Accelerated Benders decomposition for enhanced co-optimized T&D system planning

This paper addresses the decision-making problem associated with generation and network investments within the context of co-optimized transmission and distribution system planning. The proposed expansion planning problem differs from existing formulations due to the joint consideration of three maj...

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Autores: Rodrigues Faria, Wandry, Rodrigues Pereira Junior, Benvindo, Muñoz Delgado, Gregorio, Arroyo Sánchez, José Manuel, Contreras Sanz, Javier
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universidad de Castilla-La Mancha
Repositorio:RUIdeRA. Repositorio Institucional de la UCLM
OAI Identifier:oai:ruidera.uclm.es:10578/42909
Acceso en línea:https://hdl.handle.net/10578/42909
Access Level:acceso abierto
Palabra clave:benders decomposition
co-optimized transmission and distribution planning
network and generation investment decisions
stochastic programming
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spelling Accelerated Benders decomposition for enhanced co-optimized T&D system planningRodrigues Faria, WandryRodrigues Pereira Junior, BenvindoMuñoz Delgado, GregorioArroyo Sánchez, José ManuelContreras Sanz, Javierbenders decompositionco-optimized transmission and distribution planningnetwork and generation investment decisionsstochastic programmingThis paper addresses the decision-making problem associated with generation and network investments within the context of co-optimized transmission and distribution system planning. The proposed expansion planning problem differs from existing formulations due to the joint consideration of three major complicating factors. First, discrete generation investments are considered at both system levels, thereby requiring binary decision variables. Secondly, the nonlinear behavior of the distribution network is accurately modeled using second-order cone programming. In addition, both long- and short-term uncertainty sources are precisely characterized by a scenario-based stochastic programming framework. The proposed model is cast as a mixed-integer second-order cone program that is challenging for the methodologies previously used for solving simpler instances of co-optimized transmission and distribution planning. In order to circumvent this computational issue, this paper presents an enhanced and novel application of Benders decomposition featuring two acceleration strategies respectively tailored to the master problem and the subproblem into which the problem at hand is decomposed. Numerical simulations demonstrate the economic and operational advantages of the proposed approach, in the form of 75.2% cost savings and load shedding decrease down to 0, as well as its computational superiority over available solution techniques, which is backed by reductions in the running times ranging between 74.5% and 99.8%.This paper addresses the decision-making problem associated with generation and network investments within the context of co-optimized transmission and distribution system planning. The proposed expansion planning problem differs from existing formulations due to the joint consideration of three major complicating factors. First, discrete generation investments are considered at both system levels, thereby requiring binary decision variables. Secondly, the nonlinear behavior of the distribution network is accurately modeled using second-order cone programming. In addition, both long- and short-term uncertainty sources are precisely characterized by a scenario-based stochastic programming framework. The proposed model is cast as a mixed-integer second-order cone program that is challenging for the methodologies previously used for solving simpler instances of co-optimized transmission and distribution planning. In order to circumvent this computational issue, this paper presents an enhanced and novel application of Benders decomposition featuring two acceleration strategies respectively tailored to the master problem and the subproblem into which the problem at hand is decomposed. Numerical simulations demonstrate the economic and operational advantages of the proposed approach, in the form of 75.2% cost savings and load shedding decrease down to 0, as well as its computational superiority over available solution techniques, which is backed by reductions in the running times ranging between 74.5% and 99.8%.IEEE202520252025info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://hdl.handle.net/10578/42909reponame:RUIdeRA. Repositorio Institucional de la UCLMinstname:Universidad de Castilla-La ManchaInglésinfo:eu-repo/semantics/openAccessoai:ruidera.uclm.es:10578/429092026-05-27T07:36:41Z
dc.title.none.fl_str_mv Accelerated Benders decomposition for enhanced co-optimized T&D system planning
title Accelerated Benders decomposition for enhanced co-optimized T&D system planning
spellingShingle Accelerated Benders decomposition for enhanced co-optimized T&D system planning
Rodrigues Faria, Wandry
benders decomposition
co-optimized transmission and distribution planning
network and generation investment decisions
stochastic programming
title_short Accelerated Benders decomposition for enhanced co-optimized T&D system planning
title_full Accelerated Benders decomposition for enhanced co-optimized T&D system planning
title_fullStr Accelerated Benders decomposition for enhanced co-optimized T&D system planning
title_full_unstemmed Accelerated Benders decomposition for enhanced co-optimized T&D system planning
title_sort Accelerated Benders decomposition for enhanced co-optimized T&D system planning
dc.creator.none.fl_str_mv Rodrigues Faria, Wandry
Rodrigues Pereira Junior, Benvindo
Muñoz Delgado, Gregorio
Arroyo Sánchez, José Manuel
Contreras Sanz, Javier
author Rodrigues Faria, Wandry
author_facet Rodrigues Faria, Wandry
Rodrigues Pereira Junior, Benvindo
Muñoz Delgado, Gregorio
Arroyo Sánchez, José Manuel
Contreras Sanz, Javier
author_role author
author2 Rodrigues Pereira Junior, Benvindo
Muñoz Delgado, Gregorio
Arroyo Sánchez, José Manuel
Contreras Sanz, Javier
author2_role author
author
author
author
dc.subject.none.fl_str_mv benders decomposition
co-optimized transmission and distribution planning
network and generation investment decisions
stochastic programming
topic benders decomposition
co-optimized transmission and distribution planning
network and generation investment decisions
stochastic programming
description This paper addresses the decision-making problem associated with generation and network investments within the context of co-optimized transmission and distribution system planning. The proposed expansion planning problem differs from existing formulations due to the joint consideration of three major complicating factors. First, discrete generation investments are considered at both system levels, thereby requiring binary decision variables. Secondly, the nonlinear behavior of the distribution network is accurately modeled using second-order cone programming. In addition, both long- and short-term uncertainty sources are precisely characterized by a scenario-based stochastic programming framework. The proposed model is cast as a mixed-integer second-order cone program that is challenging for the methodologies previously used for solving simpler instances of co-optimized transmission and distribution planning. In order to circumvent this computational issue, this paper presents an enhanced and novel application of Benders decomposition featuring two acceleration strategies respectively tailored to the master problem and the subproblem into which the problem at hand is decomposed. Numerical simulations demonstrate the economic and operational advantages of the proposed approach, in the form of 75.2% cost savings and load shedding decrease down to 0, as well as its computational superiority over available solution techniques, which is backed by reductions in the running times ranging between 74.5% and 99.8%.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10578/42909
url https://hdl.handle.net/10578/42909
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:RUIdeRA. Repositorio Institucional de la UCLM
instname:Universidad de Castilla-La Mancha
instname_str Universidad de Castilla-La Mancha
reponame_str RUIdeRA. Repositorio Institucional de la UCLM
collection RUIdeRA. Repositorio Institucional de la UCLM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.81155

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