Reinforcement Learning Optimizes Power Dispatch in Decentralized Power Grid
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.
Cite
@article{arxiv.2407.15165,
title = {Reinforcement Learning Optimizes Power Dispatch in Decentralized Power Grid},
author = {Yongsun Lee and Hoyun Choi and Laurent Pagnier and Cook Hyun Kim and Jongshin Lee and Bukyoung Jhun and Heetae Kim and Juergen Kurths and B. Kahng},
journal= {arXiv preprint arXiv:2407.15165},
year = {2024}
}
Comments
11 pages, 6 figures