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Reinforcement learning optimizes power dispatch in decentralized power grid

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Lee,  Yongsun
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Choi,  Hoyun
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Pagnier,  Laurent
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Kim,  Cook Hyun
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Lee,  Jongshin
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Jhun,  Bukyoung
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Kim,  Heetae
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/persons/resource/Juergen.Kurths

Kurths,  Jürgen
Potsdam Institute for Climate Impact Research;

Kahng,  B.
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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


???ViewItemOverview_lblCiteAs???: https://publications.pik-potsdam.de/pubman/item/item_30714
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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.