Frequency control for islanded AC microgrid based on deep reinforcement learning

Xianggang Liu, Zhi-Wei Liu, Ming Chi and Guixi Wei

Cyber-Physical Systems, 2024, vol. 10, issue 1, 43-59

Abstract: The incorporation of intermittent and stochastic renewable energy into a microgrid creates frequent fluctuations, which provides new challenges in frequency control. This paper deals with the frequency control problem in the islanded AC microgrid (IACMG) via a model-free deep reinforcement learning (DRL) method, which includes offline learning and online control. Twin-delayed deep deterministic policy gradient is involved to improve the performance of the agent to minimise the frequency deviation. The advantage of the proposed method is self-adaptive to the uncertain IACMG model including renewable energy sources. Finally, the effectiveness and robustness of the proposed controller is demonstrated by four simulation scenarios.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/23335777.2022.2130434 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tcybxx:v:10:y:2024:i:1:p:43-59

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tcyb20

DOI: 10.1080/23335777.2022.2130434

Access Statistics for this article

Cyber-Physical Systems is currently edited by Yang Xiao

More articles in Cyber-Physical Systems from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().