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.
Rok vydání:	2024
Předmět:	Physics - Physics and Society Electrical Engineering and Systems Science - Systems and Control Nonlinear Sciences - Adaptation and Self-Organizing Systems
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
Externí odkaz:	http://arxiv.org/abs/2407.15165 Zobrazit plný text záznamu View this record from Arxiv