MICROGRID ENERGY MANAGEMENT WITH DAY AHEAD AND NOVEL EGWO SOLUTION

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dc.contributor.authorTalab, O.
dc.contributor.authorAvci, I.
dc.contributor.authorAl, Sultan, M.
dc.date.accessioned2024-09-29T16:22:39Z
dc.date.available2024-09-29T16:22:39Z
dc.date.issued2023
dc.departmentKarabük Üniversitesien_US
dc.description.abstractMicrogrids have emerged as a viable option for enhancing energy dissemination infrastructures' effectiveness, dependability, and eco-friendliness, garnering considerable interest. The optimal utilization of resources and cost reduction are critical factors in microgrid energy management. The present research introduces the Enhanced Gray Wolf Optimization (EGWO) algorithm, which aims to optimize energy management in microgrids subject to uncertain load demands and renewable energy sources. The EGWO algorithm endeavors to reduce total energy expenditure while guaranteeing a dependable and enduring power provision. The utilization of the day ahead strategy is implemented using deep learning techniques to forecast the load demand and generation within the microgrid. Subsequently, the EGWO algorithm is employed to optimize the scheduling of the various components of the microgrid, encompassing renewable energy sources and storage devices, to attain the minimum energy cost amidst conditions of uncertainty. The efficacy and efficiency of the EGWO algorithm in enhancing the energy management of microgrids is demonstrated through a comparative analysis with other optimization algorithms. This research's findings indicate that the enhanced grey wolf optimization algorithm approach yields a significant reduction in operational expenses of up to 7.5%. The implementation of energy management is facilitated by utilizing the EGWO algorithm, which yields a final cost of 841.755 €ct/kWh. Obtaining 11% of the electricity from the primary power network was imperative to satisfy the required power load. The cost associated with the referenced power amounted to a nominal 5%, indicating a comparatively modest expenditure. © 2023 Taylor's University. All rights reserved.en_US
dc.identifier.endpage2943en_US
dc.identifier.issn1823-4690
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85184030119en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage2928en_US
dc.identifier.urihttps://hdl.handle.net/20.500.14619/10191
dc.identifier.volume18en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTaylor's Universityen_US
dc.relation.ispartofJournal of Engineering Science and Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDay-ahead strategyen_US
dc.subjectEGWOen_US
dc.subjectEnergy managementen_US
dc.subjectMicrogriden_US
dc.titleMICROGRID ENERGY MANAGEMENT WITH DAY AHEAD AND NOVEL EGWO SOLUTIONen_US
dc.typeArticleen_US

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