Aug 2026· Electrical Engineering· Vol 108· 0 citations· 25 references
TL;DR
An uncertainty-aware encoding and decoding framework that fused stochastic mixed-integer quadratic programming approaches, with deep neural network (DNN) classification was developed, showing the suggested approach was able to provide voltage regulation actions that were close to optimal while achieving significant computational speed-up while ensuring feasibility in the presence of uncertainty.
The proposed MPC-guided graph convolutional network-based voltage optimization control strategy for active distribution networks with PVs and battery energy storage system shows that the proposed method effectively mitigates bidirectional voltage violations and achieves control performance and network-loss levels close...
Zhi-Hui Wang, Bo Yang, Yanxia Chen et al.· Energy Engineering· 0 citations
Findings indicate that deep reinforcement learning provides an effective optimization approach for coordinated HST and DG control and offers a useful reference for voltage regulation in active distribution networks.
The ongoing energy transition demands that distribution system operators perform advanced calculations and analyses in near-real time. This is challenging in low-voltage distribution networks, where exact network topology is often unknown or insufficiently documented. Machine learning can bring significant advancement...
Petar Labura, T. Antić, Marko Delimar et al.· 2026 International Conferenc...· 0 citations
Results demonstrate that the proposed DQN-based reconfiguration approach can enhance voltage profiles and reduce power losses under high PV penetration, highlighting the importance of considering uncertainty-driven risk assessment in computational decision-making for modern active distribution networks.
To address power-quality deterioration in increasingly complex power-system operating conditions, this paper develops a genetic-algorithm-optimized deep neural network (GA-DNN) for intelligent regulation. The GA module searches the DNN hyperparameter space, including layer depth, hidden-unit width, learning rate, batch...
The increasing penetration of inverter-based renewable energy sources (RES) presents significant challenges to power system stability by weakening grid strength, an issue often overlooked in traditional transmission network expansion planning (TNEP). To address this gap, this study introduces a novel planning frame...
Jia-Ming Li, Ke Wang, Yan Li et al.· Frontiers in Energy Research· 0 citations
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