Efficient optimal power flow learning: A deep reinforcement learning with physics-driven critic model
The transition to decarbonized energy systems presents significant operational challenges due to increased uncertainties and complex dynamics. Deep reinforcement learning (DRL) has emerged as a powerful tool for optimizing power system operations. However, most existing DRL approaches rely on approx...
Saved in:
| Main Authors: | Ahmed Sayed, Khaled Al Jaafari, Xian Zhang, Hatem Zeineldin, Ahmed Al-Durra, Guibin Wang, Ehab Elsaadany |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
|
| Series: | International Journal of Electrical Power & Energy Systems |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061525001723 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A holomorphic embedding power flow method based on dynamic power restart
by: Yi Zhang, et al.
Published: (2025-10-01) -
Vital node searcher: find out critical node measure with deep reinforcement learning
by: Guanting Du, et al.
Published: (2022-12-01) -
Learning-based control for tendon-driven continuum robotic arms
by: Nima Maghooli, et al.
Published: (2025-07-01) -
HANKEL DETERMINANT OF CERTAIN ORDERS FOR SOME SUBCLASSES OF HOLOMORPHIC FUNCTIONS
by: D. Vamshee Krishna, et al.
Published: (2022-07-01) -
Grid structure optimization using slow coherency theory and holomorphic embedding method
by: Fei Tang, et al.
Published: (2025-03-01)