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This begs the question : Is introducing Ambient Adjusted Ratings as difficult as these delays might make it seem ? The answer is no . In fact , introducing AAR can be achieved in as little as two weeks by integrating new software into the utilities ’ existing infrastructure . However , AAR is just the stepping stone towards a much more effective and future-proof solution : Dynamic Line Ratings ( DLR ). Whatever path a grid operator takes to introduce hourly AAR ratings right now should also support their move toward real-time DLR ratings .

In other words , meeting FERC ’ s AAR requirement by July 25 is only step one on the necessary journey to maximizing the transmission capacity of their existing infrastructure . DLR is the second step .

From ambient adjusted ratings to dynamic line ratings

Nearly every power line today is operated without real-time - or even daily or monthly - insights into the factors influencing power line capacity , such as wind , sun , and ambient temperature . Instead , they ’ re relying on seasonal ratings , which are no more than region-wide estimates based on historical data .

Not knowing what ’ s happening on a specific day , at a specific time , or particular points across their power lines forces companies to be very conservative in how much energy they can transmit through a given line . As a result , they ’ re leaving up to 40 percent of their grid ’ s capacity untapped .

The mandated AAR will , to a limited extent , begin to fill this gap with hourly updates . However , the only way for power grid operators to gain a holistic view of how much capacity each power line has at any given time is through DLR . DLR provides real-time insights into the conditions affecting each line that makes up a grid . This is important , considering that a single line can span different terrains and be exposed to differing weather events .

This was the case with Great River Energy earlier this year .

Minnesota ’ s Great River Energy offers a look at what ’ s possible

In March 2024 , Minnesota-based Great River Energy launched the United States ’ largest DLR project after conducting a study to identify the lines with the highest congestion costs and running a pilot project on the most congested line . It was on that line that Great River Energy installed the first four of Heimdall Power ’ s neurons , colloquially known as ‘ Magic Balls ’ to gain real-time insight into actual capacity and reduce congestion .

Through the sensors , Great River Energy could monitor the effect on capacity from real-time wind conditions as well as current , temperature , and other weather conditions . Heimdall Power ’ s software then leveraged machine learning algorithms to synthesize the data and provide a more accurate estimate of the power lines ’ real transmission capacity . Knowing this , they could

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