Optimal placement and sizing of PV sources in distribution grids using a modified gradient-based metaheuristic optimizer
The problem of the optimal placement and sizing of renewable generation sources based on photovoltaic (PV) technology in electrical distribution grids operated in medium-voltage levels was studied in this research. This optimization problem is from the mixed-integer nonlinear programming (MINLP) mod...
- Autores:
-
Montoya Giraldo, Oscar Danilo
Grisales-Noreña, Luis Fernando
Giral-Ramírez, Diego Armando
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/10689
- Palabra clave:
- Photovoltaic generation
Gradient-based metaheuristic optimizer
Radial distribution networks
Combinatorial optimization
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Optimal placement and sizing of PV sources in distribution grids using a modified gradient-based metaheuristic optimizer |
| title |
Optimal placement and sizing of PV sources in distribution grids using a modified gradient-based metaheuristic optimizer |
| spellingShingle |
Optimal placement and sizing of PV sources in distribution grids using a modified gradient-based metaheuristic optimizer Photovoltaic generation Gradient-based metaheuristic optimizer Radial distribution networks Combinatorial optimization LEMB |
| title_short |
Optimal placement and sizing of PV sources in distribution grids using a modified gradient-based metaheuristic optimizer |
| title_full |
Optimal placement and sizing of PV sources in distribution grids using a modified gradient-based metaheuristic optimizer |
| title_fullStr |
Optimal placement and sizing of PV sources in distribution grids using a modified gradient-based metaheuristic optimizer |
| title_full_unstemmed |
Optimal placement and sizing of PV sources in distribution grids using a modified gradient-based metaheuristic optimizer |
| title_sort |
Optimal placement and sizing of PV sources in distribution grids using a modified gradient-based metaheuristic optimizer |
| dc.creator.fl_str_mv |
Montoya Giraldo, Oscar Danilo Grisales-Noreña, Luis Fernando Giral-Ramírez, Diego Armando |
| dc.contributor.author.none.fl_str_mv |
Montoya Giraldo, Oscar Danilo Grisales-Noreña, Luis Fernando Giral-Ramírez, Diego Armando |
| dc.subject.keywords.spa.fl_str_mv |
Photovoltaic generation Gradient-based metaheuristic optimizer Radial distribution networks Combinatorial optimization |
| topic |
Photovoltaic generation Gradient-based metaheuristic optimizer Radial distribution networks Combinatorial optimization LEMB |
| dc.subject.armarc.none.fl_str_mv |
LEMB |
| description |
The problem of the optimal placement and sizing of renewable generation sources based on photovoltaic (PV) technology in electrical distribution grids operated in medium-voltage levels was studied in this research. This optimization problem is from the mixed-integer nonlinear programming (MINLP) model family. Solving this model was achieved by implementing a master–slave optimization approach, where the master–slave corresponded to the application of the modified gradient-based metaheuristic optimizer (MGbMO) and the slave stage corresponded to the application of the successive approximation power flow method. In the master stage, the problem of the optimal placement and sizing of the PV sources was solved using a discrete–continuous codification, while the slave stage was used to calculate the objective function value regarding the energy purchasing costs in terminals of the substation, as well as to verify that the voltage profiles and the power generations were within their allowed bounds. The numerical results of the proposed MGbMO in the IEEE 34-bus system demonstrated its efficiency when compared with different metaheuristic optimizers such as the Chu and Beasley genetic algorithm, the Newton metaheuristic algorithm, the original gradient-based metaheuristic optimizer, and the exact solution of the MINLP model using the general algebraic modeling system. In addition, the possibility of including meshed distribution topologies was tested with excellent numerical results. |
| publishDate |
2022 |
| dc.date.accessioned.none.fl_str_mv |
2022-05-09T12:12:20Z |
| dc.date.available.none.fl_str_mv |
2022-05-09T12:12:20Z |
| dc.date.issued.none.fl_str_mv |
2022-03-11 |
| dc.date.submitted.none.fl_str_mv |
2022-05-06 |
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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.spa.fl_str_mv |
Montoya, O.D.; Grisales-Noreña, L.F.; Giral-Ramírez, D.A. Optimal Placement and Sizing of PV Sources in Distribution Grids Using a Modified Gradient-Based Metaheuristic Optimizer. Sustainability 2022, 14, 3318. https://doi.org/10.3390/su14063318 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12585/10689 |
| dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.3390/su14063318 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
| identifier_str_mv |
Montoya, O.D.; Grisales-Noreña, L.F.; Giral-Ramírez, D.A. Optimal Placement and Sizing of PV Sources in Distribution Grids Using a Modified Gradient-Based Metaheuristic Optimizer. Sustainability 2022, 14, 3318. https://doi.org/10.3390/su14063318 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
| url |
https://hdl.handle.net/20.500.12585/10689 https://doi.org/10.3390/su14063318 |
| dc.language.iso.spa.fl_str_mv |
eng |
| language |
eng |
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http://purl.org/coar/access_right/c_abf2 |
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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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http://creativecommons.org/licenses/by-nc-nd/4.0/ Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://purl.org/coar/access_right/c_abf2 |
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openAccess |
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19 Páginas |
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Cartagena de Indias |
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Sustainability 2022, 14, 3318 |
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Universidad Tecnológica de Bolívar |
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Montoya Giraldo, Oscar Daniloc66dce06-2f1b-4a61-9631-60e8f37e8432Grisales-Noreña, Luis Fernando7c27cda4-5fe4-4686-8f72-b0442c58a5d1Giral-Ramírez, Diego Armandoa9612d05-bc90-49f9-94c7-20a0766e00f52022-05-09T12:12:20Z2022-05-09T12:12:20Z2022-03-112022-05-06Montoya, O.D.; Grisales-Noreña, L.F.; Giral-Ramírez, D.A. Optimal Placement and Sizing of PV Sources in Distribution Grids Using a Modified Gradient-Based Metaheuristic Optimizer. Sustainability 2022, 14, 3318. https://doi.org/10.3390/su14063318https://hdl.handle.net/20.500.12585/10689https://doi.org/10.3390/su14063318Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarThe problem of the optimal placement and sizing of renewable generation sources based on photovoltaic (PV) technology in electrical distribution grids operated in medium-voltage levels was studied in this research. This optimization problem is from the mixed-integer nonlinear programming (MINLP) model family. Solving this model was achieved by implementing a master–slave optimization approach, where the master–slave corresponded to the application of the modified gradient-based metaheuristic optimizer (MGbMO) and the slave stage corresponded to the application of the successive approximation power flow method. In the master stage, the problem of the optimal placement and sizing of the PV sources was solved using a discrete–continuous codification, while the slave stage was used to calculate the objective function value regarding the energy purchasing costs in terminals of the substation, as well as to verify that the voltage profiles and the power generations were within their allowed bounds. The numerical results of the proposed MGbMO in the IEEE 34-bus system demonstrated its efficiency when compared with different metaheuristic optimizers such as the Chu and Beasley genetic algorithm, the Newton metaheuristic algorithm, the original gradient-based metaheuristic optimizer, and the exact solution of the MINLP model using the general algebraic modeling system. In addition, the possibility of including meshed distribution topologies was tested with excellent numerical results.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_abf2Sustainability 2022, 14, 3318Optimal placement and sizing of PV sources in distribution grids using a modified gradient-based metaheuristic optimizerinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/restrictedAccesshttp://purl.org/coar/resource_type/c_2df8fbb1Photovoltaic generationGradient-based metaheuristic optimizerRadial distribution networksCombinatorial optimizationLEMBCartagena de IndiasInvestigadoresLavorato, M.; Rider, M.J.; Garcia, A.V.; Romero, R. A Constructive Heuristic Algorithm for Distribution System Planning. IEEE Trans. Power Syst. 2010, 25, 1734–1742Girbau-Llistuella, F.; Díaz-González, F.; Sumper, A.; Gallart-Fernández, R.; Heredero-Peris, D. Smart Grid Architecture for Rural Distribution Networks: Application to a Spanish Pilot Network. Energies 2018, 11, 844Helmi, A.M.; Carli, R.; Dotoli, M.; Ramadan, H.S. Efficient and Sustainable Reconfiguration of Distribution Networks via Metaheuristic Optimization. IEEE Trans. Autom. Sci. Eng. 2022, 19, 82–98.Nahman, J.; Peric, D. Optimal Planning of Radial Distribution Networks by Simulated Annealing Technique. IEEE Trans. Power Syst. 2008, 23, 790–795. [Lavorato, M.; Franco, J.F.; Rider, M.J.; Romero, R. Imposing Radiality Constraints in Distribution System Optimization Problems. IEEE Trans. Power Syst. 2012, 27, 172–180Paz-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, 4418Tolmasquim, M.T.; Linhares-Pires, J.C.; Rosa, L.P. New Strategies for Power Companies in Brazil. In European Energy Industry Business Strategies; Elsevier: Amsterdam, The Netherlands, 2001; pp. 337–374.Jerez, S.; Tobin, I.; Vautard, R.; Montávez, J.P.; López-Romero, J.M.; Thais, F.; Bartok, B.; Christensen, O.B.; Colette, A.; Déqué, M.; et al. The impact of climate change on photovoltaic power generation in Europe. Nat. Commun. 2015, 6, 10014Steffen, B.; Beuse, M.; Tautorat, P.; Schmidt, T.S. Experience Curves for Operations and Maintenance Costs of Renewable Energy Technologies. Joule 2020, 4, 359–375López, A.R.; Krumm, A.; Schattenhofer, L.; Burandt, T.; Montoya, F.C.; Oberländer, N.; Oei, P.Y. Solar PV generation in Colombia— A qualitative and quantitative approach to analyze the potential of solar energy market. Renew. Energy 2020, 148, 1266–1279.Montoya, O.D.; Grisales-Noreña, L.F.; Perea-Moreno, A.J. Optimal Investments in PV Sources for Grid-Connected Distribution Networks: An Application of the Discrete–Continuous Genetic Algorithm. Sustainability 2021, 13, 13633Kaur, 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.Muhammad, M.A.; Mokhlis, H.; Naidu, K.; Amin, A.; Franco, J.F.; Othman, M. Distribution Network Planning Enhancement via Network Reconfiguration and DG Integration Using Dataset Approach and Water Cycle Algorithm. J. Mod. Power Syst. Clean Energy 2020, 8, 86–93Prenc, R.; Skrlec, D.; Komen, V. Optimal PV system placement in a distribution network on the basis of daily power consumption and production fluctuation. In Proceedings of the Eurocon 2013, Zagreb, Croatia, 1–4 July 2013.Hraiz, M.D.; Garcia, J.A.M.; Castaneda, R.J.; Muhsen, H. Optimal PV Size and Location to Reduce Active Power Losses While Achieving Very High Penetration Level with Improvement in Voltage Profile Using Modified Jaya Algorithm. IEEE J. Photovoltaics 2020, 10, 1166–1174Valencia, A.; Hincapie, R.A.; Gallego, R.A. Optimal location, selection, and operation of battery energy storage systems and renewable distributed generation in medium–low voltage distribution networks. J. Energy Storage 2021, 34, 102158Soroudi, A. Power System Optimization Modeling in GAMS; Springer International Publishing: Berlin/Heidelberg, Germany, 2017.Montoya, O.D.; Grisales-Noreña, L.F.; Alvarado-Barrios, L.; Arias-Londoño, A.; Álvarez-Arroyo, C. Efficient Reduction in the Annual Investment Costs in AC Distribution Networks via Optimal Integration of Solar PV Sources Using the Newton Metaheuristic Algorithm. Appl. Sci. 2021, 11, 11525Wang, P.; Wang, W.; Xu, D. Optimal Sizing of Distributed Generations in DC Microgrids with Comprehensive Consideration of System Operation Modes and Operation Targets. IEEE Access 2018, 6, 31129–31140Chen, X.; Li, Z.; Wan, W.; Zhu, L.; Shao, Z. A master–slave solving method with adaptive model reformulation technique for water network synthesis using MINLP. Sep. Purif. Technol. 2012, 98, 516–530.Ahmadianfar, I.; Bozorg-Haddad, O.; Chu, X. Gradient-based optimizer: A new metaheuristic optimization algorithm. Inf. Sci. 2020, 540, 131–159Shen, T.; Li, Y.; Xiang, J. A Graph-Based Power Flow Method for Balanced Distribution Systems. Energies 2018, 11, 511.Montoya, O.D.; Gil-González, W. On the numerical analysis based on successive approximations for power flow problems in AC distribution systems. Electr. Power Syst. Res. 2020, 187, 106454Deb, S.; Abdelminaam, D.S.; Said, M.; Houssein, E.H. Recent Methodology-Based Gradient-Based Optimizer for Economic Load Dispatch Problem. IEEE Access 2021, 9, 44322–44338.Gholizadeh, S.; Danesh, M.; Gheyratmand, C. A new Newton metaheuristic algorithm for discrete performance-based design optimization of steel moment frames. Comput. Struct. 2020, 234, 106250Randall, M. Feasibility Restoration for Iterative Meta-heuristics Search Algorithms. In Developments in Applied Artificial Intelligence; Springer: Berlin/Heidelberg, Germany, 2002; pp. 168–178.Do ˘gan, B.; Ölmez, T. A new metaheuristic for numerical function optimization: Vortex Search algorithm. Inf. Sci. 2015, 293, 125–145. [Gharehchopogh, F.S.; Maleki, I.; Dizaji, Z.A. Chaotic vortex search algorithm: Metaheuristic algorithm for feature selection. Evol. Intell. 2021Sahin, O.; Akay, B. Comparisons of metaheuristic algorithms and fitness functions on software test data generation. Appl. Soft Comput. 2016, 49, 1202–1214.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, 2775–2786Grisales-Noreña, L.; Montoya, O.D.; Ramos-Paja, C.A. An energy management system for optimal operation of BSS in DC distributed generation environments based on a parallel PSO algorithm. J. 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