Multi-objective residential load scheduling approach for demand response in smart grid

In recent years, with the rapid growth of electricity demand and the development of smart grids, demand-side management had an important role. As the penetration rate of distributed renewable energy generation in the grid increases, the diurnal peak of the net load demand curve in the urban area is offset by renewable energy sources such as photovoltaics and the peak load time gradually shifts from afternoon to evening. The peak electricity load of residential buildings usually occurs in the evening, which aggravates the power balance problem. To this end, this study proposes a demand response (DR) scheduling approach for residential buildings, aimed for four types of residential building loads: interruptible and deferrable loads, noninterruptible and deferrable loads, noninterruptible and nondeferrable loads, and air conditioning loads. Nondominated sorting genetic algorithm II is used as a multi-objective optimization algorithm to search for the minimal electricity cost and minimal inconvenience index. Finally, the ASHRAE 140 standard building is used as a case and the proposed scheduling approach is evaluated under two scenarios of working and nonworking days. The proposed scheduling approach can effectively shave the peak load to off-peak load time, reduce electricity bills, and meet the occupants' comfort.