Natural gas bi-level demand response strategies considering incentives and complexities under dynamic pricing
Abstract As global energy demand rises and carbon reduction targets intensify, natural gas is gaining prominence as a clean energy source. To balance natural gas supply and demand, reduce load volatility, and enhance system stability, this paper proposes a bi-level model combining price-based and in...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-11893-z |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849332988528033792 |
|---|---|
| author | Huibin Zeng Jie Zhou Hongbin Dai |
| author_facet | Huibin Zeng Jie Zhou Hongbin Dai |
| author_sort | Huibin Zeng |
| collection | DOAJ |
| description | Abstract As global energy demand rises and carbon reduction targets intensify, natural gas is gaining prominence as a clean energy source. To balance natural gas supply and demand, reduce load volatility, and enhance system stability, this paper proposes a bi-level model combining price-based and incentive-based demand response (DR) strategies. The upper-level model uses dynamic gas pricing to guide users in adjusting consumption, thereby reducing peaks, filling valleys, and optimizing resource allocation. The lower-level model considers factors like weather and heating, creating incentives to boost user participation and flexibility. This model is solved using multi-population ensemble particle swarm optimization (MPEPSO) and Deep Q-Network (DQN) algorithms. Additionally, a spectral clustering algorithm is applied to classify load peak and valley times. For engineering applications, the model is validated using load data from a natural gas station in Xi’an, providing tailored DR strategies for various user types across heating and non-heating periods. The results demonstrate that the proposed strategy effectively smooths gas load fluctuations, alleviates supply-demand imbalances, secures supplier revenue, and maximizes user economic benefits, thereby enhancing the overall flexibility and applicability of DR. |
| format | Article |
| id | doaj-art-b27bd39d482e422e807f85ce1eae8978 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-b27bd39d482e422e807f85ce1eae89782025-08-20T03:46:01ZengNature PortfolioScientific Reports2045-23222025-07-0115113210.1038/s41598-025-11893-zNatural gas bi-level demand response strategies considering incentives and complexities under dynamic pricingHuibin Zeng0Jie Zhou1Hongbin Dai2School of Economics and Management, Chongqing Normal UniversitySchool of Big Data, Guangxi Vocational and Technical CollegeSchool of Computer and Information Science, Chongqing Normal UniversityAbstract As global energy demand rises and carbon reduction targets intensify, natural gas is gaining prominence as a clean energy source. To balance natural gas supply and demand, reduce load volatility, and enhance system stability, this paper proposes a bi-level model combining price-based and incentive-based demand response (DR) strategies. The upper-level model uses dynamic gas pricing to guide users in adjusting consumption, thereby reducing peaks, filling valleys, and optimizing resource allocation. The lower-level model considers factors like weather and heating, creating incentives to boost user participation and flexibility. This model is solved using multi-population ensemble particle swarm optimization (MPEPSO) and Deep Q-Network (DQN) algorithms. Additionally, a spectral clustering algorithm is applied to classify load peak and valley times. For engineering applications, the model is validated using load data from a natural gas station in Xi’an, providing tailored DR strategies for various user types across heating and non-heating periods. The results demonstrate that the proposed strategy effectively smooths gas load fluctuations, alleviates supply-demand imbalances, secures supplier revenue, and maximizes user economic benefits, thereby enhancing the overall flexibility and applicability of DR.https://doi.org/10.1038/s41598-025-11893-zNatural gas demand responseIncentive mechanismsDynamic pricingMulti-population ensemble particle swarm optimizationDeep reinforcement learning |
| spellingShingle | Huibin Zeng Jie Zhou Hongbin Dai Natural gas bi-level demand response strategies considering incentives and complexities under dynamic pricing Scientific Reports Natural gas demand response Incentive mechanisms Dynamic pricing Multi-population ensemble particle swarm optimization Deep reinforcement learning |
| title | Natural gas bi-level demand response strategies considering incentives and complexities under dynamic pricing |
| title_full | Natural gas bi-level demand response strategies considering incentives and complexities under dynamic pricing |
| title_fullStr | Natural gas bi-level demand response strategies considering incentives and complexities under dynamic pricing |
| title_full_unstemmed | Natural gas bi-level demand response strategies considering incentives and complexities under dynamic pricing |
| title_short | Natural gas bi-level demand response strategies considering incentives and complexities under dynamic pricing |
| title_sort | natural gas bi level demand response strategies considering incentives and complexities under dynamic pricing |
| topic | Natural gas demand response Incentive mechanisms Dynamic pricing Multi-population ensemble particle swarm optimization Deep reinforcement learning |
| url | https://doi.org/10.1038/s41598-025-11893-z |
| work_keys_str_mv | AT huibinzeng naturalgasbileveldemandresponsestrategiesconsideringincentivesandcomplexitiesunderdynamicpricing AT jiezhou naturalgasbileveldemandresponsestrategiesconsideringincentivesandcomplexitiesunderdynamicpricing AT hongbindai naturalgasbileveldemandresponsestrategiesconsideringincentivesandcomplexitiesunderdynamicpricing |