Winter windstorms are known to be among the most dangerous and loss intensive natural hazards in Europe. In order to gain a better understanding of their variability and driving mechanisms, this study analyses the temporal variability which is often referred to as serial or seasonal clustering. This is realized by developing a statistical model relating the winter storm counts to known teleconnection patterns affecting European weather and climate conditions (e.g., North Atlantic Oscillation [NAO], Scandinavian pattern [SCA], etc.). The statistical model is developed via a stepwise Poisson regression approach that is applied to windstorm counts and large-scale indices retrieved from the ERA-20C reanalysis. Significant largescale drivers accountable for the inter-annual variability of storms for several European regions are identified and compared. In addition to the SCA and the NAO which are found to be the essential drivers for most areas within the European domain, other teleconnections (e.g., East Atlantic pattern) are found to be more significant for the inter-annual variability in certain regions. Furthermore, the statistical model allows an estimation of the expected number of storms per winter season and also whether a season has the characteristic of being what we define an active or inactive season. The statistical model reveals high skill particularly over British Isles and central Europe; however, even for regions with less frequent storm events (e.g., southern and eastern Europe) the model shows adequate positive skill. This feature could be of specific interest for the actuarial sector.

Wind Farm Layout Optimization (WFLO) can be useful to minimize power losses associated with turbine wakes in wind farms. This work presents a new evolutionary WFLO methodology integrated with a recently developed and successfully validated Gaussian wake model (Bastankhah and Porté-Agel model). Two different parametrizations of the evolutionary methodology are implemented, depending on if a baseline layout is considered or not. The proposed scheme is applied to two real wind farms, Horns Rev I (Denmark) and Princess Amalia (the Netherlands), and two different turbine models, V80-2MW and NREL-5MW. For comparison purposes, these four study cases are also optimized under the traditionally used top-hat wake model (Jensen model). A systematic overestimation of the wake losses by the Jensen model is confirmed herein. This allows it to attain bigger power output increases with respect to the baseline layouts (between 0.72% and 1.91%) compared to the solutions attained through the more realistic Gaussian model (0.24–0.95%). The proposed methodology is shown to outperform other recently developed layout optimization methods. Moreover, the electricity cable length needed to interconnect the turbines decreases up to 28.6% compared to the baseline layouts.

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The study of the wind power output variability comprises different time scales. Among them, low-frequency variations can substantially modify the performance of a wind power plant during its lifetime. Although in recent years other scales, as the short-term variability or the climatological conditions of wind and the corresponding generated power have been investigated in depth, the study of the decadal and multidecadal variations is still placed in its early stages.In this work the wind power output long-term variability is analyzed for two locations of wind power relevance in Spain, during the period 1871-2009. This is attained by computing the annual wind speed Probability Density Functions (PDF) from an ensemble of atmospheric Sea Level Pressure (SLP) data set from the 20th Century Reanalysis through a statistical downscaling based in Evolutionary Algorithms. Results reveal significant trends and periodicities in multidecadal bands including 13, 25 and 46 years, as well as significant differences among both sites. The impact of the leading large-scale circulation patterns (NAO, EA, SCAND and AMO) on wind power output and its stationarity is analyzed. Results on both locations show significant and opposite seasonal couplings with these forcings. Finally, the long-term variability of the reconstructed Weibull parameters of the annual wind speed distributions are employed to derive a linear model to estimate the annual wind power.