Multi-scale data assimilation, advanced wind modelling and forecasting with emphasis to extreme weather situations for a safe large-scale wind power integration.
The integration of wind generation into power systems is affected by uncertainties in forecasting the expected power output of wind farms. 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 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.
ECMWF is leading a workpackage within the project aiming to develop and evaluate optimized ensemble prediction systems (EPS) applied to wind power prediction. This includes
Further information about the project can be found on the SAFEWIND web site.
- extending ECMWF's existing probabilistic verification suite to include diagnostics with respect to observations,
- evaluating new EPS configurations, with particular emphasis on their benefits for near-surface and extreme event predictions, and
- developing combined meteorological prediction systems based on single high-resolution
deterministic and ensemble forecasts and assess their performance.