Abstract
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.