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dc.contributor.authorLópez Santiago, Dany Mauricio-
dc.contributor.authorFrancisco Caicedo, Eduardo-
dc.date.accessioned2020-02-09T18:45:59Z-
dc.date.available2020-02-09T18:45:59Z-
dc.date.issued2019-10-03-
dc.identifier.issn17518687-
dc.identifier.urihttps://repository.usc.edu.co/handle/20.500.12421/2640-
dc.description.abstractIn energy management systems in microgrids, the combined and on-line use of optimisation and prediction rolling horizons of renewable power and demand may compromise the optimal performance. Usually, the medium-term optimal behaviour improves with additional available convergence time, while the error in predictions worsens with a broader horizon, against the using short-term predictions, which usually contributes to more accurate management. To address this problem, this study proposes the strategical and operational energy management system (SO-EMS) approach, which is based on three complementary management processes: (i) strategic energy management, based on off-line multi-objective optimisation on a rolling horizon, (ii) asynchronous decision making, which offers the ability to incorporate multiple criteria of a microgrid operator in the selection of an optimised energy plan, and (iii) operational power management, as an on-line proportional redistribution and redispatch process based on rules. The SO-EMS evaluation performed on a connected microgrid shows its ability to provide optimised plans on a rolling horizon of 24-h predictions and to adjust them to 1-m predictions. The evaluations made of the scheme also show flexibility because it is possible to redistribute the power assignments of units that are out of service. These features empower the SO-EMS in the broad power management of various connected microgrids.es
dc.language.isoenes
dc.publisherInstitution of Engineering and Technologyes
dc.subjectDecision makinges
dc.subjectOptimisationes
dc.subjectEnergy management systemses
dc.subjectDistributed power generationes
dc.titleOptimal management of electric power in microgrids under a strategic multi-objective decision-making approach and operational proportional adjustmentes
dc.typeArticlees
Appears in Collections:Artículos Científicos

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