Critical weather situations for renewable energies – Part A: Cyclone detection for wind power. (February 2017)

• Record Type: Journal Article
• Authors: Steiner, Andrea; Köhler, Carmen; Metzinger, Isabel; Braun, Axel; Zirkelbach, Mathias; Ernst, Dominique; Tran, Peter; Ritter, Bodo
• Is Part Of: Renewable energy. Volume 101(2017)
• Page Start: 41
• Page End: 50
• Publication Date: 2017-02
• Subjects: Wind energy -- Weather dependency -- Cyclone detection -- Net stability -- Transmission system operator; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2016.08.013
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: A constantly increasing share of weather dependent renewable energies in Germany's power mix poses new challenges concerning grid management and security of energy supply. An evaluation of the three year period from 2012 to 2014 reveals, that 60% of days with largest errors in the day-ahead wind power forecasts for Germany are linked to cyclones and troughs traversing the North Sea, the Baltic Sea or Germany. A cyclone detection algorithm has been developed to automatically indicate these critical weather situations. The algorithm is based on Numerical Weather Prediction model forecasts of mean sea level pressure. The cyclone detection is used to design an automated weather information tool for end-users such as Transmission System Operators (TSOs). For 2014, it identified a critical weather development in 38% of all days. The root mean square error of day-ahead wind power forecasts increased by 1% of installed capacity during these periods. A real time application of the tool is being implemented in order to support a sustainable and save integration of the increasing wind power production. It will then be provided to, and will be tested by, three German TSOs with the purpose of an operative usage to guarantee the security of supply. Highlights: Evaluation of day-ahead wind power forecasts for Germany. Large wind power errors are linked to special weather patterns. Automatic cyclone detection to indicate difficult weather patterns.