Optimal sizing of a grid independent hybrid renewable energy system incorporating resource uncertainty, and load uncertainty

• Optimum sizing of a hybrid wind–photovoltaic–battery system is formulated.
• The system is optimized in terms of minimizing the TAC and uncertainty in WS, SR, and electricity demand.
• The sizing problem is solved using particle swarm optimization algorithm-based Monte Carlo simulation.

Wind speed (WS) and solar radiation (SR) are innately uncertain and bring about more uncertainties in the power system. Therefore, due to the nonlinear nature of photovoltaic cells and wind turbines, the mean values of solar radiation and wind speed cannot be assuredly measured and a small change in these values alters the results of the study. Furthermore the mean values of WS and SR occur with a low degree of probability, that is, if the mean values one utilized in system design, it will mean that not all possible states have been considered. Therefore, in hybrid system analysis, it has been suggested that the degree of uncertainty be taken into calculation in order for all possibilities to be covered. For this purpose, the Monte Carlo Simulation Method and Particle Swarm Optimization Algorithm have been used in this article. The proposed methodology is applied to a real case study and the results are discussed. In this regard, an off-grid hybrid multisource system (photovoltaic–wind–battery) is considered, modeled, optimally sized, and compared of different seasons in terms of the total annual cost and uncertainty in WS, SR, and electricity demand.