Digital Twin-Empowered Voltage Control for Power Systems

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.

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Publication Details

Published
2024-11-29
DOI
https://doi.org/10.1109/ei264398.2024.10990476
Citations
1
Primary Topic
Electric Power Systems and Control
Type
article
Field-Weighted Citation Impact
0.21
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Digital Twin-Empowered Voltage Control for Power Systems

Yuan He, Torben Bach Pedersen, Kim G. Larsen, Jennifer Xu et al.
1 citations
Electric Power Systems and Control
0.21
article

Digital Twin-Empowered Voltage Control for Power Systems

Yuan He, Torben Bach Pedersen, Kim G. Larsen, Jennifer Xu, Yuan‐Qing Li, Tianyi Li
article en
1 citations

Abstract

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.

Guangxi University (CN), Aalborg University (DK)
Affordable and clean energy
Openalex Percentile: Top 43%
Electric Power Systems and Control
0.21
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