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The power consumption of households has been constantly growing over theyears. To cope with this growth, intelligent management of the consumptionprofile of the households is necessary, such that the households can save theelectricity bills, and the stress to the power grid during peak hours can bereduced. However, implementing such a method is challenging due to theexistence of randomness in the electricity price and the consumption of theappliances. To address this challenge, we employ a model-free method for thehouseholds which works with limited information about the uncertain factors.More specifically, the interactions between households and the power grid canbe modeled as a non-cooperative stochastic game, where the electricity price isviewed as a stochastic variable. To search for the Nash equilibrium (NE) of thegame, we adopt a method based on distributed deep reinforcement learning. Also,the proposed method can preserve the privacy of the households. We then utilizereal-world data from Pecan Street Inc., which contains the power consumptionprofile of more than 1; 000 households, to evaluate the performance of theproposed 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 electricitycost of the households can be reduced.

Distributed Deep Reinforcement Learning for Intelligent Load Scheduling in Residential Smart Grids