Joint planning of distribution and transmission system for offshore wind farms integrated with hydrogen production platforms

With the growing sophistication of offshore wind technology, the development of offshore wind‑hydrogen system is evolving as a cost-efficient approach to harnessing far offshore wind resource. However, the costs associated with inter-array cables, the offshore hydrogen production platform (OHPP) and the hydrogen pipeline constitute a substantial portion of the capital expenditure (CAPEX) for this hybrid system, underscoring the critical need for optimization in the distribution and transmission system design of offshore wind‑hydrogen system. In this study, we introduce an integrated design model for the distribution and transmission system of this hybrid system, considering local wind conditions and various hydrogen production efficiencies, with the aim of minimizing investment cost, expected power losses cost and expected fault loss cost. Given the complexity of this problem, we employ a double-layer framework for solutions. The upper layer utilizes rigorous mathematical proofs to clarify the relationship between optimal OHPP capacity and its location. Meanwhile, the lower layer addresses a nonconvex mixed-integer nonlinear programming (MINLP) problem for jointly optimizing the cable connection layout and OHPP position. Due to its intractability, we construct a relaxed mixed-integer second-order cone programming (MISOCP) model and rigorously prove that its optimal solution aligns with that of the original problem. In our computational experiments, we found that the integrated design model reduces expected costs by an average of 3.88 % compared to solving subproblems separately. Compared with other optimization methods, our optimization framework is both concise and efficient, capable of finding a globally optimal solution within an acceptable timeframe. Lastly, sensitivity analysis reveals that even a slight increase in hydrogen production efficiency can significantly reduce expected costs, each 1 % improvement in efficiency reduces total cost by 0.94 %.