Leveraging a novel grey wolf algorithm for optimization of photovoltaic-battery energy storage system under partial shading conditions

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dc.authoridDAGAL, IDRISS/0000-0002-2073-8956
dc.authoridHARRISON, AMBE/0000-0002-4353-1261
dc.contributor.authorDagal, Idriss
dc.contributor.authorIbrahim, AL-Wesabi
dc.contributor.authorHarrison, Ambe
dc.date.accessioned2025-03-09T10:49:04Z
dc.date.available2025-03-09T10:49:04Z
dc.date.issued2025
dc.departmentİstanbul Beykent Üniversitesi
dc.description.abstractPhotovoltaic (PV) systems, in conjunction with battery energy storage systems (BESS), have emerged as promising solutions for sustainable energy generation and consumption. However, the performance of these systems can be significantly impacted by partial shading conditions, which can lead to power losses and reduced efficiency. This research proposes a novel grey wolf optimization algorithm (GWO) to optimize the operation of PV-BESS systems under partial shading conditions. The GWO, inspired by the hunting behavior of grey wolves, is a robust optimization technique capable of handling complex and nonlinear problems. The proposed approach aims to maximize energy output, minimize power losses, and ensure optimal battery management. By effectively addressing the challenges posed by partial shading, this research contributes to the advancement of PV-BESS systems as reliable and efficient renewable energy solutions. The proposed system, consisting of a PV array, boost converter, MPPT controller, and battery, was evaluated using MATLAB/Simulink under various conditions. The results demonstrate that the NGWO algorithm achieves 99.89 % tracking efficiency under standard conditions and over 99.26 % under PSC, outperforming particle swarm Optimization (PSO), genetic algorithm (GA), the conventional GWO, and Perturb & Observe (P&O) methods. Notably, NGWO exhibits faster response times (0.01 s) and reduced power ripples compared to other algorithms, enhancing both energy extraction and battery efficiency. By optimizing state of charge (SOC) control, the NGWO extends battery lifespan, offering a superior solution for PV systems in challenging environments.
dc.identifier.doi10.1016/j.compeleceng.2024.109991
dc.identifier.issn0045-7906
dc.identifier.issn1879-0755
dc.identifier.scopus2-s2.0-85213962575
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.compeleceng.2024.109991
dc.identifier.urihttps://hdl.handle.net/20.500.12662/4714
dc.identifier.volume122
dc.identifier.wosWOS:001401534300001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofComputers & Electrical Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250310
dc.subjectGrey wolf optimization algorithm
dc.subjectPartial shading conditions
dc.subjectEnergy storage
dc.subjectBattery
dc.titleLeveraging a novel grey wolf algorithm for optimization of photovoltaic-battery energy storage system under partial shading conditions
dc.typeArticle

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