A multi-objective master–slave methodology for optimally integrating and operating photovoltaic generators in urban and rural electrical networks
The integration of distributed generation (DG) sources, such as photovoltaic (PV) systems, into electrical power networks presents significant challenges and opportunities. With the increasing penetration of renewable energy sources, optimizing their placement and operation becomes crucial to ensure...
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Elsevier
2024-12-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024013148 |
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| author | Jhony Andrés Guzmán-Henao Rubén Iván Bolaños Brandon Cortés-Caicedo Luis Fernando Grisales-Noreña Oscar Danilo Montoya Jesús C. Hernández |
| author_facet | Jhony Andrés Guzmán-Henao Rubén Iván Bolaños Brandon Cortés-Caicedo Luis Fernando Grisales-Noreña Oscar Danilo Montoya Jesús C. Hernández |
| author_sort | Jhony Andrés Guzmán-Henao |
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| description | The integration of distributed generation (DG) sources, such as photovoltaic (PV) systems, into electrical power networks presents significant challenges and opportunities. With the increasing penetration of renewable energy sources, optimizing their placement and operation becomes crucial to ensure the reliability, efficiency, and economic viability of power systems. This study presents a master–slave methodology for optimally integrating and operating photovoltaic (PV) generators using multi-objective optimization. This methodology can simultaneously improve technical and economic aspects of the network by determining the best locations and power injection levels for distributed generation sources. Its master stage uses one out of three different algorithms—Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, the Non-dominated Sorting Genetic Algorithm II (NSGA-II), or the Multi-Objective Ant Lion Optimizer (MOALO)—while the slave stage is always performed by a load flow analyzer. The three algorithms in the master stage were implemented considering variable generation and demand conditions in 33 and 27 bus feeders, representing urban and rural areas respectively. The results demonstrated the effectiveness of these algorithms. NSGA-II achieved the best performance, with reductions of 32.84% in energy losses and 42.41% in operating costs (with standard deviations of 0.21% and 0.39%, respectively) for the urban system; and reductions of 21.87% in energy losses and 43.36% in operating costs (with standard deviations of 0.07% and 0.24%, respectively) for the rural system. All of this was achieved within short solution processing times during a typical day in the proposed test scenarios. |
| format | Article |
| id | doaj-art-c4a9c731ab7a40148f59b9fb87182128 |
| institution | OA Journals |
| issn | 2590-1230 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
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| series | Results in Engineering |
| spelling | doaj-art-c4a9c731ab7a40148f59b9fb871821282025-08-20T01:58:30ZengElsevierResults in Engineering2590-12302024-12-012410305910.1016/j.rineng.2024.103059A multi-objective master–slave methodology for optimally integrating and operating photovoltaic generators in urban and rural electrical networksJhony Andrés Guzmán-Henao0Rubén Iván Bolaños1Brandon Cortés-Caicedo2Luis Fernando Grisales-Noreña3Oscar Danilo Montoya4Jesús C. Hernández5Facultad de Ingenierías, Instituto Tecnológico Metropolitano, Campus Robledo, Medellín 050036, ColombiaFacultad de Ingenierías, Instituto Tecnológico Metropolitano, Campus Robledo, Medellín 050036, ColombiaFacultad de Ingeniería, Institución Universitaria Pascual Bravo, Campus Robledo, Medellín 050036, ColombiaDepartamento de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad de Talca, Curicó 3340000, Chile; Corresponding authors.Grupo de Compatibilidad e Interferencia Electromagnética, Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá 110231, Colombia; Corresponding authors.Department of Electrical Engineering, Universidad de Jaén, Campus Lagunillas s/n, Edificio A3, Jaén 23071, Spain; Corresponding authors.The integration of distributed generation (DG) sources, such as photovoltaic (PV) systems, into electrical power networks presents significant challenges and opportunities. With the increasing penetration of renewable energy sources, optimizing their placement and operation becomes crucial to ensure the reliability, efficiency, and economic viability of power systems. This study presents a master–slave methodology for optimally integrating and operating photovoltaic (PV) generators using multi-objective optimization. This methodology can simultaneously improve technical and economic aspects of the network by determining the best locations and power injection levels for distributed generation sources. Its master stage uses one out of three different algorithms—Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, the Non-dominated Sorting Genetic Algorithm II (NSGA-II), or the Multi-Objective Ant Lion Optimizer (MOALO)—while the slave stage is always performed by a load flow analyzer. The three algorithms in the master stage were implemented considering variable generation and demand conditions in 33 and 27 bus feeders, representing urban and rural areas respectively. The results demonstrated the effectiveness of these algorithms. NSGA-II achieved the best performance, with reductions of 32.84% in energy losses and 42.41% in operating costs (with standard deviations of 0.21% and 0.39%, respectively) for the urban system; and reductions of 21.87% in energy losses and 43.36% in operating costs (with standard deviations of 0.07% and 0.24%, respectively) for the rural system. All of this was achieved within short solution processing times during a typical day in the proposed test scenarios.http://www.sciencedirect.com/science/article/pii/S2590123024013148Distributed generationMulti-objective optimizationMaster–slave methodologyElectrical distribution systemPhotovoltaic generation |
| spellingShingle | Jhony Andrés Guzmán-Henao Rubén Iván Bolaños Brandon Cortés-Caicedo Luis Fernando Grisales-Noreña Oscar Danilo Montoya Jesús C. Hernández A multi-objective master–slave methodology for optimally integrating and operating photovoltaic generators in urban and rural electrical networks Results in Engineering Distributed generation Multi-objective optimization Master–slave methodology Electrical distribution system Photovoltaic generation |
| title | A multi-objective master–slave methodology for optimally integrating and operating photovoltaic generators in urban and rural electrical networks |
| title_full | A multi-objective master–slave methodology for optimally integrating and operating photovoltaic generators in urban and rural electrical networks |
| title_fullStr | A multi-objective master–slave methodology for optimally integrating and operating photovoltaic generators in urban and rural electrical networks |
| title_full_unstemmed | A multi-objective master–slave methodology for optimally integrating and operating photovoltaic generators in urban and rural electrical networks |
| title_short | A multi-objective master–slave methodology for optimally integrating and operating photovoltaic generators in urban and rural electrical networks |
| title_sort | multi objective master slave methodology for optimally integrating and operating photovoltaic generators in urban and rural electrical networks |
| topic | Distributed generation Multi-objective optimization Master–slave methodology Electrical distribution system Photovoltaic generation |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024013148 |
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