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 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.

Economic‐environmental operation of a CHHP energy hub considering uncertainties and demand response programs

“…Cogeneration of heat and electricity by FC compared with NG turbines has advantages such as high efficiency, low losses, and low emissions. Equation (35) expresses the FC's operating capacity, and the ramping capacity constraint is shown in (36). Also, (37) made a relation between the generated electrical and thermal powers, and ( 38) is concerned with the efficiency of the FC [42].…”

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Performance Analysis of Marine-Predator-Algorithm-Based Optimum PI Controller with Unified Power Flow Controller for Loss Reduction in Wind–Solar Integrated System

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Integrated Multi-Criteria Planning for Resilient Renewable Energy-Based Microgrid Considering Advanced Demand Response and Uncertainty

“…To ensure the robustness of the system designed with a guaranteed acceptable level of reliability, Monte Carlo simulations (MCSs) have been incorporated into the optimal capacity planning to account for the uncertainty of the VREs and the load demand. MCS methodology utilizes statistical sampling processes to generate random scenarios for stochastic parameters: solar irradiance [31], load demand, and wind speed that reflect their uncertainty [14]. The MCS technique models uncertainties by sampling inputs and transforming them using probability distribution functions to generate scenarios [31,32].…”

“…MCS methodology utilizes statistical sampling processes to generate random scenarios for stochastic parameters: solar irradiance [31], load demand, and wind speed that reflect their uncertainty [14]. The MCS technique models uncertainties by sampling inputs and transforming them using probability distribution functions to generate scenarios [31,32]. This strategy yields a finite number of possible scenarios for all stochastic parameters, enabling a thorough evaluation of system robustness under various conditions.…”