Hybrid Metaheuristic Optimization Methods for Optimal Location and Sizing DGs in DC Networks

In this paper is proposed a master-slave method for optimal location and sizing of distributed generators (DGs) in direct-current (DC) networks. In the master stage is used the genetic algorithm of Chu & Beasley (GA) for the location of DGs. In the slave stage three different continuous techniques are used: the Continuous genetic algorithm (CGA), the Black Hole optimization method (BH) and the particle swarm optimization (PSO) algorithm, in order to solve the problem of sizing. All of those techniques are combined to find the hybrid method that provides the best results in terms of power losses reduction and processing times. The reduction of the total power losses on the electrical network associated to the transport of energy is used as objective function, by also including a penalty to limit the power injected by the DGs on the grid, and considering all constraints associated to the DC grids. To verify the performance of the different hybrid methods studied, two test systems with 10 and 21 buses are implemented in MATLAB by considering the installation of three distributed generators. To solve the power flow equations, the slave stage uses successive approximations. The results obtained shown that the proposed methodology GA-BH provides the best trade-off between speed and power losses independent of the total power provided by the DGs and the network size. © 2019, Springer Nature Switzerland AG.

• Fecha de publicación: 2019
• Institución: Universidad Tecnológica de Bolívar
• Repositorio: Repositorio Institucional UTB
• Idioma: eng
• OAI Identifier: oai:repositorio.utb.edu.co:20.500.12585/9184
• Acceso en línea: https://hdl.handle.net/20.500.12585/9184
• Palabra clave: Direct-current networks; Distributed generation; Genetic algorithm; Metaheuristic optimization; Optimal power flow; Particle swarm optimization; DC power transmission; Distributed power generation; Economic and social effects; Electric load flow; Genetic algorithms; Location; Continuous genetic algorithms; Direct current; Distributed generator (DGs); Distributed generators; Meta-heuristic optimizations; Optimal power flows; Particle swarm optimization algorithm; Successive approximations; Particle swarm optimization (PSO)
• Rights: restrictedAccess
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/