Balancing Europe’s wind power

(source: lukasbieri/

Renewable electricity generation is highly dependent on variable weather. This causes variability in power output on different temporal scales. Variability of a few hours to a few days can be managed by storage and flexible demand (intelligent devices). Seasonal variability can be balanced by co-deployment of wind and solar photovoltaics (PV) which have seasonally opposing production potential. However, on the intermediate sub-seasonal time-scales there remains a critical multi-day to multi-week variability that can not be easily addressed by available technology.

In a recent study we show how weather regimes affect this multi-day variability of wind power output across Europe. We further demonstrate that spatial deployment of new wind farms based on this knowledge could effectively balance the critical multi-day variability and yield much more stable wind power output across a wide range of weather situations. This, however, would require a paradigm shift in national planning strategies, large-scale interconnection, and pan-European collaboration.

Grams, C.M., R. Beerli, S. Pfenninger, I. Staffell, and H. Wernli (2017). Balancing Europe’s wind power output through spatial deployment informed by weather regimes, Nature Climate Change,  7 (8), 557-562, doi:10.1038/nclimate3338. Repository version:

A related study discusses the stratospheric influences on wind power output:

Beerli, R., H. Wernli, and C.M. Grams, 2017: Does the lower stratosphere provide predictability for month-ahead wind electricity generation in Europe? Quart. J. Roy. Meteor. Soc., early online, doi:10.1002/qj.3158.

On this page key results are summarized and additional figures for individual countries provided. Use the following links for original information about the study:

Fig. 1: Geopotential height anomalies at 500 hPa attributed to one of the seven regimes, or no regime. Numbers in caption indicate annual frequency. Similar to Supplementary Fig. 1 in Grams et al. (2017) but for all year.

We have combined an extended definition of 7 Atlantic-European weather regimes (Fig. 1) with country-aggregated wind and solar photovoltaics power output from the Renewables.Ninja models for the 31-year period 1985-2015. In contrast to the classical 4 weather regimes valid only in a specific season, the 7 weather regimes reflect seasonal variability and are valid year-round. Three of the regimes are dominated by cyclones in the Atlantic region (Atlantic trough AT, Zonal regime ZO, Scandinavian Trough ScTr) with strong winds in the North and Baltic Seas as well as western Europe. The other four regimes (Atlantic Ridge AR, European blocking EuBL, Scandinavian Blocking ScBL, Greenland blocking GL) are dominated by blocking anticyclones with rather calm weather. However, even during blocked conditions, high windspeeds occur at the flank of the anticyclones. Each six-hourly time steps is attributed to one of the 7 regimes or no regime.

Fig. 2: Regime-dependent relative change in winter wind power output potential with respect to winter mean for selected countries and all of Europe (inset). Fig. 1 of Grams et al. (2017).

The relative change of country-specific wind power potential during different regimes, reveals climatological sub-regions with different regime behaviour (Fig. 2). Countries adjacent to the North and Baltic Seas experience potential for high overproduction of wind power during the cyclonic AT, ZO, and ScTr regimes (violet, red, orange in Fig. 2) while severe underproduction during the blocked regimes prevails. In particular EuBL (light green) is critical with e.g. underproduction of up to 50% in Germany. In contrast Southeastern Europe has high potential for overproduction during the blocked regimes (e.g. Greece > +30% during EuBL). Also Iberia has potential for overproduction during the blocked ScBL and GL regimes. The behaviour for all of Europe (inset) is damped, but dominated by the North Sea region with over-/underproduction of up to +-20% during cyclonic/blocked regimes. Solar PV show much less regime-dependent fluctuations.

Fig. 3: (a-c) Europe-wide winter wind power output (in GW) during different regimes and (d-f) absolute difference to winter mean power output. (a,d) in the current system, (b,e) in the planned system, (c,f) in an alternate system with spatial deployment considering weather regime-dependent wind patterns across Europe. Fig. 4 from Grams et al. (2017).

Current wind farm deployment is biased towards the North Sea region. Therefore already today Europe experiences a difference of mean wind power output of 22 GW between the blocked EuBL and cyclonic AT regimes (Fig. 3 a,d). As future wind farm deployment is also planned predominantly in the North Sea region this will increase to 51 GW in 2030 (Fig. 3 b,d). During weather regime transitions changes in wind power output of more than 100 GW within a few days would occur with such an unbalanced deployment (Fig. 4). However, a spatial deployment exploiting the weather regime-dependent wind patterns could balance wind power output for all of Europe across a wide range of weather situations (Fig. 3 c,f) and stabilise multi-day fluctuations on the manageable level of today (cf. Fig. 3c and 3f) while yielding the same winter mean output as the planned scenario.

Fig. 4. Time series of six-hourly European wind power output in winter 2014/15. Solid lines raw data, bold lines 5-day running mean representing the weather regime time-scales. Wind farms as in the current, planned, and balanced scenarios in black, orange, green. Colors on the x-axis show currently active weather regime.

The balanced scenario assumes new capacity installed in the Balkans, Northern Scandinavia, and Iberia rather than in the North and Baltic Seas.


An exemplary time series of wind power output with weather conditions of winter 2014/15 demonstrates the variability of wind power output in the different scenarios (Fig. 4). In the planned scenario (orange) high fluctuations occur in particular at the onset, transition, and decay of regimes. For instance in mid-January 2015 the end of an AT regime results in a decrease in wind power output of 110 GW within a few days from 150 GW during AT to 40 GW when the regime life-cycle ended.

Fig. 5. Distribution of six-hourly capacity factors of the European wind fleet in the current, planned, and balanced scenarios in winter. Colors indicate distribution for times attributed to a specific regime (solid, cyclonic; dashed blocked regimes). Bold black line shows distribution for the entire season. Fig. 5c-e from Grams et al. (2017).

Such power ramps are difficult to manage by the electricity grid. However, the balanced deployment (green) would stabilise this ramp at about 50 GW, levels already experienced in the current system.

The distribution of six-hourly capacity factors is skewed towards low production and has a tail to high production in the current, and planned systems.  The balanced scenario yields a normal distribution across all regimes with strongly reduced variability and a shift towards higher CF in the blocked regimes (Fig. 5, Supplementary Fig. 15 in Grams et al. 2017).

Our results show that a spatial deployment of new wind farms based on a profound understanding of continent-scale weather regimes can substantially reduce multi-day fluctuations of wind power output irrespective of how the rest of the European power system develops. This, however, would require a paradigm shift in planning strategies, large-scale interconnection, and collaboration for a truly pan-European energy system.

The following table figures link to panels of weather regime-dependent absolute capacity factors (CF) and their relative change (ΔCF) with respect to the seasonal climatology for all of Europe and all individual countries. They are sorted from North to South according to the sub-regions with similar weather regime-dependent fluctuations.

wind winter spring summer autumn
 solar winter spring summer autumn

Below we show the relative change (ΔCF) in power output for selected countries as maps (Supplementary Figs. 4 and 6 from Grams et al. 2017).

Weather regime-dependent relative change in solar PV electrictiy generation
Weather regime-dependent relative change in wind electrictiy generation