Energy hubs (EHs), due to their multiple nature in the production, consumption, and storage of energy, as well as the ability to participate in different energy markets, have made their optimal and profitable scheduling important for operators. Considering the literature review, one of the main motivations of this paper is the use of biogas as a pivotal fuel and through production using biomass in the structure of EHs. Therefore, this paper proposes a linearized optimization framework for optimal scheduling of a biogasbased EH for participation in day-ahead (DA) electricity and thermal energy markets. The proposed EH directly converts local biomass into biogas, thereby providing the fuel to generate electricity and thermal. This EH comprises digester, biogas storage, electric heat pump (EHP), biogas burner CHP and boiler, solar farm, electrical storage, and internal electrical and thermal loads. In this framework, the uncertainties related to solar radiation and the DA price are modeled to generate random scenarios using the Monte-Carlo method. The proposed EH is simulated for numerical studies based on data from Finland's two selected spring and autumn days. The results show the optimal performance of the EH because it can participate in the electricity and thermal markets by using the biogas produced inside it and providing complete internal loads, and earns a decent income. In the autumn, operating the EH is more economical than in the spring. Moreover, comparative results have shown that eliminating the biogas unit and using natural gas significantly increases the expected costs of EH.
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.
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