Coupled Q-Learning-Based Routing Reconstruction Method for Collaborative Operation of Transmission Network and Data Network
Year: 2025
Type: article
Abstract: As power systems evolve, diverse power services increase bandwidth demands, posing challenges like variable transmission loads and slow data transfer. Routing reconstruction dynamically adjusts paths, balances loads, and reduces delays, ensuring reliable power service data. However, current technologies lack global state awareness, integrated risk-delay optimization, and efficient algorithms. This paper introduces a unified model and risk evaluation framework for both data and power transmission networks. Considering the enduring operational demands of the transmission network, a joint minimization strategy is devised which focuses on minimizing both transmission delays and risks. Furthermore, a coupled Q-learning methodology for collaborative network operation is introduced, which sets routing priorities and resolves differences via cost to enhance routing results. Simulations validate that the proposed methodology drastically decreases transmission risks and delays.
Authors: Yue Hu, Yanan Wang, Wei Zhao, Li Shang, Yuhang Pang, Juan Pan, Tongtong Zhang, Weiwei Dou
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4411405806