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

2023Conference paper Restricted access
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dc.contributor.authorPedroso, André Felipe Pereira
dc.contributor.authorAmoura, Yahia
dc.contributor.authorPereira, Ana I.
dc.contributor.authorFerreira, Ângela P.
dc.date.accessioned2024-01-15T11:23:53Z
dc.date.available2024-01-15T11:23:53Z
dc.date.issued2023
dc.description.abstractThis 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.pt_PT
dc.description.sponsorshipThe authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020), SusTEC (LA/P/0007/2021). This work has been supported by NORTE-01-0247- FEDER-072615 EPO - Enline Power Optimization - The supra-grid optimization software.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationPedroso, André Felipe Pereira; Amoura, Yahia; Pereira, Ana I.; Ferreira, Ângela P. (2023). A hybrid genetic algorithm for optimal active power curtailment considering renewable energy generation. In 23rd International Conference on Computational Science and Its Applications (ICCSA). Cham: Springer, p. 479-494. ISBN 978-303137107-3pt_PT
dc.identifier.doi10.1007/978-3-031-37108-0_31pt_PT
dc.identifier.isbn978-303137107-3
dc.identifier.urihttp://hdl.handle.net/10198/29179
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringerpt_PT
dc.relationLA/P/0007/2021pt_PT
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectEnergy curtailmentpt_PT
dc.subjectOptimal power flowpt_PT
dc.subjectGenetic algorithmpt_PT
dc.subjectInterior pointpt_PT
dc.subjectActive-setpt_PT
dc.titleA hybrid genetic algorithm for optimal active power curtailment considering renewable energy generationpt_PT
dc.typeconference paper
dspace.entity.typePublication
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F05757%2F2020/PT
oaire.citation.endPage494pt_PT
oaire.citation.startPage479pt_PT
oaire.citation.title23rd International Conference on Computational Science and Its Applications (ICCSA)pt_PT
oaire.citation.volume14105pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
person.familyNameAmoura
person.familyNamePereira
person.familyNameFerreira
person.givenNameYahia
person.givenNameAna I.
person.givenNameÂngela P.
person.identifier.ciencia-id1C1C-915D-DB4E
person.identifier.ciencia-id0716-B7C2-93E4
person.identifier.ciencia-id2211-6787-D936
person.identifier.orcid0000-0002-8811-0823
person.identifier.orcid0000-0003-3803-2043
person.identifier.orcid0000-0002-1912-2556
person.identifier.ridF-3168-2010
person.identifier.ridM-8188-2013
person.identifier.scopus-author-id15071961600
person.identifier.scopus-author-id55516840300
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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