Henrik Hassel

An approach for modelling interdependent infrastructures in the context of vulnerability analysis

Technical infrastructures of the society are becoming more and more interconnected and interdependent, i.e. the function of an infrastructure influences the function of other infrastructures. Disturbances in one infrastructure therefore often traverse to other dependent infrastructures and possibly even back to the infrastructure where the failure originated. It is becoming increasingly important to take these interdependencies into account when assessing the vulnerability of technical infrastructures. In the present paper, an approach for modelling interdependent technical infrastructures is proposed. The modelling approach considers structural properties, as employed in graph theory, as well as functional properties to increase its fidelity and usefulness. By modelling a fictional electrified railway network that consists of five systems and interdependencies between the systems, it is shown how the model can be employed in a vulnerability analysis. The model aims to capture both functional and geographic interdependencies. It is concluded that the proposed modelling approach is promising and suitable in the context of vulnerability analyses of interdependent systems.

Reliability and vulnerability analyses of critical infrastructures: Comparing two approaches in the context of power systems

Society depends on services provided by critical infrastructures, and hence it is important that they are reliable and robust. Two main approaches for gaining knowledge required for designing and improving critical infrastructures are reliability analysis and vulnerability analysis. The former analyses the ability of the system to perform its intended function; the latter analyses its inability to withstand strains and the effects of the consequent failures. The two approaches have similarities but also some differences with respect to what type of information they generate about the system. In this view, the main purpose of this paper is to discuss and contrast these approaches. To strengthen the discussion and exemplify its findings, a Monte Carlo-based reliability analysis and a vulnerability analysis are considered in their application to a relatively simple, but representative, system the IEEE RTS96 electric power test system. The exemplification reveals that reliability analysis provides a good picture of the system likely behaviour, but fails to capture a large portion of the high consequence scenarios, which are instead captured in the vulnerability analysis. Although these scenarios might be estimated to have small probabilities of occurrence, they should be identified, considered and treated cautiously, as probabilistic analyses should not be the only input to decision-making for the design and protection of critical infrastructures. The general conclusion that can be drawn from the findings of the example is that vulnerability analysis should be used to complement reliability studies, as well as other forms of probabilistic risk analysis. Measures should be sought for reducing both the vulnerability, i.e. improving the system ability to withstand strains and stresses, and the reliability, i.e. improving the likely behaviour.

Towards a System-Oriented Framework for Analysing and Evaluating Emergency Response

Information can be provided by studying and evaluating past emergencies and the response in connection to them. This information would then be useful in efforts directed at preventing, mitigating and/or preparing for future emergencies. However, the analysis and evaluation of emergency response operations is not an easy task, especially when the operation involves several cooperating actors (e.g. the fire and rescue services, the police, the emergency medical services, etc.). Here, we identify and discuss four aspects of this challenge: (1) issues related to the values governing the evaluation, (2) issues related to the complexity of the systems involved, (3) issues related to the validity of the information on which the analysis and evaluation is based and (4) issues related to the limiting conditions under which the emergency response system operated. An outline of a framework for such an analysis and evaluation, influenced by systems theory, accident investigation theories and programme evaluation theories dealing with the above aspects, is introduced, discussed and exemplified using empirical results from a case study. We conclude that the proposed framework may provide a better understanding of how an emergency response system functioned during a specific operation, and help to identify the potential events and/or circumstances that could significantly affect the performance of the emergency response system, either negatively or positively. The insights gained from using the framework may allow the actors involved in the response operation to gain a better understanding of how the emergency response system functioned as a whole, as well as how the actors performed as individual components of the system. Furthermore, the information can also be useful for actors preparing for future emergencies.

Identifying critical components in technical infrastructure networks

A new method for identifying and ranking critical components and sets of components in technical infrastructures is presented. The criticality of a component or a set of components is defined as the vulnerability of the system to failure in a specific component, or set of components. The identification of critical components is increasingly difficult when considering multiple simultaneous failures. This is especially difficult when dealing with failures of multiple components with synergistic consequences, i.e. consequences that cannot be calculated by adding the consequences of the individual failures. The proposed method addresses this problem. In exemplifying the method, an analysis of an electric power distribution system in a Swedish municipality is presented. It is concluded that the proposed method facilitates the identification of critical sets of components for large-scale technical infrastructures.

Vulnerability analysis of interdependent critical infrastructures : case study of the Swedish railway system

Critical infrastructures provide essential services which enable our society to function. Disruptions in infrastructures can have widespread effects, not only for the originating infrastructure but also, through mutual dependencies, for other infrastructures. Identifying vulnerabilities inherent in these system-of-systems is thus highly critical for the proactive management and avoidance of future crises. A modelling approach for interdependent technical infrastructures is proposed and three perspectives for the analysis of vulnerabilities are introduced, addressing the complexities associated with comprehensively analysing technical interdependent infrastructures. An empirical analysis of the railway system in southern Sweden is conducted, a system consisting of seven interdependent supporting systems. It is concluded that the proposed modelling approach and the three perspectives of vulnerability analysis give valuable insights for the proactive risk management of technical infrastructures.

Topological performance measures as surrogates for physical flow models for risk and vulnerability analysis for electric power systems.

Critical infrastructure systems must be both robust and resilient in order to ensure the functioning of society. To improve the performance of such systems, we often use risk and vulnerability analysis to find and address system weaknesses. A critical component of such analyses is the ability to accurately determine the negative consequences of various types of failures in the system. Numerous mathematical and simulation models exist that can be used to this end. However, there are relatively few studies comparing the implications of using different modeling approaches in the context of comprehensive risk analysis of critical infrastructures. In this article, we suggest a classification of these models, which span from simple topologically-oriented models to advanced physical-flow-based models. Here, we focus on electric power systems and present a study aimed at understanding the tradeoffs between simplicity and fidelity in models used in the context of risk analysis. Specifically, the purpose of this article is to compare performance estimates achieved with a spectrum of approaches typically used for risk and vulnerability analysis of electric power systems and evaluate if more simplified topological measures can be combined using statistical methods to be used as a surrogate for physical flow models. The results of our work provide guidance as to appropriate models or combinations of models to use when analyzing large-scale critical infrastructure systems, where simulation times quickly become insurmountable when using more advanced models, severely limiting the extent of analyses that can be performed.

Organizational Adaptation in Multi‐Stakeholder Crisis Response: An Experimental Study

Modern day crises demand organizations to collaborate and adapt to new roles, functions and structures. In such situations, lack of collaborative behaviour and openness between organizations can result in reduced adaptive ability. Therefore, it is important to facilitate collaboration between organizations. We have studied the extent to which crisis managers are prepared to work with personnel and resources from organizations other than their own when responding to crises. An experiment was designed with four different organizations in Sweden, which involved decision making concerning whether the participants systematically favoured their own organization over others. Findings indicate that increasing familiarity and expectation of future cooperation with other organizations increased the likelihood that decision makers would be prepared to work with other organizations in joint crisis management.

Risk and vulnerability analysis in practice: evaluation of analyses conducted in Swedish municipalities

Risk and vulnerability analysis (RVA) can benefit the process of preventing and preparing for disasters, both by generating a basis for making decisions and by enhancing risk awareness, safety culture and response capacity through the RVA process itself. In studying and understanding the practices related to RVA, insights can be gained regarding ways in which the RVA can be improved in society, as well as into how methods for RVA can be designed to suit the particular context. However, studies of this sort are rare. This paper presents an evaluation of RVA performed by Swedish municipalities, which are important actors in the Swedish emergency management system. This is done by employing a systematic, design science approach outlined in the paper. Document studies and interviews were used to collect data on the analyses performed by the municipalities, and the evaluation shows that there is room for improvement. The results can be especially relevant for municipalities developing their RVA practices, as well as for other actors performing similar types of analyses.

Modelling, Simulation and Vulnerability Analysis of Interdependent Technical Infrastructures

In this chapter, a modelling framework for interdependent technical infrastructures is presented. As input to the framework, characteristics of interdependencies and the objectives of the modelling framework are discussed. The overall approach is to divide the modelling of technical infrastructures into two parts, a topological part and a functional part. The topological part describes the structure and how components are connected. The functional part describes the flow of the infrastructure and how the system reacts when strains affect it. This generic approach of how to model individual infrastructures then enables the inclusion of dependencies in and analyses of a “system-of-system” model. Three perspectives of vulnerability analyses are also presented: global vulnerability, critical components and geographical vulnerability. The presented framework is utilized in, but not limited to, the context of vulnerability analyses in Chap. 6 for two different types of interdependent technical infrastructures.

Impact of Functional Models in a Decision Context of Critical Infrastructure Vulnerability Reduction

Critical infrastructures provide essential services for the functioning of the society, and disruption of these services can lead to large-scale consequences. Hence, it is of utmost importance to analyze the vulnerability of critical infrastructures and use the results to guide decisions of improving their robustness towards strains. Since real-life critical infrastructures can be regarded as complex systems, such analyses can prove challenging. To analyze the system response of strains affecting the system, several different models have been suggested in the scientific literature. These range from simplistic static topological models to more advanced engineering models. The benefit of using more simplistic models is that they are computationally less burdensome and hence more comprehensive and explorative vulnerability studies can be carried out, with the downside that they might not accurately enough describe system performance. More advanced engineering models on the other hand capture the system performance more accurately, with the downside that they are computationally burdensome and hence less comprehensive studies can be carried out. Here the focus is on how different functional models impacts estimated effectiveness of improvement strategies in a decisions context from a vulnerability perspective. More precisely nine different functional models are used, from static network theoretical models to engineering models, to assess the vulnerability of a test system, the IEEE RTS96 transmission power system. The results from these analyses are then used in a decision context to identify critical components and for suggesting structural improvements of the system. The overarching questions is whether the use of different models will have an impact on the decision of which components are deemed most critical and what improvement strategies are ranked highest. Preliminary results suggest that the use of model impacts the decision, and hence care should be taken when using these models to inform decisions of critical infrastructure improvement. (Less)