Average Wind Power Density Research Articles

Wind power plant site selection problem solution using GIS and resource assessment and analysis of wind energy potential by estimating Weibull distribution function for sustainable energy production: The case of Bitlis/Turkey

Wind energy, being a sustainable energy source, is a benign, dependable, limitless, and cost-effective source of energy. This study involved the creation of a wind power plant site selection map at a provincial scale using a GIS application. The map took into account both environmental risks and environmental criteria. A wind energy measurement station of the PCE-FWS 20 type, equipped with a touch screen, was installed in the Rahva campus of Bitlis Eren University. Over the course of one year, data on wind direction, wind speed, temperature, relative humidity, and air pressure were collected at 5-s intervals and averaged over 20-min periods. The purpose of this study was to assess the suitability of the measured region for wind energy potential. A comprehensive statistical analysis was conducted using all the data collected during this period to assess the wind energy potential in the studied region. The Weibull distribution was employed to analyse the wind energy potential of this particular region. We employed graphical techniques to ascertain the Weibull distribution. The wind data analysis and simulation were conducted using the Matlab application. Analysed were the monthly statistical wind energy potential of the region, wherein the average wind speed and power density values were determined and presented through graphs at 20-min intervals. Upon analyzing the map generated using the Geographical Information Systems (GIS) technique, it has been ascertained that Bitlis Eren University Rahva campus is located in favorable areas.

Assessment of Wind Energy Potential in Kolaka Using Era5-land Reanalysis Data

This research aims to evaluate the potential for wind energy in Kolaka Regency using ERA5-Land reanalysis data from the output of the European Center for Medium-Range Weather Forecasts (ECMWF) model, which has a spatial resolution of 9 km with hourly temporal resolution for the period of 2001 to 2020. This research also compared the quality of ERA5-Land reanalysis wind speed data on data from nearby meteorological stations using statistical assessment methods. The analysis results reveal that the highest average wind speed is discovered at a height of 80 to 150 meters with a range of 4.99 to 5.93 m/s. The spatial distribution map of wind power density indicates that the energy potential at a height of 80 to 150 meters can reach 60 to 170 W/m2. The largest wind energy potential in Kolaka Regency can be generated in the DJF period (December-January-February) with the highest average monthly wind power density in January and the JJA period (June-July-August) with the highest average in July. In general, Kolaka Regency can be classified as an area with first-class wind energy potential, so it tends to be more suitable for the development of small-scale power plants. The suitable locations for developing small-scale wind power plants in Kolaka Regency are the southern Pomalaa coast and the Tanggetada coast because both locations have better wind energy sources than other areas.

Measure-correlate-predict methods to improve the assessment of wind and wave energy availability at a semi-exposed coastal area

To reduce the carbon footprint of near-shore and land-based Recirculating Aquaculture Systems (RAS), there is interest to harness renewable energy from local sources. When estimating power availability from wind and waves, it is imperative to ensure that the metocean resource data accurately represents the energy flux at the specific site. Here, four measure-correlate-predict (MCP) models are investigated for wind speed, significant wave height and peak wave period. The MCP relationships are obtained from higher spatial resolution wind and wave data, from a weather research and forecasting model, met-mast measurements at Stord airport (wind), and spectral downscaled DNORA/SWAN model (wave), correlated to the NORA3 database over a common time interval. These relations are used to improve the predictions of metocean parameters from the NORA3 model at the RAS location for the period 2012–2021, and hence allow more accurate estimates of renewable power availability. The prediction accuracy of the MCP models improves in all four cases when an appropriate data sorting criterion and algorithm is implemented. Between 2012 and 2021, average wind power density and wave power density are lowest in July at 182 W/m2 and 0.6 kW/m, and highest in December at 538 W/m2 and 7.5 kW/m, respectively.

Wind energy potential assessment: a case study in Central India

Access to sustainable energy is important for economic development and environmental protection. The shift towards renewable energy is essential for mitigating climate change and ensuring long-term energy sustainability. Though developing nations possess significant renewable energy capacity, transitioning to renewable sources remains a challenge. India's expanding wind energy sector has the potential to lower carbon dioxide emissions and produce energy in a sustainable manner. This research uses the Windographer software to conduct a comprehensive analysis of the wind energy potential at a distinct site in Nagpur, India. The assessment includes monthly average wind speeds, wind rose diagrams that show the prevailing wind directions, and wind power density (WPD) calculations. These factors together establish the site's classification. In this study, Weibull parameters are derived using four distinct methods to estimate the probability distribution function. The average WPD is found to be 13.63 and 71.08 W/m2 at heights of 10 and 100 m above ground level, respectively. Through wind turbine output analysis, numerous turbine models are assessed for suitability at the specified location. The Windtec DD 3000-125 model, interestingly, has a maximum capacity factor of 9.27%. The results highlight comparatively low wind potential in the studied region. Nevertheless, a wind turbine at this location can produce about 2 412 379 kW of electricity annually using the aforementioned wind turbine model.

Wind Power Potential of Kaniga Region, Rwanda

The purpose of this study was to investigate the potential of wind power in Kaniga, Rwanda. Wind speed and wind direction are the most important characteristics for assessing wind energy potential of a location using suitable probability density functions. In this study, a hybrid-Weibull probability density function was used to analyze data from Kaniga station. Kaniga is located in Gicumbi District, in the Northern Province of Rwanda. Onsite hourly wind speed and wind direction data for the year 2022 were analyzed using Computer programmes. The research was conducted to reveal the wind power potential of Kaniga and other major wind stations in the country as an approach to address energy shortage problem in the country. It was found that the average annual wind speed for Kaniga, Nyarwambu, and Byumba were 4.5m/s, 3.7m/s and 4.1m/s respectively, while corresponding dominant wind directions for the stations were 3200, 1800 and 1500 respectively. The average annual wind power density of Kaniga was found to be 1,285.4W/m2, while the annual power densities for Nyarwambu and Byumba were determined as 689.4W/m2 and 805.4W/m2 respectively. The other stations in Rwanda have the average annual power densities ranging from 6.99 W/m2 to 138.2W/m2. From these findings, it is clear that Kaniga has the greatest wind power potential among all other studied wind stations in the country.

Global wind energy resources decline under climate change

Wind energy is poised to play a major role in the energy transition. The objective of this work is to investigate the effects of climate change on global wind energy resources. For this purpose, a multi-model ensemble is constructed with selected Global Climate Models. Climate change is considered through the most recent scenarios, the Shared Socioeconomic Pathways. We find a significant decline in wind resources by 2100 relative to current levels. The decline is particularly evident in the mid-latitudes of the Northern Hemisphere – heavily populated regions where it matters especially, given the need for renewable energy production to increase substantially to decarbonise the energy supply. Exceptions to the global decline do exist, but in tropical and polar regions, far less populated. Depending on the climate-change scenario and the region, by 2100 changes may exceed 30 % of current average wind power density values. Additionally, we uncover a global increase in the variability of wind power density regardless of the scenario, which may be expected to affect the production of wind energy and its integration into the electricity networks. Recognising these effects of climate change is important in planning the energy transition and, more specifically, the contribution of wind energy.

Wind Energy Resources at Antarctic Stations Based on ERA5

The harsh scientific research environment of Antarctic stations demands a reliable energy supply; however, traditional methods not only pose a challenge in supply but also harm the environment. Antarctic energy supply has become a new choice for energy development in Antarctica due to its abundant wind energy resources. Using ERA5 10 m wind field reanalysis data, we compared and analyzed the correlation (r) and Root Mean Square Error (RMSE) between some observation stations and reanalysis data, with correlations above 0.67 and root mean square error below 2.3. This indicates that the accuracy of the ERA5 data is suitable for resource assessment at stations in Antarctica. We assessed the wind energy potential of the Great Wall, Zhongshan, Kunlun and Taishan Stations. The results show that the annual distribution and long-term trend of wind energy at Taishan Station are the best, followed by the Great Wall, Zhongshan and Kunlun Stations. Taishan Station has stable wind direction and abundant wind energy, the average wind power density is 800 W/m2, with an annual growth trend of 2.02 W/m2·yr−1. The effective wind speed occurrence and energy level occurrence are generally above 90% and the coefficient of variation is generally below 0.8. The dominant direction of wind energy is northeast and the wind direction is stable, which is conducive to the development and utilization of wind energy.

An evaluation of the expected wind dynamics in the black sea in the context of the climate change

The main target of this work is to carry out an analysis of the most credible scenarios concerning the expected dynamics of the wind climate along the 21st century in the Black Sea basin. The data provided by the RCA4 climate wind model were processed and analysed. Three different Representative Concentration Pathway (RCP) scenarios are considered. These are RCP2.6, RCP4.5, and RCP8.5. The study is structured considering two 40-year time intervals. These are the near future (2021–2060), and the next 40=year time interval (2061–2100), denoted as the distant future. Moreover, in order to have a mean of comparison, the 40-year time interval (1980–2019) denoted as the recent past was also analysed. Two different parameters are processed and analysed in this study. These are the average wind power density and the maximum wind speed. The results indicate a moderate enhancement of the average wind power in the western part of the sea (of about10–20%). The coastal areas from the western and north-western parts of the sea have been identified as the most energetic and they are more appropriate for harvesting wind energy. At the same time, the results of this work indicate that in the basin of the Black Sea an increase of the maximum wind speed values is expected in the future. From this perspective, the future high storms in this basin are expected to have quite similar features with the hurricanes from the Atlantic Ocean.

Comparison of deterministic wind speed generation models for wind erosion modeling

The diurnal patterns of hourly wind speed data are fundamentally important in precise wind erosion modeling. Several deterministic wind speed simulation models, including the Guo (1/π), Guo (1/2), wind generator (WINDGEN), Ephrath, Debele, and Goudriaan models, have been developed to quantitatively generate hourly wind speed data. In this study, the performances of these popular deterministic wind speed generation models were investigated using observed hourly wind speed data obtained from 191 climate stations in arid and semi-arid areas of China. The performances of the Guo (1/π), Guo (1/2) and WINDGEN models were the best for hourly wind speed generation. Linear regression analysis revealed that all models tended to underestimate wind speeds. The patterns of diurnal variation and the amplitude of the measured hourly wind speed data generally governed the model performance. The performances of the Guo (1/π), WINDGEN, Guo (1/2), Goudriaan, Debele, and Ephrath models successively decreased for the daily average erosive wind power density (AWPD) estimation. To improve the Guo (1/π) and Guo (1/2) models for AWPD evaluation, the calibrated parameters (π and 2) of the second term in the Guo (1/π) and Guo (1/2) models showed different values for each station. The accuracy of the AWPD obtained by the Guo (1/π) and Guo (1/2) models with the calibrated parameters were significantly improved, which could further influence the accuracy of wind erosion modeling. More studies are required to investigate the relationship between the calibrated parameters in the Guo (1/π) and Guo (1/2) models and geographic conditions linked with wind erosion.

Wind Energy Resource Potential Evaluation based on Statistical Distribution Models at Four Selected Locations in Amhara Region, Ethiopia

In Ethiopia, and particularly in the Amhara region, the government as well as the concerned organization would not give special attention to establishing wind energy plants. Lack of scientific research inputs about potential assessment can be one of the reasons behind it. In this paper, a wind energy potential assessment for Debel, Malawa, Enwari, and Ayba Eyesus sites in the Amhara region has been investigated. Five statistical distribution methods namely Weibull 3P, Weibull 2P, Rayleigh 2P, Normal, and Lognormal are used to fit the data to the probability density function and cumulative distribution function. The proposed parameter estimation method, to precisely predict the values of the shape parameter, scale parameter, and location parameter, was the Maximum Likelihood Estimation Method (MLE). To analyze the goodness of fit of the models, Kolmogorov, Andersen Darling, and Chi-Square have been used. The test indicated that Weibull 3P is the best fitting method, except for Ayba Eyesus, which is suited to Weibull 2P. For Debel, Malawa, Enwari, and Ayba Eyesus, the maximum annual average wind power density was found to be 74.291 W/m2, 19.183 W/m2, 68.972 W/m2, and 49.221 W/m2 correspondingly. The evaluations show that VENSYS 87 turbine model has better performance in all three sites except Enwari, where Inox Wind DF 100 is favored. With their best performance turbine, the capacity factor of the sites is determined as 14%, 7%, 12%, and 14% for Debel, Malawa, Enwari, and Ayba Eyesus respectively. Furthermore, Economical analysis by initial cost, lifetime, operation, and maintenance cost, has been carried out to estimate the cost of energy. With VENSYS 87 turbine model, the three sites' present value costs are $5,479,586, while it costs $7,306,115 in Enwari with Inox Wind DF 100 turbine. The cost of electricity per kWh is estimated to be $0.00231, $0.00455, $0.00391, and $0.00312 for Debel, Malawa, Enwari, and Ayba Eyesus respectively, and it is significantly lower than the cost from Ethiopian electric utility (EEU), which is around 0.009$/kwh. Access to electricity in Ethiopia was reported at 45% in 2019. This indicates there is a shortage of energy in the country. This kind of study can help authorities and policymakers in taking into account wind power to mitigate energy poverty in the country.

Green Energy: An Ideal Energy Solution for Sustainable Development of Afar Region, Ethiopia

Green energy is the primary concern for the sustainable development of Ethiopia’s Afar region. The study’s goals are to present scientific evidence of the Afar region’s energy potential to researchers and industry sectors. We used solar shortwave, radiation transfer model, miniscale meteorological model for the Weather Research Forecast (WRF), and spatial and temporal simulation as research techniques. The data show that the Afar region has an energy potential of 239.9 W/m2 average solar radiation flux, 2.102 MW·h/m2 average annual solar density, 131.18 W/m2 average wind power density at h = 10 m , and 204.5 W/m2 average wind power density at h = 50 m . We discovered that solar energy and wind energy are potential energy sources in the Afar region for energy consumption such as solar cooking, solar lighting, and small DC applications.

WIND ENERGY POTENTIAL AND WIND CHARACTERISTICS FOR THE DISTRICTS OF THE CENTRAL DEVELOPMENT REGION OF THE REPUBLIC OF MOLDOVA

The poor energy security worldwide and especially at the national level, requires all entities to look for effective solutions to rectify the current situation by turning to renewable energy sources and energy efficiency. The purpose of this paper is to study the wind energy potential and wind characteristics for second-level administrative-territorial entities (districts) from the Central Development Region, Republic of Moldova, which includes 13 districts: Anenii Noi, Călărași, Criuleni, Dubăsari, Hâncești, Ialoveni, Nisporeni, Orhei, Rezina, Străseni, Șoldănesti, Telenesti and Ungheni. The Wind Atlas Method accompanied by the special Wind Atlas Analysis and Application Program, data on wind speed and direction measured by the Technical University and the orographic map of the Republic of Moldova was used for the research. For each individual district there were: calculated and presented the maps of the average annual wind speed and wind power density at the height of 100 m above ground level; classified the district territory according to the power density value and also calculated the theoretical wind power possible to install. Following the calculations made for the thirteen analyzed districts, it was found that the average annual wind speed at the height of 100 m is between 6.70 and 7.40 m/s, and the wind power density -between 287 and 398 W/m2. The highest average annual speed equal to 7.40 m/s and windpower density equal to 398 W/m2 was identified in Nisporeni district and the lowest inIaloveni, Rezina, Șoldănești and Dubăsari districts.

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.

Method to describe the distribution of wind velocity and its application in wind resource evaluation

AbstractA convenient and effective method based on vector analysis was proposed in this paper to quantitatively describe the pattern of the wind speed distribution curve. This method can accurately describe the steepness of the single‐peak curve by using two parameters of concentration degree (CD) and concentration period (CP). Through the analysis of wind speed data in China over the past 40 years, this paper found that regional wind resources with larger CD were poorer, while regional wind resources with larger CP were richer. In addition, there were obvious cubic relationships between the CP and the average wind power density. These two parameters could reflect the richness of wind energy and realize the comparisons of wind resources across time and scales. In comparison with conventional approaches, this method is simpler and avoids fitting process, which has broad promotion prospects in the field of power grids.

Wind speed Weibull distribution and wind energy potential of Chandpur, Bangladesh

Weibull distribution goes particularly well with wind speed data and is necessary to assess wind energy harnessing potential at a site. For the best estimation of the statistical distribution parameters at Chandpur (23.21116° N; 90.64237° E), located at the southern part of Bangladesh, six models: graphical method, method of moments, power density method, Justus method, Lysen method and maximum likelihood method have been analyzed for 18.8 m, 40.2 m and 59.9 m above ground level (AGL) using measured wind speed data for 2014-2017. Out of the six models, Justus method has showed the best result for estimation of Weibull parameters at all heights AGL with average RMSE of 0.0082 and R2 of 0.9650. Further more to evaluate the wind energy potential at modern wind turbine hub height of 100 m, firstly the wind shear exponent at the site (the average value is 0.37 considering the 10 m elevation of the site above sea level) is estimated from the wind data of 3 heights; secondly average wind speed (4.88 m/s), wind power density (108.8 Wm-2), Weibull distribution parameters (shape factor 2.29 and scale factor 5.33 m/s) and wind turbine power curve related wind speeds (cut in 2.28 m/s, rated 9.33 m/s and cut out 21.03 m/s)have been extrapolated at the hub height. The average wind speed and wind power density values show that the class of the site is 1 in the worldwide accepted wind power classification, based on long term practical wind farm installation experiences; the low cut in speed (<3 m/s) shows that suitable wind turbines for the site are commercially unavailable for the site and turbines with 3 m/s cut in speed have average annual capacity factor of 16.62 %, less than the commercially viable capacity factor >25 %. The wind power class (< class 3) and capacity factor (<25 %) strictly reveals that Chandpur is not suitable for wind energy exploration in near future until a high technical improvement of wind turbines and reduction of relevant costs. DUJASE Vol. 6 (2) 99-108, 2021 (July)

WIND ENERGY POTENTIAL IN YOBE, NORTHEASTERN NIGERIA: AN AIDED TECHNIQUE THROUGH GEOGRAPHIC INFORMATION SYSTEM AND REMOTE SENSING

The role of wind energy and other renewable energy sources in combating climate change cannot be overemphasized. Many countries including Nigeria have so far implemented policy measures to promote renewable energy. Moreover, the most important step to be taken in order to effectively harness renewable energy is to estimate their potentials in terms of viability and sustainability. This study was focused on evaluate the wind energy potential zones in Yobe State, northeast Nigeria, using integrated multiple criteria decision-making analysis (MCDA), Geographical Information System and Remote Sensing (GIS-RS), and Analytic Hierarchy Process (AHP) methods. Weibull distribution statistical method was applied to validate the GIS-RS based analysis. The study revealed a wind speed variation ranged from 1.48 to 7.15 m/s and an average annual wind power density of about 80 W/m2. About 90 % of study area has about 90 % wind power density of about 85 W/m2 and below while the remaining 10 % is covered by wind power density ranged between 85 and 616.89 W/m2. The results of the study showed that the area is viable for wind power generation and distribution. Furthermore, the wind energy resource will yield favorable economic gain for the study area. Thus the study concluded that Yobe is a suitable area for wind power generation both at medium scale generation, and standalone connection system.

REGARDING THE CHARACTERISTICS OF THE WIND IN NORTHERN REGION DISTRICTS OF THE REPUBLIC OF MOLDOVA

The study is focused on three districts in the northern region of the Republic of Moldova – Briceni, Edinet and Ocnita. The maps were calculated and presented in terms of average annual wind speed and wind power density at a height of 100 m above ground level, wind rose and Weibull wind speed distribution. In the calculations performed for the three analyzed districts it was found that the average annual wind speed is between 6.84 and 7.35 m / s, and the wind power density – between 450 and 593 W / m2. The highest annual average speed equal to 7.35 m / s was identified in Ocnita district. At the same time, having the maps of the wind speed and the power density, the convenient locations for the construction of the wind farms can be selected. For each district involved in the study, a location with a pronounced wind potential was identified, recommended for the location of any wind farms.

THE POTENTIAL OF OFFSHORE WIND ENERGY IN THE EAST COAST OF PENINSULAR MALAYSIA, TERENGGANU

A detailed understanding of wind characteristics is very important for offshore wind energy development. A 26 years of wind speed data (1993-2018) were retrieved using Radar Altimeter Database System (RADS) to assess the potentiality of offshore wind energy in Terengganu waters. Seasonal assessment and wind energy density derivation was carried out to choose the potential location for wind energy development. This study highlights the multi-criteria site suitability analysis using Analytical Hierarchy Process (AHP) and is supported by the geographical information system (GIS) by developing a suitability map. The site suitability analysis considered a few criteria, such as seasonal assessment, physical, environmental, and wind resources. Theoretically, the Terengganu area possessed strong wind during the Northeast monsoon with an average of 3.46m/s and experienced up to 6 m/s during this monsoon. For offshore areas, which is more than 50km from the coastline, Terengganu waters experienced a maximum of wind speed more than 5m/s and the average wind power density varied from 40W/m2 to 60W/m2. While Tenggol Island possessed a maximum wind speed between 3m/s to 5m/s and produce up to 40W/m2 of average wind energy density. From the suitability analysis, a few areas are identified as the potential location with an optimum resource of wind energy. Even though, Malaysia is located at low wind area, this research will help organisation or governments to plan suitable technology and policy for harvesting wind energy.

Estimation of the Wind Energy Potential in Various North Algerian Regions

This investigation aims to model and assess the wind potential available in seven specific regions of North Algeria. These regions, i.e., Batna, Guelma, Medea, Meliana, Chlef, Tiaret, and Tlemcen, are known for their traditional agriculture. The wind data are obtained from the National Agency of Meteorology (NAM), and a Weibull distribution is applied. In the first part of this study, the wind potential available in these sites is assessed. Then, different models are used to estimate the wind system’s annual recoverable energy for these regions. We are interested in wind pumping for possible use to meet the needs of irrigation water in rural areas. Four kinds of wind turbines are explored to determine the possibility of wind energy conversion. In addition, the effects of the heights of the pylon holding the turbines are inspected by considering four cases (10, 20, 40, and 60 m). This estimation showed that the annual mean wind velocity varies from 2.48 to 5.60 m/s at a level of 10 m. The yearly values of Weibull parameters (k and c) at the studied sites varied within 1.61–2.43 and 3.32–6.20 m/s, respectively. The average wind power density ranged from 11.48 (at Chlef) to 238.43 W/m2 (at Tiaret), and the monthly wind recoverable potential varied from 16.64 to 138 W/m2.

GIS-based optimization \u2013 achieving Austria\u2019s 2030 wind energy target

In this paper, we take a look at Austria’s renewable energy targets established in the Renewable Energy Expansion Act (EAG), aiming to annually generate an additional 10 TWh of wind power by 2030. We conduct a GIS (geographic information system)-based analysis to determine average wind power density in Austria on a cellular level while considering prohibited regions, such as national parks, where building wind turbines might not be allowed. The calculated expansion potential for all remaining regions of Austria is allocated to the closest corresponding transmission nodes. Furthermore, we suggest an optimization algorithm to geographically distribute the expansion of wind power capacity to applicable transmission nodes. Finally, we conduct a case study to validate the algorithm using historical data on expansion and utilize it to predict an annual scenario for wind power expansion from 2021 to 2030 on a regional level. The total expansion required to achieve the goal of 10 TWh is assessed to be 4 GW based on predefined full load hours while assuming an exponential increase in annually added capacity (from 250 MW in 2021 to 590 MW in 2030).