Offshore wind energy is expected to be the most significant source of future electricity supply in Europe. Offshore wind farms are located far from the shores, requiring a fleet of various types of vessels to access sites when maintaining offshore wind turbines. The employment of the vessels is costly, accounting for the majority of the total O&M costs for offshore wind energy. Therefore, configuring the size and mix of the vessel fleet to support maintenance operations in a cost-effective manner is an issue of importance to enhance economics of offshore wind sector. In this paper, a discrete event simulation based model is proposed to present how a mixed vessel fleet with the specific configuration, including crew transfer vessels, field support vessels, and heavy lift vessels, performs maintenance for an offshore wind farm. The economic performance of the vessel fleet under a predetermined condition-based opportunistic maintenance strategy is investigated by using the model. A metaheuristic algorithm, simulated annealing, is employed to find the optimal fleet size and mix to make leasing decisions with the minimum costs. The performance of the developed approaches is evaluated by using a generic offshore wind farm in the North Sea. The sensitivity analysis is performed to investigate the most influential O&M factors.

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Effective operation and maintenance (O&M) management is significant for enhancing the economic performance of offshore wind farms. Despite recent research progress in O&M, there remains a gap in integrating health prognostics and spare parts inventory into decision-making processes at the scale of offshore wind farms. To bridge this gap, this paper develops an optimisation framework integrating these aspects to establish cost-effective joint maintenance and inventory policies. In the framework, a maintenance policy is firstly developed to plan maintenance actions based on component health and maintenance opportunities. Meanwhile, in order to support maintenance implementation, a multi-echelon inventory network using (s, S) policies is proposed to store diverse units across distinct warehouses. A genetic algorithm (GA) is then employed to identify the optimal policy, aiming to minimise overall costs. Upon developing the optimisation framework, in order to illustrate the application of the proposed approach in practice, a numerical simulation of a generic offshore wind farm in the North Sea is performed. Results demonstrate that comprehensive O&M management considering interrelationship between maintenance and inventory policies reduces overall costs, showcasing its capacity in strengthening the economic performance. Finally, sensitivity analysis is performed to investigate the most influential O&M factors, providing actionable insights for O&M management.

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Europe’s offshore wind capacity is expected to reach 450 GW by 2050, meeting 30% of Europe’s electricity demand. With the increase of installed capacity, the costs invested in O&M will also increase significantly considering O&M cost is one of the biggest contributors to life cycle costs. The improvement of O&M management for offshore wind farms, especially maintenance logistics, represents a significant cost-reduction opportunity and will continue to be a primary factor in shaping the future development of the offshore wind sector. Recent research provides clear insights into maintenance logistics management, categorizing decisions into three levels, strategic, tactical, and operational. Maintenance strategies and resource organization are strategic and tactical decisions respectively, with a long lasting influence on offshore wind farms. With sensors and communication technologies, wind farm owners/operators and service providers can use the health information of wind farms to design maintenance strategies and organize maintenance resources, and utilize new data to update decisions to realize a closed-loop manner. Thus the research question of this thesis is how to improve the effectiveness of maintenance strategy and resource organization for offshore wind farms and move towards a closed-loop decision-making approach? In this thesis, an open-loop predictive opportunistic maintenance strategy utilizing predicted component failures and maintenance opportunities is developed first. Then, the influence of inaccuracy or uncertainty in model parameters is quantified on maintenance performance and strategies. The significance of different uncertainties is ranked, and suggestions are provided to cope with the uncertain decision-making environment. Next, the approaches are proposed to organize the primary maintenance resources, i.e., spare parts and service vessels, to support the implementation of the open-loop maintenance strategy in a cost-effective manner. Finally, the open-loop maintenance strategy develops towards a closed-loop maintenance strategy that is able to capture dynamic wind farm states and mitigate the influence of model parameter uncertainties, reducing more revenue losses than open-loop approaches. Overall, this thesis provides a series of approaches for offshore wind farm owners and operators and maintenance service providers to instruct the strategic and tactical maintenance logistics for offshore wind farms, showing the potential for improving the effectiveness and moving towards a closed-loop manner.@en

The determination of maintenance strategies is subject to complexity and uncertainty arising from variable offshore wind farm states and inaccuracies in model parameters. The most common method in the existing studies is to adopt an open-loop approach to optimize a maintenance strategy. However, this approach lacks the ability to capture periodic operational state of the wind farm and the awareness of eliminating uncertainty. Consequently, the determined strategy is inadequate to instruct maintenance activities, inducing excessive revenue losses. In this paper, a closed-loop maintenance strategy optimization method is proposed for decision-makers to identify a more profitable manner of wind farm maintenance management. The life-cycle maintenance optimization problem is decomposed into a sequence of sub-optimization problems covering multiple time periods by using a rolling-horizon approach. Each sub-optimization problem is intentionally designed based on the monitored state of the wind farm and the available reliability, availability, and maintainability (RAM) database. Meanwhile, the decision maker consciously mitigates the parameter uncertainty in the maintenance model gradually by updating the current database. Compared to conventional strategies covering the entire lifetime of wind farms, the proposed maintenance strategy is periodically adjusted to provide a series of sub-strategies. The proposed approach was applied in a simulation experiment, a generic small-scale offshore wind farm, to assess its performance. Computational results show that adapting maintenance strategies based on the current state of the wind farm can reduce revenue losses in comparison to conventional open-loop strategies. In addition, the benefits of updating the RAM database in decreasing revenue losses is revealed.

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While offshore wind energy is showing enormous potential, effective approaches to enhance its economics are being sought at the same time. The design of maintenance strategy is a type of strategic decision-making for offshore wind farms, aiming to improve energy production and reduce maintenance expenses. As a complicated and challenging task, the maintenance decision-making is confronted with various types of uncertainty in the model. The presence of uncertainty affects the estimation of maintenance performance, and renders the determined maintenance decisions sub-optimal or even inappropriate. In this paper, the authors propose an integrated decision-making framework incorporating i) a maintenance model which is applied to estimate maintenance performance, including maintenance costs and production losses, ii) a probabilistic uncertainty modelling approach which is used to characterize different types of uncertainty and a Monte Carlo method is adopted to generate stochastic scenarios, and iii) a multi-objective optimization method used to find the optimal decisions in the presence of conflict between multiple objectives. The uncertainties considered in the model include the stochastic attributes of time to failure, deviation between real and predicted failure times of components, and uncertain maintenance consequences. The proposed framework was applied in a generic 150MW-offshore wind farm located in the North sea. Results demonstrate that the deterministic scenario underestimates the maintenance costs and production losses, leading to the consequence that the developed maintenance strategy becomes unsatisfactory. A new series of solutions including priority solutions and trade-offs is provided for decision-makers to satisfy different goals while involving uncertainty. In addition, the influence of different uncertainties on the maintenance performance is quantified to assess the significance. The proposed optimization framework constitutes a useful decision-making tool to instruct the long-term maintenance strategy for offshore wind farms in a practical environment involving a high degree of uncertainty.

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Operation & maintenance (O&M) costs account for a large portion of total life cycle cost for onshore wind energy, and the amount is estimated to be more for offshore wind energy. Developing a sound opportunistic maintenance strategy is a solution to reduce O&M costs and enhance wind energy's competitiveness. When the wind farm is located offshore, turbines are not only subject to degradation but also the impact from the harsh marine environment. However, the degradation is mainly regarded as the only cause of the failure in the existing opportunistic maintenance models for the offshore wind energy sector. At the same time, too frequent preventive dispatch of maintenance teams exists on some occasions. This paper proposes a maintenance strategy for offshore wind farms integrating three types of maintenance opportunities. In addition to the maintenance opportunities created by degradation failures and incidents, an age-based opportunity is introduced to improve the trigger of preventive dispatch. A numerical example is presented to illustrate the effectiveness of the proposed strategy. The comparative analysis shows 2.6% and 1.5% annual cost can be reduced respectively when compared with two traditional opportunistic maintenance strategies in the base scenario.

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The increasing capacity of offshore wind energy around the world brings challenges in Operation and Maintenance (O&M) management. Over the past years, many studies have focused on developing sound maintenance strategies in order to minimize maintenance cost or maximize availability. One of the promising maintenance strategies is opportunistic maintenance due to its potential to combine maintenance activities and save maintenance efforts. In these models, a common assumption is made that input parameters are deterministic and maintenance decisions are made based on these assumed deterministic input parameters. However, offshore wind farm maintenance in the practical world is a complicated task where multiple types of uncertainty exist. These uncertainties may affect evaluation or output of the maintenance model, making maintenance decisions sub-optimal or even inappropriate. In this paper, a probabilistic simulation-based approach integrating an uncertainty module and a simulation module is proposed to study the influence of the uncertainties on maintenance performance. We identify the primary input parameters which should be considered as uncertainty but are simplified to be deterministic values in offshore wind energy maintenance models. These deterministic parameters are modelled as stochastic values in the uncertainty module to generate uncertainty scenarios. The simulation module for opportunistic maintenance is developed to quantity the expected maintenance cost and lost production. The most influential uncertainties are identified. Valuable information and suggestions are provided to offshore wind farm owners for future decision-making and project management.@en

A sound maintenance strategy is significant for wind energy development, because it can strengthen the competitiveness of wind power. The design and development of maintenance strategies for wind energy is a complicated topic which involves failure modes, degeneration process, weather conditions, spare parts management, accessibility for repair, availability of maintenance resources and so on. If the maintenance decisions are irrational or maintenance actions cannot be planned reasonably, a large amount of unnecessary cost and production losses will be induced. In recent years, many researchers have concentrated on improving maintenance for wind energy assets. This paper makes a comprehensive review of the state of the art in maintenance strategy optimization for wind energy, and addresses main subjects existing in maintenance optimization, including maintenance policies, scope of optimization model, optimization objectives, influence factors and solution techniques. Concluding comments and research directions of potential interest are summarized at the end of the paper. The future maintenance strategies should comprehensively consider the changeable scenario and mutual dependencies to satisfy multiple objectives simultaneously in the wind farm level. The proposed maintenance strategy may be applied and tested in a real wind system to validate its effectiveness and efficiency.

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Operation and maintenance (O&M) costs account for a large proportion of the total costs for offshore wind energy. Performing a reasonable maintenance strategy is an effective approach to reduce O&M costs and gain more profits. In this paper, an opportunistic maintenance strategy for offshore wind turbine systems considering maintenance intervals of each subsystem is proposed to minimize the total maintenance cost. First, a Non-homogeneous Continuous-Time Markov Process based state transition model is established to study degradation process of subsystems. The influence of maintenance time schedule on the maintenance cost is studied to obtain the optimal maintenance intervals of each subsystems. Then, an opportunistic maintenance model considering economic dependencies between multiple subsystems is proposed to optimize the maintenance strategy by combining maintenance activities of individual subsystems to a grouping maintenance activity. A numerical example is used to indicate the significant effectiveness of the maintenance model. The result shows that the total maintenance cost of an offshore wind turbine system will be reduced by adopting the opportunistic maintenance strategy when compared with conventional preventive maintenance strategy.

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