Population dynamic human behavioral models for smart grid demand side management

Demand side management (DSM) plays a critical role in scheduling and optimizing the energy consumption in the smart grid. Considering the critical yet complex human behavior issue, we develop new population dynamic models to describe the behavior of DSM users and use them to analyze the performance of DSM. We first introduce an accurate Markov model for the DSM population. We show that this model can be converted into a form similar to the popular SIR (susceptible-infected-recovered) model in mathematical biology. Then, we formulate a composite model that integrates the new DSM user behavioral model with a game theoretic DSM scheme. The convergence and the equilibrium of the composite model are studied both analytically and numerically. Experiments are conducted to determine the important model parameters.

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