An mi-sdp model for optimal location and sizing of distributed generators in dc grids that guarantees the global optimum
This paper deals with a classical problem in power system analysis regarding the optimal location and sizing of distributed generators (DGs) in direct current (DC) distribution networks using the mathematical optimization. This optimization problem is divided into two sub-problems as follows: the op...
- Autores:
-
Gil-González, Walter
Molina-Cabrera, Alexander
Montoya, Oscar Danilo
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:20.500.12585/9948
- Palabra clave:
- Branch and bound method
Convex optimization
Distributed generation
Mixed-integer semidefinite programming
Power losses minimization
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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dc.title.spa.fl_str_mv |
An mi-sdp model for optimal location and sizing of distributed generators in dc grids that guarantees the global optimum |
title |
An mi-sdp model for optimal location and sizing of distributed generators in dc grids that guarantees the global optimum |
spellingShingle |
An mi-sdp model for optimal location and sizing of distributed generators in dc grids that guarantees the global optimum Branch and bound method Convex optimization Distributed generation Mixed-integer semidefinite programming Power losses minimization LEMB |
title_short |
An mi-sdp model for optimal location and sizing of distributed generators in dc grids that guarantees the global optimum |
title_full |
An mi-sdp model for optimal location and sizing of distributed generators in dc grids that guarantees the global optimum |
title_fullStr |
An mi-sdp model for optimal location and sizing of distributed generators in dc grids that guarantees the global optimum |
title_full_unstemmed |
An mi-sdp model for optimal location and sizing of distributed generators in dc grids that guarantees the global optimum |
title_sort |
An mi-sdp model for optimal location and sizing of distributed generators in dc grids that guarantees the global optimum |
dc.creator.fl_str_mv |
Gil-González, Walter Molina-Cabrera, Alexander Montoya, Oscar Danilo Grisales-Noreña, Luis Fernando |
dc.contributor.author.none.fl_str_mv |
Gil-González, Walter Molina-Cabrera, Alexander Montoya, Oscar Danilo Grisales-Noreña, Luis Fernando |
dc.subject.keywords.spa.fl_str_mv |
Branch and bound method Convex optimization Distributed generation Mixed-integer semidefinite programming Power losses minimization |
topic |
Branch and bound method Convex optimization Distributed generation Mixed-integer semidefinite programming Power losses minimization LEMB |
dc.subject.armarc.none.fl_str_mv |
LEMB |
description |
This paper deals with a classical problem in power system analysis regarding the optimal location and sizing of distributed generators (DGs) in direct current (DC) distribution networks using the mathematical optimization. This optimization problem is divided into two sub-problems as follows: the optimal location of DGs is a problem, with those with a binary structure being the first sub-problem; and the optimal sizing of DGs with a nonlinear programming (NLP) structure is the second sub-problem. These problems originate from a general mixed-integer nonlinear programming model (MINLP), which corresponds to an NP-hard optimization problem. It is not possible to provide the global optimum with conventional programming methods. A mixed-integer semidefinite programming (MI-SDP) model is proposed to address this problem, where the binary part is solved via the branch and bound (B&B) methods and the NLP part is solved via convex optimization (i.e., SDP). The main advantage of the proposed MI-SDP model is the possibility of guaranteeing a global optimum solution if each of the nodes in the B&B search is convex, as is ensured by the SDP method. Numerical validations in two test feeders composed of 21 and 69 nodes demonstrate that in all of these problems, the optimal global solution is reached by the MI-SDP approach, compared to the classical metaheuristic and hybrid programming models reported in the literature. All the simulations have been carried out using the MATLAB software with the CVX tool and the Mosek solver. |
publishDate |
2020 |
dc.date.issued.none.fl_str_mv |
2020-10-30 |
dc.date.accessioned.none.fl_str_mv |
2021-02-08T15:38:55Z |
dc.date.available.none.fl_str_mv |
2021-02-08T15:38:55Z |
dc.date.submitted.none.fl_str_mv |
2021-02-03 |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.hasVersion.spa.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.spa.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
status_str |
publishedVersion |
dc.identifier.citation.spa.fl_str_mv |
Gil-González, W.; Molina-Cabrera, A.; Montoya, O.D.; Grisales-Noreña, L.F. An MI-SDP Model for Optimal Location and Sizing of Distributed Generators in DC Grids That Guarantees the Global Optimum. Appl. Sci. 2020, 10, 7681. https://doi.org/10.3390/app10217681 |
dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12585/9948 |
dc.identifier.url.none.fl_str_mv |
https://www.mdpi.com/2076-3417/10/21/7681 |
dc.identifier.doi.none.fl_str_mv |
10.3390/app10217681 |
dc.identifier.eissn.none.fl_str_mv |
2076-3417 |
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 |
Gil-González, W.; Molina-Cabrera, A.; Montoya, O.D.; Grisales-Noreña, L.F. An MI-SDP Model for Optimal Location and Sizing of Distributed Generators in DC Grids That Guarantees the Global Optimum. Appl. Sci. 2020, 10, 7681. https://doi.org/10.3390/app10217681 10.3390/app10217681 2076-3417 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
url |
https://hdl.handle.net/20.500.12585/9948 https://www.mdpi.com/2076-3417/10/21/7681 |
dc.language.iso.spa.fl_str_mv |
eng |
language |
eng |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
dc.rights.uri.*.fl_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
dc.rights.accessRights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
dc.rights.cc.*.fl_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://purl.org/coar/access_right/c_abf2 |
eu_rights_str_mv |
openAccess |
dc.format.extent.none.fl_str_mv |
19 páginas |
dc.format.mimetype.spa.fl_str_mv |
application/pdf |
dc.publisher.place.spa.fl_str_mv |
Cartagena de Indias |
dc.source.spa.fl_str_mv |
Appl. Sci. 2020, 10(21), 7681 |
institution |
Universidad Tecnológica de Bolívar |
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Gil-González, Walterce1f5078-74c6-4b5c-b56a-784f85e52a08Molina-Cabrera, Alexander01b29f76-a1f3-4151-a070-ce883ba39849Montoya, Oscar Danilo8a59ede1-6a4a-4d2e-abdc-d0afb14d4480Grisales-Noreña, Luis Fernando7c27cda4-5fe4-4686-8f72-b0442c58a5d12021-02-08T15:38:55Z2021-02-08T15:38:55Z2020-10-302021-02-03Gil-González, W.; Molina-Cabrera, A.; Montoya, O.D.; Grisales-Noreña, L.F. An MI-SDP Model for Optimal Location and Sizing of Distributed Generators in DC Grids That Guarantees the Global Optimum. Appl. Sci. 2020, 10, 7681. https://doi.org/10.3390/app10217681https://hdl.handle.net/20.500.12585/9948https://www.mdpi.com/2076-3417/10/21/768110.3390/app102176812076-3417Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarThis paper deals with a classical problem in power system analysis regarding the optimal location and sizing of distributed generators (DGs) in direct current (DC) distribution networks using the mathematical optimization. This optimization problem is divided into two sub-problems as follows: the optimal location of DGs is a problem, with those with a binary structure being the first sub-problem; and the optimal sizing of DGs with a nonlinear programming (NLP) structure is the second sub-problem. These problems originate from a general mixed-integer nonlinear programming model (MINLP), which corresponds to an NP-hard optimization problem. It is not possible to provide the global optimum with conventional programming methods. A mixed-integer semidefinite programming (MI-SDP) model is proposed to address this problem, where the binary part is solved via the branch and bound (B&B) methods and the NLP part is solved via convex optimization (i.e., SDP). The main advantage of the proposed MI-SDP model is the possibility of guaranteeing a global optimum solution if each of the nodes in the B&B search is convex, as is ensured by the SDP method. Numerical validations in two test feeders composed of 21 and 69 nodes demonstrate that in all of these problems, the optimal global solution is reached by the MI-SDP approach, compared to the classical metaheuristic and hybrid programming models reported in the literature. All the simulations have been carried out using the MATLAB software with the CVX tool and the Mosek solver.19 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_abf2Appl. Sci. 2020, 10(21), 7681An mi-sdp model for optimal location and sizing of distributed generators in dc grids that guarantees the global optimuminfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Branch and bound methodConvex optimizationDistributed generationMixed-integer semidefinite programmingPower losses minimizationLEMBCartagena de IndiasGharehpetian, G.B.; Agah, S.M.M. Distributed Generation Systems: Design, Operation and Grid Integration; Butterworth-Heinemann: Oxford, UK, 2017.Garces, A. On the Convergence of Newton’s Method in Power Flow Studies for DC Microgrids. IEEE Trans. Power Syst. 2018, 33, 5770–5777.Opiyo, N.N. A comparison of DC-versus AC-based minigrids for cost-effective electrification of rural developing communities. Energy Rep. 2019, 5, 398–408.Tamilselvan, V.; Jayabarathi, T.; Raghunathan, T.; Yang, X.S. Optimal capacitor placement in radial distribution systems using flower pollination algorithm. Alex. Eng. J. 2018, 57, 2775–2786.Elsheikh, A.; Helmy, Y.; Abouelseoud, Y.; Elsherif, A. Optimal capacitor placement and sizing in radial electric power systems. Alex. Eng. J. 2014, 53, 809–816.Ivanov, O.; Neagu, B.-C.; Grigoras, G.; Gavrilas, M. Optimal Capacitor Bank Allocation in Electricity Distribution Networks Using Metaheuristic Algorithms. Energies 2019, 12, 4239.Prabha, D.R.; Jayabarathi, T.; Umamageswari, R.; Saranya, S. Optimal location and sizing of distributed generation unit using intelligent water drop algorithm. Sustain. Energy Technol. Assess. 2015, 11, 106–113.Ayodele, T.; Ogunjuyigbe, A.; Akinola, O. Optimal location, sizing, and appropriate technology selection of distributed generators for minimizing power loss using genetic algorithm. J. Renew. Energy 2015, 2015, 10.Mishra, S.; Das, D.; Paul, S. A comprehensive review on power distribution network reconfiguration. Energy Syst. 2016, 8, 227–284.Das, S.; Das, D.; Patra, A. Reconfiguration of distribution networks with optimal placement of distributed generations in the presence of remote voltage controlled bus. Renew. Sustain. Energy Rev. 2017, 73, 772–781.Jakus, D.; Čađenović, R.; Vasilj, J.; Sarajčev, P. Optimal Reconfiguration of Distribution Networks Using Hybrid Heuristic-Genetic Algorithm. Energies 2020, 13, 1544.Junior, A.R.B.; Fernandes, T.S.P.; Borba, R.A. Voltage Regulation Planning for Distribution Networks Using Multi-Scenario Three-Phase Optimal Power Flow. Energies 2019, 13, 159.Vaidya, P.; Rajderkar, V. Optimal Location of Series FACTS Devices for Enhancing Power System Security. In Proceedings of the 2011 Fourth International Conference on Emerging Trends in Engineering & Technology, Port Louis, Mauritius, 18–20 November 2011.Elansari, A.; Burr, J.; Finney, S.; Edrah, M. Optimal location for shunt connected reactive power compensation. In Proceedings of the 2014 49th International Universities Power Engineering Conference (UPEC), Cluj-Napoca, Romania, 2–5 September 2014.Prabhala, V.A.; Baddipadiga, B.P.; Fajri, P.; Ferdowsi, M. An overview of direct current distribution system architectures & benefits. Energies 2018, 11, 2463.Nezhadpashaki, M.A.; Karbalaei, F.; Abbasi, S. Optimal placement and sizing of distributed generation with small signal stability constraint. Sustain. Energy Grids Netw. 2020, 23, 100380.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.Montoya, O.D. A convex OPF approximation for selecting the best candidate nodes for optimal location of power sources on DC resistive networks. Eng. Sci. Technol. Int. J. 2020, 23, 527–533.Montoya, O.D.; Garrido, V.M.; Grisales-Noreña, L.F.; Gil-González, W.; Garces, A.; Ramos-Paja, C.A. Optimal Location of DGs in DC Power Grids Using a MINLP Model Implemented in GAMS. In Proceedings of the 2018 IEEE 9th Power, Instrumentation and Measurement Meeting (EPIM), Salto, Uruguay, 14–16 November 2018; pp. 1–5.Montoya, O.D.; Grisales-Noreña, L.F.; Gil-González, W.; Alcalá, G.; Hernandez-Escobedo, Q. Optimal Location and Sizing of PV Sources in DC Networks for Minimizing Greenhouse Emissions in Diesel Generators. Symmetry 2020, 12, 322.Grisales-Noreña, L.F.; Garzon-Rivera, O.D.; Montoya, O.D.; Ramos-Paja, C.A. Hybrid Metaheuristic Optimization Methods for Optimal Location and Sizing DGs in DC Networks. In Communications in Computer and Information Science; Springer International Publishing: Berlin/Heidelberg, Germany, 2019; pp. 214–225.Huang, L.; Chen, Z.; Cui, Q.; Zhang, J.; Wang, H.; Shu, J. Optimal planning of renewable energy source and energy storage in a medium- and low-voltage distributed AC/DC system in China. J. Eng. 2019, 2019, 2354–2361.Chen, Y.; Xiang, J.; Li, Y. SOCP Relaxations of Optimal Power Flow Problem Considering Current Margins in Radial Networks. Energies 2018, 11, 3164.Zheng, X.; Chen, H.; Xu, Y.; Li, Z.; Lin, Z.; Liang, Z. A mixed-integer SDP solution to distributionally robust unit commitment with second order moment constraints. CSEE J. Power Energy Syst. 2020, 6, 374–383.Gally, T.; Pfetsch, M.E.; Ulbrich, S. A framework for solving mixed-integer semidefinite programs. Optim. Methods Softw. 2017, 33, 594–632.Garcés, A. 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. 121–137.Fallat, S.M.; Johnson, C.R. Hadamard powers and totally positive matrices. 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