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 …
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 …
Stability constrained reinforcement learning for real-time voltage control
Deep reinforcement learning (RL) has been recognized as a promising tool to address the
challenges in real-time control of power systems. However, its deployment in real-world …
challenges in real-time control of power systems. However, its deployment in real-world …
Stability constrained reinforcement learning for decentralized real-time voltage control
Deep reinforcement learning has been recognized as a promising tool to address the
challenges in real-time control of power systems. However, its deployment in real-world …
challenges in real-time control of power systems. However, its deployment in real-world …
Expandable deep width learning for voltage control of three-state energy model based smart grids containing flexible energy sources
L Yin, Y Lu - Energy, 2021 - Elsevier
The article establishes a three-state energy (TSE) model for flexible energy sources (FESs)
connected to smart grids. The article proposes a unified time-scale (UTS) coordinated …
connected to smart grids. The article proposes a unified time-scale (UTS) coordinated …
Enhancing cyber resilience of networked microgrids using vertical federated reinforcement learning
This paper presents a novel federated reinforcement learning (Fed-RL) methodology to
enhance the cyber resiliency of networked microgrids. We formulate a resilient …
enhance the cyber resiliency of networked microgrids. We formulate a resilient …
Resilient Control of Networked Microgrids using Vertical Federated Reinforcement Learning: Designs and Real-Time Test-Bed Validations
S Mukherjee, RR Hossain… - … on Smart Grid, 2024 - ieeexplore.ieee.org
Improving system-level resiliency of networked microgrids against adversarial cyber-attacks
is an important aspect in the current regime of increased inverter-based resources (IBRs). To …
is an important aspect in the current regime of increased inverter-based resources (IBRs). To …
Advanced computational techniques for improving resilience of critical energy infrastructure under cyber-physical attacks
Critical energy infrastructure has undergone significant changes in the past decades, which
has made these systems more vulnerable to breakdowns due to the uncertainty and …
has made these systems more vulnerable to breakdowns due to the uncertainty and …
Substation Topology and Line Switching Control Using Deep Reinforcement Learning
R Roychowdhury, JB Ocampo… - 2022 IEEE/IAS 58th …, 2022 - ieeexplore.ieee.org
Electric Power System (EPS) is widely regarded as one of the most complex artificial
systems ever created. With the recent penetration of distributed energy resources …
systems ever created. With the recent penetration of distributed energy resources …