A Stochastic Model for Medium-Term Distribution System Planning Considering CO2 Emissions
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://hdl.handle.net/11449/218714 |
Resumo: | This paper presents a new two-stage stochastic mixed-integer linear programming model for the medium-term expansion planning of power distribution systems considering the uncertainty associated with renewable energy sources, demand, and energy prices at substations. Investment decisions comprise the installation of both 1) classical alternatives such as conductors, capacitor banks, and voltage regulators, and 2) modern alternatives such as renewable distributed generation and energy storage units. Moreover, unlike conventional planning models, the proposed approach includes a voltage-dependent load representation. The proposed model aims to find a planning strategy that minimizes the investment and operating costs while meeting network operational constraints and CO2 emissions requirements. Tests are carried out with a 69-node distribution system and the results demonstrate the effectiveness and applicability of this model as an effective means of promoting an efficient, sustainable, and environmentally-friendly network. |
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A Stochastic Model for Medium-Term Distribution System Planning Considering CO2 EmissionsDistribution system planningrenewable energy sourcesstochastic programmingvoltage-dependent modelsThis paper presents a new two-stage stochastic mixed-integer linear programming model for the medium-term expansion planning of power distribution systems considering the uncertainty associated with renewable energy sources, demand, and energy prices at substations. Investment decisions comprise the installation of both 1) classical alternatives such as conductors, capacitor banks, and voltage regulators, and 2) modern alternatives such as renewable distributed generation and energy storage units. Moreover, unlike conventional planning models, the proposed approach includes a voltage-dependent load representation. The proposed model aims to find a planning strategy that minimizes the investment and operating costs while meeting network operational constraints and CO2 emissions requirements. Tests are carried out with a 69-node distribution system and the results demonstrate the effectiveness and applicability of this model as an effective means of promoting an efficient, sustainable, and environmentally-friendly network.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Ministry of Science, Innovation and Universities of SpainUniversidad de Castilla-La ManchaSao Paulo State Univ, Dept Elect Engn, Ilha Solteira, BrazilUniv Castilla La Mancha, Escuela Tecn Super Ingn Ind, Ciudad Real, SpainSao Paulo State Univ, Dept Elect Engn, Ilha Solteira, BrazilCAPES: 001CAPES: 88887.371636/2019-00FAPESP: 2015/21972-6FAPESP: 2018/20355-1FAPESP: 2019/19632-3Ministry of Science, Innovation and Universities of Spain: RTI2018-096108-A-I00Ministry of Science, Innovation and Universities of Spain: RTI2018-098703-B-I00Universidad de Castilla-La Mancha: 2020-GRIN-29009IeeeUniversidade Estadual Paulista (UNESP)Univ Castilla La ManchaMejia, Mario A. [UNESP]Macedo, Leonardo H. [UNESP]Munoz-Delgado, GregorioContreras, JavierPadilha-Feltrin, Antonio [UNESP]IEEE2022-04-28T17:22:38Z2022-04-28T17:22:38Z2020-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject62020 International Conference On Smart Energy Systems And Technologies (sest). New York: Ieee, 6 p., 2020.http://hdl.handle.net/11449/218714WOS:000722591200098Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2020 International Conference On Smart Energy Systems And Technologies (sest)info:eu-repo/semantics/openAccess2024-07-04T19:11:45Zoai:repositorio.unesp.br:11449/218714Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T20:27:01.690649Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
A Stochastic Model for Medium-Term Distribution System Planning Considering CO2 Emissions |
title |
A Stochastic Model for Medium-Term Distribution System Planning Considering CO2 Emissions |
spellingShingle |
A Stochastic Model for Medium-Term Distribution System Planning Considering CO2 Emissions Mejia, Mario A. [UNESP] Distribution system planning renewable energy sources stochastic programming voltage-dependent models |
title_short |
A Stochastic Model for Medium-Term Distribution System Planning Considering CO2 Emissions |
title_full |
A Stochastic Model for Medium-Term Distribution System Planning Considering CO2 Emissions |
title_fullStr |
A Stochastic Model for Medium-Term Distribution System Planning Considering CO2 Emissions |
title_full_unstemmed |
A Stochastic Model for Medium-Term Distribution System Planning Considering CO2 Emissions |
title_sort |
A Stochastic Model for Medium-Term Distribution System Planning Considering CO2 Emissions |
author |
Mejia, Mario A. [UNESP] |
author_facet |
Mejia, Mario A. [UNESP] Macedo, Leonardo H. [UNESP] Munoz-Delgado, Gregorio Contreras, Javier Padilha-Feltrin, Antonio [UNESP] IEEE |
author_role |
author |
author2 |
Macedo, Leonardo H. [UNESP] Munoz-Delgado, Gregorio Contreras, Javier Padilha-Feltrin, Antonio [UNESP] IEEE |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) Univ Castilla La Mancha |
dc.contributor.author.fl_str_mv |
Mejia, Mario A. [UNESP] Macedo, Leonardo H. [UNESP] Munoz-Delgado, Gregorio Contreras, Javier Padilha-Feltrin, Antonio [UNESP] IEEE |
dc.subject.por.fl_str_mv |
Distribution system planning renewable energy sources stochastic programming voltage-dependent models |
topic |
Distribution system planning renewable energy sources stochastic programming voltage-dependent models |
description |
This paper presents a new two-stage stochastic mixed-integer linear programming model for the medium-term expansion planning of power distribution systems considering the uncertainty associated with renewable energy sources, demand, and energy prices at substations. Investment decisions comprise the installation of both 1) classical alternatives such as conductors, capacitor banks, and voltage regulators, and 2) modern alternatives such as renewable distributed generation and energy storage units. Moreover, unlike conventional planning models, the proposed approach includes a voltage-dependent load representation. The proposed model aims to find a planning strategy that minimizes the investment and operating costs while meeting network operational constraints and CO2 emissions requirements. Tests are carried out with a 69-node distribution system and the results demonstrate the effectiveness and applicability of this model as an effective means of promoting an efficient, sustainable, and environmentally-friendly network. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-01-01 2022-04-28T17:22:38Z 2022-04-28T17:22:38Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
2020 International Conference On Smart Energy Systems And Technologies (sest). New York: Ieee, 6 p., 2020. http://hdl.handle.net/11449/218714 WOS:000722591200098 |
identifier_str_mv |
2020 International Conference On Smart Energy Systems And Technologies (sest). New York: Ieee, 6 p., 2020. WOS:000722591200098 |
url |
http://hdl.handle.net/11449/218714 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2020 International Conference On Smart Energy Systems And Technologies (sest) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
6 |
dc.publisher.none.fl_str_mv |
Ieee |
publisher.none.fl_str_mv |
Ieee |
dc.source.none.fl_str_mv |
Web of Science 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_ |
1808129202987204608 |