The Appraisal of an Innovative Residential Energy Hub Framework for a Home Energy Management Paradigm Using Two-Point Estimation Technique

A novel paradigm for home energy management in a renewable energy-based residential energy hub using probabilistic optimization methods is presented in this work. Various converters and energy storage facilities, along with combined heat and power (CHP) units, plug-in hybrid electric vehicles, heat storage units, solar panels, and controllable and uncontrollable appliances are counted as a component in the energy hub. Consumer energy cost is considered as the goal of optimizing home energy management and operating restrictions of different energy hub components are considered optimization terms. In this paper, the probabilistic method of two-point estimation is employed to model the uncertainty of solar panels. Hence, a hybrid optimization algorithm including the gray wolf optimization algorithm and the shark smell optimization (SSO) algorithm has been used to solve the desired objective function under different terms and the prevailing constraints. The results show that by applying the proposed method and considering the proposed strategy in energy management, energy efficiency can be increased and the operating costs of the energy hub can be significantly reduced.