A Reinforcement Learning Approach for Integrating an Intelligent Home Energy Management System with a Vehicle-to-Home Unit

These days, users consume more electricity during peak hours, and prices are typically higher between 3:00 p.m. 11:00 If electric vehicle (EV) charging occurs the same impact on residential distribution networks increases. Thus, home energy management systems (HEMS) have been introduced to manage demand among households EVs in networks, such as a smart micro-grid (MG). Moreover, HEMS can efficiently renewable sources, solar photovoltaic (PV) panels, wind turbines, storage. Until now, no has intelligently coordinated uncertainty of MG elements. This paper investigated PV power, storage, maximum radiation hours. Several deep learning (DL) algorithms were utilized account for uncertainties. A reinforcement centralized (RL-HCPV) scheduling algorithm was developed The RL-HCPV system modelled according several constraints meet household demands sunny cloudy weather. Additionally, simulations demonstrated how proposed could incorporate uncertainty, handle response vehicle-to-home (V2H) help level appliance load profile reduce power consumption costs with sustainable production. results advantages utilizing RL V2H technology potential building storage technology.