Evaluation of wind energy potential and estimation of cost using wind energy turbines for electricity generation in north of Algeria

Belabes, B.; Youcefi, Abdelkader; Guerri, Ouahiba; Djamai, Mourad; Kaabeche, Abdelhamid

doi:10.1016/j.rser.2015.07.043

Cited by 74 publications

(32 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, Vestas 2 MW/V80 wind turbine produces highest energy output of 601.10 MWh/year and capacity factor of 42.02% in Sarh. According to [1], the value of this factor is generally affected by the intermittent nature of the wind, the availability of the machine and the efficiency of the turbine. Capacity factor usually varies from 20 to 70% in practice.…”

Section: Estimation Of Electric Power Generation By Wind Turbinesmentioning

confidence: 99%

“…Among the many clean sources of energy is wind energy which has developed very rapidly over the past 2 decades. Great technological progress has been made to reduce the cost of producing wind-generated electricity [1]. The turbine is free of fossil fuel energy and, hence, does not cause an environmental pollution when generating electricity [2].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of the cost of producing wind-generated electricity in Chad

Soulouknga

Oyedepo

Doka

et al. 2020

Int J Energy Environ Eng

View full text Add to dashboard Cite

This paper presents an economic analysis of the electricity produced by different types of wind turbines selected for Chad. Thus, the data considered for the analysis in this study are the average monthly wind speeds at selected locations, as well as the altitude value. Statistical analysis was performed using the Weibull distribution. The same energy factor allowed determining the Weibull parameters. The results obtained show that the average annual wind speed varies from 1 m/s in Am-Timan to 4.2 m/s in N'Djamena, at a height of 10 m from the ground. Weibull statistical parameters (k and c) were determined at 10, 30, 50, and 70 m. These were obtained by extrapolation using a power law based on Weibull parameters. Three models of wind turbines available on the market were used in this study: Bonus 300 kW/33, Bonus 1 MW/54, and Vestas 2 MW/V80. The performance of these wind turbines was evaluated using the calculation of the capacity factor and the annual energy produced by each type of wind turbine at 12 sites. The PVC (present value) method was used to perform an economic analysis. The lowest cost of wind power generation was obtained with the Vestas 2 MW/V80 model, with a cost per kilowatt-hour

show abstract

Section: Estimation Of Electric Power Generation By Wind Turbinesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of the cost of producing wind-generated electricity in Chad

Soulouknga

Oyedepo

Doka

et al. 2020

Int J Energy Environ Eng

View full text Add to dashboard Cite

show abstract

“…It is a probability distribution for the normally distributed logarithm wind speed variable and can be expressed by the formula given in equation (2) [18,19];…”

Section: Lognormal Distribution Functionmentioning

confidence: 99%

“…These energy sources which have a limited availability, when harnessed results in degradation of the environment. Hence to reduce the negative impact of the fossil fuels, scientists were forced to focus on cleaner energy sources which are easily available and environmental friendly [2]. The energy sources which do not exhaust when their energy is exploited are known as renewable energy resources [3].…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis of Wind Speed and Evaluation of Wind Power Density for Colaba, Mumbai

Francis¹,

Nalamutt²

2019

IJARET

View full text Add to dashboard Cite

The fuel energy resources around the globe are declining at a faster rate. The greenhouse gases emitted in the process of harnessing energy from the non-renewable resources such as coal, fossil-fuel and radioactive substances cause the global warming and there is a danger of climate change. All these factors compel the researchers to explore renewable sources for power generation. Wind is a renewable resource, which is available naturally and is practically inexhaustible. In this study, the hourly wind data collected between the years 2013 to 2015 is evaluated. The wind data at the height of 11 m was obtained from a wind station at the Indian Meteorological Department, Colaba (Mumbai, India). The highest monthly wind speed measured was 5.11 m/s and the yearly average was found to be 2.72 m/s. various distribution functions were statistically analyzed and it was observed that the Two-parameter Weibull distribution gives the best fit to the actual data. The mean wind power density was 73.013 W/m2. The maximum wind speeds can be observed between the months of June and September. The wind data analysis shows that if a domestic wind turbine is installed at this site, it will generate enough energy for power saving. This paper aims to promote the growth of small wind power plant in a developing country like India.

show abstract

“…In most cases the measured wind speed data must be adjusted to the hub height of wind turbine using the following [11][12][13]:…”

Section: Turbine Power Output and Capacity Factormentioning

confidence: 99%

Power Curve Modelling for Wind Turbines

Teyabeen

Akkari

Jwaid

2017

2017 UKSim-AMSS 19th International Conference on Computer Modelling &Amp; Simulation (UKSim)

View full text Add to dashboard Cite

The wind turbine power curve WTPC describes the relationship between wind speed and turbine power output. Power curve, provided by the manufacturer is one of the most important tools used to estimate turbine power output and capacity factor. Hence, an accurate WTPC model is essential for predicting wind energy potential. This paper presents a comparative study of various models for mathematical modelling of WTPC based on manufacturer power curve data gathered from 32 wind turbines ranging from 330 to 7580 kW. The selected models are validated by comparing the capacity factor obtained using the models based on Gamma probability density function with the capacity factor estimated using manufacturer power curves based on measured wind speed data. The selected models are also validated by comparing the instantaneous power obtained using the models with manufacturer power curve data. The accuracy of the models is evaluated using statistical criteria such as Normalized Root Mean Square Error (NRMSE), relative error (RE), and correlation coefficient (). The adopted model allows predicting the behavior of wind turbine generated under different wind speeds. Results of the analysis presented in this paper show that the power-coefficient based model presents favorable efficiency followed by general model, since they have lower values of RE in estimation of capacity factor, whereas the polynomial model showed the least accurate model.

show abstract

Evaluation of wind energy potential and estimation of cost using wind energy turbines for electricity generation in north of Algeria

Cited by 74 publications

References 51 publications

Evaluation of the cost of producing wind-generated electricity in Chad

Evaluation of the cost of producing wind-generated electricity in Chad

Statistical Analysis of Wind Speed and Evaluation of Wind Power Density for Colaba, Mumbai

Power Curve Modelling for Wind Turbines

Contact Info

Product

Resources

About