Published on 03-Dec-2013
A power grid company
Energy & Utilities
Several government agencies collaborated with China’s leading power grid operator to create the country’s most significant energy demonstration project, integrating wind power generation, solar voltaic power generation, power storage and intelligent power transmission into the power infrastructure. The project was assigned to one of five regional subsidiaries of China’s largest power grid operator, a state-owed supplier of power to one of the most populous cities in the country and most heavily industrialized regions.
To effectively integrate unpredictable wind and solar energy into the power grid, this utility sought a way to reliably forecast weather patterns.
The company collaborated with IBM® to develop an advanced analytics solution to maximize the aggregation of renewable energy to the grid.
The IBM solution helped reduce wind and solar energy forecast errors and increases wind-solar energy transmission to the electrical grid by 10 percent.
With energy demand expected to increase almost 6 percent per year through 2020, China seeks innovative solutions to hasten the development and use of renewable energy. Several government agencies collaborated with China’s leading power grid operator to create the country’s most significant energy demonstration project, integrating wind power generation, solar voltaic power generation, power storage and intelligent power transmission into the power infrastructure. The project was assigned to one of five regional subsidiaries of China’s largest power grid operator, a state-owed supplier of power to one of the most populous cities in the country and most heavily industrialized regions.
The intermittent nature of weather-dependent wind energy makes it a less predictable source than coal or oil, forcing the electric grid to continually adjust the volume of wind-generated energy it can absorb. If winds slow during peak consumption periods, utilities transfer to conventional power plants to supplement grid demand. If winds gust during nonpeak consumption periods, more energy enters the grid than transmission lines can accommodate. Overloading creates brownouts, blackouts and service disruptions. Absent a way to predict and plan for the occurrence of wind energy, only a third of this renewable resource output can be transmitted to the electrical grid. Thus government mandates require that all new power plants include wind power forecasting as an essential component of wind energy generation and transmission. This power grid operator in China sought to reliably predict wind behavior to transform it into a consistent, predictable source of electricity.
In partnership with IBM®, the organization developed a First-of-a-Kind renewable energy forecasting solution to maximize the use of wind and solar energy. Built on an integrated platform of IBM System x® and IBM Power Systems™ servers running on the IBM AIX® operating system, the hybrid data assimilation-based renewable energy forecasting solution (HyREF) gathers and analyzes near-real-time data from multiple sources including more than 60 wind turbines, hundreds of solar panels, cloud imaging technology and the state weather bureau. Data about wind strength, speed, temperature, humidity level and direction is gathered by sensors on turbines and transmitted to the HyREF solution every five minutes, combined with meteorological data from weather bureau and satellites, and transmitted through the cloud to the IBM platform through the Supervisory Control and Data Acquisition (SCADA) system. Finally it is processed by IBM DB2® and IBM SPSS® software for data analysis and modeling.
The solution develops high-accuracy, high-resolution weather forecasting models, turbulence analysis and wake models to accurately predict wind and solar power generation. Built with DB2, IBM SPSS Categories and IBM Intelligent Operations Center technology on an IBM SmartCloud® platform, the solution enables the company to make near-real-time amendments based on sensor-collected data to revise wind-solar power output forecasts, improving readiness for low and peak periods, thus increasing renewable energy generation efficiency.
• Reduces wind and solar energy forecast errors to less than 8 percent, which is less than the 25 percent required by government mandate
• Anticipates a 10 percent increase in sales of renewable energy, equivalent to RMB3 million per years
• Increases wind-solar energy transmission to the electrical grid by
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Products and services used
IBM products and services that were used in this case study.
Power Systems, System x: System x running Linux - Other
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