Development of Monte-Carlo-based stochastic scenarios to improve uncertainty modelling for optimal energy management of a renewable energy hub

The Monte-Carlo (MC) method for generating stochastic scenarios to model uncertainty has a special role in research related to energy systems, but most studies have not provided a specific criterion for choosing an appropriate probability distribution function for using MC. This paper develops a new process for applying MC to improve uncertainty modelling based on Anderson-Darling (AD), Kolmogorov-Smirnov (KS), and Chi-Square (CS) tests statistical. Moreover, three clustering algorithms of K-means, Fuzzy c-means, and Kantorovich distance matrix have been applied to reduce the generated scenarios. To evaluate the performance of the proposed process, a renewable energy hub involving electricity, heat, cooling, natural gas, and biomass fuel carriers, is used employing valid data. The results of numerical studies show that the quality of the scenarios in the proposed process based on statistical tests is much higher than the conventional method. Also, MC-CS has been superior to the other two proposed methods in various seasons, so that, for example, in summer, its operating cost has decreased by 3% and 4% compared to MC-KS and MC-AD, respectively.