A Multi-Objective Framework for Smart Energy Hubs: Leveraging Compressed Air Storage and Demand Response

In this paper, a multi-carrier energy hub that can generate and deliver electricity, heating, and cooling ‎energy from different sources, such as wind, solar, fuel cells, batteries, and compressed air is ‎proposed. The intelligent energy hub can also participate in electrical and thermal demand response, ‎which aims to reduce peak demand and enhance overall system efficiency. The scheduling problem is ‎a mixed-integer linear programming problem that seeks to minimize the system cost and carbon ‎dioxide emissions. To obtain optimal solutions that strike a balance between cost, emissions, and ‎decision maker's preferences, an augmented epsilon-constraint min-max fuzzy method is employed. ‎The proposed strategy's advantages are demonstrated through a case study, where it is compared with ‎other methods. The results show that the proposed approach effectively reduces the cost and ‎emissions of the smart energy hub while improving the load shape and energy hub efficiency. ‎Moreover, the results showed that the integration of compressed air systems and demand response ‎programs enhances the performance of the smart energy hub, making it more flexible and reliable. ‎The GAMS software is employed for the modeling and resolution of the scheduling issue.‎