the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
IMAP-WFO: A holistic optimization tool for bottom fixed offshore wind farm design and control
Abstract. Offshore wind farms, critical for sustainable energy production, face the challenge of optimization among many parameters influencing key performance indicators in competitive ways. This research introduces the novel Integrative Maximized Aggregated Preference Wind Farm Optimization (IMAP-WFO) framework – a comprehensive tool designed to enhance flexibility, accuracy, and uncertainty quantification in offshore wind farm design and operation. Existing methods often fall short due to limitations in adaptability and precision, especially when modeling complex multi-physical behaviors under uncertain conditions. IMAP-WFO overcomes these limitations by combining advanced statistical techniques and simulation methods. At its core are parametric design performance functions, capturing critical aspects of wind farm behavior, including energy production, material usage, and structural fatigue. These functions rely on Kriging meta-models. To address inherent uncertainty, Monte Carlo simulations provide a probabilistic assessment of outcomes. IMAP-WFO's true innovation lies in translating technical functions into socio-economic objectives, including sustainability metrics, annual energy production, capital expenditure, operational expenditure, model uncertainty, and lifetime fatigue. Stakeholders can dynamically weigh these objectives based on their preferences. A validation process ensures the accuracy of design performance functions, comparing simulated results with real-world data. IMAP-WFO's application is demonstrated through case studies: optimizing the levelized cost of energy and exploring wind farm control strategies.
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RC1: 'Comment on wes-2024-117', Anonymous Referee #1, 14 Nov 2024
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The manuscript describes an interesting tool for bottom fixed wind farms and provides relevant examples of application. The topic is of current interest for the development of offshore wind farms
It is recommended to improve the fluency of the language before publication.
The following corrections are required:
-lines 75-80
Add unit of measure. AEP is called later AED in a following section while there is not a relevant section to explain the uncertainty
-Table 1
Which is the reference for current speed values
-Line 95
How should we extend the limits of the parameters for industrial application?
-Line 106
Add the reference to Pywake
-Line 125
Add the reference for k-means clustering
-Line 132
Provide some reference of the module NetworkX
-Line 143
Provide a reference of the database Saravanan and Sridhar
-Provide some references of Table 2
-Line 162
Describe the power loss equation
-Sections 3.1.1 and 3.1.2
Describe the % difference between the real case and the model
-Line 231
Describe better what means highly accurate
-Line 234
What means adapting the cable in 4
-Section 3.2
Public details of the offshore wind farm could be described
-Line 250
What means based on 2?
-Line 252
Explain meaning of FCR
-Line 260
Provide some reference of the planned 25 MW?
-Figure 13
Rewrite "Amount of producing turbine" in better englishCitation: https://doi.org/10.5194/wes-2024-117-RC1 -
RC2: 'Comment on wes-2024-117', Anonymous Referee #2, 18 Nov 2024
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Reviewer Comments
Title: "IMAP-WFO: A holistic optimization tool for bottom-fixed offshore wind farm design and control"Dear authors,
Thank you for your submission and the hard work evident in this research. Your publication is well-prepared and touches on several relevant research topics. However, I believe that the manuscript attempts to address too many objectives simultaneously, which hinders its ability to achieve its ambitious goals effectively.
Main Comments:
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Balance Between Approach and Strategic Conclusions:
On one hand, the paper focuses on the methodology, taking reasonable shortcuts such as the extra-coarse discretization in Table 1 or using somewhat outdated mass/CapEx models like Fingersh et al. (2006). On the other hand, the results are used to draw conclusions about strategic aspects, such as the value of continuous upscaling of offshore wind turbines. These two objectives (shortcuts in modeling and drawing strategic conclusions) cannot coexist harmoniously within the scope of this publication. I am particularly skeptical about the latter objective, which seems out of reach given the fidelity of the models implemented in IMAP-WFO. -
Tone and Perception of Promotion:
At times, the manuscript feels like a commercial promotion for IMAP-WFO. While I recognize the tool’s potential value for both research and industrial applications, scientific papers are not the appropriate venue for marketing. Please ensure this comment is addressed in your revision.
Detailed Comments:
Line 5:
I am unsure whether IMAP-WFO truly overcomes the limitations of other tools. The shortcuts taken by IMAP-WFO seem to undermine the “adaptability and precision” stated in your claims.References:
References are inconsistently formatted throughout the manuscript. Proper citation styles (e.g., (Roeders et al., 2024) for parenthetical and Roeders et al., 2024 for inline) should be applied uniformly. If using LaTeX, I recommend commands \citep{} and \citet{} for consistency.Line 28:
Fatigue arises not only from annual energy production (AEP) but also from wake effects. Tightly spaced turbines exhibit low AEP and high fatigue.Line 121:
Is SeaHOWL not an aeroelastic tool? How does it provide high-fidelity tower and monopile designs?Figures 3, 4, and 5:
The font size is too small, making these figures difficult to interpret even when zoomed in. Additionally, these figures are not sufficiently discussed in the text.Section 2.2.1:
This section is too brief for one of the novel aspects of your work. Please expand this section to provide a more comprehensive explanation.Table 2:
Provide additional discussion of Table 2. For instance, what do the terms “prediction(D)” and “SD(D)” mean?Line 226:
What is meant by “AEP underestimates fatigue”? This needs clarification.Line 215:
You refer to “reference projects,” but the document leaves the reader completely in the dark about what these are. While I understand proprietary limitations, some context is essential for clarity.Line 235:
You report a 74% error in total cable weight and adjust the unit cost of cables to compensate. What is this 74% error compared to? A real wind farm? Can your results be trusted when one of your submodels deviates so significantly? This warrants further discussion.Line 270:
The relatively small importance of certain factors is an interesting result. Consider highlighting this in the conclusions. Additionally, I recommend showing trends of levelized cost of energy (LCOE) with respect to the main design variables. Is a much larger turbine truly necessary to unlock LCOE savings?Figure 13:
The figure lacks clarity due to the monochromatic palette. Using colors would make it easier to distinguish between producing (currently grey) and non-producing (currently black) wind turbines under different energy price scenarios.Line 278:
Case 3.4 is difficult to follow. Initially, you seem to optimize curtailment strategies for a given electricity price, but later the text implies reoptimization of the layout. Additionally, is operational expenditure (OpEx) constant even for non-operating turbines? That assumption appears unrealistic. Please revisit this section for clarity and accuracy.Thank you for considering these comments. I look forward to seeing a revised manuscript that addresses these concerns.
Best regards,
Citation: https://doi.org/10.5194/wes-2024-117-RC2 -
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