Rahman Dashti

This paper investigates an energy hub that integrates conventional and renewable sources, along with hydrogen and electrical energy storage systems. This paper addresses the optimal scheduling for the components of a multi-energy hub (EH) to minimize cost, emission, and consumer dissatisfaction. The proposed method includes a demand response program (DRP) and considers the uncertainty in the behavior of wind and solar resources. The optimization is carried out using Mixed-Integer Nonlinear Programming (MINLP) and the fuzzy satisfaction method to identify the optimal solution from the Pareto front. The proposed model is simulated using GAMS software in three cases: single-objective (cost), bi-objective (cost, emission), and tri-objective (cost, emission, consumer dissatisfaction). The results indicate that the implementation of DRP leads to a 4.42% reduction in costs and a 2.02% decrease in CO2 emissions. In the case of tri-objective, consumer dissatisfaction is defined considering the impact of DRP as a function of load shifting and load clipping, and its impacts are explored through sensitivity analysis. Moreover, comprehensive comparisons are conducted across various case studies. The results show that more consumer patience leads to lower costs, CO2 emissions, and consumer dissatisfaction.