4,228 research outputs found
Risk Assessment Methodology for Critical Infrastructure Protection
The European Programme for Critical Infrastructure Protection is the main vehicle for the protection of critical infrastructures in
Europe. The Directive 2008/114/EC is the legislative instrument of this programme. Risk assessment is an important element
that is mentioned throughout the Directive text. However, there is no harmonized methodology in Europe for the assessment of
interconnected infrastructures. The present work describes such a methodology and its implementation for the assessment of
critical infrastructures of European dimension. The methodology accounts for impact at asset level, evaluates the propagation of
a failure at network level due to interdependencies and assess the economic impact of critical infrastructure disruption at
national level.JRC.G.6-Security technology assessmen
Infrastructure (Resilience-oriented) Modelling Language: I®M - A proposal for modelling infrastructures and their connections
The modelling of critical infrastructures (CIs) is an important issue that needs to be properly addressed, for several reasons. It is a basic support for making decisions about operation and risk reduction. It might help in understanding high-level states at the system-of-systems layer, which are not ready evident to the organisations that manage the lower level technical systems. Moreover, it is also indispensable for setting a common reference between operator and authorities, for agreeing on the incident scenarios that might affect those infrastructures. So far, critical infrastructures have been modelled ad-hoc, on the basis of knowledge and practice derived from less complex systems. As there is no theoretical framework, most of these efforts proceed without clear guides and goals and using informally defined schemas based mostly on boxes and arrows. Different CIs (electricity grid, telecommunications networks, emergency support, etc) have been modelled using particular schemas that were not directly translatable from one CI to another. If there is a desire to build a science of CIs it is because there are some observable commonalities that different CIs share. Up until now, however, those commonalities were not adequately compiled or categorized, so building models of CIs that are rooted on such commonalities was not possible. This report explores the issue of which elements underlie every CI and how those elements can be used to develop a modelling language that will enable CI modelling and, subsequently, analysis of CI interactions, with a special focus on resilience.JRC.DG.G.6-Security technology assessmen
Infrastructure (Resilience-oriented) Modelling Language: I®ML A proposal for modelling infrastructures and their connections
The modelling of critical infrastructures (CIs) is an important issue that needs to be properly addressed, for several reasons. It is a basic support for making decisions about operation and risk reduction. It might help in understanding high-level states at the system-of-systems layer, which are not ready evident to the organisations that manage the lower level technical systems. Moreover, it is also indispensable for setting a common reference between operator and authorities, for agreeing on the incident scenarios that might affect those infrastructures. So far, critical infrastructures have been modelled ad-hoc, on the basis of knowledge and practice derived from less complex systems. As there is no theoretical framework, most of these efforts proceed without clear guides and goals and using informally defined schemas based mostly on boxes and arrows. Different CIs (electricity grid, telecommunications networks, emergency support, etc) have been modelled using particular schemas that were not directly translatable from one CI to another. If there is a desire to build a science of CIs it is because there are some observable commonalities that different CIs share. Up until now, however, those commonalities were not adequately compiled or categorized, so building models of CIs that are rooted on such commonalities was not possible. This report explores the issue of which elements underlie every CI and how those elements can be used to develop a modelling language that will enable CI modelling and, subsequently, analysis of CI interactions, with a special focus on resilienc
Resilience assessment and planning in power distribution systems:Past and future considerations
Over the past decade, extreme weather events have significantly increased
worldwide, leading to widespread power outages and blackouts. As these threats
continue to challenge power distribution systems, the importance of mitigating
the impacts of extreme weather events has become paramount. Consequently,
resilience has become crucial for designing and operating power distribution
systems. This work comprehensively explores the current landscape of resilience
evaluation and metrics within the power distribution system domain, reviewing
existing methods and identifying key attributes that define effective
resilience metrics. The challenges encountered during the formulation,
development, and calculation of these metrics are also addressed. Additionally,
this review acknowledges the intricate interdependencies between power
distribution systems and critical infrastructures, including information and
communication technology, transportation, water distribution, and natural gas
networks. It is important to understand these interdependencies and their
impact on power distribution system resilience. Moreover, this work provides an
in-depth analysis of existing research on planning solutions to enhance
distribution system resilience and support power distribution system operators
and planners in developing effective mitigation strategies. These strategies
are crucial for minimizing the adverse impacts of extreme weather events and
fostering overall resilience within power distribution systems.Comment: 27 pages, 7 figures, submitted for review to Renewable and
Sustainable Energy Review
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Enabling Resilience in Cyber-Physical-Human Water Infrastructures
Rapid urbanization and growth in urban populations have forced community-scale infrastructures (e.g., water, power and natural gas distribution systems, and transportation networks) to operate at their limits. Aging (and failing) infrastructures around the world are becoming increasingly vulnerable to operational degradation, extreme weather, natural disasters and cyber attacks/failures. These trends have wide-ranging socioeconomic consequences and raise public safety concerns. In this thesis, we introduce the notion of cyber-physical-human infrastructures (CPHIs) - smart community-scale infrastructures that bridge technologies with physical infrastructures and people. CPHIs are highly dynamic stochastic systems characterized by complex physical models that exhibit regionwide variability and uncertainty under disruptions. Failures in these distributed settings tend to be difficult to predict and estimate, and expensive to repair. Real-time fault identification is crucial to ensure continuity of lifeline services to customers at adequate levels of quality. Emerging smart community technologies have the potential to transform our failing infrastructures into robust and resilient future CPHIs.In this thesis, we explore one such CPHI - community water infrastructures. Current urban water infrastructures, that are decades (sometimes over a 100 years) old, encompass diverse geophysical regimes. Water stress concerns include the scarcity of supply and an increase in demand due to urbanization. Deterioration and damage to the infrastructure can disrupt water service; contamination events can result in economic and public health consequences. Unfortunately, little investment has gone into modernizing this key lifeline.To enhance the resilience of water systems, we propose an integrated middleware framework for quick and accurate identification of failures in complex water networks that exhibit uncertain behavior. Our proposed approach integrates IoT-based sensing, domain-specific models and simulations with machine learning methods to identify failures (pipe breaks, contamination events). The composition of techniques results in cost-accuracy-latency tradeoffs in fault identification, inherent in CPHIs due to the constraints imposed by cyber components, physical mechanics and human operators. Three key resilience problems are addressed in this thesis; isolation of multiple faults under a small number of failures, state estimation of the water systems under extreme events such as earthquakes, and contaminant source identification in water networks using human-in-the-loop based sensing. By working with real world water agencies (WSSC, DC and LADWP, LA), we first develop an understanding of operations of water CPHI systems. We design and implement a sensor-simulation-data integration framework AquaSCALE, and apply it to localize multiple concurrent pipe failures. We use a mixture of infrastructure measurements (i.e., historical and live water pressure/flow), environmental data (i.e., weather) and human inputs (i.e., twitter feeds), combined and enhanced with the domain model and supervised learning techniques to locate multiple failures at fine levels of granularity (individual pipeline level) with detection time reduced by orders of magnitude (from hours/days to minutes). We next consider the resilience of water infrastructures under extreme events (i.e., earthquakes) - the challenge here is the lack of apriori knowledge and the increased number and severity of damages to infrastructures. We present a graphical model based approach for efficient online state estimation, where the offline graph factorization partitions a given network into disjoint subgraphs, and the belief propagation based inference is executed on-the-fly in a distributed manner on those subgraphs. Our proposed approach can isolate 80% broken pipes and 99% loss-of-service to end-users during an earthquake.Finally, we address issues of water quality - today this is a human-in-the-loop process where operators need to gather water samples for lab tests. We incorporate the necessary abstractions with event processing methods into a workflow, which iteratively selects and refines the set of potential failure points via human-driven grab sampling. Our approach utilizes Hidden Markov Model based representations for event inference, along with reinforcement learning methods for further refining event locations and reducing the cost of human efforts.The proposed techniques are integrated into a middleware architecture, which enables components to communicate/collaborate with one another. We validate our approaches through a prototype implementation with multiple real-world water networks, supply-demand patterns from water utilities and policies set by the U.S. EPA. While our focus here is on water infrastructures in a community, the developed end-to-end solution is applicable to other infrastructures and community services which operate in disruptive and resource-constrained environments
A Critical Review of Robustness in Power Grids using Complex Networks Concepts
Complex network theory for analyzing robustness in energy gridsThis paper reviews the most relevant works that have investigated robustness in power grids using Complex Networks (CN) concepts. In this broad field there are two different approaches. The first one is based solely on topological concepts, and uses metrics such as mean path length, clustering coefficient, efficiency and betweenness centrality, among many others. The second, hybrid approach consists of introducing (into the CN framework) some concepts from Electrical Engineering (EE) in the effort of enhancing the topological approach, and uses novel, more efficient electrical metrics such as electrical betweenness, net-ability, and others. There is however a controversy about whether these approaches are able to provide insights into all aspects of real power grids. The CN community argues that the topological approach does not aim to focus on the detailed operation, but to discover the unexpected emergence of collective behavior, while part of the EE community asserts that this leads to an excessive simplification. Beyond this open debate it seems to be no predominant structure (scale-free, small-world) in high-voltage transmission power grids, the vast majority of power grids studied so far. Most of them have in common that they are vulnerable to targeted attacks on the most connected nodes and robust to random failure. In this respect there are only a few works that propose strategies to improve robustness such as intentional islanding, restricted link addition, microgrids and smart grids, for which novel studies suggest that small-world networks seem to be the best topology.This work has been partially supported by the project TIN2014-54583-C2-2-R from the Spanish Ministerial Commission of Science and Technology (MICYT), by the project S2013/ICE-2933 from Comunidad de Madrid and by the project FUTURE GRIDS-2020 from the Basque Government
Vulnerability Analysis of Interdependent Critical Infrastructures upon a Cyber-attack
There is an extensive literature on modelling cascading effects in Critical Infrastructures (CIs). Concerning the cascading impacts of a cyber-attack upon other CIs, a detailed scenario analysis done by the Norwegian Directorate of Civil Protection concludes that a considerable impact could be achieved. However, the analysis admits that the probability of the attack would be very low, since it would require considerable expertise and resources. We argue that a smart attacker could exploit existing knowledge on cascading impacts to plan for perfidiously-timed cyber-attacks requiring low resources that would achieve a significant disruption of CIs. To illustrate our point, we build and simulate a highly-aggregated system dynamics model using estimates of disruptions effects across CIs taken from the literature
Advancements in Enhancing Resilience of Electrical Distribution Systems: A Review on Frameworks, Metrics, and Technological Innovations
This comprehensive review paper explores power system resilience, emphasizing
its evolution, comparison with reliability, and conducting a thorough analysis
of the definition and characteristics of resilience. The paper presents the
resilience frameworks and the application of quantitative power system
resilience metrics to assess and quantify resilience. Additionally, it
investigates the relevance of complex network theory in the context of power
system resilience. An integral part of this review involves examining the
incorporation of data-driven techniques in enhancing power system resilience.
This includes the role of data-driven methods in enhancing power system
resilience and predictive analytics. Further, the paper explores the recent
techniques employed for resilience enhancement, which includes planning and
operational techniques. Also, a detailed explanation of microgrid (MG)
deployment, renewable energy integration, and peer-to-peer (P2P) energy trading
in fortifying power systems against disruptions is provided. An analysis of
existing research gaps and challenges is discussed for future directions toward
improvements in power system resilience. Thus, a comprehensive understanding of
power system resilience is provided, which helps in improving the ability of
distribution systems to withstand and recover from extreme events and
disruptions
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