Optimal Energy Scheduling Considering Consumers Preference in a Smart Home

Residential buildings are estimated to have a significant percentage of energy consumption in the world. Demand-side management (DSM) improves the grid stability by providing economic incentives with increasing flexibility in demand and reducing a peak to average ratio. The success of applications in DSM heavily depends on the welfare and satisfaction of consumers, or a significant reduction of the customers electricity bill. In this paper, the genetic algorithm is used to optimize the problem. Moreover, two scenarios are discussed. In the first one, occupants have announced their preferred time of household appliances usage. On this basis, the scheduled for the use of household appliances is optimally determined in order to minimize the cost of electricity usage in addition to meeting end-user preferences on when to use devices. In the second one, customers are offered a significant reduction in their electricity bill by changing the operation time of some of their household appliances. The proposed model is implemented for the hottest day of 2018 in New York City. The results demonstrate that the consumers preference is satisfied, and the peak load is declined down to the value, which is desirable for the utility. Besides, the model doesn't produce a new peak load when the power price is low. Hence the system stability is increased, which reflects the significant effectiveness of this model.