this dissertation provide critical understanding of cascading failures in electric-cyber infrastructures and facilitate reliable and quick detection of the risk of blackouts and precursors to cascading failures. These capabilities can guide the design of efficient communication systems and cascade aware control policies for future smart grids
An Interaction Model for Simulation and Mitigation of Cascading Failures
In this paper the interactions between component failures are quantified and
the interaction matrix and interaction network are obtained. The quantified
interactions can capture the general propagation patterns of the cascades from
utilities or simulation, thus helping to better understand how cascading
failures propagate and to identify key links and key components that are
crucial for cascading failure propagation. By utilizing these interactions a
high-level probabilistic model called interaction model is proposed to study
the influence of interactions on cascading failure risk and to support online
decision-making. It is much more time efficient to first quantify the
interactions between component failures with fewer original cascades from a
more detailed cascading failure model and then perform the interaction model
simulation than it is to directly simulate a large number of cascades with a
more detailed model. Interaction-based mitigation measures are suggested to
mitigate cascading failure risk by weakening key links, which can be achieved
in real systems by wide area protection such as blocking of some specific
protective relays. The proposed interaction quantifying method and interaction
model are validated with line outage data generated by the AC OPA cascading
simulations on the IEEE 118-bus system.Comment: Accepted by IEEE Transactions on Power System
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