Site-specific fatigue estimation is an essential part of wind turbine lifetime extension, with various methods depending on data availability.The present study compares probabilistic lifetime extension assessment results for rotor blades with and without load measurements. It also addresses two key questions in such assessments: the applicability of the Frandsen model for estimating waked turbulence under complex and mixed wake conditions and the extrapolation of mid-term data over longer time periods.The case study wind turbine is SWT-2.3-93, located at the edge of the Lillgrund wind farm, situated in the & Oslash;resund Strait between Denmark and Sweden. The turbine is extensively instrumented, with 5 years of data available from its supervisory control and data acquisition (SCADA) system.Although the Frandsen turbulence estimates deviate in a different manner from measurements at below- and above-rated mean wind speeds, the model remains a conservative approach for fatigue load prediction and reliability.In the current case study, the site-specific assessment using strain gauge measurements yields a 33 % higher annual fatigue reliability index after 35 years compared to a scenario based on the Frandsen estimation combined with ambient environmental data and a generic aeroelastic model. The results also demonstrate that the sensitivity of fatigue reliability to load uncertainty is negligible when load measurements are used directly but relatively high when relying on the Frandsen model in combination with a generic aeroelastic model. Overall, the high variability of the lifetime extension in different scenarios of data availability and accuracy shows the importance and added value of high-quality measurements combined with wind-farm-level SCADA and a model updated in real time (digital twins).

Abstract Offshore wind deployment is accelerating alongside rapid growth in electricity demand from hyperscale data centers. However, prevailing renewable procurement models often decouple contractual clean energy purchases from physical system operation, shifting flexibility requirements to the broader grid. This study evaluates whether offshore hybrid wind–solar–battery power plants can directly supply high-reliability data center demand. Using the Technical University of Denmark HyDesign framework, we co-optimize offshore wind overplanting, floating photovoltaic integration, and battery storage sizing for a representative fixed-bottom offshore wind site. The data center is modeled as a constant, inflexible load, such that reliability improvements are achieved solely through supply-side design choices. Annual deterministic simulations quantify uptime and levelized cost of energy (LCOE) across wind-only and wind–solar hybrid configurations. Results show that hybridization significantly increases uptime and effective capacity relative to standalone offshore wind. However, the marginal cost of reliability rises nonlinearly as uptime approaches extreme thresholds. Achieving near-firm supply through renewable overbuild and storage alone requires substantial additional capacity and long-duration storage. The findings highlight both the technical potential and economic trade-offs of integrating large digital loads with offshore hybrid renewable systems.

Abstract. Social aspects are gaining traction in wind energy research. Increasing local opposition to wind energy projects is just one symptom of deeper-rooted challenges in the further expansion of the technology. A recent publication by Kirkegaard et al. (2023a) lays out the grand challenges related to the complex interactions between society and wind energy technology and outlines a research agenda for wind energy research from a socio-technical perspective. This article discusses these challenges in the context of a more technologically focused research audience. We begin by describing the role of social sciences in wind energy research, arguing for the diverse set of insights, research topics, and value that they can add, going beyond outdated concepts of social acceptance (such as NIMBY), and providing solutions for public engagement and planning processes, just ownership structures and value-based design. We discuss social grand challenges in five areas: (1) Project planning & spatial relations, (2) Wind turbine design & scalability, (3) Grid integration, roles & responsibilities, (4) General public perception of the technology, (5) Energy policy to support system transformation. We conclude by reflecting how social sciences and technical sciences can be better integrated to jointly advance wind energy research into a new interdisciplinary era that is able to provide holistic solutions for a transition to carbon-neutrality.

With the increased interest in floating offshore wind turbines, there is an increased focus on evaluating the lifetime of the auxiliary systems, such as the mooring lines and the dynamic power cables, because these systems are in-mature compared to bottom-mounted offshore wind farms. This paper proposes a conceptual reference dynamic power cable suitable for the 15 MW NREL reference turbine mounted on the UMaine floater installed at 82 m water depth. This provides a basis for discussing the life evaluation of dynamic cables obtained from aeroelastic simulation of the floating offshore wind turbine platform exposed to the environmental conditions of the South Brittany site in France. An electromagnetic-thermal finite element simulation is used to determine the current capacity of the cable and aeroelastic simulations are used for a simple assessment of the main failure modes as maximum cable tension, minimum bending radius and fatigue damage accumulation.

Abstract. Estimating the site-specific fatigue reliability of wind turbines is an integral part of probabilistic lifetime extension assessment. Limitations in type, accuracy, and availability of site-specific data is one of the main challenges in such estimations. The present research tackles the challenge of estimating long-term fatigue loads using short-term strain gauge measurements via statistical extrapolation. The case study wind turbine is a Siemens 2.3 MW, in the Lillgrund wind farm, located in the Øresund strait between Denmark and Sweden. The turbine is heavily instrumented and Supervisory Control and Data Acquisition (SCADA) is also available. The study also reassesses the performance of the Frandsen model – as a simplified approach for estimating higher turbulence due to wakes – in a compact wind farm layout using aeroelastic simulations of the case study wind turbine. Furthermore, it shows the sensitivity of the site-specific reliability with respect to the uncertainty in material strength, fatigue load, and damage accumulation model. The results reveal that for the case-study site, the Frandsen model underestimates turbulence in below-rated mean wind speeds and overestimates the turbulence in above-rated mean wind speeds. However, using the Frandsen model for estimating the long-term fatigue loads in the case study location leads to a 35 % lower reliability index than a site-specific assessment using data from the SCADA system and, thus, is relatively more conservative. The study reveals that the sensitivity of the fatigue reliability to the load’s uncertainty is negligible in assessment using site measurements and relatively high when using the Frandsen model. The extrapolation approach used in the current study can facilitate the use of digital twins when strain gauge measurements are unavailable for a part or the whole span of the lifetime. In addition, the assessment of the Frandsen model in the case study wind farm, as an example of a wind farm with short spacing, adds valuable information to the ongoing studies in the literature about the performance of the model in intense and mixed-waked conditions. Finally, the provided information about robustness of the reliability based on the load estimation approach, is useful for considering uncertainty in the lifetime extension assessment.

This paper presents a methodology to conduct a risk assessment of lightning damage for wind farms using a data-driven methodology. Methods exist to address risks related to fire but this approach goes further to address lightning damage and related impacts to operations and maintenance comprehensively - monetizing the costs of these impacts. The process begins with lightning data analysis and strike rate calculation. Then, the lightning damage data is analyzed to gain insight into the repair cost and impact. Lastly, the process-based cost model takes lightning strike rate and impact data, resulting in risk provision. The paper presents two case studies, one for onshore wind farms and one for offshore wind farms. The relative importance of operations and maintenance costs versus lost revenues due to downtime (liquidated damages) differ between the two cases due to underlying technical drivers (turbine sizes, wind farm performance, lightning prevalence) as well as market drivers (labor costs, electricity prices). Thus, the study demonstrates both the viability of the method in providing monetary estimates of lightning damage costs over the lifteime of a wind farm as well as the importance of site-specific analysis and models to ensure accurate estimation of those costs.

Optimization of inter-array dynamic cables for Floating Offshore Wind Farms (FOWFs) using three integer linear programs and a heuristic is presented. Design optimization of fixed-bottom offshore wind is a challenging research problem but the presence of dynamic components in FOWFs adds new complexity — as the Floating Offshore Wind Turbine (FOWT), the support structure including the station-keeping system, and the floating power cables all experience dynamic movement in reaction to wind, wave and even current forcing. In this study, dynamic modeling for the response of this system is first carried out to assess the risk of potential mechanical interference between movable elements. Subsequently, safety zones constraints are defined in the optimization to ensure minimally safe conditions for operation of the combined FOWT/support-structure/cables system. Likewise, additional constraints including maximum thermal limits, tree topology without branching, and others are incorporated. The programs follow an incremental approach. Model 1 proposes a simple way to avoid mechanical interference, Model 2 adds variables modeling mooring lines anchoring, and Model 3 increases the degrees of freedom through addition of the positioning of the touchdown point where the dynamic and static sections meet at the seabed. The applicability is illustrated through realistic case studies for a reference FOWF in Europe. Results show that: (i) Modern branch-and-cut solvers are able to solve Model 2 getting the global optimum in seconds, and (ii) further cost refining can be obtained after wrapping Model 3 in the heuristic, using Model 2 as the initial design, decreasing the cost of this layout by around 1.5% in few hours through a nonrectilinear topology.

Fatigue assessment of wind turbines involves three main sources of uncertainty: material resistance, load, and the damage accumulation model. Many studies focus on increasing the accuracy of fatigue load assessment to improve the fatigue reliability. Probabilistic modeling of the wind's turbulence standard deviation is an example of an approach used for this purpose. Editions 3 and 4 of the IEC standard for the design of wind energy generation systems (IEC 61400-1) suggest different probability distributions as alternatives for the representative turbulence in the normal turbulence model (NTM) of edition 1. There are debates on whether the suggested distributions provide conservative reliability levels, as the established design safety factors are calibrated based on the representative turbulence approach. The current study addresses the debate by comparing annual reliability based on different scenarios of NTM using a probabilistic approach. More importantly, it elaborates on the relative importance of load assessment accuracy in defining the fatigue reliability. Using the DTU 10 MW reference wind turbine and the first-order reliability method (FORM), we study the changes in the annual reliability level and its sensitivity to the three main random inputs. We perform the study considering the blade root flapwise and the tower base fore–aft moments, assuming different fatigue exponents in each load channel. The results show that integration over distributions of turbulence in each mean wind speed results in less conservative annual reliability levels than representative turbulence. The difference in the reliability levels varies according to turbulence distribution and the fatigue exponent. In the case of the tower base, the difference in the annual reliability index after 20 years can be up to 50 %. However, the model and material uncertainty have much higher effects on the reliability levels compared to load uncertainty. Knowledge about such differences in the reliability levels due to the choice of turbulence distribution is especially important, as it impacts the extent of lifetime extension through reliability reassessments.

Over the past few years, the offshore wind sector has been subject to renewed yet growing interest from the industry and from the research sphere, with a particular focus on a recently developed concept, the floating offshore wind (FOW). Because of its novelty, floating research material is found in limited quantity. This paper focuses on the layout optimization of a floating offshore wind farm (FOWF) considering multiple parameters and engineering constraints, combining floating-specific parameters together with economic indicators. Today’s common wind farm layout optimization codes do not take into account either floating-specific technical parameters (anchors, mooring lines, inter-array cables (IACs), etc.) or non-technical parameters (operational expenditure, OPEX; capital expenditure, CAPEX; and other techno-economic project parameters). In this paper, a multi-parametric objective function is used in the optimization of the layout of a FOWF, combining the annual energy production (AEP) together with the costs that depend on the layout. The mooring system and the collection system including the inter-array cables and the offshore substation are identified as layout-dependent and therefore modeled in the optimization loop. Using ScotWind site 10 as a study case, it was found with the predefined technical and economic assumptions that the profit was increased by EUR 34.5 million compared to a grid-based layout. The main drivers were identified to be the AEP, followed by the anchors and the availability associated with the failures of inter-array cables.

As a way of enhancing the profitability of renewable power plants (RPPs), this research explores the optimization of medium-voltage cable network layouts for Hybrid Power Plants (HPPs). In this work, HPPs combine wind and solar PV in the shared location with a single point of connection to the grid. Compared to traditional single-technology RPPs, HPPs have the potential to reduce costs by sharing the electrical infrastructure of the balance of the plants and the grid connection. The proposed optimization framework aims to minimize both cable investment costs and costs associated with curtailed energy due to overplanting or cable undersizing. The problem is modeled as a mixed integer linear program (MILP). Data clustering techniques are deployed to reduce computational efforts. Two optimization approaches are compared: one where cable layout is optimized separately for each technology, and another where the optimization considers both technologies simultaneously. The proposed methodology is applied to a case study in India as a proof of concept. Even though the precise numbers can differ from case to case, the results of the analyzed case study reveal 10- 15% of investment cost reductions, favoring the approach where both technologies are jointly considered. Sensitivity analysis highlights the critical role of careful data clustering when trying to capture characteristics of resources. The findings emphasize the importance of holistic optimization approaches when creating the electrical design of HPPs.

This paper explores state-of-the-art modelling and optimization methods for floating offshore wind farms. A case study is performed on three ScotWind lease areas, where the optimal turbine type and layout is assessed in terms of Annual Energy Production (AEP) using a multiple-step optimization strategy. The numerical setup relies on TopFarm (DTU), ORBIT (NREL) and Peak Wind’s in-house codes and expertise. The portfolio study reveals that across all sites, using wind turbines of higher single capacity (15MW against 11MW and 14MW) is more optimal, as the scaling-up of the nameplate power allows to save costs. The LCOE decreases to around 100$/MWh, which is consistent with the predictions for commercial floating wind projects in the coming years. The optimal layouts show an alignment of the turbines perpendicularly to the prevailing wind direction, in which their spacing is also greater to minimize the wake losses. Sensitivity analyses are carried out on key project-specific parameters and optimization inputs, such as the initial positions of the turbines, showing how a multiple-start strategy explores the whole design space and allows to validate the optima found.

Layout optimisation is essential for improving the overall performance of offshore wind farms. During the past 15 years, the use of yield optimisation algorithms has resulted in a transition from regular to more irregular farm layouts. However, since the layout affects many factors, yield optimisation alone may not maximise the overall performance. In this paper, a comparative case study is presented to quantify the effect of the wind farm layout on the overall performance of offshore wind farms. The case study was performed to investigate two performance indicators: power performance, using yield calculations with windPRO, and wake-induced tower fatigue, using the Frandsen model. It is observed that irregular wind farm layouts have a higher annual energy production compared to regular layouts. Their power production is also more persistent and less sensitive to wind direction, improving predictability and thus the market value of power output. However, one turbine location in the irregular layout has a 24 % higher effective turbulence level, leading to additional tower fatigue. As a result, fatigue-driven tower designs would require increased wall thicknesses, which would result in higher capital costs for all turbine locations. It is demonstrated in this study that layout optimisation using minimum inter-turbine spacing effectively resolves the induced wake issue while maintaining high-yield performance.

The variability of the wind turbine loads complicates fatigue assessment in the design phase, as performing simulations covering the entire lifetime is computationally expensive. The current work provides important information for assessing the uncertainty in fatigue damage estimation due to finite data. We study the sample size effect on mean, variance, and skewness of damage in each wind bin, identify the important wind bins, and study the uncertainty propagation from each wind bin to the lifetime damage using 3600 aeroelastic simulations and bootstrapping. To achieve less than 1% error in the damage estimation across all load channels in the current case study, at least 100 turbulence seeds are needed. Damage in different wind bins follows a lognormal distribution when using the conventional approach of six seeds. The provided insights and information allow the designer to achieve a specific level of accuracy for a given computational cost using strategic bin sampling.

Wind turbine design standards recommend the use of statistical modeling coupled with extrapolation of the short-term load data to long-term periods for fatigue reliability assessment. However, statistical error and computational expense can limit the accuracy of such approaches. In the case of wind turbine blades, the errors are more significant because of the high material fatigue exponent that makes the damage estimations more sensitive to variations. In addition, due to different excitation sources, the flapwise load range histogram is not unimodal, and thus its statistical modeling is complex. In the present work, we provide three methods for statistical modeling of the flapwise bending moment ranges including a novel approach based on frequency-based separation of the modes. The first two methods are simplified approaches for modeling the most crucial load ranges using unimodal distributions and the third method involves multimodal distribution fitting. The research is based on 3600 10-minute aeroelastic simulations of DTU 10MW case study wind turbine from which a benchmark damage equivalent load (DEL) is calculated. The DEL calculated by each of the three proposed methods is compared to this reference. The results show that the conventional approach based on using 6 seeds as well as using mixture models fitted on the limited data lead to under-conservative results with errors up to 23%. On the other hand, the simplified unimodal approaches provided in this work can provide conservative estimations of the fatigue damage with mean values 5% and 12% higher than the benchmark. However, the variability of the DEL estimates is higher when using unimodal extrapolation of the load ranges, and the data can be conservative by 17.5%. The proposed unimodal fits suggested for modeling and extrapolation of the blade’s load ranges provide less errors relatively and most importantly conservative DEL estimations while maintaining computational efficiency.

Wind will be a foundational energy source in the electricity grid at the heart of a future integrated energy system, replacing traditional electricity generators powered by fossil fuels and providing grid reliability services in addition to energy. Future capabilities and functions of the wind energy sector will evolve apace with the future expansion and needs of global energy infrastructure; however, wind turbines designed today will not be able to provide the services needed to form and stabilize the grid as a majority supplier. In 2017, organizers for the IEA Wind Technical Experts Meeting (TEM) #89 Grand Vision for Wind Energy workshop assembled a group of experts to consider the question of how to enable a future in which wind energy supplies more than 50% of global electricity consumption. More than 70 experts representing 15 countries attended the workshop and provided diverse perspectives for the Grand Vision for Wind Energy. The IEA Wind TEM #109 was a subsequent gathering that was convened Feb. 28-March 1, 2023, in Boulder, Colorado, USA. The IEA Wind TEM #109 meeting aimed to bring together the leaders of all working groups and the IEA Wind Technology Collaboration Programme (TCP) to identify gaps in scientific knowledge, design, and deployment practice as well as identify recommendations for collaborative pathways, initiatives, and prioritized long-term research needs that can be addressed by IEA Wind. This report captures the outcomes of this meeting of international experts: five Grand Challenge areas (The Atmosphere, The Turbine, The Plant and Grid, Environmental Co-Design, and Social Science). In addition, meeting participants identified eight crosscutting topic areas that are discusses within this report (Environment-Turbine, Turbine-Atmosphere, Atmosphere-Grid/Plant, Grid/Plant-Turbine, Grid/Plant-Environment, Atmosphere-Environment, Turbine-Social, and Social-Grid/Plant).

Green hydrogen is increasingly cited as a solution to the decarbonisation of industry. Its large-scale production is still a recent topic with uncertainties. In this paper, an economic impact assessment (EIA) method is explained. A modular and flexible cost model is generated, which estimates the LCOE (Levelized Cost of Energy) of an offshore wind farm and the LCOH (Levelized Cost of Hydrogen) of a hydrogen generation plant either as a hybrid renewable energy system (HRES) or independent from each other. The costs are estimated using a schedule-based approach, which considers the reliability, maintenance operations as well as production of both the offshore wind farm and the hydrogen generation plant. Developed EIA is demonstrated for Belgium using Mermaid Offshore Wind Farm.

The preliminary financial evaluation of wind farm profitability requires fast analysis of energy production and costs while having very little specific information around the project. Early in the design process, the selection of specific wind turbines and the layout design may not yet be defined. Techno-economic and financial analysis models have been developed to use input from a small set of high-level project characteristics to estimate major cost elements and energy production for a wind farm to support quick analysis of levelized cost of energy (LCoE), or other financial metrics. Such models are typically based on prior project data and/or very simple analytical models. However, as capabilities for financial analysis of wind farms advance, so does the desire to improve the accuracy of the physical and cost modelling of the system. In this work, we develop a surrogate model of Annual Energy Production (AEP) for offshore wind farms for financial analysis applications in the early stages of development. The surrogate is developed from an parameterized engineering model and covers a large potential wind farm design space addressing different technological and site conditions. The surrogate model uncovers the underlying structure in the model in terms of input-output relationships and achieves a coefficient of determination of 0.994. The method used to develop the surrogate model can be adapted for additional dimensions of inputs as needed.

In this work, a holistic modelling approach is developed and applied to the valuation of innovations in offshore wind energy. Two innovations are considered: 1) a more accurate modelling of vessel movement related to operations and maintenance activities and 2) an operational strategy for market participation. In both cases, a standard process was followed of first mapping the effects of the technologies on wind energy system’s levels, then assessing them through development and augmentation of a holistic cost and valuation model. The process is demonstrated for each innovation and quantifies their potential impacts to LCoE and NPV respectively. The models and methods can be extended to other innovations for quantitative assessment of potential benefits.