A Novel Solution Technique for the Expansion Planning of Modern Distribution Systems

Bibliographic Details
Main Author: Marcelo, Jonathan A. [UNESP]
Publication Date: 2023
Other Authors: Muñoz-Delgado, Gregorio, Contreras, Javier, Mantovani, José R. S. [UNESP]
Format: Conference object
Language: eng
Source: Repositório Institucional da UNESP
Download full: http://dx.doi.org/10.1109/EEEIC/ICPSEurope57605.2023.10194884
https://hdl.handle.net/11449/309466
Summary: This paper addresses the expansion planning problem of modern distribution systems using a novel solution methodology based on matheuristics. The problem is formulated as a mixed-integer quadratic programming model. The proposed technique constitutes a novel local search algorithm that, using linearized and relaxed versions of the original problem, finds successive solutions, improving at each iteration the feasibility of the original problem until attaining a high-quality near-optimal solution. Planning actions include line reconductoring and installation of distributed generation (DG) units, electrical energy storage (EES) systems, and fixed and switchable capacitor banks (CBs). Operation system variability in demand and energy resources is modeled through representative days in order to preserve EES temporal transition. Furthermore, the model guarantees CO2 reduction in order to be on track to limit global warming. The effective performance of the proposed approach in terms of solution quality and computational time has been illustrated with a case study based on a 33-node distribution system.
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spelling A Novel Solution Technique for the Expansion Planning of Modern Distribution SystemsCO2emissions reductionLocal search algorithmmodern distribution systemsquadratic programmingThis paper addresses the expansion planning problem of modern distribution systems using a novel solution methodology based on matheuristics. The problem is formulated as a mixed-integer quadratic programming model. The proposed technique constitutes a novel local search algorithm that, using linearized and relaxed versions of the original problem, finds successive solutions, improving at each iteration the feasibility of the original problem until attaining a high-quality near-optimal solution. Planning actions include line reconductoring and installation of distributed generation (DG) units, electrical energy storage (EES) systems, and fixed and switchable capacitor banks (CBs). Operation system variability in demand and energy resources is modeled through representative days in order to preserve EES temporal transition. Furthermore, the model guarantees CO2 reduction in order to be on track to limit global warming. The effective performance of the proposed approach in terms of solution quality and computational time has been illustrated with a case study based on a 33-node distribution system.São Paulo State University Dept. of Electrical EngineeringUniversidad de Castilla-La Mancha E.T.S. de Ingeniería IndustrialSão Paulo State University Dept. of Electrical EngineeringUniversidade Estadual Paulista (UNESP)E.T.S. de Ingeniería IndustrialMarcelo, Jonathan A. [UNESP]Muñoz-Delgado, GregorioContreras, JavierMantovani, José R. S. [UNESP]2025-04-29T20:15:37Z2023-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/EEEIC/ICPSEurope57605.2023.10194884Proceedings - 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023.https://hdl.handle.net/11449/30946610.1109/EEEIC/ICPSEurope57605.2023.101948842-s2.0-85168688199Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings - 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023info:eu-repo/semantics/openAccess2025-04-30T13:33:10Zoai:repositorio.unesp.br:11449/309466Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestrepositoriounesp@unesp.bropendoar:29462025-04-30T13:33:10Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv A Novel Solution Technique for the Expansion Planning of Modern Distribution Systems
title A Novel Solution Technique for the Expansion Planning of Modern Distribution Systems
spellingShingle A Novel Solution Technique for the Expansion Planning of Modern Distribution Systems
Marcelo, Jonathan A. [UNESP]
CO2emissions reduction
Local search algorithm
modern distribution systems
quadratic programming
title_short A Novel Solution Technique for the Expansion Planning of Modern Distribution Systems
title_full A Novel Solution Technique for the Expansion Planning of Modern Distribution Systems
title_fullStr A Novel Solution Technique for the Expansion Planning of Modern Distribution Systems
title_full_unstemmed A Novel Solution Technique for the Expansion Planning of Modern Distribution Systems
title_sort A Novel Solution Technique for the Expansion Planning of Modern Distribution Systems
author Marcelo, Jonathan A. [UNESP]
author_facet Marcelo, Jonathan A. [UNESP]
Muñoz-Delgado, Gregorio
Contreras, Javier
Mantovani, José R. S. [UNESP]
author_role author
author2 Muñoz-Delgado, Gregorio
Contreras, Javier
Mantovani, José R. S. [UNESP]
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (UNESP)
E.T.S. de Ingeniería Industrial
dc.contributor.author.fl_str_mv Marcelo, Jonathan A. [UNESP]
Muñoz-Delgado, Gregorio
Contreras, Javier
Mantovani, José R. S. [UNESP]
dc.subject.por.fl_str_mv CO2emissions reduction
Local search algorithm
modern distribution systems
quadratic programming
topic CO2emissions reduction
Local search algorithm
modern distribution systems
quadratic programming
description This paper addresses the expansion planning problem of modern distribution systems using a novel solution methodology based on matheuristics. The problem is formulated as a mixed-integer quadratic programming model. The proposed technique constitutes a novel local search algorithm that, using linearized and relaxed versions of the original problem, finds successive solutions, improving at each iteration the feasibility of the original problem until attaining a high-quality near-optimal solution. Planning actions include line reconductoring and installation of distributed generation (DG) units, electrical energy storage (EES) systems, and fixed and switchable capacitor banks (CBs). Operation system variability in demand and energy resources is modeled through representative days in order to preserve EES temporal transition. Furthermore, the model guarantees CO2 reduction in order to be on track to limit global warming. The effective performance of the proposed approach in terms of solution quality and computational time has been illustrated with a case study based on a 33-node distribution system.
publishDate 2023
dc.date.none.fl_str_mv 2023-01-01
2025-04-29T20:15:37Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1109/EEEIC/ICPSEurope57605.2023.10194884
Proceedings - 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023.
https://hdl.handle.net/11449/309466
10.1109/EEEIC/ICPSEurope57605.2023.10194884
2-s2.0-85168688199
url http://dx.doi.org/10.1109/EEEIC/ICPSEurope57605.2023.10194884
https://hdl.handle.net/11449/309466
identifier_str_mv Proceedings - 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023.
10.1109/EEEIC/ICPSEurope57605.2023.10194884
2-s2.0-85168688199
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Proceedings - 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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 repositoriounesp@unesp.br
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