Real-time congestion control using cascaded LSTM deep neural networks for deregulated power markets
Abstract In deregulated power markets (DPMs), transmission-line congestion has become more severe and frequent than in traditional power systems. This congestion hinders electricity markets from operating in normal competitive equilibrium. The independent system operator (ISO) is responsible for imp...
Saved in:
| Main Authors: | G. Madhu Mohan, T. Anil Kumar, A. Srujana, Yasser Fouad, Alexey Mikhaylov, Nora Baranyai, Kitmo, Ch. Rami Reddy |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-14640-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Dynamic Rescheduling Strategy for Passenger Congestion Balancing in Airport Passenger Terminals
by: Yohan Lee, et al.
Published: (2025-07-01) -
Hybrid Deep Neural Network-Based Generation Rescheduling for Congestion Mitigation in Spot Power Market
by: Anjali Agrawal, et al.
Published: (2022-01-01) -
Assessment of network traffic congestion through Traffic Congestability Value (TCV): a new index
by: Patel Nilanchal, et al.
Published: (2015-12-01) -
Congestion control algorithms in wireless sensor networks: Trends and opportunities
by: Syed Afsar Shah, et al.
Published: (2017-07-01) -
Contemporary Perspectives on Congestion in Heart Failure: Bridging Classic Signs with Evolving Diagnostic and Therapeutic Strategies
by: Mihai Grigore, et al.
Published: (2025-04-01)