A novel optimal framework for scheduling rooftop solar home appliances considering electricity, real pricing and user comfort

Abstract

With an increase in electrification trends, the energy consumption of households is expected to increase significantly in the near future. To reduce electricity bills in the electrified era, management of the demand side is inevitable. Different demand response programs, such as real-time pricing can be introduced from the grid side. The consumption side needs to adjust its consumption patterns in accordance with the price signals. Therefore, an innovative home appliance scheduling method is proposed considering the rooftop solar panels as part of energy suppliers. First, an optimization problem is formulated considering different objectives such as energy price, consumer satisfaction, and peak-to-average load ratio. Then, home appliances are divided into three major categories (shiftable, flexible, and normally operated) to adjust schedules of different load types. Then, the developed model is solved using the bald eagle search optimization algorithm (BESOA). Finally, the performance of the proposed BESOA-based method is compared with other well-known heuristic/meta-heuristic methods. In addition, an analysis of different equipment's maximal operation time is conducted for all the methods. In order to provide feedback on user comfort levels, digital twin technology is used allowing the user to adjust the scheduling of the appliances to ensure that they are comfortable. By leveraging digital twin technology, users would be able to optimize the scheduling of their rooftop solar home appliances to maximize efficiency and minimize costs. Simulation results have shown the superiority of the BESOA method over other methods in terms of daily average energy cost, peak-to-average load ratio, and consumer satisfaction.