Wind power predictability assessment from large to local scale

Large wind power penetration and efficient wind farm operation require early consideration of the impact of wind power predictability from site to regional/country and continental levels. The aim of this work is to explore the added value of introducing wind power predictability in the wind resource assessment phase for large scale spatial planning and for wind farm performance evaluation. To this end, historical wind data are converted to virtual time series of wind power production on which forecasting models operate in hindcast mode to estimate predictability information. This information can be used to anticipate operational costs and lead to a better assessment of the cost-benefit of wind energy deployment. Numerical weather prediction models give us the opportunity to evaluate wind power predictability and other forecast skills. The COSMO-EU mesoscale model wind speed data is used to create a spatial dataset of different forecast skills and visualize those in form of European predictability maps at a resolution of 7×4 km. In addition to a large capacity factor, low wind variability is also desirable for smooth wind power integration. A smoothing factor determines the areas that benefit most from wind farm aggregation resulting in increased predictability and reduced variability. These forecast skill maps provide useful information for spatial planning and can be analyzed to determine the sources of forecasting errors, either by topographical effects or meteorological phenomena. At site level, wind measurements from a reference mast are converted into wind farm production time series making use of a microscale flow model. Then CENER's LocalPred wind power forecasting model is run, emulating operational conditions to assess predictability. A test case comprising two wind farms in Spain of different terrain complexity, equipped with measurements from the resource assessment and the operational phases, is presented to illustrate a methodology for local predictability assessment. Low wind power predictability is certainly a good indicator of quality-of-energy for grid integration. Hence, this type of information should be included during the development phase of a wind farm as complementary information about its performance. In conclusion, guidelines on how to produce and evaluate wind power predictability during the planning phase are provided in order to advise end-users on the use of these new layers of information.