A comparative study based on the Genetic Algorithm (GA) method for the optimal sizing of the standalone photovoltaic system in the Ngoundiane site

A. Sadio; S. Mbodji; I. Fall; P. Sow

Research Article

A comparative study based on the Genetic Algorithm (GA) method for the optimal sizing of the standalone photovoltaic system in the Ngoundiane site

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@ARTICLE{10.4108/eai.13-7-2018.155642,
    author={A. Sadio and S. Mbodji and I. Fall and P. L. T. Sow},
    title={A comparative study based on the Genetic Algorithm (GA) method for the optimal sizing of the standalone photovoltaic system in the Ngoundiane site},
    journal={EAI Endorsed Transactions on Energy Web},
    volume={6},
    number={21},
    publisher={EAI},
    journal_a={EW},
    year={2018},
    month={10},
    keywords={Standalone PV System, Optimization, Genetic Algorithm, «Objective» function},
    doi={10.4108/eai.13-7-2018.155642}
}

A. Sadio
S. Mbodji
I. Fall
P. L. T. Sow
Year: 2018
A comparative study based on the Genetic Algorithm (GA) method for the optimal sizing of the standalone photovoltaic system in the Ngoundiane site
EW
EAI
DOI: 10.4108/eai.13-7-2018.155642

A. Sadio^1,2, S. Mbodji^1,2^,*, I. Fall², P. L. T. Sow²

1: Laboratory of Semiconductors and Solar Energy, Department of Physics, Faculty of Science and Technology, Cheikh Anta Diop University, Dakar, Senegal
2: Research team in renewable energies, materials and laser of Department of Physics, Alioune Diop University of Bambey, Bambey, Senegal

*Contact email: senghane.mbodji@uadb.edu.sn

Abstract

We study a sizing method using Artificial Intelligence Techniques (AI) to find the optimal sizes of a standalone photovoltaic system in Ngoundiane, Senegal. The sizing of the PV system is considered here as a mono-objective problem and the Total Life Cycle Cost (TLCC) is the « Objective » function to minimize. Based on some constraints and after 10 simulations, the optimisation gives, as a result, an optimal value of TLCC corresponding to the combination of 225750 WC/8100 Ah. This result show that the method using Genetic Algorithm (GA) increases considerably the photovoltaic capacity compared to the intuitive and numerical methods used in our previous works. The GA method better covers the load demand, with more long time, when compared with those obtained with numerical method. These results confirm that this method is effective and reliable because it allows the design of a PV system that satisfies the load demand of the Ngoundiane site with a lower cost.

Keywords: Standalone PV System, Optimization, Genetic Algorithm, «Objective» function

Received: 2018-05-19
Accepted: 2018-07-15
Published: 2018-10-02
Publisher: EAI

: http://dx.doi.org/10.4108/eai.13-7-2018.155642

Copyright © 2018 A. Sadio et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.