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Abstract
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Coastal regions of Iran possess substantial but underexploited potential for marine renewable energy. This study proposes and dynamically simulates a hybrid offshore system integrating oscillating water column (OWC) de
vices and offshore wind turbines (OWTs) for sustainable hydrogen production. A key novelty of this work is the comparative assessment of hybrid wind–wave system performance across Iran's northern and southern coastal regions, namely Bandar Anzali (Caspian Sea) and Bandar Abbas (Persian Gulf), using a unified modeling, control, and optimization framework. The system is modeled in TRNSYS 18 using high-resolution meteorological and oceanographic data from Meteonorm and ERA5. A hierarchical supervisory control algorithm is developed to coordinate power exchange among generation units, the electrolyzer, hydrogen storage tank, fuel cell, and the electrical grid, enabling balanced system operation under variable renewable conditions. Excess renewable electricity is directed to hydrogen production, while stored hydrogen is utilized during periods of reduced generation, with grid interaction limited to auxiliary operation. To enhance system performance, an artificial neural network–genetic algorithm (ANN–GA) optimization framework combined with Sobol sequence sampling is employed, allowing efficient optimization under annual-scale (8760 h) dynamic simulations. The results indicate pronounced regional differences in renewable energy performance, with Bandar Anzali achieving higher and more consistent energy generation due to stronger wave resources, reaching an annual output of 76880 MWh compared to 29275 MWh in Bandar Abbas. The optimized configurations demonstrate improved techno- economic and environmental performance, highlighting how integrated dynamic modeling, coordinated con
trol, and AI-driven optimization enable a robust comparison and improved design of offshore hybrid renewable hydrogen systems across distinct coastal environments
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