Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/34683
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dc.contributor.authorDimishkovska Krsteski, Natashaen_US
dc.contributor.authorIliev, Atanasen_US
dc.date.accessioned2026-01-28T10:51:40Z-
dc.date.available2026-01-28T10:51:40Z-
dc.date.issued2024-09-01-
dc.identifier.urihttp://hdl.handle.net/20.500.12188/34683-
dc.description.abstract<jats:p>This paper introduces a modification of the genetic algorithm aimed at enhancing the selection process for reproducing the next generation. This modification accelerates the optimization process and improves the outcome. The case study analyzes a grid-connected microgrid comprising renewable energy sources, a battery storage system, prosumers with installed photovoltaic generators, and consumers. The effectiveness of the proposed modification is validated through comparison with two selection algorithms commonly used in the standard genetic algorithms.</jats:p>en_US
dc.language.isoenen_US
dc.publisherInternational Journal on Information Technologies and Securityen_US
dc.relation.ispartofInternational Journal on Information Technologies and Securityen_US
dc.titleModified genetic algorithm for unit commitment of grid connected microgrids under real time pricing conditionsen_US
dc.typeArticleen_US
dc.identifier.doi10.59035/hhvg2870-
dc.identifier.urlhttps://ijits-bg.com/sites/default/files/archive/2024%28vol.16%29/No3/contents/2024-N3-08.pdf-
dc.identifier.volume16-
dc.identifier.issue3-
dc.identifier.fpage81-
dc.identifier.lpage90-
item.fulltextWith Fulltext-
item.grantfulltextopen-
crisitem.author.deptFaculty of Electrical Engineering and Information Technologies-
crisitem.author.deptFaculty of Electrical Engineering and Information Technologies-
Appears in Collections:Faculty of Electrical Engineering and Information Technologies: Journal Articles
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