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LCOE analysis of North Sea offshore wind farms using scenario-based simulation: Weather windows and maintenance strategies

Antonio Allegue-OssetUniversidade da Coruña, Centro de Investigacións Em Tecnoloxías Navais e Industriais (CITENI), Campus Industrial de Ferrol, Escola Politécnica de Enxeñaría de Ferrol, Esteiro, Ferrol, 15471, SpainCarla Fernández-BlancoUniversidade da Coruña, Centro de Investigacións Em Tecnoloxías Navais e Industriais (CITENI), Campus Industrial de Ferrol, Departamento de Enxeñaría Naval e Industrial, Escola Politécnica de Enxeñaría de Ferrol, Esteiro, Ferrol, 15471, SpainLaura Castro‐SantosUniversidade da Coruña, Centro de Investigacións Em Tecnoloxías Navais e Industriais (CITENI), Campus Industrial de Ferrol, Departamento de Enxeñaría Naval e Industrial, Escola Politécnica de Enxeñaría de Ferrol, Esteiro, Ferrol, 15471, SpainAlmudena Filgueira‐VizosoUniversidade da Coruña, Centro de Investigacións Em Tecnoloxías Navais e Industriais (CITENI), Campus Industrial de Ferrol, Departamento de Química, Escola Politécnica de Enxeñaría de Ferrol, Esteiro, Ferrol, 15471, Spain
2026en
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Litsenziya: cc-by-nc-nd

ABI

Annotatsiya

Over the last two decades, offshore wind energy has experienced strong growth in Europe, with the North Sea as the main region of development. In this context, the levelized cost of energy (LCOE) has become established as the main indicator of project profitability. However, this parameter is highly sensitive to operational factors such as weather, accessibility, and maintenance logistics. This paper develops a stochastic model based on Monte Carlo simulation that explicitly integrates weather windows, access logistics, and failure rates into the calculation of LCOE. The model is applied to the Hornsea One offshore wind farm using hourly metocean reanalysis data from ERA5, complemented with Copernicus Marine datasets, and different logistical configurations. The results show that when compared to strategies based only on CTV (Crew Transfer Vessel), the combination of SOV (Service Operation Vessel) and helicopter significantly reduces the LCOE, especially in winter conditions. This study can lay the foundation for more comprehensive logistics planning that attempts to lower the LCOE's expected value and variability.

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