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

• Published in: EAI endorsed transactions on energy web Vol. 6; no. 21; p. 155642
• Main Authors: Sadio, A., Mbodji, S., Fall, I., Sow, P.
• Format: Journal Article
• Language: English
• Published: Ghent European Alliance for Innovation (EAI) 2019
• Subjects: Artificial intelligence; Comparative studies; Computer simulation; Constraint modelling; Genetic Algorithm; Genetic algorithms; Life cycle costs; Numerical analysis; Numerical methods; Objective» function; Optimization; Photovoltaic cells; Sizing; Standalone PV System
• ISSN: 2032-944X; 2032-944X
• DOI: 10.4108/eai.13-7-2018.155642

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.