| 23 Feb 2026
Verification-Based Assessment of Modelling Assumptions in Discrete-Event Simulation of Operation and Maintenance for Floating Offshore Wind
Abstract. 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 modelling 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 harmonized 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. These differences are driven primarily by how turbine operational states are represented during maintenance activities, including off-shift periods and tow-to-port operations. Quantifying the influence of these assumptions provides generalizable insight relevant to the wider O&M modelling community, where such choices are implemented inconsistently. Building on the verified modelling 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.
General comments:
The authors address with their research an important aspect of offshore wind - modelling of operation and maintenance. While many models exist, especially for fixed bottom wind turbines, validation and verification of said models is lacking to date. The authors compare two models and present the modeling results, and most importantly driving factors for differences between the two models.
The methodology of the study is mostly well explained and fits the purpose of the study.Â
In some areas, the research article can benefit from revisions, mainly consisting of how the ideas and findings are presented.
Specific comments:
In section 2.1 it is not quite clear which modelling principles are general and common for all kinds of DES models, and which are concrete examples for the WOMBAT and UWiSE models.Â
In line 147f you write: "... to how DES-based O&M models in general represent operational states and process flows. The shared modelling principles of the DES models are summarized below:" However, there exist DES models (in the context of (fixed bottom) offshore wind O&M models) which use slightly diverging methods than the ones listed below. Some examples include:
Failure modeling: While, to my knowledge, most models use Weibull distributions for stochastic failure modelling, some models use Poisson or Bernoulli processes. Linear fatigue accumulation or exponentially increasing failure probabilities are also seen in the literature.
Weather: Recorded historical data is most commonly used, however not all of the datasets have an hourly resolution. The FINO data, which is commonly used by European researchers, includes 10min resolution for wind speeds. 30min wave measurements are also common. Additionally, sometimes simulated weather based on the historical data is used.
I believe therefore that section 2.1. will benefit from rephrasing and making the distinction between the two models investigated in this paper and the general body of DES models clearer. Â
Section 2.2.:Â
185: I am not familiar with the use of "harmonized" in the context of creating a baseline scenario - can you explain the difference of creating a baseline scenario to be used in the analysis and creating a harmonized baseline scenario? Are you hamonizing between multiple existing baseline scenarios? If there is no difference between the concepts, you might consider removing the use of harmonized in this context.
187: What is the order of magnitude of the "multiple random seeds"? If possible, provide the reader with a concrete number (i.e. "using 17 random seeds").
Figure 1 and 190ff: From the written explanantion, I am understanding that you are iteratively modifying the assumptions in the UWiSE model and then comparing those results to the baseline scenario. In Figure 1, you write "Adjust one modeling assumption in UWiSE to align with WOMBAT's assumption". This second part of aligning with WOMBAT's assumption is not mentioned in the description. Could you please explain this further?
220 and Figure 3: It would be beneficial to mark the BoP components in the overview, to make the distinction between components used in both fixed bottom and floating wind farms and those specific to floating even easier.Â
Section 3.1:
While reading about the simulation results, the reader would greatly benefit from having deeper knowledge of the two models - if at all possible, could you include all of the assumptions and boundary conditions already in Section 2? (e.g. the implementation of minimum charter period, first mentioned in 267ff), Table 2 already summarized some of the differences, this might be a good place to include this kind of information.
Section 3.2:
I do not quite understand the value of scenario 5 at this point in the paper. Scenario 1 is limiting the number of crews being transported per vessel, the hypothesis will be that this increases the time it takes to complete maintenance tasks, hence increasing the length of downtime events. Scenario 2, with uninterupted shifts, has the potential to reduce downtime, by removing the mid-day crew change and handover. These two scenarios Applied at the same time will most likely, cancel each other out to a certain extend? Similar arguments can be made for scenarios 3 and 4, one increasing (wind turbine) downtime and one having the potential to decrease (wind farm) downtime. Applying all four scenarios at once (while most likely very easy in the verification setup you have built), does not offer an obvious benefit. Later, we implicitely learn that scenario 5 makes UWiSE most like WOMBAT, could you please explain this reasoning behind scenario 5 already at the start of section 3.2. to avoid confusion?
324ff: It would be more logical to present the results for the scenarios in "chronological" order, i.e. starting with scenario 1 and ending with 4.Â
Section 3.3: The unit of k€ /MW /y as presented in Figure 15 (and formatted wrong in the text) is a concept that is not readily understood. Cost per MW or even cost per MW*time (e.g. MWh) is more readily understood. Consider refomulating, in order to avoid the double fraction, which creates ambiguity.Â
Technical corrections:
Caption Figure 1: Iteratre -> Iterate
229: suggestion to add an additional comma: towing as well as reconnection -> towing, as well as reconnection
238: replace "," with "."
323: observed variation is maintenance cost -> observed variation in maintenance cost
353: On opposite -> on the opposite
Figure 13: The labeling inside the figure is too small. Consider including just one legend (as it is the same across all scenarios, labelling the scenarios next to the graph, reducing the number of scenarios shown in the figure, a combination of the above suggestions, or any other measures to improve readability.
Section 3.3: the units for costs are formatted wrong.
394: workin -> working