IBM introduces new wind and solar forecasting system for utilities; big data analytics and weather modeling to predict output
IBM has developed an advanced power and weather modeling technology that will help utilities increase the reliability of renewable energy resources. The solution combines weather prediction and big data analytics to forecast accurately the availability of wind power and solar energy. This will enable utilities to integrate more renewable energy into the power grid, the company says.
The solution, named “Hybrid Renewable Energy Forecasting” (HyRef) uses weather modeling capabilities, advanced cloud imaging technology and sky-facing cameras to track cloud movements, while sensors on the turbines monitor wind speed, temperature and direction.
When combined with analytics technology, the data-assimilation based solution can produce accurate local weather forecasts within a wind farm as far as one month in advance, or in 15-minute increments, IBM claims.
By utilizing local weather forecasts, HyRef can predict the performance of each individual wind turbine and estimate the amount of generated renewable energy. This level of insight will enable utilities to better manage the variable nature of wind and solar, and more accurately forecast the amount of power that can be redirected into the power grid or stored. It will also allow energy organizations to easily integrate other conventional sources such as coal and natural gas.
State Grid Jibei Electricity Power Company Limited (SG-JBEPC), a subsidiary company of the State Grid Corporation of China (SGCC), is using HyRef to integrate renewable energy into the grid. This initiative led by SG-JBEPC is phase one of the Zhangbei 670MW demonstration project, the world’s largest renewable energy initiative that combines wind and solar power, energy storage and transmission. This project contributes to China’s 5-year plan to reduce its reliance on fossil fuels.
By using the IBM wind forecasting technology, phase one of the Zhangbei project aims to increase the integration of renewable power generation by 10%. This amount of additional energy can power roughly more than 14,000 homes. The efficient use of generated energy allows the utility to reduce wind and solar curtailment while analytics provides the needed intelligence to enhance grid operations.
This project builds upon another IBM smarter analytics initiative at Denmark’s Vestas Wind Systems. Vestas, together with IBM’s big data analytics and supercomputing technology, is able strategically to place wind turbines based on petabytes of data from weather reporters, tidal phases, sensors, satellite images, deforestation maps, and weather modeling research. This insight cannot only deliver improvements in energy generation but also reduce maintenance and operational costs over the life of the project.
The Hybrid Renewable Energy Forecaster represents advancements in weather modeling technology, stemming from other fundamental innovations such as Deep Thunder. Developed by IBM, Deep Thunder provides high-resolution, micro-forecasts for weather in a region—ranging from a metropolitan area up to an entire state—with calculations as fine as every square kilometer. When coupled with business data, it can help businesses and governments tailor services, change routes and deploy equipment-to minimize the effects of major weather events by reducing costs, improving service and even saving lives.