Accurate line ratings with the example of Texas

Image Author

Georg Rute, CEO

Image Blog

The cost of grid congestion in the US was $20 billion in 2022, $3 billion of which occurred in Texas (source). The grid is under increasing stress. It is already commonplace that zero marginal cost renewable energy is curtailed because the grid cannot transport the power. This raises prices for consumers next door, where fuel-burning power plants are kept in operation.

Yet in fact, much of this congestion is artificial and self-imposed due to the methods that grid operators use for calculating grid capacity. The existing grid could transport on average a third more power, if operators accounted for the power line cooling effect from actual air temperature and especially wind. A third more capacity would significantly alleviate congestion and reduce energy prices, but this must be done in a safe and secure manner.

Zooming in on a power line near San Antonio

Below you can see a map showing most of the high-voltage power lines in Texas (top left corner, image below). Each line is color coded according to its capacity over the next 48 hours, depending on actual weather conditions (from HRRR). Blue means that the line is experiencing little wind and high temperatures. Yellow means the line has in fact roughly twice the capacity compared to what the grid operators use today. The top right corner shows a selected power line near San Antonio, and the bottom graph shows the actual capacity of this line, calculated according to three different approaches.

Screenshot showing power lines in Texas (top left), a specific line near San Antonio (top right), the parameters used in our calculations (list, top right) and the line ratings over 48 hours calculated according to the three methods (bottom). Static ratings are calculated by assuming an outside temperature of around 110F, full direct sunshine and 0.61 m/s wind. Ambient ratings account with the actual outside air temperature from the weather forecast. Dynamic line ratings (DLR) account with the wind forecast as well. When it's windy the line capacity is doubled, yet when the wind is completely still, like towards the right side of the graph, the static ratings run a safety risk. If the line would be fully loaded 45 hours from now then it would risk overheating and sparking a wildfire.

Traditionally, power lines have a static rating throughout the year, which is calculated by assuming the conditions of a hot summer day with no wind. While this approach has served us very well in the past it's no longer sufficient, since the energy system is rapidly changing.

Ambient adjusted ratings use the actual forecasted air temperature as a key input. It's relatively straightforward to predict air temperature with an accuracy of a few degrees. This is a step in the right direction, but the additional benefit is marginal, as can be seen in the case of the power line near San Antonio. The green line (ambient ratings) is often not much above the orange line (static ratings).

The greatest benefit comes from the wind cooling effect. Dynamic line ratings use the wind forecast and often lead to twice the capacity (the dark green line). But it's because of the difficulty of accurately predicting wind that dynamic ratings are not yet widely used.

Reduce capacity when there’s no wind at all

Notably, dynamic line ratings are also safer. In 45 hours' time after I'm writing this blog post, the wind will be completely still near San Antonio. If this power line happens to be fully loaded at this time, then there would be a risk of overheating of the conductor. It might then sag too close to the ground and if something happens to be underneath then there's a risk of electrocution or sparking a wildfire. During such hours line capacity should be reduced to ensure safety.

The technology for implementing accurate line ratings is available today. This helps ensure the safety of our network, while at the same time increasing grid capacity, allowing more clean power onto the grid and reducing energy costs.

Read more about Grid Raven's AAR solution here:

Texas map from Wikimedia

By clicking “Accept All Cookies”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts. View our Cookie PolicyPrivacy Policy for more information.