Reinforcement Learning Optimizes Power Dispatch in Decentralized Power Grid
<|reference_start|>Reinforcement Learning Optimizes Power Dispatch in Decentralized Power Grid: 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.<|reference_end|>
@article{lee2024reinforcement,
    title={Reinforcement Learning Optimizes Power Dispatch in Decentralized Power
  Grid},
    author={Yongsun Lee, Hoyun Choi, Laurent Pagnier, Cook Hyun Kim, Jongshin Lee,
  Bukyoung Jhun, Heetae Kim, Juergen Kurths, and B. Kahng},
    journal={Chaos, Solitons and Fractals 186 (2024) 115293},
    year={2024},
    doi={10.1016/j.chaos.2024.115293},
    archivePrefix={arXiv},
    eprint={2407.15165},
    primaryClass={physics.soc-ph cs.SY eess.SY nlin.AO}
}