Modelling multi-stage energy optimization in the smart hub energy system with hybrid demand management and local supply by Electric Vehicles

Abstract

Abstract The interdependence of energy with social sustainability, economic viability, and environmental impact has led energy operators to conduct thorough investigations into energy scheduling issues. These investigations have prompted the development of new strategies for energy generation, such as the utilization of energy carriers including electrical, gas, and thermal loads. This article presents a model for multi-stage energy optimization in a smart system like energy hub, with a focus on hybrid demand management at the consumer level. The proposed multi-stage energy optimization approach aims to address demand management in the upper stage, while also minimizing energy generation costs and emission pollution in the lower stage. The hybrid demand management is proposed considering two demand response programs like optimal shifting and interrupting of the thermal and electrical demands. The modeling of emission pollution and energy generation costs in the lower stage is done by multi-criteria optimization. As well, local supply strategy is implemented via electric vehicles (EVs) in lower stage by consumers to supply self-electrical demand. The proposed two-stage multi-criteria optimization by using GAMS software is solved. On the other hand, multi-criteria optimization is handled by a modified epsilon-constraint method and max-min fuzzy decision approach in the lower stage of optimization. To validate the mentioned approach, three scenarios are compared, and the results clearly illustrate the energy optimization for minimizing the energy costs and emissions achieved through the presented approach.