Assessment of the Resources of Wind Energy in Various Regions of Algeria

Details on the wind potential during a period of about thirteen years in Algeria is given in the present work. The inspection is performed for sixteen regions covering almost all the territory of the country. The density of the mean wind power is determined for the different regions. The maps of annual and seasonal wind energy resources are also established. The characteristics of the wind velocity, as well as the potential of wind power, are determined by the Weibull distribution. From the given results, the highest values of annual mean wind speed and the annual mean wind power density are found in Adrar (P10 = 283.12 W/m2 and P50 = 646.91 W/m2), while the lowest values are observed in Skikda (P10 = 40.61 W/m2 and P50 = 115.51 W/m2, respectively).

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Assessment of wind energy resources in Nigeria – a case study of north-western region of Nigeria

International Journal of Physical Research ◽

10.14419/ijpr.v5i2.8327 ◽

2017 ◽

Vol 5 (2) ◽

pp. 83 ◽

Cited By ~ 1

Author(s):

Boluwaji Olomiyesan ◽

Onyedi Oyedum ◽

Paulinus Ugwuoke ◽

Matthew Abolarin

Keyword(s):

Power Generation ◽

Wind Speed ◽

Wind Energy ◽

Wind Power ◽

Large Scale ◽

Wind Power Generation ◽

Energy Resources ◽

Small Scale ◽

Wind Potential ◽

Wind Energy Resources

This study assesses the wind-energyresources in Nigeria by reviewing the existing literature on the subject matter, and also evaluates the wind potential in six locations in the northwest region of the country. Twenty-two years’ (1984 – 2005) wind speed data obtained from the Nigerian Meteorological Agencies (NIMET) were used in this study.Weibull two-parameter and other statistical models were employed in this analysis. Wind speed distribution across Nigeria shows that some locations in the northern part of the country are endowed with higher wind potential than others in the southern part of the country. Moreover, assessment of the wind-energy resources in the study locations reveals that wind energy potential in the region is lowest in Yelwa and highest in Kano; WPD varies from 28.30 Wm-2 to 483.72Wm-2 at 10 m AGL, 45.33 Wm-2 to 775.19 Wm-2 at 30 m AGL and 56.43 Wm-2 to 964.77 Wm-2 at 50 m AGL.Thus Kano, Sokoto and Katsina are suitable for large-scale wind power generation, while Gusau is suitable for small-scale wind power generation; whereas Yelwa and Kaduna may not be suitable for wind power production because of their poor wind potential.

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Methodical aspects of the assessment of the wind power potential

Visnyk of the Lviv University. Series Geography ◽

10.30970/vgg.2011.39.2192 ◽

2011 ◽

pp. 313-319

Author(s):

M. Syrotyuk ◽

O. Grynda

Keyword(s):

Wind Energy ◽

Key Words ◽

Wind Power ◽

Local Level ◽

The State ◽

Energy Resources ◽

Wind Potential ◽

Potential Formation ◽

Wind Energy Resources

The methodical aspects of the assessment of the wind power potential have been considered. The state of the methodics of the wind power energetic in Lviv region has been characterized. Factors of the wind potential formation for small wind energetic at the local level have been analyzed. Key words: wind energy, wind power potential, assessment of wind energy resources.

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Dynamical Downscaling of Climate Change Impacts on Wind Energy Resources in the Contiguous United States by Using a Limited-Area Model with Scale-Selective Data Assimilation

Advances in Meteorology ◽

10.1155/2014/897246 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 6

Author(s):

Bin Liu ◽

Katelyn B. Costa ◽

Lian Xie ◽

Fredrick H. M. Semazzi

Keyword(s):

United States ◽

Wind Speed ◽

Wind Energy ◽

Great Plains ◽

Climate Change Impacts ◽

Energy Resources ◽

Limited Area ◽

Current Climate ◽

Mean Wind Speed ◽

Wind Energy Resources

By using a limited-area model (LAM) in combination with the scale-selective data assimilation (SSDA) approach, wind energy resources in the contiguous United States (CONUS) were downscaled from IPCC CCSM3 global model projections for both current and future climate conditions. An assessment of climate change impacts on wind energy resources in the CONUS region was then conducted. Based on the downscaling results, when projecting into future climate under IPCC’s A1B scenario, the average annual wind speed experiences an overall shift across the CONUS region. From the current climate to the 2040s, the average annual wind speed is expected to increase from 0.1 to 0.2 m s−1over the Great Plains, Northern Great Lakes Region, and Southwestern United States located southwest of the Rocky Mountains. When projecting into the 2090s from current climate, there is an overall increase in the Great Plains Region and Southwestern United States located southwest of the Rockies with a mean wind speed increase between 0 and 0.1 m s−1, while, the Northern Great Lakes Region experiences an even greater increase from current climate to 2090s than over the first few decades with an increase of mean wind speed from 0.1 to 0.4 m s−1.

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Evaluation and Prediction of Wind Power Utilization Efficiency Based on Super-SBM and LSTM Models: A Case Study of 30 Provinces in China

Complexity ◽

10.1155/2020/8834941 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Chengyu Li ◽

Qunwei Wang ◽

Peng Zhou

Keyword(s):

Wind Energy ◽

Wind Power ◽

South China ◽

Regional Differences ◽

Development Trend ◽

Energy Resources ◽

Utilization Efficiency ◽

Central China ◽

East China ◽

Wind Energy Resources

Although China’s wind industry has made great progress in recent years, the wind abandonment phenomenon caused by the unbalanced development of regional wind power is still prominent. It is particularly important for the scientific development of wind power to accurately measure the utilization efficiency of wind power and understand its regional differences in China. This study establishes the improved super-efficiency slack-based measure (Super-SBM) model and long short-term memory (LSTM) network models, systematically and comprehensively measures and predicts the wind power utilization efficiency of 30 regions in China from 2013 to 2020, and explores regional differences in wind power utilization efficiency. Our results show the following: (1) China’s overall wind power utilization efficiency is relatively low but has been on a steady upward trend since 2013. (2) Regional differences are obvious, showing that the spatial distribution pattern of wind power utilization efficiency is greatest in Northeast China, followed by North China, East China, South China, Northwest China, and Central China. The “Three-North” region with abundant wind energy resources has relatively high wind power utilization efficiency and exhibits a good development trend. East China, South China, and Central China, where wind energy resources are relatively poor, have low wind power utilization efficiency, and their development trends are not stable and are more prone to change. (3) The utilization efficiency of wind power in coastal areas is generally better than that in inland areas. There are also differences among the thirty Chinese regions studied. Inner Mongolia and Shandong have achieved real efficiency in wind power utilization efficiency, with optimal allocation of input and output, and a good development trend. The other 28 regions have varying degrees of inefficiency, and there is still room for improvement.

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Wind Speed Change and Influence on Wind Energy in Recent 50 Years over Hexi Corridor

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1070-1072.284 ◽

2014 ◽

Vol 1070-1072 ◽

pp. 284-290

Author(s):

Yao Zong Zhang ◽

Bo Zhang ◽

Yan Yan Liu

Keyword(s):

Wind Speed ◽

Wind Energy ◽

Nonparametric Test ◽

Test Methods ◽

Hexi Corridor ◽

Mean Wind Speed ◽

Seasonal Wind ◽

Speed Change ◽

The Mean ◽

Different Characteristics

Based on wind speed data of 13 meteorological stations in 1958-2012,Mann-Kendall nonparametric test methods was been used to study on wind speed changes in Hexi Corridor.Spatial and temporal characteristics of seasonal and monthly wind speed changes was examined. (1) The maximum wind speed appeared in the higher elevations of study area, such as Wushaoling and Mazongshan station. From east to west mean wind speed increased in Hexi Corridor.For nearly 50 years wind speed had showed decreasing trend. (2)In each season Spring with an maximum mean wind speed was 3.4m/s,the Summer mean wind speed was 2.9 m/s,Autumn mean wind speed was 2.6 m/s,the mean Winter wind speed was 2.8m/s.The seasonal wind speed mainly had decline trend, each station.has different characteristics trends (3) Mean wind speed in each month was greater than 2.5m/s,maximum monthly wind speed appeared in April was 3.5m/s,the minimum wind speed appeared in the September-October was 2.53m/s,the wind speed in March,April and May was greater than the November month,December,January.In addition to Mazongshan and Wushaoling,other station monthly wind speed showed a decreasing trend.Monthly mean wind speed in Jiuquan,Dingxin and Zhangye was slow decreasing trend.Anxi,Yumen wind decreasing trend were more obvious.(4)Wind decreasing trend will have a significant impact on wind energy, wind speed changes and wind energy should be evaluated in the future.

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Assessment of Wind Energy Potential in Three Different Locations of Malaysia

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.785.621 ◽

2015 ◽

Vol 785 ◽

pp. 621-626

Author(s):

R. Shamsipour ◽

M. Fadaeenejad ◽

M.A.M. Radzi

Keyword(s):

Wind Speed ◽

Energy Density ◽

Wind Energy ◽

Wind Power ◽

Weibull Model ◽

Energy Potential ◽

Wind Speeds ◽

Wind Potential ◽

Mean Wind Speed ◽

Wind Energy Potential

In this study, wind energy potential in three different stations in Malaysia in period of 5 years is analyzed. Base on Weibull distribution parameters, the mean wind speed, wind power density and wind energy density is estimated for each defined location. Although there are many works about wind potential in Malaysia, however a few of them have been provided a comprehensive study about wind power in different places in Malaysia. According to the findings, the annual mean wind speeds indicates that the highest wind speed variation is about 2 m/s and is belonged to the Subang station and the highest wind speed is 3.5 m/s in in Kudat. It is also found that the maximum wind power densities among these three sites are 22 W/m2, 24 W/m2 and 22 W/m2 in Kudat station in January, February and September respectively. The results of the study show that as the second parameter for Weibull model, the highest wind energy density has been 190 kWh/m2 per year in Kudat and the lowest one has been about 60 kWh/m2 in Kuching.

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Climate Change Characteristics of Coastal Wind Energy Resources in Zhejiang Province Based on ERA-Interim Data

Frontiers in Physics ◽

10.3389/fphy.2021.720533 ◽

2021 ◽

Vol 9 ◽

Author(s):

Nan Wang ◽

Kai-Peng Zhou ◽

Kuo Wang ◽

Tao Feng ◽

Yu-Hui Zhang ◽

...

Keyword(s):

Wind Energy ◽

Wind Power ◽

Power Density ◽

Linear Trend ◽

Surface Wind ◽

Sea Surface ◽

Energy Resources ◽

Wind Power Density ◽

Sea Surface Wind ◽

Wind Energy Resources

The reanalysis of sea surface wind speed is compared with the measured wind speed of five offshore wind towers in Zhejiang, China. The applicability of reanalysis data in the Zhejiang coastal sea surface and the climatic characteristics of sea surface wind power density is analyzed. Results show that the reanalysis of wind field data at the height of 10 m can well capture the wind field characteristics of the actual sea surface wind field. The sea surface wind power density effective hours increases from west to east and north to south. Then Empirical orthogonal function (EOF) is used to analyze the sea surface wind power density anomaly field, and the first mode is a consistent pattern, the second mode is a North-South dipole pattern, the third mode is an East-West dipole pattern respectively. The stability of wind energy resources grows more stable with increasing distance from the coast, and the northern sea area which is far away from the coastal sea is more stable than that of the southern sea area. The yearly linear trend of sea surface wind power density is in an East-West dipole pattern respectively. The wind energy resources are more stable farther from the coast, and the wind energy resources in the northern sea are more stable than that of the southern sea. The yearly linear trend of sea surface wind power density is the East-West dipole type, the seasonal linear trend is a significant downward trend from West to East in spring, and on the contrary in summer, a non-significant trend in autumn and winter. The monthly change index shows that the linear trend near the entrance of Hangzhou Bay in Northern Zhejiang is of weak increase or decrease, which is good for wind energy development. When the wind power density is between 0 and 150 W·m−2, its frequency mainly shows the distribution trend of high in the West and low in the East, but the wind power density is between 150 and 600 W·m−2, its distribution is the opposite.

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Determining wind energy resources in Turkmenistan

Proceedings of the higher educational institutions ENERGY SECTOR PROBLEMS ◽

10.30724/1998-9903-2020-22-6-143-154 ◽

2021 ◽

Vol 22 (6) ◽

pp. 143-154

Author(s):

K. A. Saryyev

Keyword(s):

Power Plant ◽

Wind Energy ◽

Wind Power ◽

Social Issues ◽

Research Work ◽

Energy Resources ◽

Wind Power Plant ◽

Energy Potential ◽

Wind Energy Potential ◽

Wind Energy Resources

THE PURPOSE. Studying the complex issues associated with providing consumers who are away from the central power supply system with environmentally safe renewable energy source, i.e. conversion of wind energy into electrical energy and uninterrupted supply of electricity to consumers. In this area, one of the complex issues is to determine the wind energy resources in the area where it is planned to install a wind farm.The article deals with the determination of wind energy resources in Turkmenistan. Using databases obtained from meteorological stations over several years, calculations of wind energy resources by region are carried out. This will quickly and in detail analyze the wind energy potential in a particular area to assess the choice of capacity of the projected wind turbines, with the aim of ensuring sustainable development of the region and the reliability of electricity supply.METHODS. In order to estimate the wind energy resources in the area it is necessary to determine the average wind speed per year. In this research work the average annual wind speeds obtained from meteorological stations installed in different regions of Turkmenistan were used. These data were compared with the actual data obtained from the 2 kW wind power plant installed at the research site of the State Energy institute of Turkmenistan and the relevant graphs were built. The results of the obtained data can be used for optimal selection of wind power plant locations and assessment of wind energy resources of the region. RESULTS.As a result of the calculations, wind energy reserves in the region and for the regions of Turkmenistan were determined, as well as the optimal locations of wind farms.Using the technical characteristics of a 2kW wind power plant, the average annual electricity output of a wind power plant of different capacity is determined.With the correct definition of wind energy potential, there is an opportunity to solve energy, economic, environmental and social issues of the country. And also there is an opportunity to mitigate climate change on the basis of wind energy installations, and their resources, environmental benefits, goals and objectives on the scientific methodological basis in the field of wind energy for implementation of state programs for energy conservation in Turkmenistan and energy supply of the region.CONCLUSIONS. On the basis of the carried out scientific work the wind energy resources and technical potentials of wind power plants in the territory of Turkmenistan were determined and the database for compiling the wind energy cadastre was created.

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