Abstract
With the massive rise in population as well as economic growth, the energy consumption of residential end-users has increased considerably. To confront this issue distributed generations (DGs) and electrical energy storage (EES) are very crucial for meeting energy demand. In this paper, the electricity cost for residential end-users has been investigated due to integration of distributed photovoltaic (PV) and EES for IoT-enabled smart homes. Also, the benefits of energy management because of bidirectional flow of energy (H2G) have also been explored. Formulation of home energy management (HEM) problem is performed by taking PV, EES end-users comfort constraints into consideration. The Q-value based reinforcement learning (RL) strategy considering end-users priority is exploited for optimal home appliances scheduling (HAS). Simulation results shows that proposed schedule for home appliances is effective and demand response (DR) actions are accomplished. Furthermore, the reduction in both electricity consumption cost and system uncertainty has also been observed.
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