Publication
ANN-Based LMP forecasting in a distribution network with large penetration of DG
| dc.contributor.author | Soares, Tiago | |
| dc.contributor.author | Fernandes, Filipe | |
| dc.contributor.author | Morais, H. | |
| dc.contributor.author | Faria, Pedro | |
| dc.contributor.author | Vale, Zita | |
| dc.date.accessioned | 2013-05-02T15:10:58Z | |
| dc.date.available | 2013-05-02T15:10:58Z | |
| dc.date.issued | 2012 | |
| dc.date.updated | 2013-04-11T14:14:27Z | |
| dc.description.abstract | In recent years, power systems have experienced many changes in their paradigm. The introduction of new players in the management of distributed generation leads to the decentralization of control and decision-making, so that each player is able to play in the market environment. In the new context, it will be very relevant that aggregator players allow midsize, small and micro players to act in a competitive environment. In order to achieve their objectives, virtual power players and single players are required to optimize their energy resource management process. To achieve this, it is essential to have financial resources capable of providing access to appropriate decision support tools. As small players have difficulties in having access to such tools, it is necessary that these players can benefit from alternative methodologies to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), and intended to support smaller players. In this case the present methodology uses a training set that is created using energy resource scheduling solutions obtained using a mixed-integer linear programming (MIP) approach as the reference optimization methodology. The trained network is used to obtain locational marginal prices in a distribution network. The main goal of the paper is to verify the accuracy of the ANN based approach. Moreover, the use of a single ANN is compared with the use of two or more ANN to forecast the locational marginal price. | por |
| dc.identifier.doi | 10.1109/TDC.2012.6281677 | |
| dc.identifier.isbn | 978-1-4673-1934-8 | |
| dc.identifier.uri | http://hdl.handle.net/10400.22/1503 | |
| dc.language.iso | eng | por |
| dc.publisher | IEEE | por |
| dc.relation.publisherversion | http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6281677 | por |
| dc.subject | Artificial Neural Network (ANN) | por |
| dc.subject | Distributed generation | por |
| dc.subject | Locational Marginal Price (LMP) | por |
| dc.subject | Mixed Integer Linear Programming (MIP) | por |
| dc.subject | Virtual Power Player (VPP) | por |
| dc.title | ANN-Based LMP forecasting in a distribution network with large penetration of DG | 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 | Fernandes | |
| person.familyName | Faria | |
| person.familyName | Vale | |
| person.givenName | Filipe | |
| person.givenName | Pedro | |
| person.givenName | Zita | |
| person.identifier | 632184 | |
| person.identifier.ciencia-id | B212-2309-F9C3 | |
| person.identifier.ciencia-id | 721B-B0EB-7141 | |
| person.identifier.orcid | 0000-0002-4642-6950 | |
| person.identifier.orcid | 0000-0002-5982-8342 | |
| person.identifier.orcid | 0000-0002-4560-9544 | |
| person.identifier.rid | A-5824-2012 | |
| person.identifier.scopus-author-id | 7004115775 | |
| rcaap.rights | openAccess | por |
| rcaap.type | conferenceObject | por |
| relation.isAuthorOfPublication | 3a332ccf-4cef-4f64-8afa-cce8373191b2 | |
| relation.isAuthorOfPublication | 35e6a4ab-f644-4bc5-b6fc-9fd89c23d6c6 | |
| relation.isAuthorOfPublication | ff1df02d-0c0f-4db1-bf7d-78863a99420b | |
| relation.isAuthorOfPublication.latestForDiscovery | ff1df02d-0c0f-4db1-bf7d-78863a99420b |