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Multi-objective immune-commensal-evolutionary programming for total production cost and total system loss minimization via integrated economic dispatch and distributed generation installation

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dc.citedby1
dc.contributor.authorMansor M.H.en_US
dc.contributor.authorMusirin I.en_US
dc.contributor.authorOthman M.M.en_US
dc.contributor.authorid56372667100en_US
dc.contributor.authorid8620004100en_US
dc.contributor.authorid35944613200en_US
dc.date.accessioned2023-05-29T09:05:32Z
dc.date.available2023-05-29T09:05:32Z
dc.date.issued2021
dc.descriptionComputer programming; Costs; Distributed computer systems; Distributed power generation; Electric load dispatching; Evolutionary algorithms; Immune system; Multiobjective optimization; Artificial Immune System; Distributed generation installation; Economic Dispatch; Economic dispatch problems; Multi objective; Multi-objectives optimization; Symbiotic organism search; Symbiotics; System loss; Total production cost; Schedulingen_US
dc.description.abstractEconomic Dispatch (ED) problems have been solved using single-objective optimization for so long, as Grid System Operators (GSOs) previously only focused on minimizing the total production cost. In modern power systems, GSOs require not only optimizing the total production cost but also, at the same time, optimizing other important objectives, such as the total emissions of the greenhouse gasses, total system loss and voltage stability. This requires a suitable multi-objective optimization approach in ensuring the ED solution produced is satisfying all the objectives. This paper presents a new multi-objective optimization technique termed Multi-Objective Immune-Commensal-Evolutionary Programming (MOICEP) for minimizing the total production cost and total system loss via integrated Economic Dispatch and Distributed Generation installation (ED-DG). This involved the application of a weighted-sum multi-objective approach that combined with an optimization technique called Immune-Commensal-Evolutionary Programming (ICEP). The proposed MOICEP has been compared with other multi-objective techniques, which are Multi-Objective-Evolutionary Programming (MOEP) and Multi-Objective-Artificial Immune System (MOAIS). It was found that MOICEP performs very well in producing better optimization results for all the three types of Economic Dispatch (ED) problems compared to MOEP and MOAIS in terms of cheap total production costs and low total system loss. � 2021 by the authors. Licensee MDPI, Basel, Switzerland.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo7733
dc.identifier.doi10.3390/en14227733
dc.identifier.issue22
dc.identifier.scopus2-s2.0-85119699784
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85119699784&doi=10.3390%2fen14227733&partnerID=40&md5=dd2cd036636e91b8035530d3d5d815e7
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25915
dc.identifier.volume14
dc.publisherMDPIen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleEnergies
dc.titleMulti-objective immune-commensal-evolutionary programming for total production cost and total system loss minimization via integrated economic dispatch and distributed generation installationen_US
dc.typeArticleen_US
dspace.entity.typePublication
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