Search Results (84)

Reanalysis datasets, such as MERRA2, are frequently used in wind resource assessments. However, their wind speed data are typically limited to fixed altitudes that differ from wind turbine hub heights, which introduces significant uncertainty in energy yield estimations. To address this challenge, we propose a reproducible Measure–Correlate–Predict (MCP) framework that integrates Random Forest (RF) supervised learning to estimate hub-height wind speeds from MERRA2 data at 50 m. The method includes the fitting of 21 vertical wind profile models using data at 2 m, 10 m, and 50 m, with model selection based on the minimum mean square error. The approach was applied to seven wind-prone locations in the Canary Islands, selected for their strategic relevance in current or planned wind energy development. Results indicate that a three-parameter logarithmic wind profile achieved the best fit in 51.31% of cases, significantly outperforming traditional single-parameter models. The RF-based MCP predictions at different hub heights achieved RMSE metrics below 0.425 m/s across a 10-year period. These findings demonstrate the potential of combining physical modeling with machine learning to enhance wind speed extrapolation from reanalysis data and support informed wind energy planning in data-scarce regions. Full article

This study investigates the impacts of surging and pitching motions on the power generation performance and wake characteristics of an IEA 15 MW offshore wind turbine under specific inflow wind conditions. The three-dimensional, unsteady continuity equation, momentum equations, and SST k–ω turbulence model are solved numerically using the computational fluid dynamics software STAR-CCM+ (version 2206) to simulate the aerodynamic flow field around the turbine rotor and in its downstream wake region. Under the condition of an inflow wind speed of 9 m/s at hub height and a corresponding rotor rotational speed of 7.457 RPM, the surging and pitching motions of the turbine are prescribed by sinusoidal functions with a period of 45 s and amplitudes of 2.75 m and 5°, respectively. This study analyzes and quantifies the power output and wake characteristics of the turbine over a duration corresponding to 200 rotor revolutions, considering stationary, surging, and pitching conditions. The results indicate that the surging and pitching motions of the turbine cause reductions in the mean power output of 2.18% and 3.54%, respectively, compared to a stationary condition. The surging and pitching motions also lead to significant wake enhancement in the downstream region, and a minimum spacing of downstream wind turbines is suggested. Full article

Offshore wind energy plays a crucial role in achieving renewable energy targets, with OWFs facing unique environmental challenges that impact turbine performance and structural demands. This study develops an advanced optimization methodology to identify the most effective layout configurations for offshore wind farms (OWFs) with monopile foundations, focusing on enhancing structural integrity and energy generation efficiency. Using a multi-criteria optimization approach, the effects of wind turbine spacing, angular orientation, and height on energy yield and monopile loading were evaluated. Based on a seven-year dataset from the Ouido site in South Korea, where the mean wind speed is 6.95 m/s at a 150 m hub height, optimized configurations were determined. For average wind conditions, a turbine spacing of 250 m, a hub height of 148 m, and an orientation angle of 36.87° minimized wake losses and distributed structural loads effectively. Under rated wind speeds of 10.59 m/s, a spacing of 282 m, a hub height of 155 m, and an orientation angle of 45° further enhanced performance. These designs reduced wake interference by 25%, decreased monopile fatigue loads by 18%, and lowered the levelized cost of electricity (LCOE) by up to 15%. This study’s findings provide a robust framework for optimizing OWFs to increase energy yield, improve operational efficiency, and ensure economic viability. Full article

To investigate the characteristics of wind turbine wakes under different aerodynamic roughness lengths, a series of LES-ALM simulations were carried out in this study. First, a sensitivity analysis of the time step of the simulation results was performed. Then, the study compared the power and thrust of wind turbines under different roughness conditions. Finally, the mean velocity deficit, added turbulence intensity, and Reynolds shear stresses in the wake were analyzed under different roughness conditions. This study finds that a 0.1 s time step can provide satisfactory results for the LES-ALM compared to a 0.02 s time step. Furthermore, for the same hub-height wind speed, the thrust coefficient varies from 0.75 to 0.8 under the different roughness levels. As the roughness length increases, the time-averaged velocity deficit and added turbulence intensity decreases, and the wake recovers more quickly at the incoming level. However, the effect of roughness length on the Reynolds shear stress is weak within the downstream range of x = 6D to 10D. For the velocity deficit, a single Gaussian function is not able to describe its vertical distribution. Additionally, under higher roughness conditions, the height of the wake center is distinctively higher than the hub height as the wake develops downstream. The findings of this paper are beneficial for selecting the approximate numerical parameters for the wake simulations and provide deeper insights into the turbulence mechanisms of wind turbine wake, which are crucial for establishing analytical models to predict the wake field. Full article

This paper proposes a method based on Computational Fluid Dynamics (CFD) and the detection of Wind Energy Extraction Latency for a given wind turbine (WT) designed for ultra-short-term (UST) wind energy forecasting over complex terrain. The core of the suggested modeling approach is the Wind Spatial Extrapolation model (WiSpEx). Measured vertical wind profile data are used as the inlet for stationary CFD simulations to reconstruct the wind flow over a wind farm (WF). This wind field reconstruction helps operators obtain the wind speed and available wind energy at the hub height of the installed WTs, enabling the estimation of their energy production. WT power output is calculated by accounting for the average time it takes for the turbine to adjust its power output in response to changes in wind speed. The proposed method is evaluated with data from two WTs (E40-500, NM 750/48). The wind speed dataset used for this study contains ramp events and wind speeds that range in magnitude from 3 m/s to 18 m/s. The results show that the proposed method can achieve a Symmetric Mean Absolute Percentage Error (SMAPE) of 8.44% for E40-500 and 9.26% for NM 750/48, even with significant simplifications, while the SMAPE of the persistence model is above 15.03% for E40-500 and 16.12% for NM 750/48. Each forecast requires less than two minutes of computational time on a low-cost commercial platform. This performance is comparable to state-of-the-art methods and significantly faster than time-dependent simulations. Such simulations necessitate excessive computational resources, making them impractical for online forecasting. Full article

This study conducts a comprehensive evaluation of four scenario experiments using the CMA_WSP, WRF, and WRF_FITCH models to enhance forecasts of hub-height wind speeds at multiple wind farms in Northern China, particularly under significant wind speed fluctuations during high wind conditions. The experiments apply various wind speed calculation methods, including the Monin–Obukhov similarity theory (ST) and wind farm parameterization (WFP), within a 9 km resolution framework. Data from four geographically distinct stations were analyzed to assess their forecast accuracy over a 72 h period, focusing on the transitional wind events characterized by substantial fluctuations. The CMA_WSP model with the ST method (CMOST) achieved the highest scores across the evaluation metrics. Meanwhile, the WRF_FITCH model with the WFP method (FETA) demonstrated superior performance to the other WRF models, achieving the lowest RMSE and a greater stability. Nevertheless, all models encountered difficulties in predicting the exact timing of extreme wind events. This study also explores the effects of these methods on the wind power density (WPD) distribution, emphasizing the boundary layer’s influence at the hub-heighthub-height of 85 m. This influence leads to significant variations in the central and coastal regions. In contrast to other methods that account for the comprehensive effects of the entire boundary layer, the ST method primarily relies on the near-surface 10 m wind speed to calculate the hub-height wind speed. These findings provide important insights for enhancing wind speed and WPD forecasts under transitional weather conditions. Full article

This study proposes a microscale flow model to estimate mean wind speed, fluctuating wind speed and wind direction over complex terrain considering the effects of topography, atmospheric stability, and turbine wakes. Firstly, the effect of topography is considered using Computational Fluid Dynamics (CFD). Next, a mesoscale model is presented to account for the effect of atmospheric stability. The effect of turbine wakes on the mean and fluctuating wind speeds are then represented by an advanced wake model. The model is validated using the measurement data of a wind farm located in the North of Japan. The measured wind data by Lidar at a reference height are horizontally extrapolated to a nearby met mast hub height and validated by a cup anemometer. Moreover, a novel averaging method is proposed to calculate a directional equivalent Monin–Obukhov length scale to account for the effect of atmospheric stability. Finally, the measured wind data at the reference height are vertically extrapolated and validated at the lidar location. The predicted mean and fluctuating wind speeds show good agreement with the measurements. Full article

We investigated the predictive capability of various configurations of the Weather Research and Forecasting (WRF) model version 4.4, to predict hub height offshore wind speed and wind power density in the Northeast US wind farm lease areas. The selected atmospheric conditions were high-pressure systems (anticyclones) coinciding with wind speed below the cut-in wind turbine threshold. There are many factors affecting the potential of offshore wind power generation, one of them being low winds, namely wind droughts, that have been present in future climate change scenarios. The efficiency of high-resolution hub height wind prediction for such events has not been extensively investigated, even though the anticipation of such events will be important in our increased reliance on wind and solar power resources in the near future. We used offshore wind observations from the Woods Hole Oceanographic Institution’s (WHOI) Air–Sea Interaction Tower (ASIT) located south of Martha’s Vineyard to assess the impact of the initial and boundary conditions, number of model vertical levels, and inclusion of high-resolution sea surface temperature (SST) fields. Our focus has been on the influence of the initial and boundary conditions (ICBCs), SST, and model vertical layers. Our findings showed that the ICBCs exhibited the strongest influence on hub height wind predictions above all other factors. The NAM/WRF and HRRR/WRF were able to capture the decreased wind speed, and there was no single configuration that systematically produced better results. However, when using the predicted wind speed to estimate the wind power density, the HRRR/WRF had statistically improved results, with lower errors than the NAM/WRF. Our work underscored that for predicting offshore wind resources, it is important to evaluate not only the WRF predictive wind speed, but also the connection of wind speed to wind power. Full article

Wind shear is one of the crucial parameters in wind resource assessment and also serves as a vital parameter and basis for determining wind turbines’ selection and hub height. Existing studies have only focused on typical underlying surface areas, but a relatively limited comprehensive analysis of wind shear characteristics in different complex environments remains. This study analyzes the daily and monthly variations in wind shear index (α) at the station scale based on the observations from 754 wind measurement towers across land surfaces in China. The distribution and empirical values of wind shear in different wind regions and underlying surface types are also investigated. The research findings indicate that the wind shear index derived from fitting the complete annual average wind speeds at multiple height levels of meteorological towers can accurately characterize the stratification state of the atmospheric boundary layer. The variation pattern of solar radiation influences the daily α value in typical regions. In mountainous and desert areas, the monthly variation tends to be higher in autumn and winter and lower in spring and summer. However, its monthly variation shows relatively smaller fluctuations in plain regions. The comprehensive α value over land regions in China is 0.135. The α values for I, II, III, and IV wind fields are 0.111, 0.163, 0.1, and 0.153, respectively. Its values for mountainous, plains, grassland, and desert regions are 0.12, 0.273, 0.123, and 0.104, respectively. By conducting statistical analysis on α values across different wind regions, guidance is provided for extrapolating surface wind speeds to hub-height wind speeds. This serves as a reference for wind energy resource assessment, wind turbine selection, and hub height determination in the atmospheric boundary layer of China. Full article

In recent years, Taiwan has actively pursued the development of renewable energy, with offshore wind power assessments indicating that 80% of the world’s best wind fields are located in the western seas of Taiwan. The aim of this study is to maximize offshore wind power generation and develop a method for predicting offshore wind power, thereby exploring the potential of offshore wind power in Taiwan. The research employs machine learning techniques to establish a wind speed prediction model and formulates a real-time wind power potential assessment method. The study utilizes long short-term memory networks (LSTM), gated recurrent units, and stacked recurrent neural networks with LSTM units as the architecture for the wind speed prediction model. Furthermore, the prediction models are categorized into annual and seasonal patterns based on the seasonal characteristics of the wind. The research evaluates the optimal model by analyzing the results of the two patterns to predict the power generation conditions for 1 to 12 h. The study region includes offshore areas near Hsinchu and Kaohsiung in Taiwan. The novelty of the study lies in the systematic analysis using multiple sets of wind turbines, covering aspects such as wind power potential assessment, wind speed prediction, and fixed and floating wind turbine considerations. The research comprehensively considers the impact of different offshore locations, turbine hub heights, rotor-swept areas, and wind field energy on power generation. Ultimately, based on the research findings, it is recommended to choose the SG 8.0-167 DD wind turbine system for the Hsinchu offshore area and the SG 6.0-154 wind turbine system for the Kaohsiung offshore area, serving as reference cases for wind turbine selection. Full article

Fog conditions at the offshore wind farm Horns Rev 2 were photographed on 16 April 2018. In this study, we present the results of an analysis of the meteorological conditions on the day of the photographs. The aim of the study was to examine satellite images, meteorological observations, wind turbine data, lidar data, reanalysis data, and wake and blockage model results to assess whether wind farm blockage was a likely cause for the formation of fog upstream of the wind farm. The analysis indicated the advection of warm and moist air mass from the southwest over a cool ocean, causing cold sea fog. Wind speeds at hub height were slightly above cut-in, and there was a strong veer in the shallow stable boundary layer. The most important finding is that the wake and blockage model indicated stagnant air mass arcs to the south and west of the wind farm. In the photographs, sea fog is visible in approximately the same area. Therefore, it is likely that the reduced wind triggered the sea fog condensation due to blockage in this area. A discrepancy between the blockage model and sea fog in the photographs appears in the southwest direction. Slightly higher winds might have occurred locally in a southwesterly direction, which may have dissolved sea fog. The wake model predicted long and narrow wind turbine wakes similar to those observed in the photographs. The novelty of the study is new evidence of wind farm blockage. It fills the gap in knowledge about flow in wind farms. Implications for future research include advanced modeling of flow phenomena near large offshore wind farms relevant to wind farm operators. Full article

The aim of this work is to provide some details regarding the energy potential of the local wind and solar resources near the Galati area (south-east of Romania) by considering the performances of a few recent technologies. Based on 22 years of ERA5 data (2001–2022), a picture concerning the renewable energy resources in the Brates Lake area is provided. Comparing the wind and solar resources with in situ and satellite data, a relatively good agreement was found, especially in regards to the average values. In terms of wind speed conditions at a hub height of 100 m, we can expect a maximum value of 19.28 m/s during the winter time, while for the solar irradiance the energy level can reach up to 932 W/m² during the summer season. Several generators of 2 MW were considered for evaluation, for which a state-of-the-art system of 6.2 MW was also added. The expected capacity factor of the turbines is in the range of (11.71–21.23)%, with better performances being expected from the Gamesa G90 generator. As a next step, several floating solar units were considered in order to simulate large-scale solar projects that may cover between 10 and 40% of the Brates Lake surface. The amount of the evaporated water saved by these solar panels was also considered, being estimated that the water demand of at least 3.42 km² of the agricultural areas can be covered on an annual scale. Full article

Taking a wind farm in the Qinghai–Tibet Plateau as the experimental site, the ZephiR Dual Mode (ZDM) LiDAR and ground-based laser LiDAR were used to scan the incoming flow and wake of the wind turbine separately. Based on wavelet analysis, the experimental study was conducted on the influence of different incoming wind speeds on the power and wake of the wind turbine. It is found that the incoming wind speeds have a great influence on the wind turbine power, and the fluctuation frequency of the wind speed is obviously higher than that of the power, that is, the scale effects of turbulence are magnified. The rotation of the wind wheel can accelerate the collapse of the large-scale turbulent structures of the incoming flow, and large-scale vortices continue to collapse into small-scale vortices, that is, the energy cascade evolution occurs. And in the wake diffusion process, the dissipation degree of the upper blade tip vortex is greater than that of the lower blade tip vortex caused by the rotation of the wind turbine. Under the same incoming flow conditions, due to the influence of tower and ground turbulence structure, the energy level connection phenomenon of the measuring points below the hub height is stronger than that above the hub height, and it weakens with the increase of the measuring distance. That is, the energy cascade of the measuring points below the hub height at 1.5 D (D is the diameter of the wind wheel) of the wake is weaker than that at 1 D of the wake. With the increase of the measuring distance of the wake, the influx of the external flow field further aggravates the momentum exchange and energy transport between the vortex clusters, that is, the influence of the external flow field gradually increases in the wake vortex pulsation. Full article

The current work focuses on establishing the parameters that influence the wind’s behavior over the Aegean and Ionian Seas and estimating the wind potential in the region based on long-term historic climate data. Combining a downscaling technique performed with the well-founded WRF-ARW computational algorithm and a number of simultaneous meteorological station time series, an attempt is made to investigate how regional changes may affect low-altitude wind speed distribution at hub height (100 m a.s.l.). The provided time-series coastal data span the entire region of interest from north to south. WRF-ARW v.3.9 is utilized to associate the geostrophic wind distribution obtained from long-term Copernicus ERA5 wind data with the localized wind potential over lower altitudes. Evaluation and correlation of the observational data to the predicted wind climate are performed, and the statistical differences that arise are investigated. High-accuracy wind resource potential maps are thus obtained in the region. Also, a few distinctive flow patterns are identified, such as wind speed cut-off regions and very high wind speed distributions, which are presented in specific southern regions of the Aegean Sea. Full article

Increasing wind capacity and capacity factors (CF) are essential for achieving the goals set by the Paris Climate Agreement. From 2010–2012 to 2018–2020, the 3-year mean CF of the global onshore wind turbine fleet rose from 0.22 to 0.25. Wind turbine siting, wind turbine technology, hub height, and curtailed wind energy are well-known CF drivers. However, the extent of these drivers for CF is unknown. Thus, the goal is to quantify the shares of the four drivers in CF development in Germany as a case. Newly developed national power curves from high-resolution wind speed models and hourly energy market data are the basis for the study. We created four scenarios, each with one driver kept constant at the 2010–2012 level, in order to quantify the share of a driver for CF change between 2010–2012 and 2019–2021. The results indicated that rising hub heights increased CF by 10.4%. Improved wind turbine technology caused 7.3% higher CF. However, the absolute CF increase amounted to only 11.9%. It is because less favorable wind turbine sites and curtailment in the later period moderated the CF increase by 2.1% and 3.6%, respectively. The drivers are mainly responsible for perennial CF development. In contrast, variations in wind resource availability drive the enormous CF inter-annual variability. No multi-year wind resource change was detected. Full article