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Application and progress of artificial intelligence technology in the field of distribution network voltage Control: A review
The increasing integration of distributed energy resources has led to heightened complexity
in distribution network models, posing challenges of uncertainty and volatility to the …
in distribution network models, posing challenges of uncertainty and volatility to the …
Deep reinforcement learning for smart grid operations: Algorithms, applications, and prospects
With the increasing penetration of renewable energy and flexible loads in smart grids, a
more complicated power system with high uncertainty is gradually formed, which brings …
more complicated power system with high uncertainty is gradually formed, which brings …
[HTML][HTML] Advancements in data-driven voltage control in active distribution networks: A Comprehensive review
Distribution systems are integrating a growing number of distributed energy resources and
converter-interfaced generators to form active distribution networks (ADNs). Numerous …
converter-interfaced generators to form active distribution networks (ADNs). Numerous …
Electrical model-free voltage calculations using neural networks and smart meter data
The proliferation of residential technologies such as photovoltaic (PV) systems and electric
vehicles can cause voltage issues in low voltage (LV) networks. During operation, voltage …
vehicles can cause voltage issues in low voltage (LV) networks. During operation, voltage …
Model-augmented safe reinforcement learning for Volt-VAR control in power distribution networks
Volt-VAR control (VVC) is a critical tool to manage voltage profiles and reactive power flow
in power distribution networks by setting voltage regulating and reactive power …
in power distribution networks by setting voltage regulating and reactive power …
Novel Data-Driven decentralized coordination model for electric vehicle aggregator and energy hub entities in multi-energy system using an improved multi-agent …
Energy hub (EH) is an independent entity that benefits to the efficiency, flexibility, and
reliability of integrated energy systems (IESs). On the other hand, the rapid emerging of …
reliability of integrated energy systems (IESs). On the other hand, the rapid emerging of …
Impact of demand side management approaches for the enhancement of voltage stability loadability and customer satisfaction index
This research work presents the tri-level optimization framework for the optimal scheduling
of grid-connected and autonomous microgrids to diminish power losses and maximize …
of grid-connected and autonomous microgrids to diminish power losses and maximize …
Artificial emotional deep Q learning for real-time smart voltage control of cyber-physical social power systems
L Yin, X He - Energy, 2023 - Elsevier
The volatility of renewable energy leads to numerous voltage changes in a short period, thus
affecting the quality of the power supply. A real-time smart voltage control framework of …
affecting the quality of the power supply. A real-time smart voltage control framework of …
Multi-task reinforcement learning for distribution system voltage control with topology changes
This letter proposes a multi-task deep reinforcement learning (DRL) approach for distribution
system voltage regulation considering topology changes via PV smart inverter control. The …
system voltage regulation considering topology changes via PV smart inverter control. The …
Meta-learning based voltage control strategy for emergency faults of active distribution networks
With the increase of energy demand and the continuous development of renewable energy
technology, active distribution networks have become increasingly important. However, the …
technology, active distribution networks have become increasingly important. However, the …