Experimental Verification of Peak Load Reduction through DR Inspired IoT- based HEMS

Handling load conditions during peak hours is a challenging task for utilities due to continuously growing demand for electricity. This often necessitates for new capacity installations by the utilities with more capital and resources. Also, residential consumers utilizing power at peak hours have to pay increased charges despite possible load rescheduling options. To avoid this, herein a demand response program inspired load management (LM) and scheduling method for the residential consumers is proposed. The proposed method was simulated and experimentally verified by designing an internet of things (IoT) based home energy management (HEM) system. The energy management module automatically manages the consumer prioritized residential loads during high peak load conditions. Simulation studies of two different cases of load categorization results in significant power savings of 50% and 92% respectively. The simulated results are validated using an IoT based HEM experimental set-up and the observed experimental results were also in line with the simulated results giving a correlation factor of about unity. Overall, it is understood that there is a potential to leverage this method in real-time residential energy management programs, which would be cost-effective for utilities as they no need to for new installations or procurement of energy and for consumers through energy bill savings.