scholarly journals Observation and Simulation of Wind Speed and Wind Power Density over Bac Lieu Region

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Hiep Van Nguyen ◽  
Pham Xuan Thanh ◽  
Nguyen Duc Nam ◽  
Nguyen Xuan Anh ◽  
Pham Le Khuong ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Power Density ◽  
Bias Correction ◽  
Local Maximum ◽  
Observation Data ◽  
Wind Power Density ◽  
Wind Speeds ◽  
Diurnal Cycles ◽  
Southern Vietnam

In this study, the WRF (Weather Research and Forecasting) model was used to simulate and investigate diurnal and annual variations of wind speed and wind power density over Southern Vietnam at 2‐km horizontal resolution for two years (2016 and 2017). The model initial and boundary conditions are from the National Centers for Environmental Prediction (NCEP) Final Analyses (FNL). Observation data for two years at 20 m height at Bac Lieu station were used for model bias correction and investigating diurnal and annual variation of wind speeds. The results show that the WRF model overestimates wind speeds. After bias correction, the model reasonably well simulates wind speeds over the research area. Wind speed and wind power density show much higher values at levels of 50–200 m above ground levels than near ground (20 m) level and significantly higher near the coastal regions than inland. Wind speed has significant annual and diurnal cycles. Both annual and diurnal cycles of wind speeds were well simulated by the model. Wind speed is much stronger during daytime than at nighttime. Low-level wind speed reaches the maximum at about 14 LT to 15 LT when the vertical momentum mixing is highly active. Wind speeds over the eastern coastal region of Southern Vietnam are much stronger in winter than in summer due to two main reasons, including (1) stronger large-scale wind speed in winter than in summer and (2) funnel effect creating a local maximum wind speed over the nearshore ocean which then transports high-momentum air inland in winter.

scholarly journals Wind energy resource assessment for Suva, Fiji, with accurate Weibull parameters

2019 ◽  
Vol 37 (3) ◽  
pp. 1009-1038 ◽  
Author(s):  
Saiyad S Kutty ◽  
MGM Khan ◽  
M Rafiuddin Ahmed
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Power Density ◽  
Energy Production ◽  
Ground Level ◽  
Resource Assessment ◽  
Above Ground Level ◽  
Application Program ◽  
Wind Power Density ◽  
Wind Speeds

Wind resource assessment is carried out for Suva, the capital of the Republic of Fiji Islands. The wind speeds at 34 m and 20 m above ground level, wind direction, atmospheric pressure, and temperature were measured for more than five years and were statistically analyzed. The daily, monthly, yearly, and seasonal averages were estimated. For the site, the overall average wind speed at 34 m above ground level is found to be 5.18 m/s. The occurrence of effective wind (between the cut-in and cut-off wind speeds of the selected turbine) is predominantly from the east. An effective wind speed of 74.175% was recorded which can be used for power generation. The turbulence intensity and wind shear coefficient are estimated. The site’s overall turbulence intensities are 12.5% and 13.72% at 34 m and 20 m above ground level, respectively. The diurnal wind shear correlated with the temperature variation very well. The overall and seasonal wind distributions are analyzed, which shows that the wind speed in Suva is mostly between 3 m/s and 9 m/s although the winter season has higher wind speeds. The Weibull parameters and the wind power density were found using 10 different methods. The wind power density is estimated to be 159 W/m2 using the best method, which is found to be the empirical method of Justus. A high-resolution map around the site is digitized and the wind power density resource map is generated using wind atlas analysis and application program. From the wind atlas analysis and application program analysis, it is seen that Suva has high potential for power generation. Five possible locations are selected for installing wind turbines and the annual energy production is estimated using wind atlas analysis and application program. The total annual energy production from the five sites is 1950 MWh. The average capacity factor of the five turbines is 17%. An economic analysis is performed which showed a payback period of 10.83 years.

scholarly journals Evaluation of the Technical Wind Energy Potential of Kisii Region Based on the Weibull and Rayleigh Distribution Models

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
N. Laban Ongaki ◽  
Christopher M. Maghanga ◽  
Joash Kerongo
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Series Data ◽  
Energy Potential ◽  
Distribution Models ◽  
Wind Power Density ◽  
Wind Speeds

Background. Global warming is a growing threat in the world today mainly due to the emission of CO2 caused by the burning of fossil fuel. Consequently, countries are being forced to seek potential alternative sources of energy such as wind, solar, and photovoltaic among many others. However, the realization of their benefits is faced with challenges. Though wind stands a chance to solve this problem, the lack of adequate site profiles, long-term behavioural information, and specific data information that enables informed choice on site selection, turbine selection, and expected power output has remained a challenge to its exploitation. In this research, Weibull and Rayleigh models are adopted. Wind speeds were analyzed and characterized in the short term and then simulated for a long-term measured hourly series data of daily wind speeds at a height of 10 m. The analysis included daily wind data which was grouped into discrete data and then calculated to represent the mean wind speed, diurnal variations, daily variations, and monthly variations. To verify the models, statistical tools of Chi square, RMSE, MBE, and correlational coefficient were applied. Also, the method of measure, correlate, and predict was adopted to check for the reliability of the data used. The wind speed frequency distribution at the height of 10 m was found to be 2.9 ms-1 with a standard deviation of 1.5. From the six months’ experiments, averages of wind speeds at hub heights of 10 m were calculated and found to be 1.7 m/s, 2.4 m/s, and 1.3 m/s, for Ikobe, Kisii University, and Nyamecheo stations, respectively. The wind power density of the region was found to be 29 W/m2. By a narrow margin, Rayleigh proves to be a better method over Weibull in predicting wind power density in the region. Wind speeds at the site are noted to be decreasing over the years. The region is shown as marginal on extrapolation to 30 m for wind energy generation hence adequate for nongrid connected electrical and mechanical applications. The strong correlation between the site wind profiles proves data reliability. The gradual decrease of wind power over the years calls for attention.

scholarly journals Analysis and Distribution of Wind Speed in Saudi Arabia

2021 ◽  
Author(s):  
Y. Al-Douri ◽  
S. A. Waheeb
Keyword(s):  
Saudi Arabia ◽  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Frequency Distribution ◽  
North West ◽  
Wind Power Density ◽  
Wind Speeds

Abstract Background: the purpose of this study is to estimate the winds, its erosion and wind power of Kingdom of Saudi Arabia. The additional value is to details wind energy in Saudi Arabia indulging updated wind speed analysis, wind speed frequency distribution and mean wind power density variation to present novel work could be added to the literature providing recent data helping for future researches and studies. Results: the updated analysis and distribution of wind energy are presented in six sites; Al Jouf, Hafar Al Batin, Riyadh, Al Wajh, Jeddah South and Sharurah of Saudi Arabia. The winds and wind energy are elaborated. The long-term annual mean values of wind speeds are found to vary between year 2000 and 2020. The annual values of wind power density are varied between year 2006 and 2020. Also, the wind speeds are researched over the entire geography of Saudi Arabia. The percent frequency distribution at different wind speeds for the mentioned six sites at 12 m for two decades is displayed. Conclusions: the long-term values of wind speed were found between 3.3 m/s in 2000 and 5 m/s in 2020. The annual wind power density values were varied between 44 W/m2 in 2006 and 88 W/m2 in 2020. In addition, the wind speeds were researched over the entire Saudi Arabia for east, north, west and south. The deduced percent frequency distribution was less than 18% of the time at 12 m.

Assessment of wind energy potential and characteristics in Qatar for clean electricity generation

2021 ◽  
pp. 0309524X2110438
Author(s):  
Carlos Méndez ◽  
Yusuf Bicer
Keyword(s):  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Wind Farm ◽  
Wind Farms ◽  
Wind Rose ◽  
Energy Potential ◽  
Wind Power Density ◽  
Wind Speeds ◽  
Wind Energy Potential

The present study analyzes the wind energy potential of Qatar, by generating a wind atlas and a Wind Power Density map for the entire country based on ERA-5 data with over 41 years of measurements. Moreover, the wind speeds’ frequency and direction are analyzed using wind recurrence, Weibull, and wind rose plots. Furthermore, the best location to install a wind farm is selected. The results indicate that, at 100 m height, the mean wind speed fluctuates between 5.6054 and 6.5257 m/s. Similarly, the Wind Power Density results reflect values between 149.46 and 335.06 W/m2. Furthermore, a wind farm located in the selected location can generate about 59.7437, 90.4414, and 113.5075 GWh/y electricity by employing Gamesa G97/2000, GE Energy 2.75-120, and Senvion 3.4M140 wind turbines, respectively. Also, these wind farms can save approximately 22,110.80, 17,617.63, and 11,637.84 tons of CO2 emissions annually.

scholarly journals Assessment of wind energy potential across varying topographical features of Tamil Nadu, India

2019 ◽  
Vol 38 (1) ◽  
pp. 175-200 ◽  
Author(s):  
Shafiqur Rehman ◽  
Narayanan Natarajan ◽  
Mangottiri Vasudevan ◽  
Luai M Alhems
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Tamil Nadu ◽  
High Energy ◽  
Energy Potential ◽  
Annual Average ◽  
Wind Power Density ◽  
Wind Energy Potential

Wind energy is one of the abundant, cheap and fast-growing renewable energy sources whose intensive extraction potential is still in immature stage in India. This study aims at the determination and evaluation of wind energy potential of three cities located at different elevations in the state of Tamil Nadu, India. The historical records of wind speed, direction, temperature and pressure were collected for three South Indian cities, namely Chennai, Erode and Coimbatore over a period of 38 years (1980-2017). The mean wind power density was observed to be highest at Chennai (129 W/m2) and lowest at Erode (76 W/m2) and the corresponding mean energy content was highest for Chennai (1129 kWh/m2/year) and lowest at Erode (666 kWh/m2/year). Considering the events of high energy-carrying winds at Chennai, Erode and Coimbatore, maximum wind power density were estimated to be 185 W/m2, 190 W/m2 and 234 W/m2, respectively. The annual average net energy yield and annual average net capacity factor were selected as the representative parameters for expressing strategic wind energy potential at geographically distinct locations having significant variation in wind speed distribution. Based on the analysis, Chennai is found to be the most suitable site for wind energy production followed by Coimbatore and Erode.

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

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1846 ◽  
Author(s):  
Teklebrhan Negash ◽  
Erik Möllerström ◽  
Fredric Ottermo
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Coastal Region ◽  
Energy Potential ◽  
Wind Power Density ◽  
Prevailing Wind Direction ◽  
Central Highland ◽  
Wind Energy Potential

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.

scholarly journals Wind potential estimation and proposed energy production in Southern Punjab using Weibull probability density function and surface measured data

2020 ◽  
pp. 014459872092074 ◽  
Author(s):  
Muhammad Sumair ◽  
Tauseef Aized ◽  
Syed Asad Raza Gardezi ◽  
Syed Ubaid Ur Rehman ◽  
Syed Muhammad Sohail Rehman
Keyword(s):  
Wind Speed ◽  
Energy Density ◽  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Energy Production ◽  
Economic Assessment ◽  
Weibull Model ◽  
Wind Power Density ◽  
Wind Potential

Current work focusses on the wind potential assessment in South Punjab. Eleven locations from South Punjab have been analyzed using two-parameter Weibull model (with Energy Pattern Factor Method to estimate Weibull parameters) and five years (2014–2018) hourly wind data measured at 50 m height and collected from Pakistan Meteorological Department. Techno-economic analysis of energy production using six different turbine models was carried out with the purpose of presenting a clear picture about the importance of turbine selection at particular location. The analysis showed that Rahim Yar Khan carries the highest wind speed, highest wind power density, and wind energy density with values 4.40 ms−1, 77.2 W/m2 and 677.76 kWh/m2/year, respectively. On the other extreme, Bahawalnagar observes the least wind speed i.e. 3.60 ms−1 while Layyah observes the minimum wind power density and wind energy density as 38.96 W/m2 and 352.24 kWh/m2/year, respectively. According to National Renewable Energy Laboratory standards, wind potential ranging from 0 to 200 W/m2 is considered poor. Economic assessment was carried out to find feasibility of the location for energy harvesting. Finally, Polar diagrams drawn to show the optimum wind blowing directions shows that optimum wind direction in the region is southwest.

Estimated changes in wind speed and wind power density over the western High Plains, 1971–2000

2012 ◽  
Vol 109 (3-4) ◽  
pp. 507-518 ◽  
Author(s):  
J. Scott Greene ◽  
Matthew Chatelain ◽  
Mark Morrissey ◽  
Steve Stadler
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Power Density ◽  
High Plains ◽  
Wind Power Density

scholarly journals The Investigation of Wind Energy Potential in Order to Estimate Power Plants in Baladeh Mazandaran, Iran

2012 ◽  
pp. 29-33
Author(s):  
S. Asghar Gholamian ◽  
S. Bagher Soltani ◽  
R. Ilka
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Power Plants ◽  
Weather Station ◽  
Energy Potential ◽  
Wind Power Density ◽  
Synoptic Weather ◽  
Wind Potential

First step for achieving wind energy is to locate points with appropriate wind power density in a country. Wind data which are recorded in a synoptic weather station, are the best way to study the wind potential of an area. In this paper wind speed period of Baladeh synoptic weather station is studied, since it has the maximum average of wind speed among 15 stations of the MAZANDARAN Province. Weibull factors k and c are calculated for 40 months from September 2006 to December 2009 and wind power density is determined based on these data. The total average of factors k and c for a height for 50 m are 1.442 m/s and 5.1256 respectively. By using the average of factors, wind power density in 50 m height will be 147.40 watt/m2 which is categorized as weak potential in wind class. However by monthly investigation it is shown that with a 50 m wind, this station can be put in medium class in hot months of the year.

scholarly journals Evaluation of Wind Potential for the Generation of Electricity in Aliero, Kebbi State

2020 ◽  
pp. 1-7
Author(s):  
A. A. Yahaya ◽  
I. M. Bello ◽  
N. Mudassir ◽  
I. Mohammed ◽  
M. I. Mukhtar
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Power ◽  
Power Density ◽  
Horizontal Wind ◽  
Average Wind Speed ◽  
Wind Power Density ◽  
North East ◽  
Average Wind ◽  
Wind Potential

One of the major developments in the technology today is the wind turbine that generates electricity and feed it directly to the grid which is used in many part of the world. The main purpose of this work is to determine the wind potential for electricity generation in Aliero, Kebbi state. Five years Data (2014-2018) was collected from the metrological weather station (Campell Scientific Model), the equipment installed at Kebbi State University of Science And Technology Aliero The data was converted to monthly and annual averages, and compared with the threshold average wind speed values that can only generate electricity in both vertical and horizontal wind turbines. The highest average wind speed 2.81 m/s was obtained in the month of January and the minimum average wind speed of 1.20 m/s in the month of October. Mean annual wind speed measured in the study area shows that there has been an increase in the wind speed from 2014 which peaked in 2015 and followed by sudden decrease to a minimum seasonal value in the year 2016. The highest wind direction is obtained from the North North-East (NNE) direction. From the results of wind power density it shows that we have highest wind power density in month of January and December with  0.8635 w/ m2 and 0.8295 w/ m2 respectively, while lowest wind power density in the month of October and September with 0.6780 w/ m2 and 0.6575 w/ m2  respectively. Result of the type Wind Turbine to be selected in the study area shows that the site is not viable for power generation using a horizontal wind turbine but the vertical wind turbine will be suitable for the generation of electricity.

Sign in / Sign up

Export Citation Format

Share Document