Assessing green hydrogen production via offshore wind in the Dutch North Sea: Complementing techno-economic simulation with machine learning and optimization

This study analyzes the production of green hydrogen using dedicated offshore wind power in the Dutch North Sea region. The analysis is based on a detailed techno-economic model that simulates physical flows and estimates the levelized cost of hydrogen (LCOH). However, the model’s outputs depend on user-provided inputs and evaluating all possible inputs is computationally infeasible. To this end, “optimization with constraint learning” is employed, where surrogate machine learning models are trained on simulation data and embedded in mixed-integer optimization problems. The surrogate models are trained on 4096 simulation runs and achieve a mean absolute percentage error of ≤ 3% for physical flow-related outputs, and an error of ≈ 10% for the LCOH-related outputs. Once trained, these surrogates enable one to solve stakeholder–specific problem instances in sub-second solve times, supporting rapid scenario analysis and trade-off exploration. • Techno-economic analysis of offshore wind-based hydrogen production in the North Sea • Simulation model captures complexity but utility is limited by computational cost • We combine simulation with “mixed-integer optimization with constraint learning” • Embedded surrogate machine learning models enable fast and flexible optimization