Thesis

Operations and maintenance modelling for the future generation of offshore wind energy

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Awarding institution
  • University of Strathclyde
Date of award
  • 2025
Thesis identifier
  • T17488
Person Identifier (Local)
  • 202157127
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Abstract
  • In today’s fluctuating economic climate, reducing the cost of energy remains a critical objective in the development and deployment of renewable technologies. Offshore wind, as a leading source of large-scale renewable power, must continually improve its cost efficiency to remain competitive. One of the most significant areas of cost reduction potential lies in Operations and Maintenance (O&M), which can account for a substantial portion of the total lifecycle cost of offshore wind farms. Over the past two decades, a wide range of strategies and modelling approaches have emerged to optimise O&M planning and execution. This thesis contributes to this evolving field by applying and extending O&M modelling techniques to address some of the emerging challenges facing next-generation offshore wind farms. The work is structured in two parts. Part I builds upon an existing O&M simulation model focused on corrective and scheduled maintenance. Although the foundational model predates this work, it is significantly extended through new functionalities, updated failure data, and novel scenario analyses. The first set of investigations examines how environmental and human factors influence maintenance strategy and long-term costs. Specifically, it explores the impacts of limited daylight, sea ice, and health and safety considerations, such as the feasibility and effectiveness of implementing night shifts. The findings highlight that human and environmental constraints must be integrated into O&M planning: night shifts offer little economic advantage in regions with extended daylight, and while operating in ice-prone waters is feasible, it introduces substantial additional costs due to the need for icebreaking vessels. These trade-offs underscore the importance of context-specific cost-benefit analysis. Further in Part I, the model is applied to assess the operational implications of turbine upscaling. The first phase evaluates the performance of larger 10 MW turbines using failure rates derived from smaller, well-established turbine classes. The second phase introduces newly collated failure rate data from literature for 15 MW turbines and explores the impact of different drivetrain configurations across various site conditions. Results show that drivetrain performance is not universally transferable across turbine sizes. For example, medium-speed geared turbines, previously seen as less favourable in other studies, performed comparably to directdrive turbines in milder metocean conditions, owing to improved reliability and reduced turbine downtime. These findings emphasise the importance of selecting drivetrain technologies based on site-specific conditions, rather than simply extrapolating from existing small-turbine trends. The analysis also reveals a persistent gap in publicly available failure rate data for next generation turbines, limiting the precision of O&M modelling in this area. Part II introduces a novel O&M model that incorporates opportunistic maintenance, a strategy that leverages curtailment periods and internal triggers within the wind farm to reduce operational costs. This new model expands the capability of traditional O&M tools by allowing maintenance planners to capitalise on underutilised windows for intervention. Through a series of case studies and sensitivity analyses, the model is benchmarked against conventional approaches. Results demonstrate that the opportunistic strategy can significantly reduce costs when curtailment is sufficiently frequent and teams are prepared to respond flexibly. However, in low-curtailment scenarios, the marginal gains may not justify the added complexity. Sensitivity analysis further identifies uncertainty in failure distributions as a key source of variability in model outcomes, reinforcing the importance of accurate, turbine-specific reliability data. The thesis concludes by synthesising the insights from both modelling approaches and providing actionable recommendations for developers, planners, and researchers working to optimise O&M strategies for future offshore wind farms. It also outlines key directions for future research, particularly in improving failure data availability and enhancing the adaptability of maintenance models to real-world operational dynamics.
Advisor / supervisor
  • Carroll, James
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DOI
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