Optimal location-allocation of storage devices and renewable-based DG in distribution systems
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.epsr.2019.02.013 http://hdl.handle.net/11449/190158 |
Resumo: | This paper proposes a mixed integer conic programming (MICP) model to find the optimal type, size, and place of distributed generators (DG) over a multistage planning horizon in radial distribution systems. The proposed planning framework focuses on the optimal siting and sizing of wind turbines, photovoltaic panels, gas turbines, and energy storage devices (ESD). Inherently, renewable energy sources and electricity demands are subject to uncertainty. To handle such probabilistic situations in decision-making, the MICP model is extended into a two-stage stochastic programming model. To obtain more practical results, annual historical data are used to generate the scenarios. For the sake of tractability, the k-means clustering technique is used to reduce the number of scenarios while keeping the correlation between the uncertain data. Due to convexity, the proposed MICP model guarantees to find the global optimal solution. To show the potential and performance of the proposed model a 69-bus radial distribution system under different conditions is dully studied and a sensitivity analysis is conducted. Results and comparisons approve its effectiveness and usefulness. |
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Optimal location-allocation of storage devices and renewable-based DG in distribution systemsConic programmingDistributed generationEnergy storageMultistage distribution system planningRenewable energy sourcesStochastic programmingThis paper proposes a mixed integer conic programming (MICP) model to find the optimal type, size, and place of distributed generators (DG) over a multistage planning horizon in radial distribution systems. The proposed planning framework focuses on the optimal siting and sizing of wind turbines, photovoltaic panels, gas turbines, and energy storage devices (ESD). Inherently, renewable energy sources and electricity demands are subject to uncertainty. To handle such probabilistic situations in decision-making, the MICP model is extended into a two-stage stochastic programming model. To obtain more practical results, annual historical data are used to generate the scenarios. For the sake of tractability, the k-means clustering technique is used to reduce the number of scenarios while keeping the correlation between the uncertain data. Due to convexity, the proposed MICP model guarantees to find the global optimal solution. To show the potential and performance of the proposed model a 69-bus radial distribution system under different conditions is dully studied and a sensitivity analysis is conducted. Results and comparisons approve its effectiveness and usefulness.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Department of Electrical Engineering São Paulo State University (UNESP)Department of Electrical Engineering and Automation Aalto University, Maarintie 8Department of Electrical Engineering São Paulo State University (UNESP)FAPESP: 2015/21972-6CNPq: 305318/2016-0Universidade Estadual Paulista (Unesp)Aalto UniversityHome-Ortiz, Juan M. [UNESP]Pourakbari-Kasmaei, MahdiLehtonen, MattiSanches Mantovani, José Roberto [UNESP]2019-10-06T17:04:16Z2019-10-06T17:04:16Z2019-07-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article11-21http://dx.doi.org/10.1016/j.epsr.2019.02.013Electric Power Systems Research, v. 172, p. 11-21.0378-7796http://hdl.handle.net/11449/19015810.1016/j.epsr.2019.02.0132-s2.0-85062327675Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengElectric Power Systems Researchinfo:eu-repo/semantics/openAccess2021-10-23T00:57:31Zoai:repositorio.unesp.br:11449/190158Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T00:57:31Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Optimal location-allocation of storage devices and renewable-based DG in distribution systems |
title |
Optimal location-allocation of storage devices and renewable-based DG in distribution systems |
spellingShingle |
Optimal location-allocation of storage devices and renewable-based DG in distribution systems Home-Ortiz, Juan M. [UNESP] Conic programming Distributed generation Energy storage Multistage distribution system planning Renewable energy sources Stochastic programming |
title_short |
Optimal location-allocation of storage devices and renewable-based DG in distribution systems |
title_full |
Optimal location-allocation of storage devices and renewable-based DG in distribution systems |
title_fullStr |
Optimal location-allocation of storage devices and renewable-based DG in distribution systems |
title_full_unstemmed |
Optimal location-allocation of storage devices and renewable-based DG in distribution systems |
title_sort |
Optimal location-allocation of storage devices and renewable-based DG in distribution systems |
author |
Home-Ortiz, Juan M. [UNESP] |
author_facet |
Home-Ortiz, Juan M. [UNESP] Pourakbari-Kasmaei, Mahdi Lehtonen, Matti Sanches Mantovani, José Roberto [UNESP] |
author_role |
author |
author2 |
Pourakbari-Kasmaei, Mahdi Lehtonen, Matti Sanches Mantovani, José Roberto [UNESP] |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Aalto University |
dc.contributor.author.fl_str_mv |
Home-Ortiz, Juan M. [UNESP] Pourakbari-Kasmaei, Mahdi Lehtonen, Matti Sanches Mantovani, José Roberto [UNESP] |
dc.subject.por.fl_str_mv |
Conic programming Distributed generation Energy storage Multistage distribution system planning Renewable energy sources Stochastic programming |
topic |
Conic programming Distributed generation Energy storage Multistage distribution system planning Renewable energy sources Stochastic programming |
description |
This paper proposes a mixed integer conic programming (MICP) model to find the optimal type, size, and place of distributed generators (DG) over a multistage planning horizon in radial distribution systems. The proposed planning framework focuses on the optimal siting and sizing of wind turbines, photovoltaic panels, gas turbines, and energy storage devices (ESD). Inherently, renewable energy sources and electricity demands are subject to uncertainty. To handle such probabilistic situations in decision-making, the MICP model is extended into a two-stage stochastic programming model. To obtain more practical results, annual historical data are used to generate the scenarios. For the sake of tractability, the k-means clustering technique is used to reduce the number of scenarios while keeping the correlation between the uncertain data. Due to convexity, the proposed MICP model guarantees to find the global optimal solution. To show the potential and performance of the proposed model a 69-bus radial distribution system under different conditions is dully studied and a sensitivity analysis is conducted. Results and comparisons approve its effectiveness and usefulness. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-10-06T17:04:16Z 2019-10-06T17:04:16Z 2019-07-01 |
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/10.1016/j.epsr.2019.02.013 Electric Power Systems Research, v. 172, p. 11-21. 0378-7796 http://hdl.handle.net/11449/190158 10.1016/j.epsr.2019.02.013 2-s2.0-85062327675 |
url |
http://dx.doi.org/10.1016/j.epsr.2019.02.013 http://hdl.handle.net/11449/190158 |
identifier_str_mv |
Electric Power Systems Research, v. 172, p. 11-21. 0378-7796 10.1016/j.epsr.2019.02.013 2-s2.0-85062327675 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Electric Power Systems Research |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
11-21 |
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_ |
1803046848529694720 |