Distributed Robust Optimization of Integrated Seaport Energy Systems under Multiple Uncertainties

To address the challenges faced by the comprehensive energy system of seaports in the presence of multiple uncertainties such as sudden changes in renewable energy output, load demand, and energy price fluctuations, this paper attempts to study a distributionally robust optimization-based collaborative scheduling method for seaport logistics and energy. Taking quay crane scheduling as the link, a seaport logistics-energy correlation model is established that connects the in-port status of ships with shore power loads. On this basis, a multi-energy coupling model including "electricity - heat - cold - gas - hydrogen" is constructed. Then, a distributionally robust optimization (DRO) framework is designed based on the Wasserstein fuzzy set, and the column-and-constraint generation (C&CG) algorithm is combined for solution, to overcome the reliance of traditional methods on deterministic optimization. Meanwhile, a dynamic adjustment strategy for the fuzzy set radius is designed to optimize the level of robust conservatism based on real-time prediction errors, thereby avoiding the excessive redundancy in resource allocation that is a drawback of traditional robust optimization.