A study on the dynamic optimization strategy of energy routers in zero-carbon ports based on digital twin technology
The global maritime industry faces urgent demands for carbon neutrality while maintaining efficiency. Ports, as critical logistics nodes, need innovative solutions for zero-carbon energy. This study proposes a dynamic optimization framework for energy routers in zero-carbon ports, leveraging digital...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | International Journal of Electrical Power & Energy Systems |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061525004454 |
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| Summary: | The global maritime industry faces urgent demands for carbon neutrality while maintaining efficiency. Ports, as critical logistics nodes, need innovative solutions for zero-carbon energy. This study proposes a dynamic optimization framework for energy routers in zero-carbon ports, leveraging digital twins to address renewable integration, real-time coordination, and carbon accountability. By synergistically integrating physics-informed modeling, federated learning, and hybrid quantum–classical optimization, the framework achieves synchronized multi-timescale energy control. A Tianjin Port case study showed 92.4% renewable utilization, 42.8% lower carbon intensity, and 29% reduced costs. Resilience was validated under extreme weather, maintaining 94.7% capacity in typhoons. Innovations include blockchain-audited carbon tracking and adversarial reinforcement learning for cybersecurity. This study bridges the gaps in temporal-spatial decoupling and multi-stakeholder coordination, offering a replicable port decarbonization blueprint aligned with IMO 2050. Challenges like sensor dependency and embodied carbon highlight future research in edge AI and circular economy. |
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| ISSN: | 0142-0615 |