An efficient framework for siting, sizing and power factor optimization of photovoltaic and wind distributed generations in the presence of capacitor banks

This paper proposes a new efficient optimization framework for siting, sizing and power factor optimization of photovoltaic (PV) and wind turbine (WT)-based distributed generations (DGs) in the presence of capacitor banks. In the proposed framework, the weighted sum of energy loss and voltage deviation index is defined as the objective function and minimized subject to several technical constraints. To solve the optimization problem, two variants of crow search algorithm (CSA), three variants of exploration-exploitation (E2) and two variants of particle swarm optimization (PSO) are employed and the results are compared. Based on the case study, simulation results demonstrate that differential CSA (CSAd) achieves more accurate and robust results than other investigated methods. In addition to the importance of power factor optimization, it is observed that in the absence of capacitor banks, two WTs (with power factors of 0.81 and 0.85) are allocated to the network and no PV installation is suggested. In the presence of capacitor banks, two WTs with unity power factors and a relatively small PV with a power factor of 0.81 are suggested for installation.