Tri-objective optimal scheduling of smart energy hub system with schedulable loads

Abstract

Many researches have investigated the optimal energy scheduling to meet the demand regarding the sustainable development issues and the economic and environmental indices. The energy hub system (EHS) is introduced as an appropriate framework in which the demands are supplied by multi carrier energy (MCE) such as electricity, natural gas, and thermal energy. In this paper, the optimal scheduling problem of an EHS is modeled as a tri-objective optimization problem in which the operation cost, the emission pollution, and the deviation of the electrical load profile from its desired value is minimized. In the proposed model, the third objective function as a Demand-side Management (DSM) strategy is considered for the optimal shifting of the electrical deferrable loads (EDLs) based on the day-ahead electrical energy prices. Moreover, the reserve scheduling is proposed by the interruptible loads (ILs) of the thermal and the electrical demand. The proposed model is solved using the augmented ε-constraint method in the General Algebraic Modeling System (GAMS) optimization software environment. The generated solutions by the augmented ε-constraint method have diverse non-dominated solutions in which the best solution is selected by the fuzzy approach. In order to validate the proposed approach, three Case studies are investigated and their results are compared with each other. The operation cost and the emission pollution of the EHS in the first case is 402130.12 $ and 36162.26 kg. With the participation of EDLs in the second case, the operation cost and the emission reduces 0.4% and 3.02%, respectively. Finally, with the simultaneous participation of EDLs and ILs in the third case, the operation cost and the emission pollution reduces 0.38% and 2.53% in comparison with the second case. • Proposing tri-objective functions in smart EHS with optimal shifting of EDLs. • Implementation of optimal shifting and strategic conversion of DSM strategies. • Proposing ILs as reserve in smart EHS. • Utilization of augmented ɛ-constraint method to solve objective functions. • Finding best solution by fuzzy approach.