Improved Mixing and Pressure Loss Formulations for Gas Network Optimization

TL;DR

This paper enhances gas network optimization models by introducing new pressure loss formulations and cuts, significantly improving computational efficiency and stability in solving complex non-convex problems.

Contribution

It develops two novel pressure loss models leveraging flow-splitting variables and introduces cuts to accelerate MINLP solving, advancing gas network optimization techniques.

Findings

MINLP run time reduced by a factor of 35

All instances solved within 2.5 minutes with new methods

Simpler pressure loss model further improved run times but was less stable

Abstract

Non-convex, nonlinear gas network optimization models are used to determine the feasibility of flows on existing networks given constraints on network flows, gas mixing, and pressure loss along pipes. This work improves two existing gas network models: a discrete mixed-integer nonlinear program (MINLP) that uses binary variables to model positive and negative flows, and a continuous nonlinear program (NLP) that implements complementarity constraints with continuous variables. We introduce cuts to expedite the MINLP and we formulate two new pressure loss models that leverage the flow-splitting variables: one that is highly accurate and another that is simpler but less accurate. In computational tests using the global solver BARON our cuts and accurate pressure loss improves: (1) the average run time of the MINLP by a factor of 35, (2) the stability of the MINLP by solving every tested instance within 2.5 minutes (the baseline model timed out on 25% of instances), (3) the stability of the NLP by solving more instances than the baseline. Our simpler pressure loss model further improved run times in the MINLP (by a factor of 48 versus the baseline MINLP), but was unstable in the context of the NLP.