Construction of 25MW Steel–Concrete Hybrid Offshore Wind Turbines

Abstract

Floating wind turbines are becoming increasingly mainstream. Floating wind turbines have strengths in terms of cost-efficiency compared to conventional wind turbines. Comparisons between floating and conventional wind turbines can be easily conducted through simulations. In this paper, we estimate the CAPEX and LCOE of fixed and floating offshore wind farms following the INNU cost model with LIR rotor technology and hybrid floater technology.

1. Introduction

Offshore wind is becoming a core pillar of power-sector decarbonization, particularly as countries seek large-scale low-carbon electricity sources that can be deployed domestically. As nearshore sites become saturated and permitting constraints intensify, offshore development is progressively expanding into deeper waters and harsher metocean environments. In this setting, floating offshore wind (FOW) is widely regarded as a key pathway for accessing high-quality wind resources in regions where fixed-bottom foundations are technically or economically constrained by water depth [1].

Despite its strategic potential, the commercialization of FOW remains fundamentally cost-driven. The dominant objective for developers and policymakers is to reduce the levelized cost of energy (LCOE) while simultaneously lowering CAPEX and de-risking installation and long-term operations. Compared with fixed-bottom systems, floating wind projects introduce additional cost drivers and uncertainties—most notably in the floating substructure, station-keeping (mooring and anchoring), port and logistics constraints, and coupled aero–hydro–servo dynamics that affect reliability and design margins [2,3]. Accordingly, techno-economic assessment (TEA) frameworks that can consistently quantify and decompose cost drivers are essential for comparing design alternatives and guiding investment decisions [4].

Two technological directions are especially relevant for next-generation offshore wind systems. The first is continued upscaling toward the 20–25 MW class and beyond, which motivates integrated design and optimization approaches that explicitly connect aerodynamic performance, structural loads, and cost [5]. In parallel, community reference designs have emerged to enable more consistent cross-study comparisons at these scales, including the IEA 15-MW and 22-MW offshore reference turbines [6] and recent 25-MW rotor design studies [7]. Within rotor innovation, wake-aware concepts—including induction-based approaches—aim to reduce wake intensity and thereby improve wind-farm energy yield and economics, especially as farms scale in size and spacing constraints tighten [8,9]. Low-induction rotor (LIR) strategies in particular deliberately reduce axial induction and wake deficits, decreasing the single-turbine power coefficient in exchange for potential gains at the plant level when wake losses dominate [8,9].

The second innovation direction concerns floating platform technology and materials. While many established floater concepts are steel-intensive, recent work highlights the growing interest in concrete and hybrid steel–concrete concepts due to potential advantages in cost, durability, scalability, and local-content manufacturing [10]. This trend is reflected in both open reference platforms and experimental validation campaigns for concrete-based floating concepts [11,12]. In techno-economic terms, platform-side design choices can materially affect total project CAPEX and risk, and recent cost frameworks explicitly consider material and concept comparisons [4].

However, a key limitation in the existing literature is that rotor-side and platform-side innovations are still examined in relative isolation. Rotor and control studies primarily focus on aerodynamic performance and wake effects, while the extent to which these improvements are translated into CAPEX and LCOE—particularly through their linkage with platform, station-keeping, and installation costs—remains limited [9]. In contrast, studies on floating platforms and associated materials emphasize hydrodynamic behavior, structural feasibility, and component-level costs, often treating the turbine configuration as exogenous and thereby overlooking turbine–platform interaction effects on overall project economics [10,12].

Furthermore, techno-economic assessment (TEA) studies of floating offshore wind frequently rely on project-specific assumptions and heterogeneous cost structures, which limit cross-study comparability. Ref. [3] highlight the absence of a comprehensive and widely applicable life-cycle cost model, underscoring the need for standardized and transparent cost frameworks. The emergence of reference designs—such as the Task 49 reference floating wind array design basis—further reinforces the importance of establishing consistent baseline assumptions for evaluating technological innovations [13].

Motivated by these gaps, this study quantifies the decreasing CAPEX and LCOE implications of a 25-MW-class offshore wind concept that combines (i) low-induction rotor (LIR) parameterization and (ii) a steel–concrete hybrid floating substructure, within a single consistent techno-economic framework. The analysis is conducted using the INNWIND.EU cost model approach [14], updated in this work to reflect recent (2025) cost conditions and parameterized using a benchmark floating wind farm case study. To isolate and quantify the marginal contributions of turbine-side versus platform-side innovation—and their interaction—four cases are evaluated: (1) a baseline configuration, (2) an LIR rotor-only case, (3) a hybrid substructure-only case, and (4) the combined LIR–hybrid configuration. This factorial structure provides a transparent decomposition of cost drivers and enables a first-order estimate of the economic potential and trade-offs associated with jointly pursuing next-generation rotor and floating platform concepts.

The remainder of this paper is organized as follows. Section 2 describes the case study, model framework, and key input assumptions for the INNWIND-based cost model and the LIR and hybrid substructure parameterizations. Section 3 presents the CAPEX and LCOE results across the four cases and discusses the dominant cost drivers and sensitivities. Section 4 concludes with implications for technology development and investment assessment, limitations of the present analysis, and directions for future work.

2. Materials and Methods

2.1. Scenario Description

The economic assessment in this study is conducted using the cost model developed within the INNWIND.eu framework [1]. This model, designed by the INNWIND.EU consortium, is a widely recognized analytical tool that provides a comprehensive approach for evaluating the financial aspects of wind energy projects and has been extensively adopted in the wind energy research community.

The INNWIND.eu cost model estimates key economic indicators, including capital expenditure (CAPEX) and the levelized cost of energy (LCOE), which are widely regarded as essential metrics for assessing the economic viability of renewable energy installations. In this analysis, LCOE is selected as the primary evaluation indicator, as it effectively captures and compares the overall cost efficiency of different power generation technologies over their operational lifetime.

The cost model incorporates a wide range of input parameters. Turbine-related input parameters include rated power (kW), rotor diameter (m), maximum tip speed (m/s), hub height (m), rated wind speed (m/s), design wind speed (m/s), blade model, drivetrain model, tower model, offshore support structure type, and a reliability surcharge (%). In addition, wind farm-level parameters are considered, including total installed capacity (MW), wake losses (%), and electrical losses (%).

Within the model structure, total project costs are categorized into two main components: turbine-related (topside) costs and substructure and construction costs. The topside cost component is primarily derived from the wind turbine (WT) price, reflecting investments associated with turbine manufacturing and assembly. In contrast, substructure and construction costs are estimated based on the balance of plant (BoP) price, which includes expenditures related to foundations, electrical infrastructure, and installation processes.

Although the original cost model was initially developed based on cost data from 2002, updating was necessary to maintain relevance and accuracy under current market conditions. Accordingly, this study refines the model using the most recent available data from 2025, ensuring consistency with contemporary industry standards, material costs, and technological advancements. This update enables a more realistic and up-to-date economic evaluation of offshore wind energy projects.

The cost model analysis is performed using benchmark parameters derived from recent academic studies on floating offshore wind turbines. This approach ensures that the assessment is grounded in state-of-the-art methodologies and reflects the latest technological developments in floating offshore wind energy.

The foundation of the present analysis is informed by a comprehensive case study of a floating offshore wind energy system located in the Celtic Sea, as presented in [15]. This study provides valuable insights into the practical and economic considerations associated with the deployment of next-generation floating offshore wind farms.

Although a substantial portion of the required numerical inputs could be directly specified and implemented within the INNWIND.eu cost model, several parameters required for a fully site-specific assessment could not be explicitly quantified at FEED level. While the present study considers a Korean floating offshore wind scenario, specifically offshore Ulsan, it does not aim to reconstruct a fully localized project-development or execution-phase cost model for that site.

Instead, the present study adopts a harmonized benchmark framework in which the key technology concepts under investigation—namely the 25 MW low-induction rotor (LIR) turbine and the hybrid floating substructure—are considered under Ulsan-oriented design conditions, while a common benchmark cost structure is retained for categories that cannot yet be robustly localized. In this sense, the Celtic Sea reference is not used as a one-to-one regional substitute for offshore Ulsan, but as a consistent techno-economic basis for comparative assessment.

In particular, the hybrid floater considered in this study is not a direct replication of the Celtic Sea reference floater. Rather, it is defined as a separate 25 MW hybrid semi-spar concept combining a steel hull, steel legs, and a concrete counterweight, reflecting the intended structural concept and installation philosophy of the Ulsan scenario. Where local estimation was possible, floater material quantities and manufacturing-related costs were estimated using the 2025 Korean standard construction cost basis.

By contrast, selected project-level and installation-related unit costs—such as common BoP, offshore logistics, and other execution-sensitive categories—were referenced from the Celtic Sea case study and adjusted where necessary to reflect the 1 GW, 40-turbine project configuration considered here. This hybrid use of localized and benchmarked inputs was adopted to preserve internal consistency across the four comparative cases. Therefore, the reported CAPEX and LCOE values should be interpreted as first-order comparative estimates under a common modeling basis, rather than as fully localized project quotations for offshore Ulsan.

Overall, the analytical framework employed in this study is based on the methodology of [15] with necessary modifications introduced to reflect the unique context of a 25 MW hybrid offshore wind turbine. These adjustments are essential to accurately model the performance characteristics and cost structure associated with larger and more advanced turbine designs.

Table 1. Validation case study model inputs in Celtic Sea from [15].

Site	Celtic Sea
Location	South coast of Ireland
Capacity	996 MW
No. of turbines	83
Water depth	80 m
Staging port	Cork, Ireland
Distance from port	22 km
Wind data	Wind_meteo_2_Europe_station 47,215 (Copernicus Climate Change C3S Programme)
Wave data	Data point 254,222; ECMWF ERA5 Reanalysis Dataset
Turbine	12 MW
Turbine cost	€13.3 m
Turbine installation	Pre-installed on substructure
Substructure details	Semi-submersible (Steel) and catenary chain mooring system with 3 lines
Substructure cost	€15.2 m
Installation method	Turbine preinstalled on substructure and floated to site
Vessels	2 × 2 tugs
Seabed preparation	3 drag embedment anchors per turbine
Seabed prep vessel	AHTS vessel
Substation	2 × 500 MW on semi-submersible platform
Substation cost	€161.7 m
Installation method	Floated to site
Vessel	AHTS and 2 tugs
Inter-array cabling (IAC) rating	66 kV
IAC length	204 km
IAC cost per km	€221 k/km
Installation method	Plough burial
IAC vessel	Cable-laying vessel
Export cable (EC) rating	4 × 220 kV
EC length	125 km
EC cost per km	€754 k/km
Installation method	Plough burial
EC vessel	Cable-laying vessel
Development, onshore substation & ancillary works	€199 m
Offshore logistics & operations base	€7 m
Project management (PM)	10% of total installation costs
Insurance & contingency	9% of total CAPEX

reports the validation case study model inputs in Celtic Sea,

Table 2. Environmental comparison with Celtic Sea paper.

	Ulsan	Celtic Sea
Wind speed (m/s)	U10, 9.82	Wind_meteo_2_Europe_station 47,215 (Copernicus Climate Change C3S Programme)
Wave height (m)	Hs_50y: 8.62	Data point 254,222; ECMWF ERA5 Reanalysis Dataset
Wave period (s)	Tp_50y: 11.25
Current speed (m/s)	C_50y: 1.0135
	C_1y: 0.661

reports the comparison between Celtic Sea and Ulsan, and Figure 1 reports the Ulsan floating offshore wind case-study schematic.

2.2. LIR Rotor Model & Hybrid Floater Model Case Study with INNWIND Cost Model Input Parameter Setting

Economic performance in this study is evaluated using the INNWIND.eu (INNU) mass-and-cost model, which provides a consistent component-level framework to translate turbine and project input parameters into capital expenditure (CAPEX) and levelized cost of energy (LCOE) metrics [9,16]. The INNWIND cost model is implemented as an Excel-based tool that combines (i) generalized mass-scaling relations for major turbine subcomponents and (ii) cost formulations that convert mass and key design descriptors into component and total costs [17]. In particular, the model supports upscaling by applying power-based scaling laws with technology-dependent exponents, while cost normalization across years is handled using a Producer Price Index (PPI) approach organized by NAICS categories—leveraging established parametric cost-scaling methodologies originally developed and validated in the NREL WindPACT/NREL cost and scaling model lineage (Chaviaropoulos et al., 2014; Fingersh et al., 2006) [16,18]. The model therefore enables transparent decomposition of cost drivers across rotor–nacelle–tower and other major contributors, and propagates these costs to an LCOE calculation under a consistent set of assumptions.

Importantly, INNWIND’s balance-of-plant (BoP) and OPEX treatments are intentionally simplified; accordingly, the framework is primarily intended for comparative assessment and “delta” (marginal) quantification of technology impacts, rather than for bankable absolute LCOE estimates [19]. This positioning is consistent with how the model has been used in the literature, including system-level turbine redesign and multidisciplinary design studies that compute CoE/LCOE using the INNWIND cost model [20], as well as offshore wind-farm design optimization studies where the capital cost formulation is explicitly adopted from the INNWIND cost model [21]. In the present work, we adopt the INNWIND cost model as a unified reference framework to parameterize inputs and extract CAPEX and LCOE outputs, enabling a controlled comparison of (i) the LIR rotor parameterization, (ii) the steel–concrete hybrid floating substructure, and (iii) their combined effect.

Based on the configuration summarized in

Table 3. Four cases of Onshore & Offshore.

	Case 1	Case 2	Case 3	Case 4
Onshore	Baseline	LIR model applied	Baseline	LIR model applied
Offshore	Baseline	Baseline	Hybrid substructure applied	Hybrid substructure applied

, a total of four cases are analyzed in this study. The primary comparison focuses on Case 4 and Case 1, which correspond to the newly developed LIR rotor model combined with the hybrid floater design and the baseline configuration, respectively. Figure 2 shows the designed Baseline and LIR rotors for 25 MW offshore wind turbines.

To minimize potential extrapolation errors in calculating the mass and cost of the 25 MW wind turbine, the IEA 15 MW RWT data was established as the baseline. For the capacity upscaling (from 15 MW to 25 MW), the component mass-cost relationships followed the cost-scaling methodology of NREL WISDEM, which shares the design philosophy of the INNWIND.eu model. Specifically, since the mass of key structural components like rotor blades and towers increase non-linearly, the built-in power-law scaling exponents were applied to derive an optimized design mass that satisfies dynamic behavior and structural load limits. This optimized mass was then used to estimate the final cost. The input parameters associated with the low-induction rotor are specified as follows in

Table 4. Comparison of key design and aerodynamic parameters for the reference 15 MW, baseline 25 MW, and LIR 25 MW turbine models.

Model	IEA 15 MW	IAE 25 MW Baseline	IAE 25 MW LIR
Power rating (MW)	15 MW	25 MW	25 MW
Turbine class	IEC IB	IEC IIC	IEC IIC
Specific power (W/m2)	332	436	335
Rotor orientation	Upwind	Upwind	Upwind
Number of blades	3	3	3
Control	VSVP	VSVP	VSVP
Cut-in, cut-out speed (m/s)	3, 25	3, 25	3, 25
Tip Speed Ratio	9	9	9
Min. rotor speed (rpm)	5	4.774	3.819
Max. rotor speed (rpm)	7.56	7.099	6.201
Rated rotor speed (rpm)	7.499	7.099	6.201
Max. tip speed (m/s)	95	100	100
Rotor diameter (m)	240	270	308
Airfoil series	FFA-W3	FFA-W3	FFA-W3
Hub height (m)	150	165	184
Power Coefficient	0.46	0.46	0.407
Thrust Coefficient	0.77	0.75	0.58
Moment Coefficient	0.17	0.16	0.12
Capacity Factor	0.499	0.447	0.473

.

To explicitly quantify the farm-level wake reduction benefits of the LIR technology, a wake superposition analysis was conducted prior to the economic evaluation using the NREL FLORIS open-source tool with the Gauss–Curl Hybrid model. A representative 5 × 5 square layout (25 turbines) was simulated. To ensure a fair comparison, the absolute distance between turbines was kept constant at 1890 m for both models. Consequently, the 270 m baseline rotor operated at a 7D spacing, while the 308 m LIR rotor operated at a relatively tighter 6.136D spacing.

Table 5. Quantified farm-level wake reduction benefits of the LIR technology.

	Baseline_270 m	LIR_308 m	Increase Rate
Wind Farm AEP [GWh]	2095.97	2285.64	9.049%

reports the quantified farm-level wake reduction benefits of the LIR technology. Despite this reduced relative spacing, the FLORIS simulation demonstrated that the LIR wind farm achieved an approximate 9.049% increase in Annual Energy Production (AEP) (Baseline: 2095.97 GWh > LIR: 2285.64 GWh). To account for uncertainties when scaling up to the target 40-turbine layout, a conservative 8% wake loss reduction (from a baseline of 18% to 10%) was adopted as the input parameter for the subsequent INNWIND economic evaluation.

In the original INNWIND (INNU) model, the substructure configuration is based on a 10 MW turbine design, and a direct cost-scaling pathway for a 25 MW floating system is not explicitly provided. In the present study, this gap is addressed by combining benchmark-derived common cost assumptions with an independently defined 25 MW hybrid floater concept. Specifically, floater material and manufacturing-related costs are localized using Korean standard construction cost data where possible, whereas selected shared installation and project-level categories are retained within the benchmark framework to ensure consistency across the compared cases.

With respect to the substructure, the baseline model employs a pure steel design, whereas the model developed in this study incorporates concrete components, which is expected to reduce the levelized cost of energy (LCOE).

To enable a consistent comparison between the baseline, LIR, hybrid, and combined cases, turbine, substructure, and construction-related costs are evaluated within the same accounting structure and techno-economic framework. This does not imply that the baseline case represents a literal Celtic Sea project or that the hybrid case represents a fully localized Korean FEED estimate; rather, all four cases are assessed as Ulsan-oriented comparative scenarios under harmonized benchmark assumptions.

The benchmark reference is used primarily to structure common installation, logistics, and BoP-type cost categories for which sufficiently reliable Korean project-level input data are not yet available. By contrast, the hybrid floater concept and its main material/manufacturing cost components are independently specified for the present 25 MW system. The final LCOE calculation therefore follows the updated INNWIND.eu-based framework used in this study, while the Celtic Sea case serves only as a consistent benchmark reference for selected shared cost categories.

The data presented in

Table 6. Results of LIR model and Hybrid case in our 25MW LIR-Hybrid System using INNU cost model input parameters compared to those of Celtic Sea setting with baseline model (Rotor-side).

(Unit: M€)	(1)	(2)	(3)	(4)	Baseline Input Note	LIR & Hybrid Input Note
Onshore (Rotor)	Baseline	LIR	Baseline	LIR model
Offshore (Floater)	Baseline	Baseline	Hybrid	Hybrid
Diameter (m)	270	308	270	308	To compare the economic feasibility based on LCOE, a baseline wind turbine with a 270 m diameter and a 25 MW capacity was additionally designed using the Cp-max technique. The optimization process focused on a low-induction rotor design featuring an ultra-large rotor diameter of 308 m and a low specific power of 335 W/m2.	WISDEM [22] is a powerful framework for multidisciplinary analysis and optimization, encompassing aerodynamics, structural analysis, and cost analysis, with the levelized cost of energy (LCOE) as the primary objective function (NREL WISDEM Team, n.d.).
Hub Height (m)	165	184	165	184
Rated speed (m/s)	11.5	11.0	11.5	11.0
Design speed (m/s)	11.5	11.0	11.5	11.0
Tower model	10 MW 10% Longer High Thrust	10 MW 10% Longer Low Thrust	10 MW 10% Longer High Thrust	10 MW 10% Longer Low Thrust
Wake Losses (%)	18	10	18	10

compare the cost structures of the baseline and LIR models, with particular emphasis on key differences in turbine supply and substructure material costs. The baseline configuration adopts a conventional steel-based substructure, whereas the hybrid model introduces a combined concrete–steel design aimed at achieving cost reductions.

Assuming that the turbine capacity is identical and that the layout within the wind farm remains unchanged, the manufacturing and installation costs of power cables, mooring systems, and substations are considered to be the same. However, due to the change in substructure, the displacement differs between the baseline and hybrid cases, with values of 29,695 tons and 31,213 tons, respectively—a difference of only 4.86%. Even when accounting for differences in substructure geometry, this marginal variation in displacement is not significant enough to require changes in the specifications of transport vessels. Therefore, the transportation and installation costs for the systems under comparison are also assumed to be identical.

Table 7. Results of LIR model and Hybrid case in our 25MW LIR-Hybrid System using INNU cost model input parameters compared to those of Celtic Sea setting with baseline model (Platform-side).

(Unit: M€)	(1)	(2)	(3)	(4)	Baseline Input Note	LIR & Hybrid Input Note
Sea and Installation cost Setting	The Celtic Sea reference case is originally configured as a 996 MW floating wind farm, whereas the present LIR design is developed at the single-turbine level (25 MW). To ensure comparability at the wind-farm scale, we extrapolated the 25 MW turbine to a 1 GW-class project by assuming 40 turbines (25 MW × 40 = 1000 MW), which is close to the 996 MW capacity of the Celtic Sea case. The sea and installation cost inputs required by the INNU cost model were then set consistently with the Celtic Sea configuration under this matched farm-scale capacity assumption.
Turbine cost	1879.0	2083.9	1879.0	2083.9	Quantity adjustment: reduced from 83 turbines (Celtic Sea) to 40 turbines (this study). Unit-cost scaling: the turbine unit cost was scaled from the Celtic Sea 12 MW turbine cost by applying a capacity upscaling factor of 2.083 (=25 MW/12 MW). Resulting assumption: unit cost = 27.7 M€ per turbine; total quantity = 40 turbines.
Etc. (IAC + EC + ancillary work)	569.2	569.2	569.2	569.2	Since the total wind-farm capacity is nearly the same as the Celtic Sea reference case, the Celtic Sea unit costs and assumptions were applied directly for inter-array/export cables and ancillary works.	Same as baseline
Offshore logistics & operations base	7.2	7.2	7.2	7.2	Since the total wind-farm capacity is nearly the same as the Celtic Sea reference case, the Celtic Sea unit costs and assumptions were applied directly for inter-array/export cables and ancillary works.
Project management (PM)	44.5	44.5	43.8	43.8
Insurance & contingency	278.6	278.6	272.4	272.4
Onshore substation construction & cable installation	33.2	33.2	33.2	33.2	Same assumptions as the Celtic Sea case study were applied.	Same as baseline
Offshore substation installation	0.058	0.058	0.058	0.058	Same assumptions as the Celtic Sea case study were applied.	Same as baseline
Anchor-mooring transport & installation	20.3	20.3	20.3	20.3	Scaled down from the Celtic Sea case in proportion to the number of turbines, i.e., (40/83)
Turbine assembly, towing & hook-up	9.5	9.5	9.5	9.5	Scaled down from the Celtic Sea case in proportion to the number of turbines, i.e., (40/83) with twofold × 2.
Cable installation	51.4	51.4	51.4	51.4	Installation duration (and associated costs) was first scaled down from the Celtic Sea case by the turbine-count ratio (40/83), and then multiplied by 1.5 to reflect additional operational requirements; for the 25 MW configuration, three towing vessels were assumed.	Same as baseline
Steel Cost (€/tonnes)	259.0	259.0	151.1	151.1	Steel material and manufacturing costs were estimated separately based on the 2025 Standard Method of Measurement/Unit Cost Schedule for Construction Works Unit cost (€): 998 Total Quantity: 259,573	Steel Quantity decrease to 151,460
Manufacturing cost (€/tonnes)	576.2	576.2	336.2	336.2	Unit cost (€): 2220 Total Quantity: 259,573	Steel Quantity decrease to 151,460
Concrete Pouring	-	-	6.2	6.2	-	Unit price = 13.85 Total Quantity = 450,080
Steel formwork	-	-	193.4	193.4	-	Unit price = 85.84 Total Quantity = 2,252,720
Scaffold	-	-	37.6	37.6	-	Unit price = 28.06 Total Quantity = 33,511
Rebar assembly (on-site)	-	-	12.2	12.2	-	Unit price = 543.22 Total Quantity = 22,520
Ready mixed concrete	-	-	36.4	36.4	-	Unit price = 78.45 Total Quantity = 463,600
Rebar	-	-	10.8	10.8	-	Unit price = 464.71 Total Quantity = 23,200
Outfitting cost	83.5	83.5	78.4	78.4	10% of substructure cost
Contingency	208.8	208.8	196.0	196.0	25% of substructure cost
Cost per drag-embedment anchor	82.1	82.1	82.1	82.1	The unit cost per drag-embedment anchor was kept consistent with the Celtic Sea case study. However, the total number of anchors was adjusted to reflect the revised mooring configuration: from 3 anchors per turbine × 83 turbines (Celtic Sea) to 6 anchors per turbine (six mooring lines) × 40 turbines (this study).
Cost of 3 × chain mooring lines	160.0	160.0	160.0	160.0	The unit cost per drag-embedment anchor was kept consistent with the Celtic Sea case study. However, the total number of anchors was adjusted to reflect the revised mooring configuration: from 3 anchors per turbine × 83 turbines (Celtic Sea) to 6 anchors per turbine (six mooring lines) × 40 turbines (this study).

reports the results of LIR model and Hybrid case in our 25MW LIR-Hybrid System using INNU cost model input parameters compared to those of Celtic Sea setting with baseline model (Plat-form-side).

2.3. CAPEX and LCOE Calculation from INNU Cost Model Method

Techno-economic performance in this study is evaluated using the INNWIND.eu (INNU) mass-and-cost model and its associated LCOE calculator. The framework provides a consistent, component-level decomposition of turbine supply costs and balance-of-plant (BoP) costs, and computes LCOE using annualized investment and annual operating costs normalized by annual energy production, following the INNWIND project methodology and consistent with parametric wind-turbine cost-scaling approaches.

Capital expenditure (CAPEX). The unit CAPEX (€/kW) is defined as the sum of turbine supply cost and BoP cost [3], and the total project CAPEX scales with installed plant capacity:

C_CAPEX = C_Turb + C_BoP,

(1)

CAPEX_total = C_CAPEX × P_plant × 1000

(2)

Annual energy production (AEP). Annual energy production is computed from installed capacity, net capacity factor, and hours per year:

AEP = P_plant × CF_net × 8760

(3)

CF_net = CF_gross × (1 − L_wake) × (1 − L_elec) × (1 − L_avail)

(4)

LCOE is calculated as the sum of an annualized investment term and an annual operating cost term, divided by AEP, consistent with the INNWIND LCOE calculator [1] and the NREL cost-scaling model lineage [4]. Annualization uses a capital recovery factor (CRF) based on the real discount rate d and project lifetime N:

CRF = d/(1 − (1 + d)^−N)

(5)

LCOE = (CAPEX_total × CRF + OPEX_annual)/AEP

(6)

where:

• C_CAPEX: unit CAPEX (€/kW), equal to turbine supply cost plus BoP cost
• C_Turb: turbine supply cost (€/kW)
• C_BoP: balance-of-plant cost (€/kW), including installation and electrical infrastructure categories represented in the INNWIND model
• P_plant: total installed plant capacity (MW)
• CF_net: net capacity factor after wake, electrical, and availability losses (-)
• CF_gross: gross (turbine) capacity factor before the above losses (-)
• L_wake, L_elec, L_avail: fractional wake, electrical, and availability losses (-)
• d: real discount rate (-)
• N: project lifetime (years)
• OPEX_annual: annual operating expenditure (€/yr) used in the INNWIND LCOE calculator

All monetary quantities should be expressed in a consistent currency basis and reference year, following the index-based cost normalization approach adopted in INNWIND and related parametric cost-scaling models.

3. Results & Analysis

The economic performance of the proposed model is evaluated using Capital expenditure (CAPEX) and Levelized Cost of Energy (LCOE), and the resulting outcomes are interpreted accordingly.

CAPEX refers to the upfront investment required to develop infrastructure such as wind power plants. In contrast, the LCOE serves as a comprehensive metric that quantifies the cost of generating one unit of electricity over the entire operational lifetime of a power generation project.

LCOE accounts for a broad range of expenditures, including capital costs, operating costs, and system maintenance costs, and is therefore widely used as a key indicator for comparing the economic feasibility of different energy sources.

In LCOE analysis, a lower value indicates higher cost efficiency and stronger economic competitiveness. If the LIR rotor model combined with the hybrid floating platform configuration achieves a lower LCOE than the current baseline, this outcome implies that the LIR–hybrid configuration is economically more advantageous. Such a configuration not only optimizes cost efficiency but also enhances price competitiveness in the renewable energy market. Consequently, achieving a lower LCOE would provide a strategic advantage, making the LIR rotor and hybrid floating platform combination a more attractive option for large-scale deployment and investment.

3.1. Case Study Results—CAPEX & LCOE

Table 8. CAPEX.

(Unit: M€/MW)	(1)	(2)	(3)	(4)
Onshore	Baseline	LIR	Baseline	LIR
Offshore	Baseline	Baseline	Hybrid	Hybrid
Total CAPEX	4.311	4.516	4.235	4.440

and

Table 9. LCOE.

(Unit: €/MWh)	(1)	(2)	(3)	(4)
Onshore	Baseline	LIR	Baseline	LIR
Offshore	Baseline	Baseline	Hybrid	Hybrid
LCOE	76.76	71.07	75.88	70.32

summarize the economic results for the four case studies designed to isolate the effects of the LIR rotor and the steel–concrete hybrid floating substructure. The baseline configuration (Case 1) yields a normalized total CAPEX of 4.311 M€/MW and an LCOE of 76.76 €/MWh. Figure 3 and Figure 4 show the overview of calculation.

When only the rotor-side innovation is introduced (Case 2) while maintaining the baseline offshore substructure and construction assumptions, the total CAPEX increases to 4.516 M€/MW, corresponding to a +4.76% change relative to Case 1. Despite this CAPEX increase, the LCOE decreases to 71.07 €/MWh, which represents a −7.41% reduction. This indicates that the LIR rotor configuration delivers a substantial improvement in cost-of-energy performance that outweighs the modest increase in upfront investment.

When only the platform-side innovation is introduced (Case 3) while keeping the topside turbine configuration at the baseline level, the total CAPEX decreases to 4.235 M€/MW (−1.76% relative to Case 1). Consistent with this CAPEX saving, the LCOE decreases slightly to 75.88 €/MWh (−1.15%). Although the magnitude of the LCOE benefit is smaller than that of the LIR rotor, the hybrid substructure demonstrates measurable economic value through reductions in substructure and construction-related costs.

When both innovations are combined (Case 4), the total CAPEX becomes 4.440 M€/MW, which is a +2.99% increase compared to the baseline. Nevertheless, the LCOE decreases to 70.32 €/MWh, corresponding to an overall −8.39% reduction relative to Case 1. Overall, these results show that the LIR rotor provides the dominant contribution to LCOE reduction, while the hybrid substructure provides an additional CAPEX-driven benefit that partially offsets the CAPEX increase associated with the LIR configuration. The combined effect is close to the sum of the individual improvements.

This limited interaction effect can be technically explained by the aerodynamic load management of the LIR design. Specifically, the optimized LIR configuration deliberately reduces the thrust coefficient (C_T) from 0.75 to 0.58. This strategic reduction effectively offsets the increase in the maximum thrust load that would otherwise be induced by expanding the rotor diameter to 308 m. Consequently, the topside modifications do not transfer excessive loads to the substructure, eliminating the need for a drastic structural redesign or substantial mass increase of the hybrid floater. This load decoupling allows the aerodynamic AEP gains and the substructure CAPEX reductions to operate almost independently.

This suggests limited interaction effects under the present cost-model assumptions while still demonstrating clear complementarity between turbine-side and platform-side innovations.

Overall, the observed reduction in LCOE highlights that design enhancements embedded in the LIR model provide substantial economic value over the project lifetime by improving cost efficiency and strengthening the economic competitiveness of the proposed configuration (all cases input parameters for calculation of CAPEX and LCOE are presented in Appendix A).

3.2. Comprehensive Sensitivity Analysis

To ensure the robustness and universality of the economic findings, a sensitivity analysis was conducted on the key variables influencing the LCOE. As shown in

Table 10. LCOE sensitivity analysis for main economical parameters (Unit: €/MWh).

Parameters	−15%	−10%	−5%	0%	5%	10%	15%
WF Capacity Factor	77.95	75.13	72.6	70.32	68.27	66.39	64.69
Turbine Cost	67.28	68.3	69.31	70.32	71.34	72.35	73.37
BoP Cost	66.88	68.03	69.18	70.32	71.47	72.62	73.77
O&M Direct Costs	66.71	67.92	69.12	70.32	71.53	72.73	73.94
Wake Loss	69.62	69.85	70.09	70.32	70.57	70.81	71.06
Discount Rate	67.29	68.28	69.3	70.32	71.37	72.43	73.5

, the evaluated parameters were varied by ±15% from their base values, including Wind Farm (WF) Capacity Factor, Turbine Cost, BoP Cost, O&M Direct Costs, Wake Loss, and Discount Rate. The sensitivity analysis results quantitatively demonstrate that the LCOE is most sensitive to the WF Capacity Factor, ranging from 64.69 to 77.95 €/MWh. Even under adverse scenarios—such as a 15% increase in Turbine Cost, BoP Cost, O&M Direct Costs, or Discount Rate—the LCOE remains robust, not exceeding 73.94 €/MWh. This explicitly verifies the scientific robustness of the proposed combined configuration’s economic benefits across varying market and environmental conditions.

4. Conclusions

This study evaluated the techno-economic implications of integrating a low-induction rotor (LIR) parameterization with a steel–concrete hybrid floating substructure for a 25 MW-class floating offshore wind turbine concept, using an updated INNWIND.eu (INNU) cost-model framework and a structured four-case design. The LIR rotor was developed through a WISDEM-based design process targeting a 308 m rotor diameter and low specific power (335 W/m²), while the platform concept adopted a semi-submersible–spar hybrid architecture that combines a steel upper hull with a concrete counterweight connected via steel legs, aiming to reduce fabrication cost by substituting part of the steel mass with concrete.

When both innovations are applied simultaneously, CAPEX becomes 4.440 M€/MW, corresponding to a +2.99% increase relative to the baseline (and −1.68% relative to the LIR-only case). Importantly, LCOE decreases to 70.32 €/MWh, corresponding to an overall reduction of −8.39% relative to the baseline (and −1.06% relative to LIR-only). These results show that the hybrid platform can partially offset the CAPEX increase associated with the LIR configuration and provides additional LCOE improvement when combined with rotor-side innovation. The combined reduction is close to the sum of the individual effects, suggesting limited interaction effects under the present cost-model assumptions while still indicating clear complementarity between rotor-side and platform-side improvements.

Overall, the results support the conclusion that LIR rotor design is the primary driver of LCOE reduction in the investigated configuration, while steel–concrete hybridization of the floater contributes additional CAPEX savings that enhance overall cost effectiveness. From an investment perspective, a ~8–9% reduction in LCOE at the wind-farm level can materially improve competitiveness for deep-water floating projects, where platform, mooring, and marine operations often dominate cost and risk. Accordingly, the reported Ulsan-based CAPEX and LCOE values should be interpreted as benchmarked comparative estimates under harmonized assumptions, rather than as fully localized project quotations.

Several limitations should be noted. First, although the LIR turbine and hybrid floater concepts were defined with the Ulsan scenario in mind, not all project-level inputs were fully localized to Ulsan conditions. The results are based on a cost-model-driven assessment with scaled turbine parameters, partially localized floater cost inputs, and benchmark-derived installation/BoP assumptions; therefore, the reported values should be interpreted as first-order comparative estimates rather than FEED-level project quotations or a fully localized investment appraisal. Second, the magnitude of LIR benefits depends on wind-farm layout, control strategy, and site-specific wake conditions; further work is needed to couple the aerodynamic assumptions more tightly to layout optimization and operational constraints. Third, uncertainties in key economic parameters (e.g., discount rate/WACC, net capacity factor, wake/electrical/availability losses, steel and concrete prices, and O&M assumptions) may shift absolute LCOE levels and modify the relative advantage across cases.

Future work should integrate (i) a more detailed engineering design description and verification of the hybrid floater (including coupled aero–hydro–servo response), (ii) site-specific installation and logistics modeling for the target region, and (iii) systematic sensitivity and uncertainty analyses for the most influential parameters. Extending the framework to compare additional floating archetypes (e.g., semisubmersible, spar, and TLP variants) and incorporating learning-curve effects would further strengthen implications for commercialization pathways of next-generation floating offshore wind systems.