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dc.contributor.authorRajagopalan, Arul
dc.contributor.authorNagarajan, Karthik
dc.contributor.authorMontoya, Oscar Danilo
dc.contributor.authorDhanasekaran, Seshathiri
dc.contributor.authorAbdul Kareem, Inayathullah
dc.contributor.authorSendraya Perumal, Angalaeswari
dc.contributor.authorLakshmaiya, Natrayan
dc.contributor.authorParamasivam, Prabhu
dc.date.accessioned2022-12-05T07:58:45Z
dc.date.available2022-12-05T07:58:45Z
dc.date.issued2022-11-29
dc.description.abstractOptimal energy management has become a challenging task to accomplish in today’s advanced energy systems. If energy is managed in the most optimal manner, tremendous societal benefits can be achieved such as improved economy and less environmental pollution. It is possible to operate the microgrids under grid-connected, as well as isolated modes. The authors presented a new optimization algorithm, i.e., Oppositional Gradient-based Grey Wolf Optimizer (OGGWO) in the current study to elucidate the optimal operation in microgrids that is loaded with sustainable, as well as unsustainable energy sources. With the integration of non-Renewable Energy Sources (RES) with microgrids, environmental pollution is reduced. The current study proposes this hybrid algorithm to avoid stagnation and achieve premature convergence. Having been strategized as a bi-objective optimization problem, the ultimate aim of this model’s optimal operation is to cut the costs incurred upon operations and reduce the emission of pollutants in a 24-h scheduling period. In the current study, the authors considered a Micro Turbine (MT) followed by a Wind Turbine (WT), a battery unit and a Fuel Cell (FC) as storage devices. The microgrid was assumed under the grid-connected mode. The authors validated the proposed algorithm upon three different scenarios to establish the former’s efficiency and efficacy. In addition to these, the optimization results attained from the proposed technique were also compared with that of the results from techniques implemented earlier. According to the outcomes, it can be inferred that the presented OGGWO approach outperformed other methods in terms of cost mitigation and pollution reduction.en_US
dc.identifier.citationRajagopalan, Nagarajan, Montoya, Dhanasekaran S, Abdul Kareem, Sendraya Perumal A, Lakshmaiya N, Paramasivam P. Multi-Objective Optimal Scheduling of a Microgrid Using Oppositional Gradient-Based Grey Wolf Optimizer. Energies. 2022;15(23)en_US
dc.identifier.cristinIDFRIDAID 2088210
dc.identifier.doihttps://doi.org/10.3390/en15239024
dc.identifier.issn1996-1073
dc.identifier.urihttps://hdl.handle.net/10037/27679
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.relation.journalEnergies
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2022 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleMulti-Objective Optimal Scheduling of a Microgrid Using Oppositional Gradient-Based Grey Wolf Optimizeren_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's license is described as Attribution 4.0 International (CC BY 4.0)