With the increasing concern on climate change and global warming, the reduction of carbon emission becomes an important topic in many aspects of human society. The development of energy Internet (EI) makes it possible to achieve better utilization of distributed renewable energy sources with the power sharing functionality introduced by energy routers (ERs). In this paper, a bottom-up EI architecture is designed, and a novel data-driven dynamical control strategy is proposed. Intelligent controllers augmented by deep reinforcement learning (DRL) techniques are adopted for the operation of each microgrid independently in the bottom layer. Moreover, the concept of curriculum learning (CL) is integrated into DRL to improve the sample efficiency and accelerate the training process. Based on the power exchange plan determined in the bottom layer, considering the stochastic nature of electricity price in the future power market, the optimal power dispatching scheme in the upper layer is decided via model predictive control. The simulation has shown that, under the bottom-up architecture, compared with the conventional methods such as proportional integral and optimal power flow, the proposed method reduces overall generation cost by 7.1% and 37%, respectively. Meanwhile, the introduced CL-based training strategy can significantly speed up the convergence during the training of DRL. Last but not least, our method increases the profit of energy trading between ERs and the main grid.

中文翻译：

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自下而上能源互联网系统的数据驱动动态控制

随着人们对气候变化和全球变暖的日益关注，减少碳排放成为人类社会诸多方面的重要课题。能源互联网（EI）的发展使得通过能源路由器（ER）引入的电力共享功能更好地利用分布式可再生能源成为可能。在本文中，设计了自底向上的EI架构，并提出了一种新颖的数据驱动的动态控制策略。通过深度强化学习（DRL）技术增强的智能控制器被用于底层每个微电网的独立运行。此外，课程学习（CL）的概念被整合到 DRL 中，以提高样本效率并加速训练过程。根据底层确定的换电方案，考虑到未来电力市场电价的随机性，通过模型预测控制确定上层的最优电力调度方案。仿真表明，在自下而上的架构下，与比例积分和最优潮流等传统方法相比，所提出的方法分别降低了7.1%和37%的总发电成本。同时，引入的基于 CL 的训练策略可以显着加快 DRL 训练过程中的收敛速度。最后但并非最不重要的一点是，我们的方法增加了 ER 和主电网之间能源交易的利润。在自下而上的架构下，与比例积分和最优潮流等传统方法相比，所提出的方法分别降低了7.1%和37%的总发电成本。同时，引入的基于 CL 的训练策略可以显着加快 DRL 训练过程中的收敛速度。最后但并非最不重要的一点是，我们的方法增加了 ER 和主电网之间能源交易的利润。在自下而上的架构下，与比例积分和最优潮流等传统方法相比，所提出的方法分别降低了7.1%和37%的总发电成本。同时，引入的基于 CL 的训练策略可以显着加快 DRL 训练过程中的收敛速度。最后但并非最不重要的一点是，我们的方法增加了 ER 和主电网之间能源交易的利润。