Comparing stochastic optimization methods to solve the medium-term operation planning problem

Detalhes bibliográficos
Autor(a) principal: Gonçalves,Raphael E. C.
Data de Publicação: 2011
Outros Autores: Finardi,Erlon C., Silva,Edson L. da, Santos,Marcelo L. L. dos
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Computational & Applied Mathematics
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1807-03022011000200003
Resumo: The Medium-Term Operation Planning (MTOP) of hydrothermal systems aims to define the generation for each power plant, minimizing the expected operating cost over the planning horizon. Mathematically, this task can be characterized as a linear, stochastic, large-scale problem which requires the application of suitable optimization tools. To solve this problem, this paper proposes to use the Nested Decomposition, frequently used to solve similar problems (as in Brazilian case), and Progressive Hedging, an alternative method, which has interesting features that make it promising to address this problem. To make a comparative analysis between these two methods with respect to the quality of the solution and the computational burden, a benchmark is established, which is obtained by solving a single Linear Programming problem (the Deterministic Equivalent Problem). An application considering a hydrothermal system is carried out.
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spelling Comparing stochastic optimization methods to solve the medium-term operation planning problemHydrothermal SystemsStochastic OptimizationMedium-Term Operation Planning ProblemNested DecompositionProgressive HedgingThe Medium-Term Operation Planning (MTOP) of hydrothermal systems aims to define the generation for each power plant, minimizing the expected operating cost over the planning horizon. Mathematically, this task can be characterized as a linear, stochastic, large-scale problem which requires the application of suitable optimization tools. To solve this problem, this paper proposes to use the Nested Decomposition, frequently used to solve similar problems (as in Brazilian case), and Progressive Hedging, an alternative method, which has interesting features that make it promising to address this problem. To make a comparative analysis between these two methods with respect to the quality of the solution and the computational burden, a benchmark is established, which is obtained by solving a single Linear Programming problem (the Deterministic Equivalent Problem). An application considering a hydrothermal system is carried out.Sociedade Brasileira de Matemática Aplicada e Computacional2011-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1807-03022011000200003Computational & Applied Mathematics v.30 n.2 2011reponame:Computational & Applied Mathematicsinstname:Sociedade Brasileira de Matemática Aplicada e Computacional (SBMAC)instacron:SBMAC10.1590/S1807-03022011000200003info:eu-repo/semantics/openAccessGonçalves,Raphael E. C.Finardi,Erlon C.Silva,Edson L. daSantos,Marcelo L. L. doseng2011-07-27T00:00:00Zoai:scielo:S1807-03022011000200003Revistahttps://www.scielo.br/j/cam/ONGhttps://old.scielo.br/oai/scielo-oai.php||sbmac@sbmac.org.br1807-03022238-3603opendoar:2011-07-27T00:00Computational & Applied Mathematics - Sociedade Brasileira de Matemática Aplicada e Computacional (SBMAC)false
dc.title.none.fl_str_mv Comparing stochastic optimization methods to solve the medium-term operation planning problem
title Comparing stochastic optimization methods to solve the medium-term operation planning problem
spellingShingle Comparing stochastic optimization methods to solve the medium-term operation planning problem
Gonçalves,Raphael E. C.
Hydrothermal Systems
Stochastic Optimization
Medium-Term Operation Planning Problem
Nested Decomposition
Progressive Hedging
title_short Comparing stochastic optimization methods to solve the medium-term operation planning problem
title_full Comparing stochastic optimization methods to solve the medium-term operation planning problem
title_fullStr Comparing stochastic optimization methods to solve the medium-term operation planning problem
title_full_unstemmed Comparing stochastic optimization methods to solve the medium-term operation planning problem
title_sort Comparing stochastic optimization methods to solve the medium-term operation planning problem
author Gonçalves,Raphael E. C.
author_facet Gonçalves,Raphael E. C.
Finardi,Erlon C.
Silva,Edson L. da
Santos,Marcelo L. L. dos
author_role author
author2 Finardi,Erlon C.
Silva,Edson L. da
Santos,Marcelo L. L. dos
author2_role author
author
author
dc.contributor.author.fl_str_mv Gonçalves,Raphael E. C.
Finardi,Erlon C.
Silva,Edson L. da
Santos,Marcelo L. L. dos
dc.subject.por.fl_str_mv Hydrothermal Systems
Stochastic Optimization
Medium-Term Operation Planning Problem
Nested Decomposition
Progressive Hedging
topic Hydrothermal Systems
Stochastic Optimization
Medium-Term Operation Planning Problem
Nested Decomposition
Progressive Hedging
description The Medium-Term Operation Planning (MTOP) of hydrothermal systems aims to define the generation for each power plant, minimizing the expected operating cost over the planning horizon. Mathematically, this task can be characterized as a linear, stochastic, large-scale problem which requires the application of suitable optimization tools. To solve this problem, this paper proposes to use the Nested Decomposition, frequently used to solve similar problems (as in Brazilian case), and Progressive Hedging, an alternative method, which has interesting features that make it promising to address this problem. To make a comparative analysis between these two methods with respect to the quality of the solution and the computational burden, a benchmark is established, which is obtained by solving a single Linear Programming problem (the Deterministic Equivalent Problem). An application considering a hydrothermal system is carried out.
publishDate 2011
dc.date.none.fl_str_mv 2011-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
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dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1807-03022011000200003
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dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/S1807-03022011000200003
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
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dc.publisher.none.fl_str_mv Sociedade Brasileira de Matemática Aplicada e Computacional
publisher.none.fl_str_mv Sociedade Brasileira de Matemática Aplicada e Computacional
dc.source.none.fl_str_mv Computational & Applied Mathematics v.30 n.2 2011
reponame:Computational & Applied Mathematics
instname:Sociedade Brasileira de Matemática Aplicada e Computacional (SBMAC)
instacron:SBMAC
instname_str Sociedade Brasileira de Matemática Aplicada e Computacional (SBMAC)
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institution SBMAC
reponame_str Computational & Applied Mathematics
collection Computational & Applied Mathematics
repository.name.fl_str_mv Computational & Applied Mathematics - Sociedade Brasileira de Matemática Aplicada e Computacional (SBMAC)
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