A climate service for the Resilience of the Electric Transmission grid to Extreme events

The project RETE (Resilience of the Electric Transmission grid to Extreme events) funded by the Italian National Plan for Resilience and Recovery, has demonstrated a prototype service designed to enhance the resilience of critical infrastructures, with a focus on the Italian national transmission grid. The focus of the service is the growing risks posed by climate related geotechnical hazards  (e.g. shallow landslides) by integrating climate intelligence with engineering and complex network science.

The concept of RETE originated from the challenges faced by TERNA in planning strategic investments to enhance the resilience of the national transmission grid against changing patterns of intense rainfall events. Such extreme events may affect the frequency of fast landslides with a potential impact on the stability of the  infrastructure and related services. To systematically tackle these challenges, RETE was shaped using a climate service development methodology that grounds the service in real user needs, applies advanced scientific models, and iteratively co-designs solutions with stakeholders.

The service architecture is built around several key tools: harmonized climate datasets, high-resolution climate projections generated via deep machine-learning (M-L) approaches, a complex network model to simulate cascading effects across the transmission grid, a geotechnical hazard model interfaced with climate inputs.

We describe the methodology adopted for the consultation and co-development of the service and the resulting  multi-scale climate resilience analysis framework tailored to the specific needs of distributed critical infrastructures like the NTG. The co-development methodology has allowed the identification of key decisions and the tailored framework for the resilience analysis at four integrated geographical scale, from national to asset specific.

At the national scale, the framework evaluates dynamically and statistically downscaled precipitation projections to investigate how highly localized extreme rainfall events may evolve under future climate scenarios. These projections are integrated with landslide susceptibility data derived from terrain models, lithology, land cover, and historical landslide inventories to identify areas of heightened risk.

At the sub-system scale, graph theory and complex network modeling are applied to analyze infrastructure resilience. Electrical grid subsets are simulated under disruption scenarios to identify critical components based on their structural and functional roles. Electrical properties are assigned to network links, and tailored topological metrics are used to evaluate system robustness, recovery capacity, and performance.

At the site-specific scale, hydro-mechanical finite element models simulate slope stability under projected climate conditions. The model incorporates slope geometry, stratigraphy, lithology, geo-structural features, and soil hydraulic and mechanical properties critical to climate-induced instability.

While the asset scale is not explicitly addressed, the framework establishes the foundation for more localized risk and cost–benefit analyses.

Finally, we illustrate possible applications of the same co-development methodology in other activities dedicated to the development of sectoral applications such as the project RIVIERADE (Improving modelling methods to produce climate services for resilient European seas and coasts in a decadal to multi-decadal horizon).