Novel hybrid fuzzy-Bees algorithm for optimal feeder multi-objective reconfiguration by considering multiple-distributed generation

This paper presents a hybrid optimal multi-objective reconfiguration method to determine an optimal size and location of multiple-distributed generation (DG) in a distribution feeder. The purposes of this research are to mitigate losses, improve voltage profile and equalize the feeder load balancing in power distribution systems. To reduce the search space, the Improved Analytical (IA) method is employed to select the optimum candidate locations for multiple-DG, while the Bees algorithm (BA) approach as a population based algorithm is used to simultaneously reconfigure and identify the optimal capacity for installation of DG units in the distribution network. In order for the algorithm to facilitate ability for multi-objective search the optimization problem is formulated to minimize fuzzy performance indices. The proposed method is validated using the IEEE 33 bus test system at nominal load. The obtained results revealed that this proposed hybrid method has superior accuracy and efficient convergence property over the other intelligent search algorithms. It is also can be concluded that the multi-objective simultaneous placement of DGs along with multi-objective reconfiguration can be more beneficial than separate single-objective optimization.