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Smart homes have assumed a pivotal role within energy communities, offering sophisticated home energy management systems tailored to end-users preferences across technical, economic, social, and environmental facets. Amid the proliferation of smart appliances and advanced communication infrastructure, these domiciles play a crucial part in optimizing energy utilization and fostering sustainable practices. Notably, they create an enabling environment for the integration of renewable energy resources, but their widespread adoption may potentially challenge the flexibility of the overarching energy system. In this paper, a novel flexibility-constrained smart home energy management framework is proposed aiming at optimizing energy payment, end-user satisfaction, and end-user self-sufficiency preferences. Diverse technologies including Combined Heat and power (CHP) units, PhotoVoltaic (PV) generation units, electrical energy storage systems, and electric vehicles are considered in the smart home where the PV unit uncertainty is modeled by a scenario-based approach. The lexicographic technique as a Multi-Criteria Decision Making (MCDM) approach is utilized to find an appropriate compromise among different targets. The proposed model is structured as a Mixed-Integer Linear Programming (MILP) problem that is solved by the CPLEX solver in the GAMS environment. The results reveal a significant disparity in energy payments when the flexibility limit falls below 40 % of the predefined amount. The results demonstrate that altering the priority order of the objective functions has led to significant changes in energy payment, end-user satisfaction, and end-user self-sufficiency, with variations of 27.4 %, 100 %, and 56.64 %, respectively. |
