Reinforcement learning and its applications in modern power and energy systems: A review
With the growing integration of distributed energy resources (DERs), flexible loads, and
other emerging technologies, there are increasing complexities and uncertainties for …
other emerging technologies, there are increasing complexities and uncertainties for …
Reinforcement learning for selective key applications in power systems: Recent advances and future challenges
With large-scale integration of renewable generation and distributed energy resources,
modern power systems are confronted with new operational challenges, such as growing …
modern power systems are confronted with new operational challenges, such as growing …
Consensus multi-agent reinforcement learning for volt-var control in power distribution networks
Volt-VAR control (VVC) is a critical application in active distribution network management
system to reduce network losses and improve voltage profile. To remove dependency on …
system to reduce network losses and improve voltage profile. To remove dependency on …
Multi-agent deep reinforcement learning for voltage control with coordinated active and reactive power optimization
The increasing penetration of distributed renewable energy resources causes voltage
fluctuations in distribution networks. The controllable active and reactive power resources …
fluctuations in distribution networks. The controllable active and reactive power resources …
Artificial intelligence to support the integration of variable renewable energy sources to the power system
The power sector is increasingly relying on variable renewable energy sources (VRE)
whose share in energy production is expected to further increase. A key challenge for …
whose share in energy production is expected to further increase. A key challenge for …
Deep reinforcement learning enabled physical-model-free two-timescale voltage control method for active distribution systems
Active distribution networks are being challenged by frequent and rapid voltage violations
due to renewable energy integration. Conventional model-based voltage control methods …
due to renewable energy integration. Conventional model-based voltage control methods …
Deep reinforcement learning-based model-free on-line dynamic multi-microgrid formation to enhance resilience
Multi-microgrid formation (MMGF) is a promising solution for enhancing power system
resilience. This paper proposes a new deep reinforcement learning (RL) based model-free …
resilience. This paper proposes a new deep reinforcement learning (RL) based model-free …
Attention enabled multi-agent DRL for decentralized volt-VAR control of active distribution system using PV inverters and SVCs
This paper proposes attention enabled multi-agent deep reinforcement learning (MADRL)
framework for active distribution network decentralized Volt-VAR control. Using the …
framework for active distribution network decentralized Volt-VAR control. Using the …
Learning to operate distribution networks with safe deep reinforcement learning
In this paper, we propose a safe deep reinforcement learning (SDRL) based method to solve
the problem of optimal operation of distribution networks (OODN). We formulate OODN as a …
the problem of optimal operation of distribution networks (OODN). We formulate OODN as a …
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 …