Version 1
: Received: 31 August 2023 / Approved: 1 September 2023 / Online: 4 September 2023 (03:50:12 CEST)
How to cite:
Thiaw, K.; Diao, A.; Sadio, A.; Mboji, S. Design and Multi-Objective Optimization of a Photovoltaic System by a Genetic Algorithm. Preprints2023, 2023090114. https://doi.org/10.20944/preprints202309.0114.v1
Thiaw, K.; Diao, A.; Sadio, A.; Mboji, S. Design and Multi-Objective Optimization of a Photovoltaic System by a Genetic Algorithm. Preprints 2023, 2023090114. https://doi.org/10.20944/preprints202309.0114.v1
Thiaw, K.; Diao, A.; Sadio, A.; Mboji, S. Design and Multi-Objective Optimization of a Photovoltaic System by a Genetic Algorithm. Preprints2023, 2023090114. https://doi.org/10.20944/preprints202309.0114.v1
APA Style
Thiaw, K., Diao, A., Sadio, A., & Mboji, S. (2023). Design and Multi-Objective Optimization of a Photovoltaic System by a Genetic Algorithm. Preprints. https://doi.org/10.20944/preprints202309.0114.v1
Chicago/Turabian Style
Thiaw, K., Amy Sadio and Senghane Mboji. 2023 "Design and Multi-Objective Optimization of a Photovoltaic System by a Genetic Algorithm" Preprints. https://doi.org/10.20944/preprints202309.0114.v1
Abstract
This study is focused on an optimal sizing method based on genetic algorithms (GA) in order to design a high-performance photovoltaic system that would be installed in the Alioune Diop University of Bambey, Senegal. The multi-objective optimization allowed us to find a better trade- off between the Total Life Cycle cost (TLCC) and the Loss of Power Supply Probability (LPSP). The goal is to minimize the TLCC while maintaining a good satisfactory of the system with a desired LPSP, under certain constraints. The expressions of the TLCC and the LPSP are established according to the total photovoltaic energy and the battery capacity. The toolbox of Matlab software is used to implement the optimization problem with twelve blocks of the site and its different corresponding loads. The obtained results have led to a several combinations of the photovoltaic and battery capacities of which the best one is selected based on the lowest LPSP, that guaranteed a better load coverage during the night. The results of this proposed method are compared with intuitive method that has been firstly applied. For the block with the lowest load demand, the proposed optimization model leads to a reduction a reduction of 70% and 48% in the storage capacity and the TLCC respectively for a LPSP of 0.007 %.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
