A quarter of 330 kV lines limit transmission capacity between Estonia and Latvia. Further, within each of those lines, nearly every span of each line might be limiting at some point in time. Dynamic Line Ratings need to cover a quarter of all spans in the network to maximize transmission capacity.
James Pearman, our data science intern, explores the inner workings of the deep learning models for downscaling wind. The model accounts with local features in the landscape one after the other, increasing or decreasing wind speeds in a 'white box' manner. Features of the terrain as well as air pressure appear to have the greatest effects on wind speed.
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We are happy to welcome Mari and Ingvar to the team! Mari joins as our Senior Power Systems Expert and Ingvar joins as a Data Scientist.
Grid Raven has been awarded €1.5 million by Enterprise Estonia. This funding will support hiring eight people into our technical team in Tallinn who will further refine our ML wind prediction and the digital twin of the grid.
Grid Raven is accepted into the European Space Agency Business Incubator. During the year-long programme we'll collaborate with space experts to leverage satellite data for improving weather forecasting models.
We are happy to welcome Mari and Ingvar to the team! Mari joins as our Senior Power Systems Expert and Ingvar joins as a Data Scientist.
Grid Raven has been awarded €1.5 million by Enterprise Estonia. This funding will support hiring eight people into our technical team in Tallinn who will further refine our ML wind prediction and the digital twin of the grid.
Grid Raven is accepted into the European Space Agency Business Incubator. During the year-long programme we'll collaborate with space experts to leverage satellite data for improving weather forecasting models.
Grid Raven and the Estonian Environment Agency, which is responsible for weather forecasting, have signed a cooperation agreement aimed at exploring the potential of machine learning for downscaling wind predictions.
A quarter of 330 kV lines limit transmission capacity between Estonia and Latvia. Further, within each of those lines, nearly every span of each line might be limiting at some point in time. Dynamic Line Ratings need to cover a quarter of all spans in the network to maximize transmission capacity.
James Pearman, our data science intern, explores the inner workings of the deep learning models for downscaling wind. The model accounts with local features in the landscape one after the other, increasing or decreasing wind speeds in a 'white box' manner. Features of the terrain as well as air pressure appear to have the greatest effects on wind speed.
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.
It takes increasingly longer for new energy projects to connect to the grid. Already, 3,000GW of renewable capacity is awaiting a grid connection, which would double the world's renewable capacity. Making better use of the existing grid would enable more clean power projects to connect to the grid quicker.
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.
Increasing grid capacity reduces the price of energy, but only if this additional capacity is available for tomorrow, in step with energy markets. DLR forecasting is required to reduce prices for consumers and accelerate the energy transition.
Power grids are starting to become a bottleneck in the transition to clean energy. Dynamic Line Rating (DLR) is a mature technology that can unlock up to 30% more capacity from the existing grid. Grid Raven is improving Dynamic Line Rating (DLR) technology by making it more accurate, resilient and scalable.
The Grid Raven Team will be attending the 2024 IEEE PES T&D Conference in Anaheim, California, May 6-9. If you will be there as well please let us know and try meeting up with our team!
The Grid Raven Team is headed back to the USA to attend the CEATI Strategy and Innovation Conference May 14-15, in Minneapolis.
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.
