Strategic Deployment of Service Vessels for Improved Offshore Wind Farm Maintenance and Availability

This section analyses the outcomes of the previously discussed cases, focusing on farm availability, failure occurrence and downtime, vessel fuel consumption, and O&M cost. The findings aim to quantify the impact of varying O&M logistics strategies on maintenance efficiency, offering a comparative perspective on operational effectiveness under different management approaches. 4.1. Farm Energy-Based Availability Energy-based availability means how much electricity can be created in practice as a percentage of its theoretical maximum potential output for some given period. This factor is an important measure that highlights how well a wind farm operates overall, and average reliability is critical for investors who will determine a project’s life expectancy based upon this figure. It also gives a way to measure the actual real-world efficiency of a particular wind farm, taking into account its environmental conditions. where the Energy_theoretical is calculated by the power curve of wind turbine and metocean data. As previously mentioned, the difference in the ideal energy output between the two sites under review, over the given duration of weather data, is anticipated to be minimal due to the similarity in their wind resources. The energy outputs are calculated to be 54,595.3 GWh and 54,573.7 GWh, respectively. Given the similar energy output, the energy-based availability can serve as a reliable basis for comparing and analysing the delivered energy or power across both locations. This approach allows for a focused assessment of how each site’s operational efficiencies and energy delivery capabilities stand in comparison, underpinning strategic decisions and optimizations in energy production and site management. Figure 5 depicts the availability percentages of the entire wind farm for the eight evaluated cases. For Site 1, Case 2 registers the highest availability. This suggests that employing SOVs may offer operational benefits, particularly in harsh sea conditions, if vessels are available immediately and there is no lead time required following a failure. However, before the cost data discussion, it is impossible to determine the cost-effectiveness of this increase in availability. When comparing Case 2 with Case 1, there is an observable improvement in availability, although this increase is less pronounced than the improvement observed at Site 2. Case 3 at Site 1 shows the lowest availability, potentially due to the added lead times for large vessels such as SOVs, CLVs, and HLVs, which could result in delayed maintenance responses and extended downtime. At Site 2, with gentler sea conditions, Case 6 demonstrates the highest availability, where the direct replacement of CTVs with SOVs, without the constraint of lead time, again suggests that CTVs’ limited weather operability is a significant factor in downtime. The lowest availability observed in Case 7 for Site 2 underscores the considerable impact that vessel lead times have on maintenance scheduling and the consequent availability of the wind farm. It also highlights the necessity to align O&M strategies with realistic operational capabilities, acknowledging that while immediate vessel deployment is ideal (like Cases 2 and 6), it is not always feasible. The results from Case 4 and Case 8, which involve stationing a SOV at the wind farm, offer a pragmatic approach towards maintenance logistics. This strategy addresses the impracticality of having no lead time for vessel deployment by providing an on-site SOV, which can significantly reduce response times for maintenance tasks. The stationed SOV serves as a middle ground, offering improved availability as shown in the results for Site 1 and Site 2. 4.2. Failure Occurrences and Downtime Figure 6 illustrates the total failure numbers for the offshore wind farm across the eight cases, reflecting the influence of each case’s O&M strategy on handling the logistical intricacies of vessel deployment, site-specific environmental conditions, and general operational efficiency. In this paper, it is presumed that all O&M tasks lead to wind turbine downtime, despite the possibility that some turbines could continue to operate at a reduced capacity, during some minor repairs. The primary focus of this study centers on vessel deployment, and the impact of tasks that do not result in downtime is considered negligible. to the assumption regarding wind turbine failure occurrence, where the chance of additional failures occurring during a non-operational period caused by an initial failure is deemed so low as to be negligible, increased farm time availability inherently allows for more opportunities for component failures, thus potentially leading to a higher failure number. This correlation suggests that the primary contributors to downtime are factors other than component repair or replacement—specifically, vessel lead times and weather suitability, as inferred from the availability analysis. In Cases 1 and 5, the simultaneous utilization of CTVs and SOVs without accounting for lead times may mask the real-world operational delays, particularly due to the CTVs’ lower tolerance for harsh weather. This could necessitate extended periods of waiting for appropriate weather windows, resulting in increased downtime (fewer failure numbers). In contrast, SOVs, which can endure harsher conditions, are solely deployed in Cases 2 and 6, potentially diminishing downtime by reducing the dependency on favourable weather windows, which could explain fewer failure numbers in Cases 1 and 5. Incorporating vessel lead times, as observed in Cases 3 and 7, introduces other expected delays in vessel availability, specifically for larger vessels like SOVs, CLVs, HLVs, and AHTSs. This may initially heighten the number of failures as maintenance is deferred. Nevertheless, the presence of a vessel, especially once an SOV is stationed on-site as in Cases 4 and 8, can increase repair efficacy and thus improve availability. Considering the environmental factors, failure numbers in Cases 5 to 8, corresponding to Site 2, are anticipated to be lower than those at Site 1 due to milder wave conditions that enable more consistent vessel operations. Despite these gentler conditions, the strategic decision to employ CTVs alongside SOVs, or to exclusively use SOVs as in Case 6, significantly influences failure numbers. SOVs’ higher operational capability in harsh weather conditions may lead to reduced weather-induced downtime, resulting in increasing failure numbers. The effect of lead time becomes clearly visible when the total downtime is compared for the various cases (as shown in Figure 7). Cases that include lead times for large vessels, like Cases 3 and 7, show significantly more downtime than those without such lead times, like Cases 1 and 5. This shows there is a significantly large operational delay due to the waiting time while a vessel and crew are prepared for deployment. Conversely, when SOVs are stationed at the farm permanently (as in Cases 4 and 8), the downtime is reduced significantly from that of Cases 3 and 7. This suggests that having SOVs on-site at all times can balance out the negative impact of having lead times for large vessels. With the removal of downtime of the vessel for deployment, small maintenance jobs can occur more regularly thus, reducing downtime overall and maintaining the energy provision of the wind farm. To illustrate directly the impact of vessel capacity on downtime, Figure 8 shows the downtime and failure numbers with “Manual Reset”, “Minor Repair” and “Annual Service” in Cases 1/2 and 5/6. This comparison clearly demonstrates the flexible effects of using both CTV and SOV in varying weather, and also the robustness of substituting the CTVs entirely with SOVs, without additional concern for lead time involved in acquiring such substitute capacity. This approach yields insights into how decisions in vessel selection, conditioned to their operational weather capacity, result in expected downtime across different site conditions in maintenance operations. Across these cases, the failure numbers do not vary significantly (shown in Figure 8a), suggesting that the type of vessel (CTV vs. SOV) does not drastically influence the frequency of failures. This indicates that operational challenges, such as manual resets, minor repairs, and annual services, occur with relatively consistent frequency regardless of the maintenance strategy employed. However, there’s a stark contrast in downtime hours (Figure 8b), particularly when comparing CTV-utilized cases to those where SOVs are employed. For instance, in Case 1 (CTVs and SOVs at Site 1 with adverse weather) versus Case 2 (SOVs replace CTVs at the same site), the reduction in downtime is substantial. The large differences in downtime can primarily be attributed to the limited weather capacity of CTVs. CTVs, with their lower threshold for operational weather conditions, are less capable of performing maintenance tasks during adverse weather, leading to increased downtime. In contrast, SOVs, designed to operate in rougher conditions, can continue maintenance operations, thereby reducing downtime. In comparing Site 1 Case 1/2 with Site 2 Case 5/6, the principal observations are the impact of different environmental conditions on downtime, despite a similar decline in the number of failures. Site 1 (harsher wave conditions) shows that Case 1 (CTV utilized) sees significantly greater downtime when compared to Case 2 (SOVs utilized). This again illustrates the effect of the more limited weather capacity of CTVs in more challenging conditions. Site 2 (milder weather) on the other hand still shows that the SOV utilized cases (Case 6) have a lower downtime compared to CTV cases (Case 5), but the variance is not as significant as was the case at Site 1. This comparison emphasizes that the environmental conditions at each site only serve to amplify the limitations of CTVs and that vessel selection is critical in order maximize for site-specific weather challenges. 4.3. Vessel Fuel Consumption The analysis of fuel consumption across the eight cases (see Figure 9), particularly focusing on the impact of vessel type and deployment strategies, reveals distinct patterns. Cases 2 and 6, where SOVs are exclusively used, show the highest total fuel consumption. This is primarily attributed to the extensive transit required for SOVs to travel between the port and the wind farm. In contrast, Crew CTVs, which are not used in these cases, typically have lower fuel consumption due to their smaller size and limited operational range. A comparison of cases where SOVs are used exclusively (Cases 2 and 6) against cases where a combination of CTVs and SOVs are used (Cases 1 and 5) shows a notable increase in fuel usage when SOVs replace CTVs. Despite having larger capacities for cargo and crew, which allow them to operate in harsher conditions, the greater fuel usage of a larger vessel combined with the penalty of having to repeatedly travel to and from the site for transit operations results in very large increases in fuel consumption. However, significant total fuel consumption reductions are observed in Cases 4 and 8, where SOVs are stationed at the wind farm. This reduction underscores the impact of transit operations on fuel use, highlighting that the major portion of fuel consumption for SOVs is attributed to traveling to and from the site. By having SOVs permanently stationed at the farm, the need for regular transit is eliminated, resulting in substantial fuel savings. The efficiency of stationed SOVs is further evidenced by the lower fuel usage in Cases 4 and 8, suggesting that when SOVs are not engaged in transit operations, their total fuel consumption can be significantly reduced. This points to the conclusion that most of the fuel consumed by SOVs in the higher usage cases is due to transit rather than on-site operations. 4.4. O&M Cost The calculation of O&M costs includes both direct and total O&M costs. Direct O&M Costs are those directly related to the O&M of the wind farm, including routine maintenance, repairs, parts replacement, labour, and the operation of service vessels, which involve charges like chart and mobilization fees and fuel costs. Total O&M costs, meanwhile, include the economic implications of lost income resulting from O&M activities. This broader perspective is necessary to fairly and accurately grasp the full economic impact of O&M on an offshore wind farm’s profitability and financial health. Lost income is regarded as a reflection of the energy production lost as a result of O&M activities, which is calculated by energy lost production multiplied by the strike price. The strike price in this study was assumed to be £100 per MWh. The inclusion of lost income in the total O&M cost calculation thus provides a more comprehensive understanding of the O&M cost of offshore wind farms. Additionally, both types of O&M costs are normalized by the energy delivered, which aims to provide a more accurate, fair, and standardized metric for evaluating and comparing the O&M logistics. It aligns the costs directly with the revenue-generating aspect of the operation, for a clear understanding of the relationship between maintenance efforts and energy production efficiency. Figure 10 parents the normalized direct O&M cost. Typically, Site 1 (Cases 1–4) shows higher O&M costs than Site 2 (Cases 5–8), due to the more challenging weather conditions at Site 1, which could increase maintenance complexity and cost. It also can be observed that Case 2 and Case 6 have the lowest O&M costs, which align with the cases where SOVs are exclusively used without considering lead times. Even though SOVs have higher fuel consumption during transit, their ability to operate in harsher weather conditions might contribute to lower total O&M costs due to reduced downtime and more efficient maintenance operations. Conversely, Case 3 and Case 7 show higher O&M costs, which could be due to the incorporation of lead times for larger vessels. The delay in deploying SOVs could lead to increased downtime and consequently higher O&M costs. In a strategic pivot, Cases 4 and 8 where SOVs are permanently stationed at the wind farm, show a mid-range O&M cost. While the fuel costs are reduced due to the lack of transit, the fixed presence of SOVs might entail higher standby charges which are reflected in the O&M costs. It should be noted that variations in direct O&M costs are relatively small across all cases, which could imply that the majority of the O&M costs are likely driven by parts repair and replacement rather than by the logistics of vessel deployment. As shown by Figure 11, the tasks associated with power cable, mooring lines breakage, major repairs, and major replacements are consistently the most expensive across all cases. This aligns with the expectation that maintenance of such critical components, due to their essential nature and the complexity of repairs or replacements, would lead to higher costs. Cases where SOVs are permanently stationed at the farm (Cases 4 and 8) show a more even distribution of costs across tasks, possibly indicating that the immediate availability of SOVs can lead to more efficient execution of repairs and replacements. However, the data suggests that the use of SOVs, whether exclusively or in combination with CTVs, does not significantly change the repair and replacement cost structure. In light of these observations, it is clear that the key to optimizing O&M strategies lies in addressing the most expensive maintenance tasks. Targeting efficiency improvements in these areas, whether through technological innovation, process optimization, or enhanced logistics planning, could yield significant cost savings. It’s important to highlight that in Cases 4 and 8, the fixed costs associated with stationed SOVs are not taken into account due to the lack of comprehensive data collection, though mobilization costs are still included, with charter costs excluded. These fixed costs could encompass regular monthly crew changes and additional supplies for the SOVs, which are considered service platforms when there is no failure occurs. The findings of the direct O&M cost suggest that repair and replacement expenses significantly outweigh the costs related to the vessel itself. Moreover, it is implied that the fixed costs for stationed SOVs are not expected to exceed the variable costs found in scenarios involving non-stationed SOVs. Consequently, the minor nature of these fixed costs does not justify the benefits provided by the stationed SOVs. The total O&M costs per MWh, including lost income due to downtime over the eight cases, are shown in Figure 12. In the realistic operational context where vessel lead times are unavoidable, the data from these cases offer a stark representation of the financial implications. Cases 3 and 7, which take these lead times into account, show significantly higher total O&M costs. This suggests that the delay in deploying large vessels such as HLV, SOV, not only increases the downtime, but also amplifies the lost income, and thereby greatly increasing the O&M costs. The lower costs incurred in Cases 2 and 6, although both cases represent unrealistic scenarios in which there are no lead times, help to demonstrate the potential cost savings. These cases illustrate a what-if scenario where immediate vessel availability could significantly reduce O&M costs. By focusing on the SOV stationing strategy, Case 4 and Case 8 show an intermediate cost level compared to when SOVs are constantly at the wind farm. This strategy brings the lead time almost to zero, which makes reactive maintenance response nearly immediate, substantially reducing lost income, and is more cost-effective relative to cases with significant lead times. As a final point, while examining the total O&M costs across site-specific differences, the harsher conditions at Site 1 produce a higher set of costs compared to Site 2, reflecting the increased frequency and complexity of needed maintenance under the more challenging environmental conditions. In summary, the narrative woven by these case studies advocates for a strategic re-evaluation of O&M logistics. It becomes clear that mitigating vessel lead times through solutions such as SOV stationing can play a pivotal role in reducing total O&M costs. While not the lowest cost solution, the presence of SOVs on-site represents a balanced approach, blending increased readiness with a more favourable cost profile, which could be instrumental in driving down the expenses associated with offshore wind farm maintenance. 4.5. Discussions This analysis of the O&M strategies for 1 GW offshore wind farms, particularly through the lens of SOV deployment and the consideration of large vessel lead times, reveals significant operational efficiencies and cost benefits. The main findings underscore the strategic advantage of SOV utilization, especially when permanently stationed at the wind farm, in mitigating the adverse effects of challenging sea conditions and unpredictable weather patterns. Exclusively and in combination with CTVs, the deployment strategy of SOVs has produced marked operational efficiency. Stationing SOVs on a permanent basis has a direct impact, not only on the rapidity and efficacy of maintenance response—and a consequent, significant reduction in downtime and associated costs—but also on a critical environmental advantage: very substantial fuel savings. This finding is pivotal, as it directly contributes to reducing the carbon footprint of wind farm maintenance operations, aligning with the global push towards more sustainable energy production methods. To contextualize the importance of these findings, consider the broader impact of fuel consumption on carbon emissions in the maritime sector. Maritime transportation is one of the most significant contributors of CO₂ to the global effort to reduce emissions, and reductions in fuel use can have the biggest absolute effect on reducing emissions. For example, there are ambitious targets to reduce greenhouse gas (GHG) emissions from shipping by at least 50% by 2050 compared to 2008 levels, as set out by the International Maritime Organization (IMO) [26]. The operational efficiencies and fuel savings that strategic placement of SOVs into offshore wind farm operations have the potential to make a truly meaningful contribution towards the achievement of these global environmental outcomes. Furthermore, the incorporation of vessel leading times, as part of a contingency analysis, into the O&M strategy further highlights the necessity for logistical planning that is pre-emptive. In addressing the influence of vessel lead times, findings surmise that such an inevitable lag in deploying vessels is one that significantly affects operational efficiency and the scheduling of maintenance activities. However, insight from Cases 4 and 8, where SOVs are permanently stationed and effectively integrate CTV capabilities, highlights a practical approach to counter the challenges imposed by vessel lead times. This strategy improvement with maintenance response times and thus reduce downtime, underpins the criticality of pre-emptively planning strategies to overcome operational disruption. Among all cases, major repairs and replacements emerge as the most cost-intensive; O&M tasks necessitating the use of HLVs represent the most cost-intensive aspects of wind farm maintenance. This insight underscores the critical need for a strategic approach to O&M tasks, one that leverages the robust capabilities of SOV and the specialized functionalities of HLVs. The strategic employment of SOVs, especially those that integrate HLV capabilities, presents a novel solution to the challenges posed by these high-cost maintenance tasks. By enabling SOVs to perform or support tasks traditionally requiring HLVs, wind farms can achieve a higher level of operational flexibility and efficiency. This integration not only streamlines the maintenance process but also significantly reduces the logistic complexities and costs associated with deploying multiple specialized vessels. The comparative analysis across different sites (with different weather conditions) further illustrates the cost-effectiveness of SOV employment under varied environmental conditions. The adaptability of SOVs to harsher weather, coupled with their operational readiness through reduced lead times, emerges as a pivotal factor in enhancing the sustainability and profitability of offshore wind farms. In summary, the strategic deployment of SOVs, particularly with reduced lead times, emerges as a critical factor in the operational success of offshore wind farms. These findings call for a re-evaluation of current O&M logistics strategies, with vessel availability and response time emerging as prominent factors for optimizing wind farm operations. Although the model has been validated in prior research, some constraints need clarification. Certain data, such as failure rates, are highly sensitive and challenging to obtain from commercial companies. The data in this paper, sourced from open databases, may not reflect the most recent developments in the offshore wind industry. Furthermore, the failure data were considered constant, although they may vary over time. Future model enhancements should incorporate temporal variations. Other research can expand on these insights, focusing on the potential that integration of advanced technologies such as digital twins and AI-driven predictive analytics into O&M operations holds in revolutionizing maintenance strategies. Such insights are vital to the continued development of offshore wind energy and align with broader efforts to more effectively incorporate renewable energy in the global energy mix.