Wind power potential of Saudi Arabia

The intent of the study reported in this paper is to make analysis of the wind speed data and annual wind energy potential at Abomsa Metehara and Ziway in Ethiopia. The wind speed data was collected from National Metrology Agency of Ethiopia, and then monthly and annual mean wind speed, wind power potential, wind energy potential and Weibull distribution parameters for these three sites have been analyzed and assessed. In the results, the monthly mean wind speeds, the measured and Weibull estimated of most probable monthly wind velocity, wind velocity carrying maximum energy, monthly wind power and wind energy density for these three sites at site locations have been estimated. Based on monthly and annually mean wind speeds at stations, annually and monthly mean wind velocities at 40 m, 60 m, 80 m, 100 m and 120 m heights via wind shear law have been extrapolated. At these heights, annual mean wind velocity carrying maximum energy, annual most probable wind velocity, annual mean wind power and annual wind energy density for these sites have been estimated. Additionally, the annual cumulative distribution function and probability density function of wind speed for the selected sites have been obtained using the Weibull distribution functions.KeywordsMean wind speedWind power densityWeibull parametersWind-driven water pump

We report an analysis of trends in 850 hPa wind speed, as manifest in the NCEP–NCAR reanalysis fields, over the Baltic region during the latter half of the 20th century. The results indicate that annual mean wind speeds over the Baltic significantly increased over the period 1953–99 with the majority of the increase being associated with increases in the upper quartile of the wind speed distribution. Accordingly, much of the change is focused on the winter season. The trends in annual and seasonal mean wind speeds are greatest in relative and absolute sense in the southwest of the Baltic basin, where they are in excess of 0.25 m s−1 per decade for the annual mean. The extremes of the wind‐speed distribution also increased by up to 5 m s−1 over the study period for the wind speed with a 50 year return period, again with the largest magnitude changes in the southwestern Baltic. These changes in wind speed are strongly linked to changes in the synoptic‐scale circulation. The majority of the increase in wintertime wind speeds is attributable to an increase in westerly anticyclonic, westerly cyclonic and northwesterly cyclonic circulation types as manifest in the Grosswetterlagen catalogue, which are in turn related to the recent prevalence of the positive phase of the North Atlantic oscillation. Copyright © 2003 Royal Meteorological Society

With the increasing consumption of fossil fuel, reducing greenhouse gas (GHG) emissions has become a serious issue that has attracted worldwide attention. Therefore, Lebanon is currently interested in utilizing renewable energy technologies to reduce energy dependence on oil reserves and GHG emissions. The present study is focused on solar and wind power potential and the economic viability of wind/solar systems for the Rayak region in Lebanon for the first time. The input data sources for the study including the Meteorological data for wind speed and NASA database for solar energy. In the assessment of wind energy, a two-parameter Weibull distribution function was used to analyze the characteristics of wind speed. Yearly and seasonal Weibull parameters were calculated for 10 m height using the Maximum likelihood method. In addition, yearly and seasonal wind power density values were calculated. The results showed that the mean annual wind speed and wind power density values were 5.884 m/s and 124.534 W/m2, respectively, during the investigation period. It can be concluded that the value of wind power density at the region was classified as marginal wind power potential and high-scale wind turbines can be used to gather wind energy potential in the region. Furthermore, predicting wind speed depends on various atmospheric factors and random variables. Therefore, 63 ANN models are developed by varying the meteorological parameters to predict the daily wind speed in the selected region. All the models with various combinations are validated and the performances of the models are analyzed using root mean squared error. The results demonstrated that the most relevant input variables for predicting the daily wind speed were found to be temperature, pressure, and relative humidity. In the assessment of wind energy, average monthly global solar radiation data were evaluated. It is found that the annual global solar radiation was 1877.41 kWh/m2, which indicates the selected region has high solar resources and is categorized as an excellent potential class. Moreover, this study provides a comprehensive and integrated feasibility analysis of 100 MW grid-connected wind and solar projects economic projects that can be developed in the country to reduce the electricity crisis and GHG emissions. Several different economic and financial indicators were calculated. The results indicate that the wind farm is a more economical option than the solar plant because of the higher values of NPV, BCR, ALCS, and IRR as well as the lower values of EB, SB, and LCOE.

ABSTRACTThe present article utilizes wind measurements from three buoys data collection stations in Ionian Sea to study the wind speed and power characteristics using the Weibull shape and scale parameters. Specifically, the site dependent, annual, and monthly mean patterns of mean wind speed, Weibull parameters, frequency distribution, most probable wind speed, maximum energy carrying wind speed, wind power density and wind energy density characteristics have been analyzed. The Weibull distribution was found to represent the wind speed distribution with more than 90% accuracy, in most of the cases. Moreover, the correlation between the percentages of times the wind speed was above cut-in-speed and the measured mean wind speed for the three selected sites, as the correlation between the aforementioned percentages and the scale parameter c were examined and were found linear. At all these sites, no definite increasing or decreasing trends in annual mean wind speed values could be detective over the data reporting period. The mean values of wind speed, scale parameter, most probable wind speed, maximum energy carrying wind speed, wind power and wind energy density values showed higher values during winter time and lower in summer time in Pylos and Zakynthos. Moreover, Pylos and Zakynthos were found to be the best sites from wind power harnessing point of view.

The wind energy is one of the prominent renewable energy source in India. In this research paper, monthly and annual wind power density (WPD) have been estimated based on the statistical approaches known as Weibull and Rayleigh distribution functions (WDF & RDF) for the hourly time series wind data measured during the year 2013. The main objective of this research has been to estimate the wind power potential at NIT Hamirpur wind monitoring facility of Himachal Pradesh in India and to propose feasibility of wind power plant. The proposed methodology is a new contribution for small and micro power generation for this region. Modeling of wind speed deviation is the most necessary obligation for the design of wind turbines. The variations in monthly and annual mean wind speed, standard deviation, and Weibull parameters along with the frequency distribution format of measured wind speeds have been presented in this paper. The monthly and annual probability density and cumulative distribution function's (CDF) have been derived from the frequency distribution. The probability density of WDF and RDF has been well fitted with the measured probability functions on monthly and yearly basis. The investigation shows that though the particular site is not suitable for grid connected applications yet available wind potential is suitable for isolated standalone systems like rural electrification, house hold electric appliances like battery charging, mechanical applications like water pumping for irrigation etc.

Annual and seasonal variations in mean wind speed and wind turbine energy production

Wind energy potential of Nigeria

The goal of this study is to quantify the world's wind power potential for the first time from data. Wind speeds are calculated at 80 m, the hub height of modern, 77‐m diameter, 1500 kW turbines. Since relatively few observations are available at 80 m, the Least Square extrapolation technique is utilized and revised here to obtain estimates of wind speeds at 80 m given observed wind speeds at 10 m (widely available) and a network of sounding stations. Tower data from the Kennedy Space Center (Florida) were used to validate the results. Globally, ∼13% of all reporting stations experience annual mean wind speeds ≥ 6.9 m/s at 80 m (i.e., wind power class 3 or greater) and can therefore be considered suitable for low‐cost wind power generation. This estimate is believed to be conservative. Of all continents, North America has the largest number of stations in class ≥ 3 (453), and Antarctica has the largest percent (60%). Areas with great potential are found in northern Europe along the North Sea, the southern tip of the South American continent, the island of Tasmania in Australia, the Great Lakes region, and the northeastern and northwestern coasts of North America. The global average 10‐m wind speed over the ocean from measurements is 6.64 m/s (class 6); that over land is 3.28 m/s (class 1). The calculated 80‐m values are 8.60 m/s (class 6) and 4.54 m/s (class 1) over ocean and land, respectively. Over land, daytime 80‐m wind speed averages obtained from soundings (4.96 m/s) are slightly larger than nighttime ones (4.85 m/s); nighttime wind speeds increase, on average, above daytime speeds above 120 m. Assuming that statistics generated from all stations analyzed here are representative of the global distribution of winds, global wind power generated at locations with mean annual wind speeds ≥ 6.9 m/s at 80 m is found to be ∼72 TW (∼54,000 Mtoe) for the year 2000. Even if only ∼20% of this power could be captured, it could satisfy 100% of the world's energy demand for all purposes (6995–10177 Mtoe) and over seven times the world's electricity needs (1.6–1.8 TW). Several practical barriers need to be overcome to fully realize this potential.

Near‐surface seasonal and annual mean wind speed in Switzerland is investigated using homogenized observations, Twentieth Century Reanalysis (20CRv2c) data and raw model output of a 75 member EURO‐COoRdinated Downscaling EXperiment regional climate model (RCM) ensemble for present day and future scenarios. The wind speed observations show a significant decrease in the Alps and on the southern Alpine slopes in the period 1981–2010. However, the 20CRv2c data reveal that the recent trends lie well within the decadal variability over longer time periods and no clear signs of a systematic wind stilling can be found for Switzerland. The ensemble of RCMs shows large biases in the annual mean wind speed over the Jura mountains, and some members also show large biases in the Alps compared to station observations. The spatial distribution of the model biases varies strongly between the RCMs, while the resolution and the driving global model have less impact on the pattern of the model bias. The RCMs are mostly able to represent the seasonality of wind speed on the Plateau but miss important details in complex terrain related to local wind systems. Most models show no significant changes in near‐surface mean wind speed until the end of the 21st century. The model ensemble changes range from a 7% decrease to a 6% increase with an ensemble mean decrease of 1 to 2%. Due to model biases, the scale mismatch between model grid and station observations and the missing representation of local winds in the simulations, the changes need to be interpreted with utmost care. Future assessments might lead to major revisions even for the sign of the projected changes, in particular over complex terrain.

Today, most electrical energy is generated by burning huge fossil fuels and special weather conditions such as acid rain and snow, climate change, urban smog, regional haze, several tornados, etc., have happened around the whole world. It is now clear that the installation of a number of wind turbine generators can effectively reduce environmental pollution, fossil fuel consumption, and the costs of overall electricity generation. Although wind is only an intermittent source of energy, it represents a reliable energy resource from a long-term energy policy viewpoint. Among various renewable energy resources, wind power energy is one of the most popular and promising energy resources in the whole world today. At a specific wind farm, the available electricity generated by a wind power generation system depends on mean wind speed (MWS), standard deviation of wind speed, and the location of installation. Since year-to-year variation on annual MWS is hard to predict, wind speed variations during a year can be well characterized in terms of a probability distribution function (pdf). This paper also addresses the relations among MWS, its standard deviation, and two important parameters of Weibull distribution. The wind resource varies with of the day and the season of the year and even some extent from year to year. Wind energy has inherent variances and hence it has been expressed by distribution functions. In this paper, we present some methods for estimating Weibull parameters, namely, shape parameter ( k ) and scale parameter ( c ). The Weibul distribution is an important distribution especially for reliability and maintainability analysis. The suitable values for both shape parameter and scale parameters of Weibull distribution are important for selecting locations of installing wind turbine generators. The scale parameter of Weibull distribution also important to determine whether a wind farm is good or not. The presented method is the analytical methods and computational experiments on the presented methods are reported.

BackgroundPakistan is a developing nation and heavily spends on the development of conventional power plants to meet the national energy demand. The objective of this paper is to investigate wind power potential of site using wind speed, wind direction, and other meteorological data collected over a period of 1 year. This type of detailed investigation provides information of wind characteristics of potential sites and helps in selecting suitable wind turbine.MethodsThe site-specific air density, wind shear, wind power density, annual energy yield, and capacity factors have been calculated at 30 and 50 m above the ground level (AGL). The Weibull parameters have been calculated using empirical (EM), maximum likelihood (MLM), modified maximum likelihood (MMLM), energy pattern (EPFM), and graphical (GM) methods to determine the other dependent parameters. The accuracies of these methods are determined using correlation coefficient (R2) and root mean square error (RMSE) values. At last, the wind energy economic analysis has been carried out at 30- and 50-m heights.ResultsThe annual mean wind speeds were found to be 5.233and 6.55 m/s at 30- and 50-m heights, respectively, with corresponding standard deviations of 2.295 and 2.176. All methods fitted very well with the measured wind speed data except GM model. The frequency of wind speed observed that Weibull distribution gave better fitting results than Rayleigh distribution at wind site. The mean wind power densities were found to be 169.4 and 416.7 W/m2 at 30- and 50-m heights. The economic analysis showed that at Nooriabad site in Pakistan, the wind energy can be produced at US$0.02189/kWh at a hub height of 50 m.ConclusionsThe results showed that the site has potential to install utility wind turbines to generate energy at the lowest cost per kilowatt-hour at height of 50 m.

Purpose The purpose of this paper is to investigate wind power potential of site using wind speed, wind direction and other meteorological data including temperature and air density collected over a period of one year. Design/methodology/approach The site-specific air density, wind shear, wind power density, annual energy yield and capacity factors have been calculated at 30 and 10 m above the ground level (AGL). The Weibull parameters have been calculated using empirical, maximum likelihood, modified maximum likelihood, energy pattern and graphical methods to determine the other dependent parameters. The accuracies of these methods are determined using correlation coefficient (R²) and root mean square error (RMSE) values. Findings The site-specific wind shear coefficient was found to be 0.18. The annual mean wind speeds were found to be 5.174 and 4.670 m/s at 30 and 10 m heights, respectively, with corresponding standard deviations of 2.085 and 2.059. The mean wind power densities were found to be 59.50 and 46.75 W/m² at 30 and 10 m heights, respectively. According to the economic assessment, the wind turbine A is capable of producing wind energy at the lowest value of US$ 0.034/kWh. Practical implications This assessment provides the sustainable solution of energy which minimizes the dependence on continuous supply of oil and gas to run the conventional power plants that is a major cause of increasing load shedding in the significant industrial and thickly populated city of Pakistan. Also, this will minimize the quarrel between the local power producer and oil and gas supplier during the peak season. Social implications This wind resource assessment has some important social implications including decreasing the environmental issues, enhancing the uninterrupted supply of electricity and decreasing cost of energy per kWh for the masses of Karachi. Originality/value The results are showing that the location can be used for installing the wind energy power plant at the lower cost per kWh compared to other energy sources. The wind energy is termed as sustainable solution at the lowest cost.

Wind resource assessment of the Southern Appalachian Ridges in the Southeastern United States

Evaluation and Analysis of Wind Resources in Jin-Jing-Ji Region of China

This work presents an assessment of the wind potential and a design methodology for a 10 MW wind farm in the Arzew industrial region, located in northwest Algeria, to improve the quality of service of the electricity grid and increase Algeria's participation in the use of renewable energy. The hourly wind data of 10 years (2005-2015) that correspond to the wind potential of the site were analyzed, such as: dominant wind directions, probability distribution, Weibull parameters, mean wind speed and power potential. The site has a mean annual wind speed of 4.46 m/s at 10m height, and enough space to locate the wind turbines. A comparative study was carried out between four wind turbine technologies to improve the site's efficiency and select the appropriate technology: PowerWind 56/ 900 kW, Nordex N50/800 kW, Vestas V50/850 kW, NEG-Micon 44/750 kW. The estimate of the energy produced using WAsP software and the choice of the optimal architectural configuration for wind turbines installation was confirmed. A techno-economic and environmental study was carried out by HOMER software, to choose the model that produces the maximum annual net energy with a competitive cost in the global wind energy market, $ 0.068/kWh, and that provides clean energy with a reduced emission of polluting gases. Finally, this work provides a good indicator for the construction of a wind farm in Arzew.