Case Study: RTE France achieves blackout prevention through weather-based Dynamic Line Rating with Elastic

Blackout Prevention through Weather Prediction

RTE, France’s high- and extra-high-voltage transmission system operator, must constantly balance generation and consumption and prevent line overheating and sag that can trigger cascading outages. To avoid blackouts caused by weather-driven thermal stress on conductors, RTE needed a way to compute Dynamic Line Ratings (DLR) that account for temperature, wind and solar radiation using Numerical Weather Prediction data.

RTE built a pipeline to ingest Météo‑France GRIB2 weather data into Kafka, process it with Logstash and index geo‑aware JSON documents in Elasticsearch for query and visualization in Kibana. The Elastic Stack delivered a scalable, stateless, replayable solution with 25% smaller index size after mapping tuning, stable memory under heavy indexing, linear ingest scaling (275k docs/min per Logstash pipeline), and geo queries over millions of records in ~2 seconds — paving the way to a multi‑billion‑record platform with X‑Pack machine learning and alerting for automated risk detection.

RTE France

Akli Rahmoun

Project Manager