Uncertainty modeling of renewable Distributed
Generations (DGs) has significant impact on optimal design of
microgrids. Although the conventional Monte-Carlo (MC)
simulation is the most accurate method for the uncertainty
modeling it is a time consuming approach. In this paper, a new
approach based on MC simulation is proposed for uncertainty
modeling. The proposed method has a high precision in spite of
lower calculation time in comparison of conventional MC
simulation. Firstly the probability density function of each energy
resources and load profile is executed. Then, optimal sizing of
DGs is considered as an optimization problem under technical
and economic constraints which is solved by genetic algorithm.
The objective function includes investment and reliability costs.
Simulation results show the effectiveness of the proposed
methodology. The results show the proposed procedure has
suitable accuracy in spite of low computation time.