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Optimal Location and Sizing of DGs in DC Networks Using a Hybrid Methodology Based on the PPBIL Algorithm and the VSA

Author

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  • Luis Fernando Grisales-Noreña

    (Grupo MATyER, Instituto Tecnológico Metropolitano, Facultad de Ingeniería, Campus Robledo, Medellín 050036, Colombia)

  • Oscar Danilo Montoya

    (Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá 110231, Colombia
    Laboratorio Inteligente de Energía, Universidad Tecnológica de Bolívar, Cartagena 131001, Colombia)

  • Ricardo Alberto Hincapié-Isaza

    (Facultad de Ingenierias, Universidad Tecnológica de Pereira, Pereira 660003, Colombia)

  • Mauricio Granada Echeverri

    (Facultad de Ingenierias, Universidad Tecnológica de Pereira, Pereira 660003, Colombia)

  • Alberto-Jesus Perea-Moreno

    (Departamento de Física Aplicada, Radiología y Medicina Física, Universidad de Córdoba, Campus de Rabanales, 14071 Córdoba, Spain)

Abstract

In this paper, we propose a master–slave methodology to address the problem of optimal integration (location and sizing) of Distributed Generators (DGs) in Direct Current (DC) networks. This proposed methodology employs a parallel version of the Population-Based Incremental Learning (PPBIL) optimization method in the master stage to solve the location problem and the Vortex Search Algorithm (VSA) in the slave stage to solve the sizing problem. In addition, it uses the reduction of power losses as the objective function, considering all the constraints associated with the technical conditions specific to DGs and DC networks. To validate its effectiveness and robustness, we use as comparison methods, different solution methodologies that have been reported in the specialized literature, as well as two test systems (the 21 and 69-bus test systems). All simulations were performed in MATLAB. According to the results, the proposed hybrid (PPBIL–VSA) methodology provides the best trade-off between quality of the solution and processing times and exhibits an adequate repeatability every time it is executed.

Suggested Citation

  • Luis Fernando Grisales-Noreña & Oscar Danilo Montoya & Ricardo Alberto Hincapié-Isaza & Mauricio Granada Echeverri & Alberto-Jesus Perea-Moreno, 2021. "Optimal Location and Sizing of DGs in DC Networks Using a Hybrid Methodology Based on the PPBIL Algorithm and the VSA," Mathematics, MDPI, vol. 9(16), pages 1-18, August.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:16:p:1913-:d:612753
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    References listed on IDEAS

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    1. Ji, Haoran & Wang, Chengshan & Li, Peng & Zhao, Jinli & Song, Guanyu & Wu, Jianzhong, 2018. "Quantified flexibility evaluation of soft open points to improve distributed generator penetration in active distribution networks based on difference-of-convex programming," Applied Energy, Elsevier, vol. 218(C), pages 338-348.
    2. Ehsan, Ali & Yang, Qiang, 2018. "Optimal integration and planning of renewable distributed generation in the power distribution networks: A review of analytical techniques," Applied Energy, Elsevier, vol. 210(C), pages 44-59.
    3. Luis Fernando Grisales-Noreña & Carlos Andrés Ramos-Paja & Daniel Gonzalez-Montoya & Gerardo Alcalá & Quetzalcoatl Hernandez-Escobedo, 2020. "Energy Management in PV Based Microgrids Designed for the Universidad Nacional de Colombia," Sustainability, MDPI, vol. 12(3), pages 1-24, February.
    4. Walter Gil-González & Oscar Danilo Montoya & Arul Rajagopalan & Luis Fernando Grisales-Noreña & Jesus C. Hernández, 2020. "Optimal Selection and Location of Fixed-Step Capacitor Banks in Distribution Networks Using a Discrete Version of the Vortex Search Algorithm," Energies, MDPI, vol. 13(18), pages 1-21, September.
    5. Luis Fernando Grisales-Noreña & Daniel Gonzalez Montoya & Carlos Andres Ramos-Paja, 2018. "Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO Techniques," Energies, MDPI, vol. 11(4), pages 1-27, April.
    6. Abdmouleh, Zeineb & Gastli, Adel & Ben-Brahim, Lazhar & Haouari, Mohamed & Al-Emadi, Nasser Ahmed, 2017. "Review of optimization techniques applied for the integration of distributed generation from renewable energy sources," Renewable Energy, Elsevier, vol. 113(C), pages 266-280.
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    Cited by:

    1. Daniel Sanin-Villa & Oscar Danilo Montoya & Luis Fernando Grisales-Noreña, 2023. "Material Property Characterization and Parameter Estimation of Thermoelectric Generator by Using a Master–Slave Strategy Based on Metaheuristics Techniques," Mathematics, MDPI, vol. 11(6), pages 1-19, March.

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