Combined Fuzzy-ACO Algorithm for Optimal Reconfiguration and Distributed Generation Allocation

This paper presents a new hybrid method for optimal multi-objective reconfiguration simultaneous determining the optimal size and location of Distributed Generation (DG) in a distribution feeder. The purposes of this research are reducing the losses, improving the voltage profile and equalizing the feeder load balancing in a distribution system. Ant Colony Optimization (ACO) approach as a Swarm Intelligence (SI) based algorithm is used to simultaneously reconfigure and identify the optimal capacity and location for installation of DG units in the distribution network. In order to facilitate the algorithm for multi-objective search ability, the optimization problem is formulated for minimizing fuzzy performance indices. The multi-objective optimization problem is transformed into a fuzzy inference system (FIS), where each objective function is quantified into a set of fuzzy objectives selected by fuzzy membership functions. The proposed method is validated using the IEEE 33 bus test system at nominal load. The obtained results prove this combined technique is more accurate and has an efficient convergence property compared to other intelligent search algorithms. Also, the obtained results lead to the conclusion that multi-objective reconfiguration along with placement of DGs can be more beneficial than separate single-objective optimization.

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