# Digital Twin-Empowered Voltage Control for Power Systems

arXiv: https://arxiv.org/abs/2412.06940 · HTML: https://mlsanity.com/paper/2412.06940/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 9 Dec 2024.

## Builds on (most cited first, 3 of 3)

- [Mastering Atari Games with Limited Data](https://mlsanity.com/paper/2111.00210/) (2021)
- [Deep Reinforcement Learning Based Volt-VAR Optimization in Smart Distribution Systems](https://mlsanity.com/paper/2003.03681/) (2020)
- [PowerGym: A Reinforcement Learning Environment for Volt-Var Control in Power Distribution Systems](https://mlsanity.com/paper/2109.03970/) (2021)

## Abstract (arXiv)

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.