biostudies-literatureTian NNational Natural Science Foundation of China1120https://www.ebi.ac.uk/biostudies/studies/S-EPMC12651277biostudies-literatureUnknown27(11)Natural gas plays a key role in the low-carbon energy transition due to its clean and efficient characteristics, yet challenges remain in balancing economic efficiency, user behavior, and carbon emission constraints in demand-side scheduling. This study proposes a low-carbon economic operation model for terminal natural gas systems, integrating price elasticity and differentiated user behavior with carbon emission management strategies. To capture diverse demand patterns, dynamic time warping k-medoids clustering is employed, while scheduling optimization is achieved through a multi-objective framework combining NSGA-III, the entropy weight (EW) method, and the VIKOR decision-making approach. Using real-world data from a gas station in Xi'an, simulation results show that the model reduces Entropy (Basel, Switzerland)Low-Carbon Economic Operation of Natural Gas Demand Side Integrating Dynamic Pricing Signals and User Behavior Modeling.PMC1265127762072363Zhao WZeng HTian NShao BZhao XfalseLow-Carbon Economic Operation of Natural Gas Demand Side Integrating Dynamic Pricing Signals and User Behavior Modeling.Natural gas plays a key role in the low-carbon energy transition due to its clean and efficient characteristics, yet challenges remain in balancing economic efficiency, user behavior, and carbon emission constraints in demand-side scheduling. This study proposes a low-carbon economic operation model for terminal natural gas systems, integrating price elasticity and differentiated user behavior with carbon emission management strategies. To capture diverse demand patterns, dynamic time warping k-medoids clustering is employed, while scheduling optimization is achieved through a multi-objective framework combining NSGA-III, the entropy weight (EW) method, and the VIKOR decision-making approach. Using real-world data from a gas station in Xi'an, simulation results show that the model reduces 2025-01-01T00:00:00Z2025 Oct2026-05-19T03:14:55.525Z2026-05-19T03:11:19.028ZS-EPMC126512774129496310.3390/e27111120