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Optimal Model Predictive Control for Virtual Inertia Control of Autonomous Microgrids

dc.contributor.authorSaleh, Amr
dc.contributor.authorHasanien, Hany M.
dc.contributor.authorTurky, Rania A.
dc.contributor.authorTurdybek, Balgynbek
dc.contributor.authorAlharbi, Mohammed
dc.contributor.authorJurado-Melguizo, Francisco
dc.contributor.authorOmran, Walid A.
dc.date.accessioned2024-06-25T10:56:09Z
dc.date.available2024-06-25T10:56:09Z
dc.date.issued2023-03
dc.description.abstractFor the time being, renewable energy source (RES) penetration has significantly increased in power networks, particularly in microgrids. The overall system inertia is dramatically decreased by replacing traditional synchronous machines with RES. This negatively affects the microgrid dynamics under uncertainties, lowering the microgrid frequency stability, specifically in the islanded mode of operation. Therefore, this work aims to enhance the islanded microgrid frequency resilience using the virtual inertia frequency control concept. Additionally, optimal model predictive control (MPC) is employed in the virtual inertial control model. The optimum design of the MPC is attained using an optimization algorithm, the African Vultures Optimization Algorithm (AVOA). To certify the efficacy of the proposed controller, the AVOA-based MPC is compared with a conventional proportional–integral (PI) controller that is optimally designed using various optimization techniques. The actual data of RES is utilized, and a random load power pattern is applied to achieve practical simulation outcomes. Additionally, the microgrid paradigm contains battery energy storage (BES) units for enhancing the islanded microgrid transient stability. The simulation findings show the effectiveness of AVOA-based MPC in improving the microgrid frequency resilience. Furthermore, the results secure the role of BES in improving transient responses in the time domain simulations. The simulation outcomes are obtained using MATLAB software.es_ES
dc.identifier.citationSaleh, A.; Hasanien, H.M.; A. Turky, R.; Turdybek, B.; Alharbi, M.; Jurado, F.; Omran, W.A. Optimal Model Predictive Control for Virtual Inertia Control of Autonomous Microgrids. Sustainability 2023, 15, 5009. https://doi.org/10.3390/su15065009es_ES
dc.identifier.issn2071-1050es_ES
dc.identifier.other10.3390/su15065009es_ES
dc.identifier.urihttps://www.mdpi.com/2071-1050/15/6/5009es_ES
dc.identifier.urihttps://hdl.handle.net/10953/2936
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relation.ispartofSustainability [2023]; [15]: [5009]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.subjectModel predictive controles_ES
dc.subjectVirtual inertiaes_ES
dc.subjectAfrican vultures optimizeres_ES
dc.subjectMicrogrides_ES
dc.subjectRenewable energyes_ES
dc.titleOptimal Model Predictive Control for Virtual Inertia Control of Autonomous Microgridses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.type.versioninfo:eu-repo/semantics/acceptedVersiones_ES

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