Repository logo
 
Publication

Demand Response Driven by Distribution Network Voltage Limit Violation: A Genetic Algorithm Approach for Load Shifting

2022Journal article Open access
Simple item page
dc.contributor.authorCanizes, Bruno
dc.contributor.authorMota, Bruno
dc.contributor.authorRibeiro, Pedro
dc.contributor.authorVale, Zita
dc.date.accessioned2023-02-01T16:29:47Z
dc.date.available2023-02-01T16:29:47Z
dc.date.issued2022
dc.description.abstractThe residential sector electricity demand has been increasing over the years, leading to an increasing effort of the power network components, namely during the peak demand periods. This demand increasing together with the increasing levels of renewable-based energy generation and the need to ensure the electricity service quality, namely in terms of the voltage profile, is challenging the distribution network operation. Demand response can play an important role in facing these challenges, bringing several benefits, both for the network operation and for the consumer (e.g., increase network components lifetime and consumers bill reduction). The present research work proposes a genetic algorithm-based model to use the consumers’ load flexibility with demand response event participation. The proposed method optimally shifts residential loads to enable the consumers’ participation in demand response while respecting consumers’ preferences and constraints. A realistic low voltage distribution network with 236 buses is used to illustrate the application of the proposed model. The results show considerable energy cost savings for consumers and an improvement in voltage profile.pt_PT
dc.description.sponsorshipThis article is a result of the Project Real-time support infrastructure and energy management for intelligent carbon-neutral smart cities (RETINA) (NORTE-01-0145-FEDER-000062), supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1109/ACCESS.2022.3182580pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/22082
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherIEEEpt_PT
dc.relationNORTE-01-0145-FEDER-000062pt_PT
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9794688pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectDemand responsept_PT
dc.subjectDistribution networkpt_PT
dc.subjectLoad flexibilitypt_PT
dc.subjectLoad shiftingpt_PT
dc.subjectVoltage profile improvementpt_PT
dc.titleDemand Response Driven by Distribution Network Voltage Limit Violation: A Genetic Algorithm Approach for Load Shiftingpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage62193pt_PT
oaire.citation.startPage62183pt_PT
oaire.citation.titleIEEE Accesspt_PT
oaire.citation.volume10pt_PT
person.familyNameCanizes
person.familyNameMota
person.familyNameRibeiro
person.familyNameVale
person.givenNameBruno
person.givenNameBruno
person.givenNamePedro
person.givenNameZita
person.identifier632184
person.identifier.ciencia-idA411-F561-E922
person.identifier.ciencia-id6019-8D23-F05A
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0002-9808-5537
person.identifier.orcid0000-0002-9875-4868
person.identifier.orcid0000-0002-7669-2862
person.identifier.orcid0000-0002-4560-9544
person.identifier.ridI-3492-2017
person.identifier.ridA-5824-2012
person.identifier.scopus-author-id35408699300
person.identifier.scopus-author-id7004115775
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication7ea215ea-79d5-42be-a870-a0c8b23cf556
relation.isAuthorOfPublication11d36edf-a3a4-4e64-86e8-4e68ee0943b9
relation.isAuthorOfPublication56d317f2-6fb8-45cd-b6d4-1cf8f0f2e36d
relation.isAuthorOfPublicationff1df02d-0c0f-4db1-bf7d-78863a99420b
relation.isAuthorOfPublication.latestForDiscovery11d36edf-a3a4-4e64-86e8-4e68ee0943b9

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
ART21_GECAD_ZAV_2022.pdf
Size:
2.11 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