State-of-the-art and real-time implementation of an IoT-based home energy management system for a cluster of dwellings

In the present day electricity demand, demand response programs support mitigating the power demand and help to improve stability. Within this framework, the Home Energy Management System (HEMS) plays a critical role in optimizing energy consumption patterns by redistributing loads from peak to off-peak hours, thereby subsequently contributing to grid stability. The existing HEMS model often fails to simultaneously address the three important issues. 1. Minimizing power bills; 2. Maintaining peak-to-average ratio (PAR); and 3. User convenience while load scheduling. These challenges are further compounded by limitations like slow convergence in the existing optimization technique. Moreover, the lack of real-world validation impedes demonstrating their effectiveness and practicality for adoption. Thus, addressing these issues, this paper proposes the real-time implementation of the results of the optimization technique via a smart plug and a principal load scheduler (PSC) supported by a mobile application. First, the data prevalent to optimization techniques is collected from the stakeholders, and secondly, the multi-objective mountain gazelle optimization (MMGO) algorithm is formulated and utilized by the PSC to schedule household applications for all the individuals within the cluster. In addition, results are validated via a hardware prototype using a smart plug. Further, the suggested method is contrasted with existing multi-objective techniques and weighted techniques to demonstrate its superiority. While tested for single dwellings, the proposed method achieves a reduction of 12.14% in electricity costs and 52.54% in PAR compared to the unscheduled loads. Notably, during peak hours, it achieves a reduction of up to 80.15% in electricity costs and a 25.07% reduction in PAR. Extending the analysis further to multiple dwellings, 50 homes in a cluster, reveals an overall cost reduction and PAR reduction of 11% and 74.68%. Additionally, the assimilation of PV systems and battery management systems into the smart home would result in lucrative benefits and a flattening of the net load demand curve.