Towards a Decision Support Tool for Assessing, Managing and Mitigating Seismic Risk of Electric Power Networks

Abstract

Recent seismic event worldwide proved how fragile the electric power system can be to seismic events. Decision Support Systems (DSSs) could have a critical role in assessing the seismic risk of electric power networks and in enabling asset managers to test the effectiveness of alternative mitigation strategies and investments on resilience. This paper exemplifies the potentialities of CIPCast, a DSS recently created in the framework of the EU-funded project CIPRNet, to perform such tasks. CIPCast enables to perform risk assessment for Critical Infrastructures (CI) when subjected different natural hazards, including earthquakes. An ad-hoc customization of CIPCast for the seismic risk analysis and management of electric power networks is featured in this paper. The international literature describes effective and sound efforts towards the creation of software platforms and frameworks for the assessment of seismic risk of electric power networks. None of them, unfortunately, achieved the goal of creating a user-friendly and ready available DDS to be used by asset managers, local authorities and civil protection departments. Towards that and building on the international literature, the paper describes metrics and methods to be integrated within CIPCast for assessing the earthquake-induced physical and functional impacts of the electric power network at component and system level. The paper describes also how CIPCast can inform the service restoration process.

A comprehensive system for semantic spatiotemporal assessment of risk in urban areas

“…The automatic approach for semantic spatiotemporal risk assessment enhances the decision support system capabilities of a pre‐existing GIS‐based system devoted to Critical Infrastructures (CI) protection (Giovinazzi et al., 2017). A view of the overall system architecture is in Figure 2, showing the functional blocks to enable risk assessment following both quantitative and qualitative methods, and the data and domain knowledge they use.…”

“…The approach is supported by a software system that allows the above‐mentioned dynamic generation of geo‐localized risk mini‐models. The system consists of the following components: a knowledge base including TERMINUS (TERritorial Management and INfrastructures ontology for institutional and industrial Usage; Coletti et al, 2019), a domain ontology formalizing knowledge concerning environment, city services and infrastructures and related risks, and a geo‐database including data on urban areas and how they vary in time; WS‐CREAM, a web service built on top of CREAM (CREAtivity Machine; De Nicola et al., 2019), implementing the computational support for automatic risk identification and ranking, by querying the ontology and using context data; and CIPCast (Giovinazzi et al., 2017), a GIS (geographic information system)‐based tool for risk analysis of critical infrastructures, enhanced with forecasting and decision support functionalities. Preliminary results, discussed in Barcaroli et al.…”

Modeling Resilience in Electrical Distribution Networks

“…CIPCast allows geographical information system, GIS-based risk assessment, and situational awareness through the continuous acquisition of different kinds of data from the field (e.g., weather forecast, infrastructure network status). Furthermore, CIPCast allows the assessment of the impacts and consequences of possible damage scenarios due to the prediction of natural hazards (such as heavy rain, flash floods, earthquakes) on the infrastructure networks and services and on the affected communities [17,18]. The present work describes RecSIM [19], a specific module of CIPCast allowing the operational resilience assessment of electrical distribution grids.…”

“…• The physical impacts induced on EDNs following earthquakes [17] and flooding events • The impact on service functionality associated with the predicted damage of CI elements (in terms of outage duration and geographical extension), also considering possible perturbation cascades toward other networks and services [29,30] • The consequences of the predicted outages, according to several metrics accounting for economic losses, reduction of citizen well-being, and impacts on the quality of service Within CIPCast, the RecSIM simulator represents the basic module for the resilience assessment of the EDN, as better described in Section 4.…”

A software platform to support the energy system resilience study

“…The creation of the software platform implies the adoption of the specific and suitable ES models, retrieving geospatial data from multiple sources. Thus, the Geographic Information System (GIS) and Database Management System (DBMS) are the key information technologies to build a visual representation of the disturbances consequences and graphical user interface for the resilience enhancement measures efficiency assessment [8].…”