A hybrid genetic particle swarm optimization for distributed generation allocation in power distribution networks. (15th October 2020) - Pesaran H.A., Mahmoud; Nazari-Heris, Morteza; Mohammadi-Ivatloo, Behnam; Seyedi, Heresh
Abstract: Distributed generation gains a noticeable attention from governments and policy-makers. The appropriate site(s) and proper size(s) recognition for these generators can improve the network performance. In this study, a new hybrid genetic particle swarm optimization method is proposed to determine the optimal allocation of distributed generators aiming to improve the total active and reactive losses and voltage regulations of the network. The objective function has been considered for the sake of clarity; however, other objective functions may be included at the same time. The method applies the genetic algorithm and the particle swarm optimization algorithms in combination on the same population to acquire both algorithms advantages. The study is performed on IEEE 33 and 69-bus networks. A specific method is proposed and employed to calculate the weight factors linked with each objective. Multi objectives of the optimization are scalarized using the calculated weight factors to avoid human decision-making interference in the optimization procedure. The proposed hybrid genetic particle swarm optimization method has better performance in comparison to the reported values of other literatures. In addition, the employed method shows improvements in the number of iterations and the standard deviation in all study cases. Highlights: Hybrid genetic algorithm - particle swarm optimization method is implemented. The algorithm is employed for optimal distributed generation allocation. Optimal allocation includes number, position and the capacity aspects of generators. The optimal capacities are identified as share of total recognized optimal required power. The developed algorithm is able to optimize the number, site and size of generators simultaneously.