Offshore wind is an established power-generation technology with deployments at utility scale in multiple coastal regions. However, around 80 % of this potential is located in water area deeper than 60 m, which is beyond the economic reach of fixed-bottom foundations . Floating offshore wind (FOW) technologies are essential to unlock these deep-water resources, providing access to higher wind speed and greater siting flexibility. The FOW sector is rapidly advancing, with roughly 250 GW of capacity currently in the global development pipeline , reflecting increased interest in FOW for deep-water areas that are not accessible to fixed-bottom foundations.

Despite these prospects, the actual development of FOW has been challenging. Since the commissioning of the world’s first FOW farm, Hywind Scotland , in 2017, only a few small-scale FOW projects have been installed globally, totaling around 250 MW, with the major ones shown in Table . The primary limitation of FOW development is its high levelized cost of electricity (LCoE), which is estimated to range between EUR 100–200 MWh⁻¹ , roughly doubled from the recent benchmarked fixed-bottom projects . The higher cost is, in general, driven by the expense of specialized floating platforms, mooring systems, and complex maintenance operations in deep water .

A major contributor to the LCoE of both fixed-bottom and floating offshore wind is the O&M cost, generally accounting for 20 %–35 % of total LCoE . Specifically, O&M costs encompass the expenses related to operating, monitoring, and maintaining offshore assets (e.g., turbines and balance of plant) throughout a wind farm’s lifetime, including the revenue losses associated with assets' downtime. The associated O&M activities are predominantly performed in a marine environment, where the logistics of dispatching resources to the offshore site are substantially dependent on weather conditions . Therefore, developing an effective logistical strategy is essential for reducing O&M costs, as poorly informed decisions can result in missed weather windows, prolonged resource use, and increased downtime-related revenue losses.

Floating offshore wind faces greater O&M challenges than fixed-bottom wind. Firstly, floating wind farms are typically located further from shore, which increases vessel transit time and reduces accessibility due to harsher metocean conditions . Secondly, the dynamics of floating platforms introduce higher levels of motion, which may accelerate fatigue and increase failure rates of turbine components , driving up the maintenance demand. Thirdly, additional balance-of-plant (BoP) infrastructure must be maintained, including floating foundations (e.g., floaters, mooring lines, anchors) and dynamic inter-array cables, which also increases demand on maintenance .

The fourth and most critical challenge is the major component replacement (MCR), which has been highlighted in several studies . MCR involves replacing large turbine components, such as blades, gearboxes, or generators, which requires specialized heavy-lift assets. In fixed-bottom offshore wind, these operations are typically performed using a jack-up vessel (JUV) or a semi-submersible crane vessel (SSCV). Both vessel types are equipped with advanced sea-keeping systems and lifting capacities of several thousand tonnes, enabling stable operations in offshore environments. However, no existing vessel at the moment, to the authors' best knowledge, can perform MCR directly on floating wind turbines, where a turbine would be in larger relative motion with the working vessel. Specifically speaking, JUVs are limited to shallow waters, as they cannot jack up in depths greater than 60 m . SSCVs, on the other hand, cannot yet achieve the precision of operation required when both the turbine and vessel are subject to large motions.

As a result, the tow-to-port (TTP) is currently the default approach for floating-wind MCR. This process is logistically complex and highly weather-dependent, with the major steps highlighted in several studies . For example, the Hywind Scotland project conducted its first MCR via the TTP approach in 2023–2024, requiring five 6 MW turbines to be towed 500 km to the port of Gulen by tugboats. Each replacement took 1–2 months on average, encompassing the activities of tow-out/tow-in, offshore disconnection/reconnection, and quayside repair . The lengthy downtime illustrates the cost implications of additional marine operations and extended turbine unavailability. Alternative approaches such as tow-to-shore, floating-to-floating, and self-hoisting cranes have been proposed to overcome the limitations of TTP . These options could reduce downtime and associated costs but face significant technological, logistical, and market barriers. For now, TTP remains the only implemented approach, although the optimal future strategy will likely depend on project location, infrastructure availability, and technological readiness.

Given these challenges, reliable O&M modeling approaches are essential to evaluate the economic implications of different strategies on FOW projects. Such models help quantify how choices like MCR strategies influence OPEX, support design-phase decisions such as the selection of floating foundation types, and assess the impact of broader maintenance scopes. In doing so, O&M modeling provides a critical basis for optimizing an O&M strategy to improve the long-term economic performance of FOW farms.

Numerous O&M models have been developed to capture the complexities of offshore wind operations, and several have been further adapted or applied to assess O&M costs for floating-wind technologies . A common challenge these models face is the validation, a process of ensuring that the conceptual model is a faithful representation of reality, which somehow requires critical data (e.g., operational information on failures, repairs, costs) that are not widely accessible to the research community. On the other hand, verification, a process of ensuring that a model has been correctly implemented according to its assumptions, remains feasible and is especially important in this context. Verification of a model with other models that have already undergone validation in related domains is considered to be a way of lowering the uncertainty in the outputs of such a model. While this approach cannot substitute full validation against real-world data, it can help uncover inconsistencies, highlight critical modeling assumptions, and build confidence that different tools are producing reliable and reproducible results. This makes verification a vital step for advancing O&M modeling in the field of FOW.

Several verification studies have attempted to compare offshore wind O&M models by running them under a common reference wind farm and O&M scope, then comparing outputs to identify sources of discrepancies. For example, the study  benchmarked four models, including NOWIcob , the University of Stavanger O&M simulation model , the ECUME model , and Strathclyde University’s OPEX model . They found notable differences in predicted availability and O&M costs, particularly under scenarios with constrained maintenance resources (e.g., limited numbers of vessels or technicians), revealing that resource bottlenecks are treated very differently across models. Another study  compared the NOWIcob model  with the ECN O&M tool  and found the variation in the calculated O&M performance metrics between these models. Although the differences in modeling assumptions were identified in this study, the discrepancies of the results could hardly be attributed to specific assumptions given that the two models relied on fundamentally different simulation approaches. More recently, the open-source O&M model developed by the National Laboratory of the Rockies (NLR; formerly the National Renewable Energy Laboratory, NREL), WOMBAT , was benchmarked against the existing tools mentioned in the previous two studies. WOMBAT generally predicted higher availability and showed differences in vessel costs (particularly for jack-up and cable-lay vessels) as well as downtime associated with major replacements and balance-of-plant failures. However, this study did not systematically trace the observed differences back to their underlying modeling assumptions.

These verification studies highlight that O&M models differ substantially in their underlying approaches, particularly in the level of detail used to represent offshore wind operations. Broadly, they can be divided into two categories. The first consists of heuristic approaches, which use simplified rules of thumb, approximations, or expert judgment to provide rapid estimates of performance. Such models do not explicitly simulate the behavior of every asset but instead rely on aggregated representations to guide decision-making . The second category employs more detailed system-level simulations, often combining agent-based and discrete-event methods to capture the behavior of individual turbines, vessels, and technicians, as well as the processes they follow during maintenance activities . While both approaches have their merits, the divergence in modeling granularity creates a key challenge for verification: it is difficult to systematically trace how differences in assumptions and inputs affect model outputs. This gap limits confidence in model predictions and hinders the use of verification as a means to improve O&M modeling practices.

The purpose of this study is to examine how modeling assumptions influence the outcomes of offshore wind O&M simulation tools using a structured verification exercise between an open-source model and a high-fidelity research model as a test case. Although the two models differ in their representation of operational processes, they share comparable input granularity and modeling scope, making them suitable for isolating the effects of specific assumptions. The objectives of this work are to

identify and categorize key modeling assumptions that differ between the tools, particularly those related to turbine operational states, repair processes, and weather-driven logistics constraints;

Two O&M models are employed in this study: the open-source WOMBAT model developed by the National Laboratory of the Rockies (NLR; formerly the National Renewable Energy Laboratory, NREL)  and the proprietary UWiSE model developed by the Netherlands Organisation for Applied Scientific Research (TNO) . WOMBAT is an openly available decision-support tool designed to evaluate the performance and cost of wind power plants during the O&M phase, facilitating research and trade-off analyses on how operational strategies or technological innovations influence wind farm performance. UWiSE, on the other hand, is a proprietary model developed and applied in collaboration with industry partners, building on decades of offshore wind expertise and earlier Energy Research Centre of the Netherlands (ECN) decision-support tools and encompassing a broader set of offshore logistics scenarios, including installation, O&M, and decommissioning for both offshore wind and offshore floating solar assets .

Both WOMBAT and UWiSE adopt an agent-based and discrete-event simulation (DES) approach, which is commonly used in offshore wind O&M simulation. In DES models, system behavior emerges from the interactions of individual agents, including turbines, vessels, and technicians, whose activities are triggered by discrete events such as failures, repair actions, or weather-driven delays. Uncertainty is represented through Monte Carlo sampling of failure processes and environmental conditions. The shared high-level modeling principles adopted in both models are summarized below.

Stochastic failure modeling. Each wind turbine is decomposed into multiple user-defined subassemblies, where each subassembly is characterized by one or more failure modes. In both WOMBAT and UWiSE, failure occurrence is represented stochastically using Weibull-based reliability formulations parameterized by the mean time before failure (MTBF) and a specified shape factor. Random sampling based on these distributions determines the occurrence of failures during the simulation time frame, capturing the probabilistic nature of component reliability behavior. In the present implementation, a single Weibull distribution is assigned to each subsystem, representing a simplified approximation of component reliability behavior.

In summary, WOMBAT and UWiSE share a comparable level of modeling granularity and structural logic, providing a strong foundation for model-to-model verification. Nevertheless, they differ in specific implementation assumptions, such as weather dependency, resource dispatch logic, and turbine state transitions, which are expected to influence the predicted outcomes. These key differences are identified in Table , serving as the basis for the subsequent verification analysis. Other differences, such as detailed planning logic in the models’ backlogs, are not fully identified and are outside the scope of this study.

The verification process, illustrated in Fig. , is designed to systematically identify, isolate, and interpret the influence of modeling assumptions on the outputs of discrete-event O&M simulation tools. While WOMBAT and UWiSE serve as the test case in this study, the procedure is general and applicable to other DES-based O&M models.

The process begins with the construction of a common baseline scenario, in which both models are configured using the same environmental conditions, turbine characteristics, and maintenance strategies. Each model is then executed under these shared input assumptions. The resulting KPI distributions are compared to assess statistical differences in model responses under nominally equivalent conditions. Next, a series of targeted verification tests is performed through an iterative comparison process. Individual modeling assumptions (listed in Table ) are sequentially modified within UWiSE to match the corresponding assumptions implemented in WOMBAT while keeping the remaining modeling assumptions unchanged. UWiSE was selected for these adjustments due to its flexible framework, which allows controlled modifications to internal logic and efficient implementation of the investigated assumptions. After each modification step, the resulting KPIs are compared against the common baseline scenario to quantify the influence of that specific modeling difference on downtime, availability, and cost. This process enables isolation of assumption-driven effects on model outcomes. It is noted that a corresponding iterative adjustment process in WOMBAT was not performed within the scope of this study due to time and resource constraints.

Overall, this structured verification framework enhances transparency in model behavior, supports reproducible comparison across tools, and provides generalizable insight into how modeling assumptions shape the results of DES-based offshore wind O&M simulations. Nevertheless, the findings should be interpreted within the scope of the adopted verification approach, where the investigated assumptions were aligned primarily through modifications in UWiSE.

Both models share a comparable input structure. Common input parameters are derived from publicly available and peer-reviewed sources to ensure transparency and reproducibility. The detailed input configurations are presented in Appendix .

Following the verification exercise, the UWiSE model is further applied to assess the performance of several alternative O&M strategies relative to the baseline scenario. These strategies, summarized in Table , represent emerging operational concepts that aim to enhance maintenance efficiency and reduce downtime in floating-wind operations. This analysis illustrates how a verified model can be utilized to quantitatively evaluate the cost-effectiveness and performance implications of innovative O&M approaches, thereby supporting stakeholders in making better-informed strategic decisions.

Each baseline simulation was performed with 20 stochastic runs to account for random failure occurrences, and the results presented in this study represent the average values across all runs. A replication analysis was conducted for both models to evaluate the adequacy of the number of stochastic runs, demonstrating that 20 stochastic runs provide sufficiently precise estimates of the reported metrics. Details of the replication analysis are provided in Appendix . Figure  compares the breakdown of annual maintenance cost and annual downtime-related revenue loss (both in the unit of thousands of euros (TEUR) MW⁻¹ yr⁻¹) estimated by WOMBAT and UWiSE. Under the defined input assumptions, approximately 70 % of the total maintenance cost is fixed, reflecting the year-round availability of self-owned resources such as tugboats, heavy-lift onshore cranes, technicians, crew transfer vessels, and the O&M base. These fixed elements are identical in both models. The remaining share is the variable cost, which is driven by model-specific calculations and includes the charter of specialized vessels (AHV, CLV, DSV) and material expenses linked to maintenance events. While both models yield similar total variable cost magnitudes, notable discrepancies are observed in a few cost items. WOMBAT estimates roughly 35 % lower material costs and nearly 500 % higher DSV costs than UWiSE. Moreover, the estimated wind farm downtime, and hence the associated revenue loss, is calculated to be about 200 % higher in WOMBAT than in UWiSE, indicating a significant difference in how operational delays and turbine status are handled between the two models.

To understand the source of deviation in material cost, Fig.  shows the average number of maintenance events per turbine per year. WOMBAT produces about 15 % fewer failure-triggered events (including minor repair, major repair, and replacement) compared with UWiSE. This difference arises from the fact that both models adopt an operation-dependent failure mechanism, where failure likelihood scales with a turbine's accumulated operational time, or uptime, rather than calendar time. As WOMBAT estimates with higher downtime, meaning turbines accumulate fewer operational hours, this eventually results in fewer failures and thus explains the lower material costs observed in this model.

The discrepancy in vessel-related costs is further examined in Fig. , which normalizes vessel cost, charter days, and mobilization times by the number of corresponding maintenance events. The normalization isolates the effect of differing event frequencies between models, as mentioned previously. In general, WOMBAT shows slightly higher charter days and number of mobilizations per event across all on-demand vessels. This partly results from WOMBAT’s implementation of a minimum charter period for on-demand vessels, which is a parameter requiring a vessel to be booked and paid for at least a number of days regardless of whether the actual operation finishes earlier. Consequently, WOMBAT may overestimate charter days when the operational duration is shorter than the predefined minimum period. This effect, however, has only a minor influence on total cost given the low frequency of AHV and CLV operations. The most substantial difference concerns the estimation of DSVs, where WOMBAT records nearly 10 times more mobilization times per event compared with UWiSE. Specifically speaking, all the DSVs in this case study are used for scheduled inspections (see Appendix ). In UWiSE, two DSVs are mobilized only once per scheduled inspection campaign in that year, while in WOMBAT, DSV mobilization appears to be repeatedly counted within the same campaign period. This modeling difference directly explains the much higher DSV cost in WOMBAT’s results.

The major discrepancy in the estimation of turbine downtime is explored. Figure  presents the monthly time-based availability simulated by both models. This metric differs slightly from energy-based availability shown earlier, as it isolates the effect of mechanical and logistical factors by excluding the influence of wind variability. As such, it provides a clearer basis for comparing the models’ prediction of a wind farm's technical availability performance. Both models exhibit variations around their mean values (shaded area in the figure), reflecting the stochastic nature of failure generation in the simulations.

In UWiSE, time-based availability occasionally drops to around 80 % in certain periods, but it is typically recovered back to a stable range of 90 %–95 % within a few months, indicating an overall equilibrium between failure occurrence and repair capacity. In contrast, WOMBAT shows a gradual and persistent decline in availability over the simulated 20-year period, reaching approximately 60 % by the end of the simulation. This divergent trend in wind farm availability suggests fundamental differences in how the two models handle event backlogs and turbine status responses over time.

To further explore the causes of these differences, Fig.  tracks the cumulative number of unsolved maintenance events, separated by event type, while Fig.  summarizes the corresponding event completion rates. The event completion rate is defined as the ratio of completed events to total demand of events generated during the simulation.

Looking at different event types, the following can be found:

For major repair and replacement, both models produce highly consistent results. These critical events are almost always resolved within a reasonable time frame, achieving completion rates of 98 %–99 %. This reflects the higher prioritization and effective resource allocation typically assigned to these types of critical failures.

To summarize the baseline analysis, both models yield broadly comparable total maintenance cost estimates, with most variations arising from the distribution among specific cost components rather than the overall magnitude. However, a clear divergence is found in downtime estimation, where WOMBAT predicts roughly twice the downtime-related revenue loss compared with UWiSE. This higher downtime aligns with WOMBAT’s lower time-based availability and lower event completion rates, pointing to less effective scheduling and resource utilization within its operational logic. These baseline findings form an essential reference point for the subsequent verification analyses.

To evaluate how individual modeling assumptions shape O&M simulation outcomes in DES models, five verification scenarios were implemented in UWiSE, as summarized in Table . Scenarios 1 to 4 each isolate a specific modeling assumption related to one of the two aspects shown below. Scenario 5, in contrast, applies all adjustments simultaneously to create a reference case in which the investigated modeling assumptions in UWiSE are aligned with those implemented in WOMBAT. This combined scenario is intended to evaluate the cumulative effect of the investigated assumptions and to assess the extent to which the two models converge when these assumptions are made consistent. The remaining differences between the models under Scenario 5 can therefore be attributed to other unexamined modeling choices or structural differences beyond the scope of this study.

Scenarios 1 and 2: resource allocation and dispatching logic

Figure  compares the total maintenance costs and downtime-related revenue loss across all scenarios relative to the WOMBAT and UWiSE baseline. Overall, total maintenance costs remain nearly constant, with only minor deviations in variable cost components such as on-demand vessel chartering and material usage. This limited effect is due to the fact that the tested assumptions primarily affect the planning efficiency of frequent, low-cost activities such as minor repairs and inspections, whose associated resources (e.g., CTVs and technicians) are already treated as fixed annual costs in this study. Consequently, the observed variation in maintenance costs is insignificant between verification scenarios.

Scenarios 1 and 2, which focus on resource dispatching and technician shift patterns, show only marginal influence on downtime loss. In Scenario 1, limiting one crew team to be carried by a CTV slightly increases downtime (< 1 %), while in Scenario 2, assuming continuous technician shifts (06:00–22:00) without handover inefficiencies in between reduces downtime by about 2 %. Since these changes affect only the efficiency of non-critical tasks that do not directly halt turbine operation, their overall impact on downtime remains limited.

Pronounced differences appear in the scenarios where assumptions about turbine operational status during maintenance are altered. In Scenario 3, turbines are modeled to remain offline during off-shift hours (22:00–06:00) when multi-day repair or inspection tasks are ongoing. This assumption nearly doubles the total downtime loss compared with the baseline, as non-operational nighttime hours accumulate substantially over time, particularly during weather-limited seasons. Based on common operational practice, turbines often remain operational during non-working hours for minor, non-critical maintenance. Therefore, WOMBAT’s similar assumption may contribute to a tendency toward higher downtime estimates relative to typical operational behavior. Scenario 4 explores the electrical connectivity of upstream turbines during tow-to-port operations. The total downtime decreases by approximately 30 % when these upstream turbines are allowed to remain operational while the failed turbine is disconnected and towed to port. This scenario underscores the sensitivity of downtime estimates to assumptions about inter-array cable disconnection and power routing. Although technical solutions to maintain upstream production during TTP are being researched, current industry practice still typically requires shutting down upstream turbines during towing for electrical safety reasons .

Scenario 5, which integrates all adjusted assumptions, results in approximately 50 % higher downtime than the UWiSE baseline, bringing its output much closer to the one from WOMBAT. Nonetheless, even after aligning all investigated assumptions, WOMBAT still predicts 30 % higher downtime loss than UWiSE, likely due to deeper differences in resource planning approaches and weather dependency modeling that are beyond the present study’s scope.

Figure  shows the average number of maintenance events per turbine per year. As failure generation depends on operational exposure time, scenarios with higher downtime (e.g., Scenarios 1 and 3) produce fewer failures, while those with lower downtime (e.g., Scenarios 2 and 4) exhibit slightly more. These systematic yet minor differences confirm that operational-state assumptions indirectly influence failure occurrence with different calculated turbine uptime.

The temporal evolution of time-based availability is shown in Fig.  comparing different scenarios. As expected, the trend mirrors the downtime loss results, where scenarios with lower downtime result in higher average availability. Moreover, the figure also reveals that the treatment of turbine operational status significantly affects the dynamics of wind farm availability. Scenario 3 exhibits more pronounced seasonal fluctuations than the baseline, with availability dropping from 90 % to as low as 60 % during harsh-weather periods. In contrast, Scenario 4 maintains more stable availability above 90 % most of the time. Across all UWiSE scenarios, availability eventually recovers a few months after it reaches its local lowest, contrasting with WOMBAT’s continuously declining trend, even when all modeling assumptions are aligned (Scenario 5). This persistent difference likely stems from deeper modeling mechanisms or algorithmic treatments that were not included among the examined assumptions.

To further assess planning efficiency, Fig.  tracks the cumulative number of unsolved maintenance events by type, and Fig,  summarizes the event completion rates. These results highlight the effects of resource allocation logic that are not directly visible from the downtime comparisons, as shown earlier. In Scenario 1, the restriction of vessel dispatch efficiency causes an accumulation of minor repair and inspection events, with completion rates dropping from 99 % to 93 % and from 59 % to 20 %, respectively. Conversely, in Scenario 2, which assumes more efficient crew utilization, the inspection completion rate increases from 59 % to 90 %. Although these logistical differences strongly influence task completion dynamics, they do not substantially affect downtime. This is because turbines are assumed to remain operational during the period where non-critical tasks are not fully finished.

Overall, the verification analysis shows that assumptions governing turbine operational states during maintenance are the dominant drivers of downtime and availability estimates in discrete-event O&M simulations. Whether turbines are modeled to remain online during off-shift hours or during tow-to-port operations directly determines the magnitude of production loss. In contrast, logistical assumptions, such as dispatching rules or technician shift structures, have smaller standalone effects but strongly influence maintenance efficiency and can amplify downtime impacts when combined with operational-state assumptions. These findings highlight modeling choices that are widely treated differently across DES O&M tools, underscoring their broader relevance for improving the consistency, transparency, and credibility of logistics simulation models beyond the two examined here.

Figure  presents the comparison of total maintenance costs and downtime-related revenue losses across the tested O&M strategies.

For the SOV-based strategy, total maintenance costs increase by TEUR 8.2 MW⁻¹ yr⁻¹ compared to the baseline, mainly due to the significantly higher charter rate of a SOV relative to multiple CTVs. On the other hand, the higher maintenance cost is partially offset by a reduction in downtime losses of TEUR 4.6 MW⁻¹ yr⁻¹, as the SOV enables more efficient personnel transfer, longer shift hours, reduced transit time, and greater weather tolerance. As a result, the net overall cost increases by TEUR 3.6 MW⁻¹ yr⁻¹ (3 % higher than the baseline).

These downtime dynamics are further illustrated in Fig. , which shows the temporal evolution of time-based availability under different strategies. The FTF-based strategy maintains the most stable availability throughout the simulation, consistently staying within the 90 %–95 % range. This result highlights that in situ replacements via an advanced working vessel can effectively mitigate the turbine downtime associated with the current tow-to-port approach.

Overall, the results suggest that the FTF-based strategy offers the greatest potential for enhancing wind farm operational performance, reducing overall maintenance cost (including downtime-related revenue losses downtime loss) by around 5 % relative to the baseline scope. Nonetheless, these outcomes are sensitive to key economic and technical inputs that vary across projects, such as cost allocation between fixed and variable cost items, vessel day rates, O&M scope, and electricity price dynamics. Ultimately, the analysis underscores the value of a verified model as a transparent and quantitative framework for systematically evaluating the long-term implications of alternative O&M strategies in floating offshore wind.

This study applied a structured verification framework to examine how modeling assumptions influence the outputs of discrete-event O&M simulation tools for floating offshore wind. Using an open-source model and a high-fidelity research model as a test case, the analysis demonstrated that even under harmonized input conditions, differences in operational logic and process representation can lead to substantial variation in predicted downtime, availability, and cost outcomes. Because these mechanisms are widely shared across DES-based O&M models, the insights gained extend beyond the two tools examined and are relevant to the broader logistics modeling community.

Across all verification scenarios, assumptions governing turbine operational states during maintenance emerged as the dominant drivers of downtime-related production loss. In particular, decisions about whether turbines remain operational during technicians’ off-shift hours or during tow-to-port operations produced the largest changes in downtime and availability. Logistical assumptions, such as dispatching rules and technician shift structures, had smaller standalone effects but strongly influenced task execution efficiency and interacted with operational-state assumptions to amplify overall impacts. These findings highlight modeling choices that are often treated differently across O&M simulation tools and thus represent key targets for improving consistency, transparency, and reproducibility in floating-wind O&M modeling.

The verified modeling framework was then used to evaluate alternative O&M strategies relevant for floating wind. The floating-to-floating major component replacement strategy achieved the greatest performance improvement, reducing total O&M costs by approximately 5 %, while a condition-based maintenance approach reduced costs by around 3 %. These results illustrate the value of combining verified modeling foundations with scenario analysis to assess emerging operational concepts and their cost implications.

Overall, this work delivers three main contributions: (i) a transparent verification approach for diagnosing how modeling assumptions influence DES O&M outcomes, (ii) generalizable insights into the operational-state and logistical mechanisms that shape downtime and availability predictions, and (iii) a demonstration of how verified models can support the evaluation of future O&M strategies for floating offshore wind. Together, these contributions strengthen the credibility, interpretability, and decision-support value of simulation tools used across the offshore wind community.

While this study provides valuable insights into the verification and comparative behavior of offshore wind O&M simulation models, several limitations remain and highlight areas for further improvement. These limitations broadly fall into four categories:

Data-related limitations stem primarily from the absence of publicly available operational data for large-scale floating turbines. The current study relies on maintenance and failure datasets extrapolated from smaller fixed-bottom turbines in the 2–4 MW range. Although scaling relationships are applied to approximate 15 MW turbine behavior, the validity of these adjustments remains uncertain, as larger machines are expected to experience different loading conditions, failure modes, and accessibility constraints. Consequently, both the failure frequency and the associated maintenance cost or duration may diverge from real-world performance. Future work should therefore prioritize empirical data collection from early commercial floating wind farms to enable more accurate calibration and validation of model inputs.

Addressing these challenges, through improved data acquisition, enriched operational modeling, deeper algorithmic transparency, and integration of more comprehensive financial assessments, will be essential for advancing both the modeling accuracy and the practical decision-making relevance of offshore wind O&M simulation tools. Future work could, for example, leverage operational data from original equipment manufacturers (OEMs) and wind farm operators to validate and refine maintenance and logistics models while incorporating more realistic dispatch strategies, contractual arrangements, and operational decision-making processes.