Optimum allocation of multiple type and number of DG units based on IEEE 123-bus unbalanced multi-phase power distribution system

Abstract

The increasing demand for clean energy has resulted in the growth of renewable energy-based distributed generation (DG) penetration in the power distribution networks (PDN). The crucial point here is to find the optimum allocation of DGs to minimize the power loss and enhance the system performance overall. In this study, the Equilibrium Optimizer (EO) algorithm-based method is proposed to determine the location, size, connection type, and power factor of three different types of multiple DGs based on a three-phase 123-bus unbalanced PDN. The complex 123-bus UPDN has been very little studied to date, and most existing research works on the DG allocation problem have been conducted using a co-simulation platform. In this work, an unbalanced three-phase backward forward load flow (UBFLF) algorithm is written and executed under the MATLAB environment. This approach has decreased the complexity of co-simulation and provided advantages like optimizing the power factor value and the connection type (Delta/Wye) of DGs easily. Six more recent and well-established optimization algorithms such as PSO, JFO, BO, SMA, FDA, and GBO are also applied under the same test conditions to solve the Type-III DGs allocation problem. The results obtained are compared with the proposed method to show the effectiveness of the method in terms of elapsed time, statistical variables (such as standard deviation, median, variance and so on), converge speed, and best/worst case active power loss values. The efficacy of the presented method is also validated by comparing the results obtained using three distinct platforms as MATLAB/Simulink, IEEE-PES data, and OpenDSS. The detailed and comprehensive analyses show that the proposed approach demonstrates the lowest power loss, more enhancement in voltage profile, and the superiority of the EO algorithm in the application of a 123-bus unbalanced power distribution network.