Novel Hybrid Fuzzy-Intelligent Water Drops Approach for Optimal Feeder Multi Objective Reconfiguration by Considering Multiple-Distributed Generation

This paper presents a new hybrid method for optimal multi-objective reconfiguration in a distribution feeder in addition to determining the optimal size and location of multiple-Distributed Generation (DG). The purposes of this research are mitigation of losses, improving the voltage profile and equalizing the feeder load balancing in distribution systems. To reduce the search space, the improved analytical method has been employed to select the optimum candidate locations for multiple-DGs, and the intelligent water drops approach as a novel swarm intelligence based algorithm is used to simultaneously reconfigure and identify the optimal capacity 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 proposed method is validated using the Tai-Power 11.4-kV distribution system as a real distribution network. The obtained results proved that this combined technique is more accurate and has the lowest fitness value as compared with other intelligent search algorithms. Also, the obtained results leadto the conclusion that multi-objective simultaneous placement of DGs along with reconfiguration can be more beneficial than separate single-objective optimization

A SAIWD-Based Approach for Simultaneous Reconfiguration and Optimal Siting and Sizing of Wind Turbines and DVR units in Distribution Systems

In this paper, a combination of simulated annealing (SA) and intelligent water drops (IWD) algorithm is used to solve the nonlinear/complex problem of simultaneous reconfiguration with optimal allocation (size and location) of wind turbine (WT) as a distributed generation (DG) and dynamic voltage restorer (DVR) as a distributed flexible AC transmission systems (DFACT) unit in a distribution system. The objectives of this research are to minimize active power loss, minimize operational cost, improve voltage stability, and increase the load balancing of the system. For evaluation purposes, the proposed algorithm is evaluated using the Tai-Power 11.4-kV real distribution network. The impacts of the optimal placement of the WT, DVR, and WT with DVR units are separately evaluated. The results are compared in terms of statistical indicators. By comparing all the testing scenarios, it is observed that the multi-objective optimization evolutionary algorithm is more beneficial than its single-objective optimization counterpart. Also, the obtained results show that the proposed SAIWD method outperforms the IWD method and other intelligent search algorithms such as genetic algorithm or particle swarm optimization