Reinforcement learning for selective key applications in power systems: Recent advances and future challenges

X Chen, G Qu, Y Tang, S Low… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
With large-scale integration of renewable generation and distributed energy resources,
modern power systems are confronted with new operational challenges, such as growing …

Distributed control and communication strategies in networked microgrids

Q Zhou, M Shahidehpour, A Paaso… - … Surveys & Tutorials, 2020 - ieeexplore.ieee.org
Networked microgrids (NMGs) provide a promising solution for accommodating various
distributed energy resources (DERs) and enhancing the system performance in terms of …

Deep reinforcement learning for smart grid operations: Algorithms, applications, and prospects

Y Li, C Yu, M Shahidehpour, T Yang… - Proceedings of the …, 2023 - ieeexplore.ieee.org
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 …

Knowledge-based reinforcement learning and estimation of distribution algorithm for flexible job shop scheduling problem

Y Du, J Li, X Chen, P Duan… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Inthis study, a flexible job shop scheduling problem with time-of-use electricity price
constraint is considered. The problem includes machine processing speed, setup time, idle …

On data-driven modeling and control in modern power grids stability: Survey and perspective

X Gong, X Wang, B Cao - Applied Energy, 2023 - Elsevier
Modern power grids are fast evolving with the increasing volatile renewable generation,
distributed energy resources (DERs) and time-varying operating conditions. The DERs …

Survey on microgrids frequency regulation: Modeling and control systems

J Heidary, M Gheisarnejad, H Rastegar… - Electric Power Systems …, 2022 - Elsevier
The traditional power system structure is constantly changing due to the application of
renewable energy sources (RESs) and microgrids (MGs) into the power system network …

Fusion of microgrid control with model-free reinforcement learning: Review and vision

B She, F Li, H Cui, J Zhang, R Bo - IEEE Transactions on Smart …, 2022 - ieeexplore.ieee.org
Challenges and opportunities coexist in microgrids as a result of emerging large-scale
distributed energy resources (DERs) and advanced control techniques. In this paper, a …

A comprehensive review: study of artificial intelligence optimization technique applications in a hybrid microgrid at times of fault outbreaks

MLT Zulu, RP Carpanen, R Tiako - Energies, 2023 - mdpi.com
The use of fossil-fueled power stations to generate electricity has had a damaging effect
over the years, necessitating the need for alternative energy sources. Microgrids consisting …

[PDF][PDF] Reinforcement learning for decision-making and control in power systems: Tutorial, review, and vision

X Chen, G Qu, Y Tang, S Low… - arxiv preprint arxiv …, 2021 - authors.library.caltech.edu
With large-scale integration of renewable generation and distributed energy resources
(DERs), modern power systems are confronted with new operational challenges, such as …

Navigating the landscape of deep reinforcement learning for power system stability control: A review

MS Massaoudi, H Abu-Rub, A Ghrayeb - IEEE access, 2023 - ieeexplore.ieee.org
The widespread penetration of inverter-based resources has profoundly impacted the
electrical stability of power systems (PSs). Deepening grid integration of photovoltaic and …