FERC's intention with Ambient Adjusted Ratings is to increase grid capacity. However, a gain in capacity is not always guaranteed. Utilities have been optimizing their grids for decades and may have been knowingly using optimistic assumptions in the past. Dynamic Line Ratings are a more customized solution, helping increase capacity and maintain safety.
Just like the proverbial chain an entire transmission line can be limited by its weakest link. But identifying the weakest link is not trivial, since it depends on the weather. Dynamic Line Rating solutions must account with the wind in each span individually.
The cost of grid congestion in Texas in 2022 was $3 billion. But much of this congestion is artificial and comes from outdated line rating methods. Dynamic Line Ratings increase capacity by a third on average, helping bring down energy prices for consumers. But as the example of a power line near San Antonio shows, there are also many hours in a year when line ratings should be reduced for safety reasons.
Safety is the number one concern for all transmission service providers. Unfortunately accidents do happen despite the best efforts to prevent them. One of the factors that contributes to accidents is the fact that transmission lines may dangerously overheat on hot, sunny, windless days. Accurate wind forecasting helps reduce this risk.
We validated our prediction accuracy against measurements from Estonia's official weather stations. Grid Raven's machine learning model improves wind speed forecast accuracy by 39% compared to the existing forecast. Higher accuracy leads to increased safety and efficiency in Dynamic Line Rating applications.
Grid Raven's goal is to help increase grid transmission capacity by 30% annually on a global scale by 2030, which could bring the energy transition forward by a decade. The power grid is becoming a bottleneck in the energy transition, but the existing network is not yet used to its maximum potential. Grid Raven offers Dynamic Line Ratings, a Grid Enhancing Technology, based on our machine learning weather forecasting model. This enables grid operators to have a precise overview of the conditions of their network and helps manage the amount of power that the grid can really handle at a given time.
