A fast-specialized point estimate method for the probabilistic optimal power flow in distribution systems with renewable distributed generation
Autor(a) principal: | |
---|---|
Data de Publicação: | 2021 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.ijepes.2021.107049 http://hdl.handle.net/11449/206305 |
Resumo: | The increasing presence of renewable distributed generation (DG) units such as photovoltaic and wind power generation is a major challenge for the suitable operation of the electrical distribution systems (EDSs). Uncertainties of renewable DG units and loads, related to the stochastic nature of solar irradiation, wind speed, and consumer behavior, require efficient tools that help the distribution system operator to properly define a control plan of the EDS. Within this framework, the probabilistic optimal power flow (POPF) provides statistical information(e.g. voltage profile, power flows, and power losses) according to the variation of the stochastic variables (e.g. power demand and injection of generation units). Many available POPF methods have been designed for transmission systems and/or are based on Monte Carlo simulation (MCS), which requires a high computational effort. On the other hand, other approaches adopt analytical methods, which are not applied considering the characteristics of distribution systems. This paper proposes a fast-specialized point estimate method for the POPF in EDSs with the presence of renewable DG units, based on a linearization of the Branch Flow equations and Hong's point estimate method. Due to its convex nature, the advantage of the proposed method is to use well-established linear programming commercial solvers to solve the problem. Numerical results using the IEEE 69-bus and a real EDS demonstrate the efficiency in terms of computational burden and accuracy of the proposed method compared to MCS and Cumulant approaches. |
id |
UNSP_d169b8b5c00ca1aa05e05aa3ee661d17 |
---|---|
oai_identifier_str |
oai:repositorio.unesp.br:11449/206305 |
network_acronym_str |
UNSP |
network_name_str |
Repositório Institucional da UNESP |
repository_id_str |
2946 |
spelling |
A fast-specialized point estimate method for the probabilistic optimal power flow in distribution systems with renewable distributed generationElectrical distribution systemsNon-linear programmingPoint estimate methodsProbabilistic optimal power flowRenewable energy sourcesUncertaintiesThe increasing presence of renewable distributed generation (DG) units such as photovoltaic and wind power generation is a major challenge for the suitable operation of the electrical distribution systems (EDSs). Uncertainties of renewable DG units and loads, related to the stochastic nature of solar irradiation, wind speed, and consumer behavior, require efficient tools that help the distribution system operator to properly define a control plan of the EDS. Within this framework, the probabilistic optimal power flow (POPF) provides statistical information(e.g. voltage profile, power flows, and power losses) according to the variation of the stochastic variables (e.g. power demand and injection of generation units). Many available POPF methods have been designed for transmission systems and/or are based on Monte Carlo simulation (MCS), which requires a high computational effort. On the other hand, other approaches adopt analytical methods, which are not applied considering the characteristics of distribution systems. This paper proposes a fast-specialized point estimate method for the POPF in EDSs with the presence of renewable DG units, based on a linearization of the Branch Flow equations and Hong's point estimate method. Due to its convex nature, the advantage of the proposed method is to use well-established linear programming commercial solvers to solve the problem. Numerical results using the IEEE 69-bus and a real EDS demonstrate the efficiency in terms of computational burden and accuracy of the proposed method compared to MCS and Cumulant approaches.Department of Electrical Engineering Londrina State UniversitySchool of Energy Engineering São Paulo State University UNESPSchool of Energy Engineering São Paulo State University UNESPUniversidade Estadual de Londrina (UEL)Universidade Estadual Paulista (Unesp)Gallego, Luis A.Franco, John F. [UNESP]Cordero, Luis G. [UNESP]2021-06-25T10:29:50Z2021-06-25T10:29:50Z2021-10-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.ijepes.2021.107049International Journal of Electrical Power and Energy Systems, v. 131.0142-0615http://hdl.handle.net/11449/20630510.1016/j.ijepes.2021.1070492-s2.0-85105352482Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengInternational Journal of Electrical Power and Energy Systemsinfo:eu-repo/semantics/openAccess2021-10-23T03:12:20Zoai:repositorio.unesp.br:11449/206305Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-05-23T11:54:10.391152Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
A fast-specialized point estimate method for the probabilistic optimal power flow in distribution systems with renewable distributed generation |
title |
A fast-specialized point estimate method for the probabilistic optimal power flow in distribution systems with renewable distributed generation |
spellingShingle |
A fast-specialized point estimate method for the probabilistic optimal power flow in distribution systems with renewable distributed generation Gallego, Luis A. Electrical distribution systems Non-linear programming Point estimate methods Probabilistic optimal power flow Renewable energy sources Uncertainties |
title_short |
A fast-specialized point estimate method for the probabilistic optimal power flow in distribution systems with renewable distributed generation |
title_full |
A fast-specialized point estimate method for the probabilistic optimal power flow in distribution systems with renewable distributed generation |
title_fullStr |
A fast-specialized point estimate method for the probabilistic optimal power flow in distribution systems with renewable distributed generation |
title_full_unstemmed |
A fast-specialized point estimate method for the probabilistic optimal power flow in distribution systems with renewable distributed generation |
title_sort |
A fast-specialized point estimate method for the probabilistic optimal power flow in distribution systems with renewable distributed generation |
author |
Gallego, Luis A. |
author_facet |
Gallego, Luis A. Franco, John F. [UNESP] Cordero, Luis G. [UNESP] |
author_role |
author |
author2 |
Franco, John F. [UNESP] Cordero, Luis G. [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual de Londrina (UEL) Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Gallego, Luis A. Franco, John F. [UNESP] Cordero, Luis G. [UNESP] |
dc.subject.por.fl_str_mv |
Electrical distribution systems Non-linear programming Point estimate methods Probabilistic optimal power flow Renewable energy sources Uncertainties |
topic |
Electrical distribution systems Non-linear programming Point estimate methods Probabilistic optimal power flow Renewable energy sources Uncertainties |
description |
The increasing presence of renewable distributed generation (DG) units such as photovoltaic and wind power generation is a major challenge for the suitable operation of the electrical distribution systems (EDSs). Uncertainties of renewable DG units and loads, related to the stochastic nature of solar irradiation, wind speed, and consumer behavior, require efficient tools that help the distribution system operator to properly define a control plan of the EDS. Within this framework, the probabilistic optimal power flow (POPF) provides statistical information(e.g. voltage profile, power flows, and power losses) according to the variation of the stochastic variables (e.g. power demand and injection of generation units). Many available POPF methods have been designed for transmission systems and/or are based on Monte Carlo simulation (MCS), which requires a high computational effort. On the other hand, other approaches adopt analytical methods, which are not applied considering the characteristics of distribution systems. This paper proposes a fast-specialized point estimate method for the POPF in EDSs with the presence of renewable DG units, based on a linearization of the Branch Flow equations and Hong's point estimate method. Due to its convex nature, the advantage of the proposed method is to use well-established linear programming commercial solvers to solve the problem. Numerical results using the IEEE 69-bus and a real EDS demonstrate the efficiency in terms of computational burden and accuracy of the proposed method compared to MCS and Cumulant approaches. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-06-25T10:29:50Z 2021-06-25T10:29:50Z 2021-10-01 |
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.ijepes.2021.107049 International Journal of Electrical Power and Energy Systems, v. 131. 0142-0615 http://hdl.handle.net/11449/206305 10.1016/j.ijepes.2021.107049 2-s2.0-85105352482 |
url |
http://dx.doi.org/10.1016/j.ijepes.2021.107049 http://hdl.handle.net/11449/206305 |
identifier_str_mv |
International Journal of Electrical Power and Energy Systems, v. 131. 0142-0615 10.1016/j.ijepes.2021.107049 2-s2.0-85105352482 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
International Journal of Electrical Power and Energy Systems |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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_ |
1803045900870746112 |