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In the European renewable energy portfolio, wind has a sizeable share in the total energy production. The Nordic and Baltic energy systems in particular are benefiting from wind energy to reach the greenhouse gas emissions reduction objectives set by the EU. The wind energy production varies with time, and this intermittent characteristic imposes a challenge for full utilization of renewable energy potential. The power system operator needs to ensure timely power supply of demand. An accurate estimation of power output from a non-dispatchable generation resource such as a wind farm is essential for the operator to ensure the supply–demand balance and adequate sizing of reserve power capacity. Existing methods of feature extraction and prediction such as linear regression often overlook the significant variations or do not utilize in the model building. However, this method misinterprets the trend in data. Understanding the properties of the variations in more details would reduce the uncertainty and significantly improve the feature extraction to aid in decision making. Furthermore, as the volume, shape and type of dataset start to increase and new methods are required to extract meaningful information from the patterns in the big data. The objective of the paper is to present a novel Ramping BehaviourAnalysis (RBAθ) model that identifies and quantifies the variations in a time-varying dataset. The variations are classified into significant and stationary events. The former refers to the significant swings beyond a set threshold range and the latter refers to the swings that are relatively within the threshold limits. The features associated to each event include start time, end time, change in magnitude, persistence of an event, angle at which the event took place and frequency of occurrences of the features. In addition, the rain-flow cycles count is extracted from the original data for each event as a sum of half cycles and full cycles. The model is validated using simulated wind power production data from a virtual wind park spread across Estonia and the results are elaborated. The spatial dynamics of the virtual windfarm are captured through localized spatial autocorrelation of the events with the geospatial locations of the turbines. The results demonstrate that RBAθprecisely and accurately identify and quantify the time varying power generation into events with subsequent features. The volume of the data is significantly reduced in the process of summarizing time series data into a series of events. Thereby RBAθcan be also used for data compression and reconstruction with minor losses. The system operators can use the proposed algorithm in operational scheduling, maintenance and investment-capacity building decisions.

The use of wind turbines to create wind energy is one of the main alternatives to the traditional technologies of power generation. The exclusion of combustion products emissions at thermal power plants that operate on hydrocarbon fuel, as well as the exclusion of the fuel component of the cost of electricity generation makes the wind power technology very attractive. However, the rigor of the operation requirements of wind turbines as part of power systems, low density of the flow of primary energy source and the lack of control of it, low utilization of installed capacity, limited operating life, shutdowns in the conditions of squally gusts of wind and ice formation, large areas of alienated land, the impact of noise and infrasonic vibrations and the problems of utilization of large-size structural elements and foundations require a comprehensive analysis of conditions of wind turbines application. Despite the absence of desert areas and of restrictions on the construction of ultra-high structures the analysis of natural and climatic conditions of Belarus demonstrates favorable natural and landscape conditions for the development of wind power generation. The principal task is to choose the location of wind power plants with due regard to environmental requirements, temperature and humidity conditions, terrain and geological features of the location. The results of calculations of the wind flow conditions showed the preference for the joint application of the Weibull and Rayleigh functions that provide the confidence interval of the approximation of the wind speed function, while the terrain specific features make it possible to expect to obtain higher values of the established capacity utilization factor. The development of a distributed energy generation accompanied by Smart Grid technology wide use over electric networks (which would provide new opportunities for consumers and make it possible to eliminate the monopoly of powerful power plants and to reduce burden of basic costs of big power production) ought to be considered as obvious prospect of wind power plants application.

In many countries, some people living in the vicinity of wind power production areas report having symptoms that they intuitively associate with wind turbines. Recently public discussions have focused especially on wind turbine infrasound. However, scientific evidence supporting an association is lacking. The aim of this study was to assess the association between exposure to wind turbines and the prevalence of self-reported symptoms, diseases and medications.A cross-sectional questionnaire study (n = 2,828) was conducted in the vicinity of five wind power production areas in Finland in 2015–2016. Each area had 3–16 turbines with a nominal power of 2.4–3.3 MW. The response rate was 50% (n = 1,411). Continuous and categorised (≤ 2.5, > 2.5–5, > 5–10 km) distance between the respondents’ home and the closest wind turbine was used to represent exposure to wind turbines. Wind turbine sound pressure level outdoors could be reliably modelled only for the closest distance zone where the yearly average was 34 dB and maximum 43 dB.The data on symptoms (headache, nausea, dizziness, tinnitus, ear fullness, arrhythmia, fatigue, difficulties in falling asleep, waking up too early, anxiety, stress), diseases (hypertension, heart insufficiency, diabetes), and medications (analgesics for headache, joint/muscle pain and other pain, and medication for sleep disturbance, anxiety and depression, and hypertension) was obtained from the questionnaire. Logistic regression analyses were adjusted for age, sex, marital status, education, work situation, smoking, alcohol consumption, physical activity, body mass index, and hearing problems.Annoyance and sleep disturbance due to wind turbine noise were inversely associated with the distance to the closest wind turbine. The prevalence of symptoms, diseases and medications was essentially the same in all distance categories. In multivariate regression modelling, the odds ratio estimates were generally close to unity and statistically non-significant. Beyond annoyance and sleep disturbance, there were no consistent associations between exposure to wind turbines and self-reported health problems. The results do not support the hypothesis that broadband sound or infrasound from wind turbines could cause the proposed health problems.

A cost-effective and efficient wind energy production trend leads to larger wind turbine generators and drive for more advanced forecast models to increase their accuracy. This paper proposes a combined forecasting model that consists of empirical mode decomposition, fuzzy group method of data handling neural network, and grey wolf optimization algorithm. A combined K-means and identifying density-based local outliers is applied to detect and clean the outliers of the raw supervisory control and data acquisition data in the proposed forecasting model. Moreover, the empirical mode decomposition is employed to decompose signals and pre-processing data. The fuzzy GMDH neural network is a forecaster engine to estimate the future amount of wind turbines energy production, where the grey wolf optimization is used to optimize the fuzzy GMDH neural network parameters in order to achieve a lower forecasting error. Moreover, the model has been applied using actual data from a pilot onshore wind farm in Sweden. The obtained results indicate that the proposed model has a higher accuracy than others in the literature and provides single and combined forecasting models in different time-steps ahead and seasons.

An atmosphere−ocean coupled model is proposed as an optimal numerical prediction method for the offshore wind resource. Meteorological prediction models are mainly used for wind speed prediction, with active studies using atmospheric models. Seawater mixing occurring at sea due to solar radiation and wind intensity can significantly change the sea surface temperature (SST), an important variable for predicting wind resources and energy production, considering its wind effect, within a short time. This study used the weather research forecasting and ocean mixed layer (WRF-OML) model, an atmosphere−ocean coupled model, to reflect time-dependent SST and sea surface fluxes. Results are compared with those of the WRF model, another atmospheric model, and verified through comparison with observation data of a meteorological mast (met-mast) at sea. At a height of 94 m, the wind speed predicted had a bias and root mean square error of 1.09 m/s and 2.88 m/s for the WRF model, and −0.07 m/s and 2.45 m/s for the WRF-OML model, respectively. Thus, the WRF-OML model has a higher reliability. In comparing to the met-mast observation data, the annual energy production (AEP) estimation based on the predicted wind speed showed an overestimation of 15.3% and underestimation of 5.9% from the WRF and WRF-OML models, respectively.

Wind power is one of the most established renewable power resources yet it is also one of the most volatile resources. This poses a key challenge for successfully integrating wind power at a large scale into the power grid. Here we present an analysis of the time scales associated with wind power from hourly to seasonal fluctuations and how combining spatially distributed wind power sources helps to reduce its volatility. The analysis, based on observed wind speeds, is then generalised in a simple statistical model to develop a tool which can estimate the power output profile from a particular consortium of wind power sources. As the estimator only uses the local, or the mean national, wind resource and the mean distance between the sites to estimate the joint power output profile, it can be used by developers to estimate the reliability of their joint power output and to form the most effective consortium.

This paper reviews the most recent and relevant research into the variability characteristics of wind and solar power resources in Europe. The background for this study is that wind and solar resources will probably constitute major components of the future European power system. Such resources are variable, and EU plans to balance the variability with more grids and demand response. Thus, planning for the future power system requires an in-depth understanding of the variability. Resource variability is a multi-faceted concept best described using a range of distinct characteristics, and this review is structured on the basis of seven of these: Distribution Long-Term (hours to years), Distribution Short-Term (less than one hour), Step Changes, Autocorrelation, Spatial Correlation, Cross Correlation and Predictable Patterns. The review presents simulations and empirical results related to resource variability for each of these characteristics. Results to date reveal that the variability characteristics of the future power system is limited understood. This study recommends the development of a scheme for greater systematic assessment of variability. Such a scheme will contribute to the understanding of the impacts of variability and will make it possible to compare alternative power production portfolios and impacts of grid expansions, demand response and storage technologies.