Optimal Power Dispatch of Distributed Generators in Direct Current Networks Using a Master–Slave Methodology that Combines the Salp Swarm Algorithm and the Successive Approximation Method
This paper addresses the Optimal Power Flow (OPF) problem in Direct Current (DC) networks by considering the integration of Distributed Generators (DGs). In order to model said problem, this study employs a mathematical formulation that has, as the objective function, the reduction in power losses a...
- Autores:
-
Rosales Muñoz, Andrés Alfonso
Grisales-Noreña, Luis Fernando
Montano, Jhon
Montoya, Oscar Danilo
Giral-Ramírez, Diego Armando
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/10434
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/10434
https://doi.org/10.3390/electronics10222837
- Palabra clave:
- Optimal power flow
Power flow problem
Optimization algorithms
DC networks
Electrical energy
Combinatorial optimization
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Optimal Power Dispatch of Distributed Generators in Direct Current Networks Using a Master–Slave Methodology that Combines the Salp Swarm Algorithm and the Successive Approximation Method |
| title |
Optimal Power Dispatch of Distributed Generators in Direct Current Networks Using a Master–Slave Methodology that Combines the Salp Swarm Algorithm and the Successive Approximation Method |
| spellingShingle |
Optimal Power Dispatch of Distributed Generators in Direct Current Networks Using a Master–Slave Methodology that Combines the Salp Swarm Algorithm and the Successive Approximation Method Optimal power flow Power flow problem Optimization algorithms DC networks Electrical energy Combinatorial optimization LEMB |
| title_short |
Optimal Power Dispatch of Distributed Generators in Direct Current Networks Using a Master–Slave Methodology that Combines the Salp Swarm Algorithm and the Successive Approximation Method |
| title_full |
Optimal Power Dispatch of Distributed Generators in Direct Current Networks Using a Master–Slave Methodology that Combines the Salp Swarm Algorithm and the Successive Approximation Method |
| title_fullStr |
Optimal Power Dispatch of Distributed Generators in Direct Current Networks Using a Master–Slave Methodology that Combines the Salp Swarm Algorithm and the Successive Approximation Method |
| title_full_unstemmed |
Optimal Power Dispatch of Distributed Generators in Direct Current Networks Using a Master–Slave Methodology that Combines the Salp Swarm Algorithm and the Successive Approximation Method |
| title_sort |
Optimal Power Dispatch of Distributed Generators in Direct Current Networks Using a Master–Slave Methodology that Combines the Salp Swarm Algorithm and the Successive Approximation Method |
| dc.creator.fl_str_mv |
Rosales Muñoz, Andrés Alfonso Grisales-Noreña, Luis Fernando Montano, Jhon Montoya, Oscar Danilo Giral-Ramírez, Diego Armando |
| dc.contributor.author.none.fl_str_mv |
Rosales Muñoz, Andrés Alfonso Grisales-Noreña, Luis Fernando Montano, Jhon Montoya, Oscar Danilo Giral-Ramírez, Diego Armando |
| dc.subject.keywords.es_CO.fl_str_mv |
Optimal power flow Power flow problem Optimization algorithms DC networks Electrical energy Combinatorial optimization |
| topic |
Optimal power flow Power flow problem Optimization algorithms DC networks Electrical energy Combinatorial optimization LEMB |
| dc.subject.armarc.none.fl_str_mv |
LEMB |
| description |
This paper addresses the Optimal Power Flow (OPF) problem in Direct Current (DC) networks by considering the integration of Distributed Generators (DGs). In order to model said problem, this study employs a mathematical formulation that has, as the objective function, the reduction in power losses associated with energy transport and that considers the set of constraints that compose DC networks in an environment of distributed generation. To solve this mathematical formulation, a master–slave methodology that combines the Salp Swarm Algorithm (SSA) and the Successive Approximations (SA) method was used here. The effectiveness, repeatability, and robustness of the proposed solution methodology was validated using two test systems (the 21- and 69-node systems), five other optimization methods reported in the specialized literature, and three different penetration levels of distributed generation: 20%, 40%, and 60% of the power provided by the slack node in the test systems in an environment with no DGs (base case). All simulations were executed 100 times for each solution methodology in the different test scenarios. The purpose of this was to evaluate the repeatability of the solutions provided by each technique by analyzing their minimum and average power losses and required processing times. The results show that the proposed solution methodology achieved the best trade-off between (minimum and average) power loss reduction and processing time for networks of any size. |
| publishDate |
2021 |
| dc.date.issued.none.fl_str_mv |
2021-11-18 |
| dc.date.accessioned.none.fl_str_mv |
2022-02-02T20:45:48Z |
| dc.date.available.none.fl_str_mv |
2022-02-02T20:45:48Z |
| dc.date.submitted.none.fl_str_mv |
2022-02-01 |
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info:eu-repo/semantics/article |
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info:eu-repo/semantics/restrictedAccess |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
| dc.identifier.citation.es_CO.fl_str_mv |
Rosales Muñoz, A.A.; Grisales-Noreña, L.F.; Montano, J.; Montoya, O.D.; Giral-Ramírez, D.A. Optimal Power Dispatch of Distributed Generators in Direct Current Networks Using a Master–Slave Methodology that Combines the Salp Swarm Algorithm and the Successive Approximation Method. Electronics 2021, 10, 2837. https://doi.org/10.3390/electronics10222837 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12585/10434 |
| dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.3390/electronics10222837 |
| dc.identifier.instname.es_CO.fl_str_mv |
Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
| identifier_str_mv |
Rosales Muñoz, A.A.; Grisales-Noreña, L.F.; Montano, J.; Montoya, O.D.; Giral-Ramírez, D.A. Optimal Power Dispatch of Distributed Generators in Direct Current Networks Using a Master–Slave Methodology that Combines the Salp Swarm Algorithm and the Successive Approximation Method. Electronics 2021, 10, 2837. https://doi.org/10.3390/electronics10222837 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
| url |
https://hdl.handle.net/20.500.12585/10434 https://doi.org/10.3390/electronics10222837 |
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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 - vol. 10 n° 22 (2021) |
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Universidad Tecnológica de Bolívar |
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Rosales Muñoz, Andrés Alfonso1cadd052-2b2e-4872-b1d3-7679f6be5f2aGrisales-Noreña, Luis Fernando7c27cda4-5fe4-4686-8f72-b0442c58a5d1Montano, Jhon5edc0c05-f7f1-4a81-8b30-3981975c221dMontoya, Oscar Danilo8a59ede1-6a4a-4d2e-abdc-d0afb14d4480Giral-Ramírez, Diego Armandoa9612d05-bc90-49f9-94c7-20a0766e00f52022-02-02T20:45:48Z2022-02-02T20:45:48Z2021-11-182022-02-01Rosales Muñoz, A.A.; Grisales-Noreña, L.F.; Montano, J.; Montoya, O.D.; Giral-Ramírez, D.A. Optimal Power Dispatch of Distributed Generators in Direct Current Networks Using a Master–Slave Methodology that Combines the Salp Swarm Algorithm and the Successive Approximation Method. Electronics 2021, 10, 2837. https://doi.org/10.3390/electronics10222837https://hdl.handle.net/20.500.12585/10434https://doi.org/10.3390/electronics10222837Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarThis paper addresses the Optimal Power Flow (OPF) problem in Direct Current (DC) networks by considering the integration of Distributed Generators (DGs). In order to model said problem, this study employs a mathematical formulation that has, as the objective function, the reduction in power losses associated with energy transport and that considers the set of constraints that compose DC networks in an environment of distributed generation. To solve this mathematical formulation, a master–slave methodology that combines the Salp Swarm Algorithm (SSA) and the Successive Approximations (SA) method was used here. The effectiveness, repeatability, and robustness of the proposed solution methodology was validated using two test systems (the 21- and 69-node systems), five other optimization methods reported in the specialized literature, and three different penetration levels of distributed generation: 20%, 40%, and 60% of the power provided by the slack node in the test systems in an environment with no DGs (base case). All simulations were executed 100 times for each solution methodology in the different test scenarios. The purpose of this was to evaluate the repeatability of the solutions provided by each technique by analyzing their minimum and average power losses and required processing times. The results show that the proposed solution methodology achieved the best trade-off between (minimum and average) power loss reduction and processing time for networks of any size.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 - vol. 10 n° 22 (2021)Optimal Power Dispatch of Distributed Generators in Direct Current Networks Using a Master–Slave Methodology that Combines the Salp Swarm Algorithm and the Successive Approximation Methodinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/restrictedAccesshttp://purl.org/coar/resource_type/c_2df8fbb1Optimal power flowPower flow problemOptimization algorithmsDC networksElectrical energyCombinatorial optimizationLEMBCartagena de IndiasGurven, M.; Walker, R. Energetic demand of multiple dependents and the evolution of slow human growth. Proc. R. Soc. B Biol. 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In Proceedings of the 2015 Third World Conference on Complex Systems (WCCS), Marrakech, Morocco, 23–25 November 2015; pp. 1–7.Mirjalili, S.; Jangir, P.; Mirjalili, S.Z.; Saremi, S.; Trivedi, I.N. Optimization of problems with multiple objectives using the multi-verse optimization algorithm. Knowl.-Based Syst. 2017, 134, 50–71Gil-González, W.; Montoya, O.D.; Holguín, E.; Garces, A.; Grisales-Noreña, L.F. Economic dispatch of energy storage systems in dc microgrids employing a semidefinite programming model. J. Energy Storage 2019, 21, 1–8.Gil-González, W.; Montoya, O.D.; Holguín, E.; Garces, A.; Grisales-Noreña, L.F. Economic dispatch of energy storage systems in dc microgrids employing a semidefinite programming model. J. Energy Storage 2019, 21, 1–8.. Garcés, A. On the convergence of Newton’s method in power flow studies for DC microgrids. IEEE Trans. Power Syst. 2018, 33, 5770–5777Rosales-Muñoz, A.A.; Grisales-Noreña, L.F.; Montano, J.; Montoya, O.D.; Perea-Moreno, A.J. Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Direct Current Electrical Networks. 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