Open AccessQuantifying the Predictability of a ‘Dunkelflaute’ Event by Utilizing a Mesoscale Model
Author(s) -
Bowen Li,
Sukanta Basu,
Simon Watson,
H.W.J. Russchenberg
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1618/6/062042
Subject(s) - weather research and forecasting model , predictability , meteorology , mesoscale meteorology , overcast , renewable energy , environmental science , north american mesoscale model , benchmark (surveying) , wind power , cloud cover , climatology , event (particle physics) , computer science , cloud computing , global forecast system , geography , engineering , cartography , geology , physics , quantum mechanics , sky , electrical engineering , operating system
In the coming decades, both wind and solar power production will be playing increasingly important roles in Europe’s energy economy. It is absolutely essential that power grids are resilient against any unusual weather phenomena. One such meteorological phenomenon, “Dunkelflaute”, is causing serious concern to the renewable energy industry, which is primarily characterized by calm winds and overcast conditions. For example, a Dunkelflaute event happened in the Netherlands on 30th April 2018 leading to a significant shortfall in renewable energy generation requiring emergency intervention by the system operator. By analyzing this case, this paper investigates the performance of a state-of-the-art mesoscale model, Weather Research and Forecasting (WRF), in forecasting a Dunkelflaute event. Multiple WRF simulations are driven using real-time Global Forecast System (GFS) operational data over a range of prediction horizons. For comparison, a benchmark run is carried out using ERA5 reanalysis data as boundary conditions. Through validation using a variety of measured data covering onshore and offshore areas, wind speed is shown to be more predictable than cloud-cover in this particular case study.