ERNCIP training for professionals in Critical Infrastructure Protection: from risk management to resilience

This report, about the ERNCIP Pilot “Training for professionals in Critical Infrastructure Protection: from risk management to resilience”, contains an analysis of the roadmap followed by the JRC in establishing, in cooperation with DG Home, a first-of-its-kind training event strongly based on the European Programme for Critical Infrastructure Protection (EPCIP). This deliverable contains references to all the steps that this project implied; from its embryonic conceptualisation, passing through the validation of its functional requirements and modules, to its final execution in Brussels from the 21st to the 23rd of June 2016. The aim of this document is to disseminate the methodologies and material collected during the execution of the project and provide useful references, topics and suggestions to educators and trainers - and their organisations - that are willing to organise or fine-tune courses on Critical Infrastructure Protection and Resilience with a focus on European policies and strategies. The ERNCIP Office's goal, following the publication of this report, is to receive feedback from institutions and experts that have made use of the Course’s material in view to integrate them in the execution of new iterations of training events. The Course’s material could also be used by DG HOME as one of the actions put in place to foster the improvement of the “external domain” of the European programme for Critical Infrastructure Protection (EPCIP). The fact that the EPCIP also aims at reaching out to neighbouring countries of the Union, in view to establish CIP-related form of cooperation, puts the “training” among the most useful and direct tools to be exploited to achieve such objective.JRC.E.2-Technology Innovation in Securit

Traditional vs. novel approaches to coastal risk management: A review and insights from Italy

Coastal areas frequently face critical conditions due to the lack of adequate forms of land use planning, environmental management and inappropriate coastal risk management, sometimes leading to unexpected and undesired environmental effects. Risk management also involves cultural aspects, including perception. However, the acknowledgement of risk perception by stakeholders and local communities, as one of the social pillars of risk analysis, is often lacking.. Starting from an overview of the risk concept and the related approaches to be addressed, the paper investigates the evolution of coastal risk management with a focus on the Italian case study. Despite the design and adoption of national policies to deal with coastal risks, coastal management still shows in Italy a fragmented and poorly coordinated approach, together with a general lack of attention to stakeholder involvement. Recent efforts in the design of plans aiming at reducing risks derived from climate change and mitigating their impacts (National Strategy on Climate Change Adaptation; National Climate Change Adaptation Plan; National Recovery and Resilience Plan activities) should be effective in updating knowledge about climate change risks and in supporting national adaptation policies

Resilience-Driven Post-Disruption Restoration of Interdependent Critical Infrastructure Systems Under Uncertainty: Modeling, Risk-Averse Optimization, and Solution Approaches

Critical infrastructure networks (CINs) are the backbone of modern societies, which depend on their continuous and proper functioning. Such infrastructure networks are subjected to different types of inevitable disruptive events which could affect their performance unpredictably and have direct socioeconomic consequences. Therefore, planning for disruptions to CINs has recently shifted from emphasizing pre-disruption phases of prevention and protection to post-disruption studies investigating the ability of critical infrastructures (CIs) to withstand disruptions and recover timely from them. However, post-disruption restoration planning often faces uncertainties associated with the required repair tasks and the accessibility of the underlying transportation network. Such challenges are often overlooked in the CIs resilience literature. Furthermore, CIs are not isolated from each other, but instead, most of them rely on one another for their proper functioning. Hence, the occurrence of a disruption in one CIN could affect other dependent CINs, leading to a more significant adverse impact on communities. Therefore, interdependencies among CINs increase the complexity associated with recovery planning after a disruptive event, making it a more challenging task for decision makers. Recognizing the inevitability of large-scale disruptions to CIs and their impacts on societies, the research objective of this work is to study the recovery of CINs following a disruptive event. Accordingly, the main contributions of the following two research components are to develop: (i) resilience-based post-disruption stochastic restoration optimization models that respect the spatial nature of CIs, (ii) a general framework for scenario-based stochastic models covering scenario generation, selection, and reduction for resilience applications, (iii) stochastic risk-related cost-based restoration modeling approaches to minimize restoration costs of a system of interdependent critical infrastructure networks (ICINs), (iv) flexible restoration strategies of ICINs under uncertainty, and (v) effective solution approaches to the proposed optimization models. The first research component considers developing two-stage risk-related stochastic programming models to schedule repair activities for a disrupted CIN to maximize the system resilience. The stochastic models are developed using a scenario-based optimization technique accounting for the uncertainties of the repair time and travel time spent on the underlying transportation network. To assess the risks associated with post-disruption scheduling plans, a conditional value-at-risk metric is incorporated into the optimization models through the scenario reduction algorithm. The proposed restoration framework is illustrated using the French RTE electric power network. The second research component studies the restoration problem for a system of ICINs following a disruptive event under uncertainty. A two-stage mean-risk stochastic restoration model is proposed to minimize the total cost associated with ICINs unsatisfied demands, repair tasks, and flow. The model assigns and schedules repair tasks to network-specific work crews with consideration of limited time and resources availability. Additionally, the model features flexible restoration strategies including a multicrew assignment for a single component and a multimodal repair setting along with the consideration of full and partial functioning and dependencies between the multi-network components. The proposed model is illustrated using the power and water networks in Shelby County, Tennessee, United States, under two hypothetical earthquakes. Finally, some other topics are discussed for possible future work

Smart grid

Tese de mestrado integrado em Engenharia da Energia e do Ambiente, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2016The SG concept arises from the fact that there is an increase in global energy consumption. One of the factors delaying an energetic paradigm change worldwide is the electric grids. Even though there is no specific definition for the SG concept there are several characteristics that describe it. Those features represent several advantages relating to reliability and efficiency. The most important one is the two way flow of energy and information between utilities and consumers. The infrastructures in standard grids and the SG can classified the same way but the second one has several components contributing for monitoring and management improvement. The SG’s management system allows peak reduction, using several techniques underlining many advantages like controlling costs and emissions. Furthermore, it presents a new concept called demand response that allows consumers to play an important role in the electric systems. This factor brings benefits for utilities, consumers and the whole grid but it increases problems in security and that is why the SG relies in a good protection system. There are many schemes and components to create it. The MG can be considered has an electric grid in small scale which can connect to the whole grid. To implement a MG it is necessary economic and technical studies. For that, software like HOMER can be used. However, the economic study can be complex because there are factors that are difficult to evaluate beyond energy selling. On top of that, there are legislation and incentive programs that should be considered. Two case studies prove that MG can be profitable. In the first study, recurring to HOMER, and a scenario with energy selling only, it was obtained a 106% reduction on production cost and 32% in emissions. The installer would have an $8 000 000 profit in the MG’s lifetime. In the second case, it was considered economic services related to peak load reduction, reliability, emission reduction and power quality. The DNO had a profit of$41,386, the MG owner had $29,319 profit and the consumers had a$196,125 profit. We can conclude that the MG with SG concepts can be profitable in many cases

Aide à la décision pour l’analyse de la vulnérabilité des réseaux d’infrastructure face à une crise de catastrophe naturelle

Cette thèse traite de la Vulnérabilité des réseaux face aux catastrophes naturelles. Elle part du constat que les infrastructures telles que les réseaux d’eau, d’électricité influencent considérablement les conséquences indirectes des catastrophes naturelles. Elle vise donc à modéliser la vulnérabilité dans de telles situations pour une prise de décision efficace. La démarche scientifique est divisée en deux parties complémentaires. La première traite de la vulnérabilité des dits systèmes, tandis que la seconde se concentre sur le processus d’aide à la décision à mettre en œuvre en vue de réduire la vulnérabilité. L’analyse proprement dite de la vulnérabilité repose sur la modélisation des objets de l’analyse. Pour ce faire nous adopterons une représentation par la théorie des graphes. La revue de la littérature à ce niveau nous a permis d’identifier les structures de graphe les mieux adaptées au contexte de la thèse. Dans un environnement d’analyse multi réseau, les interdépendances, c’est-à-dire les liens entre les composants d’un même réseau ou de réseaux différents-sont un facteur déterminant pour tout modèle de vulnérabilité. Nous avons ainsi proposé un modèle compatible avec la théorie des graphes. Sont distingués deux types d’interdépendances. La première est fonctionnelle (dépendance), et la seconde est dysfonctionnelle (influence). La vulnérabilité quant à elle, est déterminée par une approche basée sur la simulation. Elle est composée d’une première partie relative à l’aptitude du système à résister à l’évènement redouté ; et d’une seconde partie relative à son aptitude à se recouvrer des conditions opérationnelles après la catastrophe. Le calcul de la vulnérabilité est un point d’entrée pour assister l’analyste dans sa prise de décision. La deuxième partie aborde ce thème. Elle est elle-même divisée en deux sous parties : La première traite du processus à mettre en œuvre pour la gestion de la crise ; la deuxième du Système Interactif d’Aide à la Décision réalisé. Une méthodologie d’aide à la décision et supportée par un outil informatique. À l’ère de l’internet et des réseaux sociaux, il est envisageable de déployer l’application sur internet. ABSTRACT : This thesis deals with infrastructure network vulnerability analysis in the natural disaster context. It starts from the observation that infrastructure such as water supply or power grid has significant influence on natural disasters’ indirect consequences. The aim is to model the vulnerability to take efficient actions. The scientific approach is divided into two complementary parts. The first one deals with the vulnerability assessment, while the second one focuses on the decision aiding process to be implemented for the assessed vulnerability reducing. The proper vulnerability analysis is based on the analysis objects modelling. In order to achieve this, we will adopt a graph theory representation. A literature review will allow us to identify the graph structure which best suits the context of the thesis. In a multi network analysis environment, interdependences, i.e. relationships between components of the same network or different networks - are a determining factor for any vulnerability model. We have thus proposed an approach to model interdependence compatible with the graph theory. There are two types of relationships: the one first is functional (dependence), while the second one is dysfunctional (influence). The vulnerability is assessed by a simulation-based approach. It is composed of one part relating to the system ability to resist the feared event; and the other part relative to its ability to be back on its nominal state after the disaster. When the vulnerability is determined, the next step will be to take the necessary decisions to manage it. This part on the decision aiding is itself divided into two sub parts: first of all the process to be used for the crisis management is established. Then a methodology for decision aiding is proposed and results on a Decision Support System development. In the age of the internet and social networks, it is possible to deploy the application on the internet

Decision support for infrastructure network vulnerability assessment in natural disaster crisis situations

This thesis deals with infrastructure network vulnerability analysis in the natural disaster context. It starts from the observation that infrastructure such as water supply or power grid has significant influence on natural disasters’ indirect consequences. The aim is to model the vulnerability to take efficient actions. The scientific approach is divided into two complementary parts. The first one deals with the vulnerability assessment, while the second one focuses on the decision aiding process to be implemented for the assessed vulnerability reducing. The proper vulnerability analysis is based on the analysis objects modelling. In order to achieve this, we will adopt a graph theory representation. A literature review will allow us to identify the graph structure which best suits the context of the thesis. In a multi network analysis environment, interdependences, i.e. relationships between components of the same network or different networks - are a determining factor for any vulnerability model. We have thus proposed an approach to model interdependence compatible with the graph theory. There are two types of relationships: the one first is functional (dependence), while the second one is dysfunctional (influence). The vulnerability is assessed by a simulation-based approach. It is composed of one part relating to the system ability to resist the feared event; and the other part relative to its ability to be back on its nominal state after the disaster. When the vulnerability is determined, the next step will be to take the necessary decisions to manage it. This part on the decision aiding is itself divided into two sub parts: first of all the process to be used for the crisis management is established. Then a methodology for decision aiding is proposed and results on a Decision Support System development. In the age of the internet and social networks, it is possible to deploy the application on the internet

An investigation to improve community resilience using network graph analysis of infrastructure systems

PhD ThesisDisasters can have devastating effects on our communities and can cause great suffering to the people who reside within them. Critical infrastructure underpins the stable functioning of these communities and the severity of disasters is often linked to failure of these systems. Traditionally, the resilience of infrastructure systems is assessed by subjecting physically based models to a range of hazard scenarios. The problem with this approach is that it can only inform us of inadequacies in the system for the chosen scenarios, potentially leaving us vulnerable to unforeseen events. This thesis investigates whether network graph theory can be used to give us increased confidence that the system will respond well in untested scenarios by developing a framework that can identify generic system characteristics and hence describe the underlying resilience of the network. The novelty in the work presented in this thesis is that it overcomes a shortcoming in existing network graph theory by including the effects of the spatial distribution of geographically dispersed systems. To consider spatial influence, a new network generation algorithm was developed which incorporated rules that connects system components based on both their spatial distribution and topology. This algorithm was used to generate proxy networks for the European, US and China air traffic networks and demonstrated that the inclusion of this spatial component was crucial to form the highly connected hub airports observed in these networks. The networks were then tested for hazard tolerance and in the case of the European air traffic network validated using data from the 2010 Eyjafjallajökull eruption. Hazard tolerance was assessed by subjecting the networks to a series of random, but spatially coherent, hazards and showed that the European air traffic network was the most vulnerable, having up to 25% more connections disrupted compared to a benchmark random network. This contradicts traditional network theory which states that these networks are resilient to random hazards. To overcome this shortcoming, two strategies were employed to improve the resilience of the network. One strategy ‘adaptively’ modified the topology (crises management) while the other ‘permanently’ modified it (hazard mitigation). When these modified networks were subjected to spatial hazards the ‘adaptive’ approach Page i produced the most resilient network, having up to 23% fewer cancelled air routes compared to the original network, for only a 5% change in airport capacity. Finally, as many infrastructure networks are flow based systems, an investigation into whether graph theory could identify vulnerabilities in these systems was conducted. The results demonstrated that by using a combination of both physically based and graph theory metrics produced the best predictive skill in identifying vulnerable nodes in the system. This research has many important implications for the owners and operators of infrastructure systems. It has demonstrated the European air traffic network to be vulnerable to spatial hazard and shown that, because many infrastructure networks possess similar properties, may therefore be equally vulnerable. It also provides a method to identify generic system vulnerabilities and strategies to reduce these. It is argued that as this research has considered generic networks it can not only increase infrastructure resilience to known threats but also to previously unidentified ones and therefore is a useful method to help protect these systems to large scale disasters and reduce the suffering for the people in the communities who rely upon them.EPSR

Improvements in the acoustical modelling of traffic noise prediction: theoretical and experimental results

2009 - 2010Traffic acoustical noise is one of the most important component of the urban environmental pollution in densely populated areas all over the world. A very recent ACI-Censis study [1] on Italian urban areas shows that car is the favorite mean of transportation for 90% of population. In particular, this study shows that during years ranging from 2000 to 2007, the number of circulating vehicles is grown of 14.5%. To this growth did not always correspond an improvement of national street network. This problem can be evidenced by the high growth of the traffic charge on urban, sub-urban and extra-urban roads, with a clear impact on costs, security and environment, even in term of acoustical noise. A similar tendency can be observed in the framework of many european countries. Traffic noise affects areas surrounding roads especially when high traffic load and high speed conditions occur and can lead to a degradation of the quality of life in residential areas. The impact of noise on mental and physical health and on daily activities has been widely documented in the scientific literature [2, 3, 4]. In particular a continuous exposure to acoustical noise may affect sleep and/or conversation, may lead to perception of annoyance, may cause hearing loss, cardiovascular problems etc. As a consequence, during last years, a large number of anti-noise laws, ordinances and regulations were decreed by many national governments and international institutions. Looking to Italy, it is the D.P.C.M. 01.03.1991 [5] which regulates noise pollution matters, giving the main acoustical elements definitions such as maximum limit of noise exposure in inner and external environment, acoustic zoning criteria, etc. Then the Framework Law n. 447/1995 has defined a general policy on the noise pollution that has been implemented in different decrees and regulations. Among these, one of the most interesting is the D.M.A. 16.03.1998 ”Noise pollution detection and measurement method” (Tecniche di rilevamento e di misurazione dell’inquinamento acustico) which deals with the vehicular and railway noise detection procedure. Moreover the D.Lgs 194/2005 (Attuazione della direttiva 2002/49/CE relativa alla determinazione e alla gestione del rumore ambientale) establishes the method to set the acoustic indicator for the different kind of noise sources such as vehicular traffic. In this Ph.D. thesis our aim is to improve the current prediction tools for traffic noise prediction in non trivial situations such as traffic lights, traffic jam, intersections etc., accounting some aspects of traffic dynamics by the use of traffic models (TM), i.e. following the leader model and Cellular Automata. This thesis is organized as follows. In the first chapter we briefly discuss the main features of sound and noise propagation. In the second chapter we focus our attention on vehicle noise emission and existing traffic noise models (TNM) while in the third we present a new noise prediction procedure: GERIAN2009. In chapter four some general features of physics of road traffic and transportation are discussed. In the last three chapters we propose an integration of traffic noise model and traffic dynamic model in the ”following the leader” and Cellular Automata (CA) framework, with a particular attention on road’s intersection issue. [edited by author]IX n.s