IBM has developed an advanced power and weather modeling technology to help utilities integrate renewable energy into the power grid more reliably.
The new technology combines weather prediction and analytics to accurately forecast the availability of wind and solar energy.
The solution, Hybrid Renewable Energy Forecasting (HyRef) uses weather modeling capabilities, advanced cloud imaging technology and sky-facing cameras to track cloud movements.
For wind power applications, sensors on the turbines monitor wind speed, temperature and direction.
By using local weather forecasts, IBM said that HyRef can predict the performance of each individual wind turbine and estimate the amount of generated renewable energy.
IBM claimed that the 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.
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 power grid.
The initiative led by SG-JBEPC is phase one of the Zhangbei 670MW demonstration project, a renewable energy project that combines wind and solar power, energy storage and transmission.
The project aims to increase the integration of renewable power generation by 10% with the help of IBM wind forecasting technology.
IBM Global Energy and Utilities Industry general manager Brad Gammons said: "Applying analytics and harnessing big data will allow utilities to tackle the intermittent nature of renewable energy and forecast power production from solar and wind, in a way that has never been done before.
"We have developed an intelligent system that combines weather and power forecasting to increase system availability and optimise power grid performance."
The 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 to strategically place wind turbines based on petabytes of data from weather reporters, tidal phases, sensors, satellite images, deforestation maps and weather modeling research.