Multi-scale data assimilation, advanced wind model.. (SafeWind)
Multi-scale data assimilation, advanced wind modeling and forecasting with emphasis to extreme weather situations for a secure large-scale wind power integration
Date du début: 1 sept. 2008,
Date de fin: 31 août 2012
The integration of wind generation into power systems is affected by uncertainties in the forecasting of expected power output. Misestimating of meteorological conditions or large forecasting errors (phase errors, near cut-off speeds etc), are very costly for infrastructures (i.e. unexpected loads on turbines) and reduce the value of wind energy for end-users. The state of the art in wind power forecasting focused so far on the "usual" operating conditions rather than on extreme events. Thus, the current wind forecasting technology presents several strong bottlenecks. End-users urge for dedicated approaches to reduce large prediction errors or predict extremes at local scale (gusts, shears) up to a European scale as extremes and forecast errors may propagate. Similar concerns arise from the fields of external conditions and resource assessment, where the aim is to minimize project failure. The aim of this project is to substantially improve wind power predictability in challenging or extreme situations and at different temporal and spatial scales. Going beyond this, wind predictability is considered as a system design parameter linked to the resource assessment phase, where the aim is to take optimal decisions for the installation of a new wind farm. The project concentrates on: using new measuring devices for a more detailed knowledge of the wind speed and energy available at local level; develop strong synergy with research in meteorology; develop new operational methods for the very-short-term for warning/alerting that use coherently collected meteorological and wind power data distributed over Europe to early detect and forecast extreme events; develop models to improve medium term wind predictability; develop a European vision of wind forecasting taking advantage of existing operational forecasting installations at various European end-users. Finally, the new models will be implemented into pilot operational tools for evaluation by the end-users in the project.
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