A Hybrid Times-Scale Assessment Method for Wind Power Density in Complex Mountainous Areas Based on Improved Xgboost

Since the East Vietnam Sea has an advantageous geographical location and rich natural resources, we can develop and manage islands and reefs in this region reasonably to declare national sovereignty. Based on 1096 scenes of QuikSCAT wind data of 2006–2009, wind power density at 10 m hight is calculated to evaluate wind energy resources of the East Vietnam Sea. With a combination of wind power density at 70 m hight calculated according to the power law of wind energy profile and reef flats extracted from 35 scenes of Landsat ETM+ images, installed wind power capacity of every island or reef is estimated to evaluate wind power generation of the East Vietnam Sea. We found that the wind power density ranges from levels 4–7, so that the wind energy can be well applied to wind power generation. The wind power density takes on a gradually increasing trend in seasons. Specifically, the wind power density is lower in spring and summer, whereas it is higher in autumn and winter. Among islands and reefs in the East Vietnam Sea, the installed wind power capacity of Hoang Sa archipelago is highest in general, the installed wind power capacity of Truong Sa archipelago is at the third level. The installed wind power capacity of Discovery Reef, Bombay Reef, Tree island, Lincoln island, Woody Island of Hoang Sa archipelago and Mariveles Reef, Ladd Reef, Petley Reef, Cornwallis South Reef of Truong Sa archipelago is relatively high, and wind power generation should be developed on these islands first.

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Assessment of Wind Energy for Nevada Using Towers and Mesoscale Modeling

ASME 2007 Energy Sustainability Conference ◽

10.1115/es2007-36198 ◽

2007 ◽

Cited By ~ 1

Author(s):

Darko Koracin ◽

Richard L. Reinhardt ◽

Marshall B. Liddle ◽

Travis McCord ◽

Domagoj Podnar ◽

...

Keyword(s):

High Resolution ◽

Wind Energy ◽

Wind Power ◽

Power Density ◽

Mesoscale Model ◽

Horizontal Resolution ◽

Mesoscale Modeling ◽

Annual Cycles ◽

Wind Power Density ◽

Mountainous Regions

The main objectives of the study were to support wind energy assessment for all of Nevada by providing two annual cycles of high-resolution mesoscale modeling evaluated by data from surface stations and towers, estimating differences between these annual cycles and standard wind maps, and providing wind and wind power density statistics at elevations relevant to turbine operations. In addition to the 65 existing Remote Automated Weather Stations in Nevada, four 50-m-tall meteorological towers were deployed in western Nevada to capture long-term wind characteristics and provide database input to verify and improve modeling results. The modeling methodology using Mesoscale Model 5 (MM5) was developed to provide wind and wind power density estimates representing mesoscale effects that include actual synoptic forcing during the two annual cycles (horizontal resolution on the order of 2 and 3 km). The results from the two annual simulation cycles show similar wind statistics with an average difference of less than 100 W/m2. The available TrueWind results for the wind power density at 50 m show greater values of wind power density compared to both MM5-simulated annual cycles for most of the area. However, mainly in the Sierras and the mountainous regions of southern and eastern Nevada, the MM5 simulations indicate greater values for wind power density. The results of this study suggest that the synthesis of the data from a network of tower observations and high-resolution mesoscale modeling is a crucial tool for assessing the wind power density in Nevada and, more generally, other topographically developed areas.

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

Wind Engineering ◽

10.1177/0309524x211043855 ◽

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.

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

Energy Exploration & Exploitation ◽

10.1177/0144598719875276 ◽

2019 ◽

Vol 38 (1) ◽

pp. 175-200 ◽

Cited By ~ 7

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.

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Using Weather Predictions in a Grid for Efficient Regional Wind Power Density Forecasting

2020 IEEE XXVII International Conference on Electronics, Electrical Engineering and Computing (INTERCON) ◽

10.1109/intercon50315.2020.9220236 ◽

2020 ◽

Author(s):

Ismael Sanchez ◽

Miguel G. Lobo

Keyword(s):

Wind Power ◽

Power Density ◽

Wind Power Density ◽

Density Forecasting

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Very short-term wind power density forecasting through artificial neural networks for microgrid control

Renewable Energy ◽

10.1016/j.renene.2019.07.067 ◽

2020 ◽

Vol 145 ◽

pp. 1517-1527 ◽

Cited By ~ 9

Author(s):

Fermín Rodríguez ◽

Ane M. Florez-Tapia ◽

Luis Fontán ◽

Ainhoa Galarza

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Wind Power ◽

Power Density ◽

Short Term ◽

Wind Power Density ◽

Density Forecasting ◽

Artificial Neural ◽

Microgrid Control

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

Energies ◽

10.3390/en13071846 ◽

2020 ◽

Vol 13 (7) ◽

pp. 1846 ◽

Cited By ~ 2

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.

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

Energy Exploration & Exploitation ◽

10.1177/0144598720920748 ◽

2020 ◽

pp. 014459872092074 ◽

Cited By ~ 2

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.

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