Uncertainty-resilient hierarchical optimization for cross-regional hydrogen energy storage planning and scheduling

The effective planning and scheduling of the cross-regional Hydrogen Energy Storage Systems (HESS) can address the uneven spatial and temporal distribution of renewable energy sources. Hierarchical modeling has been identified as a leading method for this problem due to its flexibility and robustness in handling uncertainties and intermittency. However, existing methods still face significant challenges, such as high model complexity and low computational efficiency. To this end, this paper introduces a novel concept of dynamic transmission pricing, innovatively quantifying the difficulty of transferring stored energy between neighboring regions. This concept leads to price-based energy trading decisions, significantly simplifying the scheduling process by eliminating discrete variables for cross-regional connection. Furthermore, a hierarchical optimization framework is developed by integrating simulated annealing (SA) for strategic planning, with linear programming (LP) and cost flow transformation (CFT) for operational scheduling. Numerical tests conducted on HESS networks consisting of 10, 50, 100, and 1000 cities demonstrate that the proposed method impressively enhances computational efficiency and algorithm convergence, with CFT proving particularly effective by dramatically reducing computation time. • Introducing dynamic transmission pricing to streamline hierarchical HESS modeling. • Integrating simulated annealing with linear programming for efficient energy planning and scheduling. • Quantifying region-to-region transfer challenges for smarter HESS energy trading. • Applying cost flow transformation to enhance convergence and avoid local optima.