The energy sector is highly dependent on climate variability for electricity generation, maintenance activities and demand. In recent years, a few climate services have appeared that provide tailored information for the energy sector. In particular, seasonal climate predictions of wind speed have proven useful to the wind power industry. However, most of the service users are ultimately interested in forecasts of electricity generation instead of wind. Although power generation depends on many factors other than wind conditions, the capacity factor is a suitable indicator to quantify the impact of wind variability on production. In this paper a methodology to produce seasonal predictions of capacity factor for a range of turbine classes is proposed for the first time. The strengths and weaknesses of the method are discussed and the forecast quality is evaluated for an application example over Europe.

This work was funded by the H2020 project S2S4E (GA 776787), the COPERNICUS service contract CLIM4ENERGY (C3S_441_Lot2_CEA), and the projects INDECIS and MEDSCOPE cofunded by the H2020 ERA-net ERA4CS. The research leading to these results has also received funding from the Ministerio de Ciencia, Innovación y Universidades (MICINN) as part of the CLINSA project (CGL2017-85791-R). We acknowledge NOAA/ESRL Physical Sciences Division to provide wind speed records from the Boulder Atmospheric Observatory. Cabauw data have been retrieved from CESAR database and Dr. Fred Bosveld (KNMI). Thanks to Elena Kozlova (University of Exeter) for sharing CVO data. The BMWi (Bundesministerium fuer Wirtschaft und Energie) and the PTJ (Projekttraeger Juelich) provided the FINO1 mast data. We also thank Hans Verhoef (ECN) and Dr. Frank Beyrich (DWD) for sharing Ijmuiden and Lindenberg data, respectively. We acknowledge the providers of the NWTC M2 mast data [26]. Thanks to WASA (Wind Atlas for South Africa) for providing WM01 tall tower data. Long-term mean wind speed data at hub heights was obtained from DTU Wind Energy Global Wind Atlas, funded by Danish Energy Agency EUDP 11-II, Globalt Vind Atlas J.nr. 64011-0347. Authors want to thank Pierre-Antoine Bretonnière for technical support with the datasets.

Peer Reviewed