A Multi-objective Hybrid Algorithm for Optimal Planning of Distributed Generation

Distributed generations (DGs) have been constantly incorporating into the distribution systems. The optimal planning (sizing and sitting) of the DGs by applying harmony search (HS)-based hybrid genetic algorithm integrated adaptive particle swarm optimization (GA-APSO) technique is exhibited in this work. The fitness evolution function has investigated as the multi-objective function (FMO), which depends on the five significant indexes such as active power loss, reactive power loss, total cost generation index, voltage deviation, and load balancing index. The ideal solution has attained by minimizing the multi-objective (MO) fitness function by deploying HS-based GA-PSO strategy. The projected work will be implemented on IEEE standard 33- and 69-bus radial distribution networks. The performance of the suggested algorithm has analyzed dependent on the five measures, such as active and reactive power loss, voltage profile improvement, reduction in voltage deviation, and cost of operation of generation. The legitimacy of the assessed outcomes has affirmed by comparing with some of the notable optimization algorithms.