GE announced the availability of three new grid analytics that combine domain knowledge with artificial intelligence (AI) and machine learning (ML) to help solve challenges in electric grid operations. The analytics use data from across transmission and distribution networks to help achieve greater operational efficiency. The portfolio includes:
- Storm Readiness utilizes high-resolution weather forecasts, outage history, crew response and geographic information system (GIS) data to more accurately forecast storm impact and prepare response crews and equipment ahead of impending weather. GE’s Storm Readiness analytics helps to reduce outage restoration time, predict future outages, reduce operational spend and improve crew safety.
- Network Connectivity helps to correct and maintain network data integrity. Data errors, which often arise due to manual input of information at the user or equipment level, can hinder emergency and outage response and lead to poor customer experiences. GE’s Network Connectivity algorithms use GIS and other operational system data to help detect, recommend and correct pervasive errors. With better data, utilities can more efficiently dispatch crews, helping to reduce outage restoration time and avoid incorrect outage notifications to customers.
- Effective Inertia provides enhanced visibility into transmission system operations. The operation of transmission networks continues to grow in complexity in part due to the influx of renewable generation. This has led to a massive displacement of “system inertia,” or the resiliency of power generation, given spikes in customer demand or reduced supply, due to unforeseen decreases in wind or sunlight. Ineffective management of a transmission system could result in blackouts and major financial and reputational penalties. GE’s Effective Inertia analytic uses ML to help to facilitate the measurement and forecasting of system inertia and help to enable a more stable grid.
The new grid analytics are connected via GE’s common Digital Energy data fabric. Unifying data on a scalable platform can help to drive efficiencies, allowing data stored in one location to be utilized by multiple solutions across the energy value chain, from generation to consumption. Users can potentially realize a network-effect of value, where improvements from one application amplify the benefits of another.