Assessment of wind and solar energy resources in Batna, Algeria

Aksas, Mounir; Gama, Amor

doi:10.1016/j.egypro.2011.05.053

Cited by 14 publications

(9 citation statements)

References 4 publications

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“…The Weibull distribution is a function characterized by two parameters and can represent a broad wind regime The probability density function of the Weibull distribution is given by the following expression [1], [2], [4], [7], [15]:…”

Section: ) Weibull Distributionmentioning

confidence: 99%

“…Given the serious effects that the environment suffers, there is a growing interest in venturing into the generation of electricity from renewable energy sources (RE), whose main virtue is the minimum or zero emission of carbon dioxide, ideal for achieving sustainable development in populations. Given the range of ER resources, the vast majority of countries focus on the development and optimization of solar and wind technology [1]. For the particular case of wind energy (WE), a measure of energy flow per unit area [2] and, according to [3], the interest in developing wind farms, focuses on methods to assess resource potential wind and how to increase the efficiency of wind turbines (and with it wind farms), so that this leads to greater profitability of the project, by reducing operation and capital costs, taking into account their generation capacity, annual growth of installed capacity, efficiency and long-term competitive cost [4], [5], [6], [7], [8], [9].…”

Section: Introductionmentioning

confidence: 99%

“…To address the randomness of the resource, research has been carried out on the analysis and application of probability distribution functions (PDF) that allow characterizing the wind resource [1], [3], [11], [14], [15], in order to study the feasibility and development of wind generation projects [3], [8], [9], [16]; and, therefore, to reduce the probability that the energy market is characterized by volatile and irregular prices between supply and demand [13]. Of the PDF, the Weibull distribution (of two parameters) has been the most used [1], [2], [4], [6], [7], [8], [10], [14], for the estimation (characterization) of the resource and the production of wind power, due to its simplicity and flexibility in the analysis of a wide range of data, however, according to the cited by [16], this distribution does not It is recommended for calm wind regimes (low wind speeds). These probability distribution studies in WE have been carried out in different countries in Asia, North America, Europe and North Africa, covering different time series, locations, temporal resolution, height at which the data is collected, among others [10].…”

Section: Introductionmentioning

confidence: 99%

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Statistical methodologies for wind resource analysis, case: Catatumbo region - Norte de Santander, Colombia

Lopez-Rodriguez¹,

Serrano-Rico²,

Florez-Serrano³

2020

REPQJ

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In this article, we analyze 4 probability distribution functions to characterize the wind resource at northeast zone of Colombia, based on data of taken from existing meteorological stations in three towns in the zone of Norte de Santander, Colombia. The data is sampled each 10 minutes, obtaining average values per hour every day, for each month of the year. The probability distributions studied are Weibull, Rayleigh, Log-Logistics and Gamma. In the case of the Weibull distribution, 5 methods were used to estimate two principal parameters of the distribution; shape and scale. For other distributions only was used one. The distributions for each data set were determined, exposing the best fit for each zone according to the 3 performance indices considered (RMSE, , R2). The main result obtained is the identification of the probability distribution that presents a better adjustment to the existing wind speed data in the zone where the meteorological stations are located. Specifically, it was identified that the probability distributions that best fit the data, for each month of the year, are Log-Logistics and Gamma. Further, the identification of the probability distribution that best fits to the wind speed data, allows to characterize with a greater degree of accuracy the wind potential of a zone.

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Section: ) Weibull Distributionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Statistical methodologies for wind resource analysis, case: Catatumbo region - Norte de Santander, Colombia

Lopez-Rodriguez¹,

Serrano-Rico²,

Florez-Serrano³

2020

REPQJ

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“…During the bibliography search, several models of the power coefficient were found, for example: [7] Proposed the following model:…”

Section: Aerodynamicsmentioning

confidence: 99%

Application and Control of a Doubly Fed Induction Machine Integrated in Wind Energy System

Fatima¹,

Assia²,

Hamdaoui³

2019

I2M

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The global wind energy capacity has increased rapidly in this last decade and became the fastest developing renewable energy technology. But unbalances in wind energy are highly impacting the energy conversion and this problem can be resolved by a doubly Fed Induction Generator (DFIG) which is one of the most commonly deployed large grid-attached wind turbine systems. The objective of this paper is to study this system, as the first the mathematical model is developed in the coordinates of Park d-q, then the power transfer between the stator and the network is realized by action on the rotor signals via a bidirectional converter. Independent control of active-reactive power is provided by conventional controllers (PIs). Then, we realized the implementation of the control by an orientation of the rotor flux (FOC) which presents an attractive solution to have an operation with the best performances of the Machine, in the applications with variable speeds. The analysis of the simulation results under the Matlab / Simulink environment of this control approach clearly shows that the system perfectly follows the reference values and, consequently, provides a good static and dynamic performance of the machine under study. 2 turbin total g J J J J K

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“…The second approach is the assessment of wind potential and the design of wind energy conversion systems. In this context, we can mention the studies by Himri et al [26][27][28][29][30][31] who were among the first to give a statistical analysis of the wind speed in different regions of Algeria and the work on the evaluation of wind resources at different sites in Algeria (Boudia et al [32][33][34][35][36][37], Benmedjahed et al [38][39], Bouzid et al [40], Diaf et al [41], Aksas et al [42]).…”

Section: Introductionmentioning

confidence: 99%

Wind Energy Resource Assessment in South Western of Algeria

Abderrahim¹,

Ghellai²,

Bouzid³

et al. 2019

MMEP

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The aim of this paper is to evaluate the wind potential in southwestern Algeria, according to months, seasons and entire years and contribute to the updating of the wind map in Algeria at 10 m from the ground, using hourly data for wind collected over a period of more than 30 years. The wind data analysis was done using the Weibull function at 10 m from the ground. Then we did the statistical analysis, which includes several fundamental properties, such as Weibull parameters, mean wind speed and average power density. The results give the city of Tindouf as the one with the highest annual average speed with 5.39 m/s at 10 m from the ground. As for the temporal study, it gives that spring is the best windy period.

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Assessment of wind and solar energy resources in Batna, Algeria

Cited by 14 publications

References 4 publications

Statistical methodologies for wind resource analysis, case: Catatumbo region - Norte de Santander, Colombia

Statistical methodologies for wind resource analysis, case: Catatumbo region - Norte de Santander, Colombia

Application and Control of a Doubly Fed Induction Machine Integrated in Wind Energy System

Wind Energy Resource Assessment in South Western of Algeria

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