Sizing optimization of a wind pumping plant: Case study in Sfax, Tunisia

Abstract

The effectiveness of wind water pumping plant depends on the available wind potential in the region and on the plant components sizing. This paper presents an algorithm for wind potential assessment based on the widely used Weibull distribution. As many methods are adopted to determine Weibull parameters, an improvement version based on the selection of the most accurate method and the establishment of a huge database using an artificial neural network (ANN) is proposed. Since the site wind performance is evaluated, the wind generator blades surface is computed on the basis of the variation limits of the monthly wind potential and the well height of rise. The sizing principal considers the calculation of the gravity centre of the general function of surface. Results are illustrated using meteorological database provided by the National Institute of Meteorology (INM) corresponding to Sfax, Tunisia. Obtained results confirm that the modified maximum likelihood method (MMLM) is the most accurate one as it provides a monthly error between À11:6% and 2:3%. Hence, a typical pumping plant, with monthly water need of 15 m 3 month located in Sfax, Tunisia, requires 37 m 2 as optimum blades surface.

Sizing optimization tool for wind/photovoltaic/battery plant considering potentials assessment and load profile

“…k and c are the two parameters of the Weibull distribution. This distribution is the most famous approach that permits to establish the wind potential assessment [7]. The probability density function of the wind speed is defined as:…”

“…In previous work [7], the parameters k and c were determined by the MMLM method. The wind potential is calculated on the basis of the Weibull distribution using its shape and scales parameters.…”

Optimum Sizing Algorithm for An Off Grid Plant Considering Renewable Potentials and Load Profile

“…and are the two parameters of the Weibull distribution. This distribution is the most famous approach that permits to establish the wind potential assessment (Brahmi et al 2012). The probability density function of the wind speed is defined as:…”

“…It computes the optimum sizes of the considered farm components: the surface of the wind generator blades, the PV array surface, the tank volume, and the battery capacity. The algorithm should ensure the load energy need throughout the year by maximizing the use of renewable energies (Brahmi et al 2012).…”

“…These methods are compiled to compute and using the available monthly meteorological database. In previous work Brahmi et al (2012), the parameters and were determined by the MMLM method. The wind potential is calculated on the basis of the Weibull distribution using its shape and scales parameters.…”