Hybrid systems modeling for gas transmission network
This work addresses operational challenges in gas transmission networks during crises, but it appears incremental as it applies existing hybrid modeling and reinforcement learning methods to this specific domain.
The paper tackles the control and management of gas transmission networks during crisis situations by modeling them as hybrid systems and using reinforcement learning for decision-making, with simulations demonstrating the method's efficiency.
Gas Transmission Networks are large-scale complex systems, and corresponding design and control problems are challenging. In this paper, we consider the problem of control and management of these systems in crisis situations. We present these networks by a hybrid systems framework that provides required analysis models. Further, we discuss decision-making using computational discrete and hybrid optimization methods. In particular, several reinforcement learning methods are employed to explore decision space and achieve the best policy in a specific crisis situation. Simulations are presented to illustrate the efficiency of the method.