Technology to Drive the Future of Renewable Energy was Developed from IBM Labs.

Technology to Drive the Future of Renewable Energy was Developed from IBM Labs. The company announced advanced power and weather modeling technology that will help increase the reliability of renewable energy sources. This solution combines weather forecasting and analytics to accurately predict the availability of wind power and solar power. This will provide an opportunity for utilities to integrate more renewable energy into the electricity grid, significantly improving clean energy for consumers and businesses, and reducing carbon emissions.

Called Hybrid Renewable Energy Forecasting (HyRef) – Hybrid Renewable Energy Forecasting – it uses weather-facing cameras, advanced cloud imaging technology and weather model capabilities to track the movement of clouds, while receivers in turbines monitor wind speed, temperature and direction. Combined with analytics technology, this data assimilation-based solution can accurately generate regional weather forecasts at a wind farm one month ahead, in increments of 15 minutes. Using regional weather forecasts, HyRef can predict the performance of each wind turbine and calculate the amount of renewable energy produced. Forecasts at this level will enable utilities to better manage the changing nature of wind and sun and more accurately predict the amount of power that can be directed or stored into the electrical grid. It will also allow energy organizations to easily integrate other sources such as coal and natural gas.

IBM Global Business Services Energy and Utilities Industry Leader Serhan Özhan said: “Using big data by applying analytical methods will help energy and infrastructure service organizations to get rid of the continuity problem of renewable energy and to use the energy to be produced from the sun and wind, in an unprecedented way. will enable them to foresee. We have developed an intelligent system that combines weather and energy forecasting to increase the availability of renewable systems and optimize power grid performance.”

State Grid Jibei Electricity Power (SG-JBEPC), a subsidiary of the China State Grid Corporation (SGCC), is among the companies using the HyRef solution to integrate renewable energy with grids. This initiative by SG-JBEPC represents the first phase of the Zhangbei 670MW demonstration project, the world's largest renewable energy initiative combining wind and solar power, energy storage and transmission. The first phase of the Zhangbei project, which uses IBM's wind forecasting technology, aims to increase renewable energy generation integration by up to 5 percent. This additional amount of energy means a rate that can power over 10 homes. Efficient use of generated energy enables utilities to reduce wind and solar constraints, while analytics provide the additional intelligence needed to improve grid operations.

This project is actually based on another IBM initiative implemented at Vestas Wind Systems of Denmark, the world's manufacturer of wind power turbines. Vestas, using IBM's big data analytics and super computing technology, wind turbines; Weather reporters can strategically position them based on petabytes of data from sources such as tidal phases, sensors, satellite imagery, deforested area maps and weather modeling surveys. These ingredients not only provide improvements in energy production, but also reduce maintenance and operational costs throughout the project.

The Hybrid Renewable Energy Forecaster represents a new step in weather modeling technology, similar to the Deep Thunder project previously developed by IBM, which provides high-resolution, micro-scale weather forecasts for a region ranging in size from a metropolis to an entire country with detailed calculations. Combined with business data, it can help businesses and governments change the way they serve specific situations and purposes, and deploy equipment to minimize the adverse effects of major weather events to reduce costs and even save lives.

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Source :technologyread

📩 16/09/2013 22:57

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