Due to the stochastic nature of renewable energy sources (RES) and electric vehicles (EV) load demand, large scale penetration of these resources in the power systems can stress the reliable network performance, such as reducing power quality, increasing power losses, and voltage deviations. These challenges must be minimized by optimal planning based on the variable output from RES to meet the additional demand caused by EV charging. In this paper, a novel method for optimal allocation and sizing of RES and EV charging stations simultaneously and managing vehicle charging process is provided. A multi-objective optimization problem is formulated to obtain objective variables in order to reduce power losses, voltage fluctuations, charging and demand supplying costs, and EV battery cost. In this optimization problem, the location and capacity of RES and EV charging stations are the optimization variables. Coefficients which are dependent on wind speed, solar radiation, and hourly peak demand ratio for the management of the EV charging pattern in off-peak load hours are introduced. Genetic Algorithm-Particle Swarm Optimization (GA-PSO) hybrid improved optimization algorithm is used to solve the optimization problem in five different scenarios. The performance of the proposed method on IEEE 33- bus system has been investigated to validate the effectiveness of the novel GA-PSO method to optimal sitting and sizing of RES and EV charging stations simultaneously.
