A Case Study of Hybrid Renewable Energy System Optimization for an Island Community based on Particle Swarm Optimization

Authors

  • Ramia Ouderni Computer Laboratory for Electrical Systems, LR11ES26, INSAT, University of Carthage, Tunisia https://orcid.org/0000-0002-7132-0867
  • Bechir Bouaziz Computer Laboratory for Electrical Systems, LR11ES26, INSAT, University of Carthage, Tunisia https://orcid.org/0000-0003-1785-8639
  • Faouzi Bacha Computer Laboratory for Electrical Systems, LR11ES26, INSAT, University of Carthage, Tunisia
Volume: 14 | Issue: 3 | Pages: 14367-14373 | June 2024 | https://doi.org/10.48084/etasr.7256

Abstract

Due to their small size and isolated energy systems, islands face a significant energy supply challenge. To develop sustainable energy systems, Hybrid Renewable Energy Systems (HRES) help in the generation of electricity in island zones, as they are a clean and inexhaustible source of energy. The purpose of this study is to optimize the allocation of Renewable Energy Sources (RES) on an island in Tunisia. To ensure efficient management between the total power generation and the total community load demand, an Energy Management System (EMS) is required. This paper presents the integration of an optimal EMS using Particle Swarm Optimization (PSO) to directly allocate and optimize the energy generated by an HRES. In addition, the PSO algorithm is applied to regulate energy production, consumption, and storage to maximize the utilization of the available renewable sources while satisfying load requirements. The results exhibit that this approach is effective for the dynamic optimization of energy management in an HRES, contributing to a more efficient and sustainable utilization of energy resources.

Keywords:

hybrid renewable energy system, energy management system, modeling system, PSO

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References

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How to Cite

[1]
R. Ouderni, B. Bouaziz, and F. Bacha, “A Case Study of Hybrid Renewable Energy System Optimization for an Island Community based on Particle Swarm Optimization”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 3, pp. 14367–14373, Jun. 2024.

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