Wind Resource Estimation Research Articles

Improving the Modeled Variability Estimates of Offshore Winds in Northern Europe by Nudging ASCAT-Derived Winds

Abstract The paper aims to demonstrate how to enhance the accuracy of offshore wind resource estimation, specifically by incorporating near-surface satellite-derived wind observations into mesoscale models. We utilized the Weather Research and Forecasting (WRF) Model and applied observational nudging by integrating ASCAT data over offshore areas to achieve this. We then evaluated the accuracy of the nudged WRF Model simulations by comparing them with data from ocean oil platforms, tall masts, and a wind lidar mounted on a commercial ferry crossing the southern Baltic Sea. Our findings indicate that including satellite-derived ASCAT wind speeds through nudging enhances the correlation and reduces the error of the mesoscale simulations across all validation platforms. Moreover, it consistently outperforms the control and previously published WRF-based wind atlases. Using satellite-derived winds directly in the model simulations also solves the issue of lifting near-surface winds to wind turbine heights, which has been challenging in estimating wind resources at such heights. The comparison of the 1-yr-long simulations with and without nudging reveals intriguing differences in the sign and magnitude between the Baltic and North Seas, which vary seasonally. The pattern highlights a distinct regional pattern attributed to regional dynamics, sea surface temperature, atmospheric stability, and the number of available ASCAT samples. Significance Statement We aim to showcase a method for improving the precision of hub-height estimation of wind resources offshore. This involves integrating wind observations obtained from near-surface satellites into the model simulations. To assess the accuracy of the simulations, we compare the simulated winds to data gathered from multiple offshore sources, including oil platforms, tall masts, and a wind lidar installed on a commercial ferry.

Wind energy relevant characteristics of turbulence over boreal forests

Turbulence statistics from three tall meteorological masts and LES in forested landscapes are compared to standard turbulence models used for wind turbine design. The comparison is split into different atmospheric conditions to highlight the impact of stratification on the character of turbulence. The aim of the work is to clarify to which extent standard turbulence models are accurate over forested regions. To this end, different spectral measures such as power spectra and coherence are examined as well as vertical profiles of turbulence characteristics relevant to the design and siting of wind turbines. The measurements are used to investigate vertically separated 2-point statistics and the LES to investigate laterally separated statistics. The results show that in neutral stratification and for smaller separation distances, in the order of half a radius, the standard turbulence models apply, but in non-neutral stratification, particularly in stable conditions and for larger separations the disparity between observations and standard turbulence grow. This effect is mainly attributed to the effect of stratification, while features in the turbulence statistics specifically related to the forest cover is absent at heights relevant to wind energy. The results of the study are expected to be of interest for turbine design purposes as well as wind resource estimation and wind modelling in forested areas.

Statistical study of the influence of the data sampling interval on the estimation of wind turbine energy

Main deficiency for wind power is variability. It is really difficult to predetermine the wind’s potential since wind velocity cannot be controlled or predicted with pinpoint accuracy. In this paper, two different methods are applied in order to estimate the monthly wind energy for different sites; those estimations are done using Weibull distribution and calculating energy with direct integration methodology. In additional, the influence of the data sampling interval is studied. Results show that the computational method based on the integration of the power is more accurate. Also, it is shown that the hourly time resolution provides satisfactory accuracy in wind resource estimation, in comparison with one minute’s resolution. Results allow better design of wind or hybrid systems. Moreover, wind speed acquisitions are minimized. A shorter time of treatment and less expensive measurement equipments are required.

Exploring the Potential of Sentinel-1 Ocean Wind Field Product for Near-Surface Offshore Wind Assessment in the Norwegian Arctic

The exploitation of offshore wind resources is a crucial step towards a clean energy future. It requires an advanced approach for high-resolution wind resource evaluations. We explored the suitability of the Sentinel-1 Level-2 OCN ocean wind field (OWI) product for offshore wind resource assessments. The SAR data were compared to in situ observations and three reanalysis products: the global reanalysis ERA5 and two regional reanalyses CARRA and NORA3. This case study matches 238 scenes from 2022 for the Goliat station, an oil platform located 85 km northwest of Hammerfest in the Barents Sea, where a new offshore wind park has been proposed. The analysis showed that despite their unique limitations in spatial and temporal resolutions, all data sources have similar statistical properties (RMSE, correlation coefficient, and standard deviation). The Weibull parameters characterizing the wind speed distributions showed strong similarities between the Sentinel-1 and all reanalysis data. The Weibull parameters of the in situ measurements showed an underestimation of wind speed compared to all other sources. Comparing the full reanalysis datasets with the subsets matching the SAR scenes, only slight changes in Weibull parameters were found, indicating that, despite its low temporal resolution, the Sentinel-1 Level 2 OWI product can compete with the more commonly used reanalysis products in the estimation of offshore wind resources. Its high spatial resolution, which is unmatched by other methods, renders it especially valuable in offshore areas close to complex coastlines and in resolving weather events at a smaller scale.

Comparison between the short-term observed and long-term estimated wind power density using Artificial Neural Networks. A case study

The economic feasibility of a wind project is dependent on the wind regime since it relies on the power output of the turbines over the installation’s working life. Consequently, the interannual variability of wind speed at a potential wind energy conversion site is an issue of capital importance. Usually a wind data measurement campaign is limited to a period no longer than one year (i.e. short-term). Therefore, the process of decision-making for wind farm constructors must be based in this short-term data. Various methods have been proposed in the scientific literature for estimation of the long-term wind speed characteristics at such sites. These methods use simultaneous measurements of the wind speed at the site in question and at one or several nearby reference sites with a long history of wind data measurements. In this paper, long-term wind power densities which have been estimated through the use Artificial Neural Networks (ANNs), will be compared to those which have been calculated by means of the short-term wind data (i.e. considered to be representative of long-term wind performance). Mean hourly wind speeds and directions calculated in a 10 year period of time at six weather stations located on six different islands in the Canarian Archipelago (Spain) were used in this study. Among the different conclusions which this study revealed, we can highlight that the wind resource estimation based on ANNs is better than that dependant on short-term wind data. This is true when the correlation coefficient between the reference and candidate weather station is of 0.6.

The properties of the global offshore wind turbine fleet

Offshore wind capacity increased massively in recent years. Sufficient wind potential must be available to realize the ambitious goals regarding further wind capacity expansion. Thus, the goal of this study is to quantify the meteorological, geographical, and technical properties of the current global offshore wind turbine fleet. The offshore wind turbine fleet is classified by twelve factors that belong to meteorological, geographical, and technical factor categories. A comprehensive wind turbine site dataset is evaluated, including 5473 sites in Europe and 3404 in Asia. The factors at the wind turbine sites are derived using publicly available datasets, including a reanalysis wind speed dataset, geodata, and technical properties. The results of this study are that the global median (1) wind speed is 8.7 m/s, (2) water depth is 17 m and (3) distance to shore is 27 km. The distance to the nearest wind turbine is 5.1 times the rotor diameter. The factors studied have a high regional variability. While Asian offshore wind turbines operate in shallower water closer to the shores than European, European wind turbine sites provide higher wind resources. The regional differences indicate that the wind potential and wind turbine siting criteria differ depending on the country. The factor values obtained are reference values for future offshore wind resource assessments and estimates of the wind potential from a regional to global scale.

Wind Retrieval from Constellations of Small SAR Satellites: Potential for Offshore Wind Resource Assessment

The planning of offshore wind energy projects requires wind observations over long periods for the establishment of wind speed distributions. In the marine environment, high-quality in situ observations are sparse and restricted to point locations. Numerical modeling is typically used to determine the spatial variability of the wind resource. Synthetic Aperture Radar (SAR) observations from satellites can be used for retrieval of wind fields over the ocean at a high spatial resolution. The recent launch of constellations of small SAR satellites by private companies will improve the sampling of SAR scenes significantly over the coming years compared with the current sampling rates offered by multi-purpose SAR missions operated by public space agencies. For the first time, wind fields are retrieved from a series of StriX SAR scenes delivered by Synspective (Japan) and also from Sentinel-1 scenes delivered by the European Space Agency. The satellite winds are compared with wind speed observations from the FINO3 mast in the North Sea. This leads to root-mean-square errors of 1.4–1.8 m s−1 and negative biases of −0.4 m s−1 and −1.0 m s−1, respectively. Although the Geophysical Model Functions (GMF) applied for wind retrievals have not yet been tuned for StriX SAR observations, the wind speed accuracy is satisfactory. Through conditional sampling, we estimate the wind resource from current and future SAR sampling scenarios where the number of SAR satellites in orbit is increasing over time. We find that hourly samples are needed to fully capture the diurnal wind speed variability at the site investigated. A combination of SAR samples from current missions with samples from clusters of small SAR satellites can yield the necessary number of wind speed samples for accurate wind resource estimation. This is particularly important for sites with pronounced diurnal wind speed variability. An additional benefit of small SAR satellites is that wind speed variability can be mapped at the sub-km scale. The very high spatial resolution is valuable for characterizing the wind conditions in the vicinity of existing offshore wind farms.

Use of the Halphen distribution family for mean wind speed estimation with application to Eastern Canada

We introduce the family of three-parameter heavy-tailed distributions, the Halphen distribution family (HDF), to model the mean wind speed for the purpose of wind energy estimation. The HDF has a number of properties favorable to model wind speed data, such as lower bound at zero (absence of location parameter), flexibility to cover a large range of shapes, and an explicit form of moment generating functions. We examined 126 stations in Eastern Canada (125 stations were heavy-tailed with positive excess kurtosis) and found that HDF provides fit superior to the most commonly used distribution for this purpose, the two-parameter Weibull distribution, in 100% of the stations according to the Akaike information criterion (AIC). HDF was compared against 4 two-parameter models (Gaussian, Weibull, Gamma, and inverse Gamma) and 3 three-parameter models (generalized extreme value, generalized Gamma and Burr). The most common best-fit distribution for the stations in the Eastern Canada case study is the HDF (46% according to AIC). The results of the case study show that wind observations cannot be fit by the Halphen inverse type B, but can be modelled by Halphen A and especially well by Halphen B (minimum of Kolmogorov-Smirnov statistic among candidate distributions). 87 stations exhibited class D tail behavior. No correlation between tail behavior class and best-fit distribution was observed. We encourage the use of Halphen A and Halphen B as candidate distributions for wind resource estimation studies that are based on mean wind speed estimation.

A comparative study of five numerical methods for the estimation of Weibull parameters for wind energy evaluation at Eastern Jerusalem, Palestine

Wind power provides a clean and feasible solution to generate electricity. The development of wind power applications requires a deep analysis of wind profiles and an accurate prediction of wind energy at a study site. This work explores the distribution of wind speed to estimate the two Weibull parameters (shape and scale) that are widely utilized for modeling and providing an accurate and efficient estimation of wind resource and power. These two parameters are calculated based on measured daily wind speed data from 2008 to 2018, collected in Jerusalem, Palestine. Three assessment criteria were used to assess the goodness of fit; they are Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and chi-square. The findings revealed that of the five estimation methods being considered in this study, both the Empirical Method (EM) and the Method of Moment (MoM) were the most accurate in determining the values of the Weibull shape and scale parameters to approximate wind speed distribution at the study site. Based on the goodness-of-fit tests, both methods provide lower values of the used assessment criteria. The statistical performance tests rejected the Energy Pattern Factor Method (EPFM) as an adequate method due to the higher values of chi-square and revealed that the Maximum Likelihood (MLM) and the Modified Maximum Likelihood (MMLM) methods ranked third and fourth, respectively.

A Framework-Based Wind Forecasting to Assess Wind Potential with Improved Grey Wolf Optimization and Support Vector Regression

Wind energy is one of the most promising alternates of fossil fuels because of its abundant availability, low cost, and pollution-free attributes. Wind potential estimation, wind forecasting, and effective wind-energy management are the critical factors in planning and managing wind farms connected to wind-pooling substations. Hence, this study proposes a hybrid framework-based approach for wind-resource estimation and forecasting, namely IGWO-SVR (improved grey wolf optimization method (IGWO)-support vector regression (SVR)) for a real-time power pooling substation. The wind resource assessment and behavioral wind analysis has been carried out with the proposed IGWO-SVR optimization method for hourly, daily, monthly, and annual cases using 40 years of ERA (European Center for Medium-Range Weather Forecast reanalysis) data along with the impact of the El Niño effect. First, wind reassessment is carried out considering the impact of El Niño, wind speed, power, pressure, and temperature of the selected site Radhapuram substation in Tamilnadu, India and reported extensively. In addition, statistical analysis and wind distribution fitting are performed to demonstrate the seasonal effect. Then the proposed model is adopted for wind speed forecasting based on the dataset. From the results, the proposed model offered the best assessment report and predicted the wind behavior with greater accuracy using evaluation metrics, namely root mean square error (RMSE), mean absolute error (MAE), and mean squared error (MSE). For short-term wind speed, power, and El Niño forecasting, IGWO-SVR optimization effectively outperforms other existing models. This method can be adapted effectively in any potential locations for wind resource assessment and forecasting needs for better renewable energy management by power utilities.

Wind turbine ice detection using AEP loss method: A case study

This paper describes the comparison of a statistical and numerical case study of wind resource assessment and estimation of resultant Annual Energy Production due to ice of a wind park in ice prone cold region. Three years Supervisory Control and Data Acquisition data from a wind park located in arctic region is used for this study. Statistical analysis shows that the relative power loss due to icing related stops is the main issue for this wind park. While Larsen wake model is used for the CFD simulations, where results show that it is important to use the wake loss model for CFD simulations of wind resource assessment and AEP estimation of a wind park. A preliminary case study about wind park layout optimization has also been carried out which shows that AEP can be improved by optimizing the wind park layout and CFD simulations can be a good tool.

Advancing Wind Resource Assessment in Complex Terrain with Scanning Lidar Measurements

The planning and realization of wind energy projects requires an as accurate and precise wind resource estimation as possible. Standard procedures combine shorter on-site measurements with the application of numerical models. The uncertainties of the numerical data generated from these models are, particularly in complex onshore terrain, not just rather high but typically not well quantified. In this article we propose a methodology for using a single scanning Doppler wind lidar device to calibrate the output data of a numerical flow model and with this not just quantify but potentially also reduce the uncertainties of the final wind resource estimate. The scanning lidar is configured to perform Plan Position Indicator (PPI) scans and the numerical flow data are projected onto this geometry. Deviations of the derived from the recorded line-of-sight wind speeds are used to identify deficiencies of the model and as starting point for an improvement and tuning. The developed methodology is demonstrated based on a study for a site in moderately complex terrain in central Germany and using two rather different types of numerical flow models. The findings suggest that the use of the methodology and the introduced scanning wind lidar technology offers a promising opportunity to control the uncertainty of the applied flow models, which can otherwise only be estimated very roughly.

Probabilistic Modelling of 80 m Mast Measured Wind Resource: A case study

ABSTRACT Eliciting information regarding available energy in the wind resource is intricately linked to the viability of a wind power project. The energy output of wind power plant is affected primarily by the wind speed characteristics. Probability distributions have proved to be useful for stochastic modeling of the wind resource and power potential estimation. This work fits ten most popular wind speed marginal distributions and four mixture distributions to 80 m mast measured 10-min averaged wind speed data in Odisha, India. The yearly mean wind speed is found to be 5.95 m/s with a coefficient of variation of 52% whereas the highest monthly mean wind speed of 9.14 m/s occurs in May. It is found that only generalized extreme value distribution could fit the yearly wind speed whereas a number of marginal and mixture distributions are found suitable for modeling monthly and seasonal wind speeds. The best fitted Weibull shape and scale parameters are 2.00 and 6.72 m/s, respectively. The annual wind power density (WPD) of 247 W/m 2 with a very high summer WPD of 414 W/m 2 confirms the feasibility of commercial wind power development.

Understanding and mitigating the impact of data gaps on offshore wind resource estimates

Abstract. Like almost all measurement datasets, wind energy siting data are subject to data gaps that can for instance originate from a failure of the measurement devices or data loggers. This is in particular true for offshore wind energy sites where the harsh climate can restrict the accessibility of the measurement platform, which can also lead to much longer gaps than onshore. In this study, we investigate the impact of data gaps, in terms of a bias in the estimation of siting parameters and its mitigation by correlation and filling with mesoscale model data. Investigations are performed for three offshore sites in Europe, considering 2 years of parallel measurement data at the sites, and based on typical wind energy siting statistics. We find a mitigation of the data gaps' impact, i.e. a reduction of the observed biases, by a factor of 10 on mean wind speed, direction and Weibull scale parameter and a factor of 3 on Weibull shape parameter. With increasing gap length, the gaps' impact increases linearly for the overall measurement period while this behaviour is more complex when investigated in terms of seasons. This considerable reduction of the impact of the gaps found for the statistics of the measurement time series almost vanishes when considering long-term corrected data, for which we refer to 30 years of reanalysis data.

Multi-lidar wind resource mapping in complex terrain

Abstract. Scanning Doppler lidars have great potential for reducing uncertainty of wind resource estimation in complex terrain. Due to their scanning capabilities, they can measure at multiple locations over large areas. We demonstrate this ability with dual-Doppler lidar measurements of flow over two parallel ridges. The data have been collected using two pairs of scanning lidars operated in a dual-Doppler mode during the Perdigão 2017 measurement campaign. There the scanning lidars mapped the flow 80 m above ground level along two ridges, which are considered favorable for wind turbine siting. The measurements are validated with sonic wind measurements at each ridge. By analyzing the collected data, we found that wind speeds are on average 10 % higher over the southwest ridge compared to the northeast ridge. At the southwest ridge, the data show, for approach flow normal to the ridge, a change of 20 % in wind speed along the ridge. Fine differences like these are difficult to reproduce with computational flow models, as we demonstrate by comparing the lidar measurements with Weather Research and Forecasting large-eddy simulation (WRF-LES) results. For the measurement period, we have simulated the flow over the site using WRF-LES to compare how well the model can capture wind resources along the ridges. We used two model configurations. In the first configuration, surface drag is based purely on aerodynamic roughness, whereas in the second configuration forest canopy drag is also considered. We found that simulated winds are underestimated in WRF-LES runs with forest drag due to an unrealistic forest distribution on the ridge tops. The correlation of simulated and observed winds is, however, improved when the forest parameterization is applied. WRF-LES results without forest drag overestimated the wind resources over the southwest and northeast ridges by 6.5 % and 4.5 %, respectively. Overall, this study demonstrates the ability of scanning lidars to map wind resources in complex terrain.

Comparison of three probability distributions and techno-economic analysis of wind energy production along the coastal belt of Pakistan

Continuous probability distributions have long been used to model the wind data. No single distribution can be declared accurate for all locations. Therefore, a comparison of different distributions before actual wind resource assessment should be carried out. Current work focuses on the application of three probability distributions, i.e. Weibull, Rayleigh, and lognormal for wind resource estimation at six sites along the coastal belt of Pakistan. Four years’ (2015–2018) wind data measured each 60-minutes at 50 m height for six locations were collected from Pakistan Meteorological Department. Comparison of these distributions was done based on coefficient of determination ( R2), root mean square error, and mean absolute percentage deviation. Comparison showed that Weibull distribution is the most accurate followed by lognormal and Rayleigh, respectively. Wind power density ( PD) was evaluated and it was found that Karachi has the highest wind speed and PD as 5.82 m/s and 162.69 W/m2, respectively, while Jiwani has the lowest wind speed and PD as 4.62 m/s and 76.76 W/m2, respectively. Furthermore, feasibility of annual energy production (AEP) was determined using six turbines. It was found that Vestas V42 shows the worst performance while Bonus 1300/62 is the best with respect to annual energy production and Bonus 600/44 is the most economical. Finally, sensitivity analysis was carried out.

Integration of small-scale surface properties in a new high resolution global wind speed model

A new model for mapping the near-surface wind speed L-moments on a high spatial resolution scale (250 m × 250 m) is introduced (GloWiSMo). The target variables are the first five L-moments of 6146 globally distributed wind speed time series. ERA5 reanalysis wind speed available on a 0.25° × 0.25° grid was used as predictor representing the large-scale wind field. Eleven predictors derived from a land cover model and a digital elevation model were applied to integrate the influence of small-scale surface properties on the wind field. The model is based on a least-squares boosting approach which is a machine learning algorithm. The parameters of the Kappa and Wakeby distribution were estimated based on the modeled L-moments. By applying the power law, the near-surface wind speed distribution can be extrapolated to any hub height. Here, we selected a typical wind turbine hub height of 120 m to demonstrate the potential of GloWiSMo. It was found that the relevance of a predictor on the spatial variability of the wind resource changes with the size of the investigation area. While the roughness length is a decisive factor for the large-scale spatial variability of the wind resource, the relative elevation is an important factor for the small-scale spatial variability. Rigorous model evaluation was performed using a validation dataset containing 598 globally distributed wind speed time series. The coefficient of determination calculated for the first L-moment was found to be 0.83. Based on the evaluation results, we argue that the developed model enables accurate and spatially explicit wind resource estimates at a very high spatial resolution.

Similarity functions and a new [formula omitted] closure for predicting stratified atmospheric surface layer flows in complex terrain

Most of the k−ε closures for modeling stratified surface layer are derived from the classical similarity functions and might fail in complex terrain due to limitations of the classical similarity functions. Despite the classical similarity functions to limit the flux Richardson number Rf, we present new similarity functions estimated from field measurement in full range of Rf and propose a k−ε model using the new similarity functions to improve predictions of stratified surface layer flows in complex terrain. Measurements show that the classical similarity functions are partially valid in complex terrain and the wind shear under strongly stable conditions is constrained at large Rf. According to numerical studies in two areas of complex terrain, models using the classical similarity functions and the new similarity functions both present good predictions of convective airflows in complex terrain. The new similarity functions are shown to significantly improve the k−ε model in predicting stably stratified airflows in complex terrain by constraining the wind shear at large Rf, while the classical similarity functions without limiting the wind shear lead to significantly misestimating the wind speedup factor under stable conditions. Using the proposed model to predict flows in wind farm could benefit wind resource estimation and wind power forecasting.

Wind resource mapping and energy estimation in complex terrain: A framework based on field observations and computational fluid dynamics

The proposed framework produces wind resource maps and estimates the energy production in complex terrain by combining computational fluid dynamics (CFD) with observations from an arbitrary number of masts. It provides a fast and comprehensive solution to the research gap that is to obtain accurate CFD inflow conditions and to mitigate the modeling error by incorporating observations to produce a wind resource map and estimate energy yield. Wind data are processed by direction bins both to initialize simulations and to combine with the latter. The wind resource map is then obtained by superposing the power density maps from each sector. Ultimately, a database containing different turbine models and hub heights is used to filter the best performing cases. Validation at two complex terrain sites reveals that the wind flow model is sufficiently accurate using the proper parameters, and that reasonable inflow conditions and assimilated wind speed fields are achieved. The framework was tested at a complex site in Brazil and was more sensitive to the number of simulated wind directions than to grid refinement. This research provides a comprehensive contribution toward wind resource mapping in complex terrain because it is computationally fast and flexible in the number of masts.

Wind Turbine Wake Effects on Wind Resource Assessments – a Case Study

This paper describes a case study of wind turbine wake loss effects on wind resource assessment in cold region. One year wind park SCADA data is used. Computational Fluid Dynamics (CFD) based numerical simulations are carried out for wind resource assessment and estimation of resultant Annual Energy Production (AEP). Numerical results are compared with the field SCADA data, where a good agreement is found. To better understand the wind flow physics and effects of wind turbine turbulence wake loss effects, three different wake loss models are used for the numerical simulations, where results with wake model is found in best agreement with the AEP estimation from field SCADA data. A detailed comparison of all wind turbines is also presented with the gross AEP. A preliminary case study about wind park layout optimization has also been carried out which shows that AEP can be improved by optimizing the wind park layout and CFD simulations can be used as a tool in this regards.