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Smart microgrid management: a hybrid optimisation approach

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dc.contributor.authorAmoura, Yahia
dc.contributor.authorPereira, Ana I.
dc.contributor.authorLima, José
dc.contributor.authorFerreira, Ângela P.
dc.contributor.authorBoukli-Hacene, Fouad
dc.contributor.authorAbdelfettah, Kerboua
dc.date.accessioned2023-03-15T15:47:00Z
dc.date.available2023-03-15T15:47:00Z
dc.date.issued2020
dc.description.abstractThe association of distributed generators, energy storage systems and controllable loads close to the energy consumers gave place to a small-scale electrical network called microgrid. The stochastic behavior of renewable energy sources, as well as the demand variation, can lead in some cases to problems related to the reliability of the microgrid system. On the other hand, the market price of electricity from mainly non-renewable sources becomes a concern for a simple consumer due to its high costs. An innovative optimization method, combining linear programming, based on the simplex method, with the particle swarm optimisation algorithm is used to develop an energy management system. The management is performed considering a smart city’s consumption profile, two management scenarios have been proposed to characterize the relation price versus gas emissions for optimal energy management. The simulation results have demonstrated the reliability of the optimisation approach on the energy management system in the optimal scheduling of the microgrid generators power flows, having achieved a better energy price compared to a previous study with the same data. The computational results identified the optimal set-points of generators in a smart city supplied by a microgrid while ensuring consumer comfort, minimising greenhouse gas emissions and guarantee an appropriate operating price for all consumers in the smart city. The energy management system based on the proposed optimisation approach gave an inverse correlation between economic and environmental aspects, in fact, a multi-objective optimisation approach is performed as a continuation of the work proposed in this paper.pt_PT
dc.description.sponsorshipThis work has been supported by Fundação La Caixa and FCT — Fundação para a Ciência e Tecnologia within the Project Scope: UIDB/05757/2020
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationAmoura, Yahia; Pereira, Ana I.; Lima, José; Ferreira, Ângela P.; Boukli hacene, Fouad; Abdelfettah, Kerboua (2020). Smart microgrid management: a hybrid optimisation approach. In Research Square.pt_PT
dc.identifier.doi10.21203/rs.3.rs-118351/v1
dc.identifier.urihttp://hdl.handle.net/10198/27751
dc.language.isoengpt_PT
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectMicrogridpt_PT
dc.subjectSmart sustainable citiespt_PT
dc.subjectEnergy management systempt_PT
dc.subjectParticle swarm optimisationpt_PT
dc.subjectLinear programmingpt_PT
dc.titleSmart microgrid management: a hybrid optimisation approachpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameAmoura
person.familyNamePereira
person.familyNameLima
person.familyNameFerreira
person.givenNameYahia
person.givenNameAna I.
person.givenNameJosé
person.givenNameÂngela P.
person.identifierR-000-8GD
person.identifier.ciencia-id1C1C-915D-DB4E
person.identifier.ciencia-id0716-B7C2-93E4
person.identifier.ciencia-id6016-C902-86A9
person.identifier.ciencia-id2211-6787-D936
person.identifier.orcid0000-0002-8811-0823
person.identifier.orcid0000-0003-3803-2043
person.identifier.orcid0000-0001-7902-1207
person.identifier.orcid0000-0002-1912-2556
person.identifier.ridF-3168-2010
person.identifier.ridL-3370-2014
person.identifier.ridM-8188-2013
person.identifier.scopus-author-id15071961600
person.identifier.scopus-author-id55851941311
person.identifier.scopus-author-id55516840300
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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