More renewables.
Faster integration and less avoidable curtailment on existing lines.
Scaling Renewable Integration with Hybrid Predictive Grid Capacity Intelligence.
Measure where evidence matters most. Then model to scale.
38 lines~1,100 km of 110 kV & 330 kV grid brought into a hybrid DLR view.
+50%Avg. capacity above design rating uncovered in earlier pilots.
10 GWLithuania's renewable target by 2030.
Smarter use of existing infrastructure is one of the most cost-effective levers in the energy transition: this is what it looks like in practice.
The challenge.
How do you carry twice the renewables without building new lines?.
Lithuania aims to double both electricity demand and renewable generation within a decade. Installed wind and solar has already reached roughly 6 GW (more than 60% of total generation capacity) and is heading toward 10 GW. That growth puts real pressure on transmission capacity, system reliability and operational planning.
For Litgrid, the task is about integrating more renewables on the infrastructure already in service, not rebuilding it. But static and seasonal line ratings are necessarily conservative. They apply a crude, fixed limit that can constrain power flows well below the capacity a line may safely provide under actual and forecast conditions.
Understanding capacity span by span across 38 lines and 1,100 km of network is impractical with traditional methods, especially as weather, renewable output, and demand change hour by hour. The team needed a scalable way to see how much capacity the existing grid really has and where.
The solution.
A Hybrid DLR Solution that combines targeted physical sensing with predictive digital modelling.
The solution combines Enline’s GridSight® DLR with Energiot’s self-powered conductor-mounted sensors. Sensors provide direct measurements of conductor temperature, sag and ground clearance on selected spans, while GridSight® DLR combines this evidence with weather forecasts, asset data and electrical information.
This gives Litgrid a more dynamic, forecast-ready view of transmission capacity helping it make better use of existing assets while supporting the integration of more renewable generation.
The principle behind it is simple: no grid can be made flexible through field measurement alone.
Targeted sensing delivers direct, point-in-time evidence. Enline´s digital twin modelling scales that evidence into predictive capacity intelligence across the wider network, anticipating how conditions, and therefore capacity, will change ahead of time.
Rather than watching individual lines, GridSight® watches the grid as a whole, modelling each span individually rather than applying one crude standard across the network. In many parts, the capacity is far higher than a static rating assumes.
The outcome.
Measure on critical spans, model digitally across the wider grid, where
scale matters most.
The hybrid deployment gives Litgrid a scalable operational framework for a more variable power system. Energiot’s sensors provide direct field measurements on selected sections giving Litgrid local evidence on priority assets and providing a robust reference for Enline´s model validation.
Enline’s GridSight® DLR estimates and forecasts line ampacity not only on instrumented sections, but across non-instrumented areas of the network as well. This allows Litgrid to extend capacity intelligence beyond the physical footprint of sensors and use it in the day ahead, intra-day and real time operations.
Lower cost.
Capacity unlocked without building new transmission.
Every horizon.
Day-ahead, intraday and real-time decisions on one framework.
Dynamic Line Rating makes it possible to significantly increase line capacity at considerably lower cost than full line reconstruction. In reaching our investment decision, we were able to draw on the results of completed pilot trials.