Hybrid systems modeling for gas transmission network

TL;DR

This paper models gas transmission networks as hybrid systems and employs reinforcement learning for decision-making in crisis management, demonstrating the approach's effectiveness through simulations.

Contribution

It introduces a hybrid systems framework for gas network control and applies reinforcement learning methods for crisis decision-making.

Findings

Reinforcement learning effectively explores decision space.

Hybrid modeling aids in analyzing complex gas networks.

Simulations show improved crisis management policies.

Abstract

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.