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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.

The main goal of generation expansion planning (GEP) and transmission expansion planning (TEP) is to expand the power system to satisfy the increasing demand of electricity while maintaining efficient operation of the system. The major objective of this study is to propose a dynamic, robust GEP–TEP expansion planning in the presence of wind farms considering both long‐ and short‐term uncertainties. The suggested model allows implementing information‐gap decision theory on multi‐year long‐term uncertainties, such as demand growth and future increase in production capacity to decrease the risk in long‐term decisions. Additionally, a scenario‐based approach is employed for short‐term uncertainties in demand and wind power production in a 1‐year time horizon. The main advantage of the proposed model is to enhance the power system robustness against the uncertainties corresponding to forecast errors. To verify the robustness of the suggested expansion planning model, it is applied to the Garver 6‐bus and IEEE 24‐bus test systems. [ABSTRACT FROM AUTHOR]

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.

This study investigates optimal wind power generator bidding strategies in the real‐time electricity market. The goal is to maximise its operating profit by determining the optimal amount of wind power to bid in the real‐time market. A bi‐level stochastic optimisation model is proposed in which the upper‐level problem is minimising the negative profit of wind power producers, while the low‐level problem clears the real‐time market. The uncertainties in the wind power production, thermal power, hydro power, demand, and energy storage are considered in the stochastic model. This study utilises a mathematical programming problem with equilibrium constraints (MPEC) and Karush–Kuhn–Tucker (KKT) conditions to transform the bi‐level problem into an equivalent single‐level mixed‐integer linear problem (MILP). Case studies obtained with the IEEE RTS‐24 Bus system demonstrate the effectiveness of the proposed model and the effect of scenarios on the wind power producer's profit. [ABSTRACT FROM AUTHOR]

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.

Maintaining a real time balance between energy consumption and production is challenging when faced with increasing penetration of Renewable Energy Sources (RES) because of the increased variability in generation output. Demand-Side Management (DSM) techniques address this issue by steering consumers’ energy off-take, thereby enabling further penetration of RES. Present paper addresses the problem of overproduction from distribution grid connected wind generation. We present and analyze two business cases in the Belgian-European energy landscape for using upward consumption flexibility to deal with excessive wind power injection. We focus on the perspective of the flexibility providers and the strategic choice they face in choosing the business partner that maximizes their expected financial compensation. Evolutionary game theory is used to model this strategic choice and to provide a framework for quantifying realistic financial compensation bounds based on real world market and wind production data for multiple locations in Belgium. Results show that in a competitive market setting compensation payments for flexible power consumption are higher when dealing with higher wind forecast error levels. These results validate the economic benefits of having accurate wind production forecasts. [ABSTRACT FROM AUTHOR]

When energy trading companies enter into long-term agreements with wind power producers, where a fixed price is paid for the fluctuating production, they are facing a joint price and volumetric risk. Since the pay-off of such agreements is non-linear, a hedging portfolio would ideally consist of not only forwards, but also a basket of e.g. call and put options. Illiquidity and an almost non-existent market for options challenge however the optimal hedging of joint price and volumetric risk in many market places. Here, we consider the case of the Danish power market, and exploit its strong positive correlation with the much more liquid German market to construct a proxy hedge. We propose a three-dimensional mixed vine copula to model the evolution of the Danish and German spot electricity prices and the Danish wind power production. We construct a realistic hedging portfolio by identifying various instruments available in the market, such as real options in the form of the right to transfer electricity across the border and the right to convert electricity to heat. Using the proposed vine copula to determine optimal hedging decisions, we show that significant benefits are to be drawn by extending the hedging portfolio with the proposed instruments. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of market coupling on (1) electricity prices of Hungarian and Romanian markets and (2) the influence of renewable generation on price regimes by employing the Markov regime-switching model with time-varying transition probabilities. The study provides the evidence of the changes in regimes since market coupling. The results show that the persistence and occurrences of Hungarian price drops are significantly increased. Meanwhile, Romanian prices exhibit less and shorter living price jumps. Considering time-varying transition probabilities as functions of wind power production in Romania, the study also reveals that market coupling changed the influence of wind power production on the regime-switching mechanism of electricity prices. [ABSTRACT FROM AUTHOR]

In highly polluted regions, it might be necessary to operate power plants enforcing appropriate emission limits to help ensuring air quality. If the air quality is low and pollutants are difficult to diffuse, few additional emissions should be allowed to avoid further deteriorating the air quality. Wind power helps reducing pollution levels as it substitutes polluting thermal production. However, wind and air pollution are generally anti‐correlated. Storage can be used to shift wind power production from hours when higher pollution levels are allowed to hours when lower pollution levels are allowed to help meeting emission constraints at critical hours. Storage availability also achieves a comparatively lower production cost for the system as a whole. To represent the stochastic nature of wind power production and weather‐dependent emission limits, the authors propose a two‐stage stochastic programming model to analyse the trade‐off emission versus cost in an electric energy system with storage facilities. The first stage represents the day‐ahead scheduling, while the second one represents the real‐time operation under different scenarios. The authors analyse the impact of emission limits and/or storage on generation scheduling by comparing different models using an illustrative example and a case study based on the IEEE 118‐node system. [ABSTRACT FROM AUTHOR]

With the growing share of wind power production in the electric power grids, many critical challenges to the grid operators have been emerged in terms of the power balance, power quality, voltage support, frequency stability, load scheduling, unit commitment and spinning reserve calculations. To overcome such problems, numerous studies have been conducted to predict the wind power production, but a small number of them have attempted to improve the prediction accuracy by employing the multidimensional meteorological input data. The novelties of this study lie in the proposal of an efficient and easy to implement very short-term wind power prediction model based on the k-nearest neighbor classifier (kNN), in the usage of wind speed, wind direction, barometric pressure and air temperature parameters as the multi-tupled meteorological inputs and in the comparison of wind power prediction results with respect to the persistence reference model. As a result of the achieved patterns, we characterize the variation of wind power prediction errors according to the input tuples, distance measures and neighbor numbers, and uncover the most influential and the most ineffective meteorological parameters on the optimization of wind power prediction results. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive robust network-constrained AC unit commitment (AC-UC) model. This model is formulated as a min–max–min optimization problem, which allows representing the uncertain nature of wind power productions in terms of bounded intervals. A tri-level decomposition algorithm using primal and dual cutting planes is introduced to solve this AC-UC problem and to find a robust commitment schedule withstanding the worst realizations of uncertain wind power. To reduce the computation burden and to obtain a tractable formulation, the AC power flow equations are convexified using a Signomial transformation. The proposed AC-UC model is illustrated using the IEEE 24-bus reliability test system. Simulation results demonstrate the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

We consider a wind power producer (WPP) participating in a dynamically evolving two settlement power market. We study the utility of energy storage for a WPP in maximizing its expected profit. With random wind and price processes, the optimal forward contract and storage charging/discharging decisions are formulated as solutions of an infinite horizon stochastic optimal control problem. For the asymptotically small storage capacity regime, we precisely characterize the maximum profit increase brought by utilizing energy storage. We prove that, in this regime, an optimal policy uses storage to compensate for power delivery shortfall/surplus in real time, without changing the forward contracts from the optimal ones in the absence of energy storage. This policy also serves as an approximately optimal policy for the case of relatively small storage capacity. We also design a policy based on model predictive control (MPC) that is approximately optimal for general storage capacities. We numerically evaluate the developed policies for wind and price processes with representative statistics from real world data. It is observed that, as expected, the simple small storage approximation policy performs closely to the optimum when storage is relatively small, while the more complex stochastic MPC policy performs better for larger storage capacities. [ABSTRACT FROM PUBLISHER]

The wind energy industry has grown very fast among the developed countries as it is remarkably clean and has a better quality in power system design. Consequently, with the increase of wind power applications in power systems, wind farms are needed for participation in network operations using efficient control strategies. This paper offers a comparative study on the performance of wind farm system equipped with doubly fed induction generators (DFIG) wind turbine. The study concentrates on the wind farm controller, which distributes power reference among wind turbines based on the demands of operator control system, while reducing their structural loads. The proposed controller is based on optimization technique in order to minimize the destructive fluctuation of torque and generator speed, in addition to supplying a satisfactory quality of power. The suggested wind farm control system has a hierarchical structure with both a central control level and a local control level. The performance of the control strategies is evaluated through simulations of wind farm controller equipped with DFIG wind turbines and proper results in solving the problem of both power and load optimizations are illustrated. [ABSTRACT FROM AUTHOR]

Using 11-month, 15-min averaged measurements by nacelle anemometers at 274 wind turbines, we studied the spatial and temporal variability of wind speed in a wind farm over an area of about 20 km by 20 km located in mountainous terrain. The intra-farm variability is compared between winter and summer: during summer the dominant time scale of wind speed fluctuation is the diurnal cycle of 24 h. During winter, however, the dominant scale is 48 h. The spatial variability of wind speed on scales ≤ O (10 km) is closely associated with the temporal variability on scales ≤ O (10 h). Over a time span ≤ 3 h rapid drop-offs in wind speed occur more often than rapid increases, and the rapid drop-offs are more frequent during summer than winter. The intra-farm spatial variability of wind speed has a diurnal cycle with that reaches its maximum in the nighttime and its minimum around midday. For accurate wind power modeling, it may be of importance to consider the intra-farm variability of wind speed distribution. We found that when energy production exceeds 200 MW, its estimation based on the averaged wind speed is significantly larger than when based on the speeds at individual turbines. [ABSTRACT FROM AUTHOR]

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.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]