Optimum design of an off-grid PV/WT/FC/battery based microgrid for sustainable and cost-effective energy optimization

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dc.contributor.authorHOUABES Mourad (Co-Auteur)
dc.date.accessioned2025-09-11T10:38:59Z
dc.date.available2025-09-11T10:38:59Z
dc.date.issued2024
dc.descriptionStudies in Engineering and Exact Sciences, Curitiba, v.5, n.2, p. 01-16, 2024 DOI: 10.54021/seesv5n2-011
dc.description.abstractThis paper presents an optimized microgrid based on a PV/wind/battery/fuel cell hybrid power system with a battery and hydrogen tank storage system for an islanded residential load. This advanced system is optimized using Smell Agent Optimization (SAO) and Genetic Algorithm (GA). The system is meticulously designed to meet the energy demand while minimizing the total annual cost. Simulation findings revealed that both SAO and GA yield comparable outcomes, as both effectively optimized the system, ensuring sustainability and efficiency in system sizing. Also, the optimized results obtained from SAO appear to be more effective in enhancing the overall performance of hybrid energy systems. The study demonstrates that the application of these algorithms not only optimizes the design but also contributes significantly to the reliability and economic feasibility of the microgrid. The dual storage approach, incorporating both battery and hydrogen tank, proves to be a robust solution for maintaining continuous power supply under diverse weather conditions. The findings suggest that the proposed system configuration and optimization methodology can serve as a benchmark for future studies in the field of renewable energy systems
dc.identifier.issn2764-0981
dc.identifier.urihttp://dspace.ensti-annaba.dz:4000/handle/123456789/772
dc.language.isoen
dc.publisherStudies in Engineering and Exact Sciences
dc.subjectHybrid Renewable Energy Sizing
dc.subjectGenetic Algorithm
dc.subjectMicrogrid
dc.subjectTotal Annual Cost.
dc.subjectSustainability
dc.subjectSmell Agent Optimization
dc.titleOptimum design of an off-grid PV/WT/FC/battery based microgrid for sustainable and cost-effective energy optimization
dc.typeArticle
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