Comparing stochastic optimization methods to solve the medium-term operation planning problem
Autor(a) principal: | |
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Data de Publicação: | 2011 |
Outros Autores: | , , |
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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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 |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1807-03022011000200003 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1807-03022011000200003 |
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 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
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) |
instacron_str |
SBMAC |
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) |
repository.mail.fl_str_mv |
||sbmac@sbmac.org.br |
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1754734890243325952 |