Limitations of wind power availability over Europe: a conceptual study

Abstract

Abstract. Wind field statistics are evaluated from the ERA-40 data bank covering a period of 44 years with a temporal resolution of 6 h. Instantaneous wind speed values are provided in geographic cells of size 1 • ×1 • (lat/long) for surface (10 m) and 1000 hPa pressure heights. Potential wind power generation is estimated in two steps. Firstly, the wind speed at hub height is approximated from surface data based on the statistical analysis of the wind and geopotential records for 1000 hPa pressure level. Secondly, the wind speed values are transformed by an idealised power curve fitted for measured data. The model time series are fed into various hypothetical electric networks. The main quantity of interest is the aggregated output from the networks. A reference power time series is determined for a static network connecting each continental site and an envelope of 1 • around the shorelines (representing off-shore locations) over Europe. This time series exhibits a low average value and a marked annual periodicity. Wind power integration over limited areas results in higher average outputs at the expense of stronger fluctuations. The long-range spatial correlations of the wind field limit the level of fluctuations strongly which can not be eliminated either by an increase of the area of integration or by dynamic control. This study is fully conceptual, however it demonstrates the limitations of wind power integration over Europe.

Principal Mismatch Patterns Across a Simplified Highly Renewable European Electricity Network

“…The planning of large-scale energy systems is also affected by the spatial variability of the weather. Wind power generation is correlated up to a length scale of about 500 km [4,[14][15][16]. Approaches based on spatial correlations, such as the optimal portfolio theory [17][18][19] and the copula method [20], are used for systemic resource assessments of renewables and for analysis of national and continental power grids.…”

Characterization of wind power resource in the United States

“…Many of these observations are at different heights, and different schemes have been used to adjust the wind speeds to the wind turbine hub heighs. Kiss and Jánosi (2008) used the ECMWF's (European Center for Medium-range Weather Forecasting) ERA-40 (ECMWF ReAnalysis) reanalysis eastward and northward winds at 10 m to study wind field statistics over Europe. Larsen and Mann (2009) also used reanalysis data from NCEP/NCAR (National Center for Environmental Prediction/National Center for Atmospheric Research) to estimate the geostrophic wind U.…”

Large scale integration of wind power – influence of geographical allocation

“…Both [4] and [5] indicate the importance of understanding how wind production varies with wind conditions between different regions. However, Kiss and Jánosi [6] suggest that the distribution of wind power over large areas in Europe will not substantially help decrease the variability in the aggregated wind power output. They found the minimum level of production of 1.1% (of rated power) for the theoretical case of wind power being equally distributed over the entire European continent.…”

“…The correlation is weak (below 0.5) for distances over 200 -500 km. However, the fitness of the correlation coefficient as a measure of the potential of smoothing out tops and downs in the wind system output, may be questioned based on the findings of Kiss and Janosí [6].…”

“…Considering limitations in the wind speed data in the previous work of the authors [5] and the questioning that distributing wind power over large areas will not substantially help decrease the variability of power output [6], it is important to further investigate the influence of geographical allocation of wind power, especially applying wind speed data with as high a resolution as possible. Thus, the aim of this work is to apply wind speed data with a higher resolution as input to the model given in [5].…”