Data-driven modelling method and application based on energy multi-layer network structure of energy hub
Energy hub (EH) is a complex system integrating multiple energy sources, playing a crucial role in the Energy Internet (EI). Conventional modelling methods often treat energy sources separately, failing to capture the full dynamic interactions and operational complexities. This paper proposes a nove...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-04-01
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| Series: | Automatika |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/00051144.2025.2479927 |
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| author | Qingsen Cai Luochang Wu Chunyang Gao |
| author_facet | Qingsen Cai Luochang Wu Chunyang Gao |
| author_sort | Qingsen Cai |
| collection | DOAJ |
| description | Energy hub (EH) is a complex system integrating multiple energy sources, playing a crucial role in the Energy Internet (EI). Conventional modelling methods often treat energy sources separately, failing to capture the full dynamic interactions and operational complexities. This paper proposes a novel multi-layer network structure (MNS) for modelling EH, which synchronizes energy flows and optimizes control parameters for energy consumption reduction. The method integrates equipment performance curves into the network, providing a dynamic model that is computationally feasible for real-world applications. In project implementation, the dynamic control method is applied hourly between 8:00 and 17:00, with specific case studies for winter and summer days. The results show that the optimized control strategy can achieve up to 70% energy cost savings in summer and 20% savings in winter while maintaining equipment efficiency above 65% in summer and 60% in winter. The energy consumption costs before and after optimization are significantly reduced, as demonstrated by the comparative analysis. The proposed approach not only enhances system performance but also provides practical implications for optimizing energy hubs in diverse operational conditions. |
| format | Article |
| id | doaj-art-0a83fc76cc8344ce841ac5cffe10fba7 |
| institution | Kabale University |
| issn | 0005-1144 1848-3380 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Automatika |
| spelling | doaj-art-0a83fc76cc8344ce841ac5cffe10fba72025-08-20T03:40:34ZengTaylor & Francis GroupAutomatika0005-11441848-33802025-04-0166233535210.1080/00051144.2025.2479927Data-driven modelling method and application based on energy multi-layer network structure of energy hubQingsen Cai0Luochang Wu1Chunyang Gao2Northwest Engineering Corporation Limited, Power China, Xi'an, People’s Republic of ChinaInstitute of Water Resources and Electric Power, Xi'an University of Technology, Xi'an, People’s Republic of ChinaInstitute of Water Resources and Electric Power, Xi'an University of Technology, Xi'an, People’s Republic of ChinaEnergy hub (EH) is a complex system integrating multiple energy sources, playing a crucial role in the Energy Internet (EI). Conventional modelling methods often treat energy sources separately, failing to capture the full dynamic interactions and operational complexities. This paper proposes a novel multi-layer network structure (MNS) for modelling EH, which synchronizes energy flows and optimizes control parameters for energy consumption reduction. The method integrates equipment performance curves into the network, providing a dynamic model that is computationally feasible for real-world applications. In project implementation, the dynamic control method is applied hourly between 8:00 and 17:00, with specific case studies for winter and summer days. The results show that the optimized control strategy can achieve up to 70% energy cost savings in summer and 20% savings in winter while maintaining equipment efficiency above 65% in summer and 60% in winter. The energy consumption costs before and after optimization are significantly reduced, as demonstrated by the comparative analysis. The proposed approach not only enhances system performance but also provides practical implications for optimizing energy hubs in diverse operational conditions.https://www.tandfonline.com/doi/10.1080/00051144.2025.2479927Energy hubenergy internetmulti-layer structurematrix modeldata drivenenergy management |
| spellingShingle | Qingsen Cai Luochang Wu Chunyang Gao Data-driven modelling method and application based on energy multi-layer network structure of energy hub Automatika Energy hub energy internet multi-layer structure matrix model data driven energy management |
| title | Data-driven modelling method and application based on energy multi-layer network structure of energy hub |
| title_full | Data-driven modelling method and application based on energy multi-layer network structure of energy hub |
| title_fullStr | Data-driven modelling method and application based on energy multi-layer network structure of energy hub |
| title_full_unstemmed | Data-driven modelling method and application based on energy multi-layer network structure of energy hub |
| title_short | Data-driven modelling method and application based on energy multi-layer network structure of energy hub |
| title_sort | data driven modelling method and application based on energy multi layer network structure of energy hub |
| topic | Energy hub energy internet multi-layer structure matrix model data driven energy management |
| url | https://www.tandfonline.com/doi/10.1080/00051144.2025.2479927 |
| work_keys_str_mv | AT qingsencai datadrivenmodellingmethodandapplicationbasedonenergymultilayernetworkstructureofenergyhub AT luochangwu datadrivenmodellingmethodandapplicationbasedonenergymultilayernetworkstructureofenergyhub AT chunyanggao datadrivenmodellingmethodandapplicationbasedonenergymultilayernetworkstructureofenergyhub |