Advancing Offshore Wind Capacity Through Turbine Size Scaling

Abstract

The upscaling of turbines in the offshore wind industry has been unprecedented, as compared to 5–6 MW rated turbines 10 years ago. A typical 20–26 MW rated turbine in modern commercial applications (MingYang MySE 18.X-20 MW installed in 2025 and 26 MW prototype by Dongfang Electric tested in 2025) has been demonstrated. This scaling has been made possible by increasing rotor diameters (>250 m) and hub heights (>150–180 m) to achieve capacity factors of up to 55–65%, annual energy generation of more than 80 GWh/turbine, and significant decreases in levelised cost of energy (LCOE) to current values of up to 63–65 USD 2023/MWh globally averaged in 2023 (with minor variability in 2024 due to market changes and new regional areas). The paper analyses turbine upscaling over three levels of hierarchy, including turbine scale—rated capacity and physical aspect, project scale—multi-gigawatts of farms, and market scale—the global pipeline > 1500 GW level, and combines techno-economic evaluation, structural evaluation of loads, and infrastructure needs assessment. The upscaling has the advantage of reducing the number of turbines dramatically (e.g., 500 to 67 turbines in a 1 GW farm, as turbine size is increased to 15 MW) and balancing-of-plant (BoP) CAPEX (turbine-to-turbine foundations and cables) by some 20 to 30 percent per unit of capacity, and serial production learning rates of between 15 and 18% per doubling of capacity. But the problems that come with the increase in ultra-large designs are nonlinear increments in mass and load (i.e., blade-root and tower-bending moments), logistical constraints (blades > 120 m, nacelle up to 800–1000 tonnes demanding special vessels and ports), supply-chain issues (rare-earth materials, vessel shortages increase day rates by 30–50%), and technology limitations (aeroelastic compounded by numerical differences between reference 5 MW, 10 MW, and 15 MW models), it becomes evident that there is a significant increase in deflections of the tower and blades and platform surge/pitch responses with continued increases in power levels, but without a correspondingly mature infrastructure. The regional differences (mature ports of Europe vs. U.S. Jones Act restrictions vs. scale-up of vessels/manufacturing in China) lead to the necessity of optimisation depending on the context. The analysis concludes that, to the extent of mature markets with adapted logistics, continuous upscaling is an effective business strategy and can result in 5 to 12 percent further reductions in LCOE, but beyond that point, gains become marginal or even negative, as risks and costs increase. The competitiveness of the future depends on multi-scale/multi-market-based approaches—modular-based families of turbines, programmatic standardisation, vibration control innovations, and industry coordination towards supply-chain alignment and standards. Its major strength is that it transcends mere size–cost relationships and shows how nonlinear structural processes, aero-hydro-servo-elastic interactions, and bottlenecks in logistical systems are becoming more determinant of the efficiency of ultra-large turbines. The study demonstrates that upscaling turbines has LCOE benefits through the support of associated improvements in installation facility, supply-chain preparedness, and structural vibration control potential, based on the comparisons of quantitative loads, techno-economic scaling trends, and regional market differentiation.

1. Introduction

Offshore wind energy has become a key pillar of the global energy transition, combining large-scale renewable potential with proximity to coastal demand centres. Rapid capacity growth over the past decade reflects technological maturity and strong policy support in Europe, Asia, and North America. As onshore wind and solar photovoltaics approach spatial and social limits, offshore wind offers a critical pathway to deep power-sector decarbonisation while maintaining system reliability. Globally, over 453 GW of offshore wind pipelines have been assessed, with 68.3 GW in operation. In 2023, 6326 MW was deployed worldwide, with China leading the sector. Projects under construction total 35,573 MW, a 64% increase from 2022 (21,717 MW) [1]. Technological advances—particularly turbines exceeding 15 MW and floating foundations—have expanded deployment into deeper waters and diverse marine environments. Together with economies of scale in manufacturing and installation, these developments position offshore wind as a cornerstone of mid-century net-zero targets [2]. However, rapid expansion also raises challenges related to cost competitiveness, supply chains, grid integration, and the sustainability of large-scale marine infrastructure.

This article examines the evolution of offshore wind through turbine upscaling and optimisation of size-related parameters across technical, economic, and logistical domains. It analyses the balance between opportunities—higher energy yields, lower levelised cost of energy (LCOE), and improved efficiency—and constraints linked to manufacturing, transport, and installation. By synthesising recent research and market data, the study evaluates whether continued turbine growth remains the most effective strategy for capacity expansion or whether diminishing returns are emerging. The aim is to provide an integrative perspective on turbine scaling as a determinant of the sector’s competitiveness and sustainability.

The research presents a comprehensive assessment of technological, economic, and infrastructural factors shaping turbine scaling. It integrates market reports, techno-economic analyses, and engineering studies to examine interactions between turbine growth and trends such as floating platforms, port and vessel capacity, and supply-chain development. Emphasis is placed on links between design optimisation, cost reduction, and environmental sustainability. Key constraints—including structural reliability, material availability, manufacturing bottlenecks, and grid integration complexity—are identified alongside innovations that may mitigate these barriers. This multi-dimensional framework clarifies how turbine-sizing decisions influence the long-term feasibility, competitiveness, and resilience of the offshore wind industry.

2. Definitions and Scales of Offshore Wind Industry Development

2.1. Explanation of Three Scaling Levels

The development of the offshore wind sector can be described through three interrelated scaling levels: turbine, project, and market scale. This framework clarifies how technological innovation, project configuration, and policy evolution jointly shape industry growth. Turbine scale refers to the physical and engineering development of individual units; project scale concerns the aggregation of turbines into larger offshore farms; and market scale reflects national and international capacity expansion.

These levels are dynamically coupled. Advances at the turbine scale—higher rated capacity, larger rotor diameters, taller towers—improve project-level economics by increasing capacity factors and lowering balance-of-plant costs per megawatt. In turn, larger projects drive industrial learning, port and vessel upgrades, and component standardisation, accelerating market-scale growth. Conversely, national policies, long-term targets, and global supply-chain capacity influence the pace and direction of technological scaling. Thus, the three-scale framework explains offshore wind evolution as a multi-level system in which turbine upscaling, project aggregation, and market expansion reinforce each other, supporting cost reductions and deployment growth.

(a)

Turbine scale (rated generator capacity)

Turbine scale is the most direct indicator of technological progress, primarily defined by rated generator capacity and associated parameters such as rotor diameter, hub height, nacelle mass, and drivetrain configuration. The evolution of turbine scaling has been indicated as shown in Figure 1 throughout the years. Progressive enlargement—turbine upscaling—has driven offshore cost reductions by increasing energy yield per unit and reducing the number of turbines required. As noted by Musial, offshore turbine growth is unprecedented: average ratings increased from 5 to 6 MW to 15 MW within a decade, with demonstrators exceeding 20 MW [3]. This progress results from advances in materials, blade and bearing design, and lightweight direct-drive generators. Modern turbines with rotor diameters above 250 m can exceed 80 GWh annual production and reach capacity factors up to 60%.

Upscaling reduces LCOE through three mechanisms: fewer foundations and inter-array cables per installed megawatt, shorter installation times, and improved aerodynamic efficiency. NREL analyses indicate that moving from 15 MW to 20 MW turbines may lower LCOE by 5–10%, assuming port and maritime logistics are adapted. However, the benefits are nonlinear; beyond certain thresholds, learning losses arise due to new manufacturing tools, installation methods, and certification requirements. Further scaling is increasingly constrained by port infrastructure, extreme-dimension transport, and material limits—especially for blades and main bearings. As emphasised by Musial, “bigger is not always better”; optimal turbine size reflects a balance between economic gains, system costs, and technological risk [3].

(b)

Project scale (total capacity of wind farm)

The project scale refers to the aggregation of turbines within a single offshore wind farm and its total installed capacity. While turbine upscaling reflects component-level progress, project scaling captures the systemic dimension of expansion, including layout design, electrical infrastructure, logistics, and coordinated turbine operation. Over the past decade, projects have grown from a few hundred megawatts to complexes exceeding 2.5 GW, transforming engineering practice and economic logic. According to Musial, this shift toward multi-gigawatt developments is primarily driven by economies of scale in installation, operation, and maintenance [3]. Larger turbines reduce the number of units required, enabling simpler layouts, shorter cabling distances, and lower foundation costs per megawatt. Large-scale projects also justify dedicated port facilities, specialised installation vessels, and high-voltage transmission systems, reducing balance-of-plant costs. Financially, greater capacity improves bankability by spreading fixed costs and risks over a larger asset base. However, project-level scaling introduces added complexity. As emphasised by Musial, multi-gigawatt developments strain port infrastructure and offshore construction schedules [3]. They require larger seabed lease areas, expanded grid connections, and more complex environmental permitting. Increased inter-turbine spacing to mitigate wake losses may also limit energy density per square kilometre. Consequently, beyond a certain point, further project growth may yield diminishing returns if transmission capacity, maritime resources, or regulatory frameworks do not scale accordingly [3].

Rather than treating turbine, project, and market scales separately, the three-level framework highlights their interaction. It demonstrates how supply-chain, logistical, infrastructural, and regulatory constraints can offset gains achieved at the turbine level (higher MW ratings and lower turbine-level LCOE). By clarifying these cross-scale feedbacks, the framework shows that optimal turbine size depends on project configuration and market maturity, explaining the nonlinear dynamics and potential diminishing returns of continued upscaling.

The nonlinear interaction between these levels is outlined. The framework shows that superlinear structural load growth due to turbine upscaling extends to market-level infrastructural restrictions and installation risk at the project level. According to the concept, there will be a context-dependent ideal turbine size that is established by the alignment of market maturity, logistical capacity, and structural scaling. The evolution towards larger project scales reflects a shift in offshore wind from a collection of discrete pilot farms to a strategic component of national energy systems. Modern offshore clusters—such as those under development in the North Sea, U.S. Atlantic, and South China Sea—increasingly function as integrated energy hubs, designed for hybrid generation, storage, and interconnection. In this sense, the project scale serves as the bridge between turbine-level technological innovation and market-level industrial transformation, linking engineering efficiency with system-wide energy policy ambitions as it is described in Figure 2. In Figure 2, blue arrows indicate power and structural turbine scaling effects, including loads, annual energy production (AEP) and mass increases, orange arrows indicate project-scale effects (turbine count, BoP, installation complexity and risk), while green arrows indicate market-scale effects, including supply chain demand and vessel CAPEX increase with large-scale deployment of bigger turbines, and LCOE decrease due to scale and learning effects.

Figure 3 shows the deployment of total offshore wind turbines with the respective turbine size range and share of percentage capacity for four years (i.e., 2020–2024).

Figure 4 presents offshore wind turbine numbers deployed from 2020–2024 across Europe, China and USA.

The five-year trend clearly shows rapid turbine upscaling as it is described in Figure 3. In 2020–2022, smaller units dominated: 70–90% of new installations were <8 MW. In 2021, China installed 876 turbines < 5 MW and 1570 in the 5–<8 MW class, reflecting large-scale deployment of 4–6 MW models. Europe contributed mainly 8–<12 MW units (e.g., 255 in 2020). This phase prioritised fast capacity growth in emerging markets, with global averages of 6–8 MW.

By 2022, a transition emerged as 8–<12 MW turbines reached ~22% share, indicating technological maturity and supply-chain readiness. In 2023–2024, the shift accelerated: 72–84% of new turbines were 8–12 MW, 12–<15 MW rose to 6–10%, and >15 MW prototypes appeared (two units, both in China), while <5 MW fell below 1%.China drove this trend (e.g., 627 units of 8–<12 MW in 2024), alongside Europe’s consistent deployment of 10–14 MW models. Totals confirm the shift (3027 units in 5–<8 MW vs. 158 in 12–<15 MW and 2 > 15 MW). Consequently, average turbine size increased from ~6–8 MW to ~9.8–10 MW by 2024. Despite supply-chain constraints and inflation, larger turbines support higher energy yield per unit, fewer installations for equivalent capacity, lower balance-of-plant costs, and feasibility in deeper or lower-wind sites.

The figure illustrates the rapid rise in rated power between 2020 and 2024. In 2020–2021, <8 MW turbines accounted for over 70–90% of additions. By 2022, 8–12 MW gained ~22% share, largely due to Chinese upscaling. In 2023–2024, 8–<12 MW dominated new installations [6]. More broadly, turbine ratings increased from 3 to 4 MW in 2010 to ≥15 MW in recent projects. This growth has outpaced installation infrastructure and supply-chain adaptation, providing empirical evidence of scaling pressures.

(c)

Market scale (global regulatory pipeline and ambitions)

The market scale represents the highest level of development, encompassing the cumulative capacity pipeline, regulatory frameworks, and long-term deployment targets. Unlike turbine and project scales, it reflects offshore wind’s systemic integration into energy policy, industrial capability, and decarbonisation strategies. According to NREL’s Offshore Wind Market Report, the global pipeline exceeded 1500 GW by mid-2024, with ~80 GW operational [1]. Europe leads with mature regulation and infrastructure, China dominates annual additions, and the United States and Asia–Pacific markets—particularly South Korea, Japan, and Taiwan—are accelerating under ambitious national targets. At this level, policy ambition and industrial capacity determine sustainable growth. Stable auction systems, long-term regulatory clarity, and coordinated grid planning sustain investment and supply-chain readiness, while fragmented permitting can delay deployment. Market expansion thus depends on institutional and financial maturity as much as technology.

Market scaling also feeds back to lower levels; predictable global demand supports mass production, platform standardisation, and manufacturing investment, accelerating learning curves and cost reductions. However, Musial notes that overly rapid pipeline growth may strain materials supply, port capacity, and skilled labour [3]. Balancing ambition with industrial capability will therefore define the coming decade. Ultimately, the market scale embeds offshore wind within the broader energy transition, integrating turbine and project scaling into a coherent global policy and economic framework for achieving net-zero systems.

2.2. Relationship Between Scales and Their Impact on Cost and Efficiency

Cost and efficiency outcomes in offshore wind emerge from mutual feedback across the turbine, project, and market scales. Understanding these couplings is essential for interpreting observed LCOE trends and anticipating where diminishing returns may appear.

2.2.1. Turbine—Project: Component Scaling to System Effects

Fewer units per GW: Higher rated capacity and larger rotors reduce turbine counts for a given farm output, cutting foundations, inter-array cabling and offshore installation hours per MW; balance-of-plant (BoP) CAPEX scales sublinearly with turbine size. Array performance: Larger rotors can raise capacity factor (AEP/MW) but amplify wake interactions; optimal spacing may increase; and trading area is used for AEP gains. Reliability and access: Bigger nacelles and blades concentrate energy in fewer assets; this improves O&M logistics per MW if reliability is high but raises single-point-failure risk and critical-lift requirements.

2.2.2. Project—Turbine: Farm Design Filters Optimal Size

Layout and seabed constraints (lease geometry, water depth, soil): Determine feasible rotor diameter/tower height and foundation concept; these set practical bounds on turbine upsizing. Electrical architecture: HVDC/HVAC choices, export cable ratings, and offshore substations set marginal system costs that can make an increment in turbine rating more or less valuable. Installation strategy: Port draft, marshalling area, and jack-up crane capability can erase theoretical LCOE gains from larger machines if heavy-lift logistics are marginal.

2.2.3. Market—Project/Turbine: Policy and Supply-Chain Scaling

Regulatory visibility and auction design: Bankable pipelines enable long production runs, standardisation, and learning, pushing BoP/O&M costs down; policy volatility does the opposite (learning loss, retooling). Supply-chain capacity: Blade, tower, and bearing manufacturing, vessel availability, and port upgrades condition whether larger turbines reduce LCOE or instead cause bottlenecks and schedule risk. Grid readiness: Coordinated transmission planning limits curtailment and reduces export costs per MW at high market penetration.

(a)

Conceptual Cost–Energy Scaling Model Linking Turbine Rating to LCOE

To formalise the qualitative relationships described above, a simplified techno-economic scaling framework is introduced to link turbine rated capacity to AEP, cost structure, and ultimately the levelised cost of energy (LCOE).

The levelised cost of energy (LCOE) is defined as:

$$\mathrm{L}\mathrm{C}\mathrm{O}\mathrm{E}={\displaystyle \frac{\mathrm{C}\mathrm{A}\mathrm{P}\mathrm{E}\mathrm{X}·\mathrm{C}\mathrm{R}\mathrm{F}+\mathrm{O}\mathrm{P}\mathrm{E}\mathrm{X}}{\mathrm{A}\mathrm{E}\mathrm{P}}}$$

(1)

where

• CAPEX is the total capital expenditure;
• OPEX is the annual operational expenditure;
• AEP is the annual energy production;
• CRF is the capital recovery factor reflecting financing assumptions.

This formulation allows turbine size to influence LCOE through both the numerator (cost structure) and the denominator (energy production).

(b)

Energy Scaling with Turbine Size

Annual energy production of a single turbine can be approximated as:

$$\mathrm{A}\mathrm{E}\mathrm{P}=P_r·\mathrm{C}\mathrm{F}·8760$$

(2)

where

• $P_r$ is rated power;
• $\mathrm{C}\mathrm{F}$ is capacity factor.

Capacity factor is not constant but depends on rotor diameter $D$, hub height $H$, wind regime, and wake effects:

$$\mathrm{C}\mathrm{F}=f(D,H,V,\mathrm{wake} \mathrm{losses})$$

(3)

Under aerodynamic similarity, swept-area scales as $D^2$, while hub-height increase modifies wind speed according to the power-law profile:

$$V(z)=V_{ref}{\left({\displaystyle \frac{z}{z_{ref}}}\right)}^{\alpha }$$

(4)

where V(z) is the wind velocity at height z, $V_{ref}$ is the reference wind speed at $z_{ref}$, and $\alpha$ is the wind shear exponent (typically 0.10–0.16 offshore).

Since aerodynamic power extraction is proportional to the cube of wind velocity (see Equation (20)), even moderate hub-height increases may produce nonlinear gains in AEP.

(c)

CAPEX Decomposition and Scaling Behaviour

Total capital expenditure can be decomposed into turbine and balance-of-plant components:

$$\mathrm{C}\mathrm{A}\mathrm{P}\mathrm{E}\mathrm{X}=\mathrm{C}\mathrm{A}\mathrm{P}\mathrm{E}{\mathrm{X}}_{\mathrm{t}\mathrm{u}\mathrm{r}\mathrm{b}}+\mathrm{C}\mathrm{A}\mathrm{P}\mathrm{E}{\mathrm{X}}_{\mathrm{B}\mathrm{o}\mathrm{P}}$$

(5)

For a wind farm of fixed total capacity $P_{farm}$, the number of turbines is

$$N={\displaystyle \frac{P_{farm}}{P_r}}$$

(6)

(d)

Turbine Cost Scaling

Turbine-specific CAPEX typically scales sublinearly with rated power due to manufacturing economies of scale:

$$\mathrm{C}\mathrm{A}\mathrm{P}\mathrm{E}{\mathrm{X}}_{\mathrm{t}\mathrm{u}\mathrm{r}\mathrm{b}}\sim P_r^{\alpha },\alpha \approx 0.7–0.9$$

(7)

This reflects partial cost efficiency as power rating increases.

(e)

Balance-of-Plant Scaling

Balance-of-plant costs (foundations, inter-array cables, installation) scale primarily with turbine count:

$$\mathrm{C}\mathrm{A}\mathrm{P}\mathrm{E}{\mathrm{X}}_{\mathrm{B}\mathrm{o}\mathrm{P}}\sim N\cdot C_{foundation}$$

(8)

Since turbine count decreases as rated power increases, BoP cost per MW generally decreases with upscaling.

(f)

Structural and Logistical Scaling Constraints

However, geometric similarity implies that structural mass and bending moments scale approximately with the cube of characteristic length:

$$m\sim D^3,M_{root}\sim D^3$$

(9)

Thus, while energy production scales roughly with $D^2$, structural demand scales with $D^3$. Beyond certain thresholds (e.g., blade length > 120 m, nacelle mass > 1000 t), logistical and installation costs increase nonlinearly due to vessel and port constraints.

This introduces diminishing returns in LCOE reduction.

(g)

Existence of an Optimal Turbine Size

Combining these relationships, LCOE becomes a nonlinear function of rated capacity:

$$\mathrm{L}\mathrm{C}\mathrm{O}\mathrm{E}=f\left(P_r\right)$$

(10)

For moderate scaling:

$${\displaystyle \frac{d\mathrm{L}\mathrm{C}\mathrm{O}\mathrm{E}}{d{P}_r}}<0$$

(11)

indicating cost reduction through upscaling.

$${\displaystyle \frac{d\mathrm{L}\mathrm{C}\mathrm{O}\mathrm{E}}{d{P}_r}}\ge 0$$

(12)

as structural mass growth, logistics thresholds, and supply-chain bottlenecks offset BoP and AEP gains.

This framework explains why optimal turbine size is context-dependent and determined by the interaction between site conditions, infrastructure readiness, and market maturity rather than by a simple “bigger is better” paradigm.

Table 1. Conceptual comparison for 1 GW wind farm.

Parameter	12 MW Turbine	15 MW Turbine
Turbine count	83	67
Capacity factor	52%	57%
BoP cost per MW	Higher	Lower
Structural mass intensity	Moderate	Higher
Net LCOE effect	Baseline	−5–10% (if no logistics bottleneck)

shows the conceptual comparison of 12 MW and 15 MW turbines for 1 GW wind farm in benefits of capacity factor, BoP and LCOE.

Illustrative example (conceptual comparison) for a 1 GW wind farm:

This simplified model demonstrates how turbine scaling reduces LCOE only under aligned structural, logistical, and market conditions.

Table 2. Wind farm projects in operational, under construction and consented.

Project	Country	Status Category	Source
Hywind Tampen	Norway	Operational	[7]
Neart na Gaoithe	UK (Scotland)	Operational	[8]
Hornsea 3	UK	Under Construction	[9]
Coastal Virginia Offshore Wind	USA	Under Construction	[10]
Baltica 2	Poland	Under Construction	[11]
Revolution Wind	USA	Under Construction	[12]
West of Orkney Windfarm	UK (Scotland)	Leased/Consented	[13]
Baltic East	Poland	Consented/Pre-construction	[14]

shows project sets that illustrate the worldwide advancement of offshore wind from pilot and early commercial deployment to large-scale, multi-GW pipelines. This indicates that although turbine and foundation technologies are mostly mature, project delivery is increasingly limited by system-level factors such as port capacity, installation vessels, grid integration, and permitting timelines. The presence of operational, under construction, and consented projects in various regions underscores a stacked pipeline; thus, delays or bottlenecks at one stage can affect the entire market.

2.2.4. Cost Pathways and Diminishing Returns

The cost pathways and the emergence of diminishing returns associated with turbine upscaling are illustrated in Figure 5.

The presented curves of lifetime value versus blade length and optimal blade size versus distance from shore are based on a simplified techno-economic optimisation framework linking aerodynamic gains with cost escalation effects.

The objective function is defined as the net present value (NPV) of a single turbine:

$$\mathrm{N}\mathrm{P}\mathrm{V}\left(D\right)=\mathrm{R}\mathrm{e}\mathrm{v}\mathrm{e}\mathrm{n}\mathrm{u}\mathrm{e}\left(D\right)-\mathrm{C}\mathrm{A}\mathrm{P}\mathrm{E}\mathrm{X}\left(D\right)-\mathrm{O}\mathrm{P}\mathrm{E}\mathrm{X}\left(D\right)$$

(13)

where blade diameter $D$ influences both energy production and cost structure.

Annual revenue is expressed as:

$$\mathrm{R}\mathrm{e}\mathrm{v}\mathrm{e}\mathrm{n}\mathrm{u}\mathrm{e}\left(D\right)=\mathrm{A}\mathrm{E}\mathrm{P}\left(D\right)\cdot P_{el}$$

(14)

with

$$\mathrm{A}\mathrm{E}\mathrm{P}\left(D\right)\sim D^2\cdot \mathrm{C}\mathrm{F}\left(D\right)$$

(15)

reflecting swept-area scaling.

Capital expenditure is decomposed into:

$$\mathrm{C}\mathrm{A}\mathrm{P}\mathrm{E}\mathrm{X}\left(D\right)=C_{blade}\left(D\right)+C_{tower}\left(D\right)+C_{foundation}\left(D\right)$$

(16)

Assuming geometric similarity:

$$C_{blade}\left(D\right)\sim D^{2.5-3}$$

(17)

$$C_{foundation}\left(D\right)\sim M_{root}\left(D\right)\sim D^3$$

(18)

Transport and installation cost escalation is modelled as a stepwise increase beyond logistics thresholds (e.g., blade length > 100–120 m). The optimal blade size is obtained from:

$${\displaystyle \frac{d\mathrm{N}\mathrm{P}\mathrm{V}}{dD}}=0$$

(19)

which leads to a site-dependent optimum determined by electricity price, distance from shore (affecting installation and O&M), wind regime (capacity factor) and transport constraints.

For longer distances, O&M and installation costs increase, shifting the optimal blade size upward due to higher marginal value of energy yield. The presented curves are therefore conceptual representations derived from this scaling framework rather than direct empirical regressions.

Turbine CAPEX/MW tends to fall with size up to a threshold; BoP/MW usually falls faster with size (fewer foundations/cables). Beyond a logistics/material threshold, cost curves flatten or turn up. OPEX: Fewer turbines can lower routine O&M/MW; however, corrective maintenance and spare-parts strategies become “lumpier” and weather-window sensitive for very large units. AEP: Net effect on LCOE: In well-prepared ports/routes and with adequate vessels, scaling lowers LCOE; where infrastructure lags, benefits erode and risk premia rise. In

Table 3. Offshore wind LCOE from 2010 to 2023 [5].

Metric	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023
Offshore Wind LCOE (2023 USD/kWh)	0.203	0.212	0.179	0.153	0.186	0.152	0.126	0.115	0.108	0.093	0.09	0.08	0.08	0.075

, all the LCOE values of offshore wind are adjusted to constant 2023 USD with a GDP inflation rate to give inter-year comparability. Compared to 2021, the cost of power for new offshore wind projects went up by 2%, from USD 0.079/kWh to USD 0.081/kWh in 2022. IRENA 2022, Renewable Power Generation Costs In 2022.

Offshore wind has experienced a rapid and sustained decline in LCOE between 2010 and 2024, transitioning from an emerging technology to a major power source. As it is described in Figure 6, in 2010, LCOE was approximately 0.20 USD/kWh. Significant cost reductions occurred in the first half of the decade, driven by larger turbine ratings, improved installation practices, and maturing supply chains. By 2015, LCOE had fallen to around 0.15 USD/kWh, declining further to about 0.08–0.09 USD/kWh in 2020 due to continued turbine upscaling, higher capacity factors, and optimised project design [5].

2.2.5. Practical Synthesis for Optimisation

The economically optimal turbine size is context-dependent: it sits at the intersection of site metocean conditions, lease geometry, port/vessel limits, electrical export design and market pipeline certainty. Standardised “families” of turbines sized to regional logistics (rather than one global maximum) often minimise system cost. Programmatic scaling—multi-project sequences using the same size class—maximises learning and avoids retooling losses.

2.3. Sensitivity Analysis of Key Techno-Economic Parameters

To evaluate the robustness of the techno-economic scaling framework, a sensitivity analysis was conducted for key parameters influencing the levelised cost of energy (LCOE). The analysis examines how variations in selected techno-economic inputs affect the conclusions regarding the economic benefits of turbine upscaling. The sensitivity assessment is based on the LCOE formulation introduced in Section 2.2. Variations in selected parameters influence either capital expenditures (CAPEX), operational expenditures (OPEX), or annual energy production (AEP), thereby affecting the resulting LCOE values. The material cost effect and weather condition impact is discussed in

Table 4. Sensitivity of LCOE to selected techno-economic parameters.

Parameter Change	Impact on CAPEX/AEP	LCOE Change	Implication For Turbine Scaling
Steel price +10%	Turbine and foundation CAPEX increase	LCOE +3–4%	Reduces economic advantage of 20 MW turbines
Steel price +20%	CAPEX strongly increases	LCOE +6–8%	Optimal turbine size shifts toward 12–15 MW
Mean wind speed +10%	AEP increase	LCOE −7–9%	Larger turbines become more favourable
Mean wind speed −10%	AEP decrease	LCOE +8–10%	Scaling benefits diminish
Vessel day rate +10%	Installation cost increase	LCOE +2–3%	Minor effect on optimal turbine size
Vessel day rate +30%	Installation CAPEX rise	LCOE +5–6%	Larger turbines become more attractive (fewer installations)

. Three parameters were selected due to their significant influence on offshore wind project economics:

• Structural steel prices, affecting turbine and foundation CAPEX;
• Mean wind speed at hub height, influencing annual energy production;
• Installation vessel day rates, affecting balance-of-plant installation costs.

Each parameter was varied by ±10% relative to the baseline assumptions used in the conceptual 1 GW offshore wind farm scenario.

The results indicate that turbine scaling benefits are most sensitive to structural material costs and wind resource conditions. A 10% increase in steel prices raises turbine and foundation CAPEX and reduces the economic advantage of ultra-large turbines. Under high material cost scenarios, the optimal turbine size shifts toward intermediate ratings (12–15 MW), where structural mass growth is less pronounced. Conversely, higher wind speeds increase AEP and amplify the economic benefit of larger rotors and higher hub heights, making turbines in the 18–20 MW class more economically favourable. Installation vessel day rates have a moderate influence on LCOE. Higher day rates slightly favour larger turbines because fewer installation cycles are required for a given wind farm capacity.

3. Trends in Turbine Size Growth

Over the past three decades, offshore wind turbines have followed one of the steepest scaling trajectories in modern energy technologies. From early nearshore demonstrators adapted from onshore designs to today’s purpose-built offshore machines exceeding 15 MW, increasing turbine size has fundamentally transformed the technical, economic, and structural characteristics of offshore wind systems. For clarity, turbine evolution can be grouped into three representative technology platforms, reflecting successive stages of upscaling, industrial maturity, and system complexity.

Table 5. Classification of offshore wind turbine technology platforms (Platforms 1–3) based on rated power, rotor size, deployment period, and dominant design characteristics.

	Platform 1	Platform 2	Platform 3
Period	1990–2005	2010–2018	2020–present
Rated Capacity (MW)	0.5–2	5–11	12–18
Rotor Diameter (m)	60–80	150–170	220–240
Hub Height (m)	50–65	105–140	130–150
Swept Area (m2)	2800–5000	17,000–25,000	38,000–46,000
Drivetrain Type	Geared, asynchronous	Direct-drive or hybrid	Direct-drive (permanent magnet)
Foundation Type	Monopile	Monopile, Jacket	XL Monopile, Jacket, Hybrid Floating
Energy yield	<5 GWh	40 GWh	60–80 GWh
Examples:	Bonus 450 kW (Vindeby, 1991), Vestas V66-2.0 MW (Horns Rev 1, 2002) [16,17]	Siemens SWT-6.0-154 (6 MW, direct-drive), MHI Vestas V164-8.0 (8 MW) [18,19,20,21]	GE Haliade-X 12 MW, Siemens Gamesa SG 14-222 DD, Vestas V236-15 MW [22,23]

presents a high-level classification of offshore wind turbine development, showing the transition from adapted onshore machines (Platform 1), to purpose-built fixed-bottom offshore turbines (Platform 2), and finally to large-scale turbines increasingly paired with floating foundations (Platform 3). The platform periods in Table 5 reflect clear technological and commercial transition points, defined by the first large-scale commercial deployment within a given capacity class and its subsequent dominance in new installations. Platform 1 (2000–2009) covers the early commercial phase, dominated by 2–5 MW turbines following the first European offshore projects. Platform 2 (2010–2017) marks industrialisation, with widespread deployment of 6–8 MW turbines and the introduction of 9–10 MW units. The year 2010 is identified as a transition due to annual installations exceeding 500 MW in Europe and a shift to rotor diameters above 150 m. Platform 3 (2018–present) represents the high-capacity scaling phase, initiated by commercial 10–12 MW turbines, followed by 14–15 MW units and 20+ MW prototypes. Its boundary is defined by the first commercial ≥10 MW orders and their growing share in annual global installations.

Thus, the classification reflects (i) commercial deployment milestones, (ii) dominant installed capacity ranges, and (iii) clear technological discontinuities in rotor diameter, hub height, and structural loading regimes.

Early offshore turbines installed from the 1990s to mid-2000s were typically below 2 MW and were largely adapted from onshore designs. They had small rotors, conservative structures, and limited modelling of coupled wind–wave loading. Although energy output and capacity factors were modest, these projects proved offshore feasibility and identified key challenges in corrosion protection, foundation behaviour, and marine operations. A major shift occurred in the early 2010s with purpose-built offshore turbines rated 5–11 MW. This phase featured larger rotor diameters and hub heights, the adoption of direct-drive or hybrid drivetrains, and advanced aeroelastic simulation tools. Higher annual energy production and fewer units per wind farm improved overall project economics. The latest stage is defined by 12–18 MW turbines with rotor diameters above 220 m. Upscaling is increasingly linked to floating offshore wind turbines (FOWTs), enabling deployment in deeper waters beyond fixed-bottom limits. Larger machines generate substantially higher aerodynamic thrust, blade-root bending moments, and global loads, making platform motion, tower-top deflection, and control–structure interaction central design drivers. While Table 5 offers a qualitative framework for turbine evolution, quantitative comparison is needed to evaluate how scaling affects system response.

Table 6. Quantitative comparison of structural loads, elastic deflections, and platform motions for representative offshore wind turbines with rated capacities of 5 MW, 10 MW, and 15 MW [2,24,25].

Parameter	5 MW—NREL OC3/OC4	10 MW—DTU FOWT	15 MW—IEA Wind FOWT	Unit
Hub height	90	119	150	m
Rotor diameter	128	178.3	240	m
Mean wind speed at hub height	13.0	13.5	13.9	m/s
Mean generator power	4.17	9.5–10	14–15	MW
Mean rotor thrust	643.6	900–1100	1600	kN
Blade-root flapwise bending moment	8.3 × 105	(1.2–1.6) × 106	(2.0–2.7) × 106	Nm
Maximum dynamic blade tip deflection	4.24–5.5	6–8	22.8	m
Normalised blade deflection (divided by rotor radius)	0.066–0.086	0.076	0.19	—
Tower-tip fore–aft displacement	0.21	≈0.35	≈0.50	m
Platform surge—mean	4.5–5.0	3–5	4–6	m
Platform surge—maximum	8–10	6–9	8–12	m
Platform pitch—mean	4.0–5.0	1.5–3.0	2.0–4.0	deg
Platform pitch—maximum	8–10	5–7	6–10	deg
Simulation software	FAST-v 6.10a-jmj & AeroDyn -v12.58	HAWC2	WISDEM and enriched by OpenFAST, HAWC2	—
Wind/wave/TI	13 m/s/[Hs ~3–5 m, Tp ~10 s [26]]/10–15% (IEC Class)	11.4 m/s/ Hs ~3–5 m, Tp ~10 s, 7–15%	10.59 m/s/Hs ~3–6 m, Tp ~10–12 s/TI: 10–15%	—
Control settings	Collective pitch PI controller. Variable speed, optimal TSR below rated (λ ≈ 7.55–8)	Collective pitch PI controller. Variable speed, optimal TSR tracking (λ ≈ 7.5)	Collective pitch via ROSCO. Variable speed, optimal TSR below rated (λ ≈ 7.5–8)	—
Averaging duration/definition of “mean/max”	600–1000 s (10–~17 min) simulations, exclude 100–200 s transients	600–1800 s (10–30 min) simulations	600–1000 s (10 min standard)	—

therefore compares representative 5 MW, 10 MW, and 15 MW turbines using widely adopted reference models and fully coupled aero-hydro-servo-elastic simulations.

The values in Table 6 are given as minimum–maximum ranges to capture structural response variability across representative IEC 61400-3 adaptations [27] design load cases under rated operation (DLC 1.2). They are based on fully coupled aero-hydro-servo-elastic simulations near rated wind speed, with turbulence intensity of 10–15% (IEC Class B/C) and aligned offshore wave conditions (Hs ≈ 2–6 m, Tp ≈ 8–12 s). The results for the NREL 5 MW turbine use OC3/OC4 OpenFAST benchmarks; DTU 10 MW values follow published HAWC2 load cases; and the IEA 15 MW response is derived from OpenFAST simulations consistent with IEA Wind Task 37 documentation.

The reported intervals represent the spread of structural response across comparable rated operational load cases, not statistical uncertainty or site-specific variability, ensuring consistency and reproducibility of the cross-scale comparison. Environmental inputs correspond to baseline IEC DLC 1.2 offshore simulations (normal production near rated wind)—TI 10–15% (IEC Class B/C NTM), Hs ~2–4 m, Tp ~8–12 s, co-aligned wind and waves without current or misalignment—consistent with common NREL/DTU/IEA validation practice and IEC 61400-3 adaptations [26].

The 22.8 m blade tip deflection of the IEA 15 MW Offshore Reference Wind Turbine corresponds to the maximum dynamic deflection obtained under IEC 61400-3 DLC 1.2 normal production conditions [24], at rated wind speed (~10.59 m/s at hub height), TI = 10–15%, and typical North Sea wave conditions (Hs ≈ 3–6 m, Tp ≈ 10–12 s). This value represents a transient maximum from fully coupled dynamic simulation, not a static deflection.

Overall, Table 6 shows that turbine upscaling produces a substantial, nonlinear increase in aerodynamic loads, structural deflections, and global system response. Blade tip deflection and root bending moments rise markedly with rated power due to larger rotor diameters and reduced relative stiffness. Platform surge and pitch motions also intensify, especially for floating configurations, reflecting greater sensitivity to low-frequency aerodynamic and hydrodynamic excitation. These results indicate that future offshore wind scaling is increasingly limited by structural dynamics, platform behaviour, and control-system performance, examined in the following sections.

Table 7. Extrapolated scaling trends for 20–25 MW offshore wind turbines (based on square–cube and aerodynamic similarity assumptions) [1,4,24,28,29,30,31].

Parameter	15 MW	20 MW (est.)	25 MW (est.)	Unit
Rotor diameter	240	270–280	300+	m
Hub height	150	165–175	180–200	m
Mean rotor thrust	1600	2000–3000	2500–2800	kN
Blade-root bending moment	2.0–2.7 × 106	3.2–3.8 × 106	4.5–5.5 × 106	Nm
Nacelle mass	700–800	900–1100	1200–1400	t
Installation crane capacity	~1500	2500+	3000+	t
Estimated CAPEX change vs. 15 MW	baseline	+6–10%	+15–25%	–

shows the extrapolated values of parameters in the application of turbine scaling with the base line of 15 MW turbine.

The extrapolated results indicate that the transition from 15 MW to 20–25 MW does not represent linear scaling. While rated power increases by 33–66%, blade-root bending moments increase by approximately 40–100%, and nacelle mass may grow by up to 75%. This illustrates the structural and logistical discontinuity associated with ultra-large turbine deployment. The extrapolated values presented in Table 7 are derived from first-order geometric and aerodynamic similarity assumptions. To formalise the scaling behaviour, the following governing relationships are considered. Aerodynamic power scaling:

$$P\sim {\displaystyle \frac{1}{2}}\rho A{V}^3$$

(20)

Since swept area $A\sim D^2$, rated power scales approximately as:

P∼D²

(21)

Structural mass scaling (square–cube law):

m_blade∼D³

(22)

Blade-root bending moment:

M_root∼F·D∼(ρAV²)·D∼D³

(23)

Thus, while power increases proportionally to $D^2$, structural mass and bending loads increase proportionally to $D^3$, leading to a superlinear growth in structural demand relative to energy output. To illustrate this imbalance,

Table 8. Relative geometric and structural scaling of 15–25 MW offshore wind turbines based on aerodynamic similarity and square–cube law assumptions.

Parameter	15 MW	20 MW	25 MW
Rated power (relative)	1.00	1.33	1.67
Rotor diameter (relative)	1.00	1.12–1.15	1.25
Swept area (relative)	1.00	1.25	1.56
Blade mass (est.)	1.00	1.40–1.60	1.80–2.10
Root bending moment	1.00	1.45–1.70	1.90–2.20

presents the relative scaling between 15 MW, 20 MW and 25 MW turbines.

A cross-technology benchmark complements the quantitative comparison.

Table 9. Comparative maximum tower-tip displacement, normalised deflection, and maximum base bending for representative turbines (including onshore); values consolidated from peer-reviewed sources and reference models. “—” = not reported/not applicable [32,33,34,35,36,37].

Turbine/Configuration	Support Type	Hub Height [m]	Max. Tower-Tip Displacement [m]	Normalised Deflection [-]	Max Base Bending Moment [MNm]
NREL 5 MW (No TMD)	Floating—tension-leg platform (TLP)	90	2.050	0.0228	—
IEA 15 MW, Baseline without blade-pitch control	Fixed-bottom—monopile	150	0.95	0.00633	500
IEA 15 MW with blade-pitch control	Fixed-bottom—monopile	150	1.48	0.00987	720

summarises maximum tower-tip displacement, normalised deflection (maximum displacement divided by hub height), and maximum base bending moment (where available), with a “Support type” column distinguishing land-based, fixed-bottom offshore, and floating platforms. In addition to the IEA 15 MW reference turbine with active pitch control, a variant without blade-pitch-based vibration control was analysed to assess structural response without aerodynamic load regulation. Both cases were evaluated under identical environmental and operational conditions, enabling direct assessment of pitch-control effects on tower-top displacement and foundation bending moments. As all commercial offshore turbines use active pitch control, the controlled case represents practical design conditions, while the fixed-blade case provides a structural sensitivity reference. Table 10 summarises regional optimisation trends for offshore turbine size relative to market scale (2025–2026). In Europe, 14–18 MW turbines with 220–260 m rotors are supported by established infrastructure, enabling 1–3 GW projects (up to 5+ GW clusters) at 0–60 m (fixed-bottom) and >50–200+ m (floating), with LCOE of 70–100 USD/MWh for bottom-fixed systems. China deploys 18–26 MW turbines (260–300+ m rotors) in 3–5+ GW arrays and targets floating installations at 40–200+ m depths, aiming for LCOE below 70 USD/MWh by 2030. The Asia–Pacific region (excluding China) pursues gradual scaling to 10–15 MW with typhoon-resistant designs, while emerging markets in Latin America and Africa initiate 8–15 MW pilot projects in shallow waters (~0.1 GW scale) with higher initial LCOE due to limited infrastructure.

Turbine sizing in typhoon-prone regions (e.g., the U.S. Gulf of Mexico and the Northwest Pacific) must prioritise resistance to extreme winds exceeding 70 m/s during Category 4–5 events. As wind loads scale with the square of wind speed and swept area, the square–cube rule amplifies structural demands in ultra-large turbines. Increasing rotor diameter from 200 to 250 m can raise blade-root bending moments by ~56% under normal conditions, while typhoon loads may cause nonlinear escalation, doubling or tripling fatigue and ultimate loads due to aeroelastic effects [38,39].

Table 10. Optimising turbine size relative to market scale: Regional comparison (2025–2026 data and projections).

	Europe	China	USA	Asia–Pacific (Excluding China)	Latin America, Africa
Turbine Size (MW)	14–18 (commercially deployed)	18–26 (prototypes dominant)	12–18 (early commercial deployment)	10–15 (progressive upscaling)	8–15 (initial deployments)
Rotor Diameter (m)	220–260	260–300+	200–260	180–236	160–220
Foundation Type	Fixed-bottom (monopile/jacket) Floating (semisubmersible) emerging	Floating (semisubmersible) advanced large-monopile	Fixed-bottom (monopile/jacket) Floating planned	Fixed (monopile/jacket) Floating pilots	Fixed-bottom
Water Depth Ranges (m)	Fixed: 0–60 m. Floating: >50–200+ m	Fixed: 0–50 m. Floating: 40–200+ m	Fixed: 20–60 m. Floating: >50–200+ m	Fixed: 0–60 m. Floating: >50–200+ m	Fixed: 0–50 m (pilots)
Project Scale (GW)	1–3 (individual); Up to 5+ GW clusters	2–5+ GW (large-scale clusters)	0.8–2 GW (individual); 3–5 GW (clusters)	0.5–2 (nearly projected) scaling to 3+	0.1–1 (pilots); 1–3 (future)
Key System Optimisation Drivers	Mature port and installation infrastructure; standardised turbine platforms; cross-project learning effects	Massive domestic supply chain, fast permitting, state-driven, 20+ MW push (Mingyang, Dongfang)	Jones Act constraints; port and vessel upgrades; incremental turbine scaling	Design adaptation for extreme weather (e.g., typhoons); localisation strategies; regional supply-chain development	Pilot projects; international finance; limited infrastructure
Projected LCOE Range (USD/MWh)	70–100 (fixed-bottom) 120–180 (early floating)	60–90, target <70 by 2030	100–150 (fixed-bottom) 150–200+ (floating)	80–130 (target decline post-2030)	120–200+ (initial commercial phase)
Technology Status	Commercially deployed/firm orders	Commercial + prototype	Early commercial deployment	Commercial/scaling phase	Announced/pilot projects
Primary Drivers of LCOE Variability	Wind resource (8–11 m/s), WACC 4–7%, port maturity, installation vessel availability, foundation type	State-backed financing, lower WACC, domestic supply-chain integration, shallow-water dominance	WACC 7–10%, Jones Act vessel constraints, port upgrade CAPEX, immature supply chain	Typhoon-resistant design, extreme-wind loading, localisation requirements, grid connection costs	High financing risk, limited port infrastructure, small project scale, higher logistics costs
Sources	[4,5]	[4,40]	[4,28]	[4]	[4]

Table 10. Optimising turbine size relative to market scale: Regional comparison (2025–2026 data and projections).

	Europe	China	USA	Asia–Pacific (Excluding China)	Latin America, Africa
Turbine Size (MW)	14–18 (commercially deployed)	18–26 (prototypes dominant)	12–18 (early commercial deployment)	10–15 (progressive upscaling)	8–15 (initial deployments)
Rotor Diameter (m)	220–260	260–300+	200–260	180–236	160–220
Foundation Type	Fixed-bottom (monopile/jacket) Floating (semisubmersible) emerging	Floating (semisubmersible) advanced large-monopile	Fixed-bottom (monopile/jacket) Floating planned	Fixed (monopile/jacket) Floating pilots	Fixed-bottom
Water Depth Ranges (m)	Fixed: 0–60 m. Floating: >50–200+ m	Fixed: 0–50 m. Floating: 40–200+ m	Fixed: 20–60 m. Floating: >50–200+ m	Fixed: 0–60 m. Floating: >50–200+ m	Fixed: 0–50 m (pilots)
Project Scale (GW)	1–3 (individual); Up to 5+ GW clusters	2–5+ GW (large-scale clusters)	0.8–2 GW (individual); 3–5 GW (clusters)	0.5–2 (nearly projected) scaling to 3+	0.1–1 (pilots); 1–3 (future)
Key System Optimisation Drivers	Mature port and installation infrastructure; standardised turbine platforms; cross-project learning effects	Massive domestic supply chain, fast permitting, state-driven, 20+ MW push (Mingyang, Dongfang)	Jones Act constraints; port and vessel upgrades; incremental turbine scaling	Design adaptation for extreme weather (e.g., typhoons); localisation strategies; regional supply-chain development	Pilot projects; international finance; limited infrastructure
Projected LCOE Range (USD/MWh)	70–100 (fixed-bottom) 120–180 (early floating)	60–90, target <70 by 2030	100–150 (fixed-bottom) 150–200+ (floating)	80–130 (target decline post-2030)	120–200+ (initial commercial phase)
Technology Status	Commercially deployed/firm orders	Commercial + prototype	Early commercial deployment	Commercial/scaling phase	Announced/pilot projects
Primary Drivers of LCOE Variability	Wind resource (8–11 m/s), WACC 4–7%, port maturity, installation vessel availability, foundation type	State-backed financing, lower WACC, domestic supply-chain integration, shallow-water dominance	WACC 7–10%, Jones Act vessel constraints, port upgrade CAPEX, immature supply chain	Typhoon-resistant design, extreme-wind loading, localisation requirements, grid connection costs	High financing risk, limited port infrastructure, small project scale, higher logistics costs
Sources	[4,5]	[4,40]	[4,28]	[4]	[4]

Aero-hydro-servo-elastic simulations (e.g., Bladed, FAST) quantify trade-offs between rotor diameter and wind resistance. In moderate climates, large rotors (e.g., 236 m for 16 MW) maximise AEP, whereas in typhoon zones, diameter reductions of 20–30% can lower peak blade-root moments by up to 81% and tower-base moments by 17%, with only 10–15% AEP loss. Reinforced towers and shortened blades ensure compliance with IEC Class T (57 m/s, 50-year return) [38,39]. Optimising the load–diameter ratio and maintaining thrust coefficients below 0.8 at cut-out speeds (25–30 m/s, up to 40 m/s for resilient designs) supports modular turbine families—for example, reducing a 15 MW, 240 m rotor to 180–200 m in high-risk areas, achieving 5–8% LCOE savings through lower reinforcement CAPEX despite minor AEP losses. Yaw misalignment relative to typhoon tracks can further reduce blade deflections by up to 50%.

Regional strategies reflect these adaptations. In China, typhoon-resistant large turbines integrate active pitch control and lower specific power to balance yield and loads. In the U.S. Southeast and Gulf of Mexico, reinforced smaller rotors (100–120 m, 4–6 MW) are favoured, accepting a 15–20% LCOE premium due to lower power prices. Future work should incorporate probabilistic risk assessments with site-specific metocean data, including multi-stage typhoon effects, to refine engineering optimisation [39,41,42,43,44].

4. Technical Drivers for Turbine Upscaling

4.1. Reduction in Turbine Count for a Given Farm Capacity

As rated generator power and rotor size increase, fewer turbines are needed to deliver the same total installed capacity, which directly translates into lower infrastructure, installation, and maintenance requirements. For example, achieving a 1 GW offshore wind farm using 2 MW turbines (typical of early 2000s installations) would require approximately 500 units, whereas deploying 15 MW turbines would reduce that number to around 67 units. This tenfold decrease in turbine count fundamentally transforms project design and execution.

From a balance-of-plant (BoP) perspective, larger turbines reduce the number of foundations, inter-array cables, and substations required per gigawatt installed. Since each foundation—especially large monopiles or jackets—can account for (up to 25–30% of total CAPEX), lowering turbine count while maintaining capacity significantly cuts BoP costs. Fewer turbines also simplify installation logistics, reducing the number of heavy-lift operations, vessel campaigns, and weather-dependent installation windows.

While turbine count reduction delivers measurable efficiency gains, its effectiveness depends on parallel infrastructure development, as discussed in Section 7. Without adequate port and vessel capacity, theoretical scaling benefits may be partially offset.

The breakdown in

Table 11. Consolidated offshore wind CAPEX breakdown (reference 15 MW fixed-bottom project, 2023 USD).

CAPEX Component	Share of Total CAPEX (%)	Cost Range (USD/kW)	Notes
Turbine (nacelle, rotor, tower)	35–45	900–1200	OEM supply, ex-works
Foundation & substructure	20–30	500–800	Monopile/jacket, excl. scour
Electrical infrastructure (array + export cable + substation)	15–20	400–600	Offshore + onshore substation
Installation & logistics	8–15	200–400	Vessel day-rates sensitive
Development & project management	5–8	100–200	Permitting, engineering
Contingency & insurance	3–5	—	Risk-dependent

represents a consolidated reference structure based on recent offshore market reports (2022–2024) and corresponds to a utility-scale 15 MW-class fixed-bottom project in North Sea conditions. All percentage shares in this manuscript refer to this consolidated structure unless explicitly stated otherwise. Floating projects exhibit higher substructure and installation shares.

4.2. Increased Energy Capture from Higher Hub Heights and Larger Rotors

Upscaling of wind turbines increases annual energy production (AEP) by using taller hub heights and larger rotor diameters. Higher hubs access stronger, more stable winds, and larger swept areas capture more kinetic energy from the wind.

The relationship between hub height and mean wind speed is expressed by Formula (4). Even modest increases in hub height led to nonlinear gains in energy production, since power output scales with the cube of wind speed $P\propto V^3$ (3).

To ensure that the commonly cited wind shear relationship has direct quantitative implications, Table 6 has been extended to include mean wind speed at hub height, calculated using a power-law wind profile with offshore shear exponent α = 0.12, a mean reference wind speed of 10 m/s, and a hub-height reference of 100 m. The results illustrate the systematic increase in available wind resource with turbine upscaling and provide a consistent aerodynamic basis for subsequent thrust and bending-moment calculations.

$$\mathrm{Power} \mathrm{proportionality} (3) \mathrm{yields} V_{150}=10{{\displaystyle \frac{150}{100}}}^{0.12}=10.49 \mathrm{m}/\mathrm{s}$$

$$\mathrm{Power} \mathrm{ratio}: {\displaystyle \frac{P_{150}}{P_{100}}}={\left({\displaystyle \frac{14.9}{10}}\right)}^3=1.157,\mathrm{thus}: P_{150}=1.157P_{100}$$

For example, raising hub height from 100 m to 150 m, depending on site-specific conditions and atmospheric stability, can yield 15.7% of AEP increase. Larger rotors further enhance energy yield by increasing the swept area, which is directly proportional to the square of the wind wheel diameter. The shift from 120 m to 240 m rotors—characteristic of current 15 MW turbines—represents a fourfold increase in swept area, allowing turbines to capture more low-speed wind and operate efficiently over a broader range of wind conditions. This also improves capacity factors, with modern 14–18 MW turbines achieving 55–60%, compared to 35–40% for 5–6 MW models a decade earlier.

A turbine upscaling combination with active control technologies (e.g., lidar-based feedforward pitch control) can be used to reduce curtailment losses and improve grid stability. However, the most effective rotor scaling should be able to strike a balance between aerodynamic effectiveness and stress on the material, especially in areas where typhoons frequently occur, and the blade should be reinforced [45].

The capacity factor (CF) values discussed in this section refer to net project-level capacity factors, including availability losses, electrical losses, and typical wake effects, unless otherwise specified. Early offshore wind projects (2005–2012), typically deploying 3–4 MW turbines with rotor diameters below 120 m and hub heights around 80–100 m, reported net capacity factors in the range of 35–40%, depending on site wind conditions and array layout. In contrast, recent large-scale projects employing 14–15 MW turbines with rotor diameters exceeding 220 m and hub heights above 140 m report projected net capacity factors in the range of 50–60% under high-wind North Sea conditions. This increase is not solely attributable to rated power growth, but primarily increased rotor swept area relative to rated power, higher hub heights accessing stronger and less turbulent wind regimes, improved aerodynamic performance and control systems, reduced wake losses due to lower turbine count per installed GW, and deployment in higher-quality wind resource areas.

Therefore, the observed increase in capacity factor reflects a combined effect of turbine scaling, site selection, and project-level optimisation, rather than a direct linear consequence of increased rated power alone.

4.3. Cost Reductions per Megawatt Through Capacity Scale

Perhaps the most influential outcome of turbine upscaling is the progressive reduction in cost per installed megawatt, achieved through both engineering and economic scaling effects. Larger turbines generate more power per unit of infrastructure, spreading fixed project costs—such as foundations, electrical systems, and installation logistics—over greater capacity, thereby lowering the capital cost per megawatt (CAPEX/MW) and LCOE (see

Table 12. Offshore wind turbines cost evolution with structural vibration mitigation impacts.

Period	Turbine Rating (MW)	Baseline CAPEX (€M/MW)	Baseline LCOE (€/MWh)	Structural Vibration Issues	Mitigation Strategy	DEL Reduction (%)	Fatigue Life Impact (%)	Indicative LCOE Impact [5]
Early 2000s	2–3	>4.0	>150–180	Limited tower–blade coupling, conservative design margins	Passive structural damping	2–5	3–8	<1
2010s	6–8	2.8–3.0	<100	Increased tower height and side to side vibrations, fatigue-driven design	Passive TMD (tower or nacelle)	5–10	8–10	1–2
2010s	6–8	2.8–3.0	<100	Variable operational conditions, multiple vibration modes	MR-based, Semi-active TMD/TVA	8–12	10–18	1–3
2020s	12–15	2.0–2.5	50–70	Strong aero-hydro-servo-elastic coupling, wave–wind interaction	Passive TMD (optimised)	5–10	8–15	1–2
2020s	12–15	2.0–2.5	50–70	Multi-mode vibration, narrow design margins	MR-based, Semi-active TMD/TVA	8–15	12–20	1–3
2020s (Floating)	10–15	3.0–4.5	80–120	Platform tower coupling, low-frequency resonance	MR-based/hybrid TMD/TVA	10–18	15–25	2–4

).

At the start of the offshore wind industry in the early 2000s, projects based on 2–3 MW turbines typically exhibited CAPEX values above €4 million/MW and LCOE levels exceeding €150–180/MWh. As turbine capacity increased to 6–8 MW during the 2010s, average project costs declined to around €2.8–3.0 million/MW, with LCOE falling below €100/MWh.

By the 2020s, large-scale projects using 12–15 MW turbines—such as Dogger Bank (UK) or Borssele (NL)—achieved CAPEX reductions of €2.0–2.5 million/MW and LCOE levels in the range of €50–70/MWh [46,47], which shows a significant decline in CAPEX and LCOE with the increase in the turbine rating and project scale.

Early offshore wind farms emphasised structural design with limited dynamic optimisation. As towers grew taller and more flexible in the 2010s, vibration and fatigue issues emerged, leading to passive and semi-active TMDs/TVAs, while floating turbines required more advanced motion control.

Upscaling is driven by capacity scale efficiency. Nonlinear costs-foundations, cabling, installation, O&M, and grid infrastructure are declining per MW as turbine size increases, reducing turbine counts, vessel time, and overall project expenditure.

DEL reductions are expressed relative to a baseline configuration without dedicated vibration mitigation measures, evaluated under representative IEC operational load cases near rated wind speed. Fatigue life impacts are estimated assuming linear damage accumulation (Miner’s rule). Indicative LCOE impacts reflect order-of-magnitude effects resulting from structural mass optimisation, life extension, and reduced maintenance interventions. Values represent typical ranges reported in simulation-based and experimental studies and are site-dependent.

From a manufacturing perspective, upscaling also promotes economies of serial production and supply-chain consolidation. As the industry converges around a smaller number of high-capacity turbine platforms (12–18 MW class), component standardisation and learning effects further reduce unit costs. According to (26), the cumulative learning rate for offshore wind is approximately 18%, meaning that every doubling of installed capacity results in an 18% cost reduction, largely attributable to scaling effects.

The learning rate of approximately 18% per cumulative capacity doubling refers in this study to total installed offshore wind CAPEX (including turbine, foundation, electrical infrastructure, and installation), rather than turbine ex-works cost alone.

The learning rate (LR) is derived from a standard one-factor experience curve formulation:

$$C=C_0{\left({\displaystyle \frac{Q}{Q_0}}\right)}^{-b}$$

(24)

where C is the specific CAPEX (USD/kW), Q is cumulative installed offshore wind capacity (GW); b is the learning exponent.

Taking logarithms:

$$\mathrm{l}\mathrm{n}C=\mathrm{l}\mathrm{n}{C}_0-b\mathrm{l}\mathrm{n}Q$$

(25)

The learning rate is then defined as:

$$\mathrm{L}\mathrm{R}=1-2^{-b}$$

(26)

Using publicly reported global offshore CAPEX data and cumulative capacity from 2010 to 2023, a log–log linear regression was performed. The fitted exponent b corresponds to a learning rate of approximately 16–20%, with a central estimate of ~18% per cumulative doubling.

It is important to note that this rate reflects total project CAPEX and therefore captures combined effects of turbine scaling, supply-chain maturation, installation efficiency, and financing improvements. It should not be interpreted as a pure turbine manufacturing learning rate.

However, further turbine upscaling cannot rely solely on geometric enlargement. Ecodesign principles become increasingly important at higher power ratings. As structural mass grows approximately with the cube of characteristic dimensions, material efficiency and lifecycle optimisation become decisive factors in maintaining cost competitiveness. Ecodesign strategies—including modular blade segmentation, recyclable composite materials, reduced rare-earth dependency in generators, and design-for-disassembly approaches—directly influence the economic viability of ultra-large turbines. In this sense, ecodesign is not an environmental add-on, but a structural scaling strategy that determines whether further size increases remain beneficial in LCOE terms.

It is important to note that turbine upscaling also introduces counterbalancing cost pressures related to component mass, logistics, and vessel requirements. Nacelles exceeding 800 tonnes and blades over 110 metres in length demand new generations of port cranes, transport barges, and heavy-lift vessels. Without corresponding infrastructure upgrades, these logistical constraints may erode part of the economic gains from turbine scaling [3].

Nevertheless, when supported by parallel infrastructure and supply-chain development, turbine upscaling remains the dominant driver of cost competitiveness in offshore wind. It has enabled the sector to evolve from a niche demonstration technology into a mainstream pillar of the global energy transition. Future 20–25 MW turbine platforms—combined with floating foundation systems—are projected to reduce LCOE to €40–55/MWh by the mid-2030s, placing offshore wind on par with conventional generation sources in terms of cost and reliability.

Recent techno-economic sensitivity analyses indicate that the LCOE reduction achieved when moving from 12 MW to 15 MW turbines ranges between 5 and 12%, depending on site conditions. However, further scaling from 15 MW to 20 MW yields a smaller incremental reduction of approximately 2–5%, assuming no major infrastructure bottlenecks. When steel prices increase by 50%, this incremental benefit can diminish to below 1%, effectively neutralising the economic advantage of ultra-large turbines.

Minimisation of support-structure vibrations is critical to reducing LCOE, as it limits fatigue accumulation, prolongs structural service life, reduces operating and maintenance requirements, and optimises energy extraction through stable rotor positioning. Enhanced dynamic performance also enables structural material mass minimisation and improves overall system reliability, thereby contributing to reductions in both capital and lifetime costs. In this context, novel vibration control strategies arise, such as an optimal-based TVA control approach [48], incorporating actuator nonlinearities to overcome force-tracking inaccuracies and address operational constraints, experimentally validated for onshore wind turbine towers using an MR damper [49,50], both as a standalone device and in combination with small electric drive support (yielding hybrid H-MR-TVA) [51,52,53], and subsequently extended to FOWTs, where it demonstrated effective tower deflection mitigation under TVA stroke control for aligned and misaligned wind–wave conditions [54,55,56]. The results summarised in

Table 13. Comparative summary: Passive TMD/TVA vs. hybrid H-MR-TVA (stochastic excitation considered unless stated otherwise).

Aspect	Passive TMD/TVA (Reference) Simulation [55] Experiment [53] Experiment [56]	H-MR-TVA Simulation [55]	H-MR-TVA Scaled Land-Based Experiment [53]	H-MR-TVA Scaled Monopile-Supported Experiment [56]	Key Implication
Absorber mass	20 t	10 t (−50%)	—	—	Significant top-mass reduction
	13.7 t	—	13.7 t	—	Significant efficiency gain
	34 t	—	—	34 t
Control strategy	Passive, fixed tuning	Hybrid (H-MR-TVA)			Real-time adaptability
Maximum tower deflection (aligned)	Baseline	−11%	−57% (steady-state harmonic)	−47% (steady-state harmonic)	Superior maximum response mitigation
Maximum tower deflection (misaligned)	Baseline	−4.3%/−4.8% (45°/90°)			Robust under multidirectional loading
RMS tower deflection (aligned)	Baseline	−4.2%	—	−41%	Improved fatigue-related performance
TVA stroke demand	High	−18.6% (aligned), −22.2%/−34.4% (misaligned)	+22% (steady-state harmonic)	+1.9% (steady-state harmonic)	Controlled space and mechanical limits
Overall efficiency	Mass-dependent	Control-dominated			Control-dominated performance, partially decoupled from mass

are derived from OpenFAST-based numerical simulations of the 20-ton passive TMD vs. 10-ton H-MR-TVA configuration for the TLP-supported NREL 5 MW wind turbine [55]; experimental tests of a scaled-down 13.7-ton passive TVA/H-MR-TVA designed for the land-based Vensys82 wind turbine structure under harmonic rotor thrust excitation [53] (a force scale factor of 1.754 × 10⁻³ was assumed); and experimental tests of a scaled-down 34-ton passive/hybrid TVA developed for the monopile-supported NREL 5 MW structure under harmonic and stochastic wind–wave regimes [56] (scale factor 7.054 × 10⁻⁴). The numerically evaluated performance indicators correspond to aero-hydro-elastic simulations conducted under a wind field approximated by a Weibull distribution (mean 8.86 m/s, standard deviation 4.63 m/s), combined with wave actions simulated using a Bretschneider spectrum with a significant height of 2.5 m and a peak period of 8.1 s, with the wave direction set at 0°, 45°, and 90° relative to the wind [55]. For experimental tests, either harmonic excitation series were applied [53,56], or a scaled-down OpenFAST-derived turbulent wind field with a mean speed of 12 m/s, generated according to the IEC 1-ED3 model, was used together with an irregular Pierson–Moskowitz wave spectrum characterised by a significant height of 6 m and a peak spectral period of 10 s, with the wind and wave directions aligned [56]. The percentage reductions reported in Table 13 represent relative improvements with respect to the reference (passive) TVA solutions.

The results presented in Table 13 highlight the significant potential of advanced vibration mitigation strategies in the context of wind turbine upscaling. As turbine ratings and hub heights increase, structural flexibility and dynamic loads grow accordingly, making effective vibration reduction essential for controlling fatigue, limiting extreme responses, and enabling cost-efficient structural design. The numerically analysed H-MR-TVA system demonstrates that a 10-ton device can outperform a conventional 20-ton passive absorber under both aligned and misaligned wind–wave conditions. The experimentally validated hybrid onshore solution exhibits the highest tower deflection reduction of 57%, at the expense of a slightly increased stroke demand. The H-MR-TVA system, tested experimentally on a scaled-down offshore wind turbine model, provides a 47% maximum and 41% RMS reduction in tower deflection with a stroke demand similar to that of a passive TVA. Three analyses under review differed in their assumptions: either priority was given to reducing the critical and fatigue loads of the tower compared with the passive TVA system (at the expense of a slightly increased TVA stroke) [53], and the best load-reduction effectiveness while maintaining a similar damper stroke and minimising the actuator power was pursued [56], or absorber stroke and mass were minimised while allowing a moderate reduction in tower deflection indicators compared with the passive TMD system [55]. By reducing maximum and RMS tower deflections while simultaneously adjusting absorber mass and stroke, the control approach can be tailored to preferable TVA locations and lighter structural solutions. These findings indicate that, in the context of wind turbine upscaling, vibration mitigation performance is governed not solely by absorber mass but by control authority, system architecture, and adaptability—including the concurrent operation of an MR damper with a small electric actuator, and the potential integration of an inerter—implying that increasing turbine scale does not necessarily require a significant increase in the mass of vibration reduction systems, while still offering substantial potential for LCOE reduction in next-generation high-capacity units.

5. Benefits of Turbine Upscaling

Upscaling the offshore wind turbines can also deliver transformational benefits to the industry by improving technical performance, economic sustainability, and promoting a broader sustainability agenda. Upscaling in rated capacity, rotor diameter, and hub height is realised to enhance the energy capture efficiency reduction in impact of infrastructure. This is particularly useful when it comes to FOWTs, which can incorporate larger units that are floating and stabilised using advanced semisubmersible or tension-leg platforms (TLP) to access reach with deeper waters with higher wind speeds. The other capability of the upscaling concept is easier integration with other hybrid energy sources. Transitioning to turbines in the 15–20 MW range has not only lowered the levelised cost of energy but also facilitated market growth by making multi-gigawatt projects more feasible with simpler operation needs [1]. Similarly, turbine scaling contributed to a 63% decrease from USD 207.335/MWh in 2010 to USD 76.284/MWh in 2023 in the global weighted-average offshore wind LCOE [5]. Considering digital-twin (DT) technologies can improve predictive maintenance and supply-chain resilience.

Importantly, DT technologies should not be treated as an independent innovation layer, but rather as an enabling mechanism for turbine upscaling. As turbine dimensions increase, structural flexibility, aeroelastic coupling, and load uncertainty grow nonlinearly. DT allows real-time monitoring and model updating of ultra-large turbines, reducing uncertainty margins in design and operation. This, in turn, enables designers to limit conservative over-dimensioning of structural components, partially offsetting the square–cube law penalties associated with scaling. Therefore, DT acts as a risk-mitigation tool that sustains the economic feasibility of 15–25 MW class turbines.

5.1. Reduction in Turbine Count for a Given Farm Capacity

One of the most immediate and quantifiable benefits of turbine upscaling is the reduction in the number of turbines required to achieve a target wind farm capacity. The reduction in turbine count described in Section 4.1 translates into broader system-level benefits. Beyond balance-of-plant savings, lower turbine density simplifies offshore logistics, reduces installation risk concentration, and enhances project bankability, allowing developers to shorten offshore installation periods and reduce exposure to adverse weather conditions, lowers the operational and maintenance burden, as each unit now contributes a greater share of total production, enabling more efficient asset management and predictive maintenance scheduling.

At large project scales, concentrating generation capacity in fewer high-performance units enables more predictable O&M scheduling and improved asset management strategies. Therefore, turbine count reduction should be interpreted not only as a cost mechanism but as a structural enabler of multi-gigawatt offshore deployment.

5.2. Increased Energy Capture from Higher Hub Heights and Larger Rotors

The process of upscaling helps capture better energy sources through higher hub heights and wider rotor diameters, where the high wind resources generate increased capacity factors and AEP.

Scaling hub height and rotor diameter remains the key path to improving offshore turbine economics. As noted by Musial, future 20–25 MW turbines with 180–200 m hub heights and > 260 m rotors could reach a CF of 65%, narrowing the gap with baseload generation [3]. Taller hubs have access to 10–20% higher wind speeds, while larger rotors (220–260+) [1,3,29] quadratically expand swept area, improve aerodynamic efficiency and reduce fatigue loads, enabling 60–65% of CF [1]. For FOWRs, these features also limit platform motion, stabilising yaw and pitch control, with AEP gains of 25–35% in favourable locations when shifting to 18–20 MW systems [31,57].

In addition to pure aerodynamic gains, higher hub heights enable access to laminar, less turbulent wind layers, reducing fatigue loading and extending turbine lifetime. Combined with improved blade designs and load-control algorithms (active pitch and yaw optimisation), these factors enable modern turbines to operate closer to their rated power for longer periods.

In general, the upscaling and increased energy capture not only reduce LCOE by spreading fixed costs across more outputs but also prepare offshore wind to be a stable baseload option, which is essential in reaching the net-zero targets.

5.3. Cost Reductions per Megawatt Through Capacity Scale

Upscaling turbines provides immense cost-saving per installed MW, as they utilise economies of scale during the manufacturing, installation, and operation processes, thus improving the competitiveness of the sector. By decreasing the number of turbines required for the same plant capacity, upscaling wind turbines from 2 MW in the early 2000s to 15 MW by 2022–2023 has captured the majority of the cost and efficiency improvements [58]. CAPMW decreases with increasing turbine rating, as the BoP components scale sublinearly (like foundation costs—dominated by steel monopiles or gravity-based foundation) are reduced by fewer and larger turbines sustaining the same capacity [28,31]. Recent market data show that 15 MW platforms have already led to global LCOE savings, with offshore wind reaching a weighted average of USD 0.075/kWh in 2023, which is a result of scaling-related efficiencies [59].

Serial production efficiencies help increase this cost pathway, with standardised nacelle and blade designs allowing learning rates of 15–20% per doubling of cumulative installed capacity, according to new projections [1,28,60]. Within floating applications, upscaling is used in combination with hybrid TVA and MR dampers to achieve a longer asset life, more than 25–30 years, further reducing the OPEX through predictive analytics and drone-based inspections. Techno-economic analyses indicate that 20 MW turbines may result in further cuts to LCOE with improved drivetrain designs and lowered BoP intensity [4,29]. Such decreases are, however, contingent on the maturation of supply chains such as the recycling of blades, which are innovative in response to the demands of the circular economy. To conclude, the economies of scale of cost advantage drive offshore wind to an unsubsidised feasibility, encouraging the development of markets and in line with the global decarbonisation requirements [5].

6. Challenges and Limitations

Despite clear benefits of turbine upscaling, offshore wind faces constraints that may slow development. These include physical and engineering limits, infrastructure bottlenecks, and technological risks, particularly for FOWTs operating in dynamic marine environments. At the 20–25 MW scale, aeroelastic instabilities, supply-chain constraints, and regulatory barriers intensify, potentially offsetting LCOE gains. Addressing this requires holistic risk mitigation, including advanced FEA for structural integrity and multi-objective optimisation to balance scale and feasibility. Macroeconomic pressures—such as post-2020 inflation and raw material price volatility—have further strained project economics [3]. Decommissioning costs are also higher than previously estimated (~421 kEUR/MW): while costs per MW decline up to ~8 MW turbines, they increase beyond this due to heavy-lift vessels and operational complexity. Therefore, end-of-life considerations must be integrated into next-generation offshore wind planning [61].

6.1. Physical Constraints: Square–Cube Law Governing Power vs. Weight

Blades longer than 120 m (in 20 MW and 25 MW designs) create major transport constraints, requiring specialised vehicles, modified routes, or on-site manufacturing to bypass infrastructure limits [3]. Nacelle and component masses exceeding 800–1000 t demand ultra-heavy-lift vessels (>1500 t crane capacity) and reinforced port infrastructure; vessel shortages can raise day rates by 30–50% [3]. In

Table 14. Scaling of key wind turbine parameters with power rating (15–25 MW).

Parameter	15 MW (Base)	20 MW (k ≈ 1.155, D ≈ 277 m, Blade Length ≈ 135 m)	25 MW (k ≈ 1.291, D ≈ 310 m, Blade Length ≈ 151 m)
Blade Mass (~D3)	65 t	65 × (1.155) 3 = 100 t	65 × (1.291) 3 = 140 t
Blade-Root Bending Moment (~D3)	180 MN·m	180 × (1.155) 3 = 277 MN·m	180 × (1.291) 3 = 387 MN·m
Nacelle Mass (~D3, approximate)	821 t	821 × (1.155) 3 = 1265 t	821 × (1.291) 3 = 1770 t

, scaling from the IEA 15 MW reference to hypothetical 20 MW and 25 MW turbines follows the square–cube law: power scales with rotor diameter squared (A ∝ D²), while mass and volume scale with D³. Aerodynamically driven bending moments also scale approximately with D³ (thrust ∝ D², moment arm ∝ D).

Assumptions: 

Constant specific power rating ($P/A\approx 332$ W/m², as in the IEA 15 MW model). Scaling factor$k=\sqrt{P_{\mathit{new}}/P_{15}}$, yielding$D_{\mathrm{new}}=D_{15}\times k$.

Base parameters from IEA 15 MW: $D=240 \mathrm{m}$, blade length ≈ 117 m, blade mass = 65 t, nacelle mass ≈ 821 t, approximate maximum blade-root bending moment = 180 MN·m.

As turbine ratings exceed 15 MW, increasing aeroelastic sensitivity reinforces the need for digital twins in scaling strategies. Design margins cannot grow proportionally without eroding economic gains. Physics-informed digital twins—integrating real-time SCADA data with reduced-order structural models—enable adaptive control and predictive load mitigation, reducing fatigue uncertainty and extending service life to preserve LCOE benefits. Without such model-based optimisation, structural overdesign would offset scaling advantages. In FOWTs, hydrodynamic loads on TLPs and semisubmersibles are amplified by square–cube scaling, intensifying heave and pitch motions and reducing efficiency. Wind–wave misalignment reduces overall damping in the wave direction, potentially shortening the fatigue life of the tower without proper utilisation of structural vibration reduction systems [55]. Climate-driven growth in extreme-wind events also threatens resilience, as highlighted by recent analyses [62]. Quantitatively, increasing rotor diameter from 240 m (15 MW) to 300 m (25 MW) expands swept area by ~56%, while blade mass may rise by 80–100% under similar stiffness constraints. Resulting gravitational and aerodynamic bending loads grow disproportionately, requiring thicker laminates and reinforced roots, leading to diminishing mass-specific efficiency gains beyond 18–20 MW.

6.2. Increasing Infrastructure and Logistical Costs

Upscaling raises infrastructure and logistics demands, increasing costs for ports, vessels, and transport unless supply chains expand proportionally. Blades exceeding 100 m (up to 160 m) [63] and nacelles above 1000–1600 t require specialised heavy-lift installation vessels with dynamic positioning. Fleet shortages during 2022–2023 increased WTIV day rates by 30–50%, driven by rapid project growth, supply-chain disruptions, inflation, and competition from oil and gas; rates rose from ~USD 200,000/day in 2022 to USD 350,000/day or more for premium units by 2023–2024 [3,63,64]. For floating wind, towing assembled FOWTs from port to site increases exposure to weather-window constraints. Additional supply-chain risks include rare-earth elements for permanent magnet generators, with geopolitical tensions contributing to short-term LCOE increases before stabilisation through localisation strategies [65]. Mitigation requires coordinated investment in modular assembly and digital logistics platforms; otherwise, infrastructure bottlenecks may constrain market-scale deployment.

6.3. Technological Risks Related to New Platforms and Immature Technology

Deployment of new upscaled turbine platforms introduces technological risks due to limited real-world validation of integrated systems. Even prototypes such as the MingYang MySE 18–20 MW raise drivetrain reliability concerns, as gearbox or bearing failures in remote offshore sites can generate downtime costs approaching €500,000/day, largely driven by heavy-lift vessel requirements [3,66,67]. Gearbox failures affect nearly half of geared turbines over a 20–25-year lifecycle, with extended downtimes offshore due to vessel mobilisation. Failure rates may increase by up to 30% in larger turbines (e.g., 10 MW vs. 3 MW), raising O&M costs [68]. Floating platforms add further risk: semisubmersible and spar designs are vulnerable to vortex-induced vibrations and mooring line fatigue [3,69]. Generator annual failure rates typically range from 1 to 4%, but repairs involve long downtimes and high replacement costs [70]. Blade repairs cost ~USD 30,000, full replacement ~USD 200,000, with production losses of USD 800–1600 per day and repair times of 1–3 days [71].

7. Infrastructure and Supply-Chain Role

7.1. Port, Installation Vessel, and Manufacturing Capacity Requirements

The shift toward offshore turbines exceeding 15 MW, and advancing to 20–25 MW prototypes, places unprecedented pressure on port infrastructure, installation vessels, and manufacturing capacity. Although larger turbines offer clear economic and technical benefits, deployment depends on infrastructure adapting to growing spatial, weight, and logistical demands. Ultra-large units require expanded marshalling areas, higher quayside-bearing capacity, and deep-water berths for next-generation vessels. Blades over 115–120 m and nacelles above 800–900 t exceed the handling limits of many conventional cranes and storage facilities, leaving only a limited number of ports capable of full pre-assembly and load-out, thereby creating regional disparities. Installation vessels face parallel constraints. Modern jack-up and floating heavy-lift units require lifting heights of 180–200 m, crane capacities up to 3000–3500 t, and enhanced deck stability for large monopiles and nacelles. Vessel shortages already lag turbine scaling, potentially extending installation schedules, raising costs, and increasing weather-window exposure. Manufacturing must also scale: blade, nacelle, and tower facilities, requiring retooling for larger moulds, transport systems, and lifting equipment. Transitioning to 15–20 MW platforms demands substantial capital investment and sustained order volumes; without stable demand, manufacturers risk cost escalation and underutilised upgraded assets.

Port Talbot (UK) is upgrading to support 20–25 MW turbines, enabling 25–50 foundations annually. Planned works include channel deepening and heavy-lift infrastructure for Celtic Sea deployment of 150 turbines, matching Hinkley Point C output within three years [72]. Fujian ports (China) support installation of 20 MW prototypes (CTG—Goldwind, Mingyang, 2023–2025) with >40 m water depths and facilities for >300 m rotors [68]. Van Oord’s new WTIV (China) accommodates turbines up to 20 MW, equipped with a 3000-t crane and capability to handle 3000-t monopiles in 70 m water depths [67]. Havfram’s NG20000X vessel is designed for >20 MW units with >300 m rotors and features 3000-t cranes and jack-up operations to 70 m depths [73]. The self-elevating Gang Hang Ping 5 supports installation of 26 MW turbines and was delivered to Qingdao, China [74]. Dongfang (China) commissioned a 26 MW turbine with 150 m blades and a 310 m rotor, while Goldwind launched its 22 MW platform in December 2024 [75]. U.S. ports invest about USD 500 million per site to handle large components such as blades and nacelles, according to the American Association of Port Authorities. West Coast floating-wind developments for Humboldt and Morro Bay require USD 3–8 billion to enable 1.6–2.9 GW capacity [76,77]. European ports invest EUR 20–80 million to expand existing infrastructure, EUR 80–110 million for new 15–20 ha terminals, and around EUR 200 million for floating-wind adaptations. By 2030, approximately 30 ports will require EUR 0.5–1 billion for further expansion [78,79].

7.2. Market-Specific Infrastructure Comparisons (China, US, Europe)

Global offshore wind development shows strong regional divergence in infrastructure readiness, supply-chain maturity, and capacity to support large-scale turbine deployment. Although turbine upscaling follows a broadly uniform technological trajectory, the practical feasibility of installing 15–20 MW units depends heavily on regional industrial ecosystems shaped by policy, port capabilities, vessel availability, and historical market development. Europe has the most mature and integrated offshore wind infrastructure, built on decades of activity in the North Sea and Baltic regions. Ports such as Esbjerg, Rotterdam, and Cuxhaven offer large marshalling areas, specialised heavy-lift facilities, and multimodal logistics suited to assembling and installing the largest turbines. Despite this maturity and dense port network, Europe faces capacity pressures related to vessel shortages and port saturation as multiple national markets scale simultaneously. China has rapidly expanded its offshore wind infrastructure through state-driven industrial coordination and high annual build-out volumes. Manufacturing hubs in Guangdong, Fujian, and Jiangsu are directly linked to nearby ports, enabling efficient production-to-installation processes. Chinese shipyards are also accelerating construction of next-generation installation vessels purpose-built for turbines above 16–18 MW. The United States remains at an early stage of infrastructure development, with limitations restricting deployment of very large turbines. Northeast Corridor ports—including New Bedford, South Brooklyn Marine Terminal, and Portsmouth—are undergoing upgrades but still lack the capacity and operational integration of leading European and Chinese hubs. The absence of Jones Act-compliant heavy-lift installation vessels further complicates logistics, necessitating hybrid installation strategies that increase costs and operational complexity. These constraints make the U.S. market particularly sensitive to turbine size selection and favour gradual scaling aligned with infrastructure readiness.

In summary, regional disparities in infrastructure maturity significantly shape the economic and technical feasibility of deploying large offshore wind turbines. Europe benefits from long-established industrial integration, China from rapid state-led expansion, while the U.S. faces a more incremental development path due to regulatory and logistical barriers. Aligning turbine size strategies with regional infrastructure capabilities is therefore essential for cost-effective global offshore wind deployment.

7.3. Need for Significant Infrastructure Investment Tied to Turbine Size

The scaling of offshore wind turbines toward 18–20 MW and larger requires not only technological progress but also coordinated investment in supporting infrastructure. As turbine size increases, infrastructure demands grow nonlinearly, widening the gap between technological capability and the capacity of ports, vessels, and manufacturing facilities to manage next-generation components. Without systematic investment, this gap becomes a key bottleneck, slowing deployment, increasing project risk, and diminishing the economic value of upscaling. Larger turbines require reinforced quaysides, deeper berths for heavy-lift vessels, and substantially larger pre-assembly areas for blades, towers, and nacelles. Many ports lack the spatial or structural capacity for blades over 120 m or nacelles nearing 1000 tonnes. As a result, a limited number of specialised ports could become congestion points for national or regional offshore wind programmes unless parallel investments expand the number of suitable facilities. Installation fleet expansion likewise requires long-term capital commitments. Economic viability depends on predictable multi-year pipelines and coordination among developers, shipyards, and policymakers. Insufficient vessel construction— or delivery lagging behind turbine growth—may create installation bottlenecks, raising day rates, extending construction timelines, and heightening weather-related risks.

Manufacturing capacity must also scale. Producing ultra-large blades and towers demands new fabrication halls, larger moulds, automated handling systems, and advanced quality control. Manufacturers therefore face strategic decisions regarding the timing and location of capital investments, balancing regional demand with global supply-chain efficiencies. Overall, turbine upscaling highlights the systemic nature of infrastructure requirements. As turbine dimensions grow, offshore wind performance increasingly depends on investment across ports, vessels, and manufacturing. Regions that coordinate these investments can secure substantial competitive advantages, reducing LCOE and accelerating deployment.

8. Optimising Turbine Size Relative to Market Scale

Balancing technological capability, financial requirements, and regulatory conditions is essential for maximising offshore wind deployment, requiring turbine sizes optimised to market scale rather than maximised globally. With pipelines exceeding 1500 GW, optimal scaling is increasingly region-specific, shaped by logistics capacity, resource variability, and policy frameworks. Multi-fidelity modelling shows that optimal turbine ratings depend on market maturity: roughly 12–15 MW in emerging regions such as the U.S. Atlantic and 18–20 MW in established European markets [3]. Current strategies therefore prioritise turbine size optimisation based on economic performance, technological readiness, and cross-market flexibility to maintain resilience and feasibility.

8.1. Economic Analysis of Cost Efficiency Versus Turbine Size

Although 15–20 MW turbines reduce LCOE by 8–12% through AEP gains, benefits diminish beyond 22 MW due to rising BoP and OPEX costs [1]. Capacity-based auction incentives favour large units, yet post-2022 steel price increases of ~20% can offset these efficiencies [80]. Techno-economic studies indicate that 15–18 MW turbines on floating platforms, including TLPs, can improve CAPEX/OPEX performance in transitional depths (50–100 m), while semisubmersibles often deliver lower LCOE under site-specific optimisation. Programmatic scaling—standardising turbine sizes across consecutive projects—can generate learning effects, potentially reducing LCOE to EUR 40/MWh by 2030 [3].

Economic Analysis—Model and Sensitivity

The numerical implementation of Equation (1) was carried out for turbine ratings between 15 MW and 25 MW in order to illustrate the nonlinear behaviour predicted by Equations (10)–(12) and to identify the range of economically optimal turbine sizes. The baseline input parameters (per MW of installed capacity) were:

• CAPEX = €2.30 M/MW.
• OPEX = €85,000 /MW/year.
• Capacity factor = 57%.
• CRF = 0.0858.

Using Equation (2), the baseline annual energy production is AEP ≈ 4993 MWh/MW/year, which gives an annualised cost of €282,340/MW/year and a corresponding LCOE ≈ €56.5/MWh. After applying a standard 20% system uplift (contingency, insurance, grid, decommissioning), this yields a reference LCOE of €68/MWh for a 15 MW turbine.

For larger turbine ratings, CAPEX, OPEX, and CF were adjusted according to industry-observed scaling trends, and LCOE was calculated using a discounted cash-flow model:

• 18 MW: CAPEX = €2.23 M/MW, OPEX = €83,000, CF = 58.8% → LCOE ≈ €64/MWh, a reduction of ~6% relative to the baseline.
• 20 MW: CAPEX = €2.18 M/MW, OPEX = €82,000, CF = 60% → LCOE ≈ €61/MWh (−10.3%).
• 22 MW (peak efficiency): CAPEX = €2.20 M/MW, OPEX = €83,000, CF = 61% → LCOE ≈ €60/MWh, representing the minimum within the evaluated range (~12% reduction).
• 25 MW: CAPEX = €2.35 M/MW, OPEX = €90,000, CF = 62% → LCOE ≈ €64/MWh, indicating diminishing returns as structural mass, BoP complexity, and installation logistics costs escalate.

These results quantitatively confirm the theoretical behaviour described by Equations (10)–(12): LCOE decreases consistently between 15 MW and approximately 22 MW, after which the benefits of further upscaling weaken or reverse. Under the assumed financial and logistical constraints, the economically favourable turbine size window lies in the range of 18–22 MW.

8.2. Importance of Technology Maturity and Market Size in Cost Reduction

Two key drivers of cost reduction are technological maturity and market scale, as larger markets enable faster innovation cycles and support upscaled turbine designs. A 20-MW turbine within a 2500-MW array can deliver LCOE reductions of up to 23% compared with average global turbine and plant sizes deployed in 2019. As quantified by Shields et al. [31], turbine upsizing from 6 MW to 20 MW leads to a 20.8% reduction in BoP CAPEX and a 33.6% reduction in OPEX while keeping total plant capacity constant, supporting the 20–30% BoP savings. Growing feasibility of floating wind has shifted industry efforts toward performance enhancement, primarily to reduce LCOE. This has been achieved by increasing turbine ratings, hub heights, and rotor diameters. Taller towers and longer blades access stronger, more stable winds and provide clearance for larger swept areas. By maximising energy yield, reducing turbine counts, and lowering CAPEX and OPEX, larger configurations improve floating-wind economic performance [81]. Their larger rotors and higher towers enable 20–30% greater power output [82]. The next generation of 15–25 MW offshore turbines offer the most balanced technological comparison. When OPEX is held constant, MS-PMSG systems achieve the lowest LCOE, reducing costs by up to 7% relative to DD-IPMSG. For fixed-bottom systems, DD-LTSG offers 2–3% lower LCOE, while floating configurations show reductions of 3–5% [83].

Market expansion has also enabled mass production, exemplified by China’s 30 GW/year additions, which contribute to ~15% cost reductions per doubling through localisation and R&D synergies. Reaching market scales of 50–100 GW prevents learning interruptions and reduces the need for turbine retooling [4]. For floating offshore wind turbines, maturing MR-damper control systems reduces vibration-related costs, reinforcing the need for harmonised international standards such as IEC 61400-3-2. Consequently, technological maturity must align with market maturity to sustain long-term cost reduction trajectories.

8.3. Flexible Multi-Market Turbine Sizing Strategies

Flexible sizing strategies adjust turbine ratings to regional conditions, reducing dependence on international supply chains and mitigating local constraints [3]. Siemens Gamesa’s direct-drive platform demonstrates this approach with models up to 15 MW (e.g., SG 14-222 DD with power-boost to 15 MW and variants with rotors up to 236 m), enabling adaptation to different wind classes and extreme-load environments [27,80]. Similarly, Vestas’ V236-15.0 MW platform exemplifies multi-market applicability by using shared components across fixed-bottom and floating foundations, allowing higher ratings for moderate-wind regimes and lower ratings for extreme-wind sites. These segmented strategies stabilise LCOE and reduce development risks through standardised yet adjustable power ratings [3,84]. Hybrid portfolios—deploying 12–18 MW turbines within the same project pipeline—provide a hedge against logistical and market volatility [3]. In floating offshore wind, flexibility is enhanced through tuneable absorbers tailored to site-specific hydrodynamic and aerodynamic loads. Integrated vibration-control systems, including MR absorbers and tuned mass dampers (TMDs), reduce platform motions across semisubmersible and tension-leg foundations. As the global offshore wind pipeline reaches ~1555 GW by early 2025, multi-market sizing strategies help ensure that turbine scaling aligns with regional infrastructure trajectories, supporting resilient deployment patterns [85]. Future optimal turbine sizes will depend not only on physical scaling but also on integration of digitalisation (DT) and ecodesign principles. Regions with advanced digital monitoring and circular-economy frameworks may sustain larger turbine platforms through improved lifecycle analytics and material recovery. Conversely, emerging markets may find mid-scale turbines with simplified designs more cost-robust. DT and ecodesign thus act as enablers that shift the optimal size threshold rather than independent development pathways.

9. Digitalisation and Digital-Twin Frameworks for Offshore Wind Optimisation

As turbine sizes increase and deployments move further offshore, operation and maintenance (O&M) become more challenging. Digitalisation has emerged as a key enabler for managing wind farms in hostile environments, improving safety, and reducing O&M costs. A predictive digital-twin platform (Unity3D + OPC-UA IEC 62541) [86] demonstrated at the Hywind Tampen floating wind farm enables early failure detection and enhanced visualisation, supporting scalability to larger floating arrays [87]. Digital twins are central to lowering LCOE in floating offshore wind by replicating asset conditions for integrity management, maintenance planning, and lifecycle optimisation—critical for expansion toward multi-GW floating projects [88]. A real-time digital-twin framework based on reduced-order hydro-elastic models enables continuous monitoring and fatigue assessment at scale, overcoming computational barriers for large floating-wind arrays and supporting cost-effective deep-water deployment [89].

AI-enabled predictive maintenance further enhances performance. Digital twins and AI can reduce unscheduled downtime by up to 35% and O&M costs by 25% [90]. A 2024 Argonne National Laboratory study found AI-driven maintenance cuts overall energy-infrastructure expenses by 43–56% and reduces unnecessary crew trips by 60–66% [91]. AI-based analytics also increase energy production by 1–2% annually, improving the long-term return on investment and supporting reliable operation of 15+ MW turbines [92]. Deployment of the AI Hub platform has yielded a 12% LCOE reduction, 2% higher availability, and 30% lower maintenance expenditure [93].

Digital-twin validation using the TetraSpar prototype confirmed accuracy within 10–15% of measured field data, demonstrating feasibility for floating-wind O&M applications [94].

9.1. Operational Digitalisation and Fleet-Level Analytics

Offshore wind O&M digitalisation enhances turbine availability and overall fleet performance. Integrated digital platforms combine vessel, weather, turbine, and operational data into dashboards and control towers tracking KPIs such as downtime, standby causes, technician allocation, vessel efficiency, weather delays, and fuel use, enabling optimised decision-making and continuous operational improvement [95]. A 1% increase in availability can generate ~€1 million per GW, while a 100 MW farm adopting digital decision-based operations can gain €0.5–1.5 million in revenue and reduce O&M costs by $0.8–1.2 million. Digital Wind Hubs, supported by AI and advanced analytics, further enhance predictive maintenance and production efficiency [96]. A major benefit of digitalisation is the ability to detect fatigue damage, vibration behaviour, and structural faults using calibrated aeroelastic and structural models combined with limited SHM data. Once calibrated, these models support continuous fatigue and vibration monitoring with strong agreement between simulations and measurements, enabling scalable fleet-level SHM, reduced inspection costs, and extended asset lifetime—particularly valuable in data-rich but sensor-limited environments [97].

SCADA-based monitoring advances further enable yaw optimisation. A method using standard 10 min SCADA data corrects nacelle-direction bias and reconstructs wind direction with <2.5° uncertainty, validated across six global sites with strong correlation to lidar measurements. Its scalability and low cost make it an effective fleet-level condition-monitoring tool directly linked to power optimisation [98]. Integrated aero-hydro-servo-elastic co-simulation (BLADED–OrcaFlex) improves representation of floating turbine dynamics. For the IEA 15 MW turbine on the VolturnUS-S platform, it yields better agreement in extreme and fatigue loads and reduces modelling uncertainty relative to decoupled methods, shortening verification cycles and supporting more cost-effective FOWT designs [99]. Fleet-wide diagnostic approaches using SCADA data can also identify yaw, pitch, and anemometer errors that cause systemic underperformance. By combining space–time power-curve deviations with rotor-speed behaviour and comparing units to fleet references, long-term measurement bias and fault mechanisms can be diagnosed, often corresponding to vibration trends. This supports data-driven performance recovery and operational decision-making at scale [100].

While current digitalisation focuses on availability optimisation, logistics, and performance recovery, the next stage of maturity moves toward physics-based digital-twin frameworks. These integrate reduced-order structural models with real-time operational data, enabling continuous reconstruction of loads, fatigue accumulation, and aero-hydro-servo-elastic responses—transitioning digitalisation from analytics to full virtual representations of turbine behaviour.

9.2. Physics-Based Digital Twins for Structural Monitoring and Load Reconstruction

Digital-twin frameworks for offshore wind turbines integrate physics-based reduced-order models within IoT architectures to enable continuous reconstruction of key dynamic responses—full-field displacements, stresses, and load paths—under operational conditions. Their high-fidelity, near-real-time performance allows early detection of anomalies and fatigue, supporting condition-based maintenance, targeted inspections, and lower LCOE through faster computation compared with traditional finite-element approaches [89]. Digital twins enhance composite-structure monitoring by fusing sensor data with 3D virtual and hydro-elastic models, improving maintenance scheduling, inspection prioritisation, and fatigue-progression forecasting. This reduces unnecessary interventions and extends turbine lifetime, yielding substantial operational and economic benefits [101].

As virtual sensors, digital twins compensate for incomplete or failing measurements by predicting wind speed, loads, and other critical parameters. Their integration of sensor networks with advanced modelling improves operational decision-making, fatigue estimation, and life-extension strategies, while addressing uncertainties in load inference and sensor limitations—supporting more flexible, predictive offshore operations [102,103]. In summary, digital twins are emerging as core tools for turbine performance and structural load monitoring. Evolving beyond static simulations, they provide real-time, cloud-integrated models that combine sensing and physics-based computation to evaluate structural health, forecast fatigue, and optimise maintenance. By reducing unnecessary inspections, improving condition-based maintenance, and lowering operational risks, digital twins significantly enhance lifecycle management, cost efficiency, and long-term sustainability in large-scale offshore wind deployment.

9.3. Implications for Floating and Ultra-Large Turbines

Digitalisation becomes essential as turbines reach 15–25 MW and floating platforms move into deeper waters, where large rotors and compliant substructures intensify aero-hydro-servo-elastic coupling and fatigue complexity. Conventional design margins and periodic inspections become insufficient. For FOWTs, digital twins reconstruct coupled loads—tower-base moments, blade-root forces, platform motions, and mooring tensions—using reduced-order hydro-elastic models in real time, avoiding costly full-scale instrumentation. For fixed-bottom turbines in the 15–25 MW class, digital twins support fleet-level fatigue tracking, vibration diagnostics, and yaw/pitch misalignment detection as structural loads scale faster than aerodynamic gains. Real-time platforms also enhance motion forecasting and integrity management for floating systems, reducing modelling uncertainty and shortening design cycles relative to high-fidelity simulations. Economically, digitalisation mitigates rising single-unit risk for ultra-large turbines by enabling predictive maintenance, condition-based inspection, and early anomaly detection, stabilising LCOE.

Ultimately, digital twins shift offshore wind design from static safety-factor approaches to adaptive, data-driven lifecycle management, forming a core enabler of reliable and cost-effective scaling for next-generation fixed-bottom and floating turbines.

10. Socioeconomic Considerations in Wind Energy Deployment

While offshore wind deployment has traditionally been assessed through techno-economic metrics such as LCOE, capacity factor, and installed capacity, socioeconomic dimensions increasingly determine project outcomes. Social acceptance, employment impacts, regional development, and distributional equity now shape timelines, policy stability, and long-term market viability. Socioeconomic factors must therefore be integrated into technical planning. Social acceptance and community engagement strongly influence project success, as local attitudes depend on environmental impacts, livelihood effects, and perceived distribution of benefits. Offshore wind can create significant local employment and fiscal revenue, but only when these benefits are clearly communicated and realised; insufficient engagement can delay or undermine projects [104,105]. Public support also reinforces political and financial legitimacy for offshore wind expansion [106]. As turbine sizes grow to 15–26 MW with rotors >250 m, fewer units are required per GW, improving efficiency but increasing infrastructure demands and local community exposure [3]. A typical offshore project generates ~1000 jobs annually during 2–3 years of development and ~100 jobs per year during its 25-year operational phase [107]. Ports also act as long-term employment hubs; for example, the New Jersey Wind Port is expected to support ~1500 jobs across manufacturing, marshalling, and assembly activities [107]. Overall, offshore wind can support 25–29 FTE jobs/MW during construction and ~1.3 FTE jobs/MW in operations and maintenance [108].

In summary, socioeconomic considerations are central to successful offshore wind deployment and should be incorporated into planning, procurement, and governance. Their integration strengthens resilience, reduces social risks, and supports equitable, publicly accepted energy transitions.

11. Ecodesign and Digital Lifecycle Optimisation for Sustainable Turbine Upscaling

Ecodesign for offshore wind turbines must evolve from qualitative lifecycle considerations into a quantitative design constraint as turbine ratings enter the 20–25 MW range. At this scale, square–cube effects intensify structural mass, bending loads, and material use, making lifecycle optimisation essential for economic feasibility. Recent 15 MW and 22 MW reference turbine definitions confirm rapid increases in rotor diameter and structural mass associated with next-generation platforms [24,30]. Market evidence shows that material-related costs remain dominant contributors to offshore wind CAPEX [3,5,28]. Under these conditions, ecodesign becomes a structural requirement rather than an environmental add-on, determining whether further upscaling remains technically and economically viable.

11.1. Material Intensity Under Square–Cube Scaling

Under aerodynamic similarity assumptions, P∼D², M_root∼D³, m_structure∼D³, while energy output increases proportionally to swept area and structural mass grows superlinearly. To quantify this imbalance, a Material Intensity Index (MI) is defined:

$$\mathrm{M}\mathrm{I}={\displaystyle \frac{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{S}\mathrm{t}\mathrm{r}\mathrm{u}\mathrm{c}\mathrm{t}\mathrm{u}\mathrm{r}\mathrm{a}\mathrm{l} \mathrm{M}\mathrm{a}\mathrm{s}\mathrm{s}}{\mathrm{R}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{d} \mathrm{P}\mathrm{o}\mathrm{w}\mathrm{e}\mathrm{r}}}$$

(27)

Representative scaling used from 15 MW to 25 MW are rated power = +67%, rotor diameter = +25%, swept area = +56%, estimated blade mass = +80–100%, and root bending moment = +90–120%.

While material intensity (MI) drops substantially from 5 MW to 15 MW platforms due to balance-of-plant consolidation [31], evidence shows that beyond ~18–20 MW the decline plateaus under high material-demand conditions [28,29]. Lifecycle assessment (LCA) studies consistently find that material production and manufacturing dominate environmental burdens, accounting for most lifecycle greenhouse-gas emissions in offshore turbines [15,37]. Comparative LCAs of conventional and recyclable blade systems further highlight the strong environmental sensitivity to material selection [109]. Thus, controlling MI is central to both environmental performance and long-term cost stability. Material price volatility also constrains optimal scaling. Offshore wind market analyses show that steel and structural materials remain primary cost drivers [3,28,46]. Under scenarios of steel price escalation, LCOE gains from extreme upscaling diminish, indicating that optimal turbine size is conditional on commodity stability rather than purely on aerodynamic or structural performance [28,110].

11.2. Sensitivity of Optimal Turbine Size to Steel Price Volatility

Steel accounts for 20–30% of offshore turbine CAPEX and is the dominant contributor to foundation and tower emissions [28,46]. A simplified sensitivity analysis—assuming an optimal baseline turbine size of 18–20 MW and steel price increases of +50% and +100%—shows strong scaling sensitivity. Under a +50% scenario, CAPEX rises by ~6–9%, LCOE by ~3–5%, and the optimal economic size shifts to ~14–16 MW. Under +100% steel inflation, LCOE benefits from >18 MW turbines nearly disappear, shifting the optimal size to ~12–14 MW.

These results demonstrate that turbine upscaling cannot be assessed independently of commodity volatility [28,29,31]. Ecodesign measures—including hybrid steel–concrete towers, recycled-steel substitution (up to ~28% reduction in climate impact) [37], and modular/reusable foundation concepts—can mitigate this sensitivity and enhance long-term economic robustness.

11.3. Rotor Diameter vs. Wind Resistance Trade-Off in Extreme Environments

In typhoon-prone regions, aerodynamic loads scale with wind speed squared and rotor diameter cubed [62]. A Load-to-Energy Ratio (LER) is defined as:

$$\mathrm{L}\mathrm{E}\mathrm{R}={\displaystyle \frac{M_{root}}{\mathrm{A}\mathrm{E}\mathrm{P}}}$$

(28)

For a 240 m rotor (15 MW baseline), increasing diameter to 300 m (25 MW class) increases AEP to ~56% and root bending moment to ~90–120%.

However, under extreme wind speeds (e.g., 50–70 m/s), nonlinear aeroelastic amplification may increase fatigue damage equivalence by 2–3 times relative to moderate sites [32,33,34,50,54,111,112]. Simulations show that reducing rotor diameter by 20% in typhoon zones decreases peak flapwise root moments by up to 60–80%, reduces tower-base moments by ~15–20%, and decreases AEP only by ~10–15%. Thus, regionally optimised turbines (e.g., 15 MW platform downsized rotor for extreme climates) may provide superior lifecycle performance compared to maximal geometric scaling. Ecodesign must therefore incorporate probabilistic load modelling rather than purely geometric optimisation.

11.4. Digital-Twin-Enabled RUL Modelling and Mass Optimisation

The synergy between digitalisation and ecodesign is central to sustainable upscaling. Digital twins (DT), integrating SCADA data with physics-based structural models, enable real-time fatigue tracking and Remaining Useful Life (RUL) estimation [89,101,102,103]. RUL is defined as the predicted operational time until structural limit state:

$$RUL=f(\mathrm{cumulative} \mathrm{fatigue} \mathrm{damage}, \mathrm{load} \mathrm{spectrum}, \mathrm{material} \mathrm{degradation})$$

By reducing uncertainty in fatigue accumulation (estimated 10–20%), DT allows a reduction in conservative safety margins by 3–8%, targeted reinforcement instead of global overdesign and extension of operational lifetime by 5–10 years.

Given:

$$\mathrm{L}\mathrm{C}\mathrm{O}\mathrm{E}\sim {\displaystyle \frac{\mathrm{C}\mathrm{A}\mathrm{P}\mathrm{E}\mathrm{X}+\mathrm{O}\mathrm{P}\mathrm{E}\mathrm{X}}{\mathrm{A}\mathrm{E}\mathrm{P}\times \mathrm{L}\mathrm{i}\mathrm{f}\mathrm{e}\mathrm{t}\mathrm{i}\mathrm{m}\mathrm{e}}}$$

(29)

Extending operational lifetime from 25 to 30–35 years reduces LCOE by ~4–9% under stable OPEX, directly offsetting the square–cube material penalty of ultra-large turbines [29,31,110]. DT-integrated lifecycle assessment also supports dynamic evaluation of alternative materials (e.g., thermoplastic vs. thermoset blades) [37,109,113], enabling real-time environmental tracking and adaptive maintenance strategies. This creates a closed-loop optimisation cycle:

Load monitoring → RUL prediction → Material optimisation → Reduced mass → Lower embodied emissions → Improved LCOE.

11.5. Circular Design and Decommissioning Economics

End-of-life (EoL) costs for offshore wind projects are estimated at ~400–450 k€/MW and rise with turbine size due to increased heavy-lift vessel demand [61]. Without modular design, ultra-large turbines further elevate decommissioning risk premiums. Circular ecodesign strategies—such as thermoplastic recyclable blades [109], segmented blade architectures, bolted modular nacelles, foundation reuse during repowering, and high-value steel recovery (up to ~90% recyclability) [114]—directly mitigate these risks. Scenario modelling shows that design-for-disassembly can reduce decommissioning costs by ~10–20% and cut lifecycle emissions by ~15–30%, depending on recovery rates [61,114]. These strategies shift decommissioning from a terminal cost into a material recovery phase within a circular industrial system.

Design-for-disassembly and modular turbine designs, including segmented blades, modular nacelle subsystems, and standard tower flanges, would reduce dismantling complexity, the need to transport heavy-lift vessels, lifting/transport costs, and decommissioning costs for offshore wind farms. Adedipe and Shafiee (2021) show that modular design-for-disassembly vessel and logistics durations allow simpler and quicker component removal (e.g., fewer vessel days, less complex sequences); a 20% reduction in activity durations will result in a 17–18% reduction in total decommissioning costs, which is equal to £22.30-£41.6 million [115]. According to ORE Catapult (2021), the revised processes are such that modular recovery and blade recycling in the context of the circular economy recover up to 20% of decommissioning costs in terms of the sales value of the materials and the shortening of vessel time, which offsets the cost of disposing of them [116]. Best practices also show that strategic recycling and dismantling policies, especially of steel towers and monopile foundations, which are recycled in industrial streams, can realise up to 20% total cost savings [117]. DNV ReWind is a digital lifecycle planning tool that automates the planning process of decommissioning and recycling logistics, which saves approximately 680,000 [118]. According to the TNO 2025 report, in the baseline scenario with 15 MW turbines (134 units), full removal costs are 172.5 k€/MW. In the upscaled scenario featuring 21.5 MW turbines (94 units, ~30% fewer positions), these costs decrease to 152 k€/MW, representing a 12% reduction in ca. [119]. Modern composite recycling technologies enable the recovery of useful materials and reduce processing costs by approximately 18%, thereby increasing the economic feasibility of turbine end-of-life management [120].

11.6. Sustainable Scaling Window

Integrating structural scaling, material sensitivity, digital RUL extension, and regional load adaptation indicates a bounded optimal range for ultra-large turbines—the Sustainable Scaling Window—defined as the rating at which material intensity trends stabilise, LCOE continues decreasing under commodity volatility, RUL ≥ 30 years, and infrastructure capacity remains sufficient [4,28,29,31,121]. Current evidence places this window at ~15–20 MW for markets with mature port and vessel infrastructure. Beyond this range, further scaling requires disproportionate infrastructure investment and advanced digital lifecycle optimisation to remain feasible [24,30]. Sustainable deployment of ultra-large turbines therefore requires ecodesign, digital lifecycle management, and probabilistic structural modelling to operate as an integrated system. Without quantitative lifecycle optimisation, square–cube mass penalties and material price volatility erode economic gains from upscaling. Conversely, the synergy of digital twins and ecodesign enables controlled mass reduction, lifetime extension, and LCOE stabilisation, supporting next-generation turbine feasibility.

End-of-life circularity further strengthens this system. Recycling can reduce lifecycle impacts by ~30%, and material recovery lowers GWP by 16–20%, corresponding to ~47% impact reduction for a 15 MW turbine [122]. A 2024 New Jersey LCA showed offshore wind’s emissions at 0.013 kg CO₂/kWh, ~98% lower than fossil fuels, with ecodesign enabling an additional 15–20% reduction [123]. Eco-designed blades reduced impacts by 19% while increasing the capacity factor by 22% [124]. Parametric LCAs confirm that upscaling reduces climate-impact intensity via design efficiencies [125]. Modular and recyclable composite systems mitigate the mass effects of large blades and towers, enabling rapid virtual prototyping for 15+ MW platforms without proportional emission growth. Ecodesign thus supports environmental justification for scaling by lowering lifecycle costs, improving recoverability, and reducing O&M burdens. Industry trajectories target 15–20 MW turbines by 2030, with rising capacity factors contributing to declining environmental intensity. Offshore wind impacts per MWh are projected to fall by ~20% between 2020 and 2040, driven by scaling, lifetime extension, and technological improvement. Greenhouse-gas intensity declines by ~58% at 20 MW relative to 5 MW (225% baseline) [126]. Historical learning curves show an 86% environmental progress rate, with 14% GWP reduction per cumulative production doubling [127].

12. Future Perspectives and Strategic Considerations

Upscaling has lowered costs and boosted energy yields, making offshore wind central to the energy transition. But once capacities pass 20 MW, progress depends less on power rating or rotor size and more on system integration, infrastructure readiness, market diversity, and coordinated risk management. The core question is when further scaling is economically, operationally, and systemically justified. Nonlinear interactions among turbine size, project design, and market maturity create trade-offs that demand holistic assessment. Physical limits, logistics, and supply-chain bottlenecks can offset expected gains if scaling is not aligned with infrastructure and regulation. To review the offshore wind manufacturers and suppliers that widely supplied with vibration mitigation implications, this section adopts a forward-looking perspective, integrating insights from turbine-level engineering, project-level optimisation and market-level dynamics. It evaluates the realistic potential and constraints of ultra-large turbines, the role of multi-scale and multi-market strategies and the importance of industry-wide coordination in managing risks associated with continued upscaling. The aim is to shift from maximising turbine size to optimising system performance, long-term cost reduction and sector resilience.

12.1. Potential and Limits of Ultra-Large Offshore Wind Turbines (20+ MW)

Ultra-large offshore turbines (>20 MW) can raise AEP and capacity factors while cutting units per GW, extending historic cost-reduction trends where infrastructure is mature; yet beyond ~15–18 MW, square–cube scaling drives nonlinear growth of mass, bending moments, and fatigue in blades, towers, and drivetrains, so the added AEP can be offset by higher CAPEX, certification scope, and technical risk. Logistical demands intensify as 120–130 m+ blades and ~1000 t nacelles require scarce heavy-lift ports, transport corridors, and vessels, making coordinated upgrades essential to avoid bottlenecks that erode LCOE benefits and heightening single-point-failure exposure that raises reliability and rapid-repair requirements. Field evidence from China’s first 20 MW unit in Fujian (>40 m depth; rotor 300 m; 147 m blades; hub 174 m; CF ≈46%; AEP > 80 GWh) confirms technical feasibility, and moving from 15 MW to 20 MW reduces per-GW counts from ~67 to ~50; indicative economics show > 23% LCOE reduction from 6→20 MW via balance-of-system effects, albeit with unit CAPEX rising ~15–25% [3,128]. System-level effects remain site-dependent; in wake-limited or spatially constrained layouts, larger rotors can yield diminishing farm-level gains due to spacing and amplified wakes, while grid integration burdens (power smoothing, FRT, frequency response) scale with single-turbine rating, increasing the value of advanced controls and digital optimisation. Consequently, 20 MW-class machines are advantageous only where site conditions, grid support, and infrastructure readiness align; their role is likely selective within diversified sizing strategies that match turbine scale to local logistics, regulatory context, and market maturity, shifting the objective from maximising rating to optimising system performance, long-run cost reduction, and sector resilience.

12.2. Multi-Scale and Multi-Market Approaches to Offshore Wind Development

As offshore wind shifts from technology-led expansion to system-level optimisation, strategy moves to multi-scale and multi-market development; rather than a single global maximum turbine size, designs are aligned across turbine, project, and market scales to reflect heterogeneous conditions (wind regimes, depths, seabeds), infrastructure readiness, regulation, and supply-chain maturity. Figure 7 illustrates a systematic decision-making process for offshore wind development, targeting LCOE below $65/MWh and capacity above 1 GW. Feasibility is sequentially assessed based on site conditions (wind and depth), turbine scaling options (20+ MW vs. 15–18 MW and 12–15 MW), infrastructure readiness (ports and vessels), integration savings, and regulatory constraints. Coordinating decisions across these levels and adapting technologies to regional constraints/opportunities yields better technical feasibility and economics than uniform upscaling, improving long-term deployment resilience.

At the turbine scale, multi-market strategies favour modular, platform-based families with shared nacelles, drivetrains and controls, while flexing blade length, rating and design life; size becomes a tuneable variable for incremental optimisation across wind classes, typhoon loads and wake-limited layouts, without repeated platform redesign. At the project scale, optimal rating is co-determined by layout, foundations, electrical architecture and installation logistics; port capacity, vessel availability, cable ratings and weather windows often make slightly smaller turbines cheaper in constrained regions, whereas mature hubs can justify larger units and capture multi-project economies of scale. At the market scale, heterogeneous maturity (e.g., Northern Europe/parts of China vs. the U.S., Japan, Southeast Asia) means aligning turbine size with policy stability, infrastructure and supply-chain depth to avoid stranded assets and bottlenecks. For floating wind, platform type, moorings and motion control (including active/semi-active damping) strongly condition feasible turbine ratings; in early-stage markets, moderate sizes paired with advanced controls can outperform very large machines on less-mature platforms. Overall, multi-scale and multi-market strategies replace size maximalism with system optimisation—coordinating turbine, project and market decisions to enhance resilience, lower risk and drive long-run cost reduction through portfolios tailored to regional conditions and integrated across scales.

12.3. Importance of Industry-Wide Coordination to Manage Upscaling Risks

As turbine upscaling increasingly shifts risks from individual projects to the wider industry, effective management requires coordinated action across OEMs, developers, ports, vessel operators, grid operators, certification bodies and policymakers. Without such alignment, technological ambition, infrastructure limits and market volatility can undermine the expected advantages of larger turbines. Supply-chain synchronisation is essential, as new turbine classes demand long-lead investments in manufacturing, port capacity and installation vessels; when turbine design evolves faster than supporting infrastructure, bottlenecks inflate installation costs and delay projects. Standardisation likewise becomes critical as >20 MW platforms introduce segmented blades, new drivetrains and advanced floating systems; harmonised technical requirements, verification methods and data exchange reduce uncertainty and accelerate validation, especially in floating wind where aero-hydro-servo-elastic behaviour cannot be reliably assessed through isolated component testing. Grid coordination is increasingly important as larger units concentrate production, requiring alignment of turbine controls with grid-code compliance, transmission planning and offshore network configuration to limit congestion and curtailment. Policy stability further underpins investment by ensuring predictable auctions and infrastructure planning, whereas fragmented or rapidly changing regulation raises upscaling risk. Port infrastructure is becoming a critical point of constriction in offshore wind roll out, floating and future generation turbines require mass assembly space, access to deep water and heavy-lift capacity [129,130].

Future scaling will depend on industry-wide alignment rather than project-specific adaptations. Standardised turbine families, harmonised port and installation specifications, and coordinated certification and digital-data protocols can reduce costs and avoid repeated retooling. Investment responsibilities must be shared: ports require public–private support for heavy-lift and marshalling upgrades, new vessel construction depends on bankable project pipelines, and factory modernisation must follow stable platform standards. Coordinated development schedules are necessary to meet national and global offshore wind targets, given that permitting, port expansion and construction often require several years of lead time. Overall, further turbine upscaling presents systemic and organisational challenges as significant as the engineering ones. Realising the benefits of larger machines requires progress across technology, supply chains, infrastructure and regulatory frameworks. Only through coordinated industry-wide action can upscaling contribute to sustained cost reductions, grid reliability and long-term resilience of the offshore wind sector.

13. Conclusions

Turbine upscaling has been the dominant driver of offshore wind competitiveness, but beyond ~20 MW its benefits become conditional on system context rather than headline rating. Nonlinear growth in structural loads and logistical complexity progressively narrows marginal LCOE gains unless matched by advances in structural control, port and vessel capability, grid integration, and bankable multi-year pipelines. The sector should therefore pivot from size maximalism to multi-scale optimisation. Sensitivity analysis performed in this study confirms that the economic advantage of ultra-large turbines remains robust under moderate variations in installation costs, while material price volatility and wind resource conditions can influence the optimal turbine rating.

At the turbine scale, modular, platform-based families—tuneable in rating, rotor, and design life—enable site-specific adaptation without repeated redesign. At the project scale, optimal rating must co-evolve with array layout, foundations, electrical architecture, installation windows, and O&M strategy, translating aerodynamic gains into realised economics. At the market scale, heterogeneous infrastructure maturity and policy stability necessitate selective deployment, typically favouring 12–15 MW in emerging markets and 18–20 MW where ports, vessels, and supply chains are prepared.

For ultra-large classes, passive safety margins are insufficient. Reliability and cost-effectiveness depend on advanced control and mitigation, continuous load reconstruction, and predictive maintenance. Digitalisation—especially physics-informed digital twins—extends asset life, stabilises OPEX, and de-risks high-unit ratings through real-time condition awareness and RUL-based decisions. Long-term feasibility also requires socioeconomic integration (workforce capacity, local acceptance, regional development) and ecodesign that treats material efficiency, recyclability, and end-of-life planning as quantitative design constraints.

Active incorporation of design-to-recycle, module turbines, superior recycling techniques, and digital lifecycle planning tools in the design of turbines and in the end-of-life management of turbines significantly lowers decommissioning costs—by up to 15–20% or more due to synergistic efficiencies—and promotes sustainability in the environment, assures regulatory compliance, and creates economic value in the rapidly growing offshore wind sector as it moves towards full circularity.

The most competitive pathways will use selective deployment of 12–15 MW and 18–20 MW classes, aligned to site and system readiness, and supported by standardisation, risk-sharing, and predictable policy. Where conditions permit, >20 MW turbines can contribute meaningfully—but success will be determined less by maximum size and more by coordinated design across turbine, project, and market scales, delivering durable cost reduction, reliability, and sector resilience.