Optimization of Smart Distribution Networks Using FACTS Devices by the Novel GA-PSO Hybrid Algorithm

This paper presents a model for distribution network optimization considering a high penetration of photovoltaic (PV) sources and electric vehicle charging stations (EVCSs) based on on-load tap changing transformers (OLTC) and step voltage regulator (SVR), shunt capacitor (SC), and shunt reactor (ShR). The purpose is to prevent overvoltage due to power injection by PV sources and voltage drop due to EV charging in distribution networks. The proposed model is solved using a new hybrid algorithm called PSO-GA. Relevant studies show that with the increasing number of PSO replications, particle population variability is easily eliminated and placed in local optimization. The idea of ​​combining GA is based on the PSO introduced in this study. Crossover and mutations of GA are performed on the PSO population, which is useful for improving the overall optimal ability of particles and causing the algorithm to deviate from the local optimal point. Two different IEEE standard test networks are tested under different load scenarios to analyze the proposed model. The results reveal the performance of the proposed model.