Application of reinforcement learning in planning and operation of new power system towards carbon peaking and neutrality
To mitigate global climate change and ensure a sustainable energy future, China has
launched a new energy policy of achieving carbon peaking by 2030 and carbon neutrality …
launched a new energy policy of achieving carbon peaking by 2030 and carbon neutrality …
Safe Reinforcement Learning for Power System Control: A Review
The large-scale integration of intermittent renewable energy resources introduces increased
uncertainty and volatility to the supply side of power systems, thereby complicating system …
uncertainty and volatility to the supply side of power systems, thereby complicating system …
Review and evaluation of reinforcement learning frameworks on smart grid applications
With the rise in electricity, gas and oil prices and the persistently high levels of carbon
emissions, there is an increasing demand for effective energy management in energy …
emissions, there is an increasing demand for effective energy management in energy …
Leveraging AI for Enhanced Power Systems Control: An Introductory Study of Model-Free DRL Approaches
The power grids nowadays are facing increasing complexity and uncertainty due to the
continuously growing penetration of renewable energy sources, such as photovoltaic (PV) …
continuously growing penetration of renewable energy sources, such as photovoltaic (PV) …
Incorporating Constraints in Reinforcement Learning Assisted Energy System Decision Making: A Selected Review
Y Wei, M Tian, X Huang, Z Ding - 2022 IEEE/IAS Industrial and …, 2022 - ieeexplore.ieee.org
With the widespread use of reinforcement learning (RL) in the energy system, the damage to
the system caused by the agents' stochastic exploration is beginning to be appreciated. We …
the system caused by the agents' stochastic exploration is beginning to be appreciated. We …
A world model based reinforcement learning architecture for autonomous power system control
Renewable generation is leading to rapidly shifting power flows and it is anticipated that
traditional power system control may soon be inadequate to cope with these fluctuations …
traditional power system control may soon be inadequate to cope with these fluctuations …
[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 …
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 …
CommonPower: Supercharging Machine Learning for Smart Grids
M Eichelbeck, H Markgraf, M Althoff - arXiv preprint arXiv:2406.03231, 2024 - arxiv.org
The growing complexity of power system management has led to an increased interest in
the use of reinforcement learning (RL). However, no tool for comprehensive and realistic …
the use of reinforcement learning (RL). However, no tool for comprehensive and realistic …
[HTML][HTML] Energy Management System for an Industrial Microgrid Using Optimization Algorithms-Based Reinforcement Learning Technique
S Upadhyay, I Ahmed, L Mihet-Popa - Energies, 2024 - mdpi.com
The climate crisis necessitates a global shift to achieve a secure, sustainable, and affordable
energy system toward a green energy transition reaching climate neutrality by 2050 …
energy system toward a green energy transition reaching climate neutrality by 2050 …