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  Reinforcement learning optimizes power dispatch in decentralized power grid

Lee, Y., Choi, H., Pagnier, L., Kim, C. H., Lee, J., Jhun, B., Kim, H., Kurths, J., Kahng, B. (2024): Reinforcement learning optimizes power dispatch in decentralized power grid. - Chaos, Solitons and Fractals, 186, 115293.
https://doi.org/10.1016/j.chaos.2024.115293

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 Creators:
Lee, Yongsun1, Author
Choi, Hoyun1, Author
Pagnier, Laurent1, Author
Kim, Cook Hyun1, Author
Lee, Jongshin1, Author
Jhun, Bukyoung1, Author
Kim, Heetae1, Author
Kurths, Jürgen2, Author              
Kahng, B.1, Author
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1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 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.

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Language(s): eng - English
 Dates: 2024-07-252024-09-01
 Publication Status: Finally published
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.chaos.2024.115293
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Model / method: Machine Learning
 Degree: -

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Title: Chaos, Solitons and Fractals
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 186 Sequence Number: 115293 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/190702
Publisher: Elsevier