With increasing amounts of wind power generation installed, the steep fluctuation of wind power generation output, called ramp events, causes serious problems for power system operation. Controlling fluctuations is an important issue for increasing the amount of wind power generation as a wind farm (WF) in the future. The authors reported the scheduled operation method of WF using a battery energy storage system (BESS) and forecast data of wind power generation output. In this paper, the authors propose a new scheduled operation method of WF. In particular, we propose the application of deep reinforcement learning to decide the output schedule of WF. Moreover, we compare the conventional method, the reinforcement learning method, and the deep reinforcement learning method in terms of the number of ramp events. In addition, we calculate the reducing effect of the storage capacity of BESS. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

中文翻译：

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带有深度强化学习的电池系统风电场调度运行

随着安装的风力发电量的增加，风力发电输出的急剧波动（称为斜坡事件）给电力系统的运行带来了严重的问题。控制波动是将来增加作为风电场（WF）的风力发电量的重要问题。作者报告了使用电池储能系统（BESS）的WF的预定运行方法，并预测了风力发电的输出数据。在本文中，作者提出了一种新的WF调度操作方法。特别是，我们建议应用深度强化学习来确定WF的输出进度表。此外，就斜坡事件的数量而言，我们将传统方法，强化学习方法和深度强化学习方法进行了比较。此外，我们计算了BESS存储容量的降低效果。©2021日本电气工程师学会。由Wiley Periodicals LLC发布。