Vehicle to Grid Frequency Regulation Capacity Optimal Scheduling for Battery Swapping Station Using Deep Q-Network
Vehicle to Grid Frequency Regulation Capacity Optimal Scheduling for Battery Swapping Station Using Deep Q-Network
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DOI：
10.1109/tii.2020.2993858
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发表时间：
2021-02
影响因子：
12.3
通讯作者：
Xinan Wang;Jianhui Wang;Jianzhe Liu
中科院分区：
文献类型：
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作者：
Xinan Wang;Jianhui Wang;Jianzhe Liu
电池交换站(BSS)由于其大的电池库存容量，是快速频率调节服务(FFR)的理想候选者。此外，BSS可以为客户预充电电池，而未充电的电池可以为市场提供稳定的调节能力。然而，ACE信号和EV每小时访问计数等不确定性将随机非线性动力学引入到基于BSS的FFRS的操作中。目前，还没有量化的方法来保证其最佳的经济运行。为了缩小这一差距，本文提出了一种新的基于深度Q学习的FFRS容量动态调度策略。该方法能够实时自主调度每小时调节能力，最大化BSS提供FFRS的收益。使用真实世界数据的案例研究验证了拟议工作的有效性。
Battery swapping stations (BSSs) are ideal candidates for fast frequency regulation services (FFRS) due to their large battery stock capacity. In addition, BSSs can precharge batteries for customers and the batteries that are not in charging can provide a stable regulation capacity to the market. However, uncertainties, such as ACE signals and the EV per-hour visit counts, introduce stochastic nonlinear dynamics into the operation of a BSS-based FFRS. Currently, there is no quantification method to ensure its optimal economical operation. To close this gap, in this article, we propose a novel deep Q-learning-based FFRS capacity dynamic scheduling strategy. This method can autonomously schedule the hourly regulation capacity in real time to maximize the BSS's revenue for providing FFRS. Case studies using real-world data verify the efficacy of the proposed work.