{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Tian N"],"funding":["National Natural Science Foundation of China"],"pagination":["1120"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12651277"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["27(11)"],"pubmed_abstract":["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 "],"journal":["Entropy (Basel, Switzerland)"],"pubmed_title":["Low-Carbon Economic Operation of Natural Gas Demand Side Integrating Dynamic Pricing Signals and User Behavior Modeling."],"pmcid":["PMC12651277"],"funding_grant_id":["62072363"],"pubmed_authors":["Zhao W","Zeng H","Tian N","Shao B","Zhao X"],"additional_accession":[]},"is_claimable":false,"name":"Low-Carbon Economic Operation of Natural Gas Demand Side Integrating Dynamic Pricing Signals and User Behavior Modeling.","description":"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 ","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Oct","modification":"2026-05-19T03:14:55.525Z","creation":"2026-05-19T03:11:19.028Z"},"accession":"S-EPMC12651277","cross_references":{"pubmed":["41294963"],"doi":["10.3390/e27111120"]}}