795 research outputs found
Context-Independent Centrality Measures Underestimate the Vulnerability of Power Grids
Power grids vulnerability is a key issue in society. A component failure may
trigger cascades of failures across the grid and lead to a large blackout.
Complex network approaches have shown a direction to study some of the problems
faced by power grids. Within Complex Network Analysis structural
vulnerabilities of power grids have been studied mostly using purely
topological approaches, which assumes that flow of power is dictated by
shortest paths. However, this fails to capture the real flow characteristics of
power grids. We have proposed a flow redistribution mechanism that closely
mimics the flow in power grids using the PTDF. With this mechanism we enhance
existing cascading failure models to study the vulnerability of power grids.
We apply the model to the European high-voltage grid to carry out a
comparative study for a number of centrality measures. `Centrality' gives an
indication of the criticality of network components. Our model offers a way to
find those centrality measures that give the best indication of node
vulnerability in the context of power grids, by considering not only the
network topology but also the power flowing through the network. In addition,
we use the model to determine the spare capacity that is needed to make the
grid robust to targeted attacks. We also show a brief comparison of the end
results with other power grid systems to generalise the result.Comment: Pre-Proceedings of CRITIS '1
MATCASC: A tool to analyse cascading line outages in power grids
Blackouts in power grids typically result from cascading failures. The key
importance of the electric power grid to society encourages further research
into sustaining power system reliability and developing new methods to manage
the risks of cascading blackouts. Adequate software tools are required to
better analyze, understand, and assess the consequences of the cascading
failures. This paper presents MATCASC, an open source MATLAB based tool to
analyse cascading failures in power grids. Cascading effects due to line
overload outages are considered. The applicability of the MATCASC tool is
demonstrated by assessing the robustness of IEEE test systems and real-world
power grids with respect to cascading failures
A Topological Investigation of Phase Transitions of Cascading Failures in Power Grids
Cascading failures are one of the main reasons for blackouts in electric
power transmission grids. The economic cost of such failures is in the order of
tens of billion dollars annually. The loading level of power system is a key
aspect to determine the amount of the damage caused by cascading failures.
Existing studies show that the blackout size exhibits phase transitions as the
loading level increases. This paper investigates the impact of the topology of
a power grid on phase transitions in its robustness. Three spectral graph
metrics are considered: spectral radius, effective graph resistance and
algebraic connectivity. Experimental results from a model of cascading failures
in power grids on the IEEE power systems demonstrate the applicability of these
metrics to design/optimize a power grid topology for an enhanced phase
transition behavior of the system
Cascading failures in spatially-embedded random networks
Cascading failures constitute an important vulnerability of interconnected
systems. Here we focus on the study of such failures on networks in which the
connectivity of nodes is constrained by geographical distance. Specifically, we
use random geometric graphs as representative examples of such spatial
networks, and study the properties of cascading failures on them in the
presence of distributed flow. The key finding of this study is that the process
of cascading failures is non-self-averaging on spatial networks, and thus,
aggregate inferences made from analyzing an ensemble of such networks lead to
incorrect conclusions when applied to a single network, no matter how large the
network is. We demonstrate that this lack of self-averaging disappears with the
introduction of a small fraction of long-range links into the network. We
simulate the well studied preemptive node removal strategy for cascade
mitigation and show that it is largely ineffective in the case of spatial
networks. We introduce an altruistic strategy designed to limit the loss of
network nodes in the event of a cascade triggering failure and show that it
performs better than the preemptive strategy. Finally, we consider a real-world
spatial network viz. a European power transmission network and validate that
our findings from the study of random geometric graphs are also borne out by
simulations of cascading failures on the empirical network.Comment: 13 pages, 15 figure
How to Think About Resilient Infrastructure Systems
abstract: Resilience is emerging as the preferred way to improve the protection of infrastructure systems beyond established risk management practices. Massive damages experienced during tragedies like Hurricane Katrina showed that risk analysis is incapable to prevent unforeseen infrastructure failures and shifted expert focus towards resilience to absorb and recover from adverse events. Recent, exponential growth in research is now producing consensus on how to think about infrastructure resilience centered on definitions and models from influential organizations like the US National Academy of Sciences. Despite widespread efforts, massive infrastructure failures in 2017 demonstrate that resilience is still not working, raising the question: Are the ways people think about resilience producing resilient infrastructure systems?
This dissertation argues that established thinking harbors misconceptions about infrastructure systems that diminish attempts to improve their resilience. Widespread efforts based on the current canon focus on improving data analytics, establishing resilience goals, reducing failure probabilities, and measuring cascading losses. Unfortunately, none of these pursuits change the resilience of an infrastructure system, because none of them result in knowledge about how data is used, goals are set, or failures occur. Through the examination of each misconception, this dissertation results in practical, new approaches for infrastructure systems to respond to unforeseen failures via sensing, adapting, and anticipating processes. Specifically, infrastructure resilience is improved by sensing when data analytics include the modeler-in-the-loop, adapting to stress contexts by switching between multiple resilience strategies, and anticipating crisis coordination activities prior to experiencing a failure.
Overall, results demonstrate that current resilience thinking needs to change because it does not differentiate resilience from risk. The majority of research thinks resilience is a property that a system has, like a noun, when resilience is really an action a system does, like a verb. Treating resilience as a noun only strengthens commitment to risk-based practices that do not protect infrastructure from unknown events. Instead, switching to thinking about resilience as a verb overcomes prevalent misconceptions about data, goals, systems, and failures, and may bring a necessary, radical change to the way infrastructure is protected in the future.Dissertation/ThesisDoctoral Dissertation Civil, Environmental and Sustainable Engineering 201
Towards a Theory of Scale-Free Graphs: Definition, Properties, and Implications (Extended Version)
Although the ``scale-free'' literature is large and growing, it gives neither
a precise definition of scale-free graphs nor rigorous proofs of many of their
claimed properties. In fact, it is easily shown that the existing theory has
many inherent contradictions and verifiably false claims. In this paper, we
propose a new, mathematically precise, and structural definition of the extent
to which a graph is scale-free, and prove a series of results that recover many
of the claimed properties while suggesting the potential for a rich and
interesting theory. With this definition, scale-free (or its opposite,
scale-rich) is closely related to other structural graph properties such as
various notions of self-similarity (or respectively, self-dissimilarity).
Scale-free graphs are also shown to be the likely outcome of random
construction processes, consistent with the heuristic definitions implicit in
existing random graph approaches. Our approach clarifies much of the confusion
surrounding the sensational qualitative claims in the scale-free literature,
and offers rigorous and quantitative alternatives.Comment: 44 pages, 16 figures. The primary version is to appear in Internet
Mathematics (2005
Energy Transition and Urban Planning for Local Development. A Critical Review of the Evolution of Integrated Spatial and Energy Planning
The aim of the article is to analyse the evolution of spatial and energy planning integration, seen as a mean to foster local development, from the birth of the theme to the current prospects of shared sustainability and Decentralised Energy System (DES) solutions. The paper is a review of the evolution of the spatial and energy planning integration, exploring weaknesses
and future opportunities. After an initial period of intense theoretical elaboration, the relationship between energy and city physical-functional organization and planning is still far from finding an implementation. The article explains this lack of integration through the analyses of significant steps in the last 50 years with the aim to outline current obstacles in achieving a more comprehensive vision of energy and spatial planning. The experiences selected highlight critical aspects concerning
the trend towards the divergence of energy planning from systemic urban and spatial planning, also due to the low consideration of energy as a factor for local development. From the processes of decentralization and energy localism, some perspectives emerge which converge on the eco-energy district as a projection of the local energy community and which seem to enhance a more systemic and strategic dimension of planning
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