AC OPF for smart distribution networks: An efficient and robust quadratic approach
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/TSG.2017.2665559 http://hdl.handle.net/11449/179028 |
Resumo: | Smart grid schemes in which multiple network elements and participants are managed for the benefit of the distribution network (e.g., energy loss reduction, restoration, etc.) require sophisticated algorithms to control them in the most suitable manner while catering for network constraints. Such a complex decision making process can be solved by tailoring the ac optimal power flow (OPF) problem to the corresponding smart grid scheme. Non-linear programming ac OPF formulations, however, can suffer from scalability and robustness issues which in turn might limit their adoption. Here, a novel quadratic programming formulation is proposed and compared against the non-linear, quadratically constrained, and linearized approaches. Two cases are carried out to assess their performance: 1) management of distributed generation units to maximize renewable energy harvesting (continuous control variables) and 2) control of capacitors to minimize energy losses (discrete control variables). The results demonstrate that the proposed quadratic approach significantly outperforms the more conventional formulations in both computational efficiency and robustness. This makes it a suitable alternative to be at the heart of the decision making of complex, real-time schemes to be adopted by future smart distribution networks. |
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Repositório Institucional da UNESP |
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AC OPF for smart distribution networks: An efficient and robust quadratic approachDistribution networksNon-linear programmingOptimal power flowQuadratic programmingQuadratically constrained programmingSmart gridsSmart grid schemes in which multiple network elements and participants are managed for the benefit of the distribution network (e.g., energy loss reduction, restoration, etc.) require sophisticated algorithms to control them in the most suitable manner while catering for network constraints. Such a complex decision making process can be solved by tailoring the ac optimal power flow (OPF) problem to the corresponding smart grid scheme. Non-linear programming ac OPF formulations, however, can suffer from scalability and robustness issues which in turn might limit their adoption. Here, a novel quadratic programming formulation is proposed and compared against the non-linear, quadratically constrained, and linearized approaches. Two cases are carried out to assess their performance: 1) management of distributed generation units to maximize renewable energy harvesting (continuous control variables) and 2) control of capacitors to minimize energy losses (discrete control variables). The results demonstrate that the proposed quadratic approach significantly outperforms the more conventional formulations in both computational efficiency and robustness. This makes it a suitable alternative to be at the heart of the decision making of complex, real-time schemes to be adopted by future smart distribution networks.Universidade Estadual Paulista UNESPDepartment of Electrical and Electronic Engineering University of MelbourneSchool of Electrical and Electronic Engineering University of ManchesterFaculdade de Engenharia de Ilha Solteira Universidade Estadual Paulista UNESPUniversidade Estadual Paulista UNESPFaculdade de Engenharia de Ilha Solteira Universidade Estadual Paulista UNESPUniversidade Estadual Paulista (Unesp)University of MelbourneUniversity of ManchesterFranco, John F. [UNESP]Ochoa, Luis F.Romero, Ruben [UNESP]2018-12-11T17:33:13Z2018-12-11T17:33:13Z2018-09-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article4613-4623application/pdfhttp://dx.doi.org/10.1109/TSG.2017.2665559IEEE Transactions on Smart Grid, v. 9, n. 5, p. 4613-4623, 2018.1949-3053http://hdl.handle.net/11449/17902810.1109/TSG.2017.26655592-s2.0-850237539022-s2.0-85023753902.pdfScopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengIEEE Transactions on Smart Grid2,854info:eu-repo/semantics/openAccess2024-07-04T19:06:25Zoai:repositorio.unesp.br:11449/179028Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T18:48:33.226300Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
AC OPF for smart distribution networks: An efficient and robust quadratic approach |
title |
AC OPF for smart distribution networks: An efficient and robust quadratic approach |
spellingShingle |
AC OPF for smart distribution networks: An efficient and robust quadratic approach Franco, John F. [UNESP] Distribution networks Non-linear programming Optimal power flow Quadratic programming Quadratically constrained programming Smart grids |
title_short |
AC OPF for smart distribution networks: An efficient and robust quadratic approach |
title_full |
AC OPF for smart distribution networks: An efficient and robust quadratic approach |
title_fullStr |
AC OPF for smart distribution networks: An efficient and robust quadratic approach |
title_full_unstemmed |
AC OPF for smart distribution networks: An efficient and robust quadratic approach |
title_sort |
AC OPF for smart distribution networks: An efficient and robust quadratic approach |
author |
Franco, John F. [UNESP] |
author_facet |
Franco, John F. [UNESP] Ochoa, Luis F. Romero, Ruben [UNESP] |
author_role |
author |
author2 |
Ochoa, Luis F. Romero, Ruben [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) University of Melbourne University of Manchester |
dc.contributor.author.fl_str_mv |
Franco, John F. [UNESP] Ochoa, Luis F. Romero, Ruben [UNESP] |
dc.subject.por.fl_str_mv |
Distribution networks Non-linear programming Optimal power flow Quadratic programming Quadratically constrained programming Smart grids |
topic |
Distribution networks Non-linear programming Optimal power flow Quadratic programming Quadratically constrained programming Smart grids |
description |
Smart grid schemes in which multiple network elements and participants are managed for the benefit of the distribution network (e.g., energy loss reduction, restoration, etc.) require sophisticated algorithms to control them in the most suitable manner while catering for network constraints. Such a complex decision making process can be solved by tailoring the ac optimal power flow (OPF) problem to the corresponding smart grid scheme. Non-linear programming ac OPF formulations, however, can suffer from scalability and robustness issues which in turn might limit their adoption. Here, a novel quadratic programming formulation is proposed and compared against the non-linear, quadratically constrained, and linearized approaches. Two cases are carried out to assess their performance: 1) management of distributed generation units to maximize renewable energy harvesting (continuous control variables) and 2) control of capacitors to minimize energy losses (discrete control variables). The results demonstrate that the proposed quadratic approach significantly outperforms the more conventional formulations in both computational efficiency and robustness. This makes it a suitable alternative to be at the heart of the decision making of complex, real-time schemes to be adopted by future smart distribution networks. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-12-11T17:33:13Z 2018-12-11T17:33:13Z 2018-09-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/TSG.2017.2665559 IEEE Transactions on Smart Grid, v. 9, n. 5, p. 4613-4623, 2018. 1949-3053 http://hdl.handle.net/11449/179028 10.1109/TSG.2017.2665559 2-s2.0-85023753902 2-s2.0-85023753902.pdf |
url |
http://dx.doi.org/10.1109/TSG.2017.2665559 http://hdl.handle.net/11449/179028 |
identifier_str_mv |
IEEE Transactions on Smart Grid, v. 9, n. 5, p. 4613-4623, 2018. 1949-3053 10.1109/TSG.2017.2665559 2-s2.0-85023753902 2-s2.0-85023753902.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
IEEE Transactions on Smart Grid 2,854 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
4613-4623 application/pdf |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128982513614848 |