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Probabilistic Optimal Power Flow Solution Using a Novel Hybrid Metaheuristic and Machine Learning Algorithm

dc.contributor.authorShaheen, Mohamed A.M.
dc.contributor.authorHasanien, Hany M.
dc.contributor.authorMekhamer, Said F.
dc.contributor.authorQais, Mohammed H.
dc.contributor.authorAlghuwainem, Saad
dc.contributor.authorUllah, Zia
dc.contributor.authorTostado-Véliz, Marcos
dc.contributor.authorTurky, Rania A.
dc.contributor.authorJurado-Melguizo, Francisco
dc.contributor.authorElkadeem, Mohamed R.
dc.date.accessioned2024-12-04T13:10:53Z
dc.date.available2024-12-04T13:10:53Z
dc.date.issued2022-08-23
dc.description.abstractThis paper proposes a novel hybrid optimization technique based on a machine learning (ML) approach and transient search optimization (TSO) to solve the optimal power flow problem. First, the study aims at developing and evaluating the proposed hybrid ML-TSO algorithm. To do so, the optimization technique is implemented to solve the classical optimal power flow problem (OPF), with an objective function formulated to minimize the total generation costs. Second, the hybrid ML-TSO is adapted to solve the probabilistic OPF problem by studying the impact of the unavoidable uncertainty of renewable energy sources (solar photovoltaic and wind turbines) and time-varying load profiles on the generation costs. The evaluation of the proposed solution method is examined and validated on IEEE 57-bus and 118-bus standard systems. The simulation results and comparisons confirmed the robustness and applicability of the proposed hybrid ML-TSO algorithm in solving the classical and probabilistic OPF problems. Meanwhile, a significant reduction in the generation costs is attained upon the integration of the solar and wind sources into the investigated power systems.es_ES
dc.identifier.citationShaheen, M.A.M.; Hasanien, H.M.; Mekhamer, S.F.; Qais, M.H.; Alghuwainem, S.; Ullah, Z.; Tostado-Véliz, M.; Turky, R.A.; Jurado, F.; Elkadeem, M.R. Probabilistic Optimal Power Flow Solution Using a Novel Hybrid Metaheuristic and Machine Learning Algorithm. Mathematics 2022, 10, 3036. https://doi.org/10.3390/math10173036es_ES
dc.identifier.issn2227-7390es_ES
dc.identifier.other10.3390/math10173036es_ES
dc.identifier.urihttps://www.mdpi.com/2227-7390/10/17/3036es_ES
dc.identifier.urihttps://hdl.handle.net/10953/3452
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relation.ispartofMathematics [2022]; [10]: [3036]es_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectMachine learninges_ES
dc.subjectProbabilistic optimal power flowes_ES
dc.subjectRenewable energy sourceses_ES
dc.titleProbabilistic Optimal Power Flow Solution Using a Novel Hybrid Metaheuristic and Machine Learning Algorithmes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_ES

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