Analysis Of Wind Energy Potential Research Articles

Analysis of Wind Energy Potential in Sri Lankan Waters Based on ERA5 (ECMWF Reanalysis v5) and CCMP (Cross-Calibrated Multi-Platform)

Analysis of Wind Energy Potential in Sri Lankan Waters Based on ERA5 (ECMWF Reanalysis v5) and CCMP (Cross-Calibrated Multi-Platform)

Harnessing the power of wind: a comprehensive analysis of wind energy potential in Dhaka city

This study gives a thorough analysis on the wind energy potential in Dhaka, Bangladesh, utilizing data from NASA Power's remote sensing tools and weather data from the Bangladesh Meteorological Department (BMD). The wind speed data collected over a 22 year period at an altitude of 10 m. The results indicate that 3.07 m per second (ms−1) is Dhaka city's typical wind speed, while the maximum wind speeds were recorded in June and July. A Weibull distribution is used to observe the wind data, as well as to calculate the Weibull form parameter of 2.65 and the scale parameter of 3.43 ms−1. Based on these parameters, the most probable wind speed along with the wind speed carrying maximum energy were calculated 2.83 ms−1 and 4.28 ms−1, respectively. The highest density of energy has been found in the month of July with a value of 52.11 W/m2. According to the study, the south is the most prominent wind direction for Dhaka city. Moreover, the study analyzes the relations between energy density and other variables, like wind speed, humidity, dry bulb temperature, etc. Positive correlations between energy density, wind speed, and dry bulb temperature imply that the higher wind speeds and dry bulb temperatures result in greater energies. The study's conclusions offer intuitive information about Dhaka City's potential for wind energy and can support direct future efforts to pursue this green resource in alignment with the Sustainable Development Goals (SDGs) of Bangladesh.

Economic Analysis of Wind Energy Potential in the Archipelago Tourism Area

Economic Analysis of Wind Energy Potential in the Archipelago Tourism Area

Offshore wind energy potential analysis of Turkish Marmara and Aegean seas

Offshore wind energy potential analysis of Turkish Marmara and Aegean seas

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.

Feasibility analysis of wind energy potential along the coastline of Pakistan

Wind energy is gaining popularity, especially across the countries with strong and continuous wind currents. Pakistan is bestowed with multiple wind corridors with prospective energy extraction, mainly concentrated along the coastline. This work aims to analyze the effectiveness and feasibility of wind energy potential in Pakistan coastline by choosing four unique zones (Karachi, Ormara, Pasni, and Gawadar) through analysis and demonstration for a 50 MW wind farm. Four years of seasonal data is used for the selected zones, reflecting the wind effectiveness in wind speed, wind density, and wind directions. Computer-based predictive models and artificial neural networks are also implemented to forecast the wind effectiveness, primarily based on the observed wind seasonal data. The wind farm's feasibility is proposed through a comparative evaluation for various turbine designs considering both geographical, installation, operational and financial factors. The factors of energy output, economic feasibility, environmental impact, and fuel-saving are analyzed for multiple locations and turbine designs. Critical observations, data, and findings are presented and discussed in detail for all zones. Zone 1 has a highest capacity factor of 46% followed by 40.4% (Zone 2), 29.3% (Zone 3), 37.5% (Zone 4). The study revealed that Karachi is the best-suited location for a 50 MW wind farm with the highest wind speed and the lowest variation in wind direction annually. It also represented the highest internal rate of return, benefit-to-cost ratio, and annual saving with a minimum payback period (4.5–7.2 years) for various commercially available turbine designs.

Assessment of wind energy potential: a case study

ABSTRACT Utilization and investments of wind energy have been speedily rising in the world due to harmful side of the fossil fuels. Assessment of wind energy potential analysis is a must before making investment decision of wind farm. Due to high capacity factor of Aegean Sea, Gökçeada location is chosen for wind energy potential analysis. One year hourly measured wind velocity and direction data supplied by Turkish State Meteorological Service (TSMS) is used to assess the wind potential in the terrain. Two different approaches are used for the assessment, the former employs power-law method for calculating wind velocity field while the latter employs Windsim software based on Computational Fluid Dynamics (CFD) method. After calculating wind velocity field, Weibull method is used for energy analysis in both approaches. Results from each approach are compared. The roughness is attained at only two points that are measurement site and turbine site in power-law method while it is taken into account for the whole domain in CFD method. Besides, unlike CFD method, wind direction is not considered in power-law method. The velocity profile is calculated at the height of 60 m and considered the same everywhere depend on the roughness in power-law method. On the other hand, Windsim calculates velocity profile over the terrain in consideration by solving Reynolds-averaged Navier-Stokes (RANS) equations. In this assessment, seven Vestas V90 2 MW wind turbines are employed. According to the results of power-law method and CFD method, the capacity factor is calculated as 31.67 and 49.5%, respectively. Windsim results indicate that annual energy production (AEP) is 60.2 GWh/y regarding wake loss, and the shape factor ranges from 1.57 to 1.64 whereas scale factor ranges from 9.49 to 10.77 m/s for the locations of turbines. It is strongly advised to build a wind farm in Gökçeada location according to the results from the analysis.

Techno-economic analysis of wind energy potential in Kazakhstan

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

Statistical analysis of wind energy potential using different estimation methods for Weibull parameters: a case study

Accurate estimation of wind speed distributions is a challenging task in wind power planning and operation. The selection of convenient functions for describing wind speed distribution is a crucial requisite. In this paper, remarkable bi-parameter Weibull function is presented to estimate the wind energy potential. Weibull parameters based on different six estimation methods, namely graphical, method of moment, energy pattern factor, mean standard deviation, power density methods, and genetic algorithm are evaluated. Besides, the goodness of fit of the estimation methods is investigated via mean absolute error, root mean square error, normalized mean absolute error, Chi-square error, and regression coefficient. To plainly identify the best matching estimation method, Net Fitness test is also presented. Catalca in the Marmara region in Istanbul, Republic of Turkey, is selected to be the underlying site. The experimental results show the effectiveness of the estimation methods in modeling wind distribution but with relatively small differences in terms of performance. However, the genetic algorithm and energy pattern factor accomplish the best and worst matching estimation methods, respectively.

Comparison based on the technical and economical analysis of wind energy potential at onshore, coastal, and offshore locations in Çanakkale, Turkey

Offshore wind technology in a global context is developing with extraordinary speed, and technical constraints are gradually decreasing. However, in Turkey, offshore applications are yet to be established despite the installation of a significant number of onshore wind power plants over the last ten years. The city of Çanakkale is situated on both the Marmara and the Aegean Sea. It is located on both sides of the Çanakkale Strait, which connects the continents of Asia and Europe and occupies an important geographical position in terms of wind-energy potential. In this study, the wind-energy potential was investigated using data from 18 different meteorological stations located at onshore, coastal, and offshore stations in Çanakkale. Weibull and Rayleigh probability density functions were used to obtain wind speed distribution curves. Four wind turbines with power ranging from 1500 kW to 4500 kW for onshore locations and three wind turbines with power ranging from 2000 kW to 5000 kW for coastal and offshore locations were compared in the scope of the methods used in economic analysis [energy cost estimation (C), net present value (NPV), benefit cost ratio, payback period, and internal rate of return]. In this context, the best performances were obtained in onshore locations of Bozcaada, Çanakkale Airport, and coastal and offshore locations of Bozcaada/Damlacık L. and Lapseki/Zincirbozan L. for Vestas V112 (119 m) turbine and Vestas V90 (105 m) turbine, respectively. This study aims to contribute scientifically to the development of coastal and offshore wind energy in the region.

Measurement and analysis of wind energy potential using fuzzy based hybrid MADM approach

Nowadays in India the renewable energy sources are continuum growing to accommodate the current demands of energy. Therefore, for an effective use of this energy, a careful and critical analysis is required. As per literature review, India was reported having a massive potential as superpower source in terms of wind energy In the present research, an effort has been carried out to explore various decision making approaches such as TOPSIS, VIKOR, and Fuzzy analysis, to subsequently rank various Indian states with respect to their wind energy potential. In this perspective, potentiality indices have been found on the justification of five significant factors that influence the effective use of wind energy and then a classification has been proposed. It was found that the wind power density is the most significant parameter while the technical expertise has been found as the least significant among identified parameters. The results presented here indicates that among all alternative states of India, Tamilnadu and Maharashtra have the maximum potential to tap the wind energy potential. This study will act as a guide for various government agencies to re-evaluate and re-formulate their energy policies as well as will help various investors (under the ‘Make in India’ campaign) orientated to do business here, to take a well informed decision. The present study also provides a way to make strong policies, in the area of high wind energy potential, in order to maximize the use of renewable source of energy which allows to tackle the societal need and poverty.

Analysis of wind energy potential; A case study of Kocaeli University campus

Determination of the wind energy potential is achieved by point measurement at the place. MERRA software which is generated by NASA provides the point data values. The point data with using Windographer software gives theoretically potential energy of wind turbines in a short time. In this study, the Kocaeli University campus selected a point, where the wind turbines are planned to be implemented. The obtained output power from wind energy will be examined. MERRA software and Windographer software are being used during this energy’s production study. The wind speed and direction analysis within 1 h of resolution is being performed at some points by using MERRA program. Windographer automatically defines wind resource data such as wind speed, standard deviation, vertical wind speed, direction, temperature, pressure, and relative humidity. Within the 4 point coordinates properly identified by the software, point “B” was selected. The annual wind data of the point “B” was analyzed and the annual amount of energy was calculated. In this study, GE 1.7-100 wind turbine was used. The generated energy and turbine output power were calculated by the Windographer program.

Wind energy potential analysis using Sentinel-1 satellite: A review and a case study on Mediterranean islands

A method for assessing and mapping the wind energy potential of near- and off-shore areas by means of multi sensor satellites (i.e. the recently launched Sentinel 1) is shown in this paper and applied to a case study area in the north-west coast of the Sicily island. The presented method aims at i) preliminary identifying the best sites for wind turbine generators installation and/or ii) estimating the average wind potential in small areas (e.g. archipelagos) for energy planning purposes. Firstly, a detailed literature review of existing techniques for wind speed estimation has been carried out, considering the most traditional methods (e.g. meteorological masts), remote sensing techniques and including a thorough review on the use of Synthetic Aperture Radar (SAR) methods integrated with Geophysical Model Functions (GMFs) for wind speed retrieval. This review enables to identify CMOD5 (C Geophysical model function 5) as the best performing GMF overcoming the CMOD4 accuracy issues in high wind speed conditions. Thus, the method has been detailly described and showcased through the analysis of the case study. SAR images from the Sentinel 1 satellite have been processed by means of the Sentinel Application Platform (SNAP) software. Afterwards, the wind speed and direction have been mapped through a Geographic Information System software. Lastly, the mean wind climate has been extrapolated for a specific Region Of Interest by the Environment for Visualizing Images (ENVI) 4.8 software. Consequentially, six hot spots characterized by high-energy potential have been identified as possible sites for possible installations of wind turbine generators.

Analysis of wind energy potential and wind energy development to evaluate performance of wind turbine installation in Bali, Indonesia

In recent years, Wind power generation in Indonesia is no longer a new issue. Indonesia has average velocity from 2 m/s to 7 m/s. With the characteristic it, Indonesia is suitable for small (10 kW) and medium wind turbine installation (10-100 kW. Based on the monitoring data from meteorological, climatological, and geophysical agency (BMKG), the average wind velocity in Bali is 2 m/s – 5m/s, hence Bali has potential to development and utilization the source for wind turbine installation, There are four stations of BMKG in Bali, which each station is supervise the region. Weibull distribution has been represented on this research to calculate and determine the probability of the each of region to know the availibility of the source. Literally, Jembrana station has the lowest availability of power available from the district and cities in Bali, with 0-0.2 W/m2, compared with some districts and cities in Bali, with wind density power between 0-2.88 W/m2 and also the KHK station has the highest probabiity of wind velocity than the other regions. Reconstruction design had been done, with basic data from probability in Bali. The result shows that the redesign of wind turbine give an effective power to extract the wind source.

Technical and economic analysis of wind energy potential in Uzbekistan

Uzbekistan is the most populated country in Central Asia with a population of about 32 million people. Unfortunately, the country's electricity generation is highly dependent on fossil fuels and the electricity generation infrastructures in the country are relatively old and obsolescence. The energy security of the country could be significantly improved by investing in environmental-friendly energy sources in order to reduce its dependence on the natural gas in the future. The present study aims to assess the wind energy potential of Uzbekistan for the very first time. The hourly wind speed data of 17 different sites (10 m height) in a typical meteorological year are collected and utilized. The collected data are fitted in the widely utilized statistical two-parameter Weibull distribution function. Several performance metric indicators of annual power density, energy production, capacity factor and levelized cost of energy (LCOE) are used to assess the technical and economic feasibility of the systems. In addition, the study assesses and compares 15 different commercially available wind turbines to infer the best option for implementation in the country. The results show that the average annual wind speed, power density and energy production are in the range between 0.61 and 3.98 m/s, 1.74–88.55 W/m2, and 15.27–775.72 kWh/m2, respectively at a height of 10 m above the ground. It is observed that Nukus, Kungrad, Ak Bajtal, and Buhara are the best sites for wind energy development in Uzbekistan. The study is expected to aid policymakers, designers and investors in decision-making regarding the development and deployment of wind energy technology as a future power source in the country.

Statistical Analysis of Wind Energy Potential in Uzbekistan’s Bukhara Region Using Weibull Distribution

With the development of small business in Uzbekistan’s rural areas, there is a shortage of electric power, as well as power outages for these consumers. The use of renewable energy sources is one way to solve this problem, and this has been little studied in Bukhara oblast. A preliminary study of this problem shows that the region has the necessary capacity of renewable energy sources. This paper presents the results of a study of wind speed and wind energy potential in Bukhara oblast located in southwestern Uzbekistan. The data of wind speed and direction measured at the weather station at Bukhara’s international airport, taken at an altitude of 10 meters, were analyzed on the basis of the two-parameter Weibull distribution function. The main parameters k and c of the Weibull distribution function were determined using the empirical method. Wind speed and direction were statistically analyzed in MatLab and a graph (wind rose) was plotted. Wind energy potentials at different altitudes were also evaluated.

Techno-economic analysis of a potential energy trading link between Patagonia and Japan based on CO2 free hydrogen

Abstract With regard to the Fukushima Daiichi accident in 2011 and Japan's goal to reduce CO2 emission, the Japanese government strives for an emission free “hydrogen society” in which hydrogen will be the primary energy medium. The import of hydrogen generated by means of CO2 free wind electricity from overseas can be a promising option for Japan's prospective energy supply. Besides different other factors like specific costs of electrolyzers and hydrogen shipment over long distances, the economically reasonable export of hydrogen based on renewable energy requires low levelized costs of electricity. Within the scope of this study, the underlying idea of a hydrogen supply chain is taken up and revisited by means of a spatially highly resolved wind energy potential analysis and a detailed investigation of the supply chain elements between Patagonia and Japan. Our analysis reveals that approximately 25% of the total land area in Patagonia would be eligible. Approx. 33,000 turbines with a minimum number of 4500 full-load hours with an overall capacity of about 115 GW can be positioned. Taking into consideration the related average number of 4750 full-load hours and an electrolysis efficiency of 0.7, this leads to a potential production of about 11.5 million tons/year of hydrogen. So the wind power potential of Patagonia would theoretically be sufficient for the assumed Japanese hydrogen demand of 8.83 million tons/year. The total hydrogen pretax cost would amount to approx. 4.40 €/kgH2 at a liquid state at the harbor of Yokohama. Hence, the final specific costs of hydrogen in Japan depend on the expansion of wind power in Patagonia and therefore hydrogen based on wind energy can be cost-competitive to conventional fuels.

Research and application based on the swarm intelligence algorithm and artificial intelligence for wind farm decision system

Wind energy is an increasing concern for wind farm administrators. Effective wind energy potential analysis and accurate forecasting can reduce the operating cost of wind farms. However, many previous studies have been restricted to analyses of wind energy potential analysis and wind speed forecasting, which may result in poor decisions and inaccurate power scheduling for wind farms. This study develops a wind energy decision system based on swarm intelligence optimization and data preprocessing, which includes two modules: wind energy potential analysis and wind speed forecasting. In the wind energy potential analysis module, the parameters of the Weibull distribution are optimized by a multiple swarm intelligence optimization algorithm, which can provide better wind energy assessment results. In the wind speed forecasting module, the data preprocessing method can effectively eliminate the noise of the original wind speed time series, maintain the characteristics of the wind speed data, and improve the accuracy of the forecasting model. The numerical results show that the wind energy decision system not only provides an effective wind energy assessment, but can also satisfactorily approximate the actual wind speed forecasting. Therefore, it can serve as an effective tool for wind farm management and decision-making.

Statistical Quality Control Analysis of Wind Energy Potential for Power Generation in Benin

Energy has been and will continue to be the driver of economy of a nation. In sharp contrast, our country Nigeria is being faced with energy poverty. This may accrue to, in fact, over reliance on conventional energy sources such as hydro power, fuels and gas etc. So, there is need to take into consideration the renewable energy as potential alternative for power generation. This work seeks to assess the wind energy potential of Edo state using a statistical method: statistical quality control technique. The detailed knowledge of the wind characteristics at a site is very crucial to install and estimate the performance of a wind energy project. The monthly and yearly highest mean wind speeds were 3.28 and 2.91 m/s respectively. The most probable wind speed and wind speed carrying maximum energy were estimated 2.84 and 4.53 m/s in 2015. Result revealed that the wind speed are statistically in control. That is, the speeds of wind fall in between their respective upper and lower limits for 2015 - 16. The result of the study revealed that the wind energy potential of Edo state is quite low for electricity generation based on the two-year wind data analysed. It is suggested that this research area should be intensified by collecting wind speed data at various locations sufficient to define its potential for power generation.

Wind energy potential analysis for Thailand: Uncertainty from wind maps and sensitivity to turbine technology

ABSTRACTRapid development of wind energy has been witnessed in Thailand. However, different wind resource maps (over land) have brought great uncertainty to wind energy planning. Here, four important mesoscale wind maps were considered: DEDP (2001), World Bank (2001), Manomaiphiboon et al. (2010) of JGSEE, and DEDE (2010). The wind maps were first harmonized to a common grid at 100 m and then compared. The earlier wind maps (DEDP and World Bank) are shown to represent the lower and upper limits of predicted speed, respectively, while JGSEE and DEDE tend to be more moderate with predictions statistically closer to observations. A consolidated wind map was constructed based on their median and shown to have the best prediction performance. It was then used for the technical potential analysis, in which three large (2-MW) turbine models (two conventional and one designed for low wind speed) were considered. By GIS techniques, any land areas not feasible for large wind turbines were excluded, and the corresponding overall onshore technical potential ranges between 50 and 250 GW, depending on map and turbine model. Considering only economically feasible turbines (with capacity factors of 20%) and the median-based map, the final technical potential equals 17 GW when using the low-wind-speed model but is reduced to 5 GW with the conventional models, adequately meeting the national wind energy target of 3 GW by the year 2036. The results suggest a strong sensitivity of estimated technical potential to turbine technology and a suitability of low-wind-speed turbines for wind conditions in Thailand.