Sustainable energy transportation
Immediate results
100% adaptable to high and low demand cycles
Maximum Efficiency, Minimal Environmental Impact
Smarter, more sustainablefor a brighter future.
Calculation of power line temperature
The most direct, most economical alternative with the lowest environmental impact.
Most power line infrastructure operates at less than half capacity.
The emergence of renewables implies a change in operations. Aging infrastructure and operations. Large investments.
Expand the capacity of their transmission systems without raising the budget or putting their existing infrastructure at risk. The need for rapid implementation contrasts with lengthy planning periods that typically take years.
Boosting Efficiency and Slashing Costs
How do we do it?
- Device management
- Remote configuration Information
- Updating and customizing views
- Visualization
Use of Real-Time Weather Data.
Among all environmental conditions, wind has the highest impact on line ratings.
Furthermore, it is a key factor in the health of towers and power lines, as well as in network maintenance planning.
and Digital Twin
It will allow us to identify the most decisive areas of the DLR in the entire section and will serve as a reference for the ampacity calculations that allow us to know the flow of electrical energy that can be injected into the infrastructure.
It will be possible to visualize the current DLR as well as the predicted DLR for the next 72 hours, which will serve as a reference for the client to plan the energy transfer.
An advanced method for calculating power line transport capacity in real-time. Analysis of various environmental and operational variables, such as conductor temperature and ambient conditions.
This approach allows for more efficient and optimized management of power grids, adapting to changing conditions and demand at any given time.
DLR (Dynamic Line Rating)
grid360: IoT and AI together
in one space
To demonstrate how the WRF model can be enhanced using local-data algorithms, Libelium conducted several studies of the meteorological conditions affecting power lines.
This one performed an alaysis of wind speeds during May 2023. Data from three sources—local stations, WRF, and enhanced-WRF—were compared:
Local Station Weather Information: A precise environmental monitoring system was installed at the study location. The measurement devices used were the sensors included in this proposal. Data was collected throughout the month at 15-minute intervals, but for comparison and representation purposes, hourly data was used.
WRF Model Information: Data was obtained from the WRF model with the same time intervals as mentioned above. This data is represented in green in the graph.
grid360 Solution Information: The data obtained from the grid360 solution is displayed in red.
To obtain the grid360 data as an improvement over the WRF model, this model was used as a reference but calibrated with historical data from the local station at the specific study location.
The results showed the enhanced WRF forecast model had a lower Mean Absolute Error (MAE) of 0.48, indicating a better fit to the observed data.
Key Benefits
Libelium's grid360 solution for Energy Transportation Efficiency
Success Story
Enhancing Energy Transport Efficiency by Up to 30% with IoT and Digital Twins
Is the existing infrastructure capable of absorbing and transmitting this new energy? How can a traditional infrastructure cope with the volatility of weather-dependent energy?
We are here to help you with your project
If you’d like to learn more about how Libelium can benefit your city, contact us.
Global Senior Sales Manager, Critical Infrastructures
Trusted by companies of all industries and sizes
More than 18 years of experience in IoT support us.
