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

Luis Fernando Grisales-Noreña, Oscar Danilo Montoya, Ricardo Alberto Hincapié-Isaza, Mauricio Granada Echeverri and Alberto-Jesus Perea-Moreno
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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
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

Mathematics, 2021, vol. 9, issue 16, 1-18

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.

Keywords: direct current grids; distributed generation; direct current networks; metaheuristic optimization; parallel processing tools; power loss reduction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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