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Data Mining for Remuneration of Consumers Demand Response Participation

2020Conference object Open access
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dc.contributor.authorRibeiro, Catarina
dc.contributor.authorPinto, Tiago
dc.contributor.authorVale, Zita
dc.contributor.authorBaptista, José
dc.date.accessioned2021-09-17T13:50:19Z
dc.date.available2021-09-17T13:50:19Z
dc.date.issued2020
dc.description.abstractWith the implementation of micro grids and smart grids, new business models able to cope with the new opportunities are being developed. Virtual Power Players are a player that allows aggregating a diversity of entities, to facilitate their participation in the electricity markets and to provide a set of new services promoting generation and consumption efficiency, while improving players’ benefits. The elastic behavior of the demand consumption jointly used with other available resources such as distributed generation (DG) can play a crucial role for the success of smart grids. This paper proposes methodologies to develop strategic remuneration of aggregated consumers with demand response participation, this model uses a clustering algorithm, applied on values that were obtained from a scheduling methodology of a real Portuguese distribution network with 937 buses, 20310 consumers and 548 distributed generators. The normalization methods and clustering methodologies were applied to several variables of different consumers, 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.pt_PT
dc.description.sponsorshipThis work has received funding from the EU Horizon 2020 research and innovation program under project DOMINOES (grant agreement No 771066) and from FEDER Funds through COMPETE program and from National Funds through FCT under projects CEECIND/01811/2017 and UIDB/00760/2020.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1007/978-3-030-51999-5_27pt_PT
dc.identifier.issn1865-0929
dc.identifier.urihttp://hdl.handle.net/10400.22/18414
dc.language.isoengpt_PT
dc.publisherSpringerpt_PT
dc.relationSmart Distribution Grid: a Market Driven Approach for the Next Generation of Advanced Operation Models and Services
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007%2F978-3-030-51999-5_27pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectClusteringpt_PT
dc.subjectDistributed generationpt_PT
dc.subjectSmart gridpt_PT
dc.subjectDemand responsept_PT
dc.titleData Mining for Remuneration of Consumers Demand Response Participationpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardTitleSmart Distribution Grid: a Market Driven Approach for the Next Generation of Advanced Operation Models and Services
oaire.awardURIinfo:eu-repo/grantAgreement/EC/H2020/771066/EU
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/157466/PT
oaire.citation.conferencePlaceL'Aquila, Italypt_PT
oaire.citation.endPage338pt_PT
oaire.citation.startPage326pt_PT
oaire.citation.titleInternational Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS2020)pt_PT
oaire.citation.volume1233pt_PT
oaire.fundingStreamH2020
oaire.fundingStream6817 - DCRRNI ID
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.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameEuropean Commission
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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