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Customized Normalization Method to Enhance the Clustering Process of Consumption Profiles

dc.contributor.authorRibeiro, Catarina
dc.contributor.authorPinto, Tiago
dc.contributor.authorVale, Zita
dc.date.accessioned2021-03-09T14:26:58Z
dc.date.available2021-03-09T14:26:58Z
dc.date.issued2016
dc.description.abstractThe restructuring of electricity markets brought many changes to markets operation. To overcome these new challenges, the study of electricity markets operation has been gaining an increasing importance.With the emergence of microgrids and smart grids, new business models able to cope with new opportunities are being developed. New types of players are also emerging, allowing aggregating a diversity of entities, e.g. generation, storage, electric vehicles, and consumers. The virtual power player (VPP) facilitates their participation in the electricity markets and provides a set of new services promoting generation and consumption efficiency, while improving players` benefits. The contribution of this paper is a customized normalization method that supports a clustering methodology for the remuneration and tariffs definition from VPPs. To implement fair and strategic remuneration and tariff methodologies, this model uses a clustering algorithm, applied on normalized load values, which creates sub-groups of data according to their correlations. The clustering process is evaluated so that the number of data sub-groups that brings the most added value for the decision making process is found, according to players characteristics. The proposed clustering methodology has been tested in a real distribution network with 30 bus, including residential and commercial consumers, photovoltaic generation and storage.pt_PT
dc.description.sponsorshipThe present work was done and funded in the scope of the following projects: People Programme of the European Union's Seventh Framework Programme FP7/2007-2013/ project ELECON, REA grant agreement No 318912; EUREKA - ITEA2 Project M2MGrids with project number 13011pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1007/978-3-319-40114-0_8pt_PT
dc.identifier.isbn978-3-319-40114-0
dc.identifier.urihttp://hdl.handle.net/10400.22/17332
dc.language.isoengpt_PT
dc.publisherSpringerpt_PT
dc.relationElectricity Consumption Analysis to Promote Energy Efficiency Considering Demand Response and Non-technical Losses
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007%2F978-3-319-40114-0_8pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectClusteringpt_PT
dc.subjectConsumption profilespt_PT
dc.subjectDynamic tariffspt_PT
dc.titleCustomized Normalization Method to Enhance the Clustering Process of Consumption Profilespt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardTitleElectricity Consumption Analysis to Promote Energy Efficiency Considering Demand Response and Non-technical Losses
oaire.awardURIinfo:eu-repo/grantAgreement/EC/FP7/318912/EU
oaire.citation.conferencePlaceSeville, Spainpt_PT
oaire.citation.endPage76pt_PT
oaire.citation.startPage67pt_PT
oaire.citation.title7th International Symposium on Ambient Intelligence (ISAmI 2016)pt_PT
oaire.citation.volume476pt_PT
oaire.fundingStreamFP7
person.familyNamePinto
person.familyNameVale
person.givenNameTiago
person.givenNameZita
person.identifierR-000-T7J
person.identifier632184
person.identifier.ciencia-id2414-9B03-C4BB
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0001-8248-080X
person.identifier.orcid0000-0002-4560-9544
person.identifier.ridT-2245-2018
person.identifier.ridA-5824-2012
person.identifier.scopus-author-id35219107600
person.identifier.scopus-author-id7004115775
project.funder.identifierhttp://doi.org/10.13039/501100008530
project.funder.nameEuropean Commission
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublication8d58ddc0-1023-47c0-a005-129d412ce98d
relation.isAuthorOfPublicationff1df02d-0c0f-4db1-bf7d-78863a99420b
relation.isAuthorOfPublication.latestForDiscovery8d58ddc0-1023-47c0-a005-129d412ce98d
relation.isProjectOfPublication8fea16df-b0e1-44c7-93fa-4383c327d04f
relation.isProjectOfPublication.latestForDiscovery8fea16df-b0e1-44c7-93fa-4383c327d04f

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