Methodology for energy management strategies design based on predictive control techniques for smart grids

dc.contributor.authorPajares Ferrando, Alberto
dc.contributor.authorVivas Fernández, Francisco José
dc.contributor.authorBlasco Ferragud, Francesc Xavier
dc.contributor.authorHerrero Durá, Juan Manuel
dc.contributor.authorSegura Manzano, Francisca
dc.contributor.authorAndújar Márquez, José Manuel
dc.date.accessioned2023-12-04T08:18:50Z
dc.date.available2023-12-04T08:18:50Z
dc.date.issued2023
dc.description.abstractThis article focuses on the development of a general energy management system (EMS) design methodology using on model-based predictive control (MPC) for the control and management of microgrids. Different MPCbased EMS for microgrids have been defined in the literature; however, there is a lack of generality in the proposed that would facilitate adapting to new architectures, energy storage system technology, nature of the bus, application, or purpose. To fill this gap, a novel general formulation that is parameterizable, simple, easily interpretable, and reproducible in different microgrid architectures is presented. This is the result of the development of a novel methodology, which is also presented. It considers the state space formulation of the controller from the initial modelling phase, from the dynamics of the energy storage systems represented by their models to the subsequent definition of the optimisation problem. This is developed through the design of the general cost function and the formulation of constrains, by means of general guidelines and reference values. To evaluate the performance of the developed methodology, simulation tests were carried out for four different microgrid architectures, with different applications and objectives, also considering different generation conditions, demand profiles, and initial conditions. The results showed that, with some simple guidelines and regardless of the case study, the developed MPC controller design methodology can address the technicaleconomic optimisation problem associated with energy management in microgrids in an easy and intuitive way.es_ES
dc.description.departmentIngeniería Electrónica, de Sistemas Informáticos y Automática
dc.description.sponsorshipThis work was supported in part by grant PID2020-116616RB-C31 and grant PID2021-124908NB-I00 founded by MCIN/AEI/10.13039/ 501100011033 and by “ERDF A way of making Europe”; by the Generalitat Valenciana regional government through project CIAICO/2021/ 064, by Andalusian Regional Program of R + D + i (P20- 00730), and by the project “The green hydrogen vector. Residential and mobility application”, approved in the call for research projects of the Cepsa Foundation Chair of the University of Huelva.es_ES
dc.identifier.citationPajares, A., Vivas, F. J., Blasco, X., Herrero, J. M., Segura, F., & Andújar, J. M. (2023). Methodology for energy management strategies design based on predictive control techniques for smart grids. In Applied Energy (Vol. 351, p. 121809). Elsevier BV. https://doi.org/10.1016/j.apenergy.2023.121809es_ES
dc.identifier.doi10.1016/j.apenergy.2023.121809
dc.identifier.issn0306-2619
dc.identifier.issn1872-9118 (electrónico)
dc.identifier.urihttps://hdl.handle.net/10272/22714
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.otherModel predictive controlleres_ES
dc.subject.otherEnergy management systemes_ES
dc.subject.otherRenewable microgridses_ES
dc.subject.otherHydrogen-hybridized backup systemses_ES
dc.subject.unesco33 Ciencias Tecnológicases_ES
dc.titleMethodology for energy management strategies design based on predictive control techniques for smart gridses_ES
dc.typejournal articlees_ES
dc.type.hasVersionVoR
dspace.entity.typePublication
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relation.isAuthorOfPublication748eef77-1deb-4ca8-92e7-f9d325095c68
relation.isAuthorOfPublicationae5faff8-3c02-43cd-a650-2e754e1995fa
relation.isAuthorOfPublication.latestForDiscovery4b525d25-b6db-4d51-8433-ed44e3071d93

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