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 electric power system decision and control: Past considerations and perspectives
In this paper, we review past (including very recent) research considerations in using
reinforcement learning (RL) to solve electric power system decision and control problems …
reinforcement learning (RL) to solve electric power system decision and control problems …
Q-learning algorithms: A comprehensive classification and applications
Q-learning is arguably one of the most applied representative reinforcement learning
approaches and one of the off-policy strategies. Since the emergence of Q-learning, many …
approaches and one of the off-policy strategies. Since the emergence of Q-learning, many …
Data-driven load frequency control for stochastic power systems: A deep reinforcement learning method with continuous action search
This letter proposes a data-driven, model-free method for load frequency control (LFC)
against renewable energy uncertainties based on deep reinforcement learning (DRL) in …
against renewable energy uncertainties based on deep reinforcement learning (DRL) in …
A novel multi-agent DDQN-AD method-based distributed strategy for automatic generation control of integrated energy systems
L Xi, L Yu, Y Xu, S Wang, X Chen - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
The widely adoption of distributed renewable energy sources (DREs) effectively reduces
carbon emission and beat atmospheric haze in developing countries. However, random …
carbon emission and beat atmospheric haze in developing countries. However, random …
Real-time fast charging station recommendation for electric vehicles in coupled power-transportation networks: A graph reinforcement learning method
With the increasing penetration rate of electric vehicles, the fast charging demands of
electric vehicles will have a significant influence on the operation of coupled power …
electric vehicles will have a significant influence on the operation of coupled power …
Reinforcement learning algorithms with function approximation: Recent advances and applications
X Xu, L Zuo, Z Huang - Information sciences, 2014 - Elsevier
In recent years, the research on reinforcement learning (RL) has focused on function
approximation in learning prediction and control of Markov decision processes (MDPs). The …
approximation in learning prediction and control of Markov decision processes (MDPs). The …
(Deep) reinforcement learning for electric power system control and related problems: A short review and perspectives
M Glavic - Annual Reviews in Control, 2019 - Elsevier
This paper reviews existing works on (deep) reinforcement learning considerations in
electric power system control. The works are reviewed as they relate to electric power …
electric power system control. The works are reviewed as they relate to electric power …
Brain-inspired deep meta-reinforcement learning for active coordinated fault-tolerant load frequency control of multi-area grids
J Li, T Zhou, H Cui - IEEE transactions on automation science …, 2023 - ieeexplore.ieee.org
This paper proposes an active coordinated fault tolerance load frequency control (AFCT-
LFC) method, which effectively prevents sudden frequency changes caused by unit actuator …
LFC) method, which effectively prevents sudden frequency changes caused by unit actuator …
Smart generation control based on multi-agent reinforcement learning with the idea of the time tunnel
L Xi, J Chen, Y Huang, Y Xu, L Liu, Y Zhou, Y Li - Energy, 2018 - Elsevier
One of the significant solutions for hazy is to reduce carbon emission by introducing
renewable energy on a large scale. However, the large-scale integration of new energy will …
renewable energy on a large scale. However, the large-scale integration of new energy will …