LAUSR

Abstract Energy hub as a novel model which has provided more efficiency, flexibility, and reliability. Also, due to using different multi carriers as renewable and non-renewable sources and storage devices in energy hubs, optimal operation scheduling becomes more challenging. In this paper, the day-ahead scheduling of energy hub is studied in the presence of wind turbine, electrical, thermal, and a novel water-based storage called Pico hydel energy storage. A useful method using the Monte-Carlo and k-mines clustering method is developed to estimate the uncertainty associated with wind speed, load, and market price. On the other hand, Conditional Value at Risk and Second-Order Stochastic Dominance (SSD) method are applied as a risk-averse method. To find the best operation performance of energy hub, multiple objectives consist of economic, emission pollution, electrical, and load profile deviation are defined. As well, the demand response program for gas, electricity, water, and thermal are implemented to improve the energy hub performance. Mixed-integer non-linear program formulation for the day-ahead scheduling of energy hub has been solved by executing CPLEX solver of GAMS optimization software in five different cases. Simulation results show that the hub's operation cost and emission reduce up to 4.8% and 5.6% by implementing four objective functions in the presence of the Pico hydel energy storage and demand response programing.