Floating offshore wind (FOW) is a renewable energy source that is set to play an essential role in addressing climate change and the need for sustainable development. However, due to the increasing threat of climate emergency, more wind turbines are required to be deployed in deep water locations, further offshore. This presents heightened challenges for accessing the turbines and performing maintenance, leading to increased costs. Naturally, methods to reduce operational expenditure (OpEx) are highly desirable. One method that shows potential for reducing OpEx of FOW is LIDAR-assisted pitch control. This approach uses wind velocity measurements from a nacelle-mounted LIDAR to enable feedforward control of floating offshore wind turbines (FOWTs) and can result in reductions to the variations of structural loads. Results obtained from a previous study of combined feedforward collective and individual pitch control (FFCPC + FFIPC) are translated to OpEx reductions via reduced component failure rates for future FOW developments, namely, in locations awarded in the recent ScotWind leasing round. The results indicate that LIDAR-assisted pitch control may allow for an up to 5% reduction in OpEx, increasing to up to 11% with workability constraints included. The results varied across the three ScotWind sites considered, with sites furthest from shore reaping the greatest benefit from LIDAR-assisted control. This work highlights the potential savings and reduction in the overall levelised cost of energy for future offshore wind turbine projects deliverable through the implementation of LIDAR-assisted pitch control.

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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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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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As new offshore wind development sites move further from shore and existing sites enter their post-subsidy operating period, it is expected that operational expenditure (OpEx) will increase. In order to overcome these challenges, a more flexible and cost-effective maintenance solution is needed. One such solution is opportunistic maintenance (OM). This work provides an overview of the maintenance strategy used within other industries before providing an in-depth review of the work specific to offshore wind. The existing literature fails to agree on the specific definition of the term. This work proposes an all-encompassing definition of the term, reviewing maintenance ‘opportunities’ and their corresponding ‘action/response’. The review found that maintenance opportunities are either internal or weather-based, with each opportunity having a pre-determined trigger/response. This work proposes the introduction of a market-based opportunity, which has not been previously considered. As offshore wind farms now face increasing curtailment and negative pricing threats, this new OM framework, OM+, view these periods as maintenance opportunities. OM+ also provides a new definition for recording and reporting availability — moving from time/energy-based availability to market-based availability.

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New wind turbine technologies and designs are being explored in order to reduce the cost of energy from offshore wind farms. Two potential routes to a lower cost of energy are the X- Rotor Concept (XRC) and Multi-Rotor System (MRS) turbines. A key cost saving for both Novel concepts included in this paper is operation and maintenance (O&M) costs savings. The major component replacement cost for conventional horizontal axis, XRC and MRS turbines are examined and the benefits of the concepts are provided in this paper. A review on existing decision support systems for offshore wind farm O&M planning is presented with a focus on how applicable these previous models are to novel turbine concepts, along with analysis of how the influential factors can be modified to effectively model XRC and MRS.

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This paper presents the key operations & maintenance (O&M) modelling inputs for fixed-bottom wind (FBW) and highlights the adaptations required for floating offshore wind (FOW) uses. The work also highlights major repair strategies such as tow to shore (T2S) and discusses the limitations and constraints which arise in an operational context. The technical and economic feasibility of such O&M strategies requires rethinking of weather risks and constraints, new vessel technologies and operational costs. The work also collates and reviews existing FBW models which have been adapted for FOW uses and analyses O&M inputs for a tow to shore operation. Findings show that there is ambiguity in literature for tug speeds and disconnection/reconnection times of the turbine system. A performed case study investigates the sensitives of both parameters through a weather window analysis of ScotWind sites. Recommendations for future practises, including additional O&M modelling considerations and inputs for FOW uses are given.

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As the installed capacity of individual turbines increases, so do costs associated with manufacture and maintenance. One proposed solution to this problem is the Multi-Rotor System (MRS) which utilises many small rotors to yield the same energy capture as a single large turbine. The operational advantage of the MRS is the built in redundancy between rotors on the same structure. However, despite this advantage, an increase in number of components is likely to result in an increase in transfers. This work examines the balance between additional crew and vessel requirements for such a structure against the expected savings in downtime due to redundancy and small rotor power rating. Three scenarios are analysed to determine the distribution of the failures which contribute to downtime. The study aims to find the optimal vessel fleet which limits downtime without drastically increasing direct operational expenditure (OpEx). As site size increases, the impact of global failures, which shut down the whole asset, is lessened. However, there is a significant increase in the number of vessels required to reduce downtime to <10% of the total OpEx. While a large fleet can offer significant downtime savings, there are practical limitations and challenges which must be acknowledged.

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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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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