Publication
Data mining techniques contributions to support electrical vehicle demand response
| dc.contributor.author | Soares, João | |
| dc.contributor.author | Ramos, Sérgio | |
| dc.contributor.author | Vale, Zita | |
| dc.contributor.author | Morais, H. | |
| dc.contributor.author | Faria, Pedro | |
| dc.date.accessioned | 2013-05-02T15:01:32Z | |
| dc.date.available | 2013-05-02T15:01:32Z | |
| dc.date.issued | 2012 | |
| dc.date.updated | 2013-04-11T14:12:07Z | |
| dc.description.abstract | The introduction of Electric Vehicles (EVs) together with the implementation of smart grids will raise new challenges to power system operators. This paper proposes a demand response program for electric vehicle users which provides the network operator with another useful resource that consists in reducing vehicles charging necessities. This demand response program enables vehicle users to get some profit by agreeing to reduce their travel necessities and minimum battery level requirements on a given period. To support network operator actions, the amount of demand response usage can be estimated using data mining techniques applied to a database containing a large set of operation scenarios. The paper includes a case study based on simulated operation scenarios that consider different operation conditions, e.g. available renewable generation, and considering a diversity of distributed resources and electric vehicles with vehicle-to-grid capacity and demand response capacity in a 33 bus distribution network. | por |
| dc.identifier.doi | 10.1109/TDC.2012.6281444 | |
| dc.identifier.isbn | 978-1-4673-1934-8 | |
| dc.identifier.uri | http://hdl.handle.net/10400.22/1502 | |
| dc.language.iso | eng | por |
| dc.publisher | IEEE | por |
| dc.relation.publisherversion | http://ieeexplore.ieee.org/xpl/abstractKeywords.jsp?arnumber=6281444 | por |
| dc.subject | Classification | por |
| dc.subject | Clustering | por |
| dc.subject | Data mining | por |
| dc.subject | Demand response | por |
| dc.subject | Electric vehicle | por |
| dc.subject | Mixed Integer Non-Linear Programming (MINLP) | por |
| dc.title | Data mining techniques contributions to support electrical vehicle demand response | por |
| dc.type | conference object | |
| dspace.entity.type | Publication | |
| oaire.citation.conferencePlace | Orlando, Florida, USA, 2012 | por |
| oaire.citation.title | IEEE Power and Energy Society Transmission and Distribution conference (T&D2012) | por |
| person.familyName | Soares | |
| person.familyName | Carvalho Ramos | |
| person.familyName | Vale | |
| person.familyName | Faria | |
| person.givenName | João | |
| person.givenName | Sérgio Filipe | |
| person.givenName | Zita | |
| person.givenName | Pedro | |
| person.identifier | 1043580 | |
| person.identifier | 632184 | |
| person.identifier.ciencia-id | 1612-8EA8-D0E8 | |
| person.identifier.ciencia-id | 6D1F-C495-6660 | |
| person.identifier.ciencia-id | 721B-B0EB-7141 | |
| person.identifier.ciencia-id | B212-2309-F9C3 | |
| person.identifier.orcid | 0000-0002-4172-4502 | |
| person.identifier.orcid | 0000-0002-1120-5656 | |
| person.identifier.orcid | 0000-0002-4560-9544 | |
| person.identifier.orcid | 0000-0002-5982-8342 | |
| person.identifier.rid | A-5824-2012 | |
| person.identifier.scopus-author-id | 35436109600 | |
| person.identifier.scopus-author-id | 7004115775 | |
| rcaap.rights | openAccess | por |
| rcaap.type | conferenceObject | por |
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