A novel Monte Carlo based modeling strategy for wind based renewable energy sources

This paper introduces a new algorithm strategy in order to model wind based renewable energy sources which are used for planning purposes in distribution systems. Initially, the available data of the wind speeds are divided into seasonal data (i.e. the available data of each season is separated) then the available separated data is divided into hourly data (i.e. 24-hours for each season). This algorithm is based on Monte Carlo Simulation Method which considers the stochastic nature of the wind power through the correct determination of the appropriate cumulative distribution function. Monte Carlo Simulation technique is utilized for obtaining the most likelihood wind turbine output power at each hour at each season. The results of the proposed strategy is compared with another probabilistic model to show the effectiveness of the proposed algorithm. The proposed algorithm is tested using MATLAB environment and the results and comparisons show that the proposed modeling algorithm gives accurate results.