Optimal location and sizing of distributed generators in dc networks using a hybrid method based on parallel pbil and pso
This paper addresses the problem of the locating and sizing of distributed generators (DGs) in direct current (DC) grids and proposes a hybrid methodology based on a parallel version of the Population-Based Incremental Learning (PPBIL) algorithm and the Particle Swarm Optimization (PSO) method. The...
- Autores:
-
Grisales-Noreña, Luis Fernando
Montoya, Oscar Danilo
Ramos-Paja, Carlos Andrés
Hernandez-Escobedo, Quetzalcoatl
Perea-Moreno, Alberto-Jesus
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/9996
- Palabra clave:
- Direct current grids
Distributed generation
Combinatorial optimization
Parallel processing tool
Optimal power flow analysis
Power loss reduction
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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| dc.title.spa.fl_str_mv |
Optimal location and sizing of distributed generators in dc networks using a hybrid method based on parallel pbil and pso |
| title |
Optimal location and sizing of distributed generators in dc networks using a hybrid method based on parallel pbil and pso |
| spellingShingle |
Optimal location and sizing of distributed generators in dc networks using a hybrid method based on parallel pbil and pso Direct current grids Distributed generation Combinatorial optimization Parallel processing tool Optimal power flow analysis Power loss reduction LEMB |
| title_short |
Optimal location and sizing of distributed generators in dc networks using a hybrid method based on parallel pbil and pso |
| title_full |
Optimal location and sizing of distributed generators in dc networks using a hybrid method based on parallel pbil and pso |
| title_fullStr |
Optimal location and sizing of distributed generators in dc networks using a hybrid method based on parallel pbil and pso |
| title_full_unstemmed |
Optimal location and sizing of distributed generators in dc networks using a hybrid method based on parallel pbil and pso |
| title_sort |
Optimal location and sizing of distributed generators in dc networks using a hybrid method based on parallel pbil and pso |
| dc.creator.fl_str_mv |
Grisales-Noreña, Luis Fernando Montoya, Oscar Danilo Ramos-Paja, Carlos Andrés Hernandez-Escobedo, Quetzalcoatl Perea-Moreno, Alberto-Jesus |
| dc.contributor.author.none.fl_str_mv |
Grisales-Noreña, Luis Fernando Montoya, Oscar Danilo Ramos-Paja, Carlos Andrés Hernandez-Escobedo, Quetzalcoatl Perea-Moreno, Alberto-Jesus |
| dc.subject.keywords.spa.fl_str_mv |
Direct current grids Distributed generation Combinatorial optimization Parallel processing tool Optimal power flow analysis Power loss reduction |
| topic |
Direct current grids Distributed generation Combinatorial optimization Parallel processing tool Optimal power flow analysis Power loss reduction LEMB |
| dc.subject.armarc.none.fl_str_mv |
LEMB |
| description |
This paper addresses the problem of the locating and sizing of distributed generators (DGs) in direct current (DC) grids and proposes a hybrid methodology based on a parallel version of the Population-Based Incremental Learning (PPBIL) algorithm and the Particle Swarm Optimization (PSO) method. The objective function of the method is based on the reduction of the power loss by using a master-slave structure and the consideration of the set of restrictions associated with DC grids in a distributed generation environment. In such a structure, the master stage (PPBIL) finds the location of the generators and the slave stage (PSO) finds the corresponding sizes. For the purpose of comparison, eight additional hybrid methods were formed by using two additional location methods and two additional sizing methods, and this helped in the evaluation of the effectiveness of the proposed solution. Such an evaluation is illustrated with the electrical test systems composed of 10, 21 and 69 buses and simulated on the software, MATLAB. Finally, the results of the simulation demonstrated that the PPBIL–PSO method obtains the best balance between the reduction of power loss and the processing time. |
| publishDate |
2020 |
| dc.date.issued.none.fl_str_mv |
2020-11-01 |
| dc.date.accessioned.none.fl_str_mv |
2021-02-15T16:09:59Z |
| dc.date.available.none.fl_str_mv |
2021-02-15T16:09:59Z |
| dc.date.submitted.none.fl_str_mv |
2021-02-12 |
| dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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info:eu-repo/semantics/article |
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info:eu-repo/semantics/publishedVersion |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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publishedVersion |
| dc.identifier.citation.spa.fl_str_mv |
Grisales-Noreña, Luis F.; Montoya, Oscar D.; Ramos-Paja, Carlos A.; Hernandez-Escobedo, Quetzalcoatl; Perea-Moreno, Alberto-Jesus. 2020. "Optimal Location and Sizing of Distributed Generators in DC Networks Using a Hybrid Method Based on Parallel PBIL and PSO" Electronics 9, no. 11: 1808. https://doi.org/10.3390/electronics9111808 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12585/9996 |
| dc.identifier.url.none.fl_str_mv |
https://www.mdpi.com/2079-9292/9/11/1808 |
| dc.identifier.doi.none.fl_str_mv |
10.3390/electronics9111808 |
| dc.identifier.eissn.none.fl_str_mv |
2079-9292 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Tecnológica de Bolívar |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Universidad Tecnológica de Bolívar |
| identifier_str_mv |
Grisales-Noreña, Luis F.; Montoya, Oscar D.; Ramos-Paja, Carlos A.; Hernandez-Escobedo, Quetzalcoatl; Perea-Moreno, Alberto-Jesus. 2020. "Optimal Location and Sizing of Distributed Generators in DC Networks Using a Hybrid Method Based on Parallel PBIL and PSO" Electronics 9, no. 11: 1808. https://doi.org/10.3390/electronics9111808 10.3390/electronics9111808 2079-9292 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
| url |
https://hdl.handle.net/20.500.12585/9996 https://www.mdpi.com/2079-9292/9/11/1808 |
| dc.language.iso.spa.fl_str_mv |
eng |
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eng |
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http://purl.org/coar/access_right/c_abf2 |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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info:eu-repo/semantics/openAccess |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://purl.org/coar/access_right/c_abf2 |
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openAccess |
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27 páginas |
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application/pdf |
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Cartagena de Indias |
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Electronics 2020, 9(11), 1808 |
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Universidad Tecnológica de Bolívar |
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Grisales-Noreña, Luis Fernando7c27cda4-5fe4-4686-8f72-b0442c58a5d1Montoya, Oscar Danilo8a59ede1-6a4a-4d2e-abdc-d0afb14d4480Ramos-Paja, Carlos Andrésac1a64c3-4089-49e6-9f7a-b2285dd56903Hernandez-Escobedo, Quetzalcoatl6d13cf66-c5cb-46ae-9a39-26767b00d93dPerea-Moreno, Alberto-Jesuse78da438-8ed5-40ab-a12c-74e84e6d691b2021-02-15T16:09:59Z2021-02-15T16:09:59Z2020-11-012021-02-12Grisales-Noreña, Luis F.; Montoya, Oscar D.; Ramos-Paja, Carlos A.; Hernandez-Escobedo, Quetzalcoatl; Perea-Moreno, Alberto-Jesus. 2020. "Optimal Location and Sizing of Distributed Generators in DC Networks Using a Hybrid Method Based on Parallel PBIL and PSO" Electronics 9, no. 11: 1808. https://doi.org/10.3390/electronics9111808https://hdl.handle.net/20.500.12585/9996https://www.mdpi.com/2079-9292/9/11/180810.3390/electronics91118082079-9292Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarThis paper addresses the problem of the locating and sizing of distributed generators (DGs) in direct current (DC) grids and proposes a hybrid methodology based on a parallel version of the Population-Based Incremental Learning (PPBIL) algorithm and the Particle Swarm Optimization (PSO) method. The objective function of the method is based on the reduction of the power loss by using a master-slave structure and the consideration of the set of restrictions associated with DC grids in a distributed generation environment. In such a structure, the master stage (PPBIL) finds the location of the generators and the slave stage (PSO) finds the corresponding sizes. For the purpose of comparison, eight additional hybrid methods were formed by using two additional location methods and two additional sizing methods, and this helped in the evaluation of the effectiveness of the proposed solution. Such an evaluation is illustrated with the electrical test systems composed of 10, 21 and 69 buses and simulated on the software, MATLAB. Finally, the results of the simulation demonstrated that the PPBIL–PSO method obtains the best balance between the reduction of power loss and the processing time.27 páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Electronics 2020, 9(11), 1808Optimal location and sizing of distributed generators in dc networks using a hybrid method based on parallel pbil and psoinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Direct current gridsDistributed generationCombinatorial optimizationParallel processing toolOptimal power flow analysisPower loss reductionLEMBCartagena de IndiasPúblico generalSchonbergerschonberger, J.; Duke, R.; Round, S.D. DC-Bus Signaling: A Distributed Control Strategy for a Hybrid Renewable Nanogrid. 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Optimal allocation of combined DG and capacitor for real power loss minimization in distribution networks.Int. J. Electr. Power Energy Syst. 2013, 53, 967–973. 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