Optimization of Network Reconfiguration and Distributed Generation Allocation with Nonlinear Load Models Using Growth Optimizer

The distribution network (DN) has large-scale and operates at low-voltage level.This causes a high-power loss and voltage drop that negatively affects different load types in the DN.This paper optimizes the network reconfiguration along with distributed generation allocation (REC-DGA) in the DN with different load models to minimize power loss of the DN and consider maintaining the radial-topology, the grid and distributed generation power limits and improving the voltage and current profiles.The five load models, which have voltage-dependent power characteristics, are used to evaluate the effects of the simultaneous REC-DGA solution.A new metaheuristic algorithm namely growth optimizer (GO) is adapted for searching the optimal solution for the simultaneous REC-DGA.The performance of GO is also compared to particle-swarm-optimization (PSO) and genetic-algorithm (GA).The results evaluated on the 33-node DN show that the optimal simultaneous REC-DGA helps to dramatically reduce power loss.The power loss reduction compared to the initial configuration of the cases of constant power, industrial, residential, commercial, and mixed loads is respectively 74.98%, 73.89%, 72.08%, 70.83% and 72.41%.Furthermore, the optimal REC-DGA also helps enhance the voltage, current profiles, and the power of load demand in the DN.In comparison with PSO and GA, GO achieves the better performance than PSO and GA and the better results compared with previous methods.