【摘要】 由于电动汽车的普及性和家用电器的灵活性，在动态电价下对家庭能源系统进行能源调度优化电力成本和保障居民舒适度是可行的。本文提出一种基于数据驱动的深度强化学习家庭能源管理方法。首先，为揭示影响电动汽车充电行为的多种不确定因素，引入一种结合驾驶员经验、突发事件和交通状况的改进数学模型描述电动汽车在家庭能源系统中的动态能量需求。其次，提出一种解耦优势演员-评论家(DA2C)算法，通过缓解策略和价值共享网络导致的过拟合问题提升能源优化性能。此外，策略函数和价值函数的解耦网络确保了所提方法在不可见场景中的泛化性。最后，将所提方法与现有方法进行综合实验比较。结果表明，该方法能在不同场景下优化用电成本并兼顾居住舒适度。更多还原

【Abstract】 Considering the popularity of electric vehicles and the flexibility of household appliances, it is feasible to dispatch energy in home energy systems under dynamic electricity prices to optimize electricity cost and comfort residents. In this paper, a novel home energy management(HEM) approach is proposed based on a data-driven deep reinforcement learning method. First, to reveal the multiple uncertain factors affecting the charging behavior of electric vehicles(EVs), an improved mathematical model integrating driver’s experience, unexpected events, and traffic conditions is introduced to describe the dynamic energy demand of EVs in home energy systems. Second, a decoupled advantage actor-critic(DA2C) algorithm is presented to enhance the energy optimization performance by alleviating the overfitting problem caused by the shared policy and value networks. Furthermore, separate networks for the policy and value functions ensure the generalization of the proposed method in unseen scenarios. Finally,comprehensive experiments are carried out to compare the proposed approach with existing methods, and the results show that the proposed method can optimize electricity cost and consider the residential comfort level in different scenarios.更多还原