User satisfaction-based genetic algorithm for load shifting in smart grid

Abstract

This paper presents a new load shifting strategy for smart grid systems based on both power consumers’ day-ahead power forecast and their Service Level Agreement (SLA) in order to reduce their electricity bills, guaranties user satisfaction, and for smart grid system to reduce as well the overall power consumption at the peak hours. We provide an analytical model that formulated the load shifting process as a cost minimization problem. A Genetic Algorithm (GA) approach based on a two dimensional chromosome representation is used to solve the optimization problem by collecting a day-ahead forecast and SLAs as an input from the power consumers. The output of the GA consists of giving the best power task plan for the day-ahead which satisfy all consumers in terms of minimizing their consumption bill and reduces the peak demand. Experimental results using simulation show that the proposed load shifting strategy not only guaranty SLA requirements but it reduces the total cost by more than 16%, and in general it achieves a substantial cost savings of 38% compared to the recent algorithms from the literature.