Scheduled Operation of Wind Farm with Battery System Using Deep Reinforcement Learning

被引：4

作者：

Futakuchi, Mamoru ^{[1
]}

Takayama, Satoshi ^{[1
]}

Ishigame, Atsushi ^{[1
]}

机构：

[1] Osaka Prefecture Univ, Power Syst Res Grp, Naka Ku, 1-1 Gakuen Cho, Sakai, Osaka 5998531, Japan

来源：

IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING | 2021年 / 16卷 / 05期

关键词：

wind generation; battery energy storage system; scheduled operation; reinforcement learning; deep learning; deep reinforcement learning;

D O I：

10.1002/tee.23348

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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. (c) 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

引用

页码：696 / 703

页数：8

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