Enhanced Coati Optimization Algorithm for Static and Dynamic Transmission Network Expansion Planning Problems

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dc.authoridDosoglu, M. Kenan/0000-0001-8804-7070;
dc.contributor.authorDemirbas, Muhammet
dc.contributor.authorDosoglu, M. Kenan
dc.contributor.authorDuman, Serhat
dc.date.accessioned2025-10-11T20:48:15Z
dc.date.available2025-10-11T20:48:15Z
dc.date.issued2025
dc.departmentDüzce Üniversitesien_US
dc.description.abstractThe power systems are becoming more and more complex due to the inclusion of new components and increasing load demand. Consequently, it is imperative to incorporate additional generation units and transmission links into the system. Transmission Network Expansion Planning (TNEP) seeks to include generation units and transmission lines into the system at optimal locations and minimal costs. Mathematical techniques are extensively employed to address the problem. Nonetheless, mathematical methods necessitate extensive computation durations. Consequently, novel solution strategies are under investigation. The TNEP problem is characterized by an innovative and effective metaheuristic optimization techniques. This study presents a novel Opposition Based Learning and Fitness Distance Balance based Coati Optimization Algorithm (FDBCOA-OBL) designed to address Static and Dynamic TNEP problems. An extensive experimental investigation was undertaken to evaluate the efficacy of the suggested method in addressing the benchmark test suites and the TNEP problem. The FDBCOA-OBL algorithm, utilizing the Elite OBL approach, surpassed all other comparative versions in addressing the benchmark test problems. The Wilcoxon analysis indicates that it lost 6 problems, tied in 110, and won 166 problems. The proposed approach resolved the TNEP problem in 6, 25, and 93-bus test systems. The Static TNEP solution was applied to the 6 and 25 bus test systems, while the Dynamic Multistage TNEP method was utilized in the 93-bus test system. The acquired investment expenses were compared to the research already documented in the literature. The findings indicate that the suggested method demonstrates robust performance.en_US
dc.identifier.doi10.1109/ACCESS.2025.3544523
dc.identifier.endpage35100en_US
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85218756692en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage35068en_US
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2025.3544523
dc.identifier.urihttps://hdl.handle.net/20.500.12684/21830
dc.identifier.volume13en_US
dc.identifier.wosWOS:001433330600020en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIEEE Accessen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_WOS_20250911
dc.subjectOptimizationen_US
dc.subjectHeuristic algorithmsen_US
dc.subjectMetaheuristicsen_US
dc.subjectCostsen_US
dc.subjectPlanningen_US
dc.subjectInvestmenten_US
dc.subjectLoad flowen_US
dc.subjectPower transmission linesen_US
dc.subjectPower system dynamicsen_US
dc.subjectGenetic algorithmsen_US
dc.subjectCoati optimization algorithmen_US
dc.subjectfitness-distance balance methoden_US
dc.subjectopposition-based learningen_US
dc.subjecttransmission network expansion planning problemen_US
dc.titleEnhanced Coati Optimization Algorithm for Static and Dynamic Transmission Network Expansion Planning Problemsen_US
dc.typeArticleen_US

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