Optimal expansion planning of microgrids clusters: A robust collaborative approach

Integrating electrical demands and distributed generators into microgrids facilitates their coordination and enables safe and reliable power supply to remote areas. When multiple microgrids share the same geographical area and transmission network, they can be organized into clusters to exchange ene...

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Detalles Bibliográficos
Autores: Tostado-Véliz, Marcos, Horrilo-Quintero, Pablo, García-Triviño, Pablo, Fernández-Ramírez, Luis Miguel, Jurado-Melguizo, Francisco
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2025
País:España
Institución:Universidad de Jaén
Repositorio:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
OAI Identifier:oai:ruja.ujaen.es:10953/6215
Acceso en línea:https://www.sciencedirect.com/science/article/pii/S2352467725003868?via%3Dihub
https://hdl.handle.net/10953/6215
Access Level:acceso abierto
Palabra clave:Column-and-Constraint-Generation algorithm
Polyhedral uncertainty set
Microgrids cluster
Robust optimization
3306.02
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spelling Optimal expansion planning of microgrids clusters: A robust collaborative approachTostado-Véliz, MarcosHorrilo-Quintero, PabloGarcía-Triviño, PabloFernández-Ramírez, Luis MiguelJurado-Melguizo, FranciscoColumn-and-Constraint-Generation algorithmPolyhedral uncertainty setMicrogrids clusterRobust optimization3306.02Integrating electrical demands and distributed generators into microgrids facilitates their coordination and enables safe and reliable power supply to remote areas. When multiple microgrids share the same geographical area and transmission network, they can be organized into clusters to exchange energy in a peer-to-peer fashion, improving the overall efficiency and economy of the system. This paper proposes a novel methodology for optimal expansion planning of microgrid clusters, explicitly considering resource sharing. The model preserves the privacy of each microgrid by exchanging only boundary information. A three-level formulation is presented, incorporating uncertainties in renewable generation and demand through polyhedral uncertainty sets, whose bounds are determined using a novel clustering strategy. The resulting model is solved with a tailored algorithm based on robust optimization and a column-and-constraint generation scheme. The methodology is tested on a three-microgrid cluster, demonstrating its ability to manage uncertainty robustly and adapt to different levels of risk and budget constraints. In the case study, increasing robustness leads to higher costs (+31 %), lower renewable generation (-13 %), and increased unserved energy (+60 %). Finally, sensitivity analyses on fuel costs and the number of microgrids show that the proposed approach scales well with system size.The authors acknowledge to the Spanish Ministry of Science and Innovation, for granting the research Project “Development of power-flow models for microgrid clusters” MICI/AEI PID2021-123633OB-C31, , Knowledge Generation Projects 2021, Spain, and by “ERDF A way of making Europe”, by “ERDF/EU”. The authors are grateful to University of Jaén for covering the Article Processing Charges under the agreement: Universidad de Jaén/CBUA.Elsevier202520252025info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttps://www.sciencedirect.com/science/article/pii/S2352467725003868?via%3Dihubhttps://hdl.handle.net/10953/6215reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaéninstname:Universidad de JaénInglésSustainable Energy, Grids and Networks 2025; 44: 102004Attribution 3.0 Spainhttp://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccessoai:ruja.ujaen.es:10953/62152026-06-24T12:41:07Z
dc.title.none.fl_str_mv Optimal expansion planning of microgrids clusters: A robust collaborative approach
title Optimal expansion planning of microgrids clusters: A robust collaborative approach
spellingShingle Optimal expansion planning of microgrids clusters: A robust collaborative approach
Tostado-Véliz, Marcos
Column-and-Constraint-Generation algorithm
Polyhedral uncertainty set
Microgrids cluster
Robust optimization
3306.02
title_short Optimal expansion planning of microgrids clusters: A robust collaborative approach
title_full Optimal expansion planning of microgrids clusters: A robust collaborative approach
title_fullStr Optimal expansion planning of microgrids clusters: A robust collaborative approach
title_full_unstemmed Optimal expansion planning of microgrids clusters: A robust collaborative approach
title_sort Optimal expansion planning of microgrids clusters: A robust collaborative approach
dc.creator.none.fl_str_mv Tostado-Véliz, Marcos
Horrilo-Quintero, Pablo
García-Triviño, Pablo
Fernández-Ramírez, Luis Miguel
Jurado-Melguizo, Francisco
author Tostado-Véliz, Marcos
author_facet Tostado-Véliz, Marcos
Horrilo-Quintero, Pablo
García-Triviño, Pablo
Fernández-Ramírez, Luis Miguel
Jurado-Melguizo, Francisco
author_role author
author2 Horrilo-Quintero, Pablo
García-Triviño, Pablo
Fernández-Ramírez, Luis Miguel
Jurado-Melguizo, Francisco
author2_role author
author
author
author
dc.subject.none.fl_str_mv Column-and-Constraint-Generation algorithm
Polyhedral uncertainty set
Microgrids cluster
Robust optimization
3306.02
topic Column-and-Constraint-Generation algorithm
Polyhedral uncertainty set
Microgrids cluster
Robust optimization
3306.02
description Integrating electrical demands and distributed generators into microgrids facilitates their coordination and enables safe and reliable power supply to remote areas. When multiple microgrids share the same geographical area and transmission network, they can be organized into clusters to exchange energy in a peer-to-peer fashion, improving the overall efficiency and economy of the system. This paper proposes a novel methodology for optimal expansion planning of microgrid clusters, explicitly considering resource sharing. The model preserves the privacy of each microgrid by exchanging only boundary information. A three-level formulation is presented, incorporating uncertainties in renewable generation and demand through polyhedral uncertainty sets, whose bounds are determined using a novel clustering strategy. The resulting model is solved with a tailored algorithm based on robust optimization and a column-and-constraint generation scheme. The methodology is tested on a three-microgrid cluster, demonstrating its ability to manage uncertainty robustly and adapt to different levels of risk and budget constraints. In the case study, increasing robustness leads to higher costs (+31 %), lower renewable generation (-13 %), and increased unserved energy (+60 %). Finally, sensitivity analyses on fuel costs and the number of microgrids show that the proposed approach scales well with system size.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://www.sciencedirect.com/science/article/pii/S2352467725003868?via%3Dihub
https://hdl.handle.net/10953/6215
url https://www.sciencedirect.com/science/article/pii/S2352467725003868?via%3Dihub
https://hdl.handle.net/10953/6215
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Sustainable Energy, Grids and Networks 2025; 44: 102004
dc.rights.none.fl_str_mv Attribution 3.0 Spain
http://creativecommons.org/licenses/by/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution 3.0 Spain
http://creativecommons.org/licenses/by/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
instname:Universidad de Jaén
instname_str Universidad de Jaén
reponame_str RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
collection RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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