Floating offshore wind farms operating in deep-water environments face substantial operational and maintenance challenges that significantly impact project economics. While these installations access stronger wind resources unavailable to fixed-bottom turbines, they incur higher and more uncertain O&M costs due to remote locations, weather constraints, and specialized equipment requirements. Discrete-event simulation (DES) models have become standard industry tools for predicting O&M performance and comparing maintenance strategies, but researchers have identified a critical problem: inconsistent modeling assumptions between different tools lead to divergent results, reducing confidence in their use for capital investment decisions.
A new verification study examined two widely-used DES-based models configured identically around a deep-water floating-wind reference case. The analysis applied a structured framework to isolate how specific assumptions influence simulation outputs. While the models produced comparable maintenance cost estimates, they diverged substantially on wind farm availability and downtime-related revenue losses—metrics that often dwarf direct maintenance expenses in total O&M cost calculations. The primary driver of these differences lay in how each model represented turbine operational states during maintenance activities, particularly during technician off-shift periods and extended tow-to-port operations for major component replacement.
These findings expose a transparency gap in the offshore wind modeling community, where such operational assumptions are implemented inconsistently and often undocumented. By quantifying the influence of each assumption, the research provides generalizable guidance applicable across multiple DES platforms and maintenance planning tools.
Beyond verification, the study evaluated emerging maintenance strategies, including service operation vessel-based logistics, floating-to-floating major component replacement without port towing, and condition-based maintenance triggered by real-time monitoring rather than fixed schedules. These alternative approaches yielded cumulative O&M cost reductions of up to 5% relative to baseline strategies. The findings advance model transparency and reproducibility while demonstrating how verified simulation tools can rigorously assess novel operational concepts, supporting better decision-making as floating offshore wind technology matures.
