An interval-based model for stochastic optimal scheduling of multi carrier energy hubs in the presence of multiple sources of uncertainty

Multi-carrier energy hubs (MCEHs) are an innovative concept capable of meeting various types of energy demand simultaneously. MCEHs involve numerous components, many of which have varying degrees of uncertainty. Therefore, a detailed model is essential for managing the energy needs of MCEHs while addressing these uncertainties. This paper proposes a novel MCEH optimization model that incorporates renewable energy resources and addresses the uncertainty associated with an electric vehicle parking lot (EVPL), renewable energy generation, and multi-energy consumption, assessing their impacts on the operational cost of the hubs. The model considers three types of energy inputs: electrical power, gas, and water, corresponding to three types of demands met by various resources. Furthermore, the hubs are capable of trading electrical energy with each other and with the external grid. To reduce carbon emissions, hydrogen vehicles and EVs are utilized, enhancing the operational efficiency of the multi-hub system. This study models the uncertainty of EVPLs, renewable energy generation, and multi-energy consumption using a stochastic, scenario-based approach. An interval-based approach is employed to address the uncertainty of retail electricity prices. The numerical results demonstrate the effectiveness of the proposed multi-energy, multi-hub management strategy, achieving a 19.48 % reduction in operational costs. However, the mean overall operational cost increased by 2.54 %.