Quantifying the Predictability of a 'Dunkelflaute' Event by Utilizing a Mesoscale Model
Bowen Li (TU Delft - Atmospheric Remote Sensing)
Sukanta Basu (TU Delft - Atmospheric Remote Sensing)
Simon J. Watson (TU Delft - Wind Energy)
Herman W.J. Russchenberg (TU Delft - Geoscience and Remote Sensing)
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Abstract
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.
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