Modeling Resilience in Electrical Distribution Networks

Tofani, Alberto; D’Agostino, Gregorio; Pietro, Antonio Di; Giovinazzi, Sonia; Porta, Luigi La; Parmendola, Giacomo; Pollino, Maurizio; Rosato, Vittorio

doi:10.5772/intechopen.85917

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…The concept allows integrating the qualitative approaches with those based on a more complex quantitative resilience analysis (e.g. [30,34,35] or [22]). In addition, the work in the background of this paper has clearly shown, that the current research on resilience has a number of different aspects: from those focusing on the "resilience of and within a network" (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…Defining the functionality in the above way enables to precisely and quantitatively define the resilience curve in scenario time, e.g. for the main characteristic points in time [22]: t 0 : time before the event or starting point of the scenario. Based on the resilience curve (or functionality curve), it is then possible to define the resulting macro-indicators, as illustrated in the notional diagram in Figure 4, such as: •…”

Section: Assessing Resilience An Infrastructure During An Adverse Event: Functionality Level (Fl)mentioning

confidence: 99%

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Resilience and Situational Awareness in Critical Infrastructure Protection: An Indicator-Based Approach

Jovanović¹,

Chakravarty²,

Jelić³

2021

Issues on Risk Analysis for Critical Infrastructure Protection

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The paper proposes a concept enabling quantitative assessment of resilience in critical entities developed in the European projects SmartResilience and InfraStress. The concept aims at combining simple communication-related advantages of simplified assessments results (such as “resilience very high” or “resilience very low”) with the advantages of the in-depth assessments (e.g. analysis of multiple sensor data). The paper describes the main elements of the innovative, indicator-based concept, starting with the “resilience cube” at the top, and continuing with the multi-level, hierarchical, indicator-based assessment methodology. The concept allows analyzing and assessing different aspects of practical resilience management. One can assess the resilience level of an entity at a given point in time, monitor their resilience level over time and benchmark it. One can also model and analyze the functionality of a system during a particular (threat) scenario, as well as stress-test it. The same methodology allows to optimize investment in improving resilience (e.g. in further training, in equipment, etc.), in a transparent and intuitive way. A resilience indicator database (over 4,000 indicators available) and a suite of tools (primarily developed within SmartResilience and InfraStress projects) and a repository of over 20 application cases and 300 scenarios, support application of the methodology. The concept has been discussed and agreed with over 50 different organizational stakeholders and is being embedded into the new ISO 31050 standard currently under development. Its “life-after-the-project” will be ensured by the dedicated “resilience rating initiative (ERRA)”. Although the concept and the tool in the form of the “ResilienceTool” were developed primarily for the resilience assessment of critical infrastructure (the “smart” ones in particular), they can be used for resilience assessment of other systems and through the extension of the, already initiated, implementation of AI techniques (machine learning) to make the ResilienceTool even more versatile and easier to use in the future.

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Section: Discussionmentioning

confidence: 99%

Section: Assessing Resilience An Infrastructure During An Adverse Event: Functionality Level (Fl)mentioning

confidence: 99%

Resilience and Situational Awareness in Critical Infrastructure Protection: An Indicator-Based Approach

Jovanović¹,

Chakravarty²,

Jelić³

2021

Issues on Risk Analysis for Critical Infrastructure Protection

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“…There are nonetheless a few studies dealing with the assessment of resilience of the EDN. Tofani et al, 2019 focuses on one specific attribute of resilience, namely, the readiness. The authors investigated how a EDN can better recover its service functionality after a crisis event by managing and deploying the available resources rapidly and effectively.…”

Section: Introductionmentioning

confidence: 99%

Resilience Modeling of Flood Induced Electrical Distribution Network Failures: Munich, Germany

2021

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Of many defining characteristics for a flood resilient city and its infrastructure networks, mitigating flooding impacts and recovering quickly to a pre-flood state are to be considered of high importance. With a likely increase in the frequency and intensity of future heavy precipitation and flooding events in Europe, the vulnerability of the electrical distribution network of Maxvorstadt, Munich will also increase. These facts justify the need for quantifying how the electrical distribution network would respond to flooding, and more so, how stakeholders can better prepare for such an event. For a synthetic electrical distribution network of Maxvorstadt, the timing and location of network components failure due to flooding and affected persons without power have been computed for a combination of realistic future flooding events via the Electrical Network Flood Resilience Model developed in this study. It has been learned that most buildings, and therefore their inhabitants, lose power due to the failure of a specific component, Medium Voltage—Low Voltage transformer buses, and that flood risk solutions should focus on protecting network components from inundation to ensure its functionality through flooding events. Solutions like dry proofing such components before severe flooding occurs is recommended for several neighborhoods analyzed in this study.

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“…For example, when electric transmission or distribution networks are affected by disturbances such as floods, in general, mitigation and restoration actions are performed through protection and automation devices and manual interventions to reduce the duration of the outage and preserve the power supply to critical systems such as hospitals. [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Integrating Resilience in Time-based Dependency Analysis: A Large-Scale Case Study for Urban Critical Infrastructures

Rosato¹,

Pietro²,

Kotzanikolaou³

et al. 2021

Issues on Risk Analysis for Critical Infrastructure Protection

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As critical systems shall withstand different types of perturbations affecting their functionalities and their service level, resilience is a very important requirement. Especially in an urban critical infrastructures where the occurrence of natural events may influence the state of other dependent infrastructures from various different sectors, the overall resilience of such infrastructures against large scale failures is even more important. When a perturbation occurs in a system, the quality (level) of the service provided by the affected system will be reduced and a recovery phase will be triggered to restore the system to its normal operation level. According to the implemented recovery controls, the restoration phase may follow a different growth model. This paper extends a previous time-based dependency risk analysis methodology by integrating and assessing the effect of recovery controls. The main goal is to dynamically assess the evolution of recovery over time, in order to identify how the expected recovery plans will eventually affect the overall risk of the critical paths. The proposed recovery-aware time-based dependency analysis methodology was integrated into the CIPCast Decision Support System that enables risk forecast due to natural events to identify vulnerable and disrupted assets (e.g., electric substations, telecommunication components) and measure the expected risk paths. Thus, CIPCast can be valuable to Critical Infrastructure Operators and other Emergency Managers involved in a crisis assessment to evaluate the effect of natural and anthropic threats affecting critical assets and plan proper countermeasures to reduce the overall risk of degradation of services. The proposed methodology is evaluated in a real scenario, which utilizes several infrastructures and Points of Interest of the city of Rome.

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Modeling Resilience in Electrical Distribution Networks

Cited by 7 publications

References 19 publications

Resilience and Situational Awareness in Critical Infrastructure Protection: An Indicator-Based Approach

Resilience and Situational Awareness in Critical Infrastructure Protection: An Indicator-Based Approach

Resilience Modeling of Flood Induced Electrical Distribution Network Failures: Munich, Germany

Integrating Resilience in Time-based Dependency Analysis: A Large-Scale Case Study for Urban Critical Infrastructures

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