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Production Line Optimization to Minimize Energy Cost and Participate in Demand Response Events

2021Artigo científico Acesso aberto
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dc.contributor.authorMota, Bruno
dc.contributor.authorGomes, Luis
dc.contributor.authorFaria, Pedro
dc.contributor.authorRamos, Carlos
dc.contributor.authorVale, Zita
dc.contributor.authorCorreia, Regina
dc.date.accessioned2021-01-27T11:52:57Z
dc.date.available2021-01-27T11:52:57Z
dc.date.issued2021
dc.descriptionThis article belongs to the Special Issue The Artificial Intelligence Technologies for Electric Power Systemspt_PT
dc.description.abstractThe scheduling of tasks in a production line is a complex problem that needs to take into account several constraints, such as product deadlines and machine limitations. With innovative focus, the main constraint that will be addressed in this paper, and that usually is not considered, is the energy consumption cost in the production line. For that, an approach based on genetic algorithms is proposed and implemented. The use of local energy generation, especially from renewable sources, and the possibility of having multiple energy providers allow the user to manage its consumption according to energy prices and energy availability. The proposed solution takes into account the energy availability of renewable sources and energy prices to optimize the scheduling of a production line using a genetic algorithm with multiple constraints. The proposed algorithm also enables a production line to participate in demand response events by shifting its production, by using the flexibility of production lines. A case study using real production data that represents a textile industry is presented, where the tasks for six days are scheduled. During the week, a demand response event is launched, and the proposed algorithm shifts the consumption by changing task orders and machine usage.pt_PT
dc.description.sponsorshipThis work has received funding from Portugal 2020 under SPEAR project (NORTE-01-0247-FEDER-040224) and from FEDER Funds through COMPETE program and from National Funds through (FCT) under the project UIDB/00760/2020, and CEECIND/02887/2017.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3390/en14020462pt_PT
dc.identifier.issn1996-1073
dc.identifier.urihttp://hdl.handle.net/10400.22/16757
dc.language.isoengpt_PT
dc.relationCEECIND/02887/2017pt_PT
dc.relationResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development
dc.relation.publisherversionhttps://doi.org/10.3390/en14020462pt_PT
dc.subjectDemand-side managementpt_PT
dc.subjectDemand responsept_PT
dc.subjectFlexibilitypt_PT
dc.subjectGenetic algorithmpt_PT
dc.subjectProduction linept_PT
dc.subjectTasks schedulingpt_PT
dc.titleProduction Line Optimization to Minimize Energy Cost and Participate in Demand Response Eventspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00760%2F2020/PT
oaire.citation.issue2pt_PT
oaire.citation.startPage462pt_PT
oaire.citation.titleEnergiespt_PT
oaire.citation.volume14pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameFaria
person.familyNameVale
person.givenNamePedro
person.givenNameZita
person.identifier632184
person.identifier.ciencia-id6F19-CB63-C8A8
person.identifier.ciencia-idB212-2309-F9C3
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0002-8597-3383
person.identifier.orcid0000-0002-5982-8342
person.identifier.orcid0000-0002-4560-9544
person.identifier.ridA-5824-2012
person.identifier.scopus-author-id7004115775
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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