Search Results (106)

This study investigates how offshore wind farm layout geometry influences farm-level performance using a computationally efficient Jensen–Park wake model combined with directionally resolved Weibull wind-speed statistics. A fixed-capacity 1.8 GW case study, consisting of 120 V236-15.0 MW turbines, is used to examine the effects of grid aspect ratio, inter-turbine spacing, cumulative row skew, and global layout rotation on wake losses, annual energy production (AEP), and capacity factor under representative offshore screening assumptions. Structured layouts with identical turbine count and installed capacity are compared with a regular baseline grid to isolate geometric effects within a consistent modelling framework. For the nominal offshore Jensen wake-expansion coefficient, k = 0.04, the highest sampled AEP is obtained for the 5 × 24 configuration, which produces 8930.69 GWh yr⁻¹ and a capacity factor of 56.64%. The regular baseline produces 7397.50 GWh yr⁻¹ and a capacity factor of 46.91%, corresponding to a 20.73% AEP increase for the highest sampled layout. However, the performance differences among Layouts D–F are small, indicating a high-performing layout plateau rather than a clearly separated optimum. The contribution of this paper is therefore not a new wake model, optimisation algorithm, or general offshore design rule. Instead, this study provides an auditable screening workflow that documents modelling assumptions, parameter bounds, coordinate transformations, convergence checks, sensitivity analyses, and spatial-efficiency indicators for one turbine model, one turbine count, one synthetic wind rose, and a limited set of structured row–column layouts. Full article

Vertical-axis wind turbines (VAWTs) are recognized as a viable option for wind energy farms due to their compact design and suitability for different wind settings, such as urban and offshore environments. VAWT wind farms have been studied with respect to various turbine spacing and configurations that demonstrate that the VAWT wind farm is well-suited for improving efficiency while requiring less land, compared to horizontal-axis wind turbines (HAWTs). Moreover, the use of combined dual-rotor configurations has recently given attention as a passive strategy to enhance the aerodynamic performance of VAWTs. Despite these advances, the optimal arrangement of VAWT farms, including inter-turbine distances, clustering configurations, and land-use efficiency of such dual rotors, has yet to be explored. This study investigates different clustering scenarios, including vertically aligned pairs and staggered clusters of three turbines, to evaluate their impact on power capture and land usage for a dual-rotor straight-blade vertical-axis wind turbine (DR-SBVAWT). The 2D-dimensional transient (URANS) numerical simulations are conducted using the k-$\omega$ SST turbulence model. Performance indices, namely, total power coefficient and improvement relative to standalone turbines, are analyzed. Wake effects are investigated through detailed velocity contour plots of the wind field. Results reveal that a DR-SBVAWT turbine arrangement can enhance wind farm performance by approximately 25% for two turbines and about 20% for three staggered turbines, with required spacing of 1.5 D and 2.5–3 D, respectively (Here, D is the outer diameter of the DR-SBVAWT). The study overall provides insights into the optimal placement and configuration of DR-SBVAWTs for maximizing energy output while minimizing land usage, offering guidance for the design of more efficient VAWT farms. Full article

This paper analyzes the fatigue loads of wind turbines in a floating offshore wind farm (FOWF) whose layout can be reconfigured. Such wind farm reconfiguration will be useful for wake effect mitigation in varying wind conditions. As an example FOWF, a farm with three 5 MW floating offshore wind turbine (FOWT) models on semi-submersible platforms, developed by the National Laboratory of the Rockies (NLR) (formerly the National Renewable Energy Laboratory (NREL)), is considered. Simulations for the example FOWF are conducted with various realistic turbulent wind and irregular wave conditions in the medium-fidelity wind farm simulator FAST.Farm. Using the simulation data, fatigue analysis is conducted by calculating the damage equivalent loads (DELs) using the computational tool MLife at critical components of the three FOWTs. The analysis results demonstrate the potential of reconfigurable FOWFs in not only increasing power outputs but also reducing fatigue loads for many critical components of turbines. Full article

The deployment of offshore wind farms (OWFs) has increased impressively over the last decade. While a group of frontrunner countries has led early deployment, the offshore wind sector is expanding to new regions; the Thracian Sea represents a promising area for OWFs deployment due to its favorable wind and wave climate. The successful implementation of OWFs projects depends on a comprehensive understanding of local environmental conditions, with particular emphasis on complex wind–wave interactions quantification, as well as on robust and representative power performance evaluation. In the present paper, hourly environmental data spanning 29 years (1993–2021), including wind and wave parameters, are utilized to quantify joint probability distributions at selected four locations in the Thracian Sea. Corresponding environmental contours are derived and presented using a probabilistic model for given return period. The joint probability distributions of wind and wave conditions are estimated and the environmental contour surfaces for 50- and 100-year return periods are calculated and presented for generic use. Furthermore, the power production of an OWF comprising nine IEA 15 MW turbine units arranged in an orthogonal grid layout is assessed through a numerical model developed in an open access computational tool. The model accounts for key physical processes influencing OWF capacity performance, including wake interactions, atmospheric conditions, turbine control strategies, and layout effects. The results indicate a substantial value of annual energy production and capacity factor for different zones within Thracian Sea achieving a value of 526 GWh and 44%, respectively. The presented results provide practical guidance for OWFs development in the Thracian Sea and contributes to reducing uncertainty in early-stage project planning and future engineering studies. Full article

This paper examines how inflow-state discretisation affects wake-loss and annual energy production (AEP) estimates for offshore wind farms. A reproducible workflow is presented for constructing weighted inflow-state ensembles from long-term offshore wind datasets using empirical wind-speed–direction occurrence frequencies. Hub-height wind speeds are reconstructed from multi-level wind data using a time-varying power–law shear exponent, after which the wind climatology is discretised using configurable directional sectors and wind-speed bins. The methodology was evaluated using both a controlled synthetic wind dataset and offshore climatological datasets processed through the same inflow-state and wake-modelling workflow. The analysis quantified how directional resolution, wind-speed bin width, and sector-mean inflow representations affect predicted turbine power, wake loss, and AEP relative to empirical reference cases. For the synthetic dataset, replacing the within-sector wind-speed distribution with a single sector-mean wind speed produced an annual power difference of 12.58%, with seasonal differences ranging from 6.66% in JJA to 13.91% in DJF. Offshore wake-model calculations showed the same overall behaviour. Reducing the empirical inflow-state ensemble from 1593 to 416 retained states changed annual AEP by only 0.03% and wake loss by $-0.03$ percentage points, whereas the sector-mean inflow representation increased predicted AEP by 18.40% and wake loss by 5.13 percentage points relative to the empirical reference case. The results show that preserving the within-sector wind-speed distribution has a larger influence on predicted wake loss and AEP than moderate reductions in retained state count or directional resolution for the datasets and layouts considered here. Empirical inflow-state ensembles using 36 directional sectors together with 1 $\mathrm{m}$${\mathrm{s}}^{-1}$ or 2 $\mathrm{m}$${\mathrm{s}}^{-1}$ wind-speed bins remained within 0.03% of the higher-resolution annual AEP reference while reducing the number of retained inflow states by approximately 74%, with a corresponding reduction in the number of wake-model evaluations required. Full article

Accurate performance prediction for large offshore wind turbines requires a principled treatment of uncertainty in both the wind resource and the rotor design parameters. In the present work, we develop a surrogate-based, multi-level uncertainty quantification (UQ) framework coupling a physics-based Blade Element Momentum (BEM) solver with a spectral Polynomial Chaos Expansion (PCE) surrogate that replaces the expensive Monte Carlo loop and apply it to the IEA 15 MW offshore reference wind turbine. The framework is completed by Sobol variance-based global sensitivity analysis. The contribution is methodological rather than algorithmic: although each individual ingredient (PCE, Sobol, BEM, and Jensen) is well established, their joint deployment in a single, internally consistent, end-to-end probabilistic workflow that simultaneously delivers (i) aerodynamic–structural UQ with analytical Sobol ranking, (ii) a like-for-like cross-comparison of three reference turbines, (iii) a quantitative leading-edge icing degradation study, and (iv) a farm-level wake-steering optimization on the same IEA 15 MW reference rotor yields a unified probabilistic envelope from which manufacturing tolerances, cold-climate investment thresholds, and farm-layout/control trade-offs can be read off consistently. Five input parameters are treated as random variables: hub-height wind speed (Weibull, k = 2.2, c = 9.8 m/s), air density, blade chord length, twist angle, and rotor speed. A degree-4 sparse PCE is built by non-intrusive spectral projection using N = 5000 Sobol quasi-random realizations, which allows the Sobol indices to be recovered analytically from the expansion coefficients at essentially no extra cost. Three parallel engineering studies complement the core UQ analysis: (A) a head-to-head comparison of the NREL 5 MW, DTU 10 MW, and IEA 15 MW reference turbines; (B) a quantitative assessment of leading-edge ice accretion at four severity levels; and (C) a Jensen-based wake optimization for a 25-turbine offshore array with static wake steering. The main results are as follows: the turbine reaches C_p,max = 0.480 at λ_opt = 8.51, and an annual energy production (AEP) of 71,261 MWh/year (PCE: 70,840 ± 2,140 MWh/year, 95% CI). Wind speed emerges as the dominant driver of C_p variance (S₁ = 0.412), followed by blade twist (0.198) and chord (0.143). Severe icing (30 kg/m) reduces C_p by 18.2% and increases the blade-root Damage Equivalent Load (DEL) by 18.5%. For the array, the optimal spacing (s_x = 8D, s_y = 6D) gives a farm efficiency of 89.6% and 1296 GWh/year, and a 15° wake-steering offset adds a further +3.2% to farm AEP. Compared with plain Monte Carlo, the sparse PCE delivers the same statistics with about 36% fewer model evaluations and a relative error below 0.8%. Full article

Given the energy-intensive operations and considerable carbon emissions of offshore oil and gas platforms (OOGPs) in deep-sea regions, adopting floating offshore wind farms (FOWFs) as power sources offers substantial benefits. However, the expenses associated with dynamic submarine cables constitute a substantial portion of the capital expenditure (CAPEX) for this hybrid system, highlighting the crucial need for optimization in the power collection system design. In this study, we present a mixed-integer quadratic programming (MIQP) model designed to reduce both the costs of investment and power losses associated with dynamic submarine cables, taking into account the influence of the wake effect in local wind conditions. Due to the complexity of this problem, we employ the Benders’ decomposition method to reformulate it into a master problem and a slave problem. Additionally, two valid inequalities are specifically incorporated into the master problem to accelerate the solution process. These constraints are derived from a heuristic combination of various cable connection configurations and a greedy-based spanning tree structure. Through multiple case studies, we first demonstrate the accuracy and rapid convergence of our method. Furthermore, we reveal that as the wind farm grows in size, the influence of the wake effect becomes increasingly pronounced. Full article

The increasing deployment of dense offshore wind turbine monopile foundations pose significant challenges for accurately simulating tidal-flow modification and energy transport at the array scale. Balancing physical realism with computational efficiency remains a key challenge in hydrodynamic modelling of offshore wind farms. In this study, an established drag-based source-term approach is implemented through a dedicated module developed within the TELEMAC-3D framework to represent the momentum-blocking effects of offshore wind-farm arrays. A representative dense 8 × 10 wind turbine monopile array configuration is constructed in a typical tidal channel to systematically examine array-induced tidal-flow responses. The results indicate that the drag-based source-term approach preserves the regional-scale tidal flow structure while effectively capturing array-induced local velocity adjustments and pronounced downstream wake attenuation and recovery. Detailed analyses further reveal distinct spatial and temporal characteristics of the velocity response, including the decay and recovery of velocity deviations downstream of the array. In addition, the monopile array induces a clear modulation of flow kinetic energy, characterized by enhanced energy dissipation and a finite array-scale redistribution of kinetic energy. These findings demonstrate that this approach efficiently simulates the array-scale hydrodynamic and energetic impacts of large offshore wind farms and contribute to a better understanding of array-induced tidal flow modification and energy redistribution. Full article

Offshore wind power currently shows the trend of a larger wind turbine capacity and deep-sea wind farm sites. Traditional wind farms with single-type wind turbines can hardly accommodate the dual requirements of high-efficiency power generation and wake effect mitigation of wind farms. Moreover, the wind shear of fixed wind turbines and the platform motion of floating wind turbines result in insufficient adaptability to a hybrid wind farm with multi-type wind turbines. To address that issue, this paper takes offshore wind farms with a multi-type hybrid layout for wake optimization. Firstly, based on the wind shear model, the influence of hub height difference for fixed wind turbines is analyzed, and the platform motion of semi-submersible floating wind turbines is evaluated through MoorDyn. On this basis, the wake optimization strategy for maximizing the total power generation of a wind farm is proposed based on the Gaussian Curl Hybrid model, which realizes three-dimensional wake control by considering the hub height difference and floating platform motion of multi-type wind turbines. The case study demonstrates that the multi-type hybrid layout itself has inherent wake suppression and optimization potential. The fixed wind farm with a row–column hybrid layout achieves an average power generation efficiency of 65.25%, which is superior to the single-type layout. For the floating wind farm with an inner–outer hybrid layout, the displacement misalignment effect is significant, with a maximum offset of 21.66 m in surge and 10.32 m in sway, and the total power is increased by 6.87 MW. And a hierarchical wake control mode matching multi-type wind turbines is formed. It provides a novel wake regulation mechanism for the design and operation of hybrid offshore wind farms. Full article

This study compares the performance of two wind farm sites located in Northern Europe: an onshore site and an offshore area in the eastern Baltic Sea region. This study investigates the optimisation of wind farm performance within a fixed project area by maximising annual energy production (AEP) and increasing energy density. Three wake-loss scenarios (≤10%, ≤15%, and ≤20%) were examined to assess the sensitivity of layout optimisation to aerodynamic interaction constraints. Several layout configurations were analysed to reduce wake losses and enhance overall energy output. Wind conditions were assessed using NORA3 reanalysis data, and wake interactions were modelled using the Jensen wake model to estimate AEP. Both wind farms were further compared across key criteria, including cost, power generation efficiency, installation and maintenance requirements, and site availability. Offshore wind farms achieve 1.5–1.7 times higher energy density under similar spatial conditions. However, offshore levelised cost of energy (LCOE) remains roughly 25% higher due to higher capital and infrastructure costs, while onshore LCOE demonstrates better economic performance, driven by lower CAPEX and O&M expenses. The findings highlight the trade-offs between cost efficiency and wake-driven energy performance for onshore and offshore wind development in the eastern Baltic Sea region. Full article

The wake that forms behind a yawing wind turbine is a complex flow region that can affect the performance of downstream turbines in offshore wind farms. It contains various flow features, including velocity deficit, shear layers, and vortex structures, which evolve in both time and space. Understanding this behavior is important for the design and operation of large-scale offshore wind farms. In this work, large-eddy simulations combined with proper orthogonal decomposition are used to study the wake development behind the National Renewable Energy Laboratory five-megawatt offshore wind turbine under both aligned and yawed inflow conditions. The results indicate that yawing the rotor leads to a lateral shift in the wake and increased asymmetry, with a stronger shear layer forming on one side. This asymmetry promotes enhanced mixing between the wake and the surrounding flow, contributing to a faster downstream recovery of the velocity field. The proper orthogonal decomposition analysis shows that the most energetic modes are associated with large-scale wake deflection and meandering, while higher-order modes correspond to smaller and less stable flow structures within the shear layer. The temporal evolution of these modes illustrates how the wake responds to the yaw maneuver and gradually reaches a new quasi-steady state. Overall, the study provides insight into the influence of yaw on wind turbine wake dynamics and demonstrates the applicability of combining large-eddy simulation with proper orthogonal decomposition for wake analysis in offshore wind farm studies. Full article

Wakes generated by upstream turbines in an offshore wind farm severely reduce the efficiency and power output of downstream turbines. Wind farm layout optimization offers a way to alleviate these negative impacts, where the main challenge lies in accurate and efficient evaluation across a vast number of potential configurations. Analytical wake models are crucial tools for this optimization, owing to their superb ability to efficiently predict wake distributions. This paper evaluates and discusses recent advances and persistent challenges in analytical wake modelling for layout optimization of wind farms. While the Jensen model remains efficient for discrete searches, the models capturing radial velocity gradients have become a preferred choice for high-fidelity optimization designs. Advanced models show the transition to full wakes to cover near-wake characteristics and complex inflow conditions. Motion corrections and physically based superposition methods improve the performance evaluation of floating offshore wind farms. Multi-objective optimization frameworks balance energy production and fatigue life by the integration of turbulence modelling. However, the increasing scale of modern wind turbines, the dynamic complexity of floating offshore wind farms, the clustering, and the model validation of large-scale wind farms present significant challenges to the applicability of these models. This paper highlights these emerging limitations in optimization problems, clarifying that addressing the gaps in these specific areas is essential for the development of high-fidelity optimizations and the design of future large-scale offshore wind turbine clusters. Full article

Offshore wind energy offers substantial potential. However, its inherent intermittency leads to the frequent occurrence of offshore wind energy droughts, which pose challenges to electricity system stability. Mitigation measures aim to reduce the number, duration, or impacts of such droughts. Among the different mitigation approaches, supply-side strategies act directly on wind power generation at the wind farm level. Nevertheless, the effectiveness of supply-side mitigation strategies remains poorly understood. This study addresses these gaps by systematically quantifying the potential of three supply-side mitigation strategies: (i) spatial diversification of wind farm locations, (ii) advances in wind turbine technology, and (iii) reductions in downtime and wake losses, to minimize the number and duration of wind energy droughts across 40 key exclusive economic zones (EEZ) worldwide. Hourly, daily, and monthly drought characteristics for both moderate and extreme offshore wind energy droughts are analyzed using wind data from the ERA5 reanalysis for the period 1993–2022. The results show that spatial diversification across multiple sub-regions is the most effective strategy for mitigating offshore wind energy droughts at the EEZ scale. In addition, the effectiveness of all mitigation strategies exhibits pronounced scale-dependent limitations, which are most evident at the monthly time scale. Overall, this study provides a robust basis for energy-policy decisions and highlights the importance of supply-side mitigation for enhancing the reliability of future electricity systems. Full article

Offshore wind-farm performance remains constrained by persistent wake deficits and turbulence that compound across intra-farm, intra-cluster, and inter-cluster scales, particularly under atmospheric neutral–stable stratification. A concept is advanced whereby offshore wind-farm yield may be augmented by pairing conventional horizontal-axis wind turbines (HAWTs) with lighter-than-air parafoil systems that entrain higher-momentum air and re-energise wakes, complementing yaw/induction-based wake control and enabling higher array energy density. A concise synthesis of wake physics and associated challenges motivates opportunities for active momentum re-injection, while a review of kite technologies frames design choices for lift generation and spatial keeping. Stability and control, spanning static and dynamic behaviours, tether dynamics, and response to extreme meteorological conditions, are identified as key challenges. System-integration pathways are outlined, including alignment and mounting options relative to turbine rows and prevailing shear. A staged validation programme is proposed, combining high-fidelity numerical simulation with wave-tank testing of coupled mooring–tether dynamics and wind-tunnel experiments on scaled arrays. Evaluation metrics emphasise net energy gain, fatigue loading, availability, and Levelized Cost of Energy (LCOE). The paper concludes with research directions and recommendations to guide standards and investment, and with a quantitative assessment of the techno-economic significance of kite–HAWT integration at scale. Full article

Lighter-than-air parakites deployed at sea in the close proximity of wind turbines may offer the possibility of mitigating wake losses encountered in large offshore wind farms. Such devices, having an order of magnitude similar to wind turbine rotors, can divert the stronger winds available at high altitudes to the lower level within the atmospheric boundary layer to enhance the wind flow between turbines. Mooring the parakites directly to the offshore wind turbine support structures would avoid the need for additional offshore structures. This paper investigates a novel and simple approach for mooring a parakite to an offshore wind turbine. The proposed approach exploits the lift forces of the inflatable parakite to reduce the tower bending moment at the base of the turbine induced by the rotor thrust. An iterative numerical model coupling the parakite loads to a catenary cable piecewise model is developed in Python 3.12.7 to quantify the bending moment reduction and shear load variations at the wind turbine tower base induced by the different kite geometries, windspeeds, and mooring cable lengths. The numerical model revealed that the proposed approach for mooring parakites can substantially reduce the tower bending loads experienced during rotor operation without considerably increasing the shearing forces. It was estimated that the tower bending moment decreased by 7.7% at the rated wind speed, where the rotor thrust is at its maximum, while the corresponding shear force increased by 0.6%. At higher wind speeds, where the magnitude of the rotor thrust decreases, the percentage reduction in bending moment gradually increases to 51.7% at a wind speed of 24 m/s, with the corresponding shear force increasing by only around 4.6%. Furthermore, while upscaling the parakite augments the tower bending moment reduction, changes in cable length had little effect on bending moment reduction and shear increase. Full article