scholarly journals Evaluation of the Energy Performance of the Amougdoul Wind Farm, Morocco

2017 ◽  
Vol 7 (2) ◽  
pp. 692
Author(s):  
Asma Ezzaidi ◽  
Mustapha Elyaqouti ◽  
Lahoussine Bouhouch ◽  
Ahmed Ihlal
Keyword(s):  
Wind Turbine ◽  
Calculation Method ◽  
Wind Farm ◽  
Shape Parameter ◽  
Scale Parameter ◽  
Energy Performance ◽  
Energy Output ◽  
Power Calculation ◽  
Power Curve ◽  
Useful Power

This paper is concerned with the assessment of the the performance of the Amougdoul wind farm. We have determined the Weibull parameters; namely the scale parameter, <em>c</em> (m/s) and shape parameter, <em>k</em>. After that, we have estimated energy output by a wind turbine using two techniques: the useful power calculation method and the method based on the modeling of the power curve, which is respectively 134.5 kW and 194.19 KW corresponding to 27% and 39% of the available wind energy, which confirm that the conversion efficiency does not exceed 40%.

scholarly journals Potential of Electricity Generation on the Western Coast of Mediterranean Sea in Egypt

2008 ◽  
Vol 45 (5) ◽  
pp. 26-38
Author(s):  
A. Ahmed Shata ◽  
S. Abdelaty ◽  
R. Hanitsch
Keyword(s):  
Wind Turbine ◽  
Mediterranean Sea ◽  
Power Law ◽  
Wind Turbines ◽  
Electricity Generation ◽  
Wind Farm ◽  
Electricity Production ◽  
Energy Output ◽  
Western Coast ◽  
Wind Potential

Potential of Electricity Generation on the Western Coast of Mediterranean Sea in EgyptA technical and economic assessment has been made of the electricity generation by wind turbines located at three promising potential wind sites: Sidi Barrani, Mersa Matruh and El Dabaa in the extreme northwest of Egypt along the Mediterranean Sea. These contiguous stations along the coast have an annual mean wind speed greater than 5.0 m/s at a height of 10 m. Weibull's parameters and the power law coefficient for all seasons have been estimated and used to describe the distribution and behavior of seasonal winds at these stations. The annual values of wind potential at the heights of 70-100 m above the ground level were obtained by extrapolation of the 10 m data from the results of our previous work using the power law. The three stations have a high wind power density, ranging from 340-425 to 450-555 W/m2at the heights of 70-100 m, respectively. In this paper, an analysis of the cost per kWh of electricity generated by two different systems has been made: one using a relatively large single 2 MW wind turbine and the other - 25 small wind turbines (80 kW, total 2 MW) arranged in a wind farm. The yearly energy output of each system at each site was determined, and the electricity generation costs in each case were also calculated and compared with those at using diesel oil, natural gas and photovoltaic systems furnished by the Egyptian Electricity Authority. The single 2 MW wind turbine was found to be more efficient than the wind farm. For all the three considered stations the electricity production cost was found to be less than 2 ϵ cent/kWh, which is about half the specific cost of the wind farm.

scholarly journals Interannual variability of wind climates and wind turbine annual energy production

2018 ◽  
Vol 3 (2) ◽  
pp. 651-665 ◽  
Author(s):  
Sara C. Pryor ◽  
Tristan J. Shepherd ◽  
Rebecca J. Barthelmie
Keyword(s):  
Wind Energy ◽  
Wind Turbine ◽  
Interannual Variability ◽  
Energy Production ◽  
Wind Farm ◽  
Wind Farms ◽  
Power Curve ◽  
Wind Speeds ◽  
Eastern Usa

Abstract. The interannual variability (IAV) of expected annual energy production (AEP) from proposed wind farms plays a key role in dictating project financing. IAV in preconstruction projected AEP and the difference in 50th and 90th percentile (P50 and P90) AEP derive in part from variability in wind climates. However, the magnitude of IAV in wind speeds at or close to wind turbine hub heights is poorly defined and may be overestimated by assuming annual mean wind speeds are Gaussian distributed with a standard deviation (σ) of 6 %, as is widely applied within the wind energy industry. There is a need for improved understanding of the long-term wind resource and the IAV therein in order to generate more robust predictions of the financial value of a wind energy project. Long-term simulations of wind speeds near typical wind turbine hub heights over the eastern USA indicate median gross capacity factors (computed using 10 min wind speeds close to wind turbine hub heights and the power curve of the most common wind turbine deployed in the region) that are in good agreement with values derived from operational wind farms. The IAV of annual mean wind speeds at or near typical wind turbine hub heights in these simulations and AEP computed using the power curve of the most commonly deployed wind turbine is lower than is implied by assuming σ=6 %. Indeed, rather than 9 out of 10 years exhibiting AEP within 0.9 and 1.1 times the long-term mean AEP as implied by assuming a Gaussian distribution with σ of 6 %, the results presented herein indicate that in over 90 % of the area in the eastern USA that currently has operating wind turbines, simulated AEP lies within 0.94 and 1.06 of the long-term average. Further, the IAV of estimated AEP is not substantially larger than IAV in mean wind speeds. These results indicate it may be appropriate to reduce the IAV applied to preconstruction AEP estimates to account for variability in wind climates, which would decrease the cost of capital for wind farm developments.

scholarly journals Power curve and wake analyses of the Vestas multi-rotor demonstrator

2019 ◽  
Author(s):  
Maarten Paul van der Laan ◽  
Søren Juhl Anderson ◽  
Néstor Ramos García ◽  
Nikolas Angelou ◽  
Georg Raimund Pirrung ◽  
...  
Keyword(s):  
Numerical Simulations ◽  
Wind Turbine ◽  
Wind Farm ◽  
Field Measurements ◽  
Power Curve ◽  
Power Performance ◽  
Near Wake ◽  
Wind Speeds ◽  
Wake Turbulence ◽  
Far Wake

Abstract. Numerical simulations of the Vestas multi-rotor demonstrator (4R-V29) are compared with field measurements of power performance and remote sensing measurements of the wake deficit by a short-range WindScanner lidar system. The simulations predict a gain of 0–2 % in power due to the rotor interaction, for wind speeds below rated. The power curve measurements also show that the rotor interaction increases the power performance below rated by 1.8 &amp;pm; 0.2 %, which can result in a 1.5 &amp;pm; 0.2 % increase in the annual energy production. The wake measurements and numerical simulations show four distinct wake deficits in the near wake, which merge into a single wake structure further downstream. Numerical simulations show that the wake recovery distance of a simplified 4R-V29 wind turbine is 1.03–1.44 Deq shorter than for an equivalent single-rotor wind turbine with a rotor diameter Deq. In addition, the numerical simulations show that the added wake turbulence of the simplified 4R-V29 wind turbine is lower in the far wake compared to the equivalent single-rotor wind turbine. The faster wake recovery and lower far-wake turbulence of such a multi-rotor wind turbine has the potential to reduce the wind turbine spacing within a wind farm while providing the same production.

scholarly journals Evaluating the Environmental Impacts and Energy Performance of a Wind Farm System Utilizing the Life-Cycle Assessment Method: A Practical Case Study

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3263 ◽  
Author(s):  
Mohamed R. Gomaa ◽  
Hegazy Rezk ◽  
Ramadan J. Mustafa ◽  
Mujahed Al-Dhaifallah
Keyword(s):  
Life Cycle Assessment ◽  
Life Cycle ◽  
Environmental Impact ◽  
Wind Turbine ◽  
Environmental Impacts ◽  
Wind Farm ◽  
Wind Farms ◽  
Energy Performance ◽  
Practical Case ◽  
Energy Payback

The ever-increasing popularity of finding alternative forms of renewable energy has seen an increased interest and utilization of wind energy. The objective of this research therefore, is to evaluate the environmental impacts and energy performance of wind farms. This study was operationalized in Jordan using a life-cycle assessment (LCA) method. The environmental impact is evaluated through lifecycle emissions that include all emissions during various phases of the project. The energy performance is illustrated by the energy indicators. The latter is the energy payback ratio (EPR) and the energy payback time (EPT). This study was conducted on a 38 Vestas V112 3-MW wind turbine located in the southern region of Tafilah in Jordan that is host to the country’s first wind farm. SimaPro 7.1 software was used as the modeling platform. Data for this study were collated from various sources, including, manufacturers, the wind turbine farm, and local subcontractors. A software database was used for the modeling process, and the data obtained modeled in accordance with ISO 14040 standards. The findings of this study indicate that the impacts of the transportation and installation phases were moderate, with the largest negative environmental impact deriving from the manufacturing phase. To remedy some of the negative impacts in these phases, green cement was used for the turbine foundation to limit the environmental impacts to be had during the installation phase, while the transportation phase saw the utilization of locally-manufactured turbines. Furthermore, an evaluation of the study’s results revealed that the energy payback period of the wind farm is approximately 0.69 year (8 months), while the payback ratio is 29, and the annual CO2 saving estimated to be at 2.23 × 108 kg, 3.02 × 108 kg, 3.10 × 108 kg for an annual generated power of 371, 501, and 515 GWh/year.

Research on the Influence of Turbulence Intensity to the Power Curve and AEP of Wind Turbine

2013 ◽  
Vol 336-338 ◽  
pp. 885-889
Author(s):  
Bo Jiao ◽  
Yang Xue ◽  
De Yi Fu ◽  
Xiao Jing Ma ◽  
Wei Bian ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Turbulence Intensity ◽  
Energy Production ◽  
Wind Farm ◽  
Negative Impact ◽  
Intensity Level ◽  
Power Curve ◽  
Power Performance

It is known that turbulence intensity will affect on power performance and Annual Energy Production (AEP) of wind turbine. But it is unknown how big the influence is. The article quantifies the concrete influence by testing. After calculating the output of wind turbine in different turbulence intensity level, it has shown that the more intensive turbulence will lead more negative impact on the output of wind turbine. The investigation provides some basis for the site sitting of wind farm.

scholarly journals Power curve and wake analyses of the Vestas multi-rotor demonstrator

2019 ◽  
Vol 4 (2) ◽  
pp. 251-271 ◽  
Author(s):  
Maarten Paul van der Laan ◽  
Søren Juhl Andersen ◽  
Néstor Ramos García ◽  
Nikolas Angelou ◽  
Georg Raimund Pirrung ◽  
...  
Keyword(s):  
Numerical Simulations ◽  
Wind Turbine ◽  
Wind Farm ◽  
Field Measurements ◽  
Power Curve ◽  
Power Performance ◽  
Wind Speeds ◽  
Wake Turbulence ◽  
Production Output ◽  
Far Wake

Abstract. Numerical simulations of the Vestas multi-rotor demonstrator (4R-V29) are compared with field measurements of power performance and remote sensing measurements of the wake deficit from a short-range WindScanner lidar system. The simulations predict a gain of 0 %–2 % in power due to the rotor interaction at below rated wind speeds. The power curve measurements also show that the rotor interaction increases the power performance below the rated wind speed by 1.8 %, which can result in a 1.5 % increase in the annual energy production. The wake measurements and numerical simulations show four distinct wake deficits in the near wake, which merge into a single-wake structure further downstream. Numerical simulations also show that the wake recovery distance of a simplified 4R-V29 wind turbine is 1.03–1.44 Deq shorter than for an equivalent single-rotor wind turbine with a rotor diameter Deq. In addition, the numerical simulations show that the added wake turbulence of the simplified 4R-V29 wind turbine is lower in the far wake compared with the equivalent single-rotor wind turbine. The faster wake recovery and lower far-wake turbulence of such a multi-rotor wind turbine has the potential to reduce the wind turbine spacing within a wind farm while providing the same production output.

Power curve model classification to estimate wind turbine power output

2018 ◽  
Vol 43 (3) ◽  
pp. 213-224 ◽  
Author(s):  
Bharti Dongre ◽  
Rajesh K Pateriya
Keyword(s):  
Wind Turbine ◽  
Wind Farm ◽  
Spline Interpolation ◽  
Specific Power ◽  
Power Curve ◽  
Model Classification ◽  
Curve Model ◽  
Modelling Methodology ◽  
Power Curves ◽  
Modelling Data

This article presents a comparative study of empirical power curve models to estimate the output power of the turbine as a function of the wind speed. In these models, modelling strategy relies on the objective of modelling, data being used for the modelling and targeted accuracy. It has been observed that models based on presumed shape of power curve lack desired accuracy since these are developed using the power ratings of wind turbine which are not sufficient to exactly replicate the turbine’s actual behaviour. The performance of various models which comes under manufacturer power curve modelling methodology has been compared with reference to commercially available wind turbines. It has been found that power curves obtained through method of least squares and cubic spline interpolation methods exactly match with manufacturer power curve, whereas 5PL method gives sufficiently accurate results. Modelling based on actual data of wind farm has been found to be a powerful technique for developing site-specific power curves.

Statistical calculation of thrust curve of a wind turbine based on available power curve and general specifications data

2021 ◽  
Author(s):  
Evgeny Atlaskin ◽  
Irene Suomi ◽  
Anders Lindfors
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Farm ◽  
Wind Farms ◽  
Power Curve ◽  
Wake Effect ◽  
Thrust Coefficient ◽  
Simple Method ◽  
Wind Speeds ◽  
Power Curves

&lt;p&gt;Power curves for a substantial number of wind turbine generators (WTG) became available in a number of public sources during the recent years. They can be used to estimate the power production of a wind farm fleet with uncertainty determined by the accuracy and consistency of the power curve data. However, in order to estimate power losses inside a wind farm due to wind speed reduction caused by the wake effect, information on the thrust force, or widely used thrust coefficient (Ct), is required. Unlike power curves, Ct curves for the whole range of operating wind speeds of a WTG are still scarcely available in open sources. Typically, power and Ct curves are requested from a WTG manufacturer or wind farm owner under a non-disclosure agreement. However, in a research study or in calculations over a multitude of wind farms with a variety of wind turbine models, collecting this information from owners may be hardly possible. This study represents a simple method to define Ct curve statistically using power curve and general specifications of WTGs available in open sources. Preliminary results demonstrate reasonable correspondence between simulated and given data. The estimations are done in the context of aggregated wind power calculations based on reanalysis or forecast data, so that the uncertainty of wake wind speed caused by the uncertainty of predicted Ct is comparable, or do not exceed, the uncertainty of given wind speed. Although the method may not provide accurate fits at low wind speeds, it represents an essential alternative to using physical Computational Fluid Dynamics (CFD) models that are both more demanding to computer resources and require detailed information on the geometry of the rotor blades and physical properties of the rotor, which are even more unavailable in open sources than power curves.&lt;/p&gt;

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