Characterizing the winter meteorological drivers of the European electricity system using targeted circulation types

Abstract

Renewable electricity is a key enabling step in the decarbonization of energy. 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, and widespread interest from both academia and industry, the meteorological drivers of European power systems remain difficult to identify and are poorly understood. The present study presents a new and generally applicable approach, targeted circulation types (TCTs). In contrast to standard meteorological weather‐regime or circulation‐typing schemes, 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 38 year reconstruction of daily electricity demand and renewable supply across Europe is used to identify the winter large‐scale circulation patterns of most interest to the European electricity grid. TCTs provide greater explanatory power for power system variability and extremes compared with standard meteorological typing. 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 (NAO), 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. While these gross qualitative patterns are robust features of the present European power systems, the detailed circulation structures are strongly affected by the amount and location of renewables installed.

Comment on essd-2020-312 Reviewer 1

“…Information regarding the spatial distribution and installed capacity of wind turbines is taken from thewindpower.net database. Validation of this model on the native ERA5 grid is available in Bloomfield et al (2020b). The models perform well compared to others in the literature, with an average daily R 2 of 0.91, and average percentage error of 10% when validated against data from ENTSOE (2018), see Appendix B for further details.…”

“…Wind power is estimated using a physically-based model driven by hourly 100m wind speeds from the ERA5 reanalysis. Prior to describing the wind-power calculation, it is first noted that the ERA5 100m wind speeds display substantial mean-biases compared to leading wind-resource assessment datasets such as the Global Wind Atlas (as previously discussed in Bloomfield et al (2020b)). A mean bias correction is therefore applied on a grid-point basis prior to conversion into wind power, as small initial wind speed biases can lead to large differences in wind power generation due to non-linearities in the chosen wind turbine power curves.…”

Seasonal predictability of Mediterranean weather regimes in the Copernicus C3S systems

“…Cipolla et al (2020), Zhang and Villarini (2019) in the Mediterranean. For their societal and economic relevance particular focus should be given to extreme events like extreme dry spells (Raymond et al 2016) and meteorological droughts (Richardson et al 2020), assessing the ability of seasonal forecasting ensembles to forecast them (Bloomfield et al 2020). In this context, state of the art seasonal prediction systems like the ones that belong to the Copernicus C3S, provide a valuable data set that is constantly updated for inter-model comparisons; their currently moderate seasonal predictive skill will benefit from a continual effort in adopting common verification diagnostics (Doblas-Reyes et al 2013).…”

Sub-seasonal forecasts of demand, wind power and solar power generation for 28 European Countries

“…converts meteorological data to energy variables at the highest possible spatial and temporal resolution available. As these conversion methods represent only minor modifications to methods that have been presented before at length in Bloomfield et al (2020b), readers are referred to Appendices A-C for a full description of how hourly time series of demand, wind and solar power are created. After these have been calculated demand can be subtracted from wind power generation to obtain demand-net-wind.…”