Coupled Q-Learning-Based Routing Reconstruction Method for Collaborative Operation of Transmission Network and Data Network

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.