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A hybrid genetic algorithm for optimal active power curtailment considering renewable energy generation

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Resumo:This paper analyzes the application of a population-based algorithm and its improvement in solving an optimal power flow problem. Simulations were performed on a 14-bus IEEE network modified to include renewable energy sources-based power plants: a wind park and two photovoltaic solar parks. In this scenario, the high penetration of intermittent energy sources in the grid makes it necessary to curtail active power during peak generation to maintain the balance between load and generation. However, European energy market regulations limit the annual curtailment of RES generators and penalize discriminatory curtailment actions between generators. This work exploits the minimization of transmission active loss while respecting its security constraints. Additionally, constraints were introduced in the optimal power flow problem to mitigate active power curtailment of the renewable source generators and to secure a non-discriminatory characteristic in curtailment decisions. The non-convex nature of the problem, intensified by the introduction of non-linear constraints, suggests the exploitation of heuristic algorithms to locate the optimal global solution. The obtained results demonstrate that a hybrid GA algorithm can improve convergence speed, and it is useful in determining the problem solution in cases where deterministic algorithms are unable to converge.
Autores principais:Pedroso, André Felipe Pereira
Outros Autores:Amoura, Yahia; Pereira, Ana I.; Ferreira, Ângela P.
Assunto:Energy curtailment Optimal power flow Genetic algorithm Interior point Active-set
Ano:2023
País:Portugal
Tipo de documento:comunicação em conferência
Tipo de acesso:acesso restrito
Instituição associada:Instituto Politécnico de Bragança
Idioma:inglês
Origem:Biblioteca Digital do IPB
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author Pedroso, André Felipe Pereira
author2 Amoura, Yahia
Pereira, Ana I.
Ferreira, Ângela P.
author2_role author
author
author
author_facet Pedroso, André Felipe Pereira
Amoura, Yahia
Pereira, Ana I.
Ferreira, Ângela P.
author_role author
contributor_name_str_mv Biblioteca Digital do IPB
country_str PT
creators_json_txt [{\"Person.name\":\"Pedroso, André Felipe Pereira\"},{\"Person.name\":\"Amoura, Yahia\",\"Person.identifier.orcid\":\"0000-0002-8811-0823\"},{\"Person.name\":\"Pereira, Ana I.\",\"Person.identifier.orcid\":\"0000-0003-3803-2043\"},{\"Person.name\":\"Ferreira, Ângela P.\",\"Person.identifier.orcid\":\"0000-0002-1912-2556\"}]
datacite.contributors.contributor.contributorName.fl_str_mv Biblioteca Digital do IPB
datacite.creators.creator.creatorName.fl_str_mv Pedroso, André Felipe Pereira
Amoura, Yahia
Pereira, Ana I.
Ferreira, Ângela P.
datacite.date.Accepted.fl_str_mv 2023-01-01T00:00:00Z
datacite.date.available.fl_str_mv 2024-01-15T11:23:53Z
datacite.date.embargoed.fl_str_mv 2024-01-15T11:23:53Z
datacite.rights.fl_str_mv http://purl.org/coar/access_right/c_16ec
datacite.subjects.subject.fl_str_mv Energy curtailment
Optimal power flow
Genetic algorithm
Interior point
Active-set
datacite.titles.title.fl_str_mv A hybrid genetic algorithm for optimal active power curtailment considering renewable energy generation
dc.contributor.none.fl_str_mv Biblioteca Digital do IPB
dc.creator.none.fl_str_mv Pedroso, André Felipe Pereira
Amoura, Yahia
Pereira, Ana I.
Ferreira, Ângela P.
dc.date.Accepted.fl_str_mv 2023-01-01T00:00:00Z
dc.date.available.fl_str_mv 2024-01-15T11:23:53Z
dc.date.embargoed.fl_str_mv 2024-01-15T11:23:53Z
dc.format.none.fl_str_mv application/pdf
dc.identifier.none.fl_str_mv http://hdl.handle.net/10198/29179
dc.language.none.fl_str_mv eng
dc.publisher.none.fl_str_mv Springer
dc.rights.cclincense.fl_str_mv http://creativecommons.org/licenses/by/4.0/
dc.rights.none.fl_str_mv http://purl.org/coar/access_right/c_16ec
dc.subject.none.fl_str_mv Energy curtailment
Optimal power flow
Genetic algorithm
Interior point
Active-set
dc.title.fl_str_mv A hybrid genetic algorithm for optimal active power curtailment considering renewable energy generation
dc.type.none.fl_str_mv http://purl.org/coar/resource_type/c_5794
description This paper analyzes the application of a population-based algorithm and its improvement in solving an optimal power flow problem. Simulations were performed on a 14-bus IEEE network modified to include renewable energy sources-based power plants: a wind park and two photovoltaic solar parks. In this scenario, the high penetration of intermittent energy sources in the grid makes it necessary to curtail active power during peak generation to maintain the balance between load and generation. However, European energy market regulations limit the annual curtailment of RES generators and penalize discriminatory curtailment actions between generators. This work exploits the minimization of transmission active loss while respecting its security constraints. Additionally, constraints were introduced in the optimal power flow problem to mitigate active power curtailment of the renewable source generators and to secure a non-discriminatory characteristic in curtailment decisions. The non-convex nature of the problem, intensified by the introduction of non-linear constraints, suggests the exploitation of heuristic algorithms to locate the optimal global solution. The obtained results demonstrate that a hybrid GA algorithm can improve convergence speed, and it is useful in determining the problem solution in cases where deterministic algorithms are unable to converge.
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funding.funder.alternateName_str_mv FCT
FCT
funding.funder.identifier_str_mv http://doi.org/10.13039/501100001871
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funding.funder.name_str_mv Fundação para a Ciência e a Tecnologia
Fundação para a Ciência e a Tecnologia
funding.name_str_mv 6817 - DCRRNI ID
6817 - DCRRNI ID
id ipb_2287fac2f4fa6580d8d99b6b3be5b447
identifier.url.fl_str_mv http://hdl.handle.net/10198/29179
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oai_identifier_str oai:bibliotecadigital.ipb.pt:10198/29179
organization_str_mv urn:organizationAcronym:ipb
person_str_mv Pedroso, André Felipe Pereira
Amoura, Yahia
Amoura, Yahia
https://www.ciencia-id.pt/1C1C-915D-DB4E
1C1C-915D-DB4E
http://orcid.org/0000-0002-8811-0823
0000-0002-8811-0823
Pereira, Ana I.
Pereira, Ana I.
https://www.ciencia-id.pt/0716-B7C2-93E4
0716-B7C2-93E4
http://orcid.org/0000-0003-3803-2043
0000-0003-3803-2043
Ferreira, Ângela P.
Ferreira, Ângela P.
https://www.ciencia-id.pt/2211-6787-D936
2211-6787-D936
http://orcid.org/0000-0002-1912-2556
0000-0002-1912-2556
publishDate 2023
publisher.none.fl_str_mv Springer
reponame_str Biblioteca Digital do IPB
repository_id_str urn:repositoryAcronym:ipb
service_str_mv urn:repositoryAcronym:ipb
spelling engSpringerpt_PTThis paper analyzes the application of a population-based algorithm and its improvement in solving an optimal power flow problem. Simulations were performed on a 14-bus IEEE network modified to include renewable energy sources-based power plants: a wind park and two photovoltaic solar parks. In this scenario, the high penetration of intermittent energy sources in the grid makes it necessary to curtail active power during peak generation to maintain the balance between load and generation. However, European energy market regulations limit the annual curtailment of RES generators and penalize discriminatory curtailment actions between generators. This work exploits the minimization of transmission active loss while respecting its security constraints. Additionally, constraints were introduced in the optimal power flow problem to mitigate active power curtailment of the renewable source generators and to secure a non-discriminatory characteristic in curtailment decisions. The non-convex nature of the problem, intensified by the introduction of non-linear constraints, suggests the exploitation of heuristic algorithms to locate the optimal global solution. The obtained results demonstrate that a hybrid GA algorithm can improve convergence speed, and it is useful in determining the problem solution in cases where deterministic algorithms are unable to converge.application/pdfpt_PTA hybrid genetic algorithm for optimal active power curtailment considering renewable energy generationPedroso, André Felipe PereiraPersonalAmoura, YahiaDSpacehttp://dspace.org/items/653c4356-dd18-4680-9774-da86a446d0e5DSpacehttp://dspace.org/items/653c4356-dd18-4680-9774-da86a446d0e5AmouraYahiaCiência IDhttps://www.ciencia-id.pt1C1C-915D-DB4EORCIDhttp://orcid.org0000-0002-8811-0823PersonalPereira, Ana I.DSpacehttp://dspace.org/items/e9981d62-2a2b-4fef-b75e-c2a14b0e7846DSpacehttp://dspace.org/items/e9981d62-2a2b-4fef-b75e-c2a14b0e7846PereiraAna I.Ciência IDhttps://www.ciencia-id.pt0716-B7C2-93E4ORCIDhttp://orcid.org0000-0003-3803-2043Researcher IDhttps://www.researcherid.comF-3168-2010Scopus Author IDhttps://www.scopus.com15071961600PersonalFerreira, Ângela P.DSpacehttp://dspace.org/items/3fec941d-79fb-4901-918d-a34ffa0195ccDSpacehttp://dspace.org/items/3fec941d-79fb-4901-918d-a34ffa0195ccFerreiraÂngela P.Ciência IDhttps://www.ciencia-id.pt2211-6787-D936ORCIDhttp://orcid.org0000-0002-1912-2556Researcher IDhttps://www.researcherid.comM-8188-2013Scopus Author IDhttps://www.scopus.com55516840300HostingInstitutionOrganizationalBiblioteca Digital do IPBe-mailmailto:dspace@ipb.ptdspace@ipb.ptISBNIsPartOf978-303137107-3DOIIsPartOf10.1007/978-3-031-37108-0_312024-01-15T11:23:53Z20232023-01-01T00:00:00ZHandlehttp://hdl.handle.net/10198/29179http://purl.org/coar/access_right/c_16ecrestricted accessEnergy curtailmentOptimal power flowGenetic algorithmInterior pointActive-set635302 bytesFundação para a Ciência e a TecnologiaResearch Centre in Digitalization and Intelligent Robotics6817 - DCRRNI IDCrossref Funder IDhttp://doi.org/10.13039/501100001871Fundação para a Ciência e a TecnologiaResearch Centre in Digitalization and Intelligent Robotics6817 - DCRRNI IDCrossref Funder IDhttp://doi.org/10.13039/501100001871other research producthttp://purl.org/coar/resource_type/c_5794conference paper2023http://creativecommons.org/licenses/by/4.0/http://purl.org/coar/access_right/c_16ecapplication/pdffulltexthttps://bibliotecadigital.ipb.pt/bitstreams/caf5da1e-4abc-459d-a5aa-6857db5aaead/download23rd International Conference on Computational Science and Its Applications (ICCSA)14105479494
spellingShingle A hybrid genetic algorithm for optimal active power curtailment considering renewable energy generation
Pedroso, André Felipe Pereira
Energy curtailment
Optimal power flow
Genetic algorithm
Interior point
Active-set
status SINGLETON
subject.fl_str_mv Energy curtailment
Optimal power flow
Genetic algorithm
Interior point
Active-set
title A hybrid genetic algorithm for optimal active power curtailment considering renewable energy generation
title_full A hybrid genetic algorithm for optimal active power curtailment considering renewable energy generation
title_fullStr A hybrid genetic algorithm for optimal active power curtailment considering renewable energy generation
title_full_unstemmed A hybrid genetic algorithm for optimal active power curtailment considering renewable energy generation
title_short A hybrid genetic algorithm for optimal active power curtailment considering renewable energy generation
title_sort A hybrid genetic algorithm for optimal active power curtailment considering renewable energy generation
topic Energy curtailment
Optimal power flow
Genetic algorithm
Interior point
Active-set
topic_facet Energy curtailment
Optimal power flow
Genetic algorithm
Interior point
Active-set
url http://hdl.handle.net/10198/29179
visible 1