Search Results (133)

Currently, Poland is witnessing a dynamic development of the offshore wind energy sector, which will be a key component of the national energy mix. While many international studies have addressed wind energy deployment, there is a lack of research that compares the energy and economic performance of both onshore and offshore wind farms under Polish climatic and spatial conditions, especially in relation to turbine spacing optimization. This study addresses that gap by performing a computer-based simulation analysis of three onshore spacing variants (3D, 4D, 5D) and four offshore variants (5D, 6D, 7D, 9D), located in central Poland (Stęszew, Okonek, Gostyń) and the Baltic Sea, respectively. The efficiency of wind farms was assessed in both energy and economic terms, using WAsP Bundle software and standard profitability evaluation metrics (NPV, MNPV, IRR). The results show that the highest NPV and MNPV values among onshore configurations were obtained for the 3D spacing variant, where the energy yield leads to nearly double the annual revenue compared to the 5D variant. IRR values indicate project profitability, averaging 14.5% for onshore and 11.9% for offshore wind farms. Offshore turbines demonstrated higher capacity factors (36–53%) compared to onshore (28–39%), with 4–7 times higher annual energy output. The study provides new insight into wind farm layout optimization under Polish conditions and supports spatial planning and investment decision making in line with national energy policy goals. Full article

The wind energy resources in the northwestern desert and semi-desert grassland regions of China are abundant. However, the ramifications of large-scale centralized wind farm operations on terrestrial rodents remain incompletely understood. In May and September 2024, we employed a grid sampling method combined with burrow counting and kernel density analysis to investigate the spatial distribution of Alashan ground squirrel (Spermophilus alashanicus) burrows in different wind turbine power zones (control, 750 kW, 1500 kW, 2000 kW, and 2500 kW) at the Taiyangshan wind farm in China. Using generalized additive models and structural equation models, we analysed the relationship between burrow spatial distribution and environmental factors. The results revealed no significant linear correlation between burrow density and turbine layout density, but was significantly positively correlated with turbine power (p < 0.05). The highest burrow density was observed in the 2500 kW zone, with values of 24.43 ± 7.18 burrows/hm² in May and 21.29 ± 3.38 burrows/hm² in September (p < 0.05). The squirrels exhibited a tendency to avoid constructing burrows within the rotor sweeping areas of the turbines. The burrow density distribution exhibited a multinuclear clustering pattern in both May and September, with a northwest–southeast spatial orientation. Turbine power, aspect, and plan convexity had significant positive effects on burrow density, whereas vegetation height had a significant negative effect. Moreover, vegetation height indirectly influenced burrow density through its interactions with turbine power and relief degree. Under the combined influence of turbine power, topography, and vegetation, Alashan ground squirrels preferred habitats in low-density, high-power turbine zones with shorter vegetation, sunny slopes, convex landforms, and minimal disturbance. Full article

This paper reviews the applications of artificial intelligence (AI) in the design optimization of wind power systems, mainly including (1) wind farm layout optimization; (2) wind turbine design optimization; and (3) wind farm electrical system design optimization. Firstly, this paper introduces the general considerations in the optimal design of wind power systems and the AI methods commonly used for the optimal design of wind power systems. Then the applications of AI in the optimal design of wind farms are reviewed in detail. Finally, further research directions of using AI methods in the optimal design of wind power systems are recommended. Full article

An accurate estimation of future wind conditions is essential for calculating the annual energy produced by a wind farm. This estimation should be based on historical wind data collected over several years at the site location. However, research articles often rely on data grouped into 12 sectors. This article examines five methods to improve the speed and accuracy in the use of wind data. First, it studies the effect of inadequate Weibull parameter calculation based on historical data showing that purely mathematical fitting methods (the traditional ones) are not valid. Then, the error introduced by wind speed discretization is evaluated showing that the traditional binning of 1 m/s is not always the best choice. Next, the effect of using symmetric wind roses is examined, demonstrating that it is possible to reduce computation time by half for layouts exhibiting point symmetry, with negligible error for other layouts. After that, the effect of abrupt wind condition distributions caused by sectorization, which can alter results when searching for optimal configurations, is analyzed proposing continuous interpolation of wind data to improve result consistency. Finally, an alternative to the wind rose is proposed to provide a quick assessment of the highest-quality wind directions. Full article

Wake effects significantly reduce efficiency and increase structural loads in wind farms. Therefore, accurate and computationally efficient models are crucial for wind farm layout optimization and operational control. High-fidelity computational fluid dynamics (CFD) simulations, while accurate, are too slow for these tasks, whereas faster analytical models often lack dynamic fidelity and 3D detail, particularly under complex conditions. Existing data-driven surrogate models based on neural networks often struggle with the high dimensionality of the flow field and scalability to large wind farms. This paper proposes a novel data-driven surrogate modeling framework to bridge this gap, leveraging Neural Networks (NNs) trained on data from the high-fidelity SOWFA (simulator for wind farm applications) tool. A physics-inspired NN architecture featuring an autoencoder for spatial feature extraction and latent space dynamics for temporal evolution is introduced, motivated by the time–space decoupling structure observed in the Navier–Stokes equations. To address scalability for large wind farms, a “wind box” decomposition strategy is employed. This involves training separate NN models on smaller, canonical domains (with and without turbines) that can be stitched together to represent larger farm layouts, significantly reducing training data requirements compared to monolithic farm simulations. The development of a batch simulation interface for SOWFA to generate the required training data efficiently is detailed. Results demonstrate that the proposed surrogate model accurately predicts the 3D dynamic wake evolution for single-turbine and multi-turbine configurations. Specifically, average velocity errors (quantified as RMSE) are typically below 0.2 m/s (relative error < 2–5%) compared to SOWFA, while achieving computational accelerations of several orders of magnitude (simulation times reduced from hours to seconds). This work presents a promising pathway towards enabling advanced, model-based optimization and control of large wind farms. Full article

With the rapid development of offshore wind farms, the construction of deep sea wind farms has increasingly significant impacts on the safety of maritime navigation. This paper conducts a cluster analysis of ship trajectories based on AIS data to analyze the characteristics of ship traffic flow in the waters near the Shanghai deep sea offshore wind farm. A fuzzy hierarchical analysis method is proposed. Combined with the layout of wind farms and the navigational environment, a risk assessment model for offshore wind farm navigation is established. This model quantifies the factors that affect the safety of ship navigation due to the wind farm and evaluates the navigation risks in the surrounding waters. The results of the research show that the construction of wind farms increases traffic density, interferes with traditional shipping routes, and consequently increases the risk of collisions. The fuzzy hierarchical analysis method has good operability and feasibility in the safety assessment of offshore wind farms, and can provide effective support for future safety assessment of offshore wind farms. The sections are arranged as follows: Firstly, the background and significance of the paper are introduced, as well as the current research status. Secondly, an overview of Shanghai offshore wind farms and their nearby shipping routes is introduced, and then the risk situation of existing wind farms is pointed out. Then the risk assessment method is carried out, and the navigational risk of offshore wind farms is evaluated. Finally, the paper proposes measures to reduce the navigational risk of ships in the vicinity of wind farms. Full article

Floating Offshore Wind Turbines (FOWTs) are gaining traction as a solution for harnessing wind energy in deepwater regions where traditional fixed-bottom turbines may not be viable due to water depth. This paper investigates the feasibility and optimization of a floating wind farm in a tropical cyclone (typhoon) region, using the IEA 15 MW turbine and semi-submersible floaters. Because of the extreme environment, the FOWT’s mooring system requires nine catenary chains in a 3 × 3 pattern, which has a large footprint. One challenge in the wind farm design is fitting the FOWTs in a limited area and minimizing wake effects. This research compares a linear layout and an offset grid layout, focusing on the effects of spacing and wake dynamics. The results show that while the linear layout maintains optimal power generation without energy loss, the offset grid layout, although resulting in 2% energy loss, offers greater spatial efficiency for larger-scale projects. The findings highlight the importance of balancing energy efficiency with spatial optimization, particularly for large offshore wind farms. This study explores the use of the Gauss–Curl hybrid model in wake modeling, and the methodology employed provides insights into FOWT placement and mooring system arrangement. The result concludes that a wind farm containing twelve (12) units of 15 MW wind turbines can achieve the 7.0 MW/km² power generation density required by a regulatory government agency. It proves the technical feasibility of a wind farm congested with large mooring systems in a tropical cyclone region. Full article

Wind energy is a vital renewable resource with substantial economic and environmental benefits, yet its spatial variability poses significant optimization challenges. This study advances wind farm layout optimization by employing a systematic genetic algorithm (GA) tuning approach using the design of experiments (DOE) method. Specifically, a full factorial 2² DOE was utilized to optimize crossover and mutation coefficients, enhancing convergence speed and overall algorithm performance. The methodology was applied to a hypothetical wind farm with unidirectional wind flow and spatial constraints, using a fitness function that incorporates wake effects and maximizes energy production. The results demonstrated a 4.50% increase in power generation and a 4.87% improvement in fitness value compared to prior studies. Additionally, the optimized GA parameters enabled the placement of additional turbines, enhancing site utilization while maintaining cost-effectiveness. ANOVA and response surface analysis confirmed the significant interaction effects between GA parameters, highlighting the importance of systematic tuning over conventional trial-and-error approaches. This study establishes a foundation for real-world applications, including smart grid integration and adaptive renewable energy systems, by providing a robust, data-driven framework for wind farm optimization. The findings reinforce the crucial role of systematic parameter tuning in improving wind farm efficiency, energy output, and economic feasibility. Full article

The coupling of offshore wind energy with hydrogen production involves complex energy flow dynamics and management challenges. This study explores the production of hydrogen through a PEM electrolyzer powered by offshore wind farms and Lithium-ion batteries. A digital twin is developed in Python with the aim of supporting the sizing and carrying out a techno-economic analysis. A controller is designed to manage energy flows on an hourly basis. Three scenarios are analyzed by fixing the electrolyzer capacity to meet a steel plant’s hydrogen demand while exploring different wind farm configurations where the electrolyzer capacity represents 40%, 60%, and 80% of the wind farm. The layout is optimized to account for the turbine wake. Results reveal that when the electrolyzer capacity is 80% of the wind farm, a better energy balance is achieved, with 87.5% of the wind production consumed by the electrolyzer. In all scenarios, the energy stored is less than 5%, highlighting its limitation as a storage solution in this application. LCOE and LCOH differ minimally between scenarios. Saved emissions from wind power reach 268 ${\mathrm{kton}}_{{\mathrm{CO}}_2}$/year while those from hydrogen production amount to 520 ${\mathrm{kton}}_{{\mathrm{CO}}_2}$/year, underlying the importance of hydrogen in hard-to-abate sectors. Full article

The wind farm layout optimization problem (WFLOP) aims to maximize wind energy utilization efficiency and mitigate energy losses caused by wake effects by optimizing the spatial layout of wind turbines. Although Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been widely used in WFLOP due to their discrete optimization characteristics, they still have limitations in global exploration capability and optimization depth. Meanwhile, the Differential Evolution algorithm (DE), known for its strong global optimization ability and excellent performance in handling complex nonlinear problems, is well recognized in continuous optimization issues. However, since DE was originally designed for continuous optimization scenarios, it shows insufficient adaptability under the discrete nature of WFLOP, limiting its potential advantages. In this paper, we propose a Fractional-Order Difference-driven DE Optimization Algorithm called FODE. By introducing the memory and non-local properties of fractional-order differences, FODE effectively overcomes the adaptability issues of advanced DE variants in WFLOP’s discreteness while organically applying their global optimization capabilities for complex nonlinear problems to WFLOP to achieve more efficient overall optimization performance. Experimental results show that under 10 complex wind farm conditions, FODE significantly outperforms various current state-of-the-art WFLOP algorithms including GA, PSO, and DE variants in terms of optimization performance, robustness, and applicability. Incorporating more realistic wind speed distribution and wind condition data into modeling and experiments, further enhancing the realism of WFLOP studies presented here, provides a new technical pathway for optimizing wind farm layouts. Full article

The effects of an increase in output power owing to the close arrangement of vertical-axis wind turbines (VAWTs) are well known. With the ultimate goal of determining the optimal layout of a wind farm (WF) for VAWTs, this study proposes a new method for quickly calculating the flow field and power output of a virtual WF consisting of two-dimensional (2-D) miniature VAWT rotors. This new method constructs a flow field in a WF by superposing 2-D velocity numerical data around an isolated single VAWT obtained through a computational fluid dynamics (CFD) analysis. In the calculation process, the VAWTs were gradually increased one by one from the upstream side, and a calculation subroutine, in which the virtual upstream wind speed at each VAWT position was recalculated with the effects of other VAWTs, was repeated three times for each arrangement with a temporal number of VAWTs. This method includes the effects of the velocity gradient, secondary flow, and wake shift as models of turbine-to-turbine interaction. To verify the accuracy of the method, the VAWT rotor power outputs predicted by the proposed method for several types of rotor pairs, four-rotor tandem, and parallel arrangements were compared with the results of previous CFD analyses. This method was applied to four virtual WFs consisting of 16 miniature VAWTs. It was found that a layout consisting of two linear arrays of eight closely spaced VAWTs with wide spacing between the arrays yielded a significantly higher output than the other three layouts. The high-performance layout had fewer rotors in the wakes of the other rotors, and the induced flow speeds generated by the closely spaced VAWTs probably mutually enhanced their output power. Full article

Floating offshore wind turbines (FOWTs) have become a promising solution for harnessing wind energy in deeper seas. However, the complex interplay between FOWT layout, mooring line patterns, and wake effects significantly influences the overall performance of a floating offshore wind farm (FOWF). This paper proposes a novel optimization methodology that integrates mooring line constraints into the FOWF layout optimization process. The wake-induced power deficit is considered, whereas the vortices are neglected. The new method considers the constraint areas for each FOWT, which are defined based on both mooring line buffer zones and wind turbine buffer zones. By defining constraint areas, the optimization process ensures that FOWTs are optimally positioned while avoiding interference and collisions. By carefully considering the buffer zones, the power potential of FOWFs with three-line, four-line, and six-line mooring configurations can be improved by 122%, 100%, and 78%, respectively. Then, a genetic algorithm is employed to optimize the FOWT positions and mooring line angles simultaneously. The effectiveness of the proposed method is demonstrated through a case study in Guangdong, resulting in a significant 5% increase in power output potential compared to conventional approaches. This research contributes to the advancement of FOWT layout optimization and provides valuable insights for the design and deployment of future FOWFs. Full article

Offshore wind energy plays a crucial role in achieving renewable energy targets, with OWFs facing unique environmental challenges that impact turbine performance and structural demands. This study develops an advanced optimization methodology to identify the most effective layout configurations for offshore wind farms (OWFs) with monopile foundations, focusing on enhancing structural integrity and energy generation efficiency. Using a multi-criteria optimization approach, the effects of wind turbine spacing, angular orientation, and height on energy yield and monopile loading were evaluated. Based on a seven-year dataset from the Ouido site in South Korea, where the mean wind speed is 6.95 m/s at a 150 m hub height, optimized configurations were determined. For average wind conditions, a turbine spacing of 250 m, a hub height of 148 m, and an orientation angle of 36.87° minimized wake losses and distributed structural loads effectively. Under rated wind speeds of 10.59 m/s, a spacing of 282 m, a hub height of 155 m, and an orientation angle of 45° further enhanced performance. These designs reduced wake interference by 25%, decreased monopile fatigue loads by 18%, and lowered the levelized cost of electricity (LCOE) by up to 15%. This study’s findings provide a robust framework for optimizing OWFs to increase energy yield, improve operational efficiency, and ensure economic viability. Full article

In recent years, offshore wind power has become increasingly relevant as a key alternative for contributing to the global economy’s decarbonization. Also, the accelerated technological development of the offshore wind turbine influences the increase in size and weight of its main components. This requires an appropriate port infrastructure to support the installation, operation, and maintenance and future decommissioning of offshore wind farms, and especially to serve as an area for manufacturing these components, addressing logistical challenges associated with land transport. This research aims to identify the factors that characterize a suitable port to support the offshore wind industry, also bringing the new green port industry concept. A systematic literature review was conducted via analyses of 126 documents, and a survey procedure was applied to validate the proposed model. As a result, a characterization model was proposed that includes 71 factors classified into 6 dimensions: physical characteristics, port layout, connectivity, port operation, port–farm performance optimization, and governance for sustainability, which is the main novelty of this study. The results contribute to the advancement of the offshore wind energy sector and can provide significant benefits for regional development and local communities with offshore wind potential. Full article

Wind farm layout optimization is essential to maximize the energy production of renewable energy systems. A new layout optimization method for offshore wind farms is proposed to minimize power deficits due to the wake effect without limitation on the number of turbines, the shape, or the extension of the concession area. The main engine of the algorithm is a gradient-descent method in which throughout the optimization process, new turbines are progressively and randomly included within the concession area and quickly expand outward, looking for areas with less perturbation, in turn, pushing the previous ones. When the optimization process ends, to avoid local maxima, it enters into a process of suppression of the set of locations that cause the greatest potential (power deficit). Then, a map of potential for the entire area is created, and a greedy algorithm places new turbines to complete the layout with the final number of turbines. The overall process is completed in 25 s. To drastically speed up the search process and the creation of the potential map, a simplification has been validated and added: for turbines affected by multiple wakes, the resulting power has been calculated by using a linear aggregation of power deficits, instead of the usual linear (or quadratic) aggregation of speed deficits. Owing to this type of aggregation, an analogy is established between power deficit and repulsive non-isotropic electrostatic potential energy, which allows using the properties of conservative fields. Thanks to this, the process is 20 times faster than any other layout optimization algorithm found in the revised literature. Irregular concession areas are easily treated using Stokes’ theorem to detect outer points. Full article