A Hydraulic-Thermal Decoupled Multiobjective Optimization Approach for Balancing Supply Security and Profit in a Gas Pipeline Network under Extreme Conditions

Under extreme conditions such as cold waves, regional natural gas pipeline networks may face a sharp increase in demand together with limited gas supply capacity, making dispatch a multiobjective problem that must balance supply security and economic benefits. Meanwhile, the strong coupling between nonlinear hydraulic-thermal processes and discrete equipment operating states makes conventional mixed-integer nonlinear programming methods difficult to solve, while isothermal models may overlook thermal safety risks. This paper develops a multiobjective optimization model that considers priority-weighted user satisfaction and total profit and proposes a hydraulic-thermal decoupled iterative algorithm. In the hydraulic problem, SOS2 constraints are used to approximate nonlinear pipeline flow equations, while ANN-based linear surrogate constraints are introduced for compressor station operation. Thermal safety cutting planes are generated dynamically through alternating iterations between hydraulic optimization and full-network thermal simulation. Tests on benchmark networks and a typical provincial supply network in China show that the proposed approach maintains good solution accuracy while improving the solution efficiency of a large-scale network by 7.6 times, with all cases converging within 15 iterations. The Pareto frontier reveals a clear trade-off between supply satisfaction and profit under a limited gas supply. The results also show that the proposed approach can identify loads with high transportation costs and low gas prices as preferred candidates for shedding, thereby releasing transmission capacity for high-priority and high-price users, while avoiding the underestimation of compressor energy consumption and local thermal safety risks caused by isothermal simplification.