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dc.contributor.authorHorrillo Quintero, Pablo 
dc.contributor.authorGarcía Triviño, Pablo 
dc.contributor.authorHosseini, Ehsan 
dc.contributor.authorGarcía Vázquez, Carlos Andrés 
dc.contributor.authorSánchez Sainz, Higinio 
dc.contributor.authorUgalde Loo, Carlos E.
dc.contributor.authorPéric, Vedran S.
dc.contributor.authorFernández Ramírez, Luis Miguel 
dc.contributor.otherIngeniería Eléctricaes_ES
dc.date.accessioned2024-12-03T07:24:52Z
dc.date.available2024-12-03T07:24:52Z
dc.date.issued2024
dc.identifier.isbn9798350340266
dc.identifier.issn2643-2978
dc.identifier.issn2641-0184
dc.identifier.urihttp://hdl.handle.net/10498/33984
dc.description.abstractMulti-energy microgrids (MEMGs) have emerged as an effective solution for reducing greenhouse gas emissions. These systems leverage the coordination of multiple energy vectors to enhance efficiency and achieve greater independence from the main grid. This paper introduces a dynamic fuzzy-logic energy management system (EMS) designed for a MEMG that encompasses gas and electricity energy vectors. The thermal network of the MEMG comprises a gas boiler, an electric boiler, and a heat load. In parallel, the electrical network consists of a photovoltaic (PV) system, a battery energy storage system, an electric load, and a connection with the grid. The EMS plays a crucial role in evaluating the PV power generation and electric demand, and it adjusts the water temperature in the electric boiler to minimize reliance on the local grid. To evaluate the effectiveness of the MEMG and EMS, a simulation spanning 4.5 hours was conducted under various operating conditions for sun irradiance, heat, water, and electric demand. The results demonstrate the capability of the fuzzylogic based EMS to reduce the dependence on the local grid, thereby showcasing the suitability of this approach in MEMGs.es_ES
dc.description.sponsorshipThis work was partially supported by Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación, and Unión Europea (Grant TED2021-129631B-C32 supported by MCIN/AEI/10.13039/501100011033 and NextGenerationEU/PRTR).es_ES
dc.formatapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisher2024 IEEE International Conference on Industrial Technology (ICIT)es_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceICIT 2024 - 2024 25th International Conference on Industrial Technologyes_ES
dc.subjectMulti-energy microgridses_ES
dc.subjectFuzzy-logices_ES
dc.subjectEnergy management systemes_ES
dc.subjectPV systemes_ES
dc.subjectBattery bankes_ES
dc.subjectThermal vectores_ES
dc.titleFuzzy Control for Multi-Energy Microgridses_ES
dc.typeconference outputes_ES
dc.rights.accessRightsopen accesses_ES
dc.identifier.doi10.1109/ICIT58233.2024.10540675
dc.relation.projectIDinfo:eu-repo/grantAgreement/MCIN/AEI/10.13039/501100011033 and NextGenerationEU/PRTR/ TED2021-129631B-C32es_ES
dc.type.hasVersionAMes_ES


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
This work is under a Creative Commons License Attribution-NonCommercial-NoDerivatives 4.0 Internacional