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A Comparative Study of Genetic Algorithm and Particle Swarm Optimization for Hybrid Renewable Systems with Battery and Hydrogen System
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| Author(s) |
Aqib KHAN, Mathieu BRESSEL, Dhaker ABBES, Arnaud DAVIGNY, Belkacem OULD BOUAMAMA |
| Abstract |
This paper presents a comparative study ofGenetic Algorithm (GA) and Particle Swarm Optimization(PSO) for optimal sizing of two hybrid renewableenergy systems: Solar with Battery and Grid, and Solarwith H2 System and Grid. Real-time energy consumptiondata from a university is used to model these systems,aiming to minimize costs while meeting energy demandsand ensuring reliability. The performance of GA andPSO is compared based on solution quality, convergencespeed, and computational efficiency. Results show that GAprovides robust configurations, while PSO offers fasterconvergence. These findings support efficient and practicalhybrid system design. |
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| Filename: | 0249-epe2025-full-16001451.pdf |
| Filesize: | 2.517 MB |
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| Type |
Members Only |
| Date |
Last modified 2025-08-31 by System |
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