Optimal power flow in direct-current power grids via black hole optimization
This paper addresses the Optimal Power Flow (OPF) problem in DC power microgrids via a combinatorial optimization technique known as Black Hole Optimization (BHO). Such optimization method allows to solve OPF problems via algorithmic strategies trough a master-slave formulation. In the master stage,...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2019
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/8769
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/8769
- Palabra clave:
- Black hole optimization
Direct-current networks
Gauss-seidel numerical method
Metaheuristic optimization
Optimal power flow
Power losses minimization
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Optimal power flow in direct-current power grids via black hole optimization |
| title |
Optimal power flow in direct-current power grids via black hole optimization |
| spellingShingle |
Optimal power flow in direct-current power grids via black hole optimization Black hole optimization Direct-current networks Gauss-seidel numerical method Metaheuristic optimization Optimal power flow Power losses minimization |
| title_short |
Optimal power flow in direct-current power grids via black hole optimization |
| title_full |
Optimal power flow in direct-current power grids via black hole optimization |
| title_fullStr |
Optimal power flow in direct-current power grids via black hole optimization |
| title_full_unstemmed |
Optimal power flow in direct-current power grids via black hole optimization |
| title_sort |
Optimal power flow in direct-current power grids via black hole optimization |
| dc.subject.keywords.none.fl_str_mv |
Black hole optimization Direct-current networks Gauss-seidel numerical method Metaheuristic optimization Optimal power flow Power losses minimization |
| topic |
Black hole optimization Direct-current networks Gauss-seidel numerical method Metaheuristic optimization Optimal power flow Power losses minimization |
| description |
This paper addresses the Optimal Power Flow (OPF) problem in DC power microgrids via a combinatorial optimization technique known as Black Hole Optimization (BHO). Such optimization method allows to solve OPF problems via algorithmic strategies trough a master-slave formulation. In the master stage, the total power generated by each Distributed Generator (DG) is determined by the BHO, while the slave strategy is entrusted with solving the resulting conventional power flow problem via a classical Gauss-Seidel (GS) numerical method. For comparison purposes, this work uses nonlinear optimization methods available in General Algebraic Modeling System (GAMS) as well as continuous metaheuristic optimization techniques. Two test feeders with 21 and 69 nodes were considered for validating the proposed hybrid BHO-GS optimization method, which enables to demonstrate its applicability, robustness and efficiency compared to conventional approaches. The results of all the simulations were obtained via MATLAB 2017a. © 2019 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING. |
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2019 |
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2019-11-06T19:05:22Z |
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2019-11-06T19:05:22Z |
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2019 |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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info:eu-repo/semantics/article |
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Artículo |
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publishedVersion |
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Advances in Electrical and Electronic Engineering; Vol. 17, Núm. 1; pp. 24-32 |
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1336-1376 |
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https://hdl.handle.net/20.500.12585/8769 |
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10.15598/aeee.v17i1.3069 |
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Universidad Tecnológica de Bolívar |
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Repositorio UTB |
| identifier_str_mv |
Advances in Electrical and Electronic Engineering; Vol. 17, Núm. 1; pp. 24-32 1336-1376 10.15598/aeee.v17i1.3069 Universidad Tecnológica de Bolívar Repositorio UTB |
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https://hdl.handle.net/20.500.12585/8769 |
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eng |
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eng |
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Atribución-NoComercial 4.0 Internacional |
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VSB-Technical University of Ostrava |
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2019-11-06T19:05:22Z2019-11-06T19:05:22Z2019Advances in Electrical and Electronic Engineering; Vol. 17, Núm. 1; pp. 24-321336-1376https://hdl.handle.net/20.500.12585/876910.15598/aeee.v17i1.3069Universidad Tecnológica de BolívarRepositorio UTBThis paper addresses the Optimal Power Flow (OPF) problem in DC power microgrids via a combinatorial optimization technique known as Black Hole Optimization (BHO). Such optimization method allows to solve OPF problems via algorithmic strategies trough a master-slave formulation. In the master stage, the total power generated by each Distributed Generator (DG) is determined by the BHO, while the slave strategy is entrusted with solving the resulting conventional power flow problem via a classical Gauss-Seidel (GS) numerical method. For comparison purposes, this work uses nonlinear optimization methods available in General Algebraic Modeling System (GAMS) as well as continuous metaheuristic optimization techniques. Two test feeders with 21 and 69 nodes were considered for validating the proposed hybrid BHO-GS optimization method, which enables to demonstrate its applicability, robustness and efficiency compared to conventional approaches. The results of all the simulations were obtained via MATLAB 2017a. © 2019 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING.Universidad Nacional de Colombia, Departamento Administrativo de Ciencia, Tecnología e Innovación, COLCIENCIAS, C2018P020, Department of Science, Information Technology and Innovation, Queensland Government, P17211Recurso electrónicoapplication/pdfengVSB-Technical University of Ostravahttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2https://www2.scopus.com/inward/record.uri?eid=2-s2.0-85067041743&doi=10.15598%2faeee.v17i1.3069&partnerID=40&md5=1e6f665d9ca690ea338902db0546c5d2Scopus 57209250499Scopus 56919564100Scopus 57208126635Scopus 55791991200Optimal power flow in direct-current power grids via black hole optimizationinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Black hole optimizationDirect-current networksGauss-seidel numerical methodMetaheuristic optimizationOptimal power flowPower losses minimizationVelasquez, O.S.Montoya, O.D.Garrido Arévalo, Víctor ManuelGrisales-Noreña L.F.Montoya, O.D., Gil-Gonzalez, W., Grisales-Norena, L.F., Optimal Power Dispatch of DGs in DC Power Grids: A Hybrid Gauss-Seidel Genetic-Algorithm Methodology for Solving the OPF Problem (2018) WSEAS Transactions on Power Systems, 13 (33), pp. 335-346. , 2224-350XPatterson, M., Macia, N.F., Kannan, A.M., Hybrid Microgrid Model Based on Solar Photovoltaic Battery Fuel Cell System for Intermittent Load Applications (2015) IEEE Transactions on Energy Conversion, 30 (1), pp. 359-366. , ISSN 0885-8969Ellabban, O., Abu-Rub, H., Blaab-Jerg, F., Renewable energy resources: Current status, future prospects and their enabling technology (2014) Renewable Sustainable Energy Reviews, 39, pp. 748-764Parhizi, S., Lotfi, H., Khodaei, A., Bahramirad, S., State of the art in research on microgrids: A review (2015) IEEE Access, 3 (1), pp. 890-925Garces, A., Uniqueness of the power flow solutions in low voltage direct current grids (2017) Electric Power Systems Research, 151 (1), pp. 149-153Li, J., Liu, F., Wang, Z., Low, S., Mei, S., Optimal Power Flow in Stand-alone DC Microgrids (2018) IEEE Transactions on Power Systems, 33 (5), pp. 5496-5506. , ISSN 0885-8950Garces, A., On Convergence of Newtons Method in Power Flow Study for DC Microgrids (2018) IEEE Transactions on Power Systems., 33 (5), pp. 5770-5777Simpson-Porco, J.W., Dorfler, F., Bullo, F., On Resistive Networks of Constant-Power Devices (2015) IEEE Transactions on Circuits and Systems II: Express Briefs, 62 (8), pp. 811-815Montoya, O.D., Grisales-Norena, L.F., Gonzalez-Montoya, D., Ramos-Paja, C., Garces, A., Linear power flow formulation for low-voltage DC power grids (2018) Electric Power Systems Research, 163 (1), pp. 375-381. , 0378-7796Gil-Gonzalez, W., Montoya, O.D., Holguin, E., Garces, A., Grisales-Norena, L.F., Economic dispatch of energy storage systems in dc microgrids employing a semidefinite programming model (2019) Journal of Energy Storage, 21 (1), pp. 1-8Montoya, O.D., Gil-Gonzalez, W., Garces, A., Optimal Power Flow on DC Microgrids: A Quadratic Convex Approximation (2018) IEEE Transactions on Circuits and Systems II: Express Briefs, 99 (1), p. 1. , 1549-7747Hasan, Z., El-Hawary, M.E., Optimal Power Flow by Black Hole Optimization Algorithm (2014) IEEE Electrical Power and Energy Conference. Calgary: IEEE, pp. 134-141Gillessen, E.S., Genzel, R., (2009) Tracking Stars Orbiting the Milky Way’s Central Black Hole, 720p. , https://www.youtube.com/watch?v=duoHtJpo4GY, Youtube [online]Bouchekara, H.R.E.H., Optimal power flow using black-hole-based optimization approach (2014) Applied Soft Computing, 24 (1), pp. 879-888Piotrowski, A.P., Napiorkowski, J.J., Rowinski, P.M., How novel is the novel black hole optimization approach? (2014) Information Sciences, 267 (1), pp. 191-200. , ISSN 0020-0255Bouchekara, H.R.E.H., Optimal design of electromagnetic devices using a black-hole-based optimization technique (2013) IEEE Transactions on Magnetics, 49 (12), pp. 5709-5714. , ISSN 0018-9464Grisales-Norena, L.F., Montoya, D.G., Ramos-Paja, C.A., Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO Techniques (2018) Energies, 11 (1018), pp. 1-27Montoya, O.D., Solving a Classical Optimization Problem Using GAMS Optimizer Package: Economic Dispatch Problem Implementation (2017) Ingenieria Y Ciencia, 13 (26), pp. 39-63(2019) GAMS Development, , https://www.gams.com/download/, onlineMandal, S., Elephant swarm water search algorithm for global optimization (2018) SADHANA, 43 (2), pp. 1-21. , 0973-7677http://purl.org/coar/resource_type/c_6501ORIGINALDOI10_15598aeee_v17i1_3069.pdfapplication/pdf706740https://repositorio.utb.edu.co/bitstream/20.500.12585/8769/1/DOI10_15598aeee_v17i1_3069.pdfdb69afc13a7236e020182482ef6b3a44MD51TEXTDOI10_15598aeee_v17i1_3069.pdf.txtDOI10_15598aeee_v17i1_3069.pdf.txtExtracted texttext/plain35613https://repositorio.utb.edu.co/bitstream/20.500.12585/8769/4/DOI10_15598aeee_v17i1_3069.pdf.txtab462d1104830891e36acdbbe6b75dbcMD54THUMBNAILDOI10_15598aeee_v17i1_3069.pdf.jpgDOI10_15598aeee_v17i1_3069.pdf.jpgGenerated Thumbnailimage/jpeg91305https://repositorio.utb.edu.co/bitstream/20.500.12585/8769/5/DOI10_15598aeee_v17i1_3069.pdf.jpg4020489b17bb0abcd9aefbb95ca3bdfcMD5520.500.12585/8769oai:repositorio.utb.edu.co:20.500.12585/87692023-05-26 11:06:45.876Repositorio Institucional UTBrepositorioutb@utb.edu.co |