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...

Full description

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
Acceso en línea:
https://hdl.handle.net/20.500.12585/9948
https://www.mdpi.com/2076-3417/10/21/7681
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/
id UTB2_3b68b7ef8b44beffbc6ede28c9845af3
oai_identifier_str oai:repositorio.utb.edu.co:20.500.12585/9948
network_acronym_str UTB2
network_name_str Repositorio Institucional UTB
repository_id_str
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
bitstream.url.fl_str_mv https://repositorio.utb.edu.co/bitstream/20.500.12585/9948/1/116.pdf
https://repositorio.utb.edu.co/bitstream/20.500.12585/9948/2/license_rdf
https://repositorio.utb.edu.co/bitstream/20.500.12585/9948/3/license.txt
https://repositorio.utb.edu.co/bitstream/20.500.12585/9948/4/116.pdf.txt
https://repositorio.utb.edu.co/bitstream/20.500.12585/9948/5/116.pdf.jpg
bitstream.checksum.fl_str_mv 85b0d942630e79e7895505b1d46a970f
4460e5956bc1d1639be9ae6146a50347
e20ad307a1c5f3f25af9304a7a7c86b6
d267712fb45268c4816ad4e92932764d
beedb792eb0a83a8ddfd1e6985c57182
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositorio Institucional UTB
repository.mail.fl_str_mv repositorioutb@utb.edu.co
_version_ 1808397599860850688
spelling 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. Linear Algebra Appl. 2007, 423, 420–427.Land, A.H.; Doig, A.G. An Automatic Method of Solving Discrete Programming Problems. Econometrica 1960, 28, 497.Dakin, R.J. A tree-search algorithm for mixed integer programming problems. Comput. J. 1965, 8, 250–255.ueno-Lopez, M.; Lemos, S.G. Electrification in non-interconnected areas: Towards a new vision of rurality in Colombia. IEEE Technol. Soc. Mag. 2017, 36, 73–79.http://purl.org/coar/resource_type/c_2df8fbb1ORIGINAL116.pdf116.pdfArtículo principalapplication/pdf309771https://repositorio.utb.edu.co/bitstream/20.500.12585/9948/1/116.pdf85b0d942630e79e7895505b1d46a970fMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.utb.edu.co/bitstream/20.500.12585/9948/2/license_rdf4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83182https://repositorio.utb.edu.co/bitstream/20.500.12585/9948/3/license.txte20ad307a1c5f3f25af9304a7a7c86b6MD53TEXT116.pdf.txt116.pdf.txtExtracted texttext/plain49522https://repositorio.utb.edu.co/bitstream/20.500.12585/9948/4/116.pdf.txtd267712fb45268c4816ad4e92932764dMD54THUMBNAIL116.pdf.jpg116.pdf.jpgGenerated Thumbnailimage/jpeg89299https://repositorio.utb.edu.co/bitstream/20.500.12585/9948/5/116.pdf.jpgbeedb792eb0a83a8ddfd1e6985c57182MD5520.500.12585/9948oai:repositorio.utb.edu.co:20.500.12585/99482021-02-15 12:46:18.68Repositorio Institucional UTBrepositorioutb@utb.edu.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