Wind energy in the Gulf of Tunis, Tunisia

Dahmouni, Anour Wajdi; Salah, M. Ben; Askri, Faouzi; Kerkeni, C.; Nasrallah, Sassi Ben

doi:10.1016/j.rser.2009.12.012

Cited by 26 publications

(8 citation statements)

References 13 publications

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“…For a period of measurement the mean wind power density (the available power of wind per unit area) is given by the following expression [6]:…”

Section: Wind Power Densitymentioning

confidence: 99%

“…3, December 2011 : 575 -582 576 potential energy and cumulative distribution function. Wind speed and direction at 20 m and 30 m above ground level and in the Gulf of Tunis were studied by [6] during 2008. The obtained results can be used to perform wind park project and confirm that the Gulf of Tunis has promising wind energy potential.…”

Section: Introductionmentioning

confidence: 99%

“…For the annual wind energy density estimation the value of 8640 h is used [6], or 8760 h if 365 days in year is required.…”

mentioning

confidence: 99%

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Potential of Wind Speed for Wind Power Generation In Perlis, Northern Malaysia

et al. 2011

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Abstrak Makalah ini menyajikan analisa karakteristik-karakteristik kecepatan angin di Keywords: weibull distribution function, wind power generation, wind speed IntroductionRenewable energy has an increasing role in achieving the goals of sustainable development, energy security and environmental protection. Nowadays, it has been recognized as one of the most promising clean energy over the world because of its falling cost, while other renewable energy technologies are becoming more expensiveWind energy is one of renewable energy which produced by continuously blowing wind and can be captured using wind turbines that convert kinetic energy from wind into mechanical energy and then into electrical energy [3]. Today, wind energy is widely used to produce electricity in many countries such as Denmark, Spain, Germany, United States, and India [4].A lot of researchers have been studying the wind speed characteristics and its potential as a wind power generation in many countries worlwide. Six kinds of numerical methods for estimating Weibull parameters were reviewed by [5]; i.e. the moment, empirical, graphical, maximum likelihood, modified maximum likelihood, and energy pattern factor method. The result showed that the maximum likelihood modified maximum likelihood and moment methods present relatively more excellent ability throughout the simulation test. From analysis of actual data, it is found that if wind speed distribution matches well with Weibull function, the six methods are applicable, but if not, the maximum likelihood method performs best followed by the modified maximum likelihood and moment methods, based on double checks including

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“…For a period of measurement the mean wind power density (the available power of wind per unit area) is given by the following expression [6]:…”

Section: Wind Power Densitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Potential of Wind Speed for Wind Power Generation In Perlis, Northern Malaysia

et al. 2011

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“…Knowledge of the wind speed patterns in these areas is essential for enabling good assessments of wind energy generation. For this purpose, in the last years, research on the local regimes of winds has been intense [1]- [4]. There are also many investigations in the literature regarding wind farms in coastal sites [5]- [7].…”

Section: Introductionmentioning

confidence: 99%

An assessment of the sea breeze energy potential using small wind turbines in peri-urban coastal areas

Bueso

Rojas

Jou

et al. 2015

Journal of Wind Engineering and Industrial Aerodynamics

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From wind speed data recorded hourly at 2 m high during 18 years (1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010) in the Llobregat Delta (15 km south of Barcelona city; northeast of the Iberian Peninsula), wind speed distributions at 10 m high were computed for the whole year and for the sea breeze period (from March 1 to September 30, from 10 to 19 local time). Weibull probability density functions fitted to the distributions were used to assess the wind energy generated by two offgrid small wind turbines: the IT-PE-100 and the HP-600W. Results from FAST and AeroDyn simulation tools were compared with those obtained by applying measured wind speeds to manufacturer power curves. Using manufacturer data, the IT-PE-100 would deliver 132 kWh during the whole year (70 kWh during the sea breeze period). From the simulations, the IT-PE-100 would deliver 155 kWh during the whole year (80 kWh during the sea breeze period). It is concluded that the sea-breeze is an interesting wind energy resource for micro-generation, not only in the Mediterranean basin but in other areas of the world with similar wind regimes, and particularly in peri-urban coastal areas where large-scale wind farms cannot be implemented.

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“…Various models have been proposed to fit wind speed data. LIU et al [3] and DAHMOUNI et al [4] pointed out that wind speed presents characteristics of high fluctuations, autocorrelation and stochastic volatility affected by various environment; thereby it is hard to forecast with a single model. SAFARI [5] and FRANDSEN et al [6] used several different probability density distributions to fit long term hourly wind speed data observed at four meteorological stations in Rwanda, including Weibull, Rayleigh, lognormal, normal and gamma distributions.…”

Section: Introductionmentioning

confidence: 99%

Characteristics for wind energy and wind turbines by considering vertical wind shear

Yan

Zhao

2015

J. Cent. South Univ.

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The probability distributions of wind speeds and the availability of wind turbines were investigated by considering the vertical wind shear. Based on the wind speed data at the standard height observed at a wind farm, the power-law process was used to simulate the wind speeds at a hub height of 60 m. The Weibull and Rayleigh distributions were chosen to express the wind speeds at two different heights. The parameters in the model were estimated via the least square (LS) method and the maximum likelihood estimation (MLE) method, respectively. An adjusted MLE approach was also presented for parameter estimation. The main indices of wind energy characteristics were calculated based on observational wind speed data. A case study based on the data of Hexi area, Gansu Province of China was given. The results show that MLE method generally outperforms LS method for parameter estimation, and Weibull distribution is more appropriate to describe the wind speed at the hub height.

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Wind energy in the Gulf of Tunis, Tunisia

Cited by 26 publications

References 13 publications

Potential of Wind Speed for Wind Power Generation In Perlis, Northern Malaysia

Potential of Wind Speed for Wind Power Generation In Perlis, Northern Malaysia

An assessment of the sea breeze energy potential using small wind turbines in peri-urban coastal areas

Characteristics for wind energy and wind turbines by considering vertical wind shear

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