Digital Twin-Empowered Voltage Control for Power Systems

Autor:	Xu, Jiachen, Li, Yushuai, Pedersen, Torben Bach, He, Yuqiang, Larsen, Kim Guldstrand, Li, Tianyi
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
Popis:	Emerging digital twin technology has the potential to revolutionize voltage control in power systems. However, the state-of-the-art digital twin method suffers from low computational and sampling efficiency, which hinders its applications. To address this issue, we propose a Gumbel-Consistency Digital Twin (GC-DT) method that enhances voltage control with improved computational and sampling efficiency. First, the proposed method incorporates a Gumbel-based strategy improvement that leverages the Gumbel-top trick to enhance non-repetitive sampling actions and reduce the reliance on Monte Carlo Tree Search simulations, thereby improving computational efficiency. Second, a consistency loss function aligns predicted hidden states with actual hidden states in the latent space, which increases both prediction accuracy and sampling efficiency. Experiments on IEEE 123-bus, 34-bus, and 13-bus systems demonstrate that the proposed GC-DT outperforms the state-of-the-art DT method in both computational and sampling efficiency. Comment: 6 pages, 1 figure, conference paper
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.06940 Zobrazit plný text záznamu View this record from Arxiv