Siemens continues to invest in wind energy R&D to …

Siemens Energy has entered into a cooperative research and development agreement (CRADA) with Lawrence Livermore National Laboratory (LLNL) to conduct atmospheric modeling research that is expected to help operators and owners operate wind farms more efficiently, while providing more power to power grids.

Under the CRADA that will span two years, Livermore will provide high-resolution, numerical weather prediction models to forecast power generated by the wind. Siemens will translate Livermore’s forecasts of wind speed and wind direction at each turbine into power collected.

Many U.S. wind parks are yielding up to 20 percent less energy than predicted because of uncertain forecasts. More accurate wind predictions will enable wind farm operators and owners to know hours or days ahead of time how wind conditions will affect power generation.

“Accurate and timely forecasts of power availability will enable turbine owners and operators to generate optimal bids on wind turbine production and, in turn, maximize both financial benefit and grid support,” said Henrik Stiesdal, Chief Technology Officer of Siemens Wind Power. “More accurate predictions also could reduce the investment risks in wind-powered projects and could improve the design of tall wind turbines to withstand the high-turbulence environment higher in the atmosphere.”

A recent study of 3,300 megawatts of wind generation in New York state performed by a member of the industry quantified improved forecasting to be worth $125 million a year to that region. Based on a conservative application of this figure, Stiesdal estimates that wind farm owners may be able to increase revenue by as much as 10 percent, making wind power more profitable and ultimately reducing the cost of energy.

“Knowing the certainty of the forecast can be useful in a day-ahead or futures market where now there are penalties for underperformance,” said Julie Lundquist, a Livermore atmospheric scientist who is heading the project. “At LLNL, we have developed improved methods for simulating the turbulent properties of the lower atmosphere, which we think will translate into a significant predictive advantage for wind energy applications.”

Siemens also established its first U.S. wind turbine R&D competence center in Boulder, Colorado, in 2008 and entered into a CRADA for the installation of a Siemens 2.3-MW pilot wind turbine with a 101-meter rotor at the National Wind Technology Center (NWTC) location south of Boulder. The company will test basic wind turbine characteristics and verify new performance-enhancing features and turbine reliability under severe weather conditions over a minimum period of three years.

In 2007, Siemens signed an agreement with the Technical University of Aachen (RWTH) to cooperate on drive train R&D, with the aim of increasing efficiency, reliability and the service life of wind turbines. The research focuses on a number of areas, including gear box design, fabrication and analysis, and bearings. This agreement marked the third cooperation project between a university and Siemens Energy’s Wind Power business unit.

Siemens has established core competence centers for wind turbine R&D in Copenhagen (Denmark), Aachen (Germany), Delft (Netherlands) and Keele (United Kingdom) and Boulder, Colorado (USA).

The Siemens Energy Sector is the world’s leading supplier of a complete spectrum of products, services and solutions for the generation, transmission and distribution of power and for the extraction, conversion and transport of oil and gas. In fiscal 2008 (ended September 30), the Energy Sector had revenues of approximately EUR22.6 billion and received new orders totaling approximately EUR33.4 billion and posted a profit of EUR1.4 billion. On September 30, 2008, the Energy Sector had a work force of approximately 83,500.

Siemens AG
Press Office Energy
Alfons Benzinger
Tel. +49 9131/18-7034
Fax: +49 9131/18-7039
mailto:alfons.benzinger@siemens.com
Reference Number: ERE 200902.027 e

Media Contact

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http://www.siemens.com/energy

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