Critical weather situations for renewable energies – Part B: Low stratus risk for solar power. (February 2017)

• Record Type: Journal Article
• Authors: Köhler, Carmen; Steiner, Andrea; Saint-Drenan, Yves-Marie; Ernst, Dominique; Bergmann-Dick, Anja; Zirkelbach, Mathias; Ben Bouallègue, Zied; Metzinger, Isabel; Ritter, Bodo
• Is Part Of: Renewable energy. Volume 101(2017)
• Page Start: 794
• Page End: 803
• Publication Date: 2017-02
• Subjects: Low stratus -- Fog -- Photovoltaic -- Power forecast -- Numerical weather prediction -- Solar radiation; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2016.09.002
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: Accurately predicting the formation, development and dissipation of fog and low stratus (LS) still poses a challenge for numerical weather prediction (NWP) models. Errors in the low cloud cover NWP forecasts directly impact the quality of photovoltaic (PV) power prediction. On days with LS, day-ahead forecast errors of Germany-wide PV power frequently lie within the magnitude of the balance energy and thus pose a challenge for maintaining grid stability. An indication in advance about the possible occurrence of a critical weather situation such as LS would represent a helpful tool for transmission system operators (TSOs) in their day-to-day business. In the following, a detection algorithm for low stratus risk (LSR) is developed and applied as post-processing to the NWP model forecasts of the regional non-hydrostatic model COSMO-DE, operational at the German Weather Service. The aim of the LSR product is to supply day-ahead warnings and to support the decision making process of the TSOs. The quality of the LSR is assessed by comparing the computed regions of LSR occurrence with a satellite based cloud classification product from the Nowcasting Satellite Facility (NWCSAF). The results show that the LSR provides additional information that should in particular be useful for risk adverse users. Highlights: Evaluation of day-ahead solar power forecasts for Germany. Large solar power errors are linked to low stratus clouds. Low stratus risk algorithm developed and verified to indicate forecast uncertainties.