Logo do repositório
 
Publicação

Differential evolution strategies for large-scale energy resource management in smart grids

2017Documento de conferência Acesso aberto
Ver registo simples
dc.contributor.authorLezama, Fernando
dc.contributor.authorSucar, Luis Enrique
dc.contributor.authorde Cote, Enrique Munoz
dc.contributor.authorSoares, João
dc.contributor.authorVale, Zita
dc.date.accessioned2021-03-08T18:19:30Z
dc.date.available2021-07-19T00:30:18Z
dc.date.issued2017
dc.description.abstractSmart Grid (SG) technologies are leading the modifications of power grids worldwide. The Energy Resource Management (ERM) in SGs is a highly complex problem that needs to be efficiently addressed to maximize incomes while minimizing operational costs. Due to the nature of the problem, which includes mixed-integer variables and non-linear constraints, Evolutionary Algorithms (EA) are considered a good tool to find optimal and near-optimal solutions to large-scale problems. In this paper, we analyze the application of Differential Evolution (DE) to solve the large-scale ERM problem in SGs through extensive experimentation on a case study using a 33-Bus power network with high penetration of Distributed Energy Resources (DER) and Electric Vehicles (EVs), as well as advanced features such as energy stock exchanges and Demand Response (DR) programs. We analyze the impact of DE parameter seing on four state-of-the art DE strategies. Moreover, DE strategies are compared with other well-known EAs and a deterministic approach based on MINLP. Results suggest that, even when DE strategies are very sensitive to the seing of their parameters, they can find beer solutions than other EAs, and near-optimal solutions in acceptable times compared with a MINLP approach.pt_PT
dc.description.sponsorshipThe present work was done and funded in the scope of the projects: Sustainability Fund CONACYT-SENER by Consejo Nacional de Ciencia y Tecnología (CONACYT) and the National Center of Innovation in Energy (CEMIE-Eolico); H2020 DREAM-GO Project (Marie Sklodowska-Curie grant agreement No 641794) and UID/EEA/00760/2013 funded by FEDER Funds through COMPETE program and by National Funds through FCTpt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1145/3067695.3082478pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/17312
dc.language.isoengpt_PT
dc.publisherACMpt_PT
dc.relationResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development
dc.relationEnabling Demand Response for short and real-time Efficient And Market Based smart Grid Operation - An intelligent and real-time simulation approach
dc.relation.publisherversionhttps://dl.acm.org/doi/10.1145/3067695.3082478pt_PT
dc.subjectDifferencial Evolutionpt_PT
dc.subjectEnergy Resource Managementpt_PT
dc.subjectEvolutionary Algorithmspt_PT
dc.subjectOptimizationpt_PT
dc.subjectSmart gridpt_PT
dc.titleDifferential evolution strategies for large-scale energy resource management in smart gridspt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardTitleResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development
oaire.awardTitleEnabling Demand Response for short and real-time Efficient And Market Based smart Grid Operation - An intelligent and real-time simulation approach
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FEEA%2F00760%2F2013/PT
oaire.awardURIinfo:eu-repo/grantAgreement/EC/H2020/641794/EU
oaire.citation.conferencePlaceBerlin, Germanypt_PT
oaire.citation.endPage1286pt_PT
oaire.citation.startPage1279pt_PT
oaire.citation.titleGECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companionpt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStreamH2020
person.familyNameLezama
person.familyNameSoares
person.familyNameVale
person.givenNameFernando
person.givenNameJoão
person.givenNameZita
person.identifier1043580
person.identifier632184
person.identifier.ciencia-idE31F-56D6-1E0F
person.identifier.ciencia-id1612-8EA8-D0E8
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0001-8638-8373
person.identifier.orcid0000-0002-4172-4502
person.identifier.orcid0000-0002-4560-9544
person.identifier.ridA-6945-2017
person.identifier.ridA-5824-2012
person.identifier.scopus-author-id36810077500
person.identifier.scopus-author-id35436109600
person.identifier.scopus-author-id7004115775
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100008530
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameEuropean Commission
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublication6a55317b-92c2-404f-8542-c7a73061cc9b
relation.isAuthorOfPublication9ece308b-6d79-4cec-af91-f2278dcc47eb
relation.isAuthorOfPublicationff1df02d-0c0f-4db1-bf7d-78863a99420b
relation.isAuthorOfPublication.latestForDiscovery6a55317b-92c2-404f-8542-c7a73061cc9b
relation.isProjectOfPublicationa00065d4-390d-45a6-b6b2-cb3dfe4c1da9
relation.isProjectOfPublication4a092e97-cc2f-4f57-8d3c-cf1709963516
relation.isProjectOfPublication.latestForDiscoverya00065d4-390d-45a6-b6b2-cb3dfe4c1da9

Ficheiros

Principais
A mostrar 1 - 1 de 1
Miniatura
Nome:
COM_GECAD_SIG_2017.pdf
Tamanho:
409.36 KB
Formato:
Adobe Portable Document Format
Ver/Abrir

Coleções

ISEP – GECAD – Comunicações em eventos científicos