An Efficient Methodology for Locating and Sizing PV Generators in Radial Distribution Networks Using a Mixed-Integer Conic Relaxation
This paper proposes a new solution methodology based on a mixed-integer conic formulation to locate and size photovoltaic (PV) generation units in AC distribution networks with a radial structure. The objective function comprises the annual expected energy costs of the conventional substation in add...
- Autores:
-
Montoya, Oscar Danilo
Ramos-Paja, Carlos Andrés
Grisales-Noreña, Luis Fernando
- 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:20.500.12585/11127
- Palabra clave:
- Photovoltaic system
Investment and operating costs
Mixed-integer conic optimization
Radial distribution networks
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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| dc.title.spa.fl_str_mv |
An Efficient Methodology for Locating and Sizing PV Generators in Radial Distribution Networks Using a Mixed-Integer Conic Relaxation |
| title |
An Efficient Methodology for Locating and Sizing PV Generators in Radial Distribution Networks Using a Mixed-Integer Conic Relaxation |
| spellingShingle |
An Efficient Methodology for Locating and Sizing PV Generators in Radial Distribution Networks Using a Mixed-Integer Conic Relaxation Photovoltaic system Investment and operating costs Mixed-integer conic optimization Radial distribution networks LEMB |
| title_short |
An Efficient Methodology for Locating and Sizing PV Generators in Radial Distribution Networks Using a Mixed-Integer Conic Relaxation |
| title_full |
An Efficient Methodology for Locating and Sizing PV Generators in Radial Distribution Networks Using a Mixed-Integer Conic Relaxation |
| title_fullStr |
An Efficient Methodology for Locating and Sizing PV Generators in Radial Distribution Networks Using a Mixed-Integer Conic Relaxation |
| title_full_unstemmed |
An Efficient Methodology for Locating and Sizing PV Generators in Radial Distribution Networks Using a Mixed-Integer Conic Relaxation |
| title_sort |
An Efficient Methodology for Locating and Sizing PV Generators in Radial Distribution Networks Using a Mixed-Integer Conic Relaxation |
| dc.creator.fl_str_mv |
Montoya, Oscar Danilo Ramos-Paja, Carlos Andrés Grisales-Noreña, Luis Fernando |
| dc.contributor.author.none.fl_str_mv |
Montoya, Oscar Danilo Ramos-Paja, Carlos Andrés Grisales-Noreña, Luis Fernando |
| dc.subject.keywords.spa.fl_str_mv |
Photovoltaic system Investment and operating costs Mixed-integer conic optimization Radial distribution networks |
| topic |
Photovoltaic system Investment and operating costs Mixed-integer conic optimization Radial distribution networks LEMB |
| dc.subject.armarc.none.fl_str_mv |
LEMB |
| description |
This paper proposes a new solution methodology based on a mixed-integer conic formulation to locate and size photovoltaic (PV) generation units in AC distribution networks with a radial structure. The objective function comprises the annual expected energy costs of the conventional substation in addition to the investment and operating costs of PV sources. The original optimization model that represents this problem belongs to the family of mixed-integer nonlinear programming (MINLP); however, the complexity of the power balance constraints make it difficult to find the global optimum. In order to improve the quality of the optimization model, a mixed-integer conic (MIC) formulation is proposed in this research in order to represent the studied problem. Numerical results in two test feeders composed of 33 and 69 nodes demonstrate the effectiveness of the proposed MIC model when compared to multiple metaheuristic optimizers such as the Chu and Beasley Genetic Algorithm, the Newton Metaheuristic Algorithm, the Vortex Search Algorithm, the Gradient-Based Metaheuristic Optimization Algorithm, and the Arithmetic Optimization Algorithm, among others. The final results obtained with the MIC model show improvements greater than USD 100,000 per year of operation. All simulations were run in the MATLAB programming environment, using its own scripts for all the metaheuristic algorithms and the disciplined convex tool known as CVX with the Gurobi solver in order to solve the proposed MIC model. |
| publishDate |
2022 |
| dc.date.accessioned.none.fl_str_mv |
2022-10-05T12:25:33Z |
| dc.date.available.none.fl_str_mv |
2022-10-05T12:25:33Z |
| dc.date.issued.none.fl_str_mv |
2022-07-27 |
| dc.date.submitted.none.fl_str_mv |
2022-09-30 |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/article |
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info:eu-repo/semantics/restrictedAccess |
| dc.type.spa.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
| dc.identifier.citation.spa.fl_str_mv |
Montoya, O.D.; Ramos-Paja, C.A.; Grisales-Noreña, L.F. An Efficient Methodology for Locating and Sizing PV Generators in Radial Distribution Networks Using a Mixed-Integer Conic Relaxation. Mathematics 2022, 10, 2626. https://doi.org/10.3390/math10152626 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12585/11127 |
| dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.3390/math10152626 |
| 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.; Ramos-Paja, C.A.; Grisales-Noreña, L.F. An Efficient Methodology for Locating and Sizing PV Generators in Radial Distribution Networks Using a Mixed-Integer Conic Relaxation. Mathematics 2022, 10, 2626. https://doi.org/10.3390/math10152626 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
| url |
https://hdl.handle.net/20.500.12585/11127 https://doi.org/10.3390/math10152626 |
| 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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17 Páginas |
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application/pdf |
| dc.publisher.place.spa.fl_str_mv |
Cartagena de Indias |
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Mathematics Vol. 10 N° 15 (2022) |
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Universidad Tecnológica de Bolívar |
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Montoya, Oscar Danilo8a59ede1-6a4a-4d2e-abdc-d0afb14d4480Ramos-Paja, Carlos Andrésac1a64c3-4089-49e6-9f7a-b2285dd56903Grisales-Noreña, Luis Fernando7c27cda4-5fe4-4686-8f72-b0442c58a5d12022-10-05T12:25:33Z2022-10-05T12:25:33Z2022-07-272022-09-30Montoya, O.D.; Ramos-Paja, C.A.; Grisales-Noreña, L.F. An Efficient Methodology for Locating and Sizing PV Generators in Radial Distribution Networks Using a Mixed-Integer Conic Relaxation. Mathematics 2022, 10, 2626. https://doi.org/10.3390/math10152626https://hdl.handle.net/20.500.12585/11127https://doi.org/10.3390/math10152626Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarThis paper proposes a new solution methodology based on a mixed-integer conic formulation to locate and size photovoltaic (PV) generation units in AC distribution networks with a radial structure. The objective function comprises the annual expected energy costs of the conventional substation in addition to the investment and operating costs of PV sources. The original optimization model that represents this problem belongs to the family of mixed-integer nonlinear programming (MINLP); however, the complexity of the power balance constraints make it difficult to find the global optimum. In order to improve the quality of the optimization model, a mixed-integer conic (MIC) formulation is proposed in this research in order to represent the studied problem. Numerical results in two test feeders composed of 33 and 69 nodes demonstrate the effectiveness of the proposed MIC model when compared to multiple metaheuristic optimizers such as the Chu and Beasley Genetic Algorithm, the Newton Metaheuristic Algorithm, the Vortex Search Algorithm, the Gradient-Based Metaheuristic Optimization Algorithm, and the Arithmetic Optimization Algorithm, among others. The final results obtained with the MIC model show improvements greater than USD 100,000 per year of operation. All simulations were run in the MATLAB programming environment, using its own scripts for all the metaheuristic algorithms and the disciplined convex tool known as CVX with the Gurobi solver in order to solve the proposed MIC model.17 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_abf2Mathematics Vol. 10 N° 15 (2022)An Efficient Methodology for Locating and Sizing PV Generators in Radial Distribution Networks Using a Mixed-Integer Conic Relaxationinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/restrictedAccesshttp://purl.org/coar/resource_type/c_2df8fbb1Photovoltaic systemInvestment and operating costsMixed-integer conic optimizationRadial distribution networksLEMBCartagena de IndiasStrezoski, L.; Stefani, I. 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Transition of Power Distribution System Planning from Passive to Active Networks: A State-of-the-Art Review and a New Proposal. In Sustainable Energy Technology and Policies; Springer: Singapore, 2017; pp. 87–117.Gomez, A.L.; Arredondo, C.A.; Luna, M.A.; Villegas, S.; Hernandez, J. Regulating the integration of renewable energy in Colombia: Implications of Law 1715 of 2014. In Proceedings of the 2016 IEEE 43rd Photovoltaic Specialists Conference (PVSC), Portland, OR, USA, 5–10 June 2016Lezama, J.M.L.; Villada, F.; Galeano, N.M. Effects of Incentives for Renewable Energy in Colombia. Ing. Univ. 2017, 21.Montoya, O.D.; Rivas-Trujillo, E.; Hernández, J.C. A Two-Stage Approach to Locate and Size PV Sources in Distribution Networks for Annual Grid Operative Costs Minimization. Electronics 2022, 11, 961.Mai, T.T.; Nguyen, P.H.; Tran, Q.T.; Cagnano, A.; Carne, G.D.; Amirat, Y.; Le, A.T.; Tuglie, E.D. An overview of grid-edge control with the digital transformation. Electr. Eng. 2021, 103, 1989–2007.Bjørnebye, H.; Hagem, C.; Lind, A. Optimal location of renewable power. Energy 2018, 147, 1203–1215Mosetti, G.; Poloni, C.; Diviacco, B. Optimization of wind turbine positioning in large windfarms by means of a genetic algorithm. J. Wind. Eng. Ind. Aerodyn. 1994, 51, 105–116. [. Kåberger, T. Progress of renewable electricity replacing fossil fuels. Glob. Energy Interconnect. 2018, 1, 48–52.Owusu, P.A.; Asumadu-Sarkodie, S. A review of renewable energy sources, sustainability issues and climate change mitigation. Cogent Eng. 2016, 3, 1167990.Betakova, V.; Vojar, J.; Sklenicka, P. Wind turbines location: How many and how far? Appl. Energy 2015, 151, 23–31Sander, L.; Jung, C.; Schindler, D. Greenhouse Gas Savings Potential under Repowering of Onshore Wind Turbines and Climate Change: A Case Study from Germany. Wind 2021, 1, 1–19O ˘guz, E.; ¸Sentürk, A.E. Selection of the Most Sustainable Renewable Energy System for Bozcaada Island: Wind vs. Photovoltaic. 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