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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用于住宅智能电网中智能负载调度的分布式深度强化学习


多年来，家庭用电量不断增长。为了应对这种增长，有必要对家庭的消费情况进行智能管理，这样家庭就可以节省电费，并减轻高峰时段对电网的压力。然而，由于电价和设备消耗的随机性的存在，实施这种方法具有挑战性。为了应对这一挑战，在本文中，我们对家庭采用了无模型方法，该方法仅适用于有关不确定因素的有限信息。更具体地说，家庭和电网之间的相互作用可以建模为非合作随机博弈，其中电价被视为随机变量。为了寻找博弈的纳什均衡（NE），我们采用基于分布式深度强化学习的方法。此外，所提出的方法可以保护家庭的隐私。然后，我们利用 Pecan Street Inc. 的真实数据（其中包含 1000 多个家庭的功耗概况）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峰均比降低约 12%，负载方差降低 11%。通过这种方式，可以降低电网的运营成本和家庭的用电成本。