Linearized Stochastic Optimization Framework for Day-Ahead Scheduling of a Biogas-Based Energy Hub Under Uncertainty

Abstract

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 biogas-based 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.