Operation and maintenance of offshore wind farms

The operation and maintenance of offshore wind farms represent a significant proportion of the lifetime cost of energy of offshore wind.

Go to the profile of Iain Dinwoodie
Jul 17, 2017
1
0
Upvote 1 Comment

Authors: Iain Dinwoodie and David McMillan 

Abstract 

The operation and maintenance of offshore wind farms represent a significant proportion of the lifetime cost of energy of offshore wind. There are a number of significant differences from maintaining wind turbines in the onshore environment and new methodologies that will be required as wind farms increase in size and distance from shore. Understanding the key cost drivers is therefore vital in order to determine optimal maintenance strategies for existing wind farms and minimising the uncertainty associated with future projects. This study identifies the key issues associated with operations and maintenance of offshore wind farms for this purpose. A review of early operational experiences in the North Sea is also presented and key lessons learned to identify. In addition, a case study based on operating strategies for major maintenance actions is included. This case study demonstrates how different strategies can impact overall costs and the uncertainty of offshore wind.

Introduction

It is estimated that the operation and maintenance (O&M) of offshore wind contribute between a quarter and a third of the total lifetime cost of energy [1 – 3]. In addition, the typically higher wind speeds and larger size of offshore wind turbines result in a higher lost revenue from downtime than in the onshore case. As a consequence, O&M is critical to the financial success of offshore wind projects and adopting optimal maintenance strategies is vital. To achieve this, a number of industrial and academic projects have maximised lessons learned from early operational experience and reduced the uncertainty associated with O&M for future projects. The performance of these early projects along with the objectives and methodologies of the research projects are reviewed in this paper.

Operations and maintenance overview

Onshore, wind turbine maintenance has traditionally been performed on a fix-on-fail approach in conjunction with annual servicing. Repairs are performed as soon as spare components are available and if needed a crane can be brought on to site. Annual servicing is carried out during spring and summer months where wind speeds are typically lowest and the impact on lost revenue is minimal. Offshore, there is a significant added complexity to major repair and replacements of components because of the increased logistical complexity of the ocean environment and scarcity of required specialist vessels. Scheduled maintenance is also potentially restricted by accessibility as there is a limit to the number of vessels available as well as uncertainty over access windows. In addition, there is a huge degree of uncertainty surrounding the reliability performance of offshore turbines with failures at early sites occurring more frequently than historical onshore rates.

Principal cost drivers for offshore wind

Offshore wind is still in the development stage and there is a lack of operational experience when compared to onshore wind which can now be considered a mature technology. The majority of operational sites are still under their warranty period where the turbine original equipment manufacturers (OEMs) are responsible for the maintenance of the turbines. There is also a large degree of commercial sensitivity surrounding the performance of different turbines and sites resulting in a lack of operational data existing in the public domain. This has resulted in a large degree of uncertainty surrounding costs and performance of offshore wind. In addition, the majority of projects that have been developed have been in shallow sites and close to shore. An estimate of lifetime costs of energy, based on early wind farms around the UK and in Europe, is shown in Fig 1 with the O&M component broken down into the key drivers [4].

Fig 1: Estimated contribution to life time cost of energy and breakdown of O&M component

Analysis of OPEX has been carried out in [5] with the breakdown of costs shown in Fig. 2 . This work also identified that overall costs are more sensitive to the access threshold of crew transfer vessels (CTVs) and the failure rate and repair duration of major components requiring heavy lift vessels. Issues surrounding the failure rate of wind turbines and accessibility of offshore wind farms are considered in Sections 3 and 4 of this paper.

Fig 2: Contributions to total and direct OPEX, adapted from [5]

When considering O&M costs there are two principal cost aspects to consider. Firstly, there are direct costs that must be paid by operators. These comprise of vessel costs, component cost, staff costs and other fixed costs such as port and insurance fees. Secondly, any lost production because of the turbine downtime can be considered as an indirect cost. This does not require a financial output but represents a lost revenue opportunity. The optimal strategy will be the one that minimises the combined direct and indirect costs as shown in Fig 3.

Fig 3: Direct cost and lost revenue trade off

As sites become larger, further from shore and in deeper waters there may be a significant change in the composition of the lifetime cost of energy. For example, the foundation and installation costs of offshore wind can be reduced by using larger wind turbines as their costs do not scale linearly with turbine size. However, this may result in the need for more specialist access solutions to perform maintenance. Alternatively, the reduction in the number of assets for the same wind farm capacity may reduce the number of offshore operations required. Floating wind turbines may have higher manufacturing costs but could allow turbines to be brought to port to perform maintenance which would dramatically change operating strategies. To achieve the optimal configuration identified in Fig. 3 , as well as address the underlying uncertainty; a number of O&M modelling tools have been developed that allow OPEX for hypothetical wind farms to be explored, a thorough review of these is provided by Hofmann [6].

Performance of Early Offshore Wind Farms

The key metric for identifying how well wind farms are performing is availability. Various definitions of availability have been used previously, therefore an IEC standard specifically focusing on the issue has been created to produce a formal definition across the industry. Availability is defined as:

‘the percentage of time that an individual wind turbine or wind farm is available to generate electricity expressed as a percentage of the theoretical maximum’ [7], calculated using the following equation from IEC Standard 61400–26 [8]

There are a small number of early wind farms that received government support and consequently provide some operational data [9 ,10]. The availability of these sites over the first three years of operation are shown in Fig. 4 . It should be noted that these sites all used similar turbine models which experienced a serial defect as well as having a similar number of turbines, distance to port and water depth. Therefore, they are not necessarily representative of the industry at large and caution should be applied when drawing wider conclusions. A detailed review of the UK round one site, identifying the issues experienced in more detail is presented in [11].

Fig 4: Availability performance of early European offshore wind farms, adapted from [11,10]

The average availability across the early sites was 80.2% with a capacity factor (see case study 1 for more details) of 29.5%. This availability level is significantly below onshore levels which historically have been above 95%, with 97% achievable for modern wind turbine designs [12]. This low level is largely attributed to the serial defect of the gearbox and generator experienced at these sites. Excluding these failures, availability performance over 90% is observed and this level has been achieved at number of later sites. Despite the serial defects, the average direct O&M costs at these sites is £12/MWh which lies within their projected budgets and agrees with the predicted contribution to overall lifetime cost of energy. In addition, the average capacity factor of 29.5% is higher than that observed onshore despite onshore availability of 97% in the UK. This figure has increased at later sites because of a combination of improved turbine design, higher wind speeds and improved maintenance techniques. For offshore wind to become cost competitive with alternative generation technologies, operation and maintenance costs must be minimised at all stages of the wind farm life and serial defects must be eradicated.

Reliability of Offshore Wind Turbines

Understanding the failure performance of turbine components and sub systems is vital in the planning stage of offshore wind projects for accurate financial forecasting; in the operations stage it is vital for ensuring that optimal resources are provisioned and to allow the most cost effective maintenance strategy to be adopted. The two key metrics relating to availability of a wind turbine are mean time to failure (MTTF) and mean time to repair (MTTR). In the wind industry, failure rate λ, which is the reciprocal of MTTF is predominantly used as failure data is typically collected on an annual basis [12]. A key issue facing offshore wind is the lack of relevant failure data that can be used for such purposes.

There is a fundamental lack of data because of the immature nature of the industry and the fact that large number of turbines are still under warranty. This problem has been exacerbated because of the small number of OEMs resulting in a lack of transparency over performance statistics. Onshore, the need for reliability databases was identified in order to identify key subsystems for design improvement. Owing to the large number of manufacturers and operators a number of anonymised datasets have been created and analysed [13–16]. Various projects have looked into reliability performance and the need for standardised performance recording as a key cost reduction opportunity in the offshore context, principally [17]. Consequently, two government led projects are being developed to produce coherent reliability databases that the industry can use in order to quantify failure performance and identify industry wide tends going forward [18,19].

One approach to predicting offshore wind turbine reliability performance is to use the historic onshore failure values to predict failure behaviour of offshore wind turbines. This has been performed in [20] and the resulting failure rates and MTTRs for different subsystems are shown in Fig 5.

Fig 5: Historical onshore failure rate and MTTR categorised as major and minor classes [20]

The key trend identified from Fig. 5 is that failure rate and MTTR are broadly, inversely proportional. Failures that occur regularly have a small MTTR whereas significant periods of downtime are associated with rare major failures of the drive train, blades, gearbox, generator and structure. In addition, these are the components that require specialist heavy lift vessels offshore and therefore have a significant additional financial cost making them critical to overall O&M performance. The one offshore data set that has sufficient information to allow a direct comparison with the onshore analysis comes from Offshore Wind Park Egmond aan Zee (OWEZ) in the Netherlands [10]. The operational reports for this site report total ‘faults’ rather than absolute failures and so are not directly comparable. However, the average number of annual visits to each turbine is reported as 7.5 per turbine over the first three years of operation and the contribution to downtime from each subsystem is reported. It is therefore possible to scale the faults to represent failure rate and estimate the MTTR. This result is provided in Fig 6.

Fig 6: Scaled failure rate and MTTR at Egmond aan Zee wind farm, adapted from [10]

Comparing the onshore based and offshore results it can be seen that the general relationship between failure rate and MTTR is observed and that the gearbox, generator and blade have the highest MTTR. The higher than expected failure rates of the generator and gearbox are attributed to the serial defects present in these subsystems. The resultant contribution to downtime is shown in Fig 7 confirming that despite being in the middle of the observed failure rates, gearbox and generator failures dominate overall downtime. Improving reliability and reducing maintenance of these subsystems is critical to the industry moving forward. An additional observation is that the next largest contribution to downtime comes from the control system despite the small MTTR. This highlights the fact that there is a significant time cost associated with any failure offshore. As wind farms are constructed in more remote locations the contribution of frequent, low impact failures on overall availability is likely to increase. This has led to investigation of failure analysis techniques such as failure mode effect analysis for offshore wind [21] in order to minimise overall failure rate and implement advanced maintenance approaches which are discussed in Section 6.

Fig 7: Contribution to downtime from different subsystems at OWEZ

Accessibility and Logistics Costs

The wave and wind climate has a significant impact on the accessibility of offshore wind farms in order to perform maintenance. Depending on the type of maintenance action required, various different categories of vessel or helicopter will be required. For minor repairs and scheduled maintenance a CTV or helicopter is used. These vessels come in various sizes ranging from 12 to 24 technician capacity and are typically limited by an access threshold of 1.5 m significant wave height ( Hs). Specialist transfer systems potentially allow these vessels to operate in greater significant wave heights, improving overall accessibility but these systems are expensive and remain rare. Average day rates for CTVS will be in the low thousands of pounds region and the number used at a particular wind farm will vary depending on the season and failure performance of the wind turbines. CTVs will be used for the majority of maintenance activities but there is a limit to the quantity and weight of equipment that can be transferred from the vessel to the wind turbine for maintenance. For larger maintenance it is therefore necessary to commission a specialist vessel.

Field support vessels (FSVs) which may or may not include dynamic positioning stabilisation capability can remain on site for several weeks, operate in wave heights up to 4 m, carry a large number of spares and transfer large components up to several hundred tonnes onto the wind turbine. These vessels are widely available but also used heavily by the oil and gas industry. Therefore, the day rate for hire are in the region of tens of thousands of pounds and also highly volatile. In addition, any chartering may be subject to a minimum duration of hire resulting in a higher cost. For major maintenance actions that require removal of blades or the largest components in the drive train even more specialist heavy lift jack up barges are required. These create a completely stable platform from which to operate and are capable of performing all major maintenance tasks for offshore wind turbines. However, these vessels are in short supply, may have a significant mobilisation time and cost and have day rates in the hundreds of thousands of pounds. Despite the low requirement for such vessels there is the potential for them to dominate vessel costs. Examples of all three vessel types are shown in Fig 8.

Fig 8: Different categories of vessel required for maintenance, (L-R) CTV, FSV and Jack-Up

The wave climate has a significant impact on the accessibility of offshore wind farms, particularly for the use of CTVs which comprise the majority or maintenance actions. The variability of wave climate at different locations around the UK is shown in Fig. 9 [22]. The impact is significant, with a 60% difference between the best and worst sites at and an access threshold of 1.5 m. This has implications for availability and operational costs at different sites. In particular, simply increasing the access threshold of maintenance vessels to improve availability will have varying efficiency depending on the site. The consequence of this has meant that several of the sites in more challenging conditions such as those off the west coast of Scotland are suspended until the technology involved has significantly matured [23].

Fig 9: Wave height exceedence probability at sites around the UK

Additional Maintenance Cost Drivers

In addition to the large cost attributed to vessels and the indirect cost associated with downtime there are various other cost components that contribute to overall O&M costs. Outside of the warranty period, there is a large direct cost associated with repair and replacement of components and a cost associated with spares holding. There is significant uncertainty surrounding these costs for future large wind turbines. As wind farms become larger and further from shore, it may make economic sense to store a number of major components that otherwise suffer from long lead times. The number of minor components required for routine maintenance will also need to be controlled to ensure that these do not result in unnecessary downtime. This may require more novel approaches such as developing offshore maintenance bases or using ‘maintenance island’ vessels that can house a large number of technicians and spares for future far shore sites.

Staff costs will also contribute significantly to the overall O&M costs for offshore wind. Identification and planning of when maintenance is required is carried out by an expert team of engineers and skilled technicians are required in order to actually perform the actions. The immature nature of the industry means that there is a general shortage of personnel with adequate experience and expertise to meet the current requirements of the industry. This shortage will become more acute unless sufficient structured training programs are put in place to meet future demands. The alternative is to bring in staff with expertise from other industries such as oil and gas and conventional power production but this could lead to increased competition between sectors and result in significantly higher wage costs. Finally, there are a number of fixed costs that will contribute to O&M costs. These include rents, port docking fees, overheads related to condition monitoring and insurance costs.

Moving Towards Advanced Asset Management

To develop optimal maintenance strategies it is necessary to have a good knowledge of the state of the system being maintained. This allows the optimisation of intervals between scheduled maintenance as well as the opportunity for preventative maintenance action to take place, minimising downtime. Adopting such approaches for onshore wind turbines has been demonstrated in principal [24, 25]. The financial costs of accessing a turbine and higher penalty associated with downtime of larger turbines make successful implementation of such approaches increasingly critical offshore. There are two principal approaches to obtaining this information. Firstly, the current state of the component or subsystem of components can be determined directly by inspection or using condition monitoring techniques. Alternatively, the remaining life of a component or subsystem can be estimated statistically based on the historical failure performance of such systems. Condition monitoring is an active area of research and development for wind turbines, a review of the latest developments can be found in [26, 27]. Modern wind turbines are large, complex machines and this has resulted in uncertainty remaining around the information provided from CM systems and in most cases a repair action will only be carried out after visual inspection. Manual inspection has a significantly higher cost than onshore because of the increased logistical effort required to obtain a technician onto a turbine. Innovations such as the use of remote control helicopter cameras are providing a reduction in the cost and health and safety risk associated with visual inspection but ultimately it is desirable that improved condition monitoring systems will remove the need all together.

Condition monitoring will allow a number of different maintenance strategies to be implemented. Classification of different strategies is shown in Fig. 10 [28] and the impact on maintenance levels for three different approaches shown in Fig. 11 . Traditional corrective approaches adopted onshore are shown on the right-hand side branch while strategies considered necessary for offshore wind are shown on the left-hand side of Fig 10. Fig 11 shows that condition-based maintenance can reduce the number of maintenance actions while avoiding downtime associated with corrective maintenance. Repairs are also cheaper than complete replacement providing a financial benefit from successfully implemented asset management strategies.

Fig 10: Schematic overview of different maintenance strategies, adapted from [28]

Fig 11: Implementation of different maintenance strategies on asset life and downtime

Conclusion

Offshore wind remains a developing industry with huge potential to become a major contributor to the future energy mix. Operations and maintenance contribute significantly to the lifetime cost of energy of offshore wind farms and is therefore a key area for cost reduction. As wind farms move further from shore into the more extreme wind and wave climates minimising the O&M costs will be vital to the success of projects. In order to do this, a greater understanding of the failure behaviour of wind turbines is required along with improved access solutions and more reliable wind turbines. This will allow the implementation of advanced asset management techniques that can simultaneously reduce the amount of downtime experienced and the direct costs associated with maintenance. Improved operational decisions are vital to achieving cost reduction by improving performance and selecting operational strategies that minimise the combination of direct cost and lost revenue as demonstrated by the case studies. The most efficient way of achieving these targets will be via collaboration and knowledge sharing across projects in order to reduce costs for the entire industry.

References 

  1. Van Bussel G. Henderson A. Morgan C. et al.: ‘ State of the art and technology trends for offshore wind energy: operation and maintenance issues’. Proc. of Offshore Wind Energy Special Topic Conf., Brussels, Belgium, 2001.
  2. Henderson A. R. Morgan C. Smith B. Sørensen H. C. Barthelmie R. J. Boesmans B.: ‘ Offshore wind energy in Europe – a review of the state-of-the-art’, Wind Energy, 2003, 6, pp. 35–52 (doi: 10.1002/we.82) .
  3. Valpy B. B. A.: ‘How to improve the cost of energy from offshore wind – technology perspectives’. Renewable UK 2010, Glasgow, UK, 2010.
  4. O.D.E. Limited: ‘Study of the Costs of Offshore Wind Generation’, dti – Department of Trade and Industry URN NUMBER: 07/779 , 2006.
  5. Feuchtwang J. Infield D.: ‘Offshore wind turbine maintenance access: a closed-form probabilistic method for calculating delays caused by sea-state’, Wind Energy, 2013, 16, pp. 1049–1066 (doi: 10.1002/we.1539).
  6. Hofmann M.: ‘A review of decision support models for offshore wind farms with an emphasis on operation and maintenance strategies’, Wind Eng., 2011, 35, pp. 1–16 (doi: 10.1260/0309-524X.35.1.1).
  7. Tavner P. J.: Offshore wind turbines: Reliability, availability and maintenance. Stevenage: Institution of Engineering and Technology: distributor Institution of Engineering and Technology (IET) , 2012.
  8. International Electrotechnical Commission: ‘IEC/TS 61400-26-1 ed1.0 – Wind turbines – Part 26–1: Time-based availability for wind turbine generating systems’, ed , 2014.
  9. DTI and BERR (Department for Business Enterprise and Regulatory Reform): ‘Offshore wind capital grants scheme annual reports: 2004–2009’.
  10. Nordzee Wind: ‘Wind Farm Egmond aan Zee Operations Reports 2007–2010’ .
  11. Feng Y. Tavner P. J. Long H.: ‘Early experiences with UK Round 1 offshore wind farms’. Proc. of the Institution of Civil Engineers: Energy, 2010, vol. 163, pp. 167–181.
  12. Tavner P. J. Xiang J. Spinato F.: ‘ Reliability analysis for wind turbines’, Wind Energy, 2007, 10, pp. 1–18 (doi: 10.1002/we.204).
  13. Wind Stats. Available at: http://www.windstats.com.
  14. Ribrant J. Bertling L. M.: ‘Survey of failures in wind power systems with focus on Swedish wind power plants during 1997–2005’, IEEE Trans. Energy Convers., March 2007, vol. 22, pp. 167–173 (doi: 10.1109/TEC.2006.889614).
  15. Stenberg A. Holttinen H.: ‘Analysing failure statistics of wind turbines in Finland’. Presented at the EWEC 2010, Warsaw, 2010.
  16. Sheng S.: ‘Report on Wind Turbine Subsystem Reliability – A Survey of Various Databases’, NREL/PR-5000-59111. Golden, CO: National Renewable Energy Laboratory , 2013.
  17. Wilkinson M. Hendriks B. Spinato F. et al.: ‘Methodology and results of the reliawind reliability field study’. Proc. European Wind Energy Conf. & Exhibition (EWEA), Warsaw, Poland, 20–23 April 2010.
  18. Faulstich S. Lyding P.: ‘The IWES Offshore O&M database project’. Operations & Maintenance Excellence for Offshore Wind, London, 2010.
  19. The Crown Estate: ‘SPARTA project to drive offshore wind cost reduction’, ed , 2014.
  20. Faulstich S. Hahn B. Tavner P. J.: ‘ Wind turbine downtime and its importance for offshore deployment’, Wind Energy, 2011, 14, pp. 327–337 (doi: 10.1002/we.421).
  21. Arabian-Hoseynabadi H. Oraee H. Tavner P. J.: ‘Failure modes and effects analysis (FMEA) for wind turbines’, Int. J. Electric. Power Energy Syst., 2010, 32, pp. 817–824 (doi: 10.1016/j.ijepes.2010.01.019) .
  22. Geos F.: ‘ Wind and wave frequency distributions for sites around the British Isles’ (Health and Safety Executive, Great Britain, 2001).
  23. Macalister T.: ‘ Scottish Power cancels £5.4bn Argyll Array offshore wind farm plan’, in The Guardian, ed , 2013.
  24. Andrawus J. Watson J. Kishk M.: ‘ Wind turbine maintenance optimisation: principles of quantitative maintenance optimization’,Wind Eng., 2007, 31, pp. 101–110 (doi: 10.1260/030952407781494467) .
  25. Besnard F. Patriksson M. Stromberg A. B. Wojciechowski A. Bertling L.: ‘An optimization framework for opportunistic maintenance of offshore wind power system’, 2009 IEEE Bucharest Powertech., 2009, 1–5, pp. 2970–2976.
  26. Crabtree C. J.: ‘Survey of commercially available condition monitoring systems for wind turbines’, Durham University, 2010.
  27. Hamilton A. Quail F.: ‘Detailed state of the art review for the different online/inline oil analysis techniques in context of wind turbine gearboxes’, J. Tribol., 2011, 133, pp. 044001–044001 (doi: 10.1115/1.4004903).
  28. Wiggelinkhuizen E. Verbruggen T. Braam H. Rademakers L. Xiang J. P. Watson S.: ‘Assessment of condition monitoring techniques for offshore wind farms’, ASME Trans. J. Sol. Energy Eng., 2008, 130, p. 031004 (doi: 10.1115/1.2931512).
  29. Dinwoodie I. McMillan D. Revie M. Lazakis I. Dalgic Y.: ‘Development of a Combined Operational and Strategic Decision Support Model for Offshore Wind’. Energy Procedia, 2013, vol. 35, pp. 157–166.

Case Study 1: Capacity at UK Offshore Wind Farms

Unlike availability which is commercially sensitive and therefore not generally published, the power produced by wind farms on a monthly basis is published in the UK by OFGEM [www.renewablesandchp.ofgem.gov.uk]. This is principally in order to provide transparency over the amount paid out via support mechanisms such as Renewable Obligation Credits and Feed-in-Tariffs but it can be used to estimate the capacity factor of offshore wind farms. Capacity factor is defined for this analysis in the following equation:

For conventional power plants capacity factor is a good indication of operating performance. Owing to the significant influence of variability in wind speed on output of wind turbines, capacity factor for is not as definitive as a performance metric. However, capacity factor can provide an insight into the O&M performance and underlying trends at individual sights and across the industry can be identified. Fig. 12 shows the annual capacity factor of UK wind farms from 2006 to 2013. Values corresponding to the commissioning phase of newer sites are discounted for clarity as insufficient data on the number of turbines grid connected at each month is available in the database.

Fig 12: Capacity factor at sites around the UK

From Fig 12, various key observations can be observed. The average capacity factor across all sites has shown a significant improvement. This is partly because of wind farms being constructed at sites with higher average wind speeds but also indicates that O&M is resulting in improved performance. The increasing trend is evident at older sites which reaffirms this observation. In addition, the early life capacity factor of newer sites is higher than older sites indicating that despite moving to more challenging locations improved performance is achievable through better operations. It can also be noted that significant inter annual variations in capacity factor occur which are outside the control maintenance strategies. Reporting availability would provide a more clarity into how maintenance strategies are improving performance for the industry.

Case Study 2: Optimal Strategy for Large Maintenance Vessels

Vessel costs contribute a significant proportion of direct O&M costs and the largest contribution comes from the specialist heavy lift vessels required for major repair and replacement of drive train components and blades. The procurement of specialist vessels for these maintenance actions are typically Jack-Up platforms. The CAPEX cost associated with such vessels are in the hundreds of millions of pounds while mobilisation costs involved in chartering can be millions of pounds along with associated day rates of hundreds of thousands. A number of operating strategies can be adopted in order to minimise the sum of spending on vessels and lost revenue. Four potential operating strategies are described below and explored over a range of wind farm size in Fig. 13 to demonstrate how different strategies are optimum for different wind farm configurations [29].

Fix on fail (FoF) – Charter vessel when after failure. Only pay for duration of the vessel charter, however, exposure to long mobilisation periods where the turbine will not be generating electricity.

Batch repair – As FoF but vessel not chartered until a threshold number of failures have occurred. Reducing total number of charters but increasing exposure to lost revenue.

Annual Charter – Short term (1–12) month yearly charter each year, failures falling outside the charter period do not receive maintenance until the start of the next charter period.

Purchase – Purchase a vessel for the duration of the wind farm life, high additional cost at start of project but removes exposure to market and lost revenue but is not flexible.

Fig 13: Heavy lift operational strategies for different wind farm sizes

For different wind farm sizes, different strategies are more cost effective. For very small wind farms the conventional FoF approach is the most effective. As wind farms become larger, purchasing a vessel becomes optimal. However, this is an inflexible strategy and increases the difficulty in financing large projects. A batch approach offers similar lifetime costs without the large up-front investment and may therefore be the preferable strategy.

 

Go to the profile of Iain Dinwoodie

Iain Dinwoodie

Senior Asset Performance Engineer , Natural Power

No comments yet.