the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Probabilistic cost modeling as a basis for optimizing the inspection and maintenance of support structures in offshore wind farms
Abstract. The operational management of offshore wind farms includes inspection and maintenance (I&M) of the turbine support structures. These activities are complex and influenced by numerous uncertain factors that affect their costs. The uncertainty in the I&M costs should be considered in decision and value of information analyses performed to optimize I&M regimes. In this paper, we present a probabilistic cost model for I&M activities in an offshore wind farm serviced by boats operating from a port base. The model is developed based on interviews with a wind farm operator, consultants, and operation and maintenance engineers, as well as on scientific literature. Various I&M methods are considered, and the model is evaluated to predict probabilistic I&M costs at different levels, i.e., wind farm, structural system, and structural component. A sensitivity analysis is performed to study the influence of the different model parameters on the overall I&M costs. Finally, the model is included in a numerical example in which the I&M regime for a steel frame subject to fatigue is optimized using risk-informed methods. The frame's characteristics are comparable to those of a jacket structure supporting an offshore wind turbine. In the example, we demonstrate that the I&M costs can be considered deterministically as expected values since they are included in the optimization on a linear basis.
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Status: open (until 30 Jun 2024)
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RC1: 'Comment on wes-2023-176', Anonymous Referee #1, 23 May 2024
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The article is an optimization framework for offshore wind farms in which the price of inspections and maintenance (I&M) is an objective to optimize. A support structure (namely steel frame) is considered in the optimizations, and a practical application example is provided. The work is a comprehensive effort, as several aspects of offshore wind farms are incorporated in the analysis, including port activities and associated costs with necessary equipment and personnel and time to complete inspections. Taking more refined assumptions in optimization frameworks is desirable as it helps reduce uncertainties in the results, constituting an interesting analysis/tool for the wind community. Therefore, the topic and tools shown are worth investigating.
1) Line 41: "Typically, operation and maintenance (O&M) of an offshore wind farm corresponds to 25% - 30% of the levelized cost of energy (LCoE) (Ambuhl and Sorensen, 2017; Kolios and Brennan, 2018; Maples et al., 2013; Röckmann et al., 2017)."
Comment: Could you provide other references to confirm these values?
2) Line 43: "One option to reduce the LCoE, is to optimize the inspection and maintenance (I&M) regime for the turbine support structures."
Comment: Can you briefly comment on the other options? And how do they compare? Why specifically look at support structures? Is it just because it is a possible way? I think here, it is missing a more well-defined motivation.
3) Figure 1. there is a mistake in the figure: some written content overlaping
4) Comment on section 2: Can't you put some of the theory equations in an appendix, and translate all the math into a nice history? Readers can go to the appendix for more details if/when needed. In the way currently written, the whole history loses a bit of the flow due to the heavy math.
5) Section 7:
5.1 In my view, Equations 21-37 are all out of place. These should have been written in the methods section.5.2 The results are superficially discussed in Figures 12 and 13. Similar problems were identified for Tables 7 and 8, and the other figures in the section.5.3 In general, this section lacks a more objective and comprehensive discussion of the numerical results for the example.Other comments
6) Your work assumed a lognormal distribution for the majority of your statistical fits. I am missing some sort of discussion on the implications of these assumptions. Why not another distribution? Could your results be changed otherwise?
7) The model is not completely reproducible, as some of the assumptions are based on interviews with project owners and stakeholders. There is little information about it in the article. Therefore, I would like to see further details on the assumptions as much as possible. What would be the implications of not having these pieces of information? Could you have done your work without it? How would it be affected? I am missing a discussion here.
Citation: https://doi.org/10.5194/wes-2023-176-RC1 -
RC2: 'Comment on wes-2023-176', Anonymous Referee #2, 14 Jun 2024
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- The second sentence of the Introduction "The integrity management..." is awkward and I don't understand what it is trying to say. There are a handful of other sentences and phrases in the manuscript like this to. Perhaps a professional editor could help.
- The fourth paragraph of the Introduction (around line 60 on page 2) throws 15 citations at the reader in one shot. I think the authors could go a bit further to describe this previous work and similarities or distinctions between them.
- Introduction line 75, "In the numerical example, we
demonstrate that the I&M costs can be considered deterministically as expected values in the analysis since they are included in the model on a linear basis." This statement and conclusion does carry through the paper from beginning to end, but it also undercuts the approach and leaves me unsure of its contributions. The authors note previous research where deterministic models were used probabilistically, so what is the meaningful difference? The authors should be more clear about what is new and novel in this work here. More thoughts on this theme in later comments as well.- Figure 1: There is overlapping text in the PDF in the top bar header of the figure
- Tables 1, 2, 3, 4: Where are these input values coming from? Data? Interviews? Modeled processes? Data for operations and maintenance is the hardest to come by, so the authors need to be precise when reporting their inputs to their model. I suggest citations in the caption.
- Page 10, "Due to the lack of data on the parameters ... their marginal probability distributions are assumed to be lognormal." Where does this assumption come from. With just a min and a max and a lack of data, seems like a uniform distribution, or maybe even a triangular distribution with a "most likely value" would be better approximations in a sparse data context. Why lognormal? Why not normal or any other distribution? This seems to be a critical assumption because the output distributions are also lognormal, so they track the input assumptions closely.
- Table 5: I am confused by this table. I understood how Tables 1-4 are converted to be lognormal based on min & max values, but where do these distributions come from? Are these inputs or outputs? If inputs, then sources and citations must be included. Also, how would a parameter such as "Campaign cost" be an input when Section 4 described the elements that comprise a "campaign".
- Figures 3-5: The outputs are clearly lognormal to match the input distributions, which asks the question of what is "probabilistic" about this model if all of the operations are linear enough to maintain the input distribution properties. Phrased another way- what is gained by using probabilistic inputs vs deterministic inputs here. Alternatively, if the authors had assumed uniform input distributions, would their conclusions be any different?
- Section 6 Sensitivity Analysis: I applaud the authors for including a sensitivity analysis, as that is typically where probabilistic analysis gives the greatest insight. However, I am struggling with the communication of results in Figures 6-9. It seems like the authors are trying to show too many dimensions of variation all at once and the important comparisons are spread out across the figures. Additionally, the figures are small and tough to read or see. Also, in the end, only three variables are compared at one time in the different color lines. The impact of the sensitivity analysis is to compare the relative impact of many variables all at once. Why not combine Figures 6-9 and all of the above/below water differences, show more comparisons, perhaps with a bar chart, and then show some perturbations, such as a bar chart with 1 or 5 inspected components. The way the sensitivity analysis is presented now, I have little meaningful takeaways or insights into the problem.
Figure 10: I don't fully understand the physics that are driving the failure model of the jacket. Is there is a time-series load signal from a turbine that is propagated to the jacket? Something more prescribed or analytical? Are all of the 22 stress concentration hot spots at the welded joints considered independent random variables? If so, how realistic is that? It seems as though the later analysis on the optimal number of inspections is highly dependent on this assumption, because of the tendency to inspect all of them as soon as one crack is found. Furthermore, there is a greater likelihood to continue to inspect all of them after a crack is found and repaired.
Section 7.2: The math on the first couple of pages here is probably best moved to an Appendix. Once the Appendix is created though, the authors should consider shunting other equations there as well to improve the paper's readability for the wind-focused audience of the journal.
Section 7.3: I don't follow what is different about this section and the results. Figure 14 looks the same as Figure 11.
Citation: https://doi.org/10.5194/wes-2023-176-RC2
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