The project is divided into three distinct activities: identifying and characterizing threats to electrical networks; assessing their vulnerability and security; and enhancing network resilience.

In the first year, the types of threats analyzed included the formation of snow sleeves on network conductors due to so-called wet snow, the impact of strong winds (which can cause trees and branches to fall on the network), and flood events resulting from intense precipitation.

To this end, the following were used: the expanded and optimized MERIDA meteorological reanalysis dataset, outputs from updated climate models to assess extreme weather events expected in the coming decades, greenhouse gas concentration measurements inducing climate change, and the collection of hazard maps for floods and landslides, including a model for simulating urban flooding.

Regarding vulnerability and security, methodologies and models were developed and refined to determine the vulnerability of electrical network components to various meteorological threats (wet snow, strong winds, and, preliminarily, floods and saline pollution), in order to estimate the failure return periods and the propagation of contingencies across the network.

To improve the assessment of seismic safety for concrete dams, a seismic wave propagation model was adopted, appropriately integrated with existing numerical analysis tools used for dam analysis. This model was used in an international comparison of numerical methods for seismic analysis of a gravity dam. The SPHERA code was also utilized for simulating floods and landslides, equipped with a flood damage scheme for electrical substations.

An advanced software tool is being developed to integrate existing resilience assessment functions with innovative capabilities for both GIS applications and probabilistic network resilience control, within the context of network operation programming and planning. Three WILD 2.0 stations were designed, built, installed, and made operational for monitoring the formation of snow sleeves on medium-voltage spans. These measurements are also useful for verifying and improving the performance of the WOLF forecasting model for estimating snow sleeve formation.