Incentive-based RTP model for balanced and cost-effective smart grid

The authors propose an intelligent real-time pricing (RTP)-based energy consumption scheduling model, which is especially applicable to more active and balanced demand management in a smart grid. Most previous research studies have not considered the incentive for subscribers who are more likely to move their consumption schedule to the off-peak period. Therefore, they considered the degree of the sacrifice made by each subscriber to determine an individualised price. As a result, the electricity unit price charged to each subscriber is different. An appropriate incentive coefficient is identified using a genetic algorithm and applied to the RTP model. This approach draws more active rescheduling of the energy consumption and enhances the fairness of a network. Compared with non-scheduling and day-ahead scheduling, the authors algorithm reduces the subscribers' total cost by an average of 24.9 and 15.9%, and increases the corresponding average fairness of the network by 16.7 and 5.4%, respectively. Moreover, they achieved a significant reduction in the peak-to-average-ratio.