Modeling Interdependencies in electricity infrastructure risk (original) (raw)
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Risk analysis of critical infrastructures emphasizing electricity supply and interdependencies
Reliability Engineering & System Safety, 2012
Failures in critical infrastructures can cause major damage to society. Wide-area interruptions (blackouts) in the electricity supply system have severe impacts on societal critical functions and other critical infrastructures, but there is no agreed-upon framework on how to analyze and predict the reliability of electricity supply. Thus, there is a need for an approach to cross-sector risk analyses, which facilitates risk analysis of outages in the electricity supply system and enables investigation of cascading failures and consequences in other infrastructures. This paper presents such an approach, which includes contingency analysis (power flow) and reliability analysis of power systems, as well as use of a cascade diagram for investigating interdependencies. A case study was carried out together with the Emergency Preparedness Group in the city of Oslo, Norway and the network company Hafslund Nett. The case study results highlight the need for cross-sector analyses by showing that the total estimated societal costs are substantially higher when cascading effects and consequences to other infrastructures are taken into account compared to only considering the costs of electricity interruptions as seen by the network company. The approach is a promising starting point for crosssector risk analysis of electricity supply interruptions and consequences for dependent infrastructures.
Interdependencies in Security of Electricity Supply
SSRN Electronic Journal, 2016
The analysis of security of electricity supply (SoES) is particularly complex due to, among others, the liberalisation process and the increasing penetration of renewable energies. Larsen et al. (2016) propose a framework based on twelve dimensions to evaluate SoES for a single jurisdiction. However, actions aimed at improving one dimension might impact others negatively, adversely affecting the overall system. Understanding how these dimensions are interrelated is thus a prerequisite for appropriate planning and resource allocation. We apply a Cross Impact Analysis (CIA) to these twelve dimensions to determine the degree to which the different dimensions depend on each other. From this we derive an influence diagram to visualise the interdependencies and a scatter plot to categorise the dimensions as independent, driver, connector or outcome. The connecting dimensions are the central elements of the feedback mechanisms of SoES, reinforcing or balancing the effect of other dimensions. Actions targeting the dimensions categorised as drivers or connectors are potentially the more effective ones a regulator can take, as the consequences will gradually ripple through the system. They affect systemwide performance, but not necessarily in the desired direction. Having an integral view of the dimensions' interdependencies provides a better understanding of the higher-order changes an intervention may cause. This enables policymakers and regulators to identifying where in the system to intervene to achieve the desired effect with the least amount of resources and with as few undesirable side-effects as possible.
Identifying Extreme Risks in Critical Infrastructure Interdependencies
Proceedings of the International Symposium for Next Generation Infrastructure, 2014
Critical infrastructures like our power generation facilities and water supply form highly interconnected networks that are mutually dependent and any failure can cascade through the network, resulting in devastating impact on health, safety and the economy. These catastrophic events/disruptions can be triggered by environmental accidents, geological/weather phenomena, disease pandemics, etc. The disruptions can be caused/exacerbated by their being unexpected, but they may actually be expected if relevant data have been accounted for. To help account for and thereby anticipate such disruptions, one way is to identify potential unforeseen interdependencies among infrastructure components that can lead to extreme disruptions upon some failure in the network. This paper shows how a simulation model for cascading failures and a risk analysis/optimization approach can be applied to search for unforeseen interdependencies and failure points that give rise to the highest risk in a network.
The aggregate energy sector criticality risk assessment
2015
One of the aims of this work is to create a method of identification of critical elements (elements or groups of elements) in the energy infrastructure, and this method should allow ranking these critical elements by relevance to consumers (the consumer could be from the systems of different energy sector).The risk estimate of an element is one of the proposed sorting criteria for critical elements or their groups. It allows assessing the importance of the combination of critical elements and takes into account the probabilities of faults of these combinations. The key result of the research is the identification of the weakest links in the system, namely those elements, the failure of which (together with other elements) would lead to the worst consequences for the consumers and response to the availability of the infrastructure element.
Risk Assessment in Complex Interacting Infrastructure Systems
Proceedings of the 38th Annual Hawaii International Conference on System Sciences, 2005
Critical infrastructures have some of the characteristic properties of complex systems. They exhibit infrequent large failures events. These events, though infrequent, often obey a power law distribution in their probability versus size. This power law behavior suggests that ordinary risk analysis might not apply to these systems. It is thought that some of this behavior comes from different parts of the systems interacting with each other both in space and time. While these complex infrastructure systems can exhibit these characteristics on their own, in reality these individual infrastructure systems interact with each other in even more complex ways. This interaction can lead to increased or decreased risk of failure in the individual systems. To investigate this and to formulate appropriate risk assessment tools for such systems, a set of models are used to study to impact of coupling complex systems. A probabilistic model and a dynamical model that have been used to study blackout dynamics in the power transmission grid are used as paradigms. In this paper, we investigate changes in the risk models based on the power law event probability distributions, when complex systems are coupled.
Risk and Interdependencies in Critical Infrastructures
This chapter presents methods for analyzing interdependencies in critical infrastructures. In general, there are three groups of methods for analyzing interdependencies; (i) conceptual, (ii) model and simulation, and (iii) empirical and knowledge based approaches. Examples of methods belonging to these groups are presented. The latter part of the chapter discusses challenges related to modeling, focusing on how to deal with complexity, trade-offs between abstraction and fidelity, choice of consequence measures, and obtaining information.
Preliminary interdependency analysis: An approach to support critical-infrastructure risk-assessment
Reliability Engineering & System Safety, 2017
We present a methodology, Preliminary Interdependency Analysis (PIA), for analysing interdependencies between critical infrastructure (CI). Consisting of two phases-qualitative analysis followed by quantitative analysisan application of PIA progresses from a relatively quick elicitation of CI-interdependencies to the building of representative CI models, and the subsequent estimation of any resilience, risk or criticality measures an assessor might be interested in. By design, stages in the methodology are both flexible and iterative, resulting in interacting CI models that are scalable and may vary significantly in complexity and fidelity, depending on the needs and requirements of an assessor. For model parameterisation, one relies on a combination of field data, sensitivity analysis and expert judgement. Facilitated by dedicated software tool support, we illustrate PIA by applying it to a complex case-study of interacting Power (distribution and transmission) and Telecommunications networks in the Rome area. A number of studies are carried out, including: 1) an investigation of how "strength of dependence " between the CIs ' components affects various measures of risk and uncertainty, 2) for resource allocation, an exploration of different, but related, notions of CI component importance, and 3) highlighting the impact of model fidelity on the estimated risk of cascades.
Environment Systems and Decisions, 2021
The electric power grid is a critical societal resource connecting multiple infrastructural domains such as agriculture, transportation, and manufacturing. The electrical grid as an infrastructure is shaped by human activity and public policy in terms of demand and supply requirements. Further, the grid is subject to changes and stresses due to diverse factors including solar weather, climate, hydrology, and ecology. The emerging interconnected and complex network dependencies make such interactions increasingly dynamic, posing novel risks, and presenting new challenges to manage the coupled human-natural system. This paper provides a survey of models and methods that seek to explore the significant interconnected impact of the electric power grid and interdependent domains. We also provide relevant critical risk indicators (CRIs) across diverse domains that may be used to assess risks to electric grid reliability, including climate, ecology, hydrology, finance, space weather, and agriculture. We discuss the convergence of indicators from individual domains to explore possible systemic risk, i.e., holistic risk arising from cross-domain interconnections. Further, we propose a compositional approach to risk assessment that incorporates diverse domain expertise and information, data science, and computer science to identify domain-specific CRIs and their union in systemic risk indicators. Our study provides an important first step towards datadriven analysis and predictive modeling of risks in interconnected human-natural systems.
Interdependent risk in interacting infrastructure systems
2007
Critical infrastructures display many of the characteristic properties of complex systems. They exhibit infrequent large failures events that often obey a power law distribution in their probability versus size. This power law behavior suggests that conventional risk analysis does not apply to these systems. It is thought that some of this behavior comes from different parts of the systems interacting with each other both in space and time. While these complex infrastructure systems can exhibit these characteristics on their own, in reality these individual infrastructure systems interact with each other in even more complicated ways. This interaction can lead to increased or decreased risk of failure in the individual systems. To investigate this, we couple two complex system models and investigate the effect of the coupling on the characteristic properties of the systems such as the probability distribution of events.
Input-output impact risk propagation in critical infrastructure interdependency
2010
An attack on a critical infrastructure such as banking and finance resulting in loss or damage will not occur in isolation because infrastructures are interdependent. The recent global financial crisis has reaffirmed that the world economies are interdependent. Infrastructure sectors have direct and indirect interdependencies and are vulnerable to each others impacts and disruptions, deliberate or accidental which can be pernicious, resulting in derivative losses that reverberate perturbations globally. Owing to the complexity of interdependency, there is the need for managers to better understand how impact risk propagates. Propagated risks due to dependency, interdependency and multi-dependency have been broken down in detail to comb out the complexity of interconnectedness and the ripple effect in a network system. Using input-output methodology and risk vulnerability coefficient factors, the paper presents an analysis of impact risk transfers and their rippled effect in critical infrastructure system. The analysis explicitly provides details of the impact risk transfer and exhibits the rudiments of the transfer. An analysis is carried out to illustrate cascading of an impact among two interdependent and seven multi-interdependent infrastructures of varying, impact values, strength of interdependent relationship, vulnerability to risk impact, resilience to external impact and source of attack with results shown in tables and graphs. The results have been obtained by trivial mathematical solution of the matrix equations. Using iteration which may be tedious, the rudiments of transfer have been exhibited by manual computation in dependent and interdependent relationships. The iterative process reveals impact risk that is attributed to network interdependency and distinguishes it from that emanating from external sources. The application of the method has been demonstrated by showing the rippled effect of an impact in a real economy using seven out of 109 infrastructure sectors. Hence, the rippled effect of risk in any number of interdependent infrastructures can also be shown. The solution of equations involving a higher (greater than three) number of infrastructures is not a task for unaided human computation.