Research on Optimization Models and Algorithms for Natural Gas Pipeline Dispatch Under Normal Conditions

With the increasing share of natural gas in the energy mix, ensuring safe and efficient pipeline operation is critical for reliable energy supply. Pipeline networks require active inventory regulation to manage operational and seasonal fluctuations, making effective dispatch strategies under complex constraints essential. A mixed-integer programming model is established to optimize dispatch with objectives of maximizing target completion, minimizing operational actions, and reducing compressor energy consumption. The network’s complex dependencies and nonlinear dynamics make the problem high-dimensional and multimodal; to address this, a tailored hybrid heuristic combining Variable Neighborhood Search (VNS) for solution exploration and Simulated Annealing (SA) for escaping local optima is developed. The algorithm generates an initial feasible solution, applies multiple neighborhood perturbation strategies, and uses a probabilistic acceptance mechanism. Interaction with simulation software enables dynamic evaluation and iterative optimization. A 24-h case study of the Northern Tai natural gas pipeline network, using actual initial inventories, gas capacities, and demand profiles, demonstrates that the method efficiently produces feasible dispatch solutions that satisfy inventory requirements while balancing target completion and energy consumption.