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dc.contributor.authorGil-González, Walter
dc.contributor.authorGarcés, Alejandro
dc.contributor.authorMontoya, Oscar Danilo
dc.contributor.authorHernández, Jesus C.
dc.identifier.citationGil-González, Walter; Garces, Alejandro; Montoya, Oscar D.; Hernández, Jesus C. 2021. "A Mixed-Integer Convex Model for the Optimal Placement and Sizing of Distributed Generators in Power Distribution Networks" Appl. Sci. 11, no. 2: 627.
dc.description.abstractThe optimal placement and sizing of distributed generators is a classical problem in power distribution networks that is usually solved using heuristic algorithms due to its high complexity. This paper proposes a different approach based on a mixed-integer second-order cone programming (MI-SOCP) model that ensures the global optimum of the relaxed optimization model. Second-order cone programming (SOCP) has demonstrated to be an efficient alternative to cope with the non-convexity of the power flow equations in power distribution networks. Of relatively new interest to the power systems community is the extension to MI-SOCP models. The proposed model is an approximation. However, numerical validations in the IEEE 33-bus and IEEE 69-bus test systems for unity and variable power factor confirm that the proposed MI-SOCP finds the best solutions reported in the literature. Being an exact technique, the proposed model allows minimum processing times and zero standard deviation, i.e., the same optimum is guaranteed at each time that the MI-SOCP model is solved (a significant advantage in comparison to metaheuristics). Additionally, load and photovoltaic generation curves for the IEEE 69-node test system are included to demonstrate the applicability of the proposed MI-SOCP to solve the problem of the optimal location and sizing of renewable generators using the multi-period optimal power flow formulation. Therefore, the proposed MI-SOCP also guarantees the global optimum finding, in contrast to local solutions achieved with mixed-integer nonlinear programming solvers available in the GAMS optimization software. All the simulations were carried out via MATLAB software with the CVX package and Gurobi
dc.format.extent15 páginas
dc.sourceApplied Sciences 2021, 11(2), 627spa
dc.titleA mixed-integer convex model for the optimal placement and sizing of distributed generators in power distribution networksspa
dcterms.bibliographicCitationKefayat, M.; Ara, A.L.; Niaki, S.N. A hybrid of ant colony optimization and artificial bee colony algorithm for probabilistic optimal placement and sizing of distributed energy resources. Energy Convers. Manag. 2015, 92, 149–161. [CrossRef]spa
dcterms.bibliographicCitationMontoya, 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. [CrossRef]spa
dcterms.bibliographicCitationMontoya, O.D.; Gil-González, W.; Grisales-Noreña, L. An exact MINLP model for optimal location and sizing of DGs in distribution networks: A general algebraic modeling system approach. Ain Shams Eng. J. 2020, 11, 409–418. [CrossRef]spa
dcterms.bibliographicCitationKatyara, S.; Staszewski, L.; Leonowicz, Z. Protection coordination of properly sized and placed distributed generations–methods, applications and future scope. Energies 2018, 11, 2672. [CrossRef]spa
dcterms.bibliographicCitationFloudas, C.A.; Pardalos, P.M. (Eds.) Encyclopedia of Optimization; Springer: Berlin/Heidelberg, Germany, 2009; [CrossRef]spa
dcterms.bibliographicCitationGrisales-Noreña, L.F.; Gonzalez-Montoya, D.; Ramos-Paja, C.A. Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO Techniques. Energies 2018, 11, 1018. [CrossRef]spa
dcterms.bibliographicCitationPrado, I.; Garces, L. Chu-Beasley genetic algorithm applied to the allocation of distributed generation. In Proceedings of the 2013 IEEE PES Conference on Innovative Smart Grid Technologies (ISGT Latin America), Sao Paulo, Brazil, 15–17 April 2013; pp. 1–
dcterms.bibliographicCitationGandomkar, M.; Vakilian, M.; Ehsan, M. A genetic–based tabu search algorithm for optimal DG allocation in distribution networks. Electr. Power Compon. Syst. 2005, 33, 1351–1362. [CrossRef]spa
dcterms.bibliographicCitationInjeti, 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. Int. J. Electr. Power Energy Syst. 2013, 45, 142–151. [CrossRef]spa
dcterms.bibliographicCitationVc, V.R. Ant Lion optimization algorithm for optimal sizing of renewable energy resources for loss reduction in distribution systems. J. Electr. Syst. Inf. Technol. 2018, 5, 663–
dcterms.bibliographicCitationReddy, P.D.P.; Reddy, V.V.; Manohar, T.G. Whale optimization algorithm for optimal sizing of renewable resources for loss reduction in distribution systems. Renew. Wind Water Sol. 2017, 4, 3. [CrossRef]spa
dcterms.bibliographicCitationSultana, S.; Roy, P.K. Krill herd algorithm for optimal location of distributed generator in radial distribution system. Appl. Soft Comput. 2016, 40, 391–404. [CrossRef]spa
dcterms.bibliographicCitationMohanty, B.; Tripathy, S. A teaching learning based optimization technique for optimal location and size of DG in distribution network. J. Electr. Syst. Inf. Technol. 2016, 3, 33–44. [CrossRef]spa
dcterms.bibliographicCitationHassanzadehFard, H.; Jalilian, A. A novel objective function for optimal DG allocation in distribution systems using meta-heuristic algorithms. Int. J. Green Energy 2016, 13, 1615–1625. [CrossRef]spa
dcterms.bibliographicCitationNekooei, K.; Farsangi, M.M.; Nezamabadi-Pour, H.; Lee, K.Y. An improved multi-objective harmony search for optimal placement of DGs in distribution systems. IEEE Trans. Smart Grid 2013, 4, 557–567. [CrossRef]spa
dcterms.bibliographicCitationOthman, 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. Int. J. Electr. Power Energy Syst. 2016, 82, 105–113. [CrossRef]spa
dcterms.bibliographicCitationSorensen, K. Metaheuristics—The metaphor exposed. Int. Trans. Oper. Res. 2015, 22, 3–18. [CrossRef]spa
dcterms.bibliographicCitationHeliodore, F.; Nakib, A.; Ismail, B.; Ouchraa, S.; Schmitt, L. Performance Evaluation of Metaheuristics. In Metaheuristics for Intelligent Electrical Networks; John Wiley & Sons: Inc.: Hoboken, NJ, USA, 2017; pp. 43–58. [CrossRef]spa
dcterms.bibliographicCitationEftimov, T.; Korošec, P. A novel statistical approach for comparing meta-heuristic stochastic optimization algorithms according to the distribution of solutions in the search space. Inf. Sci. 2019, 489, 255–273. [CrossRef]spa
dcterms.bibliographicCitationXu, X.; Li, J.; Xu, Z.; Zhao, J.; Lai, C.S. Enhancing photovoltaic hosting capacity—A stochastic approach to optimal planning of static var compensator devices in distribution networks. Appl. Energy 2019, 238, 952–962. [CrossRef]spa
dcterms.bibliographicCitationMontoya, O.D.; Molina-Cabrera, A.; Chamorro, H.R.; Alvarado-Barrios, L.; Rivas-Trujillo, E. A Hybrid Approach Based on SOCP and the Discrete Version of the SCA for Optimal Placement and Sizing DGs in AC Distribution Networks. Electronics 2020, 10, 26. [CrossRef]spa
dcterms.bibliographicCitationMontoya, O.D.; Gil-González, W.; Grisales-Noreña, L.F. Hybrid GA-SOCP Approach for Placement and Sizing of Distributed Generators in DC Networks. Appl. Sci. 2020, 10, 8616. [CrossRef]spa
dcterms.bibliographicCitationMolzahn, D.K.; Hiskens, I.A. A Survey of Relaxations and Approximations of the Power Flow Equations. Found. Trends Electr. Energy Syst. 2019, 4. [CrossRef]spa
dcterms.bibliographicCitationAlizadeh, F.; Goldfarb, D. Second-order cone programming. Math. Program. 2003, 95, 3–51. [CrossRef]spa
dcterms.bibliographicCitationBoyd, S.; Boyd, S.P.; Vandenberghe, L. Convex Optimization; Cambridge University Press: Cambridge, UK,
dcterms.bibliographicCitationLow, S.H. Convex Relaxation of Optimal Power Flow—Part I: Formulations and Equivalence. IEEE Trans. Control Netw. Syst. 2014, 1, 15–27. [CrossRef]spa
dcterms.bibliographicCitationBenson, H.Y.; Saglam, U. Mixed Integer Second Order Cone Programming: A Survey. In Theory Driven by Influential Applications; INFORMS: Catonsville, MD, USA, 2014; Chapter 2, pp. 13–36. [CrossRef]spa
dcterms.bibliographicCitationKarmarkar, N. A new polynomial-time algorithm for linear programming. Combinatorica 1984, 4, 373–395. [CrossRef]spa
dcterms.bibliographicCitationAtamturk, A.; Gómez, A. Submodularity in Conic Quadratic Mixed 0–1 Optimization. Oper. Res. 2020, 68, 609–630. [CrossRef]spa
dcterms.bibliographicCitationGrant, M.; Boyd, S. CVX: Matlab Software for Disciplined Convex Programming, Version 2.1. 2014. Available online: http: // (accessed on 3 July 2020).spa
dcterms.bibliographicCitationMoradi, M.; Abedini, M. A novel method for optimal DG units capacity and location in Microgrids. Int. J. Electr. Power Energy Syst. 2016, 75, 236–244. [CrossRef]spa
dcterms.bibliographicCitationMoradi, M.; Abedini, M. A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems. Int. J. Electr. Power Energy Syst. 2012, 34, 66–74. [CrossRef]spa
dcterms.bibliographicCitationBocanegra, S.Y.; Montoya, O.D. Heuristic Approach for Optimal Location and Sizing of Distributed Generators in AC Distribution Networks. Wseas Trans. Power Syst. 2019, 14, 113–
dcterms.bibliographicCitationSultana, S.; Roy, P.K. Multi-objective quasi-oppositional teaching learning based optimization for optimal location of distributed generator in radial distribution systems. Int. J. Electr. Power Energy Syst. 2014, 63, 534–545. [CrossRef]spa
dcterms.bibliographicCitationBayat, A.; Bagheri, A. Optimal active and reactive power allocation in distribution networks using a novel heuristic approach. Appl. Energy 2019, 233-234, 71–85. [CrossRef]spa
dcterms.bibliographicCitationMuthukumar, K.; Jayalalitha, S. Optimal placement and sizing of distributed generators and shunt capacitors for power loss minimization in radial distribution networks using hybrid heuristic search optimization technique. Int. J. Electr. Power Energy Syst. 2016, 78, 299–319. [CrossRef]spa
dcterms.bibliographicCitationKaur, S.; Kumbhar, G.; Sharma, J. A MINLP technique for optimal placement of multiple DG units in distribution systems. Int. J. Electr. Power Energy Syst. 2014, 63, 609–617. [CrossRef]spa
dcterms.bibliographicCitationGholami, K.; Parvaneh, M.H. A mutated salp swarm algorithm for optimum allocation of active and reactive power sources in radial distribution systems. Appl. Soft Comput. 2019, 85, 105833. [CrossRef]spa
dcterms.bibliographicCitationNowdeh, S.A.; Davoudkhani, I.F.; Moghaddam, M.H.; Najmi, E.S.; Abdelaziz, A.; Ahmadi, A.; Razavi, S.; Gandoman, F. Fuzzy multi-objective placement of renewable energy sources in distribution system with objective of loss reduction and reliability improvement using a novel hybrid method. Appl. Soft Comput. 2019, 77, 761–779. [CrossRef]spa
dcterms.bibliographicCitationHung, D.Q.; Mithulananthan, N. Multiple distributed generator placement in primary distribution networks for loss reduction. IEEE Trans. Ind. Electron. 2011, 60, 1700–1708. [CrossRef]spa
dcterms.bibliographicCitationJain, N.; Singh, S.; Srivastava, S. A generalized approach for DG planning and viability analysis under market scenario. IEEE Trans. Ind. Electron. 2012, 60, 5075–5085. [CrossRef]spa
dc.subject.keywordsDistributed generatorsspa
dc.subject.keywordsConvex optimizationspa
dc.subject.keywordsSecond-order cone programmingspa
dc.subject.keywordsBranch & boundspa
dc.subject.keywordsInteger optimizationspa
dc.subject.keywordsPower losses minimizationspa
dc.rights.ccAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.ccAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.identifier.instnameUniversidad Tecnológica de Bolívarspa
dc.identifier.reponameRepositorio Universidad Tecnológica de Bolívarspa
dc.publisher.placeCartagena de Indiasspa

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Universidad Tecnológica de Bolívar - 2017 Institución de Educación Superior sujeta a inspección y vigilancia por el Ministerio de Educación Nacional. Resolución No 961 del 26 de octubre de 1970 a través de la cual la Gobernación de Bolívar otorga la Personería Jurídica a la Universidad Tecnológica de Bolívar.