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, in this article, 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 noncooperative 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 1000 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 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.

中文翻译：

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分布式深度强化学习在住宅智能电网中的智能负荷调度

多年来，家庭的电力消耗一直在不断增长。为了应对这种增长，需要对住户的用电情况进行智能管理，以使住户可以节省电费，并且可以减少高峰时段对电网的压力。但是，由于电价和设备的消耗存在随机性，因此实施这种方法具有挑战性。为了解决这一挑战，在本文中，我们为家庭采用了一种无模型的方法，该方法在有关不确定因素的信息有限的情况下工作。更具体地说，可以将家庭与电网之间的互动建模为非合作随机博弈，其中电价被视为随机变量。为了搜索游戏的纳什均衡（NE），我们采用了一种基于分布式深度强化学习的方法。而且，所提出的方法可以保护住户的隐私。然后，我们利用来自Pecan Street Inc.的真实数据（其中包含1000多个家庭的功耗曲线）来评估所提出方法的性能。平均而言，结果表明我们可以实现$ 12 \％$ 降低峰均比和 $ 11 \％$减少负载差异。通过这种方法，可以降低电网的运营成本和家庭的电费。