Assessment Of Wind Energy Potential Research Articles

Wind resource and energy potential assessment of the Sujawal site, Sindh Pakistan

PurposeThe objective of this paper to assess the wind energy potential of the Sujawal site for minimizing the dependence on fossil fuels.Design/methodology/approachThe site-specific wind shear coefficient and the turbulence model were investigated. The two-parameter, k and c, Weibull distribution function was used to analyze the wind speed of the Sujawal site. The standard deviation of the site was also assessed for a period of a year. Also, the coefficient of variation was carried out to determine the difference at each height. The wind power and energy densities were assessed for a period of a year. The economic assessment of energy/kWh was investigated for selection of appropriate wind turbine.FindingsThe mean wind shear of the Sujawal site was found to be 0.274. The mean wind speed was found to be 7.458, 6.911, 6.438 and 5.347 at 80, 60, 40 and 20 m, respectively, above the ground level (AGL). The mean values of k parameter were observed to be 2.302, 2.767, 3.026 and 3.105 at 20, 40, 60 and 80 m, respectively, for a period of a year. The Weibull c m/s parameter values were found to be 8.415, 7.797, 7.265 and 6.084 m/s at 80, 60, 40 and 20 m, respectively. The mean values of standard deviation were found to be 0.765, 0.737, 0.681 and 0.650 at 20, 40, 60, and 80 m, respectively. The mean wind power density (W/m2) was found to be 287.33, 357.16, 405.16 and 659.58 for 20, 40, 60 and 80 m, respectively. The economic assessment showed that wind turbine 7 had the minimum cost/kWh US$ 0.0298.Originality/valueThe Sujawal site is suitable for installing the utility wind turbines for energy generation at the lowest cost; hence, a sustainable solution.

Assessment of wind energy potential based on Weibull and Rayleigh distribution models

The present study aims to analyze the wind power potential in two locations in Galati county, Romania, before making a decision on the appropriate area for wind turbine installation. The hourly series of wind speed and wind direction were analyzed for the period between January 2017 and December 2018, measured at two meteorological stations of the national network at 10 m height. The Weibull and Rayleigh distribution models were applied to hourly wind speed data to evaluate wind speed characteristics and wind power potentials at wind turbine height. The annual values of k parameter for the Weibull analysis range from 2.15 in 2017 to 2.1 in 2018 for the GL3 site (GL3 — Galati county measurement station number 3, with geographical location: latitude 45.47 N and longitude 28.03 E), while for GL5 site (GL5 — Galati county measurement station number 5, with geographical location: latitude 45.82 N and longitude 27.44 E), the values are 1.33 in 2017 and 1.46 in 2018, respectively. Another important factor in wind resources assessment in addition to the average wind speed is the wind speed distribution in the regime, because two wind turbines installed in two different places (locations that have the same average wind speed), may generate a very different amount of energy, due to differences in wind speed distribution in the two locations. Thus, in 2017, the average annual wind speed at the two locations was approximately the same, being 5.44 m/s at GL3 and 5.41 m/s at GL5, respectively, but the maximum values of power density for the two locations were determined to be 260 W/m2 for GL3 and 361 W/m2 for GL5, in 2017. It was observed that the average annual wind speed was higher than the most frequent wind speed. Another important conclusion is that the greatest influence on the wind power potential has a speed that produces the maximum energy of the wind regime and not the average wind speed or the most frequent wind speed. From the data analysis, it was found that application of the Rayleigh distribution is not suitable for the GL5 case, as the wind speed does not have a normal distribution.

Resource potential for wind-hydrogen power in Ukraine

The paper provides an assessment of the current wind energy potential in Ukraine, and discusses developmental prospects for wind-hydrogen power generation in the country. Hydrogen utilization is a highly promising option for Ukraine's energy system, environment, and business. In Ukraine, an optimal way towards clean zero-carbon energy production is through the development of the wind-hydrogen sector. In order to make it possible, the energy potential of industrial hydrogen production and use has to be studied thoroughly.Ukraine possesses huge resources for wind energy supply. At the beginning of 2020, the total installed capacity of Ukrainian wind farms was 1.17 GW. Wind power generation in Ukraine has significant advantages in comparison to the use of traditional sources such as thermal and nuclear energy.In this work, an assessment of the wind resource potential in Ukraine is made via the geographical approach suggested by the authors, and according to the «Methodical guidelines for the assessment of average annual power generation by a wind turbine based on the long-term wind speed observation data». The paper analyses the long-term dynamics of average annual wind speed at 40 Ukrainian weather stations that provide valid data. The parameter for the vertical wind profile model is calculated based on the data reanalysis for 10 m and 50 m altitudes. The capacity factor (CF) for modern wind turbine generators is determined. The CF spatial distribution for an average 3 MW wind turbine and the power generation potential for the wind power plants across the territory of Ukraine are mapped.Based on the wind energy potential assessment, the equivalent possible production of water electrolysis-derived green hydrogen is estimated. The potential average annual production of green hydrogen across the territory of Ukraine is mapped.It is concluded that Ukraine can potentially establish wind power plants with a total capacity of 688 GW on its territory. The average annual electricity production of this system is supposed to reach up to 2174 bln kWh. Thus, it can provide an average annual production of 483 billion Nm3 (43 million tons) of green hydrogen by electrolysis. The social efficiency of investments in wind-hydrogen electricity is presented.

Introducing a new approach for wind energy potential assessment under climate change at the wind turbine scale

An important aspect of planning the future expansion of wind energy is the consideration of changes in available wind resources due to climate change. In previous studies simulations of the future behavior of wind resources were derived from regional climate models at coarse spatial resolutions not suitable for wind energy potential assessment at the wind turbine scale. Thus, this study investigates the hypothesis that changes in future wind resources initiated by climate change will influence important aspects of small-scale wind resource assessment. A new approach is introduced that can be used to quantify the (1) spatial wind resource availability, (2) temporal wind resource availability, and (3) geographical complementarity under climate change at the wind turbine scale. The assessment of the future spatiotemporal variations in wind resources is based on an ensemble of near-surface (10 m) wind speed time series at a daily resolution for the period 1981–2099 from 35 different regional climate models. Using the highly resolved (horizontal resolution: 200 m × 200 m) Wind Speed-Wind Shear model, the near-surface wind speed time series were bias-corrected and extrapolated to a wind turbine hub height of 140 m at the sites of the current wind turbines in Germany. Bias correction was carried out by matching the quantile distributions from the regional climate models and the Wind Speed-Wind Shear model. Afterward, a power curve of a modern 3.45 MW wind turbine was applied to calculate daily capacity factors. The results indicate small long-term changes in the wind resource availability under the representative concentration pathways 4.5 and 8.5. It was found, that the influence of the interannual variability of the German wind resource exceeds the influence of climate change on the wind resource. The newly developed approach goes beyond previous wind resource assessments under climate change because it offers the opportunity for the spatially explicit investigation of different aspects of wind resource assessment which allows to develop more sophisticated wind energy expansion plans. Although the results are valid only for the study area, the proposed methodology is portable to any other region around the world.

Wind Energy Potential and Economic Assessment of Southeast of Pakistan

ABSTRACT This study aims to evaluate wind resource potential and analyze the performance of wind turbines for six onshore and near-coast sites (three commercial power plants and three new potential sites) of Pakistan. The technical and economic performance analysis of more than 200 commercially available turbines suitable for weak wind regions is performed, 20 turbines were selected based on performance. The percentage increase in annual Capacity Factor (CF) and decrease in Levelized Cost of Electricity (LCOE) for commercial sites using proposed turbine is 42–61% and 30–38% respectively compared to installed turbines on the sites. Three potential sites have wind power density as 155, 203, and 237 W/m2 and corresponding wind power class as poor, marginal and marginal respectively. The CF and LCOE of potential sites having marginal power class using PT10 turbine (Goldwind GW140/3.0) is up to 50% and 5.5 US¢/kWh respectively so these two sites are economically feasible for future wind projects. The CF and LCOE of potential sites having poor power class using PT10 turbine is 34% and 7.2 US¢/kWh. The wind turbines having lower cut-in wind speed and lower-rated wind speeds are economically feasible for sites having poor wind power class.

Wind energy potential assessment in the Persian Gulf: A spatial and temporal analysis

Despite the high capacity of wind energy in the offshore regions of Iran seas, wind power plants in these parts have not been significantly considered. In this study, the temporal and spatial wind characteristics and wind energy potential of the Persian Gulf are investigated during the years 2000–2017 based on the European Center for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis data set. The spatio-temporal distributions of the annual, seasonal, monthly, and diurnal mean wind characteristics are presented using the 18-year hourly resolution results. Statistical analysis and variability indices of the wind parameters are presented. The Weibull density function is used to assess and analyze the wind characteristics of the region. The Weibull shape k and scale c parameters are calculated using the maximum likelihood estimation (MLE) method. Finally, the wind capacities at the 12 potential locations near the populated or industrial regions around the studied area are derived. Besides, the capacity factor and the potential energy output of two commercial wind turbines are investigated, which are appropriate for the locations at 50 m and 80 m hub heights. The present results can be used to develop of wind energy plans and wind energy potential assessment in the studied area.

Assessment of wind energy potential for Tuvalu with accurate estimation of Weibull parameters

Wind resource assessments are carried out for two sites in Tuvalu: Funafuti and Nukufetau. The wind speeds at 34 and 20 m above ground level were recorded for approximately 12 months and analyzed. ...

Assessment of wind energy potential for Tuvalu with accurate estimation of Weibull parameters

Wind resource assessments are carried out for two sites in Tuvalu: Funafuti and Nukufetau. The wind speeds at 34 and 20 m above ground level were recorded for approximately 12 months and analyzed. The averages of each site are computed as the overall, daily, monthly, annual, and seasonal averages. The overall average wind speeds for Funafuti and Nukufetau at 34 m above ground level were estimated to be 6.19 and 5.36 m/s, respectively. The turbulence intensities at the two sites were also analyzed. The turbulence intensity is also computed for windy and low-wind days. Wind shear analysis was carried out and correlated with temperature variation. Ten different methods: median and quartiles method, the empirical method of Lysen, the empirical method of Justus, the moments method, the least squares method, the maximum likelihood method, the modified maximum likelihood method, the energy pattern factor method, method of multi-objective moments, and the wind atlas analysis and application program method were used to find the Weibull parameters. From these methods, the best method is used to determine the wind power density for the site. The wind power density for Funafuti is 228.18 W/m2 and for Nukufetau is 145.1 W/m2. The site maps were digitized and with the WAsP software, five potential locations were selected for each site from the wind resource map. The annual energy production for the sites was computed using wind atlas analysis and application program to be 2921.34 and 1848.49 MWh. The payback periods of installing the turbines for each site are calculated by performing an economic analysis, which showed payback periods of between 3.13 and 4.21 years for Funafuti and between 4.83 to 6.72 years for Nukufetau.

An Assessment of Onshore and Offshore Wind Energy Potential in India Using Moth Flame Optimization

Wind energy is one of the supremely renewable energy sources and has been widely established worldwide. Due to strong seasonal variations in the wind resource, accurate predictions of wind resource assessment and appropriate wind speed distribution models (for any location) are the significant facets for planning and commissioning wind farms. In this work, the wind characteristics and wind potential assessment of onshore, offshore, and nearshore locations of India—particularly Kayathar in Tamilnadu, the Gulf of Khambhat, and Jafrabad in Gujarat—are statistically analyzed with wind distribution methods. Further, the resource assessments are carried out using Weibull, Rayleigh, gamma, Nakagami, generalized extreme value (GEV), lognormal, inverse Gaussian, Rician, Birnbaum–Sandras, and Bimodal–Weibull distribution methods. Additionally, the advent of artificial intelligence and soft computing techniques with the moth flame optimization (MFO) method leads to superior results in solving complex problems and parameter estimations. The data analytics are carried out in the MATLAB platform, with in-house coding developed for MFO parameters estimated through optimization and other wind distribution parameters using the maximum likelihood method. The observed outcomes show that the MFO method performed well on parameter estimation. Correspondingly, wind power generation was shown to peak at the South West Monsoon periods from June to September, with mean wind speeds ranging from 9 to 12 m/s. Furthermore, the wind speed distribution method of mixed Weibull, Nakagami, and Rician methods performed well in calculating potential assessments for the targeted locations. Likewise, the Gulf of Khambhat (offshore) area has steady wind speeds ranging from 7 to 10 m/s with less turbulence intensity and the highest wind power density of 431 watts/m2. The proposed optimization method proves its potential for accurate assessment of Indian wind conditions in selected locations.

A framework for preliminary large-scale urban wind energy potential assessment: Roof-mounted wind turbines

Urban wind energy can provide a decentralized local source of energy for residential areas and reduce the cost of energy by avoiding the losses/costs of long-distance energy transmission. In this perspective, a preliminary assessment of urban wind energy is highly desired by turbine developers, investors and policy makers. However, given the large number of parameters involved, predictions of the wind energy potential in urban areas are very challenging. The present paper, therefore, intends to present a straightforward framework to provide a preliminary and large-scale assessment of the urban wind energy potential, i.e. at city or country scales, for roof-mounted turbines. The framework is based on four main steps: (i) collecting the building data, i.e. the number of potential candidate high-rise buildings and their height and rooftop surface area; (ii) obtaining the annual mean wind speed statistics at the height of these buildings and sorting the building data based on these statistics; (iii) obtaining the turbine characteristics and determining the average number of turbines per building roof; (iv) calculating the annual energy production (AEP). The application of the framework is then illustrated at the country scale for the Netherlands. In this case, the urban wind energy potential is assessed by considering the installation of 18,156 small wind turbines on the roofs of 1513 existing high-rise buildings in 12 major cities in the Netherlands, yielding an annual energy production of 150.1 GWh.

An Assessment of Wind Energy Potential for the Three Topographic Regions of Eritrea

This paper presents the wind energy potential and wind characteristics for 25 wind sites in Eritrea, based on wind data from the years 2000–2005. The studied sites are distributed all over Eritrea, but can roughly be divided into three regions: coastal region, western lowlands, and central highlands. The coastal region sites have the highest potential for wind power. An uncertainty, due to extrapolating the wind speed from the 10-m measurements, should be noted. The year to year variations are typically small and, for the sites deemed as suitable for wind power, the seasonal variations are most prominent in the coastal region with a peak during the period November–March. Moreover, Weibull parameters, prevailing wind direction, and wind power density recalculated for 100 m above ground are presented for all 25 sites. Comparing the results to values from the web-based, large-scale dataset, the Global Wind Atlas (GWA), both mean wind speed and wind power density are typically higher for the measurements. The difference is especially large for the more complex-terrain central highland sites where GWA results are also likely to be more uncertain. The result of this study can be used to make preliminary assessments on possible power production potential at the given sites.

Exploring the suitability of MERRA-2 reanalysis data for wind energy estimation, analysis of wind characteristics and energy potential assessment for selected sites in Pakistan

In the first part of this study, correlation between MERRA-2 reanalysis wind data and ground data is assessed for 12 selected locations. The correlation coefficient ranges from 0.17 to 0.75 among the sites. Sites with higher average wind speeds show comparatively stronger correlation. Besides, site specific factors are also investigated. In the second part, wind energy potential at same 12 locations is evaluated using high frequency (10-min interval) ground observed data. The diurnal, monthly and annual means for the sites are calculated and wind speed variance is observed utilizing wind data at six altitude levels (10m, 20m, 40m, 50m, 60m and 80m). The data is fitted to the Weibull distribution. Most probable wind speeds, wind speeds carrying maximum energy and wind power densities for all the locations are calculated for 50m and 80m height wind data. Significant variation of wind power density is observed along the height. A low cut-in speed wind turbine is selected, and annual energy production and capacity factors are estimated. Four locations with high wind power densities, namely Sujawal (355.6 W/m2), Sanghar (312.9 W/m2), Tando Ghulam Ali (288.2 W/m2) and Umerkot (252.8 W/m2) showed good potential to add wind share to global energy mix.

Wind energy potential assessment for KPT with a comparison of different methods of determining Weibull parameters

Purpose The purpose of this paper is to investigate wind power potential of site using wind speed, wind direction and other meteorological data including temperature and air density collected over a period of one year. Design/methodology/approach The site-specific air density, wind shear, wind power density, annual energy yield and capacity factors have been calculated at 30 and 10 m above the ground level (AGL). The Weibull parameters have been calculated using empirical, maximum likelihood, modified maximum likelihood, energy pattern and graphical methods to determine the other dependent parameters. The accuracies of these methods are determined using correlation coefficient (R²) and root mean square error (RMSE) values. Findings The site-specific wind shear coefficient was found to be 0.18. The annual mean wind speeds were found to be 5.174 and 4.670 m/s at 30 and 10 m heights, respectively, with corresponding standard deviations of 2.085 and 2.059. The mean wind power densities were found to be 59.50 and 46.75 W/m² at 30 and 10 m heights, respectively. According to the economic assessment, the wind turbine A is capable of producing wind energy at the lowest value of US$ 0.034/kWh. Practical implications This assessment provides the sustainable solution of energy which minimizes the dependence on continuous supply of oil and gas to run the conventional power plants that is a major cause of increasing load shedding in the significant industrial and thickly populated city of Pakistan. Also, this will minimize the quarrel between the local power producer and oil and gas supplier during the peak season. Social implications This wind resource assessment has some important social implications including decreasing the environmental issues, enhancing the uninterrupted supply of electricity and decreasing cost of energy per kWh for the masses of Karachi. Originality/value The results are showing that the location can be used for installing the wind energy power plant at the lower cost per kWh compared to other energy sources. The wind energy is termed as sustainable solution at the lowest cost.

Feasibility Assessment of Wind Energy Potential for Electricity Generation in Nigeria

Feasibility Assessment of Wind Energy Potential for Electricity Generation in Nigeria

An assessment of wind energy potential in the Beibu Gulf considering the energy demands of the Beibu Gulf Economic Rim

The Beibu Gulf Economic Rim of China is a key economic region in China as demonstrated by the “Beibu Gulf Cities Development Project” (2017) that plans to build a “Blue Livable Gulf” to balance environmental protection while providing sustainable economic development. This region has significant energy needs and is predicted to exhibit rapid growth in the future.By means of meteorological observations located at seven islands, a comprehensive statistical analysis on wind energy potential in the northern coastal part of the Beibu Gulf is conducted in this study. Specifically, wind speed, Weibull parameters, wind power density, as well as wind directions on various timescales are analyzed. The analysis shows that annual mean wind power density during 2010–2017 at 100 m above mean sea level was, respectively, 605.6, 542.0, 368.0, 282.0, 265.6, 87.6 and 321.5 W m-2 at the seven sites, with average value of 353.2 W m-2. Evidently, wind power potential demonstrates intra-annual variability, with greatest values occurring in December, while another peak value is observed in July. Wind speeds are lowest in May with another trough occurring in August. The data also display weak inter annual variability. The prevailing wind directions in the rim are mainly from opposing directions of N (winter and autumn) and S (summer).

Synergetic use of multiple scatterometers for offshore wind energy potential assessment

The synergetic offshore wind dataset derived from a combination of scatterometers (QuikSCAT, OSCAT and ASCAT-A, B) have been utilised and validated with available buoy data. It is observed that, the mean deviation in wind speed between the co-located buoy and synergetic scatterometer data is small of the order 2% and the power density showed a difference of 2.5%. The combined synergetic maps of wind speed, wind power density and practical power production from two offshore wind turbines V112 3M and G128 5M have been analysed and evaluated on a seasonal and annual basis. Further, considering the available area after exclusions, conflict regions due to shipping, visual and avian exclusions, India has a net power production of 364 GW (3190 TWh/yr) for V112 3M and 516 GW (4522 TWh/yr) for G128 5M turbines at 80 m hub height. This would reduce overall carbon emissions of 3117 and 4418 million tons respectively per year. Finally, the Levelised cost of energy is computed for two optimum sites in shallow waters off Gujarat and Tamil Nadu coasts and found to be lowest of around 106.87 €/MWh in Tamil Nadu site, suggesting an optimum site for wind farm development.

Analysis of Wind Data and Assessment of Wind Energy Potential in Lamhiriz Village, Morocco

Morocco has pursued an ambitious policy aimed at boosting renewable energies in the country. On this basis, we have examined the possibility of using wind turbines to generate electricity in Lamhiriz village in the south of Morocco, known for its strong winds. The wind speeds were statistically studied using the data recorded over one-year period collected from the mast which contains a set of measuring instruments at different altitudes. Based on several studies, we selected Weibull distribution to model the wind speeds, and we used the empirical method to estimate the scale and the shape parameters. Thereafter, a detailed analysis of seasonal and annual wind direction was presented. From the obtained results, the available energy density in this area is assessed by using WAsP software. Then, six commercial wind turbines of various sizes between 300 and 1300 kW were technically evaluated for electricity generation in Lamhiriz village by calculating their capacity factors and annual energy outputs. The results showed that the site under study is an adequate location for any of the adopted wind turbine models, where the annual energy output ranges from 1.44 to 6.75 GW h. Therefore, we can conclude that Lamhiriz have an attractive economical potential for developing large wind turbine farms.

Wind Energy Potential Assessment and Wind Turbine Performance Investigation in the Cotonou Coast (Benin Republic)

This paper presents a study of the monthly variability of wind energy potential at several heights and an investigation of the best fitting commercial wind turbine in the Cotonou coast (Benin Republic). The monthly Weibull parameters are calculated at 10 m and extrapolated at 30 and 50 m heights. The monthly Weibull wind power density and the wind speed carrying maximum energy are calculated at 10, 30 and 50 m. We showed that wind resource in the Cotonou coast is favorable for wind energy production at 30 and 50 m heights. The capacity factor of selected commercial wind turbines is calculated to investigate the best fitting wind turbine in the Cotonou coast. It turns out that Polaris 19-50 is the best fitting wind turbine in the selected turbines with a mean capacity factor of 0.49.

Wind Energy Potential Assessment in Selected Regions in Northern Cyprus Based on Weibull Distribution Function

Wind Energy Potential Assessment in Selected Regions in Northern Cyprus Based on Weibull Distribution Function

Statistical assessment of wind energy potential for power generation at Imphal, Manipur (India)

ABSTRACT In this work, two parameters Weibull distribution method and Rayleigh distribution method have been used to estimate the wind energy potential at Imphal, Manipur located in northeast India with the objective to check the feasibility of wind power generation at this location. In this study, we analyze the wind profile using the 12 years (2005–2016) wind data collected from the Meteorological center, ICAR Research Complex, Lamphelpat, Imphal, Manipur. It is observed that the wind speed drastically increased from 2009 onward except in 2013 and 2014. Subsequently, we determine the statistical parameters of collected wind speed data using four different estimation methods. Then the probability density function, cumulative distributions function, and finally wind power density per unit area are determined. It is found that mean wind power density based on actual mean wind speed is 1.214 W/m2, which is not suitable for medium or large-scale power generation. This perhaps calls for deployment of vertical axis wind turbines for harnessing wind power at small power levels.