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Reinforcement learning optimizes power dispatch in decentralized power grid

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  • 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
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    References listed on IDEAS

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    1. Benjamin Schäfer & Thiemo Pesch & Debsankha Manik & Julian Gollenstede & Guosong Lin & Hans-Peter Beck & Dirk Witthaut & Marc Timme, 2022. "Understanding Braess’ Paradox in power grids," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
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