Differentiable Simulator For Dynamic & Stochastic Optimal Gas & Power Flows

In many power systems, particularly those isolated from larger intercontinental grids, reliance on natural gas is crucial. This dependence becomes particularly critical during periods of volatility or scarcity in renewable energy sources, further complicated by unpredictable consumption trends. To ensure the uninterrupted operation of these isolated gas-grid systems, innovative and efficient management strategies are essential. This paper investigates the complexities of achieving synchronized, dynamic, and stochastic optimization for autonomous transmission-level gas-grid infrastructures. We introduce a novel methodology grounded in differentiable programming, which synergizes symbolic programming, a conservative numerical method for solving gas-flow partial differential equations, and automated sensitivity analysis powered by SciML/Julia. Our methodology refines the co-optimization landscape for gas-grid systems by grounding gas dynamics in physics-adherent simulation. We demonstrate efficiency and precision of the methodology by solving a stochastic optimal gas flow problem, phrased on an open source model of Israel's gas grid model.