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

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