  The power consumption of households has been constantly growing over the
years. To cope with this growth, intelligent management of the consumption
profile of the households is necessary, such that the households can save the
electricity bills, and the stress to the power grid during peak hours can be
reduced. However, implementing such a method is challenging due to the
existence of randomness in the electricity price and the consumption of the
appliances. To address this challenge, we employ a model-free method for the
households which works with limited information about the uncertain factors.
More specifically, the interactions between households and the power grid can
be modeled as a non-cooperative stochastic game, where the electricity price is
viewed as a stochastic variable. To search for the Nash equilibrium (NE) of the
game, we adopt a method based on distributed deep reinforcement learning. Also,
the proposed method can preserve the privacy of the households. We then utilize
real-world data from Pecan Street Inc., which contains the power consumption
profile of more than 1; 000 households, to evaluate the performance of the
proposed method. In average, the results reveal that we can achieve around 12%
reduction on peak-to-average ratio (PAR) and 11% reduction on load variance.
With this approach, the operation cost of the power grid and the electricity
cost of the households can be reduced.
