Graph Reinforcement Learning for Power Grids: A Comprehensive Survey
The rise of renewable energy and distributed generation requires new approaches to
overcome the limitations of traditional methods. In this context, Graph Neural Networks are …
overcome the limitations of traditional methods. In this context, Graph Neural Networks are …
Power grid congestion management via topology optimization with AlphaZero
M Dorfer, AR Fuxjäger, K Kozak, PM Blies… - arXiv preprint arXiv …, 2022 - arxiv.org
The energy sector is facing rapid changes in the transition towards clean renewable
sources. However, the growing share of volatile, fluctuating renewable generation such as …
sources. However, the growing share of volatile, fluctuating renewable generation such as …
Alleviating imbalanced problems of reinforcement learning when applying in real-time power network dispatching and control
X Wang, N Lu - Expert Systems with Applications, 2024 - Elsevier
Real-time power network dispatching and control (PDC) presents unique challenges that
traditional methods cannot effectively address due to the consideration of temporal dynamic …
traditional methods cannot effectively address due to the consideration of temporal dynamic …
[HTML][HTML] Heterogeneous reinforcement learning for defending power grids against attacks
Reinforcement learning (RL) has been employed to devise the best course of actions in
defending the critical infrastructures, such as power networks against cyberattacks …
defending the critical infrastructures, such as power networks against cyberattacks …
Machine Learning for Complex Cyber-Physical Systems
M Moradi - 2024 - search.proquest.com
This dissertation presents novel applications of machine learning techniques in enhancing
the security and efficiency of complex cyber-physical systems such as power grids and …
the security and efficiency of complex cyber-physical systems such as power grids and …