From Weather Forecast to Grid Decision: A Multi-Scale Compound Event Detection and Warning System for Dynamic Line Rating of Overhead Transmission Lines

The capacity of overhead transmission lines is fundamentally governed by atmospheric conditions, primarily wind speed, ambient temperature, and solar radiation, that determine the rate of conductor cooling and heating. Dynamic Line Rating (DLR) exploits this weather dependency to safely increase or decrease the permissible current beyond conservative static ratings. However, translating raw numerical weather prediction output into actionable operational warnings for transmission system operators (TSOs) requires a carefully designed warning value chain that bridges the gap between meteorological forecasts and grid management decisions.

We present a multi-scale automated detection and warning system developed within the Destination Earth (DestinE) Extremes Digital Twin initiative, co-designed with three European TSOs/DSOs. The system implements a cascading forecast-to-warning chain across three scales:

(1) Continental-scale screening using the DestinE Global Digital Twin (ECMWF IFS ensemble forecasts at ~9 km) identifies regions where compound meteorological events, specifically the co-occurrence of high temperature (>30°C), low wind speed (<2 m/s), and high solar radiation (>600 W/m²), pose a risk to transmission capacity. A five-level severity classification provides an automated first-guess warning product across the European domain.

(2) Regional refinement using AROME/ALARO forecasts (~2.5 km) extracts detailed atmospheric profiles along specific transmission corridors at multiple heights (10 to 100 m above ground) and lateral grid points, capturing the spatial heterogeneity critical in Alpine terrain where sheltered valley sections may experience unfavorable conditions while exposed ridge crossings remain favorable.

(3) On-demand hectometric detail (100 to 500 m) from the DestinE Extremes Digital Twin is triggered only when the continental screening identifies compound events, providing sub-kilometer resolution for bottleneck identification along critical line segments.

At each scale, the meteorological detection is coupled with CIGRE and IEEE 738 thermal models to translate weather conditions into quantitative ampacity forecasts (maximum permissible current in Amperes) and identify the specific line segment that limits the entire corridor's capacity, i.e. the operational bottleneck.

 

Ensemble-based probability statements are derived through k-means clustering of IFS ensemble members, producing operationally meaningful scenarios (e.g., "64% probability of mixed DLR conditions with a 200 A capacity reduction at the bottleneck segment") rather than simple mean/spread summaries. The compound event detection specifically addresses the interdisciplinary gap between meteorological severity and grid impact: high temperatures combined with calm winds can be more operationally critical than either extreme in isolation.

The system has been validated against archived AROME forecasts for Austrian Alpine transmission lines and verified against TSO operational data. We discuss the challenges of evaluating the true added value of each element in this warning chain, from the continental early warning that triggers expensive high-resolution runs to the segment-level ampacity forecast that informs real-time dispatch decisions, and the role of user co-design in ensuring that meteorological expertise translates into improved grid operation outcomes.