Multi-Objective Operational Scheduling of Natural Gas Networks with Gas Quality Limitation

Against the backdrop of natural gas energy measurements and pipeline interconnectivity, the supply–demand imbalance has become increasingly prominent in multi-source gas pipeline networks. Existing pipeline scheduling studies mostly focus solely on economic optimization or simple gas quality constraints, while rarely quantifying user satisfaction and integrating it with operational profit within a systematic multi-objective framework, leaving a critical research gap for refined scheduling under energy metering modes. This paper first develops a quantitative user satisfaction function incorporating calorific value and methane content indicators and further establishes a novel multi-objective operational scheduling model coupled with gas quality limitations, which simultaneously maximizes network operating profit and user gas supply satisfaction. The ε-constraint method combined with the GAMS/ANTIGONE solver is adopted to address the constructed Mixed-Integer Nonlinear Programming (MINLP) model. Taking a typical long-distance pipeline in China as the engineering case, a series of Pareto-optimal solutions is obtained. The results show that user satisfaction ranges from 99.70% to 99.77% and operating profit varies from 1403.07 × 104 to 1752.44 × 104 CNY. The derived Pareto frontier quantitatively reveals the inherent trade-off mechanism between user satisfaction and operating profit. The case results demonstrate the applicability of the proposed framework in this specific pipeline scenario, rather than claiming universal validity. It is acknowledged that model validation is currently limited to only one single long-distance pipeline case, with no additional case studies and no comparison with historical operation data conducted in this work. Different from conventional single-objective or simplified gas quality-optimization methods, this study enriches scheduling scheme alternatives and provides theoretical support and a practical decision-making reference for multi-source pipeline operational optimization under energy metering.