Understanding and forecasting the subseasonal meteorological drivers of the European electricity system in winter

Renewable electricity is a key enabling step to globally decarbonise the energy sector. Europe is at the forefront of renewable deployment and this has dramatically increased the weather sensitivity of the continent's power systems. Despite the importance of weather to energy systems, the meteorological drivers remain difficult to identify, and are poorly understood. This study presents a new and generally applicable approach, targeted circulation types (TCTs). In contrast to standard meteorological circulation typing methods, such as weather regimes, TCTs convolve the weather sensitivity of an impacted system of interest (in this case, the electricity system) with the intrinsic structures of the atmospheric circulation to identify its meteorological drivers.

A new, freely available, 38 year reanalysis-based reconstruction of daily electricity demand, wind power and solar power generation across Europe is created and used to identify the winter large‐scale circulation patterns of most interest to the European electricity grid. TCTs are shown to provide greater explanatory power for power system variability and extremes compared with standard weather regime analysis. Two new pairs of atmospheric patterns are highlighted, both of which have marked and extensive impacts on the European power system. The first pair resembles the meridional surface pressure dipole of the North Atlantic Oscillation, but shifted eastward into Europe and noticeably strengthened, while the second pair is weaker and corresponds to surface pressure anomalies over Central Southern and Eastern Europe. These patterns are shown to be robust features of the “present-day” European power system.

 The use of TCTs to increase the utility and skill of subseasonal forecasts during the winter season is discussed.  It is shown that TCTs provide additional useful information compared to standard “grid-point” or weather-regime techniques for applications in energy system forecasting and operations.