An exact MINLP model for optimal location and sizing of DGs in distribution networks: A general algebraic modeling system approach
This paper addresses the classical problem of optimal location and sizing of distributed generators (DGs) in radial distribution networks by presenting a mixed-integer nonlinear programming (MINLP) model. To solve such model, we employ the General Algebraic Modeling System (GAMS) in conjunction with...
- 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:20.500.12585/9246
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/9246
- Palabra clave:
- Distributed generation
Distribution systems
General algebraic modeling system
Mixed-integer nonlinear programming
Optimal location and sizing of distributed generation
Algebra
Distributed power generation
Integer programming
Location
Algebraic modeling
Distributed generator (DGs)
Distribution systems
Mixed integer nonlinear programming models
Mixed-integer nonlinear programming
Optimal locations
Radial distribution networks
Solution methodology
Nonlinear programming
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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An exact MINLP model for optimal location and sizing of DGs in distribution networks: A general algebraic modeling system approach |
| title |
An exact MINLP model for optimal location and sizing of DGs in distribution networks: A general algebraic modeling system approach |
| spellingShingle |
An exact MINLP model for optimal location and sizing of DGs in distribution networks: A general algebraic modeling system approach Distributed generation Distribution systems General algebraic modeling system Mixed-integer nonlinear programming Optimal location and sizing of distributed generation Algebra Distributed power generation Integer programming Location Algebraic modeling Distributed generator (DGs) Distribution systems Mixed integer nonlinear programming models Mixed-integer nonlinear programming Optimal locations Radial distribution networks Solution methodology Nonlinear programming |
| title_short |
An exact MINLP model for optimal location and sizing of DGs in distribution networks: A general algebraic modeling system approach |
| title_full |
An exact MINLP model for optimal location and sizing of DGs in distribution networks: A general algebraic modeling system approach |
| title_fullStr |
An exact MINLP model for optimal location and sizing of DGs in distribution networks: A general algebraic modeling system approach |
| title_full_unstemmed |
An exact MINLP model for optimal location and sizing of DGs in distribution networks: A general algebraic modeling system approach |
| title_sort |
An exact MINLP model for optimal location and sizing of DGs in distribution networks: A general algebraic modeling system approach |
| dc.subject.keywords.none.fl_str_mv |
Distributed generation Distribution systems General algebraic modeling system Mixed-integer nonlinear programming Optimal location and sizing of distributed generation Algebra Distributed power generation Integer programming Location Algebraic modeling Distributed generator (DGs) Distribution systems Mixed integer nonlinear programming models Mixed-integer nonlinear programming Optimal locations Radial distribution networks Solution methodology Nonlinear programming |
| topic |
Distributed generation Distribution systems General algebraic modeling system Mixed-integer nonlinear programming Optimal location and sizing of distributed generation Algebra Distributed power generation Integer programming Location Algebraic modeling Distributed generator (DGs) Distribution systems Mixed integer nonlinear programming models Mixed-integer nonlinear programming Optimal locations Radial distribution networks Solution methodology Nonlinear programming |
| description |
This paper addresses the classical problem of optimal location and sizing of distributed generators (DGs) in radial distribution networks by presenting a mixed-integer nonlinear programming (MINLP) model. To solve such model, we employ the General Algebraic Modeling System (GAMS) in conjunction with the BONMIN solver, presenting its characteristics in a tutorial style. To operate all the DGs, we assume they are dispatched with a unity power factor. Test systems with 33 and 69 buses are employed to validate the proposed solution methodology by comparing its results with multiple approaches previously reported in the specialized literature. A 27-node test system is also used for locating photovoltaic (PV) sources considering the power capacity of the Caribbean region in Colombia during a typical sunny day. Numerical results confirm the efficiency and accuracy of the MINLP model and its solution is validated through the GAMS package. © 2019 Ain Shams University |
| publishDate |
2019 |
| dc.date.issued.none.fl_str_mv |
2019 |
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2020-03-26T16:41:26Z |
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2020-03-26T16:41:26Z |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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info:eu-repo/semantics/article |
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Artículo |
| dc.identifier.citation.none.fl_str_mv |
Montoya O.D., Gil-González W. y Grisales-Noreña L.F. (2019) An exact MINLP model for optimal location and sizing of DGs in distribution networks: A general algebraic modeling system approach. Ain Shams Engineering Journal |
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20904479 |
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https://hdl.handle.net/20.500.12585/9246 |
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10.1016/j.asej.2019.08.011 |
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Universidad Tecnológica de Bolívar |
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Repositorio UTB |
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56919564100 57191493648 55791991200 |
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Montoya O.D., Gil-González W. y Grisales-Noreña L.F. (2019) An exact MINLP model for optimal location and sizing of DGs in distribution networks: A general algebraic modeling system approach. Ain Shams Engineering Journal 20904479 10.1016/j.asej.2019.08.011 Universidad Tecnológica de Bolívar Repositorio UTB 56919564100 57191493648 55791991200 |
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https://hdl.handle.net/20.500.12585/9246 |
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eng |
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eng |
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Atribución-NoComercial 4.0 Internacional |
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Recurso electrónico |
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Ain Shams University |
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Ain Shams University |
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2020-03-26T16:41:26Z2020-03-26T16:41:26Z2019Montoya O.D., Gil-González W. y Grisales-Noreña L.F. (2019) An exact MINLP model for optimal location and sizing of DGs in distribution networks: A general algebraic modeling system approach. Ain Shams Engineering Journal20904479https://hdl.handle.net/20.500.12585/924610.1016/j.asej.2019.08.011Universidad Tecnológica de BolívarRepositorio UTB569195641005719149364855791991200This paper addresses the classical problem of optimal location and sizing of distributed generators (DGs) in radial distribution networks by presenting a mixed-integer nonlinear programming (MINLP) model. To solve such model, we employ the General Algebraic Modeling System (GAMS) in conjunction with the BONMIN solver, presenting its characteristics in a tutorial style. To operate all the DGs, we assume they are dispatched with a unity power factor. Test systems with 33 and 69 buses are employed to validate the proposed solution methodology by comparing its results with multiple approaches previously reported in the specialized literature. A 27-node test system is also used for locating photovoltaic (PV) sources considering the power capacity of the Caribbean region in Colombia during a typical sunny day. Numerical results confirm the efficiency and accuracy of the MINLP model and its solution is validated through the GAMS package. © 2019 Ain Shams UniversityUniversidad Nacional de Colombia, UN: 38945, 58838 P17211 Universidad Tecnológica de Pereira, UTP: C2019P011, C2018P020 Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS), COLCIENCIAS: 727-2015This work was funded in part by the Administrative Department of Science, Technology, and Innovation of Colombia (COLCIENCIAS) through its National Scholarship Program, under Grant 727-2015 ; in part by Instituto Tecnológico Metropolitano de Medellín, under Project P17211; in part by Universidad Tecnológica de Bolívar, under Projects C2018P020 and C2019P011; and in part by Universidad Nacional de Colombia, under Proyect ”Estrategia de transformación del sector energético Colombiano en el horizonte de 2030 - Energética 2030” - ”Generación distribuida de energía eléctrica en Colombia a partir de energía solar y eólica” (Code: 58838, Hermes: 38945). Oscar D. Montoya received his BEE, M.Sc. and Ph.D degrees in Electrical Engineering from Universidad Tecnológica de Pereira, Colombia, in 2012 and 2014 respectively. His research interests include mathematical optimization, planning and control of power systems, renewable energies, energy storage, protective devices and smartgrids. Walter Gil-González received his BEE and M.Sc. degrees in Electrical Engineering from Universidad Tecnológica de Pereira, Colombia, in 2011 and 2013 respectively. He is currently studying a Ph.D in Electrical Engineering at Universidad Tecnológica de Pereira, Colombia. His research interests include mathematical optimization, planning and control of power systems, renewable energies, energy storage, protective devices and smartgrids. Luis F. Grisales received his BEE and M.Sc. degrees in Electrical Engineering from Universidad Tecnológica de Pereira, Colombia, in 2013 and 2015 respectively. He is currently studying a Ph.D in Engineering at Universidad Nacional de Colombia. Actually, is professor in the Instituto TecnolÓgico Metropolitano de Medellín, attached to the Department of Electromechanics and mechatronics, member of the research group MATyER. His research interests include mathematical modelling, optimization techniques, planning and control of power systems, renewable energies, energy storage, power electronic and smartgrids.Recurso electrónicoapplication/pdfengAin Shams Universityhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075329994&doi=10.1016%2fj.asej.2019.08.011&partnerID=40&md5=0e325b37e9d79a3278dfb68aab7bf338Scopus2-s2.0-85075329994An exact MINLP model for optimal location and sizing of DGs in distribution networks: A general algebraic modeling system approachinfo:eu-repo/semantics/articleArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Distributed generationDistribution systemsGeneral algebraic modeling systemMixed-integer nonlinear programmingOptimal location and sizing of distributed generationAlgebraDistributed power generationInteger programmingLocationAlgebraic modelingDistributed generator (DGs)Distribution systemsMixed integer nonlinear programming modelsMixed-integer nonlinear programmingOptimal locationsRadial distribution networksSolution methodologyNonlinear programmingMontoya O.D.Gil-González W.Grisales-Noreña L.F.Prakash, P., Khatod, D.K., Optimal sizing and siting techniques for distributed generation in distribution systems: a review (2016) Renew Sustain Energy Rev, 57, pp. 111-130Bawan, E.K., (2000), 3. , Distributed generation impact on power system case study: losses and voltage profile. In: Power engineering society summer meeting IEEE,2000. p. 1645–56Rezaee Jordehi, A., Allocation of distributed generation units in electric power systems: a review (2016) Renew Sustain Energy Rev, 56, pp. 893-905Abdelaziz, A., Ali, E., Elazim, S.A., Optimal sizing and locations of capacitors in radial distribution systems via flower pollination optimization algorithm and power loss index (2016) Eng Sci Technol, Int J, 19 (1), pp. 610-618Iqbal, F., Khan, M.T., Siddiqui, A.S., https://doi.org/10.1016/j.aej.2017.03.002, Optimal placement of DG and DSTATCOM for loss reduction and voltage profile improvement. Alexandr Eng JMahdad, B., Srairi, K., Adaptive differential search algorithm for optimal location of distributed generation in the presence of SVC for power loss reduction in distribution system (2016) Eng Sci Technol, Int J, 19 (3), pp. 1266-1282Galiveeti, H.R., Goswami, A.K., Choudhury, N.B.D., Impact of plug-in electric vehicles and distributed generation on reliability of distribution systems (2018) Eng Sci Technol, Int J, 21 (1), pp. 50-59Elmitwally, A., A new algorithm for allocating multiple distributed generation units based on load centroid concept (2013) Alexandr Eng J, 52 (4), pp. 655-663Sadiq, A., Adamu, S., Buhari, M., https://doi.org/10.1016/j.jestch.2018.09.013, Optimal distributed generation planning in distribution networks: a comparison of transmission network models with FACTS. Eng Sci Technol, Int JGrisales Noreña, L.F., Restrepo Cuestas, B.J., Jaramillo Ramirez, F.E., Location and sizing of distribted generation: a review (2017) Ciencia e Ingeniería Neogranadina, 27 (2), pp. 157-176Keane, A., Ochoa, L.F., Borges, C.L.T., Ault, G.W., Alarcon-Rodriguez, A.D., Currie, R.A.F., State-of-the-art techniques and challenges ahead for distributed generation planning and optimization (2013) IEEE Trans Power Syst, 28 (2), pp. 1493-1502Theo, W.L., Lim, J.S., Ho, W.S., Hashim, H., Lee, C.T., Review of distributed generation (DG) system planning and optimisation techniques: comparison of numerical and mathematical modelling methods (2017) Renew Sustain Energy Rev, 67, pp. 531-573Pesaran, M., A, H., Huy, P.D., Ramachandaramurthy, V.K., A review of the optimal allocation of distributed generation: objectives, constraints, methods, and algorithms (2017) Renew Sustain Energy Rev, 75 (October), pp. 293-312Dixit, M., Kundu, P., Jariwala, H.R., Incorporation of distributed generation and shunt capacitor in radial distribution system for techno-economic benefits (2017) Eng Sci Technol, Int J, 20 (2), pp. 482-493Ellabban, O., Abu-Rub, H., Blaabjerg, F., Renewable energy resources: current status, future prospects and their enabling technology (2014) Renew Sustain Energy Rev, 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, pp. 890-925Kaur, S., Kumbhar, G., Sharma, J., A MINLP technique for optimal placement of multiple DG units in distribution systems (2014) Int J Electr Power Energy Syst, 63, pp. 609-617Abido, M.A., Optimal power flow using tabu search algorithm (2002) Electr Power Comp Syst, 30 (5), pp. 469-483HassanzadehFard, H., Jalilian, A., A novel objective function for optimal DG allocation in distribution systems using meta-heuristic algorithms (2016) Int J Green Energy, 13 (15), pp. 1615-1625Bohre, A.K., Agnihotri, G., Dubey, M., Optimal sizing and sitting of DG with load models using soft computing techniques in practical distribution system (2016) IET Gener Transm Distrib, 10 (11), pp. 2606-2621Moradi, M., Abedini, M., A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems (2012) Int J Electr Power Energy Syst, 34 (1), pp. 66-74https://doi.org/10.1016/j.jesit.2017.06.001, D.P.R.P., V.R.V.C., G.M.T., Ant Lion optimization algorithm for optimal sizing of renewable energy resources for loss reduction in distribution systems. J Electr Syst Inf Technol 20185(3): 663–80Gandomkar, M., Vakilian, M., Ehsan, M., A genetic based tabu search algorithm for optimal dg allocation in distribution networks (2005) Electr Power Comp Syst, 33 (12), pp. 1351-1362Injeti, S.K., Kumar, N.P., A novel approach to identify optimal access point and capacity of multiple DGs in a small, medium and large scale radial distribution systems (2013) Int J Electr Power Energy Syst, 45 (1), pp. 142-151Sultana, S., Roy, P.K., Krill herd algorithm for optimal location of distributed generator in radial distribution system (2016) Appl Soft Comput, 40, pp. 391-404Sultana, S., Roy, P.K., Oppositional krill herd algorithm for optimal location of distributed generator in radial distribution system (2015) Int J Electr Power Energy Syst, 73, pp. 182-191ChithraDevi, S., Lakshminarasimman, L., Balamurugan, R., Stud Krill herd Algorithm for multiple DG placement and sizing in a radial distribution system (2017) Eng Sci Technol, Int J, 20 (2), pp. 748-759Grisales-Noreña, L.F., Gonzalez-Montoya, D., Ramos-Paja, C.A., Optimal sizing and location of distributed generators based on PBIL and PSO Techniques (2018) Energies, 11 (1018), pp. 1-27Mohanty, B., Tripathy, S., A teaching learning based optimization technique for optimal location and size of DG in distribution network (2016) J Electr Syst Inf Technol, 3 (1), pp. 33-44Sudabattula, S.K., M, K., Optimal allocation of solar based distributed generators in distribution system using Bat algorithm (2016) Perspect Sci, 8, pp. 270-272. , recent Trends in Engineering and Material SciencesYammani, C., Maheswarapu, S., Matam, S.K., A Multi-objective Shuffled Bat algorithm for optimal placement and sizing of multi distributed generations with different load models (2016) Int J Electr Power Energy Syst, 79, pp. 120-131Othman, M., El-Khattam, W., Hegazy, Y., Abdelaziz, A.Y., Optimal placement and sizing of voltage controlled distributed generators in unbalanced distribution networks using supervised firefly algorithm (2016) Int J Electr Power Energy Syst, 82, pp. 105-113Behera, S.R., Dash, S.P., Panigrahi, B.K., Optimal placement and sizing of DGs in radial distribution system (RDS) using Bat algorithm (2015) 2015 International conference on circuits, power and computing technologies [ICCPCT-2015], pp. 1-8Nguyen, T.P., Dieu, V.N., Vasant, P., Symbiotic organism search algorithm for optimal size and siting of distributed generators in distribution systems (2017) Int J Energy Optim Eng, 6 (3), pp. 1-28Nekooei, K., Farsangi, M.M., Nezamabadi-Pour, H., Lee, K.Y., An improved multi-objective harmony search for optimal placement of DGs in distribution systems (2013) IEEE Trans Smart Grid, 4 (1), pp. 557-567https://doi.org/10.1016/j.future.2018.12.046, S.C., A.T., Optimal power flow using Moth Swarm Algorithm with Gravitational Search Algorithm considering wind power. Future Gener Comput SystNojavan, S., Jalali, M., Zare, K., An MINLP approach for optimal dg unit's allocation in radial/mesh distribution systems take into account voltage stability index (2015) Trans Electr Eng, 39 (E2), pp. 155-165Engelmann, A., Muhlpfordt, T., Jiang, Y., Houska, B., Faulwasser, T., https://doi.org/10.1016/j.ifacol.2017.08.1095, Distributed AC optimal power flow using ALADIN, IFAC-PapersOnLine 201750(1):5536–41. In: 20th IFAC World CongressMontoya, O.D., Grajales, A., Garces, A., Castro, C.A., Distribution systems operation considering energy storage devices and distributed generation (2017) IEEE Latin Am Trans, 15 (5), pp. 890-900https://www.gams.com/download/, GAMS Development Corp., General Algebraic Modeling SystemCastillo, E., Conejo, A., Pedregal, P., García, R., Alguacil, N., Building and solving mathematical programming models in engineering and science, pure and applied mathematics: A Wiley Series of Texts, Monographs and Tracts (2001), WileyMontoya, O.D., Garces, A., Castro, C.A., Optimal conductor size selection in radial distribution networks using a mixed-integer non-linear programming formulation (2018) IEEE Latin Am Trans, 16 (8), pp. 2213-2220Montoya, O.D., Solving a classical optimization problem using GAMS optimizer package: economic dispatch problem implementation (2017) Ingenieria y ciencia, 13 (26), pp. 39-63Jamian, J., Mustafa, M., Mokhlis, H., Optimal multiple distributed generation output through rank evolutionary particle swarm optimization (2015) Neurocomputing, 152, pp. 190-198Kollu, R., Rayapudi, S.R., Sadhu, V.L.N., A novel method for optimal placement of distributed generation in distribution systems using HSDO (2014) Int Trans Electr Energy Syst, 24 (4), pp. 547-561http://purl.org/coar/resource_type/c_6501ORIGINALhttpsdoiorg101016jasej201908011.pdfapplication/pdf1647207https://repositorio.utb.edu.co/bitstream/20.500.12585/9246/1/httpsdoiorg101016jasej201908011.pdf2356244e42d1bce6997550d1dc7551b0MD51TEXThttpsdoiorg101016jasej201908011.pdf.txthttpsdoiorg101016jasej201908011.pdf.txtExtracted texttext/plain50466https://repositorio.utb.edu.co/bitstream/20.500.12585/9246/4/httpsdoiorg101016jasej201908011.pdf.txt34f373be41f940333b1ddd87ec1cad2fMD54THUMBNAILhttpsdoiorg101016jasej201908011.pdf.jpghttpsdoiorg101016jasej201908011.pdf.jpgGenerated Thumbnailimage/jpeg112346https://repositorio.utb.edu.co/bitstream/20.500.12585/9246/5/httpsdoiorg101016jasej201908011.pdf.jpg7bb10584be7e4cca920fbb2591119af9MD5520.500.12585/9246oai:repositorio.utb.edu.co:20.500.12585/92462020-10-23 05:17:05.833Repositorio Institucional UTBrepositorioutb@utb.edu.co |