Marine operations required to transfer technicians and equipment represent a significant proportion of the total cost of offshore wind. The profile of sites being considered for floating offshore wind farms (FOWFs), e.g., further from the shore and in harsher environments, indicates that these costs need to be assessed by taking into account the maintenance requirements and restricted weather windows. There is an immediate need to investigate the potential use of robotic systems in the wind farm's operations and maintenance (O&M) activities, to reduce the need for costly manned visits. The use of robotic systems can be critical, not only to replace repetitive activities and bring down the levelised cost of energy but also to reduce the health and safety risks by supporting human operators in performing the desired inspections. This paper provides a review of the state of the art in the applications of robotics for O&M of FOWFs. Emerging technology trends and associated challenges and opportunities are highlighted, followed by an outline of the agenda for future research in this domain.

Operations and maintenance (O&M) of floating offshore wind farms (FOWFs) poses various challenges in terms of greater distances from the shore, harsher weather conditions, and restricted mobility options. Robotic systems have the potential to automate some parts of the O&M leading to continuous feature‐rich data acquisition, operational efficiency, along with health and safety improvements. There remains a gap in assessing the techno‐economic feasibility of robotics in the FOWF sector. This paper investigates the costs and benefits of incorporating robotics into the O&M of a FOWF. A bottom‐up cost model is used to estimate the costs for a proposed multi‐robot platform (MRP). The MRP houses unmanned aerial vehicle (UAV) and remotely operated vehicle (ROV) to conduct the inspection of specific FOWF components. Emphasis is laid on the most conducive O&M activities for robotization and the associated technical and cost aspects. The simulation is conducted in Windfarm Operations and Maintenance cost‐Benefit Analysis Tool (WOMBAT), where the metrics of incurred operational expenditure (OPEX) and the inspection time are calculated and compared with those of a baseline case consisting of crew transfer vessels, rope‐access technicians, and divers. Results show that the MRP can reduce the inspection time incurred, but this reduction has dependency on the efficacy of the robotic system and the associated parameterization e.g., cost elements and the inspection rates. Conversely, the increased MRP day rate results in a higher annualized OPEX. Residual risk is calculated to assess the net benefit of incorporating the MRP. Furthermore, sensitivity analysis is conducted to find the key parameters influencing the OPEX and the inspection time variation. A key output of this work is a robust and realistic framework which can be used for the cost‐benefit assessment of future MRP systems for specific FOWF activities.

Decommissioning is an emerging practice for the offshore wind industry. To date, the Yttre Stengrund and Vindeby Offshore Wind Farms are the only installations to have been fully dismantled. While every project requires a decommissioning plan to gain consent; they are high-level estimates of the expected strategy, time required and costs. This is due to the lack of reliable data or experience. However; if underestimated, decommissioning may result in significant and unexpected outgoings at the end of a farm lifecycle. Simulation is an effective way to test a plan is both executable and cost-effective, as well as optimising activities for an individual site. Therefore, a stochastic tool was developed to simulate a wide range of decommissioning methods, considering the impact of uncertain factors such as weather and costs on time and expenditure. It is the first detailed simulation model developed for this crucial project phase. This paper describes the scope of the model; documents the validation activities undertaken; and demonstrates the model’s capabilities. It was found that while further validation against real-case scenarios is necessary, the model can indicatively forecast costs and time for a given strategy as well as optimise scenarios and mitigate risks.

Levelized cost of energy (LCoE) is the most common metric used in renewable energy assessments. However, this can be a very complex calculation with numerous methodologies depending on the perspective taken. Inputs including costs, energy production are generally forecasts and predictions based on publicly available information; therefore, they are key areas of uncertainty. Elements of the calculation are site or region specific such as the tax rate or inclusion of grid connection costs. The business case and financial assumptions applied will be very project specific, e.g., the discount rate applied. These numerous variables and uncertainties must be fully understood in order to effectively apply the metric or review and compare LCoEs. Therefore, this paper provides a comprehensive set of LCoE methodologies that provide a reference basis for researchers. A case study demonstrates the application of these methods and the variation in results illustrates the importance of correctly selecting the discount rate and cash flow based on the perspective and motivation of the user. Sensitivity studies further investigates the potential impact of key variables and areas of uncertainty on results. Analysis indicates that the energy production and discount rate applied will have the most significant impact on LCoE, followed by Capital Expenditure (CAPEX) costs. While the key areas of uncertainties cannot necessarily be solved, this paper promotes consistency in the application and understanding of the metric, which can help overcome its limitations.

The offshore wind sector has achieved significant cost reductions in recent years. However, there is still work to be done to maintain and surpass these savings across current and future farms. There is increased competition to reduce costs within the industry itself. Additional challenges are foreseen at future sites located further from shore, in harsher conditions and deeper waters. Larger turbines and projects also mean new equipment, logistics and maintenance requirements. Moreover, farms are approaching the decommissioning phase where there is little experience. Modelling is a safe and cost-effective way to evaluate and optimise operations. However, there is a lack of comprehensive decision-support tools, detailed enough to provide insight into the effects of technological innovations and novel strategies. To address the gap, the EU FP7 LEANWIND project developed a suite of state-of-the-art logistics optimisation and financial simulation models. They can assess a farm scenario in detail at every stage of the project lifecycle and supply-chain, identifying potential cost reductions and more efficient strategies. This paper introduces the models including: an overview of their scope and capabilities; how they can be applied; and the potential end users.

This paper has developed an operation and maintenance (O&M) model for projected 20 MW tidal stream farm case studies at two sites in the northeast Atlantic in France and at EMEC’s Fall of Warness site in the UK. The annual energy production, number of incidents, and downtimes of the farms for corrective and planned (preventive) maintenance strategies are estimated using Monte Carlo simulations that vary weather windows, repair vessel availabilities, and mean annual failure rates modelled by Weibull distributions. The trade-offs between the mean annual failure rates, time availability, O&M costs, and energy income minus the variable O&M costs were analysed. For all scenarios, a 5-year planned maintenance strategy could considerably decrease the mean annual failure rates by 37% at both sites and increase the net energy income. Based on a detailed sensitivity analysis, the study has suggested a simple decision-making method that examines how the variation in the mean annual failure rate and changes in spare-part costs would reduce the effectiveness of a preventive maintenance strategy. This work provides insights into the most important parameters that affect the O&M cost of tidal stream turbines and their effect on tidal energy management. The output of the study will contribute to decision-making concerning maintenance strategies.