Chapters authored
Modeling Resilience in Electrical Distribution Networks By Alberto Tofani, Gregorio D’Agostino, Antonio Di Pietro, Sonia Giovinazzi, Luigi La Porta, Giacomo Parmendola, Maurizio Pollino and Vittorio Rosato
Electrical distribution networks deliver a fundamental service to citizens. However, they are still highly vulnerable to natural hazards as well as to cyberattacks; therefore, additional commitment and investments are needed to foster their resilience. Toward that, this paper presents and proposes the use of a complex simulation model, called reconfiguration simulator (RecSIM), enabling to evaluate the effectiveness of resilience enhancement strategies for electric distribution networks and the required resources to implement them. The focus is, in particular, on one specific attribute of resilience, namely, the readiness, i.e., the promptness and efficiency to recover the service functionality after a crisis event by managing and deploying the available resources rapidly and effectively. RecSIM allows estimating how and to what extent technological, topological, and management issues might improve electrical distribution networks’ functionality after the occurrence of accidental faults, accounting for interdependency issues and reconfiguration possibilities. The viability of implementing RecSIM on a real and large urban network is showcased in the paper with reference to the study case of the electrical distribution network (EDN) of Rome city.
Part of the book: Infrastructure Management and Construction
Integrating Resilience in Time-based Dependency Analysis: A Large-Scale Case Study for Urban Critical Infrastructures By Vittorio Rosato, Antonio Di Pietro, Panayiotis Kotzanikolaou, George Stergiopoulos and Giulio Smedile
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.
Part of the book: Issues on Risk Analysis for Critical Infrastructure Protection
An Open-Data-Based Methodology for the Creation of a Graph of Critical Infrastructure Dependencies at an Urban Scale By Antonio Di Pietro, Alessandro Calabrese, Antonio De Nicola, Daniele Ferneti, Luisa Franchina, Josè Martì and Tommaso Ruocco
This paper presents the MARIS (Modeling infrAstructuRe dependencIes at an urban Scale) methodology, allowing the generalization of one of the possible graphs modeling Critical Infrastructure (CI, hereafter) interdependencies at an urban scale starting from uncertain data. This leverages a set of known interdependencies at the system level, topological open data of local services and Points of Interest collected at an urban scale, and some heuristics. Indeed, interdependencies at an urban scale are usually not known to decision makers (e.g., CI operators, emergency planners) due to, for example, a lack of integration of knowledge held by different critical infrastructure operators and privacy restrictions. Here, these interdependencies are determined through geographic-based strategies. The resulting graph can be a valuable input to simulate emergency scenarios of CIs in the area of interest and, thus, plan proper countermeasures.
Part of the book: Critical Infrastructure
