GE Vernova Acquires Greenbird to Accelerate GridOS Innovation and Help Utilities Reduce the Complexity of Energy Data Integration - Seite 2

“Having access to utility data in context gives grid operators an opportunity to better leverage AI for automation and potentially enables the grid to be self-describing and self-healing in the future,” said Mahesh Sudhakaran, General Manager, Grid Software at GE Vernova’s Digital business. “This is the vision we have for grid orchestration enabled by the GridOS portfolio, and this acquisition provides both the technology and the talent to help us get there.”

“We started in 2010 with a mission to simplify data integration for utilities, helping them leverage data to run smarter, more efficient analytics-driven services to accelerate the energy transition,” said Thorsten Heller, CEO of Greenbird Integration Technology. “Our technology platform is purpose-built to support a distributed data and IT architecture and aligns perfectly with GE Vernova’s vision for the GridOS federated grid data fabric and one network model, providing the data foundation utilities need to transform their operations.”

An example of the need for more connected and integrated data is evident in the exponential growth predicted for electric vehicles (EVs) – there are expected to be 77M EVs on the road by 2025 and 229M by 2030 – that will both draw from and possibly contribute to the grid as “rolling batteries” that can be tapped when demand is high and supply is low. Integration of data from multiple sources like charging stations as well as operations, forecasting, billing, and other systems can support the success of future use cases such as vehicle-to-grid (V2G) while keeping the grid safe.

Data integration is also key to solving renewables connection challenges. Approximately 1,350 gigawatts of additional, mostly sustainable power capacity is waiting to be constructed and connected to the grid. This large-scale interconnection backlog results in wasted capacity and unutilized investment. Scenario planning and grid simulations can provide an opportunity to increase line capacity and drive deeper visibility into the impact of such assets on the grid, allowing capacity to be connected faster and helping to reduce hundreds of millions of dollars of traditional back-up generators. Such use cases require the integration of forecasting, simulation, historical grid OT, sensor, line, and inertia data.