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21 results on '"Arrif, Toufik"'

18. Optical performance study of different shapes of solar cavity receivers used in central receiver system plant

Author

Arrif, Toufik, Benchabane, Adel, Guermoui, Mawloud, Gama, Amor, and Merarda, Hakim

Abstract

ABSTRACTIn the current study, we present the optical efficiency of six different shapes of cavity receivers that are mostly used in solar power tower systems. This study was conducted in applied research unit for renewable energies (URAER) at Ghardaïa province, Algeria. Each heliostat reflects the sunray into the centre point of the cavity aperture situated at 7.4 m on the top tower. In order to find the orientation of each heliostat, several parameters relating the position of the sun and heliostat tower system are estimated. Optical simulation was performed by means of TracePro® 2013 ray tracing software using the MCRT (Monte Carlo ray trace) method. The results show that conical and trapezoidal shapes perform better than other studied cavities. They mostly exhibit the highest optical efficiency reaching 92.65%, 91.81% at absorptivity 85% and an optical efficiency of 90.88% and 89.30% at absorptivity 75%.

Published
2021
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19. Optimisation of heliostat field layout for solar power tower systems using iterative artificial bee colony algorithm: a review and case study

Author

Arrif, Toufik, Benchabane, Adel, Germoui, Mawloud, Bezza, Badreddine, and Belaid, Abdelfettah

Abstract

ABSTRACTIn this study, the Iterative Artificial Bee Colony Algorithm (IABCA) methodology was proposed to perform optimisation of a heliostat field for the Solar Power Tower (SPT) system. In this respect, a complete mathematical model of annual unweighted optical efficiency considering cosine losses, shadowing-blocking losses, atmospheric attenuation losses, interception losses and mirror reflectivity was developed firstly. A simple and efficient method is adopted to identify the heliostats with the highest possibility to shade or block another heliostat. The use of this method allows us to reduce the processing time of the shadowing and blocking efficiency and avoid unnecessary calculations. A case study of PS10 situated in Spain was used to validate the mathematical model and the proposed IABCA approach. The proposed IABCA maximize the annual unweighted optical efficiency. In order to evaluate the performance of the proposed approach, seasonal investigation on four different days 21 March, 21 June, 21 September, and 21 December at solar noon time was carried out. The results show that the proposed IABCA methodology boost significantly the performance of the heliostat field compared to the original PS10 layout. Percentage improvement for each studied day in terms of efficiency is 3.46%, 1.05%, 3.2%, 1.98%, respectively.

Published
2021
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21. A Novel Hybrid Model for Solar Radiation Forecasting Using Support Vector Machine and Bee Colony Optimization Algorithm: Review and Case Study

Author

Kacem Gairaa, John Boland, Toufik Arrif, Mawloud Guermoui, Guermoui, Mawloud, Gairaa, Kacem, Boland, John, and Arrif, Toufik

Subjects
clean energy, least square-support vector machine (LS-SVM), Mathematical optimization, Optimization algorithm, 010308 nuclear & particles physics, Renewable Energy, Sustainability and the Environment, Computer science, solar radiation, 020209 energy, artificial beecolony (ABC), solar energy, Energy Engineering and Power Technology, forecasting, 02 engineering and technology, Radiation, 01 natural sciences, radiation, Support vector machine, photovoltaics, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, renewable, environment, Hybrid model, energy
Abstract

This article proposes a new hybrid least squares-support vector machine and artificial bee colony algorithm (ABC-LS-SVM) for multi-hour ahead forecasting of global solar radiation (GHI) data. The framework performs on training the least squares-support vector machine (LS-SVM) model by means of the ABC algorithm using the measured data. ABC is developed for free parameters optimization for the LS-SVM model in a search space so as to boost the forecasting performance. The developed ABC-LS-SVM approach is verified on an hourly scale on a database of five years of measurements. The measured data were collected from 2013 to 2017 at the Applied Research Unit for Renewable Energy (URAER) in Ghardaia, south of Algeria. Several combinations of input data have been tested to model the desired output. Forecasting results of 12 h ahead GHI with the ABC-LS-SVM model led to the root-mean-square error (RMSE) equal to 116.22 Wh/m2, Correlation coefficient r = 94.3%. With the classical LS-SVM, the RMSE error equals to 117.73 Wh/m2 and correlation coefficient r = 92.42%; for cuckoo search algorithm combined with LS-SVM, the RMSE = 116.89 Wh/m2 and r = 93.78%. The results achieved reveal that the proposed hybridization scheme provides a more accurate performance compared to cuckoo search-LS-SVM and the stand-alone LS-SVM.

Published
2020

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