Optimal Selection of Conductors in Three-Phase Distribution Networks Using a Discrete Version of the Vortex Search Algorithm
In this study, a new methodology is proposed to perform optimal selection of conductors in three-phase distribution networks through a discrete version of the metaheuristic method of vortex search. To represent the problem, a single-objective mathematical model with a mixed-integer nonlinear program...
- Autores:
-
Martínez-Gil, John Fernando
Moyano-García, Nicolas Alejandro
Montoya, Oscar Danilo
Alarcon-Villamil, Jorge Alexander
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/10401
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/10401
https://doi.org/10.3390/computation9070080
- Palabra clave:
- Conductor selection
Mathematical optimization
Distribution systems
Three-phase
Power flow
Energy losses
Vortex search algorithm
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Optimal Selection of Conductors in Three-Phase Distribution Networks Using a Discrete Version of the Vortex Search Algorithm |
| title |
Optimal Selection of Conductors in Three-Phase Distribution Networks Using a Discrete Version of the Vortex Search Algorithm |
| spellingShingle |
Optimal Selection of Conductors in Three-Phase Distribution Networks Using a Discrete Version of the Vortex Search Algorithm Conductor selection Mathematical optimization Distribution systems Three-phase Power flow Energy losses Vortex search algorithm LEMB |
| title_short |
Optimal Selection of Conductors in Three-Phase Distribution Networks Using a Discrete Version of the Vortex Search Algorithm |
| title_full |
Optimal Selection of Conductors in Three-Phase Distribution Networks Using a Discrete Version of the Vortex Search Algorithm |
| title_fullStr |
Optimal Selection of Conductors in Three-Phase Distribution Networks Using a Discrete Version of the Vortex Search Algorithm |
| title_full_unstemmed |
Optimal Selection of Conductors in Three-Phase Distribution Networks Using a Discrete Version of the Vortex Search Algorithm |
| title_sort |
Optimal Selection of Conductors in Three-Phase Distribution Networks Using a Discrete Version of the Vortex Search Algorithm |
| dc.creator.fl_str_mv |
Martínez-Gil, John Fernando Moyano-García, Nicolas Alejandro Montoya, Oscar Danilo Alarcon-Villamil, Jorge Alexander |
| dc.contributor.author.none.fl_str_mv |
Martínez-Gil, John Fernando Moyano-García, Nicolas Alejandro Montoya, Oscar Danilo Alarcon-Villamil, Jorge Alexander |
| dc.subject.keywords.es_CO.fl_str_mv |
Conductor selection Mathematical optimization Distribution systems Three-phase Power flow Energy losses Vortex search algorithm |
| topic |
Conductor selection Mathematical optimization Distribution systems Three-phase Power flow Energy losses Vortex search algorithm LEMB |
| dc.subject.armarc.none.fl_str_mv |
LEMB |
| description |
In this study, a new methodology is proposed to perform optimal selection of conductors in three-phase distribution networks through a discrete version of the metaheuristic method of vortex search. To represent the problem, a single-objective mathematical model with a mixed-integer nonlinear programming (MINLP) structure is used. As an objective function, minimization of the investment costs in conductors together with the technical losses of the network for a study period of one year is considered. Additionally, the model will be implemented in balanced and unbalanced test systems and with variations in the connection of their loads, i.e., ∆− and Y−connections. To evaluate the costs of the energy losses, a classical backward/forward three-phase power-flow method is implemented. Two test systems used in the specialized literature were employed, which comprise 8 and 27 nodes with radial structures in medium voltage levels. All computational implementations were developed in the MATLAB programming environment, and all results were evaluated in DigSILENT software to verify the effectiveness and the proposed three-phase unbalanced powerflow method. Comparative analyses with classical and Chu & Beasley genetic algorithms, tabu search algorithm, and exact MINLP approaches demonstrate the efficiency of the proposed optimization approach regarding the final value of the objective function |
| publishDate |
2021 |
| dc.date.issued.none.fl_str_mv |
2021-07-18 |
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2022-01-24T21:21:00Z |
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2022-01-24T21:21:00Z |
| dc.date.submitted.none.fl_str_mv |
2022-01-24 |
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info:eu-repo/semantics/article |
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info:eu-repo/semantics/restrictedAccess |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
| dc.identifier.citation.es_CO.fl_str_mv |
Martínez-Gil, J.F.; Moyano-Garcia, N.A.; Montoya, O.D.; Alarcon-Villamil, J.A. Optimal Selection of Conductors in Three-Phase Distribution Networks Using a Discrete Version of the Vortex Search Algorithm. Computation 2021, 9, 80. https://doi.org/10.3390/computation9070080 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12585/10401 |
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https://doi.org/10.3390/computation9070080 |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
| identifier_str_mv |
Martínez-Gil, J.F.; Moyano-Garcia, N.A.; Montoya, O.D.; Alarcon-Villamil, J.A. Optimal Selection of Conductors in Three-Phase Distribution Networks Using a Discrete Version of the Vortex Search Algorithm. Computation 2021, 9, 80. https://doi.org/10.3390/computation9070080 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
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https://hdl.handle.net/20.500.12585/10401 https://doi.org/10.3390/computation9070080 |
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eng |
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eng |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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info:eu-repo/semantics/openAccess |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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openAccess |
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32 páginas |
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Cartagena de Indias |
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Computation - vol. 9 n° 7 2021 |
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Martínez-Gil, John Fernando08bb4179-a169-40f3-b930-a4880d07fd01Moyano-García, Nicolas Alejandro659ff6ae-6e0f-4f6f-a103-4e591c87cb72Montoya, Oscar Danilo8a59ede1-6a4a-4d2e-abdc-d0afb14d4480Alarcon-Villamil, Jorge Alexanderafeab025-85c3-471d-9d8c-685a95aa3eb52022-01-24T21:21:00Z2022-01-24T21:21:00Z2021-07-182022-01-24Martínez-Gil, J.F.; Moyano-Garcia, N.A.; Montoya, O.D.; Alarcon-Villamil, J.A. Optimal Selection of Conductors in Three-Phase Distribution Networks Using a Discrete Version of the Vortex Search Algorithm. Computation 2021, 9, 80. https://doi.org/10.3390/computation9070080https://hdl.handle.net/20.500.12585/10401https://doi.org/10.3390/computation9070080Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarIn this study, a new methodology is proposed to perform optimal selection of conductors in three-phase distribution networks through a discrete version of the metaheuristic method of vortex search. To represent the problem, a single-objective mathematical model with a mixed-integer nonlinear programming (MINLP) structure is used. As an objective function, minimization of the investment costs in conductors together with the technical losses of the network for a study period of one year is considered. Additionally, the model will be implemented in balanced and unbalanced test systems and with variations in the connection of their loads, i.e., ∆− and Y−connections. To evaluate the costs of the energy losses, a classical backward/forward three-phase power-flow method is implemented. Two test systems used in the specialized literature were employed, which comprise 8 and 27 nodes with radial structures in medium voltage levels. All computational implementations were developed in the MATLAB programming environment, and all results were evaluated in DigSILENT software to verify the effectiveness and the proposed three-phase unbalanced powerflow method. Comparative analyses with classical and Chu & Beasley genetic algorithms, tabu search algorithm, and exact MINLP approaches demonstrate the efficiency of the proposed optimization approach regarding the final value of the objective function32 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_abf2Computation - vol. 9 n° 7 2021Optimal Selection of Conductors in Three-Phase Distribution Networks Using a Discrete Version of the Vortex Search Algorithminfo:eu-repo/semantics/articleinfo:eu-repo/semantics/restrictedAccesshttp://purl.org/coar/resource_type/c_2df8fbb1Conductor selectionMathematical optimizationDistribution systemsThree-phasePower flowEnergy lossesVortex search algorithmLEMBCartagena de IndiasMontoya, O.D.; Gil-González, W.; Hernández, J.C. Efficient Operative Cost Reduction in Distribution Grids Considering the Optimal Placement and Sizing of D-STATCOMs Using a Discrete-Continuous VSA. Appl. Sci. 2021, 11, 2175. doi:10.3390/app11052175.Fatima, S.; Püvi, V.; Arshad, A.; Pourakbari-Kasmaei, M.; Lehtonen, M. Comparison of Economical and Technical Photovoltaic Hosting Capacity Limits in Distribution Networks. Energies 2021, 14, 2405. doi:10.3390/en14092405Sorrentino, E.; Gupta, N.G. Summary of useful concepts about the coordination of directional overcurrent protections. CSEE J. Power Energy Syst. 2019. doi:10.17775/cseejpes.2018.01220Paz, M.C.R.; Ferraz, R.G.; Bretas, A.S.; Leborgne, R.C. System unbalance and fault impedance effect on faulted distribution networks. Comput. Math. Appl. 2010, 60, 1105–1114. doi:10.1016/j.camwa.2010.03.067.Cortés-Caicedo, B.; Avellaneda-Gómez, L.S.; Montoya, O.D.; Alvarado-Barrios, L.; Chamorro, H.R. Application of the Vortex Search Algorithm to the Phase-Balancing Problem in Distribution Systems. Energies 2021, 14, 1282. doi:10.3390/en14051282.Lavorato, M.; Franco, J.F.; Rider, M.J.; Romero, R. Imposing Radiality Constraints in Distribution System Optimization Problems. IEEE Trans. Power Syst. 2012, 27, 172–180. doi:10.1109/tpwrs.2011.2161349.Montoya., O.D.; Grajales, A.; Hincapié, R.A.; Granada, M. A new approach to solve the distribution system planning problem considering automatic reclosers. Ingeniare. Revista Chilena de Ingeniería 2017, 25, 415–429. doi:10.4067/s0718-33052017000300415.Wang, Z.; Liu, H.; Yu, D.; Wang, X.; Song, H. A practical approach to the conductor size selection in planning radial distribution systems. IEEE Trans. Power Deliv. 2000, 15, 350–354. doi:10.1109/61.847272Zhao, Z.; Mutale, J. Optimal Conductor Size Selection in Distribution Networks with High Penetration of Distributed Generation Using Adaptive Genetic Algorithm. Energies 2019, 12, 2065. doi:10.3390/en12112065.Montoya, O.; Grajales, A.; Hincapié, R. Optimal selection of conductors in distribution systems using tabu search algorithm. Ingeniare. Revista Chilena de Ingeniería 2018, 26, 283–295. doi:10.4067/s0718-33052018000200283.Ismael, S.M.; Aleem, S.H.E.A.; Abdelaziz, A.Y. Optimal selection of conductors in Egyptian radial distribution systems using sine-cosine optimization algorithm. In Proceedings of the 2017 Nineteenth International Middle East Power Systems Conference (MEPCON), Hibbingum, Egypt, 19–21 December 2017; IEEE: Piscataway, NJ, USA, 2017. doi:10.1109/mepcon.2017.8301170.Joshi, D.; Burada, S.; Mistry, K.D. Distribution system planning with optimal conductor selection. In Proceedings of the 2017 Recent Developments in Control, Automation & Power Engineering (RDCAPE), Noida, India, 26–27 October 2017, IEEE: Piscataway, NJ, USA, 2017. doi:10.1109/rdcape.2017.8358279.Ismael, S.M.; Aleem, S.H.E.A.; Abdelaziz, A.Y.; Zobaa, A.F. Practical Considerations for Optimal Conductor Reinforcement and Hosting Capacity Enhancement in Radial Distribution Systems. IEEE Access 2018, 6, 27268–27277. doi:10.1109/access.2018.2835165.Mandal, S.; Pahwa, A. Optimal selection of conductors for distribution feeders. IEEE Trans. Power Syst. 2002, 17, 192–197. doi:10.1109/59.982213.Falaghi, H.; Singh, C. Optimal Conductor Size Selection in Distribution Systems with Wind Power Generation. In Green Energy and Technology; Springer: Berlin/Heidelberg, Germany, 2010; pp. 25–51. doi:10.1007/978-3-642-13250-6_2.Legha, M.; Javaheri, H.; Legha, M. Optimal Conductor Selection in Radial Distribution Systems for Productivity Improvement Using Genetic Algorithm. Iraqi J. Electr. Electron. Eng. 2013, 9, 29–35. doi:10.37917/ijeee.9.1.3Rao, R.S.; Satish, K.; Narasimham, S.V.L. Optimal Conductor Size Selection in Distribution Systems Using the Harmony Search Algorithm with a Differential Operator. Electr. Power Compon. Syst. 2011, 40, 41–56. doi:10.1080/15325008.2011.621922.Lopez, L.; Hincapié, R.A.; Gallego, R.A. Planeamiento multi-objetivo de sistemas de distribución usando un algoritmo evolutivo NSGA-II. Revista Escuela de Ingeniería de Antioquía 2011, 15, 141–151.Khalil, T.M.; Gorpinich, A.V. Optimal conductor selection and capacitor placement for loss reduction of radial distribution systems by selective particle swarm optimization. In Proceedings of the 2012 Seventh International Conference on Computer Engineering & Systems (ICCES), Cairo, Egypt, 27–29 November 2012; IEEE: Piscataway, NJ, USA 2012. doi:10.1109/icces.2012.6408516.Legha, M.M.; Noormohamadi, H.; Barkhori, A. Optimal conductor selection in radial distribution using bacterial foraging algorithm and comparison with ICA method WALIA J. 2015. 31, 37–43.Abdelaziz, A.Y.; Fathy, A. A novel approach based on crow search algorithm for optimal selection of conductor size in radial distribution networks. Eng. Sci. Technol. Int. J. 2017, 20, 391–402. doi:10.1016/j.jestch.2017.02.004Abdelaziz, A.Y.; Fathy, A. A novel approach based on crow search algorithm for optimal selection of conductor size in radial distribution networks. Eng. Sci. Technol. Int. J. 2017, 20, 391–402. doi:10.1016/j.jestch.2017.02.004Hassen, S.Z.S.; Jahmeerbacus, M.I. Customer Loss Allocation Reduction Using Optimal Conductor Selection in Electrical Distribution System. In Emerging Trends in Electrical, Electronic and Communications Engineering; Springer: Singapore, 2017; Volume 416, pp. 372–379. doi:10.1007/978-3-319-52171-8.Montoya, O.D.; Garces, A.; Castro, C.A. Optimal Conductor Size Selection in Radial Distribution Networks Using a MixedInteger Non-Linear Programming Formulation. IEEE Latin Am. Trans. 2018, 16, 2213–2220. doi:10.1109/tla.2018.8528237Montoya, O.D.; Gil-González, W.; Grisales-Noreña, L.F. On the mathematical modeling for optimal selecting of calibers of conductors in DC radial distribution networks: An MINLP approach. Electr. Power Syst. Res. 2021, 194, 107072. doi:10.1016/j.epsr.2021.107072.Ismael, S.M.; Aleem, S.H.E.A.; Abdelaziz, A.Y. Optimal conductor selection in radial distribution systems using whale optimization algorithm. J. Eng. Sci. Technol. 2017, 14, 87–107Kumari, M.; Singh, V.R.; Ranjan, R. Optimal selection of conductor in RDS considering weather condition. In Proceedings of the 2018 International Conference on Computing, Power and Communication Technologies, GUCON 2018, New Delhi, India, 28–29 September 2018; pp. 647–651. doi:10.1109/GUCON.2018.8675051.Mohanty, S.; Kasturi, K.; Nayak, M.R. Application of ER-WCA to Determine Conductor Size for Performance Improvement in Distribution System. In Proceedings of the 2021 International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), Bhilai, India, 19–20 February 2021, pp. 1–5. doi:10.1109/ICAECT49130.2021.9392495.Paz-Rodríguez, A.; Castro-Ordoñez, J.F.; Montoya, O.D.; Giral-Ramírez, D.A. Optimal Integration of Photovoltaic Sources in Distribution Networks for Daily Energy Losses Minimization Using the Vortex Search Algorithm. Appl. Sci. 2021, 11, 4418. doi:10.3390/app11104418.Dogan, B.; Yuksel, A. Analog filter group delay optimization using the Vortex Search algorithm. In Proceedings of the 2015 23nd Signal Processing and Communications Applications Conference (SIU), Malatya, Turkey, 16–19 May 2015. doi:10.1109/siu.2015.7129815Serna-Suárez, I.D. A Convex Approximation for Optimal DER Scheduling on Unbal-anced Power Distribution Networks. DYNA 2019, 86, 281–291. doi:10.15446/dyna.v86n208.72886Wang, C.; Liu, P.; Zhang, T.; Sun, J. The Adaptive Vortex Search Algorithm of Optimal Path Planning for Forest Fire Rescue UAV. In Proceedings of the 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2018, Chongqing, China, 12–14 October 2018; pp. 400–403. doi:10.1109/IAEAC.2018.8577733Montoya, O.D.; Gil-Gonzalez, W.; Grisales-Norena, L.F. Vortex Search Algorithm for Optimal Power Flow Analysis in DC Resistive Networks with CPLs. IEEE Trans. Circ. Syst. II Express Briefs 2020, 67, 1439–1443. doi:10.1109/TCSII.2019.2938530Shen, T.; Li, Y.; Xiang, J. A graph-based power flow method for balanced distribution systems. Energies 2018, 11, 511. doi:10.3390/en11030511Castilho Neto, J.; Cossi, A.M. Alocação de Cabos em Redes de Distribuição de Energia Elétrica de Média Tensão (MT) Utilizando Algoritmo Chu-Beasley. In Simpósio Brasileiro de Sistemas Eletricos (SBSE). Foz do Iguacu, Brasil, 22–25 April. 2014; 1–6.Soroudi, A. Multi-Period Optimal Power Flow. In Alireza Soroudi Power System Optimization Modeling in GAMS; Springer: Singapore, 2019; Chapter 6, pp. 2017–2020.http://purl.org/coar/resource_type/c_2df8fbb1ORIGINAL[Art. 31] Optimal Selection of Conductors in_Oscar Danilo Montoya.pdf[Art. 31] Optimal Selection of Conductors in_Oscar Danilo Montoya.pdfapplication/pdf456911https://repositorio.utb.edu.co/bitstream/20.500.12585/10401/1/%5bArt.%2031%5d%20Optimal%20Selection%20of%20Conductors%20in_Oscar%20Danilo%20Montoya.pdf88d44d7ffd58fab9db19b78432b5715eMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.utb.edu.co/bitstream/20.500.12585/10401/2/license_rdf4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83182https://repositorio.utb.edu.co/bitstream/20.500.12585/10401/3/license.txte20ad307a1c5f3f25af9304a7a7c86b6MD53TEXT[Art. 31] Optimal Selection of Conductors in_Oscar Danilo Montoya.pdf.txt[Art. 31] Optimal Selection of Conductors in_Oscar Danilo 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