Data-driven Grid Resilience: Planning & Asset Intelligence for Future-Proof Transmission Networks

Transmission System Operators are facing increasing stress on their networks due to climate change, extreme weather events, accelerated electrification, and the rapid integration of renewable energy sources.
Ensuring grid resilience is no longer limited to physical robustness, but increasingly depends on the ability to anticipate risks, optimize long-term planning, and proactively manage existing assets through advanced data-driven approaches.

This challenge aims to attract innovative solutions that enhance network resilience across the full lifecycle, from strategic grid planning to operational asset management, leveraging digital technologies, advanced analytics, and system-level intelligence.

The objective of this challenge is to identify innovative solutions that:

• Improve the efficiency and robustness of grid planning under uncertainty

• Enhance the resilience and reliability of existing transmission assets

• Enable data-driven, predictive, and adaptive decision-making for TSOs

Solutions should support Terna in anticipating failures, prioritizing investments, and optimizing maintenance strategies, ultimately increasing the grid’s ability to withstand and recover from disruptive events.

We are particularly interested in 2 main areas:

1. Resilient Grid Planning and Development: innovations addressing long-term and mid-term planning challenges, such as:

   • Advanced scenario analysis integrating climate risks, extreme weather, and demand uncertainty

   • Tools for cost-benefit and resilience-based planning, beyond traditional deterministic approaches

   • Digital twins or simulation platforms for network stress testing and resilience assessment

   • AI/ML-based support for investment prioritization and grid expansion strategies

   • Integration of resilience metrics into planning and regulatory decision processes

2. Smart Infrastructure & Asset Management: solutions improving the monitoring and lifecycle management of existing grid assets, including:

   • Predictive maintenance and condition-based monitoring for transmission lines, substations, and critical components

   • Advanced analytics combining sensor data, historical failures, and environmental data

   • Asset criticality assessment and risk-based maintenance prioritization

   • Tools to optimize asset lifetime while maintaining safety and reliability

   • Platforms enabling real-time or near-real-time situational awareness of asset health