Hybrid ga-socp approach for placement and sizing of distributed generators in DC networks
This research addresses the problem of the optimal location and sizing distributed generators (DGs) in direct current (DC) distribution networks from the combinatorial optimization. It is proposed a master–slave optimization approach in order to solve the problems of placement and location of DGs, r...
- Autores:
-
Montoya, Oscar Danilo
Gil-González, Walter
Grisales-Noreña, Luis Fernando
- 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:20.500.12585/9976
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/9976
https://www.mdpi.com/2076-3417/10/23/8616/htm
- Palabra clave:
- Direct current networks
Optimal power flow analysis
Metaheuristic optimization
Master-slave optimization
Genetic algorithms
Second-order cone programming
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Hybrid ga-socp approach for placement and sizing of distributed generators in DC networks |
| title |
Hybrid ga-socp approach for placement and sizing of distributed generators in DC networks |
| spellingShingle |
Hybrid ga-socp approach for placement and sizing of distributed generators in DC networks Direct current networks Optimal power flow analysis Metaheuristic optimization Master-slave optimization Genetic algorithms Second-order cone programming LEMB |
| title_short |
Hybrid ga-socp approach for placement and sizing of distributed generators in DC networks |
| title_full |
Hybrid ga-socp approach for placement and sizing of distributed generators in DC networks |
| title_fullStr |
Hybrid ga-socp approach for placement and sizing of distributed generators in DC networks |
| title_full_unstemmed |
Hybrid ga-socp approach for placement and sizing of distributed generators in DC networks |
| title_sort |
Hybrid ga-socp approach for placement and sizing of distributed generators in DC networks |
| dc.creator.fl_str_mv |
Montoya, Oscar Danilo Gil-González, Walter Grisales-Noreña, Luis Fernando |
| dc.contributor.author.none.fl_str_mv |
Montoya, Oscar Danilo Gil-González, Walter Grisales-Noreña, Luis Fernando |
| dc.subject.keywords.spa.fl_str_mv |
Direct current networks Optimal power flow analysis Metaheuristic optimization Master-slave optimization Genetic algorithms Second-order cone programming |
| topic |
Direct current networks Optimal power flow analysis Metaheuristic optimization Master-slave optimization Genetic algorithms Second-order cone programming LEMB |
| dc.subject.armarc.none.fl_str_mv |
LEMB |
| description |
This research addresses the problem of the optimal location and sizing distributed generators (DGs) in direct current (DC) distribution networks from the combinatorial optimization. It is proposed a master–slave optimization approach in order to solve the problems of placement and location of DGs, respectively. The master stage applies to the classical Chu & Beasley genetic algorithm (GA), while the slave stage resolves a second-order cone programming reformulation of the optimal power flow problem for DC grids. This master–slave approach generates a hybrid optimization approach, named GA-SOCP. The main advantage of optimal dimensioning of DGs via SOCP is that this method makes part of the exact mathematical optimization that guarantees the possibility of finding the global optimal solution due to the solution space’s convex structure, which is a clear improvement regarding classical metaheuristic optimization methodologies. Numerical comparisons with hybrid and exact optimization approaches reported in the literature demonstrate the proposed hybrid GA-SOCP approach’s effectiveness and robustness to achieve the global optimal solution. Two test feeders compose of 21 and 69 nodes that can locate three distributed generators are considered. All of the computational validations have been carried out in the MATLAB software and the CVX tool for convex optimization. |
| publishDate |
2020 |
| dc.date.issued.none.fl_str_mv |
2020-12-02 |
| dc.date.accessioned.none.fl_str_mv |
2021-02-09T22:18:26Z |
| dc.date.available.none.fl_str_mv |
2021-02-09T22:18:26Z |
| dc.date.submitted.none.fl_str_mv |
2021-02-09 |
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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 |
Montoya, Oscar D.; Gil-González, Walter; Grisales-Noreña, Luis F. 2020. "Hybrid GA-SOCP Approach for Placement and Sizing of Distributed Generators in DC Networks" Appl. Sci. 10, no. 23: 8616. https://doi.org/10.3390/app10238616 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12585/9976 |
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https://www.mdpi.com/2076-3417/10/23/8616/htm |
| dc.identifier.doi.none.fl_str_mv |
10.3390/app10238616 |
| 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 |
Montoya, Oscar D.; Gil-González, Walter; Grisales-Noreña, Luis F. 2020. "Hybrid GA-SOCP Approach for Placement and Sizing of Distributed Generators in DC Networks" Appl. Sci. 10, no. 23: 8616. https://doi.org/10.3390/app10238616 10.3390/app10238616 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
| url |
https://hdl.handle.net/20.500.12585/9976 https://www.mdpi.com/2076-3417/10/23/8616/htm |
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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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18 páginas |
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application/pdf |
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Cartagena de Indias |
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Universidad Tecnológica de Bolívar |
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Montoya, Oscar Danilo8a59ede1-6a4a-4d2e-abdc-d0afb14d4480Gil-González, Walter1747fed9-7818-4c10-a283-efb3c73ebb27Grisales-Noreña, Luis Fernando7c27cda4-5fe4-4686-8f72-b0442c58a5d12021-02-09T22:18:26Z2021-02-09T22:18:26Z2020-12-022021-02-09Montoya, Oscar D.; Gil-González, Walter; Grisales-Noreña, Luis F. 2020. "Hybrid GA-SOCP Approach for Placement and Sizing of Distributed Generators in DC Networks" Appl. Sci. 10, no. 23: 8616. https://doi.org/10.3390/app10238616https://hdl.handle.net/20.500.12585/9976https://www.mdpi.com/2076-3417/10/23/8616/htm10.3390/app10238616Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarThis research addresses the problem of the optimal location and sizing distributed generators (DGs) in direct current (DC) distribution networks from the combinatorial optimization. It is proposed a master–slave optimization approach in order to solve the problems of placement and location of DGs, respectively. The master stage applies to the classical Chu & Beasley genetic algorithm (GA), while the slave stage resolves a second-order cone programming reformulation of the optimal power flow problem for DC grids. This master–slave approach generates a hybrid optimization approach, named GA-SOCP. The main advantage of optimal dimensioning of DGs via SOCP is that this method makes part of the exact mathematical optimization that guarantees the possibility of finding the global optimal solution due to the solution space’s convex structure, which is a clear improvement regarding classical metaheuristic optimization methodologies. Numerical comparisons with hybrid and exact optimization approaches reported in the literature demonstrate the proposed hybrid GA-SOCP approach’s effectiveness and robustness to achieve the global optimal solution. Two test feeders compose of 21 and 69 nodes that can locate three distributed generators are considered. All of the computational validations have been carried out in the MATLAB software and the CVX tool for convex optimization.18 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_abf2Hybrid ga-socp approach for placement and sizing of distributed generators in DC networksinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Direct current networksOptimal power flow analysisMetaheuristic optimizationMaster-slave optimizationGenetic algorithmsSecond-order cone programmingLEMBCartagena de IndiasPúblico generalKwon, M.; Choi, S. Control scheme for autonomous and smooth mode switching of bidirectional DC–DC converters in a DC microgrid. IEEE Trans. Power Electron. 2017, 33, 7094–7104.Garcés, A. 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Convex Optimization for the Optimal Power Flow on DC Distribution Systems. In Handbook of Optimization in Electric Power Distribution Systems; Springer: Berlin/Heidelberg, Germany, 2020; pp. 121–137Garcés, A. On the Convergence of Newton’s Method in Power Flow Studies for DC Microgrids. IEEE Trans. Power Syst. 2018, 33, 5770–5777.Garces, A. Uniqueness of the power flow solutions in low voltage direct current grids. Electr. Power Syst. Res. 2017, 151, 149–153.Hamad, A.A.; El-Saadany, E.F. Multi-agent supervisory control for optimal economic dispatch in DC microgrids. Sustain. Cities Soc. 2016, 27, 129–136.Barabanov, N.; Ortega, R.; Griñó, R.; Polyak, B. On Existence and Stability of Equilibria of Linear Time-Invariant Systems With Constant Power Loads. IEEE Trans. Circuits Syst. I 2016, 63, 114–121.Chauhan, R.K.; Chauhan, K.; Guerrero, J.M. Controller design and stability analysis of grid connected DC microgrid. J. Renew. Sustain. Energy 2018, 10, 035101.Montoya, O.D.; Gil-González, W.; Grisales-Noreña, L. Relaxed convex model for optimal location and sizing of DGs in DC grids using sequential quadratic programming and random hyperplane approaches. Int. J. Electr. Power Energy Syst. 2020, 115, 105442.Grisales-Noreña, L.; Montoya, O.D.; Ramos-Paja, C.A. An energy management system for optimal operation of BSS in DC distributed generation environments based on a parallel PSO algorithm. J. Energy Storage 2020, 29, 101488.Fantauzzi, M.; Lauria, D.; Mottola, F.; Scalfati, A. Sizing energy storage systems in DC networks: A general methodology based upon power losses minimization. Appl. Energy 2017, 187, 862–872.Castillo-Calzadilla, T.; Macarulla, A.M.; Kamara-Esteban, O.; Borges, C.E. A case study comparison between photovoltaic and fossil generation based on direct current hybrid microgrids to power a service building. J. Clean. Prod. 2020, 244, 118870.Chiodo, E.; Fantauzzi, M.; Lauria, D.; Mottola, F. 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