IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v186y2024ics0960077924008452.html
   My bibliography  Save this article

Reinforcement learning optimizes power dispatch in decentralized power grid

Author

Listed:
  • Lee, Yongsun
  • Choi, Hoyun
  • Pagnier, Laurent
  • Kim, Cook Hyun
  • Lee, Jongshin
  • Jhun, Bukyoung
  • Kim, Heetae
  • Kurths, Jürgen
  • Kahng, B.

Abstract

Effective frequency control in power grids has become increasingly important with the increasing demand for renewable energy sources. Here, we propose a novel strategy for resolving this challenge using graph convolutional proximal policy optimization (GC-PPO). The GC-PPO method can optimally determine how much power individual buses dispatch to reduce frequency fluctuations across a power grid. We demonstrate its efficacy in controlling disturbances by applying the GC-PPO to the power grid of the UK. The performance of GC-PPO is outstanding compared to the classical methods. This result highlights the promising role of GC-PPO in enhancing the stability and reliability of power systems by switching lines or decentralizing grid topology.

Suggested Citation

  • Lee, Yongsun & Choi, Hoyun & Pagnier, Laurent & Kim, Cook Hyun & Lee, Jongshin & Jhun, Bukyoung & Kim, Heetae & Kurths, Jürgen & Kahng, B., 2024. "Reinforcement learning optimizes power dispatch in decentralized power grid," Chaos, Solitons & Fractals, Elsevier, vol. 186(C).
    • Handle: RePEc:eee:chsofr:v:186:y:2024:i:c:s0960077924008452
    DOI: 10.1016/j.chaos.2024.115293
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077924008452
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2024.115293?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chsofr:v:186:y:2024:i:c:s0960077924008452. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.