Abstract
The pipeline transportation and distribution process of natural gas, from production sources to end consumers, can be divided into three stages: upstream production and supply, midstream storage and pipeline transportation, and downstream distribution and end-use. In the optimization of natural gas pipeline networks, considering the full life cycle, multiple uncertainties in planning, design, operation, and maintenance often affect the efficiency and quality of model optimization. This study addresses the uncertainty in end-user demand during the operation of natural gas pipeline net-works and investigates a scheduling method that simultaneously meets user demand while achieving coordinated optimization of operational costs and carbon emissions. Based on one year of historical demand data, a normal distribution is fitted to characterize the demand, and several representative scenarios with corresponding probabilities are extracted through clustering to capture demand uncertainty. A two-stage stochastic programming model is developed. In the first stage, the basic operational strategy of the pipeline network is determined, while in the second stage, flow, pressure, and compressor power are adjusted according to each demand scenario. The model prioritizes minimizing operational costs and employs the e-constraint method to transform the carbon emission objective into a carbon emission cap. By systematically varying the value of e, the Pareto frontier between cost and carbon emissions is delineated. Furthermore, non-linear constraints are addressed using a combination of piecewise linearization and McCormick re-laxation. The proposed model is implemented in a real-world case study in China using Python and the Gurobi solver. Results demonstrate that, compared to conventional methods, the proposed approach effectively reduce carbon emissions by 34.96%, 42.35%, and 36.84% in high, medium, and low demand scenarios. The method provides clear optimization solutions and supports in-depth analysis of results, thereby enhancing data management capabilities of pipeline systems. The model systematically accounts for uncertainties in natural gas demand, offering valuable research support and practical references for scheduling decisions in the natural gas pipeline industry.