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Open AccessArticle

Hybrid GA-SOCP Approach for Placement and Sizing of Distributed Generators in DC Networks

1
Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá 11021, DC, Colombia
2
Laboratorio Inteligente de Energía, Universidad Tecnológica de Bolívar, Cartagena 131001, Colombia
3
Grupo GIIEN, Facultad de Ingeniería, Institución Universitaria Pascual Bravo, Campus Robledo, Medellín 050036, Colombia
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(23), 8616; https://doi.org/10.3390/app10238616
Received: 13 September 2020 / Revised: 16 October 2020 / Accepted: 26 October 2020 / Published: 2 December 2020
(This article belongs to the Special Issue Control, Optimization and Planning of Power Distribution Systems)
This research addresses the problem of the optimal location and sizing distributed generators (DGs) in direct current (DC) distribution networks from the combinatorial optimization. It is proposed a master–slave optimization approach in order to solve the problems of placement and location of DGs, respectively. The master stage applies to the classical Chu & Beasley genetic algorithm (GA), while the slave stage resolves a second-order cone programming reformulation of the optimal power flow problem for DC grids. This master–slave approach generates a hybrid optimization approach, named GA-SOCP. The main advantage of optimal dimensioning of DGs via SOCP is that this method makes part of the exact mathematical optimization that guarantees the possibility of finding the global optimal solution due to the solution space’s convex structure, which is a clear improvement regarding classical metaheuristic optimization methodologies. Numerical comparisons with hybrid and exact optimization approaches reported in the literature demonstrate the proposed hybrid GA-SOCP approach’s effectiveness and robustness to achieve the global optimal solution. Two test feeders compose of 21 and 69 nodes that can locate three distributed generators are considered. All of the computational validations have been carried out in the MATLAB software and the CVX tool for convex optimization. View Full-Text
Keywords: direct current networks; optimal power flow analysis; metaheuristic optimization; master-slave optimization; genetic algorithms; second-order cone programming direct current networks; optimal power flow analysis; metaheuristic optimization; master-slave optimization; genetic algorithms; second-order cone programming
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MDPI and ACS Style

Montoya, O.D.; Gil-González, W.; Grisales-Noreña, L.F. Hybrid GA-SOCP Approach for Placement and Sizing of Distributed Generators in DC Networks. Appl. Sci. 2020, 10, 8616.

AMA Style

Montoya OD, Gil-González W, Grisales-Noreña LF. Hybrid GA-SOCP Approach for Placement and Sizing of Distributed Generators in DC Networks. Applied Sciences. 2020; 10(23):8616.

Chicago/Turabian Style

Montoya, Oscar D.; Gil-González, Walter; Grisales-Noreña, Luis F. 2020. "Hybrid GA-SOCP Approach for Placement and Sizing of Distributed Generators in DC Networks" Appl. Sci. 10, no. 23: 8616.

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