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Genetic algorithm based optimization on modeling and design of hybrid renewable energy systems

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dc.citedby144
dc.contributor.authorIsmail M.S.en_US
dc.contributor.authorMoghavvemi M.en_US
dc.contributor.authorMahlia T.M.I.en_US
dc.contributor.authorid9633224700en_US
dc.contributor.authorid7003701545en_US
dc.contributor.authorid56997615100en_US
dc.date.accessioned2023-05-16T02:47:33Z
dc.date.available2023-05-16T02:47:33Z
dc.date.issued2014
dc.description.abstractA sizing optimization of a hybrid system consisting of photovoltaic (PV) panels, a backup source (microturbine or diesel), and a battery system minimizes the cost of energy production (COE), and a complete design of this optimized system supplying a small community with power in the Palestinian Territories is presented in this paper. A scenario that depends on a standalone PV, and another one that depends on a backup source alone were analyzed in this study. The optimization was achieved via the usage of genetic algorithm. The objective function minimizes the COE while covering the load demand with a specified value for the loss of load probability (LLP). The global warming emissions costs have been taken into account in this optimization analysis. Solar radiation data is firstly analyzed, and the tilt angle of the PV panels is then optimized. It was discovered that powering a small rural community using this hybrid system is cost-effective and extremely beneficial when compared to extending the utility grid to supply these remote areas, or just using conventional sources for this purpose. This hybrid system decreases both operating costs and the emission of pollutants. The hybrid system that realized these optimization purposes is the one constructed from a combination of these sources. © 2014 Elsevier Ltd. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.enconman.2014.05.064
dc.identifier.epage130
dc.identifier.scopus2-s2.0-84902650959
dc.identifier.spage120
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84902650959&doi=10.1016%2fj.enconman.2014.05.064&partnerID=40&md5=908e74d9dba8f7568c7a5d81a75126cc
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22120
dc.identifier.volume85
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleEnergy Conversion and Management
dc.titleGenetic algorithm based optimization on modeling and design of hybrid renewable energy systemsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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