Demand side management with a human behavior model for energy cost optimization in smart grids

Demand side management (DSM) is an essential function to schedule and optimize the energy consumption in smart grids. One of the challenges of DSM is how to take the complex human behavior into consideration. In this paper, we apply a special population dynamic model to investigate the performance of DSM algorithms under human behavior effects. Specifically, we adapt the SISa (susceptible-infected-susceptible with autonomous infection) model to describe the population behavior of smart grid customers. The SISa model and the DSM algorithms interact with each other via the recovery probability and the DSM population size. The convergence and the equilibrium of the composite model are studied both analytically and numerically.