A novel hybrid fuzzy-metaheuristic strategy for estimation of optimal size and location of the distributed generators

Abstract

Optimal size and location of the distributed generators (DGs) has become a new avenue applied to achieve a proper design and to provide a better performance for the distribution system. A hybrid Fuzzy-Metaheuristic strategy has been proposed in this work to provide an optimal design for sizing and placement of different types of DGs. In the introduced strategy, the fuzzy logic based adaptive weights subroutine has been combined with a metaheuristic optimizer in conjunction with a power system model, load flow software, input/output software modules and three proposed approaches software modules suitable for various types of DGs. Minimizing active and reactive power losses as well as enhancing the voltage characteristics over the whole system using a multi objective function with dynamic adjustment of weights has been introduced in this work. Furthermore, a novel approach of variable power factor has been established and investigated as well as the previously published constant power factor and unity power factor approaches. To prove the validity of the new strategy and the novel approach, IEEE distribution systems of 33 and 66 buses have been used to test the efficiency and accuracy of the proposed strategy. A novel hybrid optimization strategy named Hybrid Fuzzy Equilibrium Optimizer (HFEO) is established to estimate the optimal size and location of three DGs inserted in the selected distribution systems. The hybrid technique is based on merging fuzzy logic to adapt the weights of the objective function dynamically with a newly developed metaheuristic Algorithm named equilibrium optimizer to achieve a better performance in the optimization process. For fair verification of the proposed technique, its results have been compared with that of five algorithms presented in literature to prove its superiority and reliability over the other state of art. For intensive comparisons, some statistical analysis has been established to show the minimum objective function, the highest speed of convergence, the least execution time and the most consistent results.