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.

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A novel algorithm is proposed to control the overall energy produced by a photovoltaic/battery bank/diesel generator renewable energy system. A renewable energy system is used to supply an off-grid connected house in Sfax, Tunisia. The algorithm computes recurrently the battery depth of discharge and diesel consumption of the diesel generator and then forecasts the photovoltaic subsystem output power every two minutes ahead. The estimated power is computed using an auto-regressive moving average model associated with a Kalman filter. During each calculation loop, hybrid system energy scheduling is accomplished considering criteria that guarantee the maximum use of generated renewable energy, minimum diesel generator operational time, and full-load need satisfaction during the whole day. Numerical simulations for two typical sunny and cloudy days in Sfax, Tunisia, are conducted in order to validate the algorithm and to compare the performance of managed system behavior with that of a standard unmanaged system. It is shown that the proposed energy management system could save fuel consumption by as much as 11.27% in the sunny day and 9.17% in the cloudy day.

The energy demand in remote area cannot be satisfied unless renewable energy based plants are locally installed. In order to be efficient, such projects should be sized on the basis of maximizing the renewable energies exploitation and meeting the consumer needs. The aim of this work is to provide an algorithm-based calculation of the optimum sizing of a standalone hybrid plant composed of a wind generator, a photovoltaic panel, a lead acid-battery bank, and a water tank. The strategy consists of evaluating the renewable potentials (solar and wind). Obtained results are entered as inputs to established generators models in order to estimate the renewable generations. The developed optimal sizing algorithm which is based on iterative approach, computes plant components sizes for which load profile meet estimated renewable generations. The approach validation is conducted for A PV/Wind/Battery based farm located in Sfax, Tunisia. Obtained results proved that the energetic need is covered and only about 4% of the generated energy is not used. Also a cost investigation confirmed that the plant becomes profitable ten years after installation.Article History: Received June 24th 2017; Received in revised form September 26th 2017; Accepted Sept 30th 2017; Available onlineCitation: Brahmi, N., Charfi, S., and Chaabene, M. (2017) Optimum Sizing Algorithm for an off grid plant considering renewable potentials and load profile. Int. Journal of Renewable Energy Development, 6(3), 213-224.https://doi.org/10.14710/ijred.6.3.213-224

The effectiveness of autonomous wind plants depends basically on the characterization, sizing, and environmental design and analysis of its renewable energy conversion system. This article presents an assessment on wind potential characterization to be used to compute the size of a wind farm turbine. Different methods are adopted to estimate parameters of the Weibull distribution. The modified maximum likelihood method is selected as the most accurate with reference to comparison between many approaches output results and measurements provided by the National Institute of Meteorology. Also, an artificial neural network-based algorithm is developed to optimize the MMLM parameters. The monthly wind potential distribution is consequently computed for Sfax, Tunisia. Obtained results are used to optimize the size calculation of wind turbine blades and battery capacity for a standalone wind farm. The proposed approach profitability is evaluated upon the lost produced energy.

Covering the energy demand of typical consumers in remote area on the basis of maximizing the renewable energies exploitation represents the main aim of most governments. Such project requires as a first step an efficient sizing of renewable energy plants. In this context, this work proposes an optimum sizing algorithm of a standalone hybrid system components: wind generator, photovoltaic panel, lead acid-battery bank, and water tank. The strategy starts by renewable potentials (solar and wind) assessment. Different generators models are developed then coupled to the established potentials in order to compute the renewable generations. Optimal plant components sizes are computed thanks an iterative approach based on the meeting of renewable generations to load profile. Representative case study of Sfax, Tunisia is investigated, offered need energetic satisfaction of agricultural farm.

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