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 …
Spatiotemporal deep learning for power system applications: a survey
M Saffari, M Khodayar - IEEE Access, 2024 - ieeexplore.ieee.org
Understanding spatiotemporal correlations in power systems is crucial for maintaining grid
stability, reliability, and efficiency. By discerning connections between spatial and temporal …
stability, reliability, and efficiency. By discerning connections between spatial and temporal …
Physics-informed graphical representation-enabled deep reinforcement learning for robust distribution system voltage control
The anomalous measurements and inaccurate distribution system physical models cause
huge challenges for distribution system optimization. This paper proposes a robust voltage …
huge challenges for distribution system optimization. This paper proposes a robust voltage …
Three-stage hierarchically-coordinated voltage/var control based on PV inverters considering distribution network voltage stability
Intermittent photovoltaic (PV) power generation brings voltage fluctuation and stability issues
to distribution networks. Meanwhile, PV inverters can support voltage/Var control (VVC) to …
to distribution networks. Meanwhile, PV inverters can support voltage/Var control (VVC) to …
A two-level energy management strategy for multi-microgrid systems with interval prediction and reinforcement learning
Setting retail electricity prices is one of the significant strategies for energy management of
multi-microgrid (MMG) systems integrated with renewable energy. Nevertheless, the need of …
multi-microgrid (MMG) systems integrated with renewable energy. Nevertheless, the need of …
Deep reinforcement learning-based adaptive voltage control of active distribution networks with multi-terminal soft open point
The integration of highly penetrated distributed generators (DGs) aggravates the rise of
voltage violations in distribution networks. Connected by multi-terminal soft open points (M …
voltage violations in distribution networks. Connected by multi-terminal soft open points (M …
Physical-assisted multi-agent graph reinforcement learning enabled fast voltage regulation for PV-rich active distribution network
Active distribution network is encountering serious voltage violations associated with the
proliferation of distributed photovoltaic. Cutting-edge research has confirmed that voltage …
proliferation of distributed photovoltaic. Cutting-edge research has confirmed that voltage …
Multi-agent reinforcement learning with policy clipping and average evaluation for UAV-assisted communication Markov game
Unmanned aerial vehicle (UAV)-assisted communication is a significant technology in 6G
communication. In order to cope with the dynamic trajectory optimization problem of the air …
communication. In order to cope with the dynamic trajectory optimization problem of the air …
Attention-Enhanced Multi-Agent Reinforcement Learning Against Observation Perturbations for Distributed Volt-VAR Control
The cloud-edge collaboration architecture has been widely adopted for distributed Volt-VAR
control (VVC) problems in active distribution networks (ADNs). To alleviate the computation …
control (VVC) problems in active distribution networks (ADNs). To alleviate the computation …
Optimal coordination for multiple network-constrained VPPs via multi-agent deep reinforcement learning
This paper proposes a multi-agent deep reinforcement learning method to coordinate
multiple microgrids owned virtual power plants (VPPs) connected in the active distribution …
multiple microgrids owned virtual power plants (VPPs) connected in the active distribution …