This challenge arises primarily from responses to future wind energy projects, where external and system-level risks become more important. While reliability remains a relevant concern, market uncertainty and curtailment, changing regulatory frameworks, financing uncertainty, and supply chain constraints are identified as key risk drivers. These factors are inherently long-term and interdependent, influencing revenue stability, cost structures, and investment decisions over the project lifetime. The survey results indicate a shift from predominantly technical and physical uncertainties towards system-level uncertainties driven by external conditions and institutional factors.

The dominance of reliability-related responses across both operational and future projects motivates this challenge. Reliability remains the single largest risk category for both project phases. In particular, failures of major non-redundant components are critical due to their potential to cause long outages, high repair costs, and significant reductions in availability and revenue.

While reliability determines failure occurrence, the survey responses also highlight the importance of how failures are managed. The strong prioritization of advanced sensing, failure prediction, and O&M decision-support tools indicates recognition that operational inefficiencies and limited predictability in O&M processes materially affect project economics. This challenge captures issues that are not reflected in failure behaviour alone, including uncertainty in repair times, logistics delays, access constraints, data availability for operational and end-of-life decisions, and the difficulty of translating condition-monitoring information into effective operational actions.

The identification of immature or unproven technology as a risk across both operational and future projects motivates the fourth sector challenge. Risks associated with technology novelty include unexpected costs, reduced availability, delayed commissioning, and increased uncertainty in performance and maintenance behaviour. These risks are amplified by rapid upscaling trends, including increasing turbine size, new drivetrain concepts, evolving grid-code requirements, and associated changes in turbine operating modes.

The growth of the wind energy industry has been enabled by a robust and globalized supply chain that supported the installation of around 100 GW of capacity per year from 2020 to 2024 (). However, this existing supply chain is inadequate to support growth targets that require higher deployment rates, expansion into new markets, diversifying suppliers to mitigate geopolitical risks, and meeting the needs of evolving technologies such as floating offshore wind (). Although many of these challenges could be mitigated by expanding networks of manufacturers, infrastructure, and skilled labour, investors remain cautious due to market volatility, inconsistent policy signals, and trade-offs between local capacity building and reliance on established global suppliers (; ). This uncertainty slows supply chain investment, which subsequently limits the ability of future wind energy projects to source and install components in a timely, cost-effective, and politically acceptable manner.

The elements of a global supply chain that are relevant for wind energy projects include raw materials, manufactured components, specialized infrastructure, and a skilled workforce (). These present themselves as risks to wind energy projects primarily as follows:

Material and component bottlenecks. The construction of a wind turbine requires specialized materials, products, and manufacturing processes. Manufacturing facilities are designed only to produce components that are specific to the wind industry (and often, specific to a single manufacturer) and cannot easily diversify their production base. Installing wind turbines – particularly offshore – requires specialized logistics and facilities that have a limited global supply. The constrained supply of these components or capabilities can drive higher costs and delays for wind projects that compete for a scarce set of resources.

Supply chain uncertainties affect project design and decision-making through a complex network of interrelated factors, which we summarize in Table .

In this study, we provide a simple assessment of the first two uncertainty dimensions to demonstrate the potential impact on LCOE driven by supply chain uncertainty. Further research is required to fill the knowledge gap of how future supply chain risks impact project design and cost decisions, such as developing dynamic supply chain investment models, causal relationships between perceived risks and financing costs, detailed accounting of industrial policies, and readiness levels of existing supply chain infrastructure.

We evaluate the impact of supply chain risks on the LCOE of representative wind projects through two coupled mechanisms: (i) stochastic uncertainty in key commodity prices, propagated through the CAPEX calculation, and (ii) variation in the cost of capital reflecting different levels of perceived geopolitical and supply chain risk. All other parameters – including OPEX, annual energy production, and project lifetime – are held at baseline values, so that the resulting LCOE variation is attributable solely to supply chain and financing risk. Because LCOE scales with the product of these factors, their combination can produce non-linear impacts on project economics.

The commodity prices that have the greatest impact on the variance of capital costs of a wind project are steel, copper, and carbon fibre which contribute some of the highest mass fractions to onshore and offshore wind projects (). Although there are other key commodities that are notable supply chain risks for wind energy projects, such as rare earth elements and iron ore, these commodities are a relatively minor part of the capital stack of a project, and price fluctuations will not greatly affect CAPEX (availability risk will be addressed through the cost of capital). Other commodities such as fibreglass used in blades have higher mass fractions but relatively low historic volatility, making them less relevant for stochastic modelling (). Therefore, we primarily assess stochastic CAPEX uncertainty due to the variability in these primary cost drivers.

We obtain long-term price indices for these commodities from the US Bureau of Labor Statistics (). Because steel and copper are globally traded commodities, these indices serve as proxies for global market dynamics. Although regional price levels may differ, it is assumed that volatility structure and jump behaviour are broadly consistent across major markets. From the historical data, we derive the following stochastic parameters:

the drift (long-term trend) of commodity prices,

We fit these parameters using a Merton jump-diffusion model (), which augments geometric Brownian motion with a compound Poisson jump process to capture sudden price shocks. This is necessary because the frequency of short-term shocks to commodity prices produces heavy tails in the log returns that are poorly captured by Gaussian models. The linear correlations between the commodity price parameters are low (under 0.1) but are still included in the Monte Carlo simulation. The historical stochastic parameters for the three commodities are summarized in Table .

Financing risk is one of the most significant drivers of LCOE, although the causal relationship between supply chain disruptions and financial parameters is not well established in the public literature. We therefore select values of the weighted average cost of capital (WACC) that correspond to different levels of project risk, without attempting to draw a direct causal link with specific geopolitical or supply chain factors. Modelling these dependencies is a promising direction for future research.

WACC is calculated from the debt–equity ratio, tax rate, and cost of debt and equity, keeping the first two constant across scenarios. The cost of debt and equity are set relative to a risk-free rate using historical spreads from US wind project data (). During 2017–2020, a period of relatively stable deployment, the debt premium over the risk-free rate was 1.73 % (); in 2023, amid global supply chain constraints, this premium rose to 3.5 % (). Both premiums are measured relative to the 10-year US Treasury yield (). The cost of equity is estimated at approximately 5 percentage points above the cost of debt. A German 10-year bond yield of 2.7 % serves as the risk-free baseline

In reality, a wind project is unlikely to achieve a debt rate equal to a risk-free government bond rate. We use this value as a minimum threshold for supply chain risk-driven financing cost impacts on LCOE.

Wind turbines are typically designed for a nominal lifetime of 20 years for onshore installations and 25 years for offshore installations, in accordance with International Electrotechnical Commission (IEC) standards (). In Europe, between approximately 60 GW () and 80 GW () of installed capacity will reach the end of design life in the coming years. The nominal design lifetime, however, does not necessarily define the actual end of operational life. End-of-life decision-making encompasses a set of interrelated technical, regulatory, and economic considerations that determine whether assets are decommissioned, repowered, refurbished, or operated under a lifetime extension (LTE) framework. The underlying uncertainties are relevant throughout the asset lifetime, even though they become most consequential in late-life phases.

From a technical perspective, structural integrity frequently permits continued operation beyond the design lifetime, because actual site-specific loads are often less severe than the conservative assumptions used in design. The monitoring of operational conditions and accumulated loads can reduce this uncertainty and support the verification of safe continued operation (). If structural integrity can be ensured, component reliability becomes the key determinant: ageing mechanisms such as wear, corrosion, and fatigue lead to increasing failure probabilities (), and epistemic uncertainty in post-design-life failure rates can substantially influence maintenance costs and availability. Aerodynamic performance may further degrade due to blade erosion (). Beyond the asset itself, changing site conditions introduce additional uncertainty through climate-change-induced annual energy production (AEP) shifts of approximately -2 % to -5 % () and evolving wake effects that may reduce AEP by 5 %–18 % under future high-deployment scenarios ().

Regulatory conditions represent a regime-level uncertainty. Lifetime extension is not governed by a harmonized framework in Europe; national regulations differ in scope, stringency, and enforcement, and may evolve over time (). Requirements for permitting, inspecting, certifying, and documenting directly affect technical feasibility, while decommissioning and removal obligations, particularly for offshore assets, introduce additional economic risk that can influence lifetime extension decisions independently of the technical condition of the asset.

Economic uncertainty in late-life phases is driven by refurbishment scope and costs, which are typically identified only after detailed inspections and vary widely across turbines, sites, and regulatory contexts (). The absence of standardized refurbishment pathways renders cost estimates highly project specific. During extended operation, OPEX is further subject to the ageing-related reliability and spare-part uncertainties described above, limiting the applicability of deterministic cost assumptions.

IEC 61400-28 () provides a technical framework covering load accumulation, inspection strategies, and certification but does not prescribe an economic decision model. Several studies have addressed LTE risk quantification, including Bayesian decision frameworks (), probabilistic fatigue-economic optimization (), and techno-economic frameworks integrating structural degradation with economic evaluation (). These approaches demonstrate that moderate reductions in target reliability may be economically acceptable, but they typically address limited subsets of the relevant uncertainties.

Table  summarizes the key uncertainty domains, their associated risks, and relevant time horizons. Late-life decision-making spans aleatory, epistemic, and regulatory uncertainty across multiple time horizons, from early-life fatigue accumulation to short-term operational decisions and long-term financing risk, and existing frameworks typically address these dimensions in isolation rather than jointly.

Building on the uncertainty dimensions in Table , we adopt a scenario-based impact assessment using lifetime extension as the central end-of-life decision. The analysis demonstrates how plausible variations in energy yield, refurbishment effort, and system reliability during extended operation propagate to project-level NPV and LCOE.

Each scenario defines:

a stochastic production modifier applied uniformly across the LTE period,

Production and refurbishment uncertainties are modelled as aggregated normal distributions, representing scenario-level uncertainty rather than individual risk realizations. For a given scenario s: 1XAEP(s)∼NμsAEP,σsAEP,2XRef(s)∼NμsRef,σsRef.

The sampled modifiers are applied as 3Productionadj=Productionbase⋅1+XAEP(s),4CRef=CRef,base+XRef(s).

In each Monte Carlo iteration, one random draw from XAEP(s) and XRef(s) is applied uniformly across all LTE years, reflecting scenario-level uncertainty in energy yield and refurbishment costs. The corresponding assumptions for production loss, refurbishment uplift, and extension duration are summarized in Table .

Uncertainty in future reliability is captured through deterministic, scenario-dependent multipliers on baseline component failure rates, applied only during the LTE period. This reflects the expectation that ageing mechanisms (wear, fatigue, corrosion, blade erosion) become increasingly relevant beyond the design life. Failure rates during the original design life remain unchanged. The complete scenario definitions – including production, refurbishment, reliability, and extension duration assumptions – are summarized in Table .

Figure  shows the distribution of total project NPV and LCOE for both the baseline case and the lifetime extension scenario. As expected, extending the operational lifetime of a wind farm is a powerful lever to increase project value and reduce the levelized cost of energy. Continued operation of an already depreciated asset allows additional production at comparatively low marginal cost, thereby lowering LCOE and increasing cumulative revenues. In addition, the postponement of typically substantial decommissioning costs further improves project economics. This should not be interpreted as an absence of risk in late-life phases but as an illustration of how the end-of-life decision can redistribute risk across time. The results indicate that LTE outcomes can vary substantially, with a spread on the order of EUR 100 million in NPV or about EUR 4 per MWh in LCOE for the considered case. This illustrative experiment does not account for electricity price volatility, inflation, regulatory risk, or catastrophic failures; future work should therefore address the end-of-life decision under the combined uncertainty dimensions summarized in Table , as well as load-managed operational strategies that actively shape the damage budget available for extended operation ().

With the increasing share of variable renewable energy (VRE) in power systems, the curtailment of wind and solar generation is a growing concern (). Curtailment reduces energy production, erodes revenues, and introduces uncertainty that is difficult to anticipate at the project level. Curtailed operation can also alter the turbine loading regime, which can influence fatigue life. This structural interaction is not considered in the present assessment. We distinguish three main types: (i) transmission-constraint curtailment, arising from insufficient grid capacity; (ii) market-driven curtailment, triggered by negative electricity prices; and (iii) wildlife-related curtailment, imposed to protect birds and bats.

Transmission-constraint curtailment occurs when local or regional generation exceeds the capacity of the grid (). Grid capacity must develop in pace with renewable deployment, yet uncertainties in the timing and scope of reinforcement remain a critical challenge: projected European redispatch volumes range from 165 to 809 TWh by 2040, depending on the expansion scenario (), and congestion is already acute in several countries (). The spatial heterogeneity of constraint levels is illustrated by Irish transmission system operator (TSO) data (): in 2023, grid-driven curtailment ranged from 0.3 % in South East Ireland to 11.4 % in North West Ireland (Fig. , right). Compensation mechanisms vary by country, from full cost-based reimbursement (e.g. Germany) to limited or no compensation (e.g. Ireland until recently), and frequent curtailment can lead to high system-balancing costs ().

Market-driven curtailment arises when wholesale electricity prices turn negative during periods of high VRE production (). Oversupply alone, however, does not push prices below zero: production-based support schemes such as feed-in premiums and contracts for difference (CfDs) remunerate per unit of energy produced and thereby incentivize generation even at negative prices. Their frequency has increased across high-penetration markets such as Germany, Denmark, and the Netherlands (Fig. , left; ), making curtailment an economically rational response. Market-driven curtailment can significantly impact project NPV and bankability, and may affect the ability to secure power purchase agreements (PPAs) or CfDs ().

Wildlife-related curtailment is imposed to mitigate bat and bird fatalities from blade collisions and barotrauma, particularly during seasonal migration and low-wind conditions (). Regulatory frameworks mandate operational restrictions such as raised cut-in speeds or seasonal shutdowns (), although smart curtailment strategies using real-time bat activity indicators can reduce AEP losses relative to blanket shutdowns (). Reported AEP losses are typically <1 %–3 % (). As wildlife curtailment affects a different operational regime (low wind speeds) than transmission or market curtailment (high wind speeds), it is not included in the quantitative assessment below.

Table  summarizes the curtailment uncertainty dimensions discussed.

Curtailment is modelled as a system-level loss mechanism reflecting both persistent structural conditions and short-term operational variability. For a given month m, curtailed production Pmcurt is computed as 5Pmcurt=cmPm, where Pm is the uncurtailed production and cm∈[0,1] denotes the fraction of energy curtailed during month m. Depending on the case, cm either represents the transmission constraint level or the ratio of hours with negative electricity prices per month.

The curtailment fraction is modelled using a hierarchical stochastic formulation: 6cm∣α,θ∼Gamma(α,θ),7α,θ∼p(α,θ).

The model separates two uncertainty sources. Epistemic uncertainty in the long-term curtailment regime is represented through predefined ranges of the Gamma parameters (α,θ), sampled once per simulation and held fixed over the project lifetime (variation ranges of ±10 % around scenario values). Conditional on this regime, aleatory uncertainty captures irreducible month-to-month variability through Eq. (). The Gamma distribution is chosen for its ability to represent positively skewed loss processes.

Five curtailment regimes are defined, informed by two empirical drivers: (i) spatially heterogeneous transmission constraints, derived from Irish TSO data for 2021–2024 (), motivating three regimes of low, medium, and high grid constraints; and (ii) cross-country variability in negative electricity price frequency across Europe, motivating two market-driven regimes with contrasting price volatility. The resulting scenario definitions are summarized in Table .

Figure  shows the distribution of project NPV, LCOE, and total lost energy for each curtailment scenario. The results are based on the assumption that curtailed energy leads to lost revenue for the project owner, with no compensation scheme considered. As a result, the analysis primarily reflects the economic loss, which in practice would not necessarily be carried by the operator alone.

The results indicate that curtailment introduces substantial economic risk. Project NPVs are shifted by several hundred million euros across scenarios and exhibit a wide spread within individual scenarios. Depending on the compensation mechanism, curtailment at these levels can render projects economically unviable. From an energy system perspective, curtailed production also represents a significant inefficiency, as this energy could have been generated at near-zero marginal cost.

Wind turbine reliability is central to performance optimization and cost control, yet various indicators are used in the literature to assess it at both system and component level (). Despite efforts to develop consistent data collection practices and taxonomies (), there is still no uniform standardization in the definition of reliability indicators, failure events, or component classifications across turbine types ().

Among the reliability indicators used in wind turbine studies, the average annual failure rate per turbine is the most widely reported, due to its simplicity and direct applicability in O&M analyses. However, reliance on this parameter may hide substantial epistemic uncertainty associated with its estimation. Identical average failure rates can arise from datasets of different sizes. For example, an annual failure rate of 0.5 may be derived from 500 failures observed over 1000 turbine years or from 15 000 failures over 30 000 turbine years. While the mean is the same in both cases, the statistical confidence in the estimate differs. Methods to quantify this uncertainty through confidence intervals or similar measures have been proposed in the literature (), yet this information is rarely reported in publicly available reliability databases. As a result, failure rates are often treated as deterministic inputs in O&M models, despite being derived from limited or heterogeneous datasets. A compilation of average failure rates reported in 12 publicly available databases (Table ) shows values spanning from 0.434 to 46.856 failures WT⁻¹ yr⁻¹, with the highest value likely an outlier due to supervisory control and data acquisition (SCADA) alarm usage. Following the approach of , the datasets were clustered by location, observation period, and mean rating; the resulting coefficients of variation (Table ) illustrate how these factors contribute to variability. This lack of uncertainty characterization introduces epistemic uncertainty that can propagate into biased estimates of intervention frequency, downtime, and O&M costs ().

Beyond statistical uncertainty in failure-rate estimation, a major source of epistemic uncertainty arises from heterogeneity across turbine fleets and study contexts. Reported failure behaviour has been shown to depend systematically on turbine technology, drivetrain concept, rated power, manufacturer, and site conditions. analyses large multi-fleet datasets and demonstrate original equipment manufacturer (OEM)-specific differences in average annual failure frequencies normalized by installed capacity. Reported values range from approximately ∼1.5 to ∼2.5 failures MW⁻¹ yr⁻¹ for onshore turbines and from ∼0.7 to ∼1.6 failures MW⁻¹ yr⁻¹ for offshore installations, highlighting substantial differences even after capacity normalization. These differences are attributed to a combination of design characteristics, component selection, operational environment, and maintenance strategy rather than random variation. Similarly, report systematic variations in failure rates at both system and component levels as a function of turbine power rating (Table ), while show that turbines of identical design installed at different sites can show different failure behaviour. Despite this evidence, in practice O&M modelling, especially in research contexts, often relies on publicly available datasets that aggregate observations across manufacturers, sites, and operational contexts, due to the limited availability of homogeneous fleet-specific data. Owing to the commercial sensitivity of reliability data, few recent large-scale datasets are publicly available, and many studies continue to rely on data collected prior to 2015, such as . The transfer of this aggregated failure-rate assumptions to a specific project therefore introduces epistemic uncertainty. This can lead to misaligned maintenance strategies and biased O&M cost estimates.

In addition to uncertainty in the overall frequency of failure events, substantial epistemic uncertainty exists in how failures are distributed across major wind turbine assemblies. conducted a comparative analysis of 13 reliability studies and failure databases, and developed a unified turbine taxonomy to enable consistent comparison across datasets. Large discrepancies were observed in the relative contribution of individual components to total failure counts. For example, the reported share of generator-related failures varies from approximately 3 % to 23 % across the analysed studies. Similar variability is reported for other major assemblies, indicating that the component-level failure distribution is highly sensitive to dataset composition and study context. This uncertainty is particularly relevant for O&M decision-making, as different components are connected to different repair times, logistics requirements, and costs. Consequently, two systems with similar overall failure rates may exhibit substantially different OPEX and availability outcomes, depending on how failures are distributed across components.

The bathtub curve is frequently used as a conceptual model for failure-rate evolution (), but empirical data from wind fleets do not confirm a clearly defined bathtub shape at the system level. Component-specific analyses reveal non-stationary behaviour, including elevated early-life failure frequencies and age-dependent trends, which differs substantially between subsystems (), as illustrated by the WinNER database (Fig. ; ).

In O&M modelling, failure occurrence is typically represented using assumed statistical distributions, most commonly Weibull formulations, which implicitly impose assumptions on the temporal structure of failures. As shown by , these distributional choices can have a significant impact on model outcomes: even when average failure rates are identical, different failure-time distributions lead to substantial differences in simulated wind farm availability. This demonstrates that these models should be interpreted as simplifying representations under uncertainty rather than as empirically validated descriptions of failure mechanisms. Consequently, assumptions about the temporal distribution of failures constitute an important source of epistemic uncertainty in O&M simulations.

In addition to epistemic uncertainty, aleatory variability in failure occurrence means that otherwise identical projects may experience substantially different maintenance histories and costs, purely due to random variation. Reliability-driven O&M assessments must therefore be interpreted in probabilistic terms.

As summarized in Table , wind turbine reliability is affected by multiple interrelated sources of uncertainty, predominantly epistemic in nature. Uncertainty in failure rates, their allocation across components, and their temporal structure propagates directly into intervention frequency, downtime, and O&M expenditures.

We model the impact of uncertainty around the reliability of wind turbine components using the Markov-chain-based OPEX module of WINPACT. The scope is limited to turbine-level components (drivetrain, blades, electrical systems, etc.); substructure and cable system failures are not included in the current model, which is acknowledged as a limitation. Failure occurrence is modelled using a two-level uncertainty framework that explicitly separates aleatory variability in the timing of failures from epistemic uncertainty in the underlying failure rates. Aleatory uncertainty reflects the inherently random nature of failure events given a fixed failure rate, while epistemic uncertainty captures imperfect knowledge of the true mean failure rate arising from limited observation periods, inconsistent failure definitions and reporting, and the transfer of reliability evidence across turbine generations, sites, and operational contexts. In the model, this epistemic uncertainty is represented by treating failure rates as random variables rather than fixed parameters, with uncertainty described using Gamma distributions, which provide a flexible and non-negative representation commonly used for rate parameters.

Epistemic uncertainty in failure rates is implemented through a structured decomposition of the coefficient of variation (CV) into a shared and an independent component. The shared component captures coherent effects – systematic biases in data sources, modelling assumptions, or limited transferability – through a single multiplicative factor sampled once per simulation and held constant over the lifetime. The independent component captures residual component-specific heterogeneity. A target CV is specified at the turbine level for each scenario, and the combined effect of shared and independent terms reproduces this target at system level.

The scenarios presented in Table  represent different levels of uncertainty about failure-rate assumptions. Scenarios represent different assumptions about epistemic uncertainty, not properties of a specific turbine or project. We define three main scenarios based on evidence from literature:

Scenario R1 [low variability]. Mature turbine model in a large homogeneous fleet with established O&M processes. The CV reflects within-fleet heterogeneity, informed by site-to-site differences reported by for Enercon turbines across three German wind farms.

Figure  illustrates the distributions of NPV, LCOE, and operational expenditure obtained for the different failure-rate uncertainty scenarios. The results demonstrate that epistemic uncertainty in the specification of failure rates has a pronounced impact on project economics. Even under the low-uncertainty scenario (R1), the spread of economic outcomes remains substantial, with a range on the order of EUR 400 million. With increasing epistemic uncertainty, the spread of outcomes grows and the distributions exhibit pronounced long tails. Although such adverse outcomes are unlikely, they imply that unfavourable combinations of assumptions can lead to very low or even negative project values. Ignoring epistemic uncertainty would therefore lead to a systematic underestimation of downside risk in investment decision-making and an overly optimistic assessment of project robustness.

Figure  shows the corresponding distributions of farm availability and the number of corrective maintenance activities. These results indicate that uncertainty in reliability assumptions propagates beyond economic metrics to technical performance indicators. In higher-uncertainty scenarios, availability may decrease substantially, while the number of corrective maintenance actions can increase sharply in extreme cases. This reflects a system-level impact, combining lost energy production with potential stress on maintenance resources and supporting infrastructure.

The presence of uncertainty-driven long-tail risks is particularly relevant for financing, insurance, and contractual risk allocation, for example, in the context of O&M guarantees or availability-based contracts.

O&M costs and downtime are fundamentally driven by two coupled factors: the reliability and maintenance requirements of the physical asset and the effectiveness of the organizational processes responsible for detecting failures, planning interventions, and restoring operation. The reliability characteristics of wind turbine components and their associated uncertainties were introduced in the previous section. In the following, we focus on inefficiencies in the O&M process itself and the uncertainties that arise from them.

For offshore wind turbines, a generic corrective maintenance process can be described as a sequence of partly overlapping steps. Following the occurrence of a failure, the turbine transitions from normal operation to a down state. This event initiates a chain of activities including fault detection and diagnosis (performed remotely or manually), the identification and procurement of spare parts and specialized equipment, waiting for suitable weather windows, the mobilization of crews and logistics, transport to site, the execution of the repair, and finally the return of the turbine to normal operation. Each step introduces delays and uncertainty, and the overall downtime is typically dominated by access, logistics, and organizational constraints rather than by the repair action itself.

Technological and process-oriented advances have been developed to reduce the time between failure and return to operation. These include advanced monitoring, diagnostics, prognostics, and, more recently, preventive and prescriptive maintenance concepts. While such approaches have the potential to improve O&M performance (), their impact is not uniform across the maintenance process. We therefore review the uncertainties and risks associated with these approaches from a system-level O&M perspective. Table  provides an overview of these risks.

Multiple monitoring and diagnostic systems, including SCADA-based condition inference (), dedicated vibration and oil-condition CMS (), and structural health monitoring (), aim to represent the true health state of turbine components. Their practical value depends not only on diagnostic accuracy but also on how uncertainty propagates from data collection to decision-making and operational actions.

Uncertainty arises at sensing, modelling, and integration levels: sensor noise, limited coverage, and partial observability (); data gaps and reliance on aggregated 10 min SCADA statistics that may miss transient faults (); and sensor degradation over time, which erodes diagnostic accuracy unless the condition-monitoring and diagnostic models are actively recalibrated.

Model-based and data-driven approaches introduce additional uncertainty through modelling simplifications, limited fidelity, and poor transferability across turbines, sites, and control configurations (). At the operational level, algorithmic detection latency compounds with system-level latency from weather, vessel, and contractual delays, so that faster diagnostics do not translate linearly into reduced downtime.

Failure detection uncertainty affects process performance in two ways: false positives trigger unnecessary interventions and availability losses, whereas false negatives enable secondary damage or catastrophic failures. Because logistics and access constraints continue to dominate total downtime, diagnostics constitute a necessary but not sufficient condition for reducing O&M risk.

Diagnostic outputs are typically provided as alarms, health indices, or remaining useful life (RUL) estimates rather than as decision-relevant risk metrics linking fault probabilities to consequence severity. Confidence bounds are rarely propagated into the decision layer, so maintenance planning continues to rely on static thresholds and heuristic rules (). Decision-theoretic frameworks, notably Bayesian pre-posterior models (), address this gap in principle but have been demonstrated only for idealized single-component cases; their extension to full wind farms with realistic logistics and organizational boundaries remains an open challenge.

Maintenance execution and weather-dependent accessibility introduce aleatory uncertainty. Empirical studies show strongly right-skewed repair-duration distributions (), where rare events (weather lock-in, vessel unavailability, campaign interference) dominate cumulative downtime (). Vessel mobilization ranges from days to months, and waiting for access frequently exceeds actual repair time (). These interactions produce heavy-tailed downtime distributions that cannot be captured by mean process parameters. Process maturity and advanced access concepts can compress but not eliminate these tails, implying that modelling approaches must explicitly represent this stochasticity.

Organizational coordination is a significant source of epistemic uncertainty. Fragmented responsibilities among owners, OEMs, and service providers lead to coordination delays, repeated site visits, and prolonged diagnosis-to-fix cycles (). The contractual misalignment of incentives and separate management of turbines, balance of plant, and marine operations further increase system-level downtime ().

The absence of widely adopted failure taxonomies and maintenance standards, a limitation already noted for reliability data in Appendix , restricts cross-farm learning and benchmarking (). Information asymmetries between OEMs, owners, and service providers further limit the availability of integrated failure, operational, and cost datasets (), so that epistemic process uncertainty persists across projects.

Finally, the adoption of advanced O&M technologies itself introduces uncertainty. Despite promising results in research settings, many condition monitoring, predictive maintenance, and digital twin concepts face a persistent gap between demonstrated technology readiness and operational deployment (). Integration with legacy SCADA and control infrastructure, limited field validation data, and emerging cybersecurity risks from increased connectivity represent additional barriers that can delay or diminish the expected benefits of digitalization in O&M practice.

To evaluate the uncertainty arising from inefficiencies in O&M processes, we use the OPEX module of WINPACT.

This experiment isolates inefficiencies in wind farm O&M execution while holding component reliability constant. The aim is to quantify the influence of operational processes on availability and OPEX, and thus on economic project performance, independently of component failure behaviour. The scenarios represent (i) variability in mean time to repair (MTTR), (ii) mean time to wait for logistics and spare parts (MTTW), and (iii) access limitations driven by sea state operability and weather-window decision-making. Failure rates are fixed at baseline values across all scenarios. In the model, these parameters determine the transition rates between the states of the underlying Markov model.

Baseline process parameters are read from the O&M input data and modified per scenario using a multiplicative mean-shift factor α and a lognormal uncertainty term with standard deviation σ: 8θ=θ0⋅αθ⋅Xθ,Xθ∼Lognormal(0,σθ), where θ∈{MTTR,MTTWL}. Accessibility is represented by an hourly access probability paccess and modified via a scenario factor αp: 9paccess=paccess,0⋅αp.

Scenario assumptions are summarized in Table . The reference scenario represents the baseline without variation of O&M process parameters.

The P1 (high process uncertainty) scenario reflects immature or highly volatile offshore operations where repair execution and logistics are strongly weather- and market-driven. Empirical downtime data show highly right-skewed distributions with many short repairs but a small number of very long events that dominate average downtime (), while vessel mobilization times of 30–60 d for offshore support and jack-up vessels indicate that logistics waiting can be extremely variable (). The reduced access factor in P1 represents conservative access strategies dominated by crew transfer vessel (CTV) limits (1.5 m significant wave height), which constrain operability to these conditions.

The P2 (typical offshore operations) scenario reflects industry-average practice in which mean repair and logistics times remain unchanged, but stochastic variability persists due to weather, coordination delays, and campaign interference.

The P3 (mature O&M processes) scenario represents learning effects, standardization, and contractual maturity, leading to modest reductions in mean repair and logistics delays, and lower variability. These improvements are consistent with the gap identified by between conservative expert-assumed repair times and more efficient data-informed practices, and with reduced exposure to high mobilization delays.

Finally, the P4 (best practice O&M) scenario reflects optimized logistics, campaign planning, condition-based maintenance, and advanced access concepts. Literature shows that moving from CTV-based access to strategies supported by a field service vessel (FSV) or jack-up expands the operability envelope through higher allowable wave heights (3–4 m versus 1.5 m), justifying a moderate increase in access probability ().

Figure  shows the NPV distributions for the different O&M efficiency scenarios and demonstrates that economic risk is driven not only by mean O&M performance but also, to a large extent, by variability in operational execution. Projects with similar expected repair and waiting times can exhibit markedly different risk profiles when downtime distributions differ in shape. In particular, heavier-tailed distributions lead to substantially higher downside risk, even if mean performance is comparable. Variance reduction in O&M processes is therefore at least as important as reducing mean repair or logistics times.

Scenarios characterized by high process uncertainty display pronounced right-skewed NPV distributions with long downside tails. These tails arise from rare but severe events such as extended logistics delays, prolonged weather inaccessibility, or coordination failures, which dominate cumulative downtime and revenue loss. While such events are infrequent, their economic impact is large and not symmetrically compensated by shorter-than-average interventions. As a result, downside deviations are not offset by upside outcomes to the same degree, leading to an asymmetry in economic risk.

Even with identical component failure rates across scenarios, differences in O&M process uncertainty result in substantial differences in NPV. Improving component reliability alone is therefore insufficient in stabilizing project economics unless accompanied by parallel improvements in organizational coordination, logistics planning, and access strategies.

Figure  illustrates the corresponding technical effects: increased process uncertainty directly reduces farm availability and increases downtime hours, translating into economic risk through lost production and increased OPEX.

The wind energy sector is undergoing a period of rapid technology upscaling, characterized by unprecedented increases in turbine rated power, rotor diameter, hub height, and system complexity. Offshore wind has been at the forefront of this trend, with commercial turbines exceeding 15 MW, and rotor diameters approaching or surpassing 230 m, while onshore turbines have also seen steady increases in size and complexity. Upscaling is primarily driven by the objective of reducing LCOE, as larger turbines increase energy capture per unit and reduce the number of turbines required for a given plant capacity, lowering balance-of-system costs such as substructures/foundations and electrical infrastructure (). However, the acceleration of turbine scaling has introduced a distinct class of operational risk rooted in technology novelty and limited empirical operating experience ().

Rapid upscaling differs from incremental technological evolution in that multiple design parameters are pushed simultaneously beyond previously validated ranges. These include structural dimensions, drivetrain torque levels, blade flexibility, power electronic ratings, and control-system complexity. New materials (e.g. high-modulus composites), drivetrain concepts (e.g. direct-drive or hybrid medium-speed systems), and advanced grid-support functionalities are often introduced concurrently. As a result, new turbine platforms enter service with limited field data, compressed prototype testing cycles, and restricted opportunities for iterative design refinement (). This creates a dominance of epistemic uncertainty, as the true long-term behaviour of critical components and systems is not yet well understood.

Operational risk from rapid upscaling propagates through several interconnected pathways. A primary concern is reliability: newer turbine generations exhibit elevated early-life failure rates in blades, main bearings, power electronics, and cable systems (). Critically, these failures tend to be systemic: design or manufacturing deficiencies may affect entire fleets, triggering serial failures and large-scale retrofit campaigns (). This challenges the stationarity and independence assumptions underlying conventional reliability models discussed above.

Upscaling also amplifies the O&M logistics uncertainties discussed in the preceding section. Larger turbines require heavier lifting equipment and specialized vessels with limited availability, prolonging corrective maintenance waiting times (). Because fewer turbines represent a larger fraction of farm capacity, single-turbine outages have a disproportionate impact on revenue ().

Supply chain risks, discussed in the context of commodity and financing uncertainty above, are further compounded by upscaling (). Manufacturing concentration among few suppliers (), non-linear quality-control challenges at extreme component scales (), and long lead times for bespoke replacement parts amplify both delivery risk and operational downtime.

Financial and insurance dimensions reflect the systemic nature of upscaling risk. Higher repair costs per event () and increased single-loss exposure lead insurers to raise premiums and tighten coverage for first-of-a-kind platforms (). OEM warranty provisions have risen to 5 %–6 % of revenues (), and technology novelty affects bankability and financing terms during due diligence ().

Table  summarizes the key uncertainty dimensions associated with rapid technology upscaling, their dominant uncertainty types, principal operational risk implications, and relevant time horizons.

Traditional quantitative risk models rely on historical failure statistics from mature fleets and are not readily transferable to novel turbine designs operating outside established envelopes (). Because upscaling-related risks are epistemic, path dependent, and coupled across the domains discussed above (), a fully quantitative impact assessment is currently not feasible without speculative assumptions. This risk category is therefore best addressed through structured qualitative analysis and scenario-based reasoning (). Closing the gap between technology upscaling and its propagation to economic risk is identified as a research priority.