Reinforcement Learning Optimizes Power Dispatch in Decentralized Power Grid
Autor: | Lee, Yongsun, Choi, Hoyun, Pagnier, Laurent, Kim, Cook Hyun, Lee, Jongshin, Jhun, Bukyoung, Kim, Heetae, Kurths, Juergen, Kahng, B. |
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Rok vydání: | 2024 |
Předmět: | |
Zdroj: | Chaos, Solitons and Fractals 186 (2024) 115293 |
Druh dokumentu: | Working Paper |
DOI: | 10.1016/j.chaos.2024.115293 |
Popis: | 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. Comment: 11 pages, 6 figures |
Databáze: | arXiv |
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