A Branch and Bound Algorithm for Transmission Network Expansion Planning Using Nonconvex Mixed-Integer Nonlinear Programming Models
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/ACCESS.2022.3166153 http://hdl.handle.net/11449/241736 |
Resumo: | The branch and bound (BB) algorithm is widely used to obtain the global solution of mixed-integer linear programming (MILP) problems. On the other hand, when the traditional BB structure is directly used to solve nonconvex mixed-integer nonlinear programming (MINLP) problems, it becomes ineffective, mainly due to the nonlinearity and nonconvexity of the feasible region of the problem. This article presents the difficulties and ineffectiveness of the direct use of the traditional BB algorithm for solving nonconvex MINLP problems and proposes the formulation of an efficient BB algorithm for solving this category of problems. The algorithm is formulated taking into account particular aspects of nonconvex MINLP problems, including (i) how to deal with the nonlinear programming (NLP) subproblems, (ii) how to detect the infeasibility of an NLP subproblem, (iii) how to treat the nonconvexity of the problem, and (iv) how to define the fathoming rules. The proposed BB algorithm is used to solve the transmission network expansion planning (TNEP) problem, a classical problem in power systems optimization, and its performance is compared with the performances of off-the-shelf optimization solvers for MINLP problems. The results obtained for four test systems, with different degrees of complexity, indicate that the proposed BB algorithm is effective for solving the TNEP problem with and without considering losses, showing equal or better performance than off-the-shelf optimization solvers. |
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A Branch and Bound Algorithm for Transmission Network Expansion Planning Using Nonconvex Mixed-Integer Nonlinear Programming ModelsBranch and bound algorithmmixed-integer nonlinear programmingoptimizationtransmission network expansion planningThe branch and bound (BB) algorithm is widely used to obtain the global solution of mixed-integer linear programming (MILP) problems. On the other hand, when the traditional BB structure is directly used to solve nonconvex mixed-integer nonlinear programming (MINLP) problems, it becomes ineffective, mainly due to the nonlinearity and nonconvexity of the feasible region of the problem. This article presents the difficulties and ineffectiveness of the direct use of the traditional BB algorithm for solving nonconvex MINLP problems and proposes the formulation of an efficient BB algorithm for solving this category of problems. The algorithm is formulated taking into account particular aspects of nonconvex MINLP problems, including (i) how to deal with the nonlinear programming (NLP) subproblems, (ii) how to detect the infeasibility of an NLP subproblem, (iii) how to treat the nonconvexity of the problem, and (iv) how to define the fathoming rules. The proposed BB algorithm is used to solve the transmission network expansion planning (TNEP) problem, a classical problem in power systems optimization, and its performance is compared with the performances of off-the-shelf optimization solvers for MINLP problems. The results obtained for four test systems, with different degrees of complexity, indicate that the proposed BB algorithm is effective for solving the TNEP problem with and without considering losses, showing equal or better performance than off-the-shelf optimization solvers.Institute of Exact and Natural Sciences Federal University of RondonópolisDepartment of Electrical Engineering São Paulo State UniversityDepartment of Systems and Energy University of CampinasDepartment of Electrical Engineering São Paulo State UniversityFederal University of RondonópolisUniversidade Estadual Paulista (UNESP)Universidade Estadual de Campinas (UNICAMP)Zoppei, Reinaldo T.Delgado, Marcos A. J.MacEdo, Leonardo H. [UNESP]Rider, Marcos J.Romero, Ruben [UNESP]2023-03-01T21:19:04Z2023-03-01T21:19:04Z2022-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article39875-39888http://dx.doi.org/10.1109/ACCESS.2022.3166153IEEE Access, v. 10, p. 39875-39888.2169-3536http://hdl.handle.net/11449/24173610.1109/ACCESS.2022.31661532-s2.0-85128257493Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengIEEE Accessinfo:eu-repo/semantics/openAccess2023-03-01T21:19:04Zoai:repositorio.unesp.br:11449/241736Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462023-03-01T21:19:04Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
A Branch and Bound Algorithm for Transmission Network Expansion Planning Using Nonconvex Mixed-Integer Nonlinear Programming Models |
title |
A Branch and Bound Algorithm for Transmission Network Expansion Planning Using Nonconvex Mixed-Integer Nonlinear Programming Models |
spellingShingle |
A Branch and Bound Algorithm for Transmission Network Expansion Planning Using Nonconvex Mixed-Integer Nonlinear Programming Models Zoppei, Reinaldo T. Branch and bound algorithm mixed-integer nonlinear programming optimization transmission network expansion planning |
title_short |
A Branch and Bound Algorithm for Transmission Network Expansion Planning Using Nonconvex Mixed-Integer Nonlinear Programming Models |
title_full |
A Branch and Bound Algorithm for Transmission Network Expansion Planning Using Nonconvex Mixed-Integer Nonlinear Programming Models |
title_fullStr |
A Branch and Bound Algorithm for Transmission Network Expansion Planning Using Nonconvex Mixed-Integer Nonlinear Programming Models |
title_full_unstemmed |
A Branch and Bound Algorithm for Transmission Network Expansion Planning Using Nonconvex Mixed-Integer Nonlinear Programming Models |
title_sort |
A Branch and Bound Algorithm for Transmission Network Expansion Planning Using Nonconvex Mixed-Integer Nonlinear Programming Models |
author |
Zoppei, Reinaldo T. |
author_facet |
Zoppei, Reinaldo T. Delgado, Marcos A. J. MacEdo, Leonardo H. [UNESP] Rider, Marcos J. Romero, Ruben [UNESP] |
author_role |
author |
author2 |
Delgado, Marcos A. J. MacEdo, Leonardo H. [UNESP] Rider, Marcos J. Romero, Ruben [UNESP] |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Federal University of Rondonópolis Universidade Estadual Paulista (UNESP) Universidade Estadual de Campinas (UNICAMP) |
dc.contributor.author.fl_str_mv |
Zoppei, Reinaldo T. Delgado, Marcos A. J. MacEdo, Leonardo H. [UNESP] Rider, Marcos J. Romero, Ruben [UNESP] |
dc.subject.por.fl_str_mv |
Branch and bound algorithm mixed-integer nonlinear programming optimization transmission network expansion planning |
topic |
Branch and bound algorithm mixed-integer nonlinear programming optimization transmission network expansion planning |
description |
The branch and bound (BB) algorithm is widely used to obtain the global solution of mixed-integer linear programming (MILP) problems. On the other hand, when the traditional BB structure is directly used to solve nonconvex mixed-integer nonlinear programming (MINLP) problems, it becomes ineffective, mainly due to the nonlinearity and nonconvexity of the feasible region of the problem. This article presents the difficulties and ineffectiveness of the direct use of the traditional BB algorithm for solving nonconvex MINLP problems and proposes the formulation of an efficient BB algorithm for solving this category of problems. The algorithm is formulated taking into account particular aspects of nonconvex MINLP problems, including (i) how to deal with the nonlinear programming (NLP) subproblems, (ii) how to detect the infeasibility of an NLP subproblem, (iii) how to treat the nonconvexity of the problem, and (iv) how to define the fathoming rules. The proposed BB algorithm is used to solve the transmission network expansion planning (TNEP) problem, a classical problem in power systems optimization, and its performance is compared with the performances of off-the-shelf optimization solvers for MINLP problems. The results obtained for four test systems, with different degrees of complexity, indicate that the proposed BB algorithm is effective for solving the TNEP problem with and without considering losses, showing equal or better performance than off-the-shelf optimization solvers. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-01 2023-03-01T21:19:04Z 2023-03-01T21:19:04Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/ACCESS.2022.3166153 IEEE Access, v. 10, p. 39875-39888. 2169-3536 http://hdl.handle.net/11449/241736 10.1109/ACCESS.2022.3166153 2-s2.0-85128257493 |
url |
http://dx.doi.org/10.1109/ACCESS.2022.3166153 http://hdl.handle.net/11449/241736 |
identifier_str_mv |
IEEE Access, v. 10, p. 39875-39888. 2169-3536 10.1109/ACCESS.2022.3166153 2-s2.0-85128257493 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
IEEE Access |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
39875-39888 |
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 |
|
_version_ |
1799965367794139136 |