A study on the dynamic optimization strategy of energy routers in zero-carbon ports based on digital twin technology - Shun Li; Xingda Fan; Zhaoyu Qi (International Journal of Electrical Power & Energy Systems)
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.