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Repowering wind farms entails upgrading or replacing old turbines with more efficient, capable, and profitable ones. This technique has the potential to boost energy production, improve grid integration, and lower operational costs. Repowering also makes use of the most recent breakthroughs in wind energy technology, ensuring that wind farms stay economically viable and contribute to the growth of renewable energy. The majority of the wind farms that are located in India were constructed in early 2000, and their capacity ratings range from 200 kW to 800 kW. The lifespans of these wind farms have finally come to an end. Repowering wind farms is a viable alternative in these regions due to the significant wind capacity that exists there. In order to find the wind potential in the site wind resource assessment is needed. This paper proposes a repowering scheme for the existing wind farm located in Kayathar, Tamil Nadu. The reduction in power loss due to the wake effect in the existing wind farm is analyzed using WAsP software and repowering scheme is proposed to increase the Annual Energy Production (AEP), Capacity Utilisation Factor (CUF). The Wind Atlas Analysis and Application Program (WAsP) is utilized in order to carry out the site's wind resource evaluation. After the wind resource assessment, individual turbine wake loss is identified, and the viability of repowering the wind farm by raising the hub height of high wake-affected turbines was investigated. Another repowering study is also carried out by installing high-capacity turbines in place of the existing turbines. [ABSTRACT FROM AUTHOR]

Abstract The objective of this study is to perform an analysis to determine the most suitable type of wind turbine that can be installed at a specific location for electricity generation, using annual measurements of wind characteristics and meteorological parameters. Wind potential analysis has shown that the analyzed location is suitable for the development of a wind farm. The analysis was carried out for six different types of wind turbines, with a power ranging from 1.5 to 3.0 MW and a hub height set at 80 m. Wind power potential was assessed using the Weibull analysis. The values of the scale coefficient c were determined, and a large monthly variation was observed, with values ranging from 1.92 to 8.36 m/s and an annual value of 4.95 m/s. Monthly values for the shape coefficient k varied between 0.86 and 1.53, with an annual value of 1.07. Additionally, the capacity factor of the turbines was determined, ranging from 17.75 to 22.22%. The Vestas turbine, with a nominal power of 2 MW and a capacity factor of 22.22%, proved to be the most efficient wind turbine for the specific conditions of the location. The quantity of greenhouse gas emissions that will be reduced if this type of turbine is implemented was also calculated, considering the average CO2 emission intensity factor (kg CO2/kWh) of the national electricity system.

The objective of this study is to perform an analysis to determine the most suitable type of wind turbine that can be installed at a specific location for electricity generation, using annual measurements of wind characteristics and meteorological parameters. Wind potential analysis has shown that the analyzed location is suitable for the development of a wind farm. The analysis was carried out for six different types of wind turbines, with a power ranging from 1.5 to 3.0 MW and a hub height set at 80 m. Wind power potential was assessed using the Weibull analysis. The values of the scale coefficient c were determined, and a large monthly variation was observed, with values ranging from 1.92 to 8.36 m/s and an annual value of 4.95 m/s. Monthly values for the shape coefficient k varied between 0.86 and 1.53, with an annual value of 1.07. Additionally, the capacity factor of the turbines was determined, ranging from 17.75 to 22.22%. The Vestas turbine, with a nominal power of 2 MW and a capacity factor of 22.22%, proved to be the most efficient wind turbine for the specific conditions of the location. The quantity of greenhouse gas emissions that will be reduced if this type of turbine is implemented was also calculated, considering the average CO2 emission intensity factor (kg CO2/kWh) of the national electricity system. [ABSTRACT FROM AUTHOR]

An accurate wind shear model is an important prerequisite in extrapolating the wind resource from lower heights to the increasing hub height of wind turbines. Based on the 1-year dataset (collected in 2014) consisting of 15-minute intervals collected at heights of 2, 10, 50, 100, and 150 m on an anemometer tower in northern China, the present study focuses on the time-varying relationship between the wind shear coefficient (WSC) and atmospheric stability and proposes a wind shear model considering atmospheric stability. Through the relationship between Monin–Obukhov (M-O) length and gradient Richardson number, the M-O length is directly calculated by wind data, and the WSC is calculated by combining the Panofsky and Dutton (PD) models, which enhances the engineering practicability of the model. Then, the performance of the model is quantified and compared with two alternative methods: the use of annual average WSC and the use of stability change WSC extrapolation. The analysis demonstrates that the proposed model outperforms the other approaches in terms of normal root mean square error (NRMSE) and normal bias (NB). More specifically, this method reduces the NRMSE and NB by 24–29% and 76–95%, respectively. Meanwhile, it reaches the highest extrapolation accuracy under unstable and stable atmospheric conditions. The results are verified using the Weibull distribution. [ABSTRACT FROM AUTHOR]

This study aims to optimize the Weather Research and Forecasting (WRF) model regarding the choice of the best planetary boundary layer (PBL) physical scheme and to evaluate the model's performance for wind energy assessment and mapping over the Iranian territory. In this initiative, five PBL and surface layer parameterization schemes were tested, and their performance was evaluated via comparison with observational wind data. The study used two-way nesting domains with spatial resolutions of 15 km and 5 km to represent atmospheric circulation patterns affecting the study area. Additionally, a seventeen-year simulation (2004–2020) was conducted, producing wind datasets for the entire Iranian territory. The accuracy of the WRF model was assessed by comparing its results with observations from multiple sites and with the high-resolution Global Wind Atlas. Statistical parameters and wind power density were calculated from the simulated data and compared with observations to evaluate wind energy potential at specific sites. The model's performance was sensitive to the horizontal resolution of the terrain data, with weaker simulations for wind speeds below 3 m/s and above 10 m/s. The results confirm that the WRF model provides reliable wind speed data for realistic wind energy assessment studies in Iran. The model-generated wind resource map identifies areas with high wind (wind speed > 5.6 m/s) potential that are currently without wind farms or Aeolic parks for exploitation of the wind energy potential. The Sistan Basin in eastern Iran was identified as the area with the highest wind power density, while areas west of the Zagros Mountains and in southwest Iran showed high aeolian potential during summer. A novelty of this research is the application of the WRF model in an area characterized by high topographical complexities and specific geographical features. The results provide practical solutions and valuable insights for industry stakeholders, facilitating informed decision making, reducing uncertainties, and promoting the effective utilization of wind energy resources in the region. [ABSTRACT FROM AUTHOR]

Wind energy resource estimates commonly depend on simulated wind speed profiles generated by reanalysis or weather models due to the lack of long time series measurements with sufficient coverage at relevant heights (roughly 90 m above ground). However, modeled data, including reanalyses, can be noisy and display a wide range of biases and errors, variously attributed to terrain effects, poor coverage of assimilated inputs, and model resolution. Wind generation records, if available at high temporal and geographical resolution, can provide a proxy for wind measurements and allow for evaluation of reanalyses and weather model wind time series. We use a 7-year-long data set of hourly, plant-level generation records from over 100 wind plants across Texas to evaluate two commonly used reanalysis data sets (MERRA2 and ERA5). Additionally, we use 1-year of records (2019) to evaluate an operational, high-resolution regional weather modeling product (HRRR v3). We find that across the region, and across all modeling products, the modeled representation of wind generation (i.e., wind speeds at hub heights passed through a power curve) has relatively small mean errors when aggregated daily, but that accuracy and hourly correlation have a strong diurnal sensitivity. Accuracy and correlation systematically decline through the evening and markedly improve after sunrise. These diurnal patterns persist even in the highest resolution model tested (HRRR v3). We hypothesize the nighttime decline in accuracy is mostly due to poorly represented boundary layer conditions, perhaps related to model representation of stability, while other uncertainties (such as wake effects) play a secondary role.

When considering offshore wind farm projects, it is essential to understand the wind conditions in the target sea area. However, in the wind resource assessment at the initial stage, wind farm developers will conduct only the assessment with a numerical model due to no observation data. In this study, in order to evaluate the prediction error of the numerical model, we compared the 0.1km-resolution WRF simulation data with observation data at Happo-Noshiro offshore site, Akita prefecture. We confirmed that WRF can generally well reproduce the annual mean wind speed and wind rose, having good correlations with the observations. However, it was also found that WRF predicted more smoother wind speed fluctuations than the observations, which appeared as characteristic errors in the frequency distribution of wind speed for a year. These errors, combined with the nonlinearity of a wind turbine power curve, was found to cause an underestimation of the annual energy production. [ABSTRACT FROM AUTHOR]

Amongst the various natural and readily available renewable energy sources, wind has been one of the most sought-after sources for energy generation due to its virtually inexhaustible nature and availability over vast geographical areas. The rise in urbanization and the development of high-rise buildings have shown great potential for local wind power generation via locally setting up small-scale turbines on rooftops, specifically for decentralized and off-grid power generation and usage. While the said technology possesses potential, a major question regarding its overall feasibility arises due to the plethora of factors to be considered during the design and implementation of such systems. The following review gives an in-depth understanding of the current status of said technology, along with a discussion of the critical factors that must be given proper attention and how they can impact the power output of the turbines implemented on rooftops. These factors generally include wind resource assessments, the urban environment’s architecture, the wind turbine’s design parameters, and power augmentation mechanisms. Following this extensive discussion, a basic framework for studies on rooftop wind turbines is proposed. This framework can act as a guide for future work on the said topic and discusses in brief the methodologies and the workflow process one can follow when it comes to assessing rooftop wind turbines.

The accurate quantification and assessment of available renewable energy resources has emerged as a research topic with high relevance to policymakers and industry. Motivated by the need for a contemporary review on the methodologies and practices prevalent in wind resource assessments, we employ a systematic analysis of 195 articles that describe large-scale wind assessments. Our review reveals significant heterogeneity in global and continental-scale potentials and geographical bias of research towards the Northern Hemisphere, despite electrification needs in regions like Africa and Latin America. A fraction of the literature attempts to explicitly include social and political barriers to wind power development, thereby defining ‘feasible’ potentials. We delve into advancements in this domain, focusing on innovative methodologies that encapsulate the viewpoints of subject experts and stakeholders in the assessment process. Our analysis underscores pressing challenges relating to data sharing and scientific reproducibility, with our findings revealing a mere 10 % of studies that offer openly available data for download. This highlights a pervasive insufficiency in the reproducibility of wind assessments. Additionally, we tackle notable hurdles concerning wind data and meteorological characterization, including an over-reliance on single-source wind data and a deficit in adequately characterizing temporal wind variability. Relatedly, we uncover a highly heterogenous approach to turbine siting and characterizing wake-related losses. These methods are frequently simplistic, potentially leading to an overestimation of wind potentials by assuming an overly optimistic capacity density. In each of these domains, we discuss the state of the art for modern wind resource assessments, propose best practices, and pinpoint crucial areas warranting future research.

This study aims to optimize the Weather Research and Forecasting (WRF) model regarding the choice of the best planetary boundary layer (PBL) physical scheme and to evaluate the model’s performance for wind energy assessment and mapping over the Iranian territory. In this initiative, five PBL and surface layer parameterization schemes were tested, and their performance was evaluated via comparison with observational wind data. The study used two-way nesting domains with spatial resolutions of 15 km and 5 km to represent atmospheric circulation patterns affecting the study area. Additionally, a seventeen-year simulation (2004–2020) was conducted, producing wind datasets for the entire Iranian territory. The accuracy of the WRF model was assessed by comparing its results with observations from multiple sites and with the high-resolution Global Wind Atlas. Statistical parameters and wind power density were calculated from the simulated data and compared with observations to evaluate wind energy potential at specific sites. The model’s performance was sensitive to the horizontal resolution of the terrain data, with weaker simulations for wind speeds below 3 m/s and above 10 m/s. The results confirm that the WRF model provides reliable wind speed data for realistic wind energy assessment studies in Iran. The model-generated wind resource map identifies areas with high wind (wind speed > 5.6 m/s) potential that are currently without wind farms or Aeolic parks for exploitation of the wind energy potential. The Sistan Basin in eastern Iran was identified as the area with the highest wind power density, while areas west of the Zagros Mountains and in southwest Iran showed high aeolian potential during summer. A novelty of this research is the application of the WRF model in an area characterized by high topographical complexities and specific geographical features. The results provide practical solutions and valuable insights for industry stakeholders, facilitating informed decision making, reducing uncertainties, and promoting the effective utilization of wind energy resources in the region.

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. [ABSTRACT FROM AUTHOR]

The evaluation of reanalysis and analysis data (estimated data) against in-situ measured data is essential to find uncertainties before its use for wind resource assessment. The performance evaluation of four different generations reanalysis datasets (NCEP-CFSR, NCEP-DOE, NCEP-NCAR and JRA-55) and two analysis datasets (NCEP-FNL and NCEP-GFS) was done against measured data for six sites using statistical analysis. A comparison of monthly mean time-series, Weibull probability distribution function and wind rose diagram of measured and estimated data was performed. The MBE and RMSE for wind speed range from −2.18 to 2.01 m/s and 1.34 to 3.00 m/s respectively; whereas MBE and RMSE for wind direction range from −34.34° to 13.90° and 40.58° to 71.28° respectively for six sites using all datasets. NCEP-CFSR data show promising results for most of the sites with the lowest errors and better correlation coefficients. NCEP-CFSR data being the new generation reanalysis having higher spatial resolution show better results compared to other reanalyses and analyses. The reanalysis and analysis wind data can be used as alternative to measured data to assess wind energy potential. [ABSTRACT FROM AUTHOR]

Air density plays an important role in assessing wind resource. Air density significantly fluctuates both spatially and temporally. But literature typically used standard air density or local annual average air density to assess wind resource. The present study investigates the estimation errors of the potential and fluctuation of wind resource caused by neglecting the spatial-temporal variation features of air density in China. The air density at 100 m height is accurately calculated by using air temperature, pressure, and humidity. The spatial-temporal variation features of air density are firstly analyzed. Then the wind power generation is modeled based on a 1.5 MW wind turbine model by using the actual air density, standard air density ρst, and local annual average air density ρsite, respectively. Using ρst overestimates the annual wind energy production (AEP) in 93.6% of the study area. Humidity significantly affects AEP in central and southern China areas. In more than 75% of the study area, the winter to summer differences in AEP are underestimated, but the intra-day peak-valley differences and fluctuation rate of wind power are overestimated. Using ρsite significantly reduces the estimation error in AEP. But AEP is still overestimated (0–8.6%) in summer and underestimated (0–11.2%) in winter. Except for southwest China, it is hard to reduce the estimation errors of winter to summer differences in AEP by using ρsite. Using ρsite distinctly reduces the estimation errors of intra-day peak-valley differences and fluctuation rate of wind power, but these estimation errors cannot be ignored as well. The impacts of air density on assessing wind resource are almost independent of the wind turbine types. [ABSTRACT FROM AUTHOR]

The evaluation results of wind energy resources directly affect the economic benefits and healthy development of wind farms. Therefore, a high-resolution wind resource assessment method for wind farms based on mesoscale atmospheric model and CFD technology is studied to accurately simulate relevant data of wind resources and improve the assessment effect. The mesoscale WRF numerical model is used to solve the regional data of wind farms and obtain the mesoscale meteorological analysis data. According to the solution results of the mesoscale atmospheric model, the wind speed profile is established, the boundary conditions and initial conditions are extracted, and the CFD micro scale model is input to obtain the wind speed and wind speed frequency at the height of the fan impeller. In order to improve the accuracy of numerical simulation of micro scale CFD model, the large eddy simulation method is used to simulate the operation of wind farms. Complete theoretical power generation evaluation based on wind speed, wind speed frequency and generator power. The experimental results show that this method can accurately simulate wind resources and accurately evaluate the theoretical power generation of wind farms. The wind farm is rated as Level 3, and the wind frequency is mainly between 4 and 10 m/s. This method can ensure that the wind farm can generate electricity all year round without damaging the wind speed. [ABSTRACT FROM AUTHOR]

Suburban areas are rich in wind resources, but wind energy characteristics are complicated by the strong turbulence disturbance of buildings. Current work on wind resource assessment is focused on the fitting of the wind speed distribution function and the measurement of wind energy abundance. The mathematical relationship between the distribution function and the main wind energy characteristics under suburban wind field conditions needs to be studied.In this study, a ZephIR 300 LiDAR wind measurement system is built in a suburban area, and wind speed data samples from heights of 11–199 m are obtained. The functional relationship between parameters is derived through a comparative analysis, which reveals that the variation of shape parameter k in the range of 1.05–1.25 causes abrupt changes in wind power density (WPD). The positive proportional relationship between the scale parameter and WPD and its correlation with height are also obtained. Turbulence intensity (TI) decrease as the scale parameter increases from 1.18 to 2.50 and fluctuates within 10.54% as the scale parameter c continues to increase from 2.50 to 5.58. The scale parameter corresponding to the minimum value of TI at each height is provided.

The characterization of wind speed and its variability at a site is important for wind resource assessment. The most readily available wind measurements are at 10 m above ground level. These measurements can then be extrapolated vertically to estimate wind power production. In this work, the Monin–Obukhov similarity method was implemented to estimate the wind speed vertical profile within the surface boundary layer for a southeast Mexican site, considering seasonal and diurnal variations of the surface boundary layer stability parameters. Additionally, a power-law method was implemented where the wind shear exponent was set following the International Electrotechnical Commission (IEC) standard and using a variable wind shear exponent. The results showed that the log-law and the variable wind shear method produce better estimates than the IEC standard. The mean power production was estimated at hub height (80 m above surface level) using anemometric data from the Mexican Wind Atlas and then compared with that calculated using the equivalent wind speed estimated from variable wind shear exponent and log-law model. No influence of the vertical wind speed variation within and on top (up to 117.5 m) of the surface boundary layer was found on the mean power production for a wind turbine with a diameter of 90 m and a hub height of 80 m.

In the framework of the wind energy network for distributed generation in urban environments for most South American countries, wind resource assessment studies have been carried out in activities to establish a suitable methodology to assess the wind potential in urban environments. Some methodologies are already published in research studies, and the wind tunnel is the most accurate solution to obtain insight into the wind resource when measurements are unavailable, which is the most frequent case. Nevertheless, besides its validity, one cannot disregard the high cost of producing a scaled urban model and access to a wind tunnel. In this sense, this paper compares results from a wind tunnel experiment and different numerical modeling approaches. Two commercial models (WindSim and Wasp Engineering) and one open-source CFD code (OpenFOAM) are used. The results from the modeling simulation concluded that CFD models could achieve lower deviations for the mean wind speed and turbulence intensity when compared with non-CFD models. With such results, CFD modeling is a promising tool for reliably evaluating wind potential in urban environments.

Abstract Wind energy resource estimates commonly depend on simulated wind speed profiles generated by reanalysis or weather models due to the lack of long time series measurements with sufficient coverage at relevant heights (roughly 90 m above ground). However, modeled data, including reanalyses, can be noisy and display a wide range of biases and errors, variously attributed to terrain effects, poor coverage of assimilated inputs, and model resolution. Wind generation records, if available at high temporal and geographical resolution, can provide a proxy for wind measurements and allow for evaluation of reanalyses and weather model wind time series. We use a 7‐year‐long data set of hourly, plant‐level generation records from over 100 wind plants across Texas to evaluate two commonly used reanalysis data sets (MERRA2 and ERA5). Additionally, we use 1‐year of records (2019) to evaluate an operational, high‐resolution regional weather modeling product (HRRR v3). We find that across the region, and across all modeling products, the modeled representation of wind generation (i.e., wind speeds at hub heights passed through a power curve) has relatively small mean errors when aggregated daily, but that accuracy and hourly correlation have a strong diurnal sensitivity. Accuracy and correlation systematically decline through the evening and markedly improve after sunrise. These diurnal patterns persist even in the highest resolution model tested (HRRR v3). We hypothesize the nighttime decline in accuracy is mostly due to poorly represented boundary layer conditions, perhaps related to model representation of stability, while other uncertainties (such as wake effects) play a secondary role.

Abstract In wind energy, the general practice in assessing the wind resource of a site is to employ a 10‐min averaging to measured wind data. However, small wind turbines (SWTs) with rotor diameters

In the pre-construction of wind farms, wind resource assessment is of paramount importance. Measurements by lidars are a source of high-fidelity data. However, they are expensive and sparse in space and time. Contrarily, Weather Research and Forecasting models generate continuous data with relatively low fidelity. We propose a hybrid approach combining measurements and output from numerical simulations for the assessment of offshore wind. Firstly, the datasets were fed onto a matrix, with columns representing the spatial lidar and WRF points, and the rows representing the time steps. Entries of the matrix reflect the wind speed, empty entries represent unobserved data. Then, matrix factorization using Gaussian process was employed for filling the missing entries with statistically calculated estimates. The model was optimized with stochastic gradient descent to apply GP without approximation methods. To evaluate the method, wind speed data along the coast of Denmark were used. The proposed technique, evaluated using two experiments, resulted in 58% more accurate results than the industrial standard method with trivial increase of computational cost. The RMSE of the proposed method ranges between 0.35 and 0.52 m/s. [Display omitted] • Novel algorithm to merge limited wind data from measurements with simulation data. • Fusion of multi-levels of fidelity data, high-fidelity as function of low fidelity. • Cross correction of measurements at multiple locations. • Significantly outperforms the standard industrial method - Windgrapher. [ABSTRACT FROM AUTHOR]

The engineering wind farm models currently used in industry can assess power losses due to turbine wake effects, but the prediction of power losses due to farm blockage is still a challenge. In this study we demonstrate a new prediction method of farm blockage losses and a possible strategy to mitigate them for a large offshore wind farm in the North Sea, by combining a common engineering wind farm model 'FLORIS' with the 'two-scale momentum theory' of Nishino and Dunstan (2020). Results show that the farm blockage losses depend significantly on the 'wind extractability' factor, which reflects the strength of mesoscale atmospheric response. For a typical range of the extractability factor (assessed using a numerical weather prediction model) the farm blockage losses are shown to vary between about 5% and 15% of the annual energy production (AEP). However, these losses may be mitigated by adjusting turbine operating points taking into account the wind extractability. It is shown that a simple adjustment of the blade pitch angle and tip-speed ratio used below the rated wind speed may increase the AEP by up to about 2%. [ABSTRACT FROM AUTHOR]

It is crucial to understand the characteristics of wind resources and optimize wind resources in the area that is being considered for offshore wind farm development. Based on the improved particle swarm optimization (IPSO) and the back propagation neural network (BPNN), the IPSO-BP hybrid intelligent algorithm model was established. The assessment of wind resource characteristics in the eastern waters of China, including average wind speed, extreme wind speed, wind power density, effective wind energy hours and wind direction distribution were all calculated. Additionally, the wind speed throughout the different years in Luchao Port, a famous seaport in China, was predicted. The results revealed that the wind power density is approximately 300 W/m2 all year round and that the effective wind energy hours take up about 92% per hour. It was also identified that the wind direction distribution is stable in Luchao Port, implying that there are better wind energy resource reserves in this region. The IPSO-BP model has a strong tracking performance for wind speed changes, and can accurately predict the wind speed change in a short period. In addition, the prediction error of the IPSO-BP model is smaller when the time of training data is closer to the target one, and it can be controlled within a 5% range. [ABSTRACT FROM AUTHOR]

In the framework of the wind energy network for distributed generation in urban environments for most South American countries, wind resource assessment studies have been carried out in activities to establish a suitable methodology to assess the wind potential in urban environments. Some methodologies are already published in research studies, and the wind tunnel is the most accurate solution to obtain insight into the wind resource when measurements are unavailable, which is the most frequent case. Nevertheless, besides its validity, one cannot disregard the high cost of producing a scaled urban model and access to a wind tunnel. In this sense, this paper compares results from a wind tunnel experiment and different numerical modeling approaches. Two commercial models (WindSim and Wasp Engineering) and one open-source CFD code (OpenFOAM) are used. The results from the modeling simulation concluded that CFD models could achieve lower deviations for the mean wind speed and turbulence intensity when compared with non-CFD models. With such results, CFD modeling is a promising tool for reliably evaluating wind potential in urban environments. [ABSTRACT FROM AUTHOR]

The current study presents a wind resource assessment (WRA) approach by combining existing approaches, including wind probability density estimation based on hourly wind speed frequency, wind power density (WPD) and wind energy density (WED), wind turbine (WT) power output and power curve modeling, and annual energy production (AEP). Wind probability density investigation employed various probability density functions (PDF), including parametric probability density functions such as Weibull, Normal, and Gamma, and non-parametric distribution, including Kernel Density Estimator (KDE). The present study also models the influence of humidity on air density for estimating WPD, WT power output, and AEP. The current study validated the proposed approach by conducting case studies for selected sites of remote Indonesian archipelago islands. AEP estimation proposed by this study can assist the site-turbine fitting design, especially for relatively moist locations. [ABSTRACT FROM AUTHOR]

Wind resource assessments are crucial for pre-construction planning of wind farms, especially offshore. This study proposes a novel hybrid model integrating Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Empirical Wavelet Transform (EWT) for enhanced wind speed forecasting. This secondary decomposition reduces forecasting complexity by processing high-frequency signals. A Bidirectional Long Short-Term Memory (BiLSTM) neural network optimized with the Grey Wolf Optimizer (GWO) is then employed for forecasting. The model's accuracy is evaluated using simulated wind speeds along the coast of Denmark, combined with lidar measurements through data fusion. This approach demonstrates significant improvements in prediction accuracy, highlighting its potential for offshore wind resource assessment. [Display omitted] • A novel algorithm is proposed to predict offshore wind speed using continuous simulation data. • A secondary signal decomposition algorithm is used to pre-process a time series and further reduce the complexity of the signal. • A Gaussian regression is constructed for the fusion of data with multiple fidelities where high-fidelity data is considered as a function of low fidelity data. • The data fusion method using gappy empirical measurement results in more accurate wind resource assessment with 40% improvement, significantly outperforming models that are trained only on a single source of data. [ABSTRACT FROM AUTHOR]

This study aimed to establish numerical models to replicate wind conditions for nearshore waters, sensitive to onshore topography, and to compare the characteristics of computational fluid dynamic (CFD) and mesoscale models. Vertical Doppler light detection and ranging (LiDAR) observation data were measured at an onshore site, which showed that wind conditions were affected by thermodynamic phenomena, such as land and sea breeze, and dynamical effects from neighboring onshore topography. The estimation accuracy of the CFD model depended on the height of the LiDAR data input. A height close to the target, such as the hub height of wind turbines, seemed appropriate as input data, considering that the accuracy of the wind speed shear replicated in a CFD numerical model may be uncertain. The mesoscale model replicated the wind through the thermodynamic effect and reliably estimated wind speed over nearshore waters without observation correction. Larger estimation errors were detected in the CFD model than in the mesoscale model, as the former could not account for thermodynamic effects. Wind conditions in water areas near complex coastlines may also be formed by thermodynamic factors, making analysis using a mesoscale model advantageous. [ABSTRACT FROM AUTHOR]

An analysis of the behavior of the wind speed using the regional climate model PRECIS in high resolution scenarios of climate change RCPs is presented. The projections indicate that throughout the century, the speed of the surface wind will continue to increase to a greater or lesser extent (depending on the scenario) in most of the national territory, mainly towards the coast north, as an intensification and westward shift of the anticyclone North Atlantic could occur. The most important thing about this increase is that allows to consolidate the current projection of the Cuban wind program, in which the construction of 13 wind farms is proposed, precisely where the wind potential of Cuba will be increased. Finally this increase is added to the wind speed outputs of the Numerical Wind Atlas of Cuba to estimate the values of wind speed over the future periods. [ABSTRACT FROM AUTHOR]

Globally, people from all walks of life are working towards a sustainable globe and the availability of energy to all human beings using clean, self‐renewed, freely available, and distributive sources. Present study utilizes wind speed data measured at 40, 60, 80, 98, and 100 m above ground level and other meteorological parameters near ground level to analyze the wind characteristics, energy yield, cost of energy, and greenhouse gasses reduction at chosen locations. The data analysis showed an increasing trend of wind shear exponent with longitude and a decreasing trend of ambient temperature with latitude. The analysis recommends Yanbu North, Hafar Al‐Batin, Sharourah, and Turaif sites with annual energy delivered to the grid of 213.5, 179.4, 167.1, and 166.7 GWh from a wind farm of 49.5 MW installed capacity as the first, second, third, and fourth choice for wind power deployment in the Kingdom of Saudi Arabia. On an average, 28.45 thousand tons CO2 equivalent greenhouse gasses can be avoided from entering into the local atmosphere at each site with a maximum of 41.1 thousand tons at Yanbu North site alone. [ABSTRACT FROM AUTHOR]

In its contribution towards fighting climate change, Tunisia aims at reducing greenhouse gas emissions across all sectors through reducing carbon intensity in the country by 41 per cent in 2030, relative to the base year 2010 The Tunisia energy sector mitigation scenario is proactive and incorporates ambitious programs for improving energyefficiency and developing renewable energy resources. This paper reviews the prospects of one of the major renewable energy sources in the country: wind energy. It presents the state of wind energy sector in the world and tracks in particular, the evolution of wind power development in Tunisia since its initiation in 2000 till the present. The paper outlines the prevailing climatic conditions, the regulatory and legal instruments on renewables as well as national policies and strategies on the energy transition. The pathways to sustainable wind energy sector development is analyzed and discussed. Additionally, the wind energy research status in Tunisia is presented and the gaps in research are explored.

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.

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. [ABSTRACT FROM AUTHOR]

The growth rate of offshore wind is increasing due to technological advancement and reduction in cost. An approach using mast measured data at coastline and reanalysis data is proposed for offshore wind resource assessment, especially for developing countries. The evaluation of fifth generation European Reanalysis (ERA5) data was performed against measured data using statistical analysis. ERA5 data slightly underestimates wind speed and wind direction with percentage bias of less than 1%. Wind resource assessment of region in Exclusive Economic Zone (EEZ) of Pakistan was performed in terms of wind speed and Wind Power Density (WPD). The range of monthly mean wind speed and WPD in the region was 4.03–8.67 m/second and 73–515 W/m2 respectively. Most-probable wind speed and dominating wind direction on corners and center of the region were found using probability distributions and wind rose diagrams respectively. Most-probable wind speed ranges 4.41–7.64 m/second and dominating wind direction is southwest. [ABSTRACT FROM AUTHOR]

In wind energy, the general practice in assessing the wind resource of a site is to employ a 10‐min averaging to measured wind data. However, small wind turbines (SWTs) with rotor diameters <15 m will have a shorter response time scale; thus, an averaging time window of 10 min is too long for accurate wind resource assessments. The current study investigates the effect of averaging time windows on wind resource assessment and power estimation of SWTs. To that end, wind data from a 1‐year measurement campaign is analysed using two short‐term averaging windows of 30 and 60 s and two long‐term averaging windows of 5 and 10 min. Although the average wind speed over the 1‐year period is 1.3 m/s with all four windows, when wind speeds higher than 3 m/s are considered, the average wind speed obtained using a 30‐s averaging window is 8% higher than that obtained using a 10‐min window. Power prediction using the 10 kW SWT shows that the predicted power using a 30‐s window is almost twice as large compared to that estimated using a 10‐min window. Finally, the rotational speed of the SWT obtained with short‐term averaging shows better agreement with that computed by direct modelling of the turbine with the measured wind speed as the input to the model. The study thus shows that the performance of SWTs will be better predicted if short‐term averaging windows are used. [ABSTRACT FROM AUTHOR]

Mexico is an attractive candidate for offshore wind energy development due to its geographical location with extensive coasts in the Pacific Ocean and Mexico's Gulf. Although potential offshore wind areas have been geographically assessed, an evaluation of the seasonal variations of the capacity factors has not been considered for the feasibility of the locations. This research identifies potential zones for offshore wind development in the Gulf of Mexico, implementing geographical restrictions such as the Economic Exclusive Zone, distance from the coast, protected areas, bathymetry, and capacity factor seasonality. Wind speeds were obtained from 39 years of reanalyses historical data and two reference wind turbines of 5 and 10 MW were included in the analysis. Three potential areas were identified from the results: the northeast Tamaulipas, the western Campeche, and the northern Yucatan. Monthly mean capacity factors above 45% were estimated from October to June, with the maximum values near 60% between March and April. Conversely, minimum values were observed from July to September but consistently higher than 30%. The analyzed zones show suitable technical conditions for offshore wind development. Further analysis is needed to validate the wind speed conditions, in addition to the evaluation of economic factors, the study of extreme weather conditions like tropical cyclones as well as characteristics in the intertropical region. • Potential zones for offshore wind development in the Gulf of Mexico are identified. • Results rely on 39 years of wind data from reanalysis models and spatial restrictions. • Monthly capacity factors are used for the seasonal analysis of selected locations. • The highest wind resources are located at the northwest and southeast Gulf of Mexico. • Monthly mean capacity factors estimated for the regions are between 25 and 60%. [ABSTRACT FROM AUTHOR]

[Introduction] Accurate wind resource data is of great significance to wind resource evaluation and power generation calculation of wind farm. Due to mechanical failure, weather factors and human influence, there are many problems in wind speed data acquisition, such as short collection time, many discontinuities, data distortion and so on, which bring a lot of trouble to the evaluation of wind resources. [Method] At present, the traditional interpolation method based on MCP method for discontinuous data interpolation and fitting in the wind power industry is not accurate enough. In this paper, the wind resource data prediction scheme based on neural network algorithm was proposed for wind speed interpolation of wind turbine and wind speed interpolation of wind measurement mast. [Result] The results show that the interpolation effect of BP neural network is better than the traditional interpolation method, and the wind speed interpolation of anemometer tower in flat terrain is better than that in complex terrain. [Conclusion] The research shows that the wind speed interpolation technology based on BP neural network method is suitable for wind speed interpolation application of wind farm, and the accuracy of wind resource assessment is significantly improved.

Various models for wind speed mapping have been developed, with increasing attention on models focusing on mapping wind speed distribution. This study extends these models to predict hourly wind speed time series at unsampled locations. A model based on the quantile mapping (QM) procedure was compared to a traditional and machine-learning model to interpolate wind speed spatially. These proposed models were also used with inputs from the ERA5 reanalysis dataset, enabling them to consider local variation in orography and large-scale wind fields. A widely used procedure for mean bias correction of reanalysis based on the Global Wind Atlas (GWA) was implemented and compared to the proposed models. It was found that the QM and machine learning model, both using input from ERA5, significantly outperformed GWA bias correction in terms of time series correlation and probability distribution. Despite being more computationally intensive than GWA bias correction, both models are recommended due to their significantly (in a statistical sense) superior performance. • Various methods for the prediction of hourly wind speed time series at unsampled locations were evaluated. • The complex approaches were more effective than the baseline model. • No single method excelled across all the evaluation criteria. • Two methods seemed promising for downscaling ERA5 wind speed data. [ABSTRACT FROM AUTHOR]

This paper presents a systematic literature review of urban wind energy harnessing in built environments. In line to foster decarbonisation in urban areas of tropical countries with decentralised renewable energy technologies, such as small wind turbines in buildings, this research has been carried out. To provide a comprehensive foundation on the topic, urban wind assessment research for various purposes was reviewed, where 48 studies for energy harnessing purposes were classified by country, region and methods. The methodologies are divided into six stages, of which environmental assessment and resilience analysis are scarce. Due to tropical countries being exposed to atmospheric events that historically cause human and material losses, potential future research could be made in line with resilience analysis through metrics that are key to the proper mass deployment of building-integrated wind turbine systems. Of 66 methods for wind prospecting, the most used are on-site measurement, computational fluid dynamics simulation and numerical climate prediction, which represent 49 %, 22 % and 17 %, respectively. Other decision-making techniques are presented with the aim of addressing new ways in strategic planning to promote the use of this technology. [Display omitted] • Urban wind energy research does not deem energy resilience analysis. • Few evaluations of urban wind energy have been carried out in the tropics. • Hurricanes represent a hazard for building-integrated wind turbines. • On-site measurement and CFD simulation are the most adopted methods. [ABSTRACT FROM AUTHOR]

Situated in the southern part and on the western coast of Thailand, the Andaman Coast covers the provinces of Ranong, Phangnga, Phuket, Krabi, Trang and Satun. Using a coupled mesoscale atmospheric model and a microscale wind flow model, along with computational fluid dynamics (CFD) modeling, this paper presents a detailed assessment of the wind energy potential for power generation along the Andaman Coast of Thailand. The climatic data are obtained from the Modern Era Retrospective analysis for Research and Applications (MERRA), along with a high-resolution topography database and Land Use Land Cover digital data. The results are compared to the equivalent wind speeds obtained with the Weather Research and Forecasting (WRF) atmospheric model. The results showed that, at 120 m above ground level (agl), the predicted wind speeds from the models proposed were 20% lower for the mesoscale model and 10% lower for the microscale model when compared to the equivalent wind speeds obtained from the WRF model. A CFD wind flow model was then used to investigate 3D wind fields at 120–125 m agl over five potential sites offering promising wind resources. The annual energy productions (AEP) and the capacity factors under three different wake loss models and for five wind turbine generator technologies were optimized for 10-MW wind power plants, as per Thailand's energy policies. With capacity factors ranging from 20 to 40%, it was found that the AEPs of the best sites were in the range of 18–36 GWh/year, with a total AEP in the vicinity of 135 GWh/year when using a single wind turbine model for the five sites studied. The combined energy productions by these wind power plants, once operational, could avoid GHG emissions of more than 80 ktons of CO2eq/year. [ABSTRACT FROM AUTHOR]

This work presents an assessment of the wind potential and a design methodology for a 10 MW wind farm in the Arzew industrial region, located in northwest Algeria, to improve the quality of service of the electricity grid and increase Algeria's participation in the use of renewable energy. The hourly wind data of 10 years (2005-2015) that correspond to the wind potential of the site were analyzed, such as: dominant wind directions, probability distribution, Weibull parameters, mean wind speed and power potential. The site has a mean annual wind speed of 4.46 m/s at 10m height, and enough space to locate the wind turbines. A comparative study was carried out between four wind turbine technologies to improve the site's efficiency and select the appropriate technology: PowerWind 56/ 900 kW, Nordex N50/800 kW, Vestas V50/850 kW, NEG-Micon 44/750 kW. The estimate of the energy produced using WAsP software and the choice of the optimal architectural configuration for wind turbines installation was confirmed. A techno-economic and environmental study was carried out by HOMER software, to choose the model that produces the maximum annual net energy with a competitive cost in the global wind energy market, $ 0.068/kWh, and that provides clean energy with a reduced emission of polluting gases. Finally, this work provides a good indicator for the construction of a wind farm in Arzew.

The engineering wind farm models currently used in industry can assess power losses due to turbine wake effects, but the prediction of power losses due to farm blockage is still a challenge. In this study we demonstrate a new prediction method of farm blockage losses and a possible strategy to mitigate them for a large offshore wind farm in the North Sea, by combining a common engineering wind farm model ’FLORIS’ with the ’two-scale momentum theory’ of Nishino and Dunstan (2020). Results show that the farm blockage losses depend significantly on the ’wind extractability’ factor, which reflects the strength of mesoscale atmospheric response. For a typical range of the extractability factor (assessed using a numerical weather prediction model) the farm blockage losses are shown to vary between about 5% and 15% of the annual energy production (AEP). However, these losses may be mitigated by adjusting turbine operating points taking into account the wind extractability. It is shown that a simple adjustment of the blade pitch angle and tip-speed ratio used below the rated wind speed may increase the AEP by up to about 2%.

It is crucial to understand the characteristics of wind resources and optimize wind resources in the area that is being considered for offshore wind farm development. Based on the improved particle swarm optimization (IPSO) and the back propagation neural network (BPNN), the IPSO-BP hybrid intelligent algorithm model was established. The assessment of wind resource characteristics in the eastern waters of China, including average wind speed, extreme wind speed, wind power density, effective wind energy hours and wind direction distribution were all calculated. Additionally, the wind speed throughout the different years in Luchao Port, a famous seaport in China, was predicted. The results revealed that the wind power density is approximately 300 W/m2 all year round and that the effective wind energy hours take up about 92% per hour. It was also identified that the wind direction distribution is stable in Luchao Port, implying that there are better wind energy resource reserves in this region. The IPSO-BP model has a strong tracking performance for wind speed changes, and can accurately predict the wind speed change in a short period. In addition, the prediction error of the IPSO-BP model is smaller when the time of training data is closer to the target one, and it can be controlled within a 5% range.

The use of renewable energy as a future energy source is attracting considerable research interest globally. In particular, there is a significant growth in wind energy utilization during the last few years. This present study through a detailed and systematic literature survey assesses the wind energy potential of Kazakhstan for the first time. Using the Weibull distribution function and hourly wind speed data, the annual power and energy density of the sites are calculated. For the 50 sites considered in this study and at a height of 10 m above the ground, the annual average wind speed, the power density, and energy production of Kazakhstan range from 0.94–5.15 m/s, 4.50–169.34 W/m2 and 39.56–1502.50 kWh/m2/yr, respectively. It was found that Fort Sevcenko, Atbasar, and Akmola are the three best locations for wind turbine installation with wind power densities of 169.34, 135.30, and 111.51 W/m2, respectively. Fort Sevcenko demonstrates the highest potential for wind energy harvesting with an energy density of 1483.46 kWh/m2/yr. For the 15 commercial wind turbines, it was observed that the annual energy production of the selected turbines ranges between 3.8 GWh/yr in Petropavlovsk to 15.4 GWh/yr in Fort Sevcenko among the top six locations. The lowest and highest capacity factors correspond to the same sites with the values of 29.21% and 58.66%, respectively. Overall, it is the intention of this study to constitute a database for the users and developers of wind power in Kazakhstan. [ABSTRACT FROM AUTHOR]