Search Results (75)

To overcome the limitations of existing evaluation methods and performance testing for wind power generation systems, this study proposes a modular framework for performance testing and comparative assessment. Methodologically, the approach establishes a baseline configuration for simulation and provides optional interfaces for experimental, hardware-in-the-loop, or bench testing under identical boundary conditions. By employing a unified metric system, the proposed method enables a comprehensive evaluation of annual energy production (AEP) gains, power curve deviations, damage equivalent loads (DEL) when cycle-resolved load histories are available, fatigue- and peak load proxy variations, efficiency fluctuations, temperature rise margins, and reliability proxy indicators. A demonstrative case study is conducted using illustrative numerical data parameterized for a generic 2.5 MW-class doubly fed wind turbine to compare three retrofit schemes: blade replacement, gearbox optimization (S2), and a pitch system upgrade. When annual energy production (AEP) is utilized as the sole metric, the blade replacement scheme yields a 3.32% increase. However, it concurrently increases the fatigue load and peak load proxies by 6.11% and 3.72%, respectively. Conversely, a comprehensive assessment incorporating load, temperature rise, vibration, and reliability identifies the gearbox optimization (S2 scheme) as the highest-ranked option under the current weighting configuration, with a reproducible overall score of 0.65. The weight sensitivity analysis further shows that the preferred scheme can change when engineering priorities change. Ultimately, this work demonstrates the proposed method’s capability to highlight the discrepancy between single-metric and holistic performance optimization, providing standardized support for scheme selection, project acceptance, and the evaluation of wind turbine retrofit schemes. Full article

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

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

Wind farm layout optimization (WFLO) plays a key role in reducing wake effect energy losses and increasing annual energy production (AEP). This paper proposes a double-stage optimization approach that incorporates staggered grid-based optimization with coordinate-based local optimization. In the first stage, staggered grid-based optimization is performed to determine optimal turbine locations within predefined grid boundaries. In the second stage, turbine positions are locally optimized within bounded regions to improve AEP efficiently without extending the search across the entire wind farm. The modified electric charged particle optimization (MECPO) algorithm is applied to evaluate five optimization approaches, including two double-stage and three single-stage approaches. The framework is tested on a wind farm covering an area of 2000 m by 2000 m with 20 turbines under single-direction, uniform multi-directional, and spatially varying wind conditions. The proposed double-stage optimization approach achieves comparable or improved net AEP while significantly reducing computational cost across different wind conditions. The method provides up to 0.36% improvement in net AEP, reduces wake losses by up to 6.84%, and decreases computational time by up to 90% compared with the coordinate-based approach. These results confirm that the proposed approach significantly enhances computational efficiency while maintaining comparable energy performance. The findings indicate that integrating staggered grid-based optimization with coordinate-based local optimization provides an effective balance between solution quality and computational efficiency, offering a practical and scalable approach for WFLO. Full article

While conventional wind towers operate at heights of 80 to 90 m across many regions, including the United States, emerging tower technologies enable higher hub heights that are expected to reduce the levelized cost of energy (LCOE) and increase profit margins. This paper investigates whether increased hub heights, as well as different turbine technologies, deliver measurable economic and performance benefits in wind-rich regions using measured and simulated wind data. First, a model for estimating hourly and monthly energy production is validated with data from a site in Minnesota. To evaluate the advantages of tall towers, the model is extended to estimate the annual energy production (AEP) at various hub heights across multiple sites using different wind datasets. The results confirm that simulated data can be effectively used for predicting AEP and capacity factors in wind-rich regions. Next, it is demonstrated that increasing the hub height by 20 m yielded an average 11% increase in AEP and an 18% reduction in LCOE. Finally, the integration of advanced turbine technologies with taller towers shows the potential to reduce the LCOE of wind power by 23% while increasing profit margins by over 40%. Full article

This study evaluates the wind energy potential of Polokwane, South Africa, using statistical distribution models to estimate wind power density (WPD) and assess turbine performance under low-wind inland conditions. Hourly wind speed and direction data (2015–2024) measured at a 10 m height above ground level (AGL) were analysed to characterise wind behaviour and assess energy availability. Four probability distributions, namely generalised logistic (GLD), generalised extreme value (GEVD), Gumbel (GD), and Weibull (WD), were fitted using the maximum likelihood (ML) method. Model performance was evaluated using Kolmogorov–Smirnov (KS), Anderson–Darling (AD), and Chi-square $\left({\chi }^2\right)$ tests, while wind power density accuracy was assessed using wind power density error (WPDE). The results showed that Polokwane is characterised by low wind speeds, with an overall mean wind speed of 2.72 m/s at 10 m AGL, reaching a low of 3.88 m/s at a hub height of 125 m. The GEVD model produced the most accurate wind power density estimate of 32.37 W/m², classifying the site within the poor wind resource category. Wind direction analysis revealed a dominant northeast sector with seasonal shifts toward the south. Wind turbine performance analysis showed improved energy generation at higher hub heights, with the Gamesa G136-4.5 MW turbine identified as the most suitable option for the site, achieving the highest net annual energy production (AEP) of 10.82 GWh/yr and the highest net capacity factor (CF) of 27.44%. These results indicate that the Polokwane site is suitable for low-to-moderate wind energy applications and small-scale distributed wind generation rather than large-scale commercial wind farm development. Full article

The wave energy resource along the Azores coast is evaluated for the present (1990–2019) and future (2030–2059) periods using the third-generation wave model WAVEWATCH III, forced by winds and sea-ice cover from the RCP8.5 EC-Earth integration dynamically downscaled with the Weather Research and Forecasting model. The results indicate that the region is characterized by a high-energy wave climate, with mean wave power values typically ranging between 30 and 40 kW/m. A statistical comparison between the two periods shows a moderate reduction in wave energy potential under future conditions, with strong spatial variability. The performance of four wave energy converters (AquaBuoy, Wavestar, Oceantec, and Atargis) is analyzed, revealing significant differences in energy production and capacity factor depending on device–site matching. A techno-economic evaluation is performed by estimating the LCOE, accounting for capital expenditure, operational costs, device lifetime, and annual energy production (AEP). The results demonstrate that economic performance is primarily driven by energy production rather than capital cost alone, and that wave energy exploitation in the Azores remains viable under near-future climate conditions. Full article

The transition to sustainable energy systems requires the effective integration of offshore wind energy with hydrogen production. In this context, the paper assesses the potential for offshore hydrogen production in eight locations, three of which are located in the Black Sea, using data from the ERA5 database (period 2016–2025) at a height of 10 m and then extrapolated to a height of 150 m. The methodology includes estimating the annual energy production for four types of offshore turbines (Siemens Gamesa (Zamudio, Spain) SG 14-236 DD, Vestas (Aarhus, Denmark) V236-15.0, GE (Rotterdam, The Netherlands) Haliade-X 13, and MingYang (Guangdong, China) MySE12-242) and correlating it with six electrolyzer configurations (PEM and AWE) in gross and net scenarios, as well as analyzing the energy compatibility related to the number of electrolyzers. The novelty of the study lies in the integrated multi-site approach and in the direct quantification of the relationship between wind production and electrolysis requirements for different turbine–electrolyzer combinations. The results indicate a variation in gross annual energy production (AEP) in the range of 45.65 to 81.11 GWh/year, while the net scenario, accounting for operational losses, ranged from 37.75 to 67.05 GWh/year, and hydrogen production between 327 and 1075 t/year, highlighting that the optimal performance is determined by the compatibility between turbine and electrolyzer and the specific energy consumption rather than the nominal power. The ${\mathrm{N}}_{\mathrm{net}}$ analysis shows that, in most cases, the energy produced by a single turbine is insufficient for the full operation of large capacity electrolyzers, resulting in a sub-unit utilization rate and necessitating the use of multiple turbines to reach the nominal operating regime. The analysis is limited to a technical assessment based on historical climatological data, excluding economic aspects, grid constraints, and variations in equipment performance over time. The results underscore the importance of integrating the sizing of offshore wind–hydrogen systems with local resources and energy conversion efficiency. 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

With hydropower supplying nearly 94% of Ethiopia’s electricity, the national power grid is extremely vulnerable to recurrent droughts and erratic rainfall. To mitigate this risk, this study examines the wind power potential across five specific locations: Arerti, Sela Dingay, Debre Berhan, Mega, and Gode. By combining on-site mast measurements with datasets from NASA and the Global Wind Atlas, we evaluated wind characteristics at industry-standard hub heights of 80 m and 100 m. The analysis focused on wind power density (WPD), Weibull stability parameters (k and c), and directional consistency. The results indicate that Gode and Mega are the premier choices for commercial development, showing average speeds above 8.5 m/s and power densities exceeding 500 W/m2 at the 100 m level. Gode stands out as the most reliable site, with a Weibull shape factor (k) of 2.8 and a scale factor (c) of 9.1 m/s. We modeled a standard 3 MW turbine while factoring in a 20% loss for real-world conditions; this yielded net annual energy productions of 9461 MWh (36% CF) for Gode, 9040 MWh (34.4% CF) for Mega, and 8619 MWh (32.8% CF) for Arerti. While Sela Dingay and Debre Berhan have lower initial yields, their feasibility improves significantly when using towers taller than 80 m. Wind rose data reveals that Gode and Arerti have highly unidirectional flows, which simplifies turbine micro-siting. Notably, Arerti provides a unique economic advantage due to its location right next to existing 132/230 kV transmission infrastructure and industrial load centers. Overall, these findings provide a definitive technical roadmap for Ethiopia to diversify its energy portfolio and meet its Climate-Resilient Green Economy (CRGE) objectives. Full article

Forests play a vital role in influencing wind flow by modifying turbulence intensity and vertical wind shear. Because wind turbines are susceptible to these conditions, accurately characterising wind flow in forested environments is vital to ensuring structural reliability and realistic energy-yield assessments. In Latvia, where approximately 51.3% of the territory is covered by forests; the likelihood of wind turbine deployment in such areas is considerable. However, wind behaviour within and above forests is complex and strongly influenced by canopy effects, which in turn affect wake dynamics, structural fatigue, and power production. Advancing research in this field is therefore crucial for improving the accuracy of wind resource assessment and supporting evidence-based engineering solutions that enable the sustainable development of wind energy. Wind conditions were evaluated using NORA3 reanalysis data. Wake effects were simulated with the Jensen wake model to estimate annual energy production (AEP), which then informed levelised cost of energy (LCOE) calculations at various hub heights. The results indicate clear seasonal variability and show that increasing hub height leads to higher AEP and lower LCOE, owing to higher wind speeds and reduced turbulence. For forest heights of 0–25 m, the AEP loss increases from 7.8% (hub height = 199 m) to 22.9% (hub height = 114 m). Higher hub heights are also less sensitive to canopy-induced variability, reducing the impact of forest-related turbulence on energy production. Full article

Accurate wind resource assessment is critical for the effective planning of wind farms, as well as for forecasting production values to ensure grid stability, yet it remains a complex challenge. This study evaluates the robustness of the Norwegian reanalysis model (NORA3) as a wind assessment tool specifically for the Baltic Sea region. The NORA3 model was validated by comparing it to observation data from four onshore locations in Latvia, collected from meteorological masts and a lidar wind measurement device. The evaluation applied correlation analysis, wind distribution and wind rose comparisons, and annual energy production (AEP) estimates. Results reveal high similarity between NORA3 and observation datasets in terms of wind speed correlation and distribution, while wind roses feature significant differences, especially for short-term observations. AEP estimates based on the NORA3 dataset are more optimistic compared to the actual observations for all investigated locations. Full article

In this study, we investigated the optimization of a wind farm layout on complex mountainous terrain in Yeongdeok, South Korea, with varying hub heights. Specifically, the energy performance of mixing two commonly used commercial models with different heights, i.e., Vestas V82 and V162, was evaluated. The impact of site scale in terms of farm area (ranging from 1 to 9 km²) on power generation and wake effects was also determined. The results obtained using WindPRO and the Wind Atlas Analysis and Application Program demonstrated that, with increased wind farm area, the annual energy production increased while wake losses decreased. Compared with the case employing hubs with a uniform height, the mixed-height case showed a decrease in wake losses of up to 1.7% while maintaining comparable AEP. The findings of this study demonstrate that combining turbines of different hub heights provides more energy-efficient layouts, even in complex mountainous terrains. Insights from these findings can be further utilized to expand wind power in complex terrain in other countries. Full article

Large-scale wind farms operate under highly unsteady atmospheric inflows, where transient turbulence, dynamic wake interactions, and inflow-wake coupling reduce energy production and exacerbate turbine loads. Over the past five years, advances in high-fidelity computational fluid dynamics (CFDs), large eddy simulation (LES), machine learning (ML)-based wake modeling, and multi-objective optimization have reshaped wind farm layout optimization under dynamic inflow conditions. This review synthesizes recent progress in five key areas: dynamic inflow and high-fidelity wake modeling (including LES-driven transient wake evolution and turbulence-resolved inflow generation), data-driven wake prediction, multi-objective layout optimization (considering the annual energy production (AEP), fatigue load constraints, and the levelized cost of energy (LCOE)), blockage modeling for complex terrain and yaw misalignment, and real-time optimization addressing inflow, turbine performance, and modeling uncertainties. Coupling transient wake models with surrogate-assisted multi-objective optimization enables a computationally efficient and physically consistent layout design. Key open challenges (dynamic wake controllability, real-time optimization under uncertainty, and integration with next-generation farm-level control systems) and future directions for enhancing large-scale wind farm resilience and cost-competitiveness are also identified. However, despite significant progress, existing models still face fundamental limitations, such as oversimplified treatment of complex turbulence structures, poor generalization under extreme or atypical conditions, and inadequate capture of long-timescale dynamic responses, which constrain their reliability in practical optimization settings. Full article