Smart home energy management optimization: An amended sparrow search algorithm for enhanced grid stability and cost efficiency

This ecosystem is suffering from a shortage of sources of energy as its population continues to grow rapidly. A large amount of energy produced every year ends up being wasted in the transmission and on a demand-side basis due to the conventional energy distribution systems. Due to this, experts around the world have been paying close attention to the problem and have developed smart grid concepts as a result. Using smart grids is the best solution for today's challenges related to energy. Presented here is an amended version of the Sparrow Search Optimization Algorithm (ASSOA), a new optimization method based on meta-heuristics. Addressing real-world challenges in both multiple and single smart homes, the recommended algorithm tackles the intricacies of appliance scheduling. Within this system model, the appliances are categorized into 2 classes: Adjustable and Non-Adjustable. The simulation outcomes affirm that ASSOA ensures grid stability, a critical aspect dependent on the peak-to-average ratio (PAR), which the suggested algorithm successfully reduces compared to unscheduled scenarios. Moreover, the offered algorithm, ASSOA, achieves minimal electricity costs while maintaining acceptable waiting times. Notably, there exists a delicate balance between electricity costs and waiting times, where reducing one often entails an increase in the other, a balance effectively struck by ASSOA.