This study presents an integrated methodology for evaluating operations and maintenance (O&M) costs for floating offshore wind turbines (FOWTs), incorporating vessel motion dynamics. By combining UWiSE, a discrete-event simulation tool, with SafeTrans, a voyage simulation software, vessel motion effects during offshore operations are modeled. The approach is demonstrated in a case study at two wind farm sites, Marram Wind and Celtic Sea C. Three major component replacement (MCR) strategies were assessed: Tow-to-Port (T2P), Floating-to-Floating (FTF), and Self-Hoisting Crane (SHC). The T2P strategy yielded the highest O&M costs—94 kEUR/MW/year at Marram Wind and 97 kEUR/MW/year at Celtic Sea C—due to the extended MCR durations (90–180 days), leading to lower availability (90–94%). In contrast, the FTF and SHC strategies offered significantly lower costs and downtime. The SHC strategy was most cost-effective, reducing costs by up to 64% while achieving 97–98% availability. The integrated approach was found to be either more restrictive or more permissive depending on the specific sea states influencing the motion responses. This variability highlights the critical role of motion-based dynamics in promoting safe and efficient O&M practices, particularly for advancing FOWT operations.

Floating solar technology has gained significant attention as part of the global expansion of renewable energy due to its potential for installation in underutilized water bodies. Several countries, including the Netherlands, have initiated efforts to extend this technology from inland freshwater applications to open offshore environments, particularly within offshore wind farm areas. This development is motivated by the synergistic benefits of increasing site energy density and leveraging the existing offshore grid infrastructure. The deployment of offshore floating photovoltaic (OFPV) systems involves assembling multiple modular units in a marine environment, introducing operational risks that may give rise to safety concerns. To mitigate these risks, weather windows must be considered prior to the task execution to ensure continuity between weather-sensitive activities, which can also lead to additional time delays and increased costs. Consequently, optimizing marine logistics becomes crucial to achieving the cost reductions necessary for making OFPV technology economically viable. This study employs a simulation-based approach to estimate the installation duration of a 5 MWp OFPV plant at a Dutch offshore wind farm site, started in different months and under three distinct risk management scenarios. Based on 20 years of hindcast wave data, the results reveal the impacts of campaign start months and risk management policies on installation duration. Across all the scenarios, the installation duration during the autumn and winter period is 160% longer than the one in the spring and summer period. The average installation durations, based on results from 12 campaign start months, are 70, 80, and 130 days for the three risk management policies analyzed. The result variation highlights the additional time required to mitigate operational risks arising from potential discontinuity between highly interdependent tasks (e.g., offshore platform assembly and mooring). Additionally, it is found that the weather-induced delays are mainly associated with the campaigns of pre-laying anchors and platform and mooring line installation compared with the other campaigns. In conclusion, this study presents a logistics modeling methodology for OFPV systems, demonstrated through a representative case study based on a state-of-the-art truss-type design. The primary contribution lies in providing a framework to quantify the performance of OFPV installation strategies at an early design stage. The findings of this case study further highlight that marine installation logistics are highly sensitive to local marine conditions and the chosen installation strategy, and should be integrated early in the OFPV design process to help reduce the levelized cost of electricity.

In offshore wind operations, favourable weather and access conditions create opportunities to perform maintenance more efficiently. This study presents a modelling framework to identify and characterize such weather-based opportunity windows for yearly tactical planning. The framework integrates method statements, resource constraints, and multi-year hindcast metocean data within the TNO UWiSE discrete-event simulation model, evaluating all possible campaign start weeks. A historical blade repair campaign conducted in 2022 at the Offshore Wind Farm Egmond aan Zee is used for validation. The model reproduces most weather days accurately, though total campaign duration is overestimated by approximately 14%, likely due to conservative repair duration assumptions. As a proof of concept, a hypothetical future campaign is simulated, identifying an optimal window between calendar weeks 13 and 28, where cost, transfers, downtime, and duration are minimized. The best week shows approximately 15% lower total cost compared to the least favourable week. Sensitivity analysis indicates that uncertainties in task duration affect both the timing and width of opportunity windows, particularly for weather-critical activities. The framework enables operators and maintenance service providers to quantitatively assess maintenance scheduling performance across a year, accounting for seasonal and interannual variability. It is applicable to a wide range of maintenance activities, with future work focusing on integration with planning workflows, asset-condition models, and weather forecasting, as well as multi-objective optimization for automated scheduling.

This study introduces an integrated methodology that incorporates vessel motion dynamics into the evaluation of operations and maintenance (O&M) costs for floating offshore wind turbines (FOWTs). By combining UWiSE, a discrete-event simulation tool, with SafeTrans, a voyage simulation software, the methodology accounts for vessel motion effects during offshore operations. The approach was demonstrated through a numerical case study at two wind farm sites, Marram Wind and Celtic Sea C, with a fictive wind farm layout of 100 × 15 MW NREL turbine on a UMaine VolturnUS-S platform. Three Major Component Replacement (MCR) strategies were assessed: Tow-to-Port (T2P), Floating-to-Floating (FTF), and Self-Hoisting Crane (SHC). The T2P strategy resulted in the highest O&M costs—94 k€/MW/year at Marram Wind and 97 k€/MW/year at Celtic Sea C—due to extended MCR durations (90-180 days), leading to lower availability (90%-94%). In contrast, the FTF and SHC strategies, which are still under development, demonstrate significantly lower costs and reduced downtime. The SHC strategy, in particular, has proven to be the most cost-effective, achieving up to a 64% reduction in costs while increasing availability to 97%-98%. The integrated approach incorporates vessel dynamics, accounting for factors such as wave direction, wave period, and vessel response to varying sea states. This allows for greater flexibility in setting operational limits, potentially permitting higher limits in favorable conditions where vessel motion impact is reduced. However, in scenarios where dynamic vessel responses lead to increased motions—such as when waves approach from the side or when the wave period is close to the vessel's natural roll period—more restrictive limits may be necessary, even if significant wave heights are lower. This flexibility or restriction highlights the importance of incorporating motion-based dynamics for emerging technologies in the evolving FOWT O&M market.

Floating offshore wind offers access to deep-water wind resources but remains challenged by high and uncertain operation and maintenance (O&M) costs. Discrete-event simulation (DES) models are widely used to evaluate O&M strategies, yet variations in modeling assumptions often lead to inconsistent estimates and limit confidence in their use for decision support. This study applies a structured verification framework to examine how key assumptions influence O&M simulation outcomes using two DES-based models configured with a deep-water floating-wind reference case. While maintenance cost estimates remain broadly consistent across models, substantial differences arise in wind farm availability and in downtime-related revenue losses, which constitute a major share of total O&M costs. This discrepancy is driven primarily by how turbine operational states are represented during maintenance activities, including technician off-shift periods and tow-to-port operations. Quantifying the influence of these assumptions provides generalizable insight relevant to the wider O&M modeling community, where such choices are implemented inconsistently between different models. Building on the verified modeling foundation, several alternative O&M strategies including service operation vessel-based logistics, floating-to-floating major component replacement, and condition-based maintenance are evaluated, yielding total O&M cost reductions of up to 5 % in the examined case. The findings strengthen model transparency and reproducibility while demonstrating how verified simulation tools can support the assessment of emerging operational concepts in floating offshore wind.

Leading-edge refurbishment (LER) is a frequent offshore wind blade-maintenance activity driven by leading-edge erosion. Rope access, the current industrial standard, is constrained by daylight-only operation, strict weather limits, and repeated setup. Blade-access platforms (BAPs) provde promising alternatives, but their offshore logistical and economic performance remains uncertain and highly site- and weather-dependent. This study provides the first simulation-based evaluation of BAP deployment for planned LER campaigns, using an inflatable BAP concept as a case study. By enabling safe night-time operation and continuous three-blade repair per deployment, the BAP reduces lifetime LER cost by 33–41% relative to rope access and shows lower sensitivity to weather variability. Two design drivers are identified to enhance BAP performance: (i) local environmental control (rain protection and especially humidity control), which expands workable weather windows and shortens seasonal campaigns, and (ii) operational wind tolerance, where limits below 12 m/s sharply reduce workable days and increase the risk of missing annual repair targets. The results inform operator decision-making, prioritize performance-critical BAP features, and demonstrate a transferable methodology for other sites, BAP concepts, and blade-repair scenarios.