A Critical Review of Safe Reinforcement Learning Techniques in Smart Grid Applications

VH Bui, S Das, A Hussain, GV Hollweg… - arXiv preprint arXiv …, 2024 - arxiv.org
The high penetration of distributed energy resources (DERs) in modern smart power
systems introduces unforeseen uncertainties for the electricity sector, leading to increased …

A Review of Safe Reinforcement Learning Methods for Modern Power Systems

T Su, T Wu, J Zhao, A Scaglione, L Xie - arXiv preprint arXiv:2407.00304, 2024 - arxiv.org
Due to the availability of more comprehensive measurement data in modern power systems,
there has been significant interest in developing and applying reinforcement learning (RL) …

A Memory-Based Graph Reinforcement Learning Method for Critical Load Restoration With Uncertainties of Distributed Energy Resource

B Fan, X Liu, G Xiao, Y Xu, X Yang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
The integration of distributed energy resources into distribution networks, marked by its
inherent uncertainties, presents a substantial challenge for devising load restoration …

Two-Stage Resilient Recovery of Unbalanced Distribution System Considering Intelligent Zoning and Merging of Microgrids

C Yin, X Wu, X Wang - IEEE Transactions on Smart Grid, 2024 - ieeexplore.ieee.org
Increasingly extreme events are threatening the resilience of distribution systems.
Conventional research usually ignores the distribution system unbalance and microgrid …

Dual anti-jamming alleviation for radio frequency/free-space optical (RF/FSO) tactical systems

TT Nguyen, KK Nguyen - IEEE Transactions on Vehicular …, 2024 - ieeexplore.ieee.org
In this paper, we design a jamming alleviation plan to protect a mixed radio frequency/free-
space optical (RF/FSO) relay tactical network in the context that both RF and FSO systems …

[PDF][PDF] Deep Graph Neural Network for Fault Detection and Identification in Distribution Systems

QH Ngo, BLH Nguyen, J Zhang, K Schoder, H Ginn… - Authorea …, 2024 - techrxiv.org
With the growing integration of renewable energy sources, the penetration of the distributed
generation leads to increasingly dynamic power distribution system topologies. This poses …

Optimal Operation of Networked Microgrids With Distributed Multi-Agent Reinforcement Learning

H Li, H He - 2024 IEEE Power & Energy Society General …, 2024 - ieeexplore.ieee.org
This paper presents distributed multi-agent deep reinforcement learning (MADRL) approach
for optimizing power flow management in networked microgrids within distribution systems …

Resiliency-driven Restoration for Unbalanced Distribution Systems with IoT-based Demand Response and DERs

PS Sarker, S Basumallik, LD Marinovici… - Authorea …, 2024 - techrxiv.org
The use of Internet-of-Things (IoT) technology in electric power distribution is growing,
specially with smart meters, automated controls, demand response (DR), and renewable …

A Load Transfer Decision Method for Distribution Networks Based on Mask-Constrained Deep Reinforcement Learning

K Ji, J Qiao, Y Chen, F Yang, Z Zhao… - 2024 9th Asia …, 2024 - ieeexplore.ieee.org
In recent years, with the rapid expansion of urban areas and the increase in electricity
consumption, the number of nodes in distribution networks has significantly grown, resulting …

Load Restoration Approach in Biogas Powered Microgrid

S Verma, A Sharma, P Bajpai - 2024 IEEE 4th International …, 2024 - ieeexplore.ieee.org
This study addresses the critical need for a robust approach to load restoration in an active
distribution network (ADN), with a focus on the utilization of Bio-Gas Plants (BGPs) as revival …