Stochastic Optimal Operation Framework of an Integrated Methane-Based Zero-CO2 Energy Hub in Energy Markets

Abstract

Energy hubs (EHs) are couplers between different energy carriers in smart grids. The optimal participation of these actors in energy markets (EMs) as active and helpful actors is essential. This paper presents a new structure of methane-based EH considering biomass fuel to participate in the EMs of electricity, heat, and natural gas (NG). For this purpose, we propose an optimal bidding framework for the EH as a MILP stochastic optimization problem. The EH does not inject any CO2 pollution into the air (zero-CO2) and converts it into valuable methane (CH4) fuel using the CH4 production unit. To model uncertain parameters, electricity market price, wind speed, and solar radiation, an LSTM-based model of deep learning is proposed for scenario generation. Moreover, the Kantorovich distance matrix method reduces the generated scenarios. Since the proposed EH structure is compatible with Finland's infrastructure, simulation studies using actual data of this country are performed on selected days. The results show that in addition to profitable operation, high flexibility, environmental friendliness, and high accuracy of uncertainty modeling, the EH has no dependence on the purchase of energy carriers.