标题： 基于深度学习的电池管理频率调节预测控制 显示英文标题

标题： Deep Learning-based Predictive Control of Battery Management for Frequency Regulation

摘要： 本文提出了一种基于深度学习的最优电池管理方案，用于频率调节（FR），该方案结合了模型预测控制（MPC）、监督学习（SL）、强化学习（RL）和高保真电池模型。 通过利用深度神经网络（DNN），得出的DNN近似策略在在线实现中计算效率高。 所提出的方案的设计过程包括两个顺序步骤：（1）SL过程，其中我们首先运行一个嵌入低保真电池模型的MPC仿真以生成训练数据集，然后基于生成的数据集，使用SL算法优化DNN近似策略；以及（2）RL过程，其中我们利用RL算法通过平衡短期经济激励和长期电池退化来提高DNN近似策略的性能。 SL过程通过提供良好的初始化加快了后续的RL过程。 通过利用RL算法，所提出的方案的一个显著特性是它可以利用在高保真电池模拟器上模拟FR策略生成的数据来调整DNN近似策略，该策略最初基于低保真电池模型。 进行了使用实际FR信号和价格数据的案例研究。 仿真结果表明，与传统MPC方案相比，所提出的基于深度学习的方案可以有效实现更高的FR参与经济效益，同时保持较低的在线计算成本。 显示英文摘要

摘要： This paper proposes a deep learning-based optimal battery management scheme for frequency regulation (FR) by integrating model predictive control (MPC), supervised learning (SL), reinforcement learning (RL), and high-fidelity battery models. By taking advantage of deep neural networks (DNNs), the derived DNN-approximated policy is computationally efficient in online implementation. The design procedure of the proposed scheme consists of two sequential processes: (1) the SL process, in which we first run a simulation with an MPC embedding a low-fidelity battery model to generate a training data set, and then, based on the generated data set, we optimize a DNN-approximated policy using SL algorithms; and (2) the RL process, in which we utilize RL algorithms to improve the performance of the DNN-approximated policy by balancing short-term economic incentives and long-term battery degradation. The SL process speeds up the subsequent RL process by providing a good initialization. By utilizing RL algorithms, one prominent property of the proposed scheme is that it can learn from the data generated by simulating the FR policy on the high-fidelity battery simulator to adjust the DNN-approximated policy, which is originally based on low-fidelity battery model. A case study using real-world data of FR signals and prices is performed. Simulation results show that, compared to conventional MPC schemes, the proposed deep learning-based scheme can effectively achieve higher economic benefits of FR participation while maintaining lower online computational cost.