Mean Wind Power Density Research Articles

Statistical Analysis of Wind Resource Assessment for Different Locations in South-Western Thailand

Weibull parameters have been widely used to evaluate wind energy potential. In this work presents wind resource assessment by statistical analysis with a Weibull distribution model for Krabi, Phuket, and Ranong weather stations in south-western Thailand. Ten-minute intervals include wind speed and wind direction of 10m from four-year records obtained by the Thai meteorological department. Four numerical methods, namely empirical method, graphical method, energy pattern factor method and maximum likelihood method are examined to estimate the Weibull parameters. The Weibull distribution obtained from each method is compared with the observed wind speed distribution by the performance tests using root mean square error, mean percentage error, and chi-square error to select a suitable method for the station area. The results revealed that the maximum likelihood method was the most accurate for Krabi and Phuket stations, and the energy pattern factor method was the most accurate for Ranong station. At a hub height of 80m, the highest mean wind speed and mean wind power density found in Krabi station were 3.25 m/s and 44.84 W/m2. The most probable wind speed value in three stations had a range from 1.80 to 2.50 m/s. The maximum wind speed carrying maximum energy found in Krabi station was 5.53 m/s. The operating probability of a wind turbine in Krabi station was 49.61%, followed by Phuket station was 46.80%, and Ranong station was 37.84%, respectively. In conclusion, all three stations had wind power potential classified as wind class 1 and can be sorted as follows: Krabi, Phuket, and Ranong stations.

Hybrid wind-solar energy resources mapping in the European Atlantic

The complementarity of offshore wind and solar resources can enhance the energy output of a hybrid farm and reduce its variability relative to a stand-alone, conventional offshore wind farm. In this work offshore wind and solar resources are characterised and mapped in a large study area covering the European Atlantic, the North and Baltic Seas, and the Canary Islands. The intra-annual and overall variabilities of wind power density and solar irradiance are investigated, and their complementarity is evaluated on the basis of their correlation. Negatively correlated regions include the seas around Ireland and Great Britain, with vast wind resources (mean wind power density ~1500 Wm−2 off W Ireland) and comparatively limited solar resources (mean solar irradiance ~100 Wm−2). Positively correlated regions include notably the Canary Islands, with the highest values of solar irradiance in the study area (mean values of ~280 Wm−2). Two study sites are chosen for more detailed investigation – one with a negative correlation, off W Ireland; the other with a positive correlation, off the Canary Islands. Even in the positively correlated regions, it is found that the correlation coefficient is never large (always under 0.2), which signals an opportunity for reducing power output variability through hybrid or co-located wind-solar farms. This, along with the other advantages of hybrid or co-located wind-solar farms (optimised use of scarce marine space, shared electrical infrastructure, shared O&M crews and vessels, etc.), attests to their potential in the European Atlantic. This potential could be realised through new hybrid or co-located wind-solar farms, or by retrofitting floating solar PV into existing offshore wind farms.

Elaboration of a Generalized Mixed Model for the wind speed distribution and an assessment of wind energy in Algerian Coastal regions and at the Capes

Understanding and evaluating wind energy involves various steps, including the observed temporal and directional variations in wind speed, choosing a suitable Probability Density Function (PDF) to describe wind regimes, and selecting an appropriate location for building wind farms. The wind speed data archived from nine meteorological stations along the northern coast of Algeria from 2017 to 2021 was used as a dataset to achieve these purposes. Zero-inflated mixed model (ZIMM) has been elaborated to model the wind speed regimes that combine two processes. The first process consists of mixing two PDFs that generate the counts data. The second process generates the residual zero-counts data. The estimation of ZIMM parameters is obtained with the Expectation–Maximization algorithm. ZIMM performances were evaluated at the selected sites using the commonly used goodness of fit (GOF) metrics: coefficient of determination (R2), root mean square error (RMSE), sum of square error (SSE), Kolmogorov–Smirnov (KS), Akaike information criterion (AIK), Bayesian Information Criterion (BIC), and compared with the widespread single PDFs: the two parameters Weibull, generalized extreme value (GEV) and Normal–Weibull mixed model. The datasets are used as input for assessing wind power and the feasibility of wind power plants in 24 Algerian Capes where the data is unavailable, using the Wind Atlas Analyses and Application Program (WAsP) and three different wind turbines, namely Nordex N50, Bonus B54, and Nordex N60. The results show significant variations in diurnal, monthly, and seasonal mean wind speed and power density. According to all GOF, the ZIMM PDF model outperforms conventional PDFs and the mixed model at the nine meteorological stations. The Bonus B54 wind turbine has the best capacity factor (Cp) with the lowest electrical production cost of all Algerian Capes, and the Cp exceeds 0.3 at 15 Capes from the 24 analyzed Capes. The highest Cp, about 0.467 with an annual electric production that reaches 4.09 GWh/year, is obtained at Cape Takouch with the lowest electrical production cost of 0.0414 $/kWh and 2.032 Kton/year of CO2 emission avoided.

Site suitability analysis of wind energy resources in different regions of Algeria’s southwestern highland

This paper presents a site suitability analysis for a 20 MW wind farm project in western Algeria’s highlands. The aim is to improve the quality of the electricity grid’s service and increase Algeria’s renewable energy utilization. The wind potential of three regions, Mecheria, El Kheiter, and Naâma, was evaluated using the Weibull function and the wind atlas analysis and application program (WAsP) with a ten-year database (2011-2021) at 10 m hub height. The assessment encompassed a comprehensive analysis of various wind resource parameters, including mean wind speed, prevailing direction, and power densities. In comparison to other sites, the Mecheria region has the best wind potential, with a mean annual wind speed of 6.31 m/s, a power density of 283 W/m2, and Weibull parameters A = 7.1 m/s and k = 2.02. These promising results prompted us to design a wind farm in this region using Power Wind 90/2000 kW turbine technology facing the predominant wind directions of the site, producing 103.91 GWh of total annual gross energy produced (gross AEP) and 103.75 GWh of total annual net energy produced (net AEP). Finally, it appears that the wind resources in the selected region are well-suited for electricity generation, offering a promising opportunity to reduce the country's dependence on fossil fuels.

Fitting wind speed and wind direction probability distribution using mixture B-spline function

Fitting wind speed and wind direction probability distribution is a crucial and fundamental task for the investigation of wind resource. This paper proposes a mixture basis(B)-spline function to fit wind speed and wind direction probability distribution. Such mixture B-spline function requires no prior assumption and eliminates the influence of subjective experience. A procedure has been developed that combines a binary genetic algorithm, maximum likelihood estimation, and a chi-squared test. This procedure can enable mixture B-spline function to adapt to various probabilistic behaviors. The wind speed and wind direction probability distributions of four North Dakota, U.S. sites are fitted by the mixture B-spline function and other five methods. Their goodness-of-fits are examined by chi-squared tests. The results indicate that the mixture B-spline function passes all the chi-squared tests and presents the highest goodness-of-fit. Additionally, the mixture B-spline function is used to calculate the mean wind power density, which can help identify sites with richer wind resources.

Offshore Wind Power Resource Assessment in the Gulf of North Suez

Growing population, industrialization, and power requirements are adversely affecting the environment through increased greenhouse gases resulting from fossil fuel burning. Global greenhouse gas mitigation targets have led nations to promote clean and self-renewable sources of energy to address this environmental issue. Offshore wind power resources are relatively more attractive due to high winds, less turbulence, minimal visualization effects, and no interaction of infrastructure. The present study aims at conducting an offshore wind power resource assessment (OWPRA) at some locations in the Gulf of North Suez. For this purpose, the long-term hourly mean wind speed (WS) and wind direction above mean sea level (AMSL), as well as temperature and pressure data near the surface, are used. The data is obtained from ERA5 (fifth generation global climate reanalysis) at six (L1–L6) chosen offshore locations. The data covers a period of 43 years, between 1979 and 2021. The WS and direction are provided at 100 m AMSL, while temperature and pressure are available near water-surface level. At the L1 to L6 locations, the log-term mean WS and wind power density (WPD) values are found to be 7.55 m/s and 370 W/m2, 6.37 m/s and 225 W/m2, 6.91 m/s and 281 W/m2, 5.48 m/s and 142 W/m2, 4.30 m/s and 77 W/m2, and 5.03 and 115 W/m2 and at 100 m AMSL, respectively. The higher magnitudes of monthly and annual windy site identifier indices (MWSI and AWSI) of 18.68 and 57.41 and 12.70 and 42.94 at the L1 and L3 sites, and generally lower values of wind variability indices, are indicative of a favorable winds source, which is also supported by higher magnitudes of mean WS, WPD, annual energy yields, plant capacity factors, and wind duration at these sites. The cost of energy for the worst and the best cases are estimated as 10.120 USD/kWh and 1.274 USD/kWh at the L5 and L1 sites, corresponding to wind turbines WT1 and WT4. Based on this analysis, sites L1, L3, and L2 are recommended for wind farm development in order of preference. The wind variability and windy site identifier indices introduced will help decision-makers in targeting potential windy sites with more confidence.

Climate-change impacts on offshore wind resources in the Mediterranean Sea

The evolution of offshore wind resources in the Mediterranean Sea up to 2100 is studied following the latest climate-change scenarios, the Shared Socioeconomic Pathways. Three scenarios are considered: a high emissions scenario, i.e., fossil-fuelled development; a business-as-usual scenario, in which current emission levels are maintained; and a low-emissions scenario. Particularly, the low-emissions scenario is of special interest since it represents the achievement of the targets set by the Paris Agreement and EU’s Green Deal, which is a novelty in this type of work. A multi-model ensemble is constructed to reduce individual uncertainties with the Global Climate Models that are found to perform best in the Mediterranean Sea. With the exception of the low-emissions scenario, the results show a generalised decline in mean wind power density and augmented variability by 2100. This decline is more pronounced in the high-emissions scenario, with a widespread drop in mean wind power density of ∼20%, rising to 30% around the Balearic Islands. By contrast, the low-emissions scenario anticipates strong, localised growth in certain areas around the Italian Peninsula and off Lebanon and Israel. As for the seasonal variability, profound changes are predicted depending on the scenario and period considered, with increases of up to 80% and decreases of up to 40% in mean seasonal wind power density. These trends have significant implications for wind energy production and can serve as a background for future downscaling initiatives. On the positive side, the comparison between the three scenarios indicates that reducing carbon emissions in line with international climate accords may indeed help to mitigate the decline in wind resources in the Mediterranean Sea and the increase in their variability.

An approach to assess offshore wind power potential using bathymetry and near-hub-height reanalysis data

Offshore wind power is emerging option in wind industry due to relatively consistent wind behavior over ocean. This study aims to access offshore wind resource employing a novel approach using bathymetry data, in-situ measured and 5th generation European Reanalysis (ERA-5) wind data. The measured wind data for four near-coast locations was used in Exclusive Economic Zone (EEZ) of Pakistan. The percentage bias for wind speed and wind direction of ERA-5 data compared to measured data for coastline location is −0.2% and −0.9% respectively, which shows suitability of ERA-5 data for offshore resource assessment with acceptable accuracy. Annual mean wind speed and wind power density of the EEZ is 6.1 m/s and 263 W/m2 respectively. The northeastern part of the EEZ near coastline of Sindh province is most suitable for offshore wind farms. An offshore wind farm site suitable for monopile foundation having wind power class as Fair was selected. The performance of more than 400 commercial wind turbines was evaluated and a 4.5 MW turbine having highest net capacity factor (NCF) was selected. The capacity factor of suggested wind farm having installed capacity of 540 MW is computed as 55% which can avoid 1.45 million tons in CO2eq emissions annually.

Impacts of climate change on wind energy potential in Australasia and South-East Asia following the Shared Socioeconomic Pathways

Wind energy is poised to play a major role in the energy transition. Fluctuations in global atmospheric circulation are expected as a result of climate change, and wind projections based on the most up-to-date scenarios of climate change, the Shared Socioeconomic Pathways (SSPs), anticipate significant changes in wind energy potential in many regions; so far, these changes have not been studied in Southeastern Asia and Australasia, a region with notable wind energy potential. This work investigates the evolution of wind power density and its temporal variability considering the latest scenarios of climate change, the SSPs. More specifically, two scenarios are considered, SSP2-4.5 and SSP5-8.5, corresponding to moderate and high emissions, respectively. As many as 18 global climate models are considered and compared against past-present data, and those that perform best are retained to build a large multi-model ensemble. The results show that projected changes in mean wind power density at the end of the 21st century are of little significance (typically below 5 %); nevertheless, this value can be far surpassed locally. In certain areas (e.g., Vietnam, Borneo) and seasons, remarkable changes in wind power density (exceeding 150 %) are anticipated. Typically, mean values and temporal variability changes are greater in the high-emissions scenario, however, seasonal variability is projected to be more pronounced in the moderate-emissions scenario. These effects of climate change on wind energy potential must be taken into account in the development of wind power in the region, for they will affect the energy production and, therefore, the economic viability of wind farms – not least in those areas where drastic changes are projected.

ASSESSMENT OF WIND ENERGY POTENTIAL IN THE BIEN DONG SEA USING CCMP SATELLITE DATA

This study assessesed the wind energy potential in the Bien Dong Sea by using the Cross-Calibrated Multi-Platform (CCMP) satellite wind data from 1991 to 2020. The results showed that the wind energy potential at 10 m height above ground is not high in the offshore areas with annual mean wind speeds ranging from 5 to 8 m/s. At heights of 100 m, 150 m, and 200 m, the greatest wind energy potential is found in the offshore areas of the North Bien Dong Sea Region, Binh Dinh - Ninh Thuan, Binh Thuan - Ca Mau, and the central part of the Gulf of Tonkin. Especially, the wind energy is quite high in the Sea of Ninh Thuan to Ba Ria - Vung Tau with annual mean wind speeds of 8 to 10 m/s, and the annual mean wind power density of 500 to 700 W/m2 at a height of 100 m. On the seasonal scale, the wind energy potential of January and October is greater than that of April and July for the orthern offshore areas while the wind energy potential of January is the greatest, followed by July and transition months for the southern offshore areas.

Offshore wind power potential analysis for different wind turbines in the Mediterranean Region, 1959–2020

We evaluated spatiotemporal characteristics of the wind power density (WPD) in the Mediterranean and the Black Sea. An hourly wind speed data from ERA5 of ECMWF is used covering 62 years between 1959 and 2020. Spatial distributions of WPD at 100 m altitude at monthly, yearly, and seasonal timescales are revealed. The average WPD in the study area is 373 W/m2, where the spatial maximums in the 62-year average map reach up to 1116 W/m2. The spatial distribution of yearly mean WPD considering 13 sub-basins revealed that the two most prominent regions are the Gulf of Lion and the Aegean Sea. The diurnal variation of WPD showed over ± 10 % variability within the considered sub-basins. Five commercial turbines are selected to investigate their performance in the study area. The capacity factors, and operational and rated power time percentages are spatially mapped. The variability in the WPD is investigated using several indicators of yearly variability index, intra-annual variability index, coefficient of variation, robust coefficient of variation, inter-quartile range, and inter-quartile ratio. The long-term trend in the monthly-mean values is investigated and it is revealed that the statistically significant downward trends manifest themselves in the Levantine Basin, Ligurian-Provençal Basin, Balearic Sea, and the Alboran Sea. The capacity factors of five widely used wind turbines are computed in the study area using both yearly and seasonal timescales.

Assessment of the wind power dynamics in the North Sea under climate change conditions

The objective of the present work is to analyse the expected dynamics of the wind power in the North Sea until the end of the twenty-first century. From this perspective, the proposed study is focused on the assessment of the average and extreme conditions, considering two future time windows. These are near future, represented by the 40-year time interval 2021–2060, and distant future, represented by the time interval 2061–2100. The wind data provided by a Regional Climate Model (RCM) is processed and analysed in this work considering the Representative Concentration Pathway (RCP) scenarios 4.5 and 8.5 (RCP4.5 and RCP8.5). The first, denoted as the realistic scenario, assumes an increase of the greenhouse gas emissions until 2040 and a stagnation and decline afterwards, while the second, denoted as the pessimistic scenario, assumes that this increase will continue along the entire 21st century and afterword. Furthermore, an analysis of the recent past wind data for the 40-year period 1980–2019 is also carried out. In this way a more comprehensive image of the wind power dynamics for both the near and distant future in the North Sea is provided. The results indicate that enhancements are expected in the North Sea, both in terms of mean wind power density, but also especially as regards the maximum wind speed. Finally, comparisons with the expected dynamics of some other similar marine environments, as the Baltic, Mediterranean and Black seas are also performed.

ASSESSMENT OF WIND ENERGY POTENTIAL FOR ACCRA, GHANA USING TWO PARAMETER WEIBULL DISTRIBUTION FUNCTION

This study investigates the predictive ability of two-parameter Weibull distribution function for Accra, Ghana using fifteen years (2002 – 2016) meteorological parameter of monthly mean wind speed data. The monthly and annual averaged wind speed was estimated to be . The annual mean values of the maximum energy – carrying wind speed and most probable wind speed were found to be and respectively. The two parameters of the Weibull statistics found in this study for Accra were within the range for the shape parameters and for the scale parameters. The results of the wind power density indicated that the location has a good prospect for wind power generation with the highest value of found in September, 2006. The linear relationship between the monthly mean wind power density and mean wind speed shows that a perfect correlation with coefficient of correlation of 99.3 % exists between them. The results obtained from this study revealed that the Weibull function was found appropriate for analyzing measured wind speed data and in predicting the wind power density for the region under investigation. Therefore, Accra has an excellent prospect for wind power generation

Techno-Economic Analysis of Wind Energy Potential in North-Eastern of Thailand

Thailand's alternative power development plan attempts to reduce the dependence of electrical power generation on fossil fuels by setting a goal of generating electricity from wind energy resources of 3,002MW within year 2,036. Moreover, the shortage of electricity in the North-eastern region of Thailand needs to be cared for to create stability of energy in Thailand. Therefore, the assessment of wind power resource potential in the north-eastern part of Thailand needs to be studied. This paper presents a technical and economic analysis of the wind power potential for five stations in the north-eastern part of Thailand: Buriram, Roi Et, Sri Sa Ket, Surin, and Ubon Ratchathani within 4 years (2017-2020) at a 10-minute interval. The data was collected from Thai Meteorological Department (TMD) at a 10m height above ground level. This paper focuses on the wind climate at 60m and 80m above ground level by using WAsP to simulate the mean wind speed and power density of each station. Moreover, this study uses Vestas V52-850kW and Vestas V90-2,000kW wind turbine generators to calculate the net annual energy production of each station. In addition to evaluating the economic analysis of investment, this study uses economic tools to evaluate the feasibility of investment. The result showed that Roi Et is the most suitable location for a wind farm. The mean wind speeds and power densities are 5.95 m/s and 265 W/m2 at 60m and 6.15 m/s and 311 W/m2 at 80m. The AEP can be generated at 18.932 GWh and 56.322 GWh at 60m and 80m heights respectively for Roi Et with a cost per unit of 0.09 $/kWh and 0.07 $/kWh. The other provinces have an AEP of 10.674 GWh for Buriram, 12.152 GWh for Sri Sa Ket, 12.969 GWh for Surin, and 8.508 GWh for Ubon Ratchathani at 60m height level, with a cost per unit of 0.15 $/kWh, 0.14 $/kWh, 0.13 $/kWh, and 0.2 $/kWh respectively. At 80m height, the AEP is 33.437 GWh with 0.11 $/kWh for Buriram, 45.737 GWh with 0.09 $/kWh for Sri Sa Kat, 41.189 GWh with 0.09 $/kWh for Surin, and 27.524 GWh with 0.14 $/kWh for Ubon Ratchathani.

Topographic Data Analysis of Observed Wind Data Recorded at 15 Different Stations in Thailand

This study focuses on the collection and observation of mean wind speed and power density of 15 stations in Thailand merged with the topographic map of the stations. The wind data was collected by installing anemometers at 10m, 15m, 20m,25m and 30m height at 15 selected stations around Thailand. Wind Analysis and Application Program (WAsP) is used to generate mean wind speed and power density. Meanwhile, roughness and surface elevation map are produced and merged with the data in WAsP. The results showed the highest wind speed in Songkhla station which was 3.16 to 12.15m/s and on the other hand data from Narathiwat showed the lowest mean wind ranging from 1.13 to 1.72m/s. Finally, Songkhla station power density ranges from 24-1372W/m2 and in Narathiwat station ranging from 2-5W/m2 in terms of power density. In Thailand, the landscape is diverse such as plateau, plains, coastal plains, land, mountains, mountain ranges and hills. Generally, the wind speeds and directions change due to landscape. For this season, to study wind resource, investigation on topography is vital.

Changes in wind energy potential over China using a regional climate model ensemble

The goal of carbon neutrality provides a new opportunity for the development of renewable energy, especially for wind power. In this paper, 11 regional climate models (RCMs) including CORDEX-EA and PRECIS at two different resolutions are used to evaluate the performance in simulating the spatio-temporal characteristics for wind speed and energy potential from 1981 to 2005. The results show that most models can reproduce the spatio-temporal patterns, and the simulation of the eastern region is better than that of the western region. Besides, most of the higher-resolution models had better simulation performance for spatial-temporal patterns and inter-annual variation. Then, the weighted multi-model ensemble is used to project the changes of the wind speed and the wind power density over China's mainland during the mid-21st century under the RCP8.5 scenario. In future, a decrease in wind speed is projected by most RCMs over China, although some regions, such as the southern parts of China, are projected to have more wind energy potential. The percentage variation of annual mean wind speed will remain in the range of ±4%. The wind power density will decrease in the north and the largest decrease will be found in the northwest. However, the annual mean wind power density in the southeast China will have an increase of 2.21% in the middle of this century. The inter-annual variation of the wind power density in most regions will increase by more than 20%. However, intra-annual variation in the wind power density is likely to decrease in most regions of China, ranging from about −20% to −50%.

Wind Energy Data Analysis and Resource Mapping of Dangla, Gojjam, Ethiopia

Energy is one of the most important factors in socio-economic development. The rapid increase in energy demand and air pollution has increased the number of ways to generate energy in the power sector. Currently, wind energy capacity in Ethiopia is estimated at 10,000 MW. Of these, however, only eight percent of its capacity has been used in recent years. One of the reasons for the low use of wind energy is the lack of accurate wind atlases in the country. Therefore, the purpose of this study is to develop an accurate wind atlas and review the wind resources using Wind Atlas Analysis and Application Program in Dangla so that the best sites used for the installation of wind farms can be easily identified. Wind climatology data, vector maps, and wind turbine data were used to model the Wind Atlas Analysis and Application Program. The wind data collected from National Meteorology Survey Agency over three years were used for the analysis. Besides, vector data for the survey area was prepared using Global Mapper V20.1. In this study, the Wind Atlas Analysis and Application Program tool was used to estimate wind resources. The wind resource map was developed at an altitude of 80 m above ground level, and a horizontal axis type wind generator called (i.e., GridStreamer Vestas V100 1.8 MW) was selected from the Wind Atlas Analysis and Application Program catalog to mimic the wind farm. The results obtained from the analysis of actual wind data showed that the minimum wind speed was 0.12083 m/s while the maximum wind speed was 9.96389 m/s. At a height of 10 m a.g.l., the mean wind speed and wind power density were 1.8 m/s and 9 w/m2, respectively. The most common wind direction was also discovered to be 210 degrees. The maximum wind speed and capacity factor were available around hills, according to the wind resource map, and the net annual energy output of the Dangla wind farm at a turbine height of 80 m a.g.l. was found to be 282.726 GWh (or, 32.27 MW). The farm’s capacity factor was also discovered to be 9.54 percent, indicating that the site is rated as low potential.

Assessment of the Resources of Wind Energy in Various Regions of Algeria

Details on the wind potential during a period of about thirteen years in Algeria is given in the present work. The inspection is performed for sixteen regions covering almost all the territory of the country. The density of the mean wind power is determined for the different regions. The maps of annual and seasonal wind energy resources are also established. The characteristics of the wind velocity, as well as the potential of wind power, are determined by the Weibull distribution. From the given results, the highest values of annual mean wind speed and the annual mean wind power density are found in Adrar (P10 = 283.12 W/m2 and P50 = 646.91 W/m2), while the lowest values are observed in Skikda (P10 = 40.61 W/m2 and P50 = 115.51 W/m2, respectively).

Statistical analysis of wind resource and energy potential assessment of Sanghar site, Sindh Pakistan

PurposeThe purpose of this paper is to assess the wind resource and energy potential of the Sanghar site for minimizing the dependence on fossil fuels and improving the environment.Design/methodology/approachThe Sanghar site wind shear coefficient and turbulence intensity factor are investigated for a period of a year. The two-parameter k and c Weibull distribution function is used to analyze the wind speed of the Sanghar site. The standard deviation, coefficient of variation, wind power density and energy density; and capacity factor was assessed for a period of a year. The economic assessment of energy/kWh is investigated for the selection of appropriate wind turbines.FindingsThe mean wind shear of the Sanghar site was found to be 0.2509. The mean wind speed was found to be 4.766, 5.534 and 6.121 at 20, 40 and 60 m above the ground level. The mean value of the k parameter was observed to be 2.433, 2.777 and 2.862 at 20, 40 and 60 m for a period of a year. The Weibull c m/s parameter was found to be 5.377, 6.245 and 6.906 m/s at 20, 40 and 60 m. The major portion of values of standard deviation was found to be in between 0.1 to 2.00 at 20, 40 and 60 m. The mean wind power density values were observed to be 88.33, 93.5 and 110.16 W/m2 at 20, 40 and 60 m; respectively, for a period of a year. The mean coefficient of variation was found to be 0.1478, 0.1205 and 0.1033 at 20, 40 and 60 m; respectively. The mean energy density was found to be 476.75, 683.08 and 866.33 kWh/m2 at 20, 40 and 60 m; respectively. The mean capacity factor for different wind turbines was ranged between 18 to 24.83 for a period of a year. The economic assessment showed that wind turbine B has the minimum cost (US$) 0.0484/kWh.Practical implicationsThe assessment provides the solution to sustainable energy generation which reduces the consumption of fuel and the effect of fluctuating price of fuel in the world market on local consumers.Social implicationsWind energy may have social implications including environmentally friendly, consistent supply of energy during the peak summer season, less unit per cost, etc.Originality/valueThe Sanghar site is new and assessed for the first time in this research work. The Sanghar site is suitable for installing utility wind turbines for energy generation at the lowest cost.

High-resolution mesoscale wind-resource assessment of Fiji using the Weather Research and Forecasting (WRF) model

This study presents a high-resolution mesoscale wind-resource assessment of the small island developing state (SIDS) of Fiji using a 10-year simulation of the Weather Research and Forecasting (WRF) model with convection-permitting resolution. Our analysis evaluates the wind speed and Weibull distributions, diurnal and seasonal wind speed patterns, resource maps of annual and seasonal wind speed, power density, model statistical analysis and interannual wind speed variability. The results reveal that the WRF-model simulated wind resource parameters are in good agreement with observations at 24 existing weather stations. At 55 m above ground, the annual mean wind speed and wind power density varies from 1.5 m/s to 8 m/s and 50 W/m2 to 300 W/m2, respectively, for onshore land areas. Higher wind speeds are observed during austral winter than in austral summer. Forty high wind-resource areas are identified in this study, which were previously unknown. This indicates that there is potential for utility-scale wind power generation at selected locations with wind speed and power density greater than 6.4 m/s and 300 W/m2 (NREL, Wind Power Class 3). An estimated 1000 MW theoretical potential installed capacity is available for utility-scale wind power applications on Viti Levu and Vanua Levu.