Probabilistic Algorithm based on 2m+1 Point Estimate Method Edgeworth considering Voltage Confidence Intervals for Optimal PV Generation
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/PMAPS53380.2022.9810644 http://hdl.handle.net/11449/240554 |
Resumo: | Photovoltaic (PV) systems widespread into distribution networks due to its environmentally friendly source of energy, cost-competitive option and system support benefits. However, traditional distribution networks were not designed to operate under a high penetration of intermittent generation posing technical challenges for grid operation and planning. Therefore, probabilistic tools become suitable to cater for uncertainties in generation and demand, thus, leading to a more realistic network representation. Furthermore, the need for harvesting potential energy in an uncertain environment are essential for an efficient grid operation. In this context, this work proposes a probabilistic algorithm based on 2m+1 Point Estimate Method Edgeworth to tackle technical issues considering voltage confidence levels that is used for maximizing PV generation. Tests in a IEEE 33 buses radial distribution system using the proposed probabilistic algorithm yields higher accuracy of cost probability distribution, voltage confidence intervals and a faster computational time when compared to Monte Carlo simulation. |
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Probabilistic Algorithm based on 2m+1 Point Estimate Method Edgeworth considering Voltage Confidence Intervals for Optimal PV Generation2m+1 Point Estimate Method EdgeworthOptimal Probability PV GenerationProbabilistic Algorithm OptimizationVoltage Confidence IntervalsPhotovoltaic (PV) systems widespread into distribution networks due to its environmentally friendly source of energy, cost-competitive option and system support benefits. However, traditional distribution networks were not designed to operate under a high penetration of intermittent generation posing technical challenges for grid operation and planning. Therefore, probabilistic tools become suitable to cater for uncertainties in generation and demand, thus, leading to a more realistic network representation. Furthermore, the need for harvesting potential energy in an uncertain environment are essential for an efficient grid operation. In this context, this work proposes a probabilistic algorithm based on 2m+1 Point Estimate Method Edgeworth to tackle technical issues considering voltage confidence levels that is used for maximizing PV generation. Tests in a IEEE 33 buses radial distribution system using the proposed probabilistic algorithm yields higher accuracy of cost probability distribution, voltage confidence intervals and a faster computational time when compared to Monte Carlo simulation.São Paulo State University Dep. of Electrical EngineeringGecad Polytechnic of PortoSão Paulo State University Dep. of Electrical EngineeringUniversidade Estadual Paulista (UNESP)Gecad Polytechnic of PortoBautista, Luis Gustavo Cordero [UNESP]Soares, JoaoBaquero, John Fredy Franco [UNESP]Vale, Zita2023-03-01T20:22:29Z2023-03-01T20:22:29Z2022-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/PMAPS53380.2022.98106442022 17th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2022.http://hdl.handle.net/11449/24055410.1109/PMAPS53380.2022.98106442-s2.0-85135022589Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2022 17th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2022info:eu-repo/semantics/openAccess2023-03-01T20:22:29Zoai:repositorio.unesp.br:11449/240554Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T17:26:28.639969Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Probabilistic Algorithm based on 2m+1 Point Estimate Method Edgeworth considering Voltage Confidence Intervals for Optimal PV Generation |
title |
Probabilistic Algorithm based on 2m+1 Point Estimate Method Edgeworth considering Voltage Confidence Intervals for Optimal PV Generation |
spellingShingle |
Probabilistic Algorithm based on 2m+1 Point Estimate Method Edgeworth considering Voltage Confidence Intervals for Optimal PV Generation Bautista, Luis Gustavo Cordero [UNESP] 2m+1 Point Estimate Method Edgeworth Optimal Probability PV Generation Probabilistic Algorithm Optimization Voltage Confidence Intervals |
title_short |
Probabilistic Algorithm based on 2m+1 Point Estimate Method Edgeworth considering Voltage Confidence Intervals for Optimal PV Generation |
title_full |
Probabilistic Algorithm based on 2m+1 Point Estimate Method Edgeworth considering Voltage Confidence Intervals for Optimal PV Generation |
title_fullStr |
Probabilistic Algorithm based on 2m+1 Point Estimate Method Edgeworth considering Voltage Confidence Intervals for Optimal PV Generation |
title_full_unstemmed |
Probabilistic Algorithm based on 2m+1 Point Estimate Method Edgeworth considering Voltage Confidence Intervals for Optimal PV Generation |
title_sort |
Probabilistic Algorithm based on 2m+1 Point Estimate Method Edgeworth considering Voltage Confidence Intervals for Optimal PV Generation |
author |
Bautista, Luis Gustavo Cordero [UNESP] |
author_facet |
Bautista, Luis Gustavo Cordero [UNESP] Soares, Joao Baquero, John Fredy Franco [UNESP] Vale, Zita |
author_role |
author |
author2 |
Soares, Joao Baquero, John Fredy Franco [UNESP] Vale, Zita |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) Gecad Polytechnic of Porto |
dc.contributor.author.fl_str_mv |
Bautista, Luis Gustavo Cordero [UNESP] Soares, Joao Baquero, John Fredy Franco [UNESP] Vale, Zita |
dc.subject.por.fl_str_mv |
2m+1 Point Estimate Method Edgeworth Optimal Probability PV Generation Probabilistic Algorithm Optimization Voltage Confidence Intervals |
topic |
2m+1 Point Estimate Method Edgeworth Optimal Probability PV Generation Probabilistic Algorithm Optimization Voltage Confidence Intervals |
description |
Photovoltaic (PV) systems widespread into distribution networks due to its environmentally friendly source of energy, cost-competitive option and system support benefits. However, traditional distribution networks were not designed to operate under a high penetration of intermittent generation posing technical challenges for grid operation and planning. Therefore, probabilistic tools become suitable to cater for uncertainties in generation and demand, thus, leading to a more realistic network representation. Furthermore, the need for harvesting potential energy in an uncertain environment are essential for an efficient grid operation. In this context, this work proposes a probabilistic algorithm based on 2m+1 Point Estimate Method Edgeworth to tackle technical issues considering voltage confidence levels that is used for maximizing PV generation. Tests in a IEEE 33 buses radial distribution system using the proposed probabilistic algorithm yields higher accuracy of cost probability distribution, voltage confidence intervals and a faster computational time when compared to Monte Carlo simulation. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-01 2023-03-01T20:22:29Z 2023-03-01T20:22:29Z |
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 |
http://dx.doi.org/10.1109/PMAPS53380.2022.9810644 2022 17th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2022. http://hdl.handle.net/11449/240554 10.1109/PMAPS53380.2022.9810644 2-s2.0-85135022589 |
url |
http://dx.doi.org/10.1109/PMAPS53380.2022.9810644 http://hdl.handle.net/11449/240554 |
identifier_str_mv |
2022 17th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2022. 10.1109/PMAPS53380.2022.9810644 2-s2.0-85135022589 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2022 17th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2022 |
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_ |
1808128812037177344 |