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 …
[PDF][PDF] Reinforcement learning for decision-making and control in power systems: Tutorial, review, and vision
With large-scale integration of renewable generation and distributed energy resources
(DERs), modern power systems are confronted with new operational challenges, such as …
(DERs), modern power systems are confronted with new operational challenges, such as …
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 …
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 …
A cooperative charging control strategy for electric vehicles based on multiagent deep reinforcement learning
The growth of electric vehicles (EVs) significantly increases the residential electricity
demand and potentially leads to the overload of the transformer in the distribution grid …
demand and potentially leads to the overload of the transformer in the distribution grid …
Powernet: Multi-agent deep reinforcement learning for scalable powergrid control
This paper develops an efficient multi-agent deep reinforcement learning algorithm for
cooperative controls in powergrids. Specifically, we consider the decentralized inverter …
cooperative controls in powergrids. Specifically, we consider the decentralized inverter …
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 …
Adaptive current differential protection for active distribution network considering time synchronization error
C Zhou, G Zou, X Du, L Zang - International Journal of Electrical Power & …, 2022 - Elsevier
To solve the impact of the integration of distributed generations on distribution network
protection, current differential protection is introduced into the active distribution networks …
protection, current differential protection is introduced into the active distribution networks …
Decentralized safe reinforcement learning for inverter-based voltage control
Inverter-based distributed energy resources provide the possibility for fast time-scale voltage
control by quickly adjusting their reactive power. The power-electronic interfaces allow these …
control by quickly adjusting their reactive power. The power-electronic interfaces allow these …
Physics-shielded multi-agent deep reinforcement learning for safe active voltage control with photovoltaic/battery energy storage systems
While many multi-agent deep reinforcement learning (MADRL) algorithms have been
implemented for active voltage control (AVC) in power distribution systems, the safety of …
implemented for active voltage control (AVC) in power distribution systems, the safety of …