• Energy Research

Wind energy in Germany has experienced high growth rates over the last few years. The set political target in the German federal state of Baden-Wuerttemberg is to raise the share of wind energy in the overall electricity supply to 10% by 2020. To achieve this goal, detailed information on wind energy potential in Baden-Wuerttemberg is necessary. This study assesses the geographical wind energy potential (GP) in Baden-Wuerttemberg giving a guideline to identify suitable locations for wind energy utilization. The focus of this investigation lies in assessing GP for the mean annual meteorological wind energy potential (MP) as well as for the mean MP in December and August providing information on the seasonal behavior of wind power availability. A GIS-based approach is employed to identify sites without geographical restrictions and with sufficient MP at hub heights of 100 m, 140 m, and 200 m. The study finds that (1) the number of possible sites for wind energy utilization is strongly limited by geographical restrictions, (2) GP is highly dependent on MP and, therefore, (3) GP varies highly throughout a year since MP depends on the seasonal pattern of wind speed in Central Europe, showing high values in winter and low values in summer.

Abstract In a large number of previous studies, the technical wind energy potential was estimated from national to global scale. Usually, it was assumed that the underlying meteorological potential remains constant over time. However, the wind resource greatly varies on different temporal scales including inter-annual and multi-decadal scales. In this study, the long-term variability of national and global technical wind energy potentials was assessed for the period 1971–2010 based on wind speed data from the coupled atmosphere/land-surface/ocean-wave model ERA-20C. Within this period, the annual national and global wind energy generation was reconstructed assuming an average number of 0.01–0.25 wind turbines per km2 sited on geographically non-restricted areas. The applied wind turbines have an average rated power of 3.67 MW and a hub height of 100 m. While no significant trend in technical wind energy potential was found in the majority of the countries studied, the Mann-Kendall and Cox-Stuart trend tests revealed significantly increasing trends in 37 countries and significantly decreasing trends in 10 countries. In addition, the results show that the inter-annual variability of the wind energy potential is influenced not only by the wind resource itself, but also by the rate of wind turbine expansion. From the presented results it is clear that the quantification of the long-term variability of the wind energy potential is an important prerequisite for controlling and adapting the expansion of wind energy on national and global scales to future electricity consumption.

Abstract The choice of a suitable theoretical wind speed distribution is an important prerequisite for accurate wind energy yield estimation. In this review, 46 studies published 2010–2018, which compared the goodness-of-fit of different theoretical parametric distributions, were evaluated. The evaluation scheme considered three aspects: (1) distributions, (2) parameter estimations methods, and (3) goodness-of-fit metrics. It was found that the two-parameter Weibull distribution is by far the most frequently (in 44 out of 46 studies) evaluated distribution. In total, 115 different distributions were fitted to wind speed data. Out of these 115 distributions, 32 distributions were recommended at least once. One reason for the large spread of recommendations can be attributed to the research design. To ensure comparability in this review, each study was rated by its scope and the amount and quality of the wind speed data used. Results from the present evaluation demonstrate that the five-parameter Wakeby and four-parameter Kappa distributions achieved the highest scores. The results also show that the maximum likelihood method, the least-squares estimation method, the moment method, and the L-moment method are the most frequently applied methods for estimating distribution parameters. For goodness-of-fit evaluation, many studies used more than two metrics. Among the most often used goodness-of-fit metrics are the Kolmogorov-Smirnov statistic, the coefficient of determination, and the Chi2 statistic. A fundamental conclusion of this review is that common research standards are needed to improve the comparability of future wind energy yield assessments. Standards are required for wind speed data, the use of distributions, and the goodness-of-fit evaluation.

Abstract Accurate modeling of empirical wind speed distributions is a crucial step in the estimation of average wind turbine power output. For this purpose, the Weibull distribution has often been fitted to empirical wind speed distributions. However, the Weibull distribution has been found to be insufficient to reproduce many wind speed regimes existing around the world. Results from previous studies demonstrate that numerous one-component distributions as well as mixture distributions provide a better goodness-of-fit to empirical wind speed distributions than the Weibull distribution. Moreover, there is considerable interest to apply a single system of distributions that can be utilized to reproduce the large majority of near-surface wind speed regimes existing around the world. Therefore, a system of wind speed distributions was developed that is capable of reproducing the main characteristics of existing wind speed regimes. The proposed system consists of two one-component distributions (Kappa and Wakeby) and one mixture distribution (Burr-Generalized Extreme Value). A random forests classifier was trained in order to select the most appropriate of these three distributions for each of 10,016 globally distributed empirical wind speed distributions. The shape of the empirical wind speed distributions was described by L-moment ratios. The L-moment ratios were used as predictor variables for the random forests classifier. The goodness-of-fit of the system of wind speed distributions was evaluated according to eleven goodness-of-fit metrics, which were merged into one comprehensive metric. It was found that the goodness-of-fit of the system of wind speed distributions is significantly better than the goodness-of-fit of any other distribution. The new system of wind speed distributions provides reasonable fit to all evaluated wind speed regimes. Furthermore, the system of wind speed distributions significantly improves wind turbine power output estimation. Its application enables a more precise, simplified and globally consistent wind resource assessment.

Abstract Based on ERA-20C data available for the period 2008–2010, the potential of six onshore wind turbine installation scenarios to cover current electricity consumption at the national and global scale was studied. The technical wind energy potential was estimated using the recently developed, highly accurate Burr-Generalized Extreme Value mixture distribution. The installation scenarios were evaluated by varying wind farm efficiency, the concentration of wind turbines, and wind turbine siting strategy for wind turbine densities of 1–25% of the land area. With the systematic installation of wind turbines at the national scale and international coordination of energy distribution, wind energy production could match current electricity consumption in the 2030s assuming a tenfold increase in current expansion rate. However, the results greatly differ from country to country mainly because of the meteorological potential, the total area available for wind turbine installation, and the population size. In industrialized countries such as China, France, Germany, and Japan, either low average annual wind energy yield or the large population prevents complete coverage of electricity consumption by wind energy at low and moderate wind turbine density. In developing countries including Ethiopia, Sudan or Kenia, where wind energy potential is high and electricity consumption is low, expansion of wind energy could greatly improve electricity supply. It was found that 98 countries could cover their current electricity consumption by an installed capacity of 0.0734 MW/km2. This installed capacity enables 73 countries to cover even a 100% increase of the current electricity consumption. Based on the range of evaluated scenarios, it is possible to estimate the upper and lower bounds of the technical potential predefined by the applied wind turbine densities.

Abstract The goals of this study were to develop and to evaluate wind turbine siting scenarios for achieving a share of about 40% (250 TWh/yr) wind energy in Germany’s gross electricity consumption. The scenarios were developed to quantify the influence of (1) available technology and repowering, (2) resource distribution, and (3) siting strategy on five siting suitability measures: (1) number of wind turbines, (2) cumulative installed capacity, (3) capacity factor, (4) main investment costs, and (5) distribution of wind turbines. The wind field in the analyzed hub height range was modeled by the wind speed-wind shear model on a 200 m × 200 m grid. Geographically restricted areas were considered as not suitable under all scenarios. For a better comparison of the scenarios, a novel wind turbine siting index was introduced by normalizing the suitability measures. It was found that an installed capacity of less than 100 GW and about 36,000 wind turbines are sufficient to generate 250 TWh/yr wind energy. If the current rate of wind energy expansion in Germany is maintained, this value can be achieved in the early 2030s. The results also demonstrate that inefficient wind turbine siting considerably extends the period and increases the number of required wind turbines. Against this background, the results of this study can be applied for a better coordination of wind turbine siting in Germany at different administrative levels. The methodology is portable to other countries and can be used to develop national siting strategies to achieve projected wind energy shares in the electricity mix.

Destructive winter storms cause recurring major damage to physical, biological, human, and managed systems in Central Europe. Therefore, detailed knowledge of their future development in many areas of human life is of great importance for planning strategic management decisions. One feature to characterise the winter storm intensity is the daily maximum gust speed for a 10-yr return period (GS10yr). In this study, the development of GS10yr under the representative concentration pathways RCP45 and RCP85 in the near future (2019–2049), mid future (2044–2074), and far future (2069–2099) was assessed. Gust speed projections were derived from 19 regional climate models (RCM) available from the EURO-CORDEX initiative. The GS10yr estimates were first bias-corrected and then combined with the historical winter storm atlas for Germany (GeWiSA) yielding highly resolved (25 m × 25 m) GS10yr grids. The results which are available on a monthly basis, indicate a significant increase in winter storm-related wind gust intensity in October under RCP45 and in November and December under RCP85 towards the end of the 21st century. The proposed methodology allows the quantification of the uncertainty associated with winter storm projections and the development of climate-sensitive storm damage models.

Abstract Among the renewable energy sources, the highest share of European net electricity generation comes from wind power. However, the European onshore wind resource’s volatile nature is a significant challenge in ensuring a constant national electricity supply. Therefore, this study examined the potential of complementary use of national wind resources available in 33 European countries. The complementarity of the national wind resources was assessed for 1971–2010 by identifying the time scales explaining the largest part of the variance in the time series of daily wind energy yield. The results of a novel combination of wavelet analysis, graph models, and dynamic time warping indicate that the wind energy yield shows the most substantial variations at the annual, seasonal, and multi-day scales. Geographical proximity is a critical factor in the complementarity of national wind energy yields. At all evaluated time scales, the wind energy yield in most central and western European countries is strongly correlated, forming a large supranational network with low potential for complementary use of national wind resources. Although the seasonal component of daily wind energy yield is shifted between the European countries by up to several weeks, which would create potential for complementary use, domestic electricity consumption often exceeds the usable wind resources. Domestic electricity consumption was found to be a major barrier to the transboundary exchange of wind energy. Based on the results obtained for the European onshore wind resource’s spatiotemporal dynamics, it must be assumed that the potential for regular complementary transboundary use of wind resources is limited.