Repository logo
 
Publication

Application-specific modified particle swarm optimization for energy resource scheduling considering vehicle-to-grid

2013Journal article Open access
Simple item page
dc.contributor.authorSoares, João
dc.contributor.authorSousa, Tiago
dc.contributor.authorMorais, Hugo
dc.contributor.authorVale, Zita
dc.contributor.authorCanizes, Bruno
dc.contributor.authorSilva, António S.
dc.date.accessioned2014-01-21T11:03:39Z
dc.date.available2014-01-21T11:03:39Z
dc.date.issued2013
dc.description.abstractThis paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding he management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.por
dc.identifier10.1016/j.asoc.2013.07.003
dc.identifier.doi10.1016/j.asoc.2013.07.003pt_PT
dc.identifier.issn1568-4946
dc.identifier.urihttp://hdl.handle.net/10400.22/3389
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherElsevierpor
dc.relation.ispartofseriesApplied Soft Computing; Vol. 13, Issue 11
dc.relation.publisherversionhttp://www.sciencedirect.com/science/article/pii/S1568494613002299por
dc.subjectApplication specific algorithmpor
dc.subjectHard combinatorial schedulingpor
dc.subjectParticle swarm optimizationpor
dc.subjectVehicle-to-grid schedulingpor
dc.titleApplication-specific modified particle swarm optimization for energy resource scheduling considering vehicle-to-gridpor
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage4280por
oaire.citation.issueIssue 11por
oaire.citation.startPage4264por
oaire.citation.titleApplied Soft Computingpor
oaire.citation.volumeVol. 13por
person.familyNameSoares
person.familyNameSousa
person.familyNameMorais
person.familyNameVale
person.familyNameCanizes
person.givenNameJoão
person.givenNameTiago
person.givenNameHugo
person.givenNameZita
person.givenNameBruno
person.identifier1043580
person.identifier126036
person.identifier80878
person.identifier632184
person.identifier.ciencia-id1612-8EA8-D0E8
person.identifier.ciencia-id2010-D878-271B
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.ciencia-idA411-F561-E922
person.identifier.orcid0000-0002-4172-4502
person.identifier.orcid0000-0002-3704-562X
person.identifier.orcid0000-0001-5906-4744
person.identifier.orcid0000-0002-4560-9544
person.identifier.orcid0000-0002-9808-5537
person.identifier.ridB-9229-2019
person.identifier.ridA-5824-2012
person.identifier.ridI-3492-2017
person.identifier.scopus-author-id35436109600
person.identifier.scopus-author-id36549396600
person.identifier.scopus-author-id21834170800
person.identifier.scopus-author-id7004115775
person.identifier.scopus-author-id35408699300
rcaap.rightsopenAccesspor
rcaap.typearticlepor
relation.isAuthorOfPublication9ece308b-6d79-4cec-af91-f2278dcc47eb
relation.isAuthorOfPublication140e3ebc-ad42-402f-84b8-a54c1fb2cfc0
relation.isAuthorOfPublicationb159f8c9-5ee1-444e-b890-81242ee0738e
relation.isAuthorOfPublicationff1df02d-0c0f-4db1-bf7d-78863a99420b
relation.isAuthorOfPublication7ea215ea-79d5-42be-a870-a0c8b23cf556
relation.isAuthorOfPublication.latestForDiscovery7ea215ea-79d5-42be-a870-a0c8b23cf556

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
COM_JoaoSoares_2013_GECAD.pdf
Size:
2.5 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

ISEP – GECAD – Artigos