Optimization of V-Trough photovoltaic concentrators through genetic algorithms with heuristics based on Weibull distributions

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dc.citation.journalTitleAPPLIED ENERGYspa
dc.contributor.authorArias-Rosales A.
dc.contributor.authorMejía-Gutiérrez R.
dc.contributor.departmentUniversidad EAFIT. Departamento de Ingeniería de Diseño
dc.contributor.researchgroupIngeniería de Diseño (GRID)spa
dc.date.accessioned2021-04-12T21:15:01Z
dc.date.available2021-04-12T21:15:01Z
dc.date.issued2018-02-15
dc.description.abstractPhotovoltaic V-Troughs use simple and low-cost non-imaging optics, namely flat mirrors, to increase the solar harvesting area by concentrating the sunlight towards regular solar cells. The geometrical dispositions of the V-Trough's elements, and the way in which they are dynamically adjusted to track the sun, condition the optical performance. In order to improve their harvesting capacity, their geometrical set-up can be tailored to specific conditions and performance priorities. Given the large number of possible configurations and the interdependence of the multiple parameters involved, this work studies genetic algorithms as a heuristic approach for navigating the space of possible solutions. Among the algorithms studied, a new genetic algorithm named “GA-WA” (Genetic Algorithm-Weibull Arias) is proposed. GA-WA uses new heuristic processes based on Weibull distributions. Several V-Trough performance indicators are proposed as objective functions that can be optimized with genetic algorithms: (i) Ce? (average effective concentration); (ii) Cost (cost of materials) and (iii) Tsp (space required). Moreover, from the integration of these indicators, three multi-objective indices are proposed: (a) ICOE (Ce? versus Cost); (b) MICOE (Ce? versus Cost and Ce? versus Tsp combined) and (c) MDICOE (similar to MICOE but with discretization considerations). The heuristic parameters of the studied genetic algorithms are optimized and their capacities are explored in a case study. The results are compared against reported V-Trough set-ups designed with the interactive software VTDesign for the same case study. It was found that genetic algorithms, such as the ones developed in this work, are effective in the performance indicators improvement, as well as efficient and flexible tools in the problem of defining the set-up of solar V-Troughs in personalized scenarios. The intuition and the more holistic exploration of a trained engineer with an interactive software can be complemented with the broader and less biased evolutionary optimization of a tool like GA-WA. © 2017 Elsevier Ltdeng
dc.identifierhttps://eafit.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=7849
dc.identifier.doi10.1016/j.apenergy.2017.11.106
dc.identifier.issn3062619
dc.identifier.issn18729118
dc.identifier.otherWOS;000425200700008
dc.identifier.otherSCOPUS;2-s2.0-85037625120
dc.identifier.urihttp://hdl.handle.net/10784/28987
dc.language.isoengeng
dc.publisherElsevier Ltd
dc.relationDOI;10.1016/j.apenergy.2017.11.106
dc.relationWOS;000425200700008
dc.relationSCOPUS;2-s2.0-85037625120
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85037625120&doi=10.1016%2fj.apenergy.2017.11.106&partnerID=40&md5=d7fe21dec32f744fbca4dfe8c6a40402
dc.rightshttps://v2.sherpa.ac.uk/id/publication/issn/0306-2619
dc.sourceAPPLIED ENERGY
dc.subject.keywordAir navigationeng
dc.subject.keywordBenchmarkingeng
dc.subject.keywordCostseng
dc.subject.keywordGenetic algorithmseng
dc.subject.keywordHeuristic algorithmseng
dc.subject.keywordHeuristic methodseng
dc.subject.keywordMultiobjective optimizationeng
dc.subject.keywordParameter estimationeng
dc.subject.keywordSolar cellseng
dc.subject.keywordSolar power generationeng
dc.subject.keywordWeibull distributioneng
dc.subject.keywordEffective concentrationeng
dc.subject.keywordEvolutionary optimizationseng
dc.subject.keywordHeuristic parameterseng
dc.subject.keywordHeuristicseng
dc.subject.keywordNew genetic algorithmseng
dc.subject.keywordPerformance indicatorseng
dc.subject.keywordPhotovoltaic concentratorseng
dc.subject.keywordSolar concentrationeng
dc.subject.keywordOptimizationeng
dc.subject.keywordcost analysiseng
dc.subject.keyworddesigneng
dc.subject.keywordenergy efficiencyeng
dc.subject.keywordfuel celleng
dc.subject.keywordgenetic algorithmeng
dc.subject.keywordheuristicseng
dc.subject.keywordoptimizationeng
dc.subject.keywordperformance assessmenteng
dc.subject.keywordphotovoltaic systemeng
dc.subject.keywordAnaseng
dc.titleOptimization of V-Trough photovoltaic concentrators through genetic algorithms with heuristics based on Weibull distributionseng
dc.typeinfo:eu-repo/semantics/articleeng
dc.typearticleeng
dc.typeinfo:eu-repo/semantics/publishedVersioneng
dc.typepublishedVersioneng
dc.type.localArtículospa

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