Search Results (344)

A high-quality set of typical scenarios is significant for power grid planning. Existing construction methods for typical scenarios do not account for the spatiotemporal correlations among renewable energy plant outputs, failing to adequately reflect the distribution characteristics of original scenarios. To address the issues mentioned above, this paper proposes a construction method for typical scenarios considering spatiotemporal correlations, providing high-quality typical scenarios for power grid planning. Firstly, a symmetric spatial correlation matrix and a temporal autocorrelation matrix are defined, achieving quantitative representation of spatiotemporal correlations. Then, distributional differences between typical and original scenarios are quantified from multiple dimensions, and a scenario reduction model considering spatiotemporal correlations is established. Finally, the genetic algorithm is improved by incorporating adaptive parameter adjustment and an elitism strategy, which can efficiently obtain high-quality typical scenarios. A case study involving five wind farms and fourteen photovoltaic plants in Belgium is presented. The rate of error between spatial correlation matrices of original and typical scenario sets is lower than 2.6%, and the rate of error between temporal autocorrelations is lower than 2.8%. Simulation results demonstrate that typical scenarios can capture the spatiotemporal correlations of original scenarios. Full article

Reliable estimation of extreme waves is essential for offshore wind turbine system design; however, site-specific conditions limit the application of one-size-fits-all statistical methods. We analyzed extreme wave conditions at potential offshore wind farm sites in South Korea using high-resolution hindcast data (1979–2022) based on the Weather Research and Forecasting (WRF) model. While previous studies have typically relied on a limited combination of distribution types and parameter estimation methods, this study systematically applied various Weibull distribution models and parameter estimation techniques to the environmental contour (EC) method. The results show that the optimal statistical approach varied by site according to the tail characteristics of the wave height distribution. The inverse second-order reliability method (I-SORM) provided the highest accuracy in regions with rapidly decaying tails, achieving root mean square error (RMSE) values of 0.21 in Shinan (using the three-parameter Weibull distribution with maximum likelihood estimation, MLE) and 0.34 in Chujado (with the method of moments, MOM). In contrast, the inverse first-order reliability method (I-FORM) yielded superior performance in areas where the tail decays more gradually, such as Yokjido (RMSE = 0.47 with MLE using the exponentiated Weibull distribution) and Ulsan (RMSE = 0.29, with MLE using the exponentiated Weibull distribution). These findings underscore the importance of selecting site-specific combinations of statistical models and estimation techniques based on wave distribution characteristics, thereby improving the accuracy and reliability of extreme design wave predictions. The proposed framework can significantly contribute to the establishment of reliable design criteria for offshore wind turbine systems by reflecting region-specific marine environmental conditions. Full article

With the large-scale integration of renewable energy into distribution networks, traditional fixed-setting overcurrent protection strategies struggle to adapt to rapid fluctuations in renewable energy (e.g., wind and photovoltaic) output. Optimizing current settings is crucial for enhancing the stability of modern distribution networks. This paper proposes an adaptive overcurrent protection method based on an improved NSGA-II algorithm. By dynamically detecting renewable power fluctuations and generating adaptive solutions, the method enables the online optimization of protection parameters, effectively reducing misoperation rates, shortening operation times, and significantly improving the reliability and resilience of distribution networks. Using the rate of renewable power variation as the core criterion, renewable power changes are categorized into abrupt and gradual scenarios. Depending on the scenario, either a random solution injection strategy (DNSGA-II-A) or a Gaussian mutation strategy (DNSGA-II-B) is dynamically applied to adjust overcurrent protection settings and time delays, ensuring real-time alignment with grid conditions. Hard constraints such as sensitivity, selectivity, and misoperation rate are embedded to guarantee compliance with relay protection standards. Additionally, the convergence of the Pareto front change rate serves as the termination condition, reducing computational redundancy and avoiding local optima. Simulation tests on a 10 kV distribution network integrated with a wind farm validate the effectiveness of the proposed method. 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

In recent times, blades that have the ability to change shape passively or actively have garnered interest due to their ability to optimize blade performance for varying flow conditions. Various versions of morphing exist, from simple chord length changes to full blade morphing with multiple degrees of freedom. These blades can incorporate smart materials or mechanical actuators to modify the blade shape to suit the wind conditions. Morphing blades have shown an ability to improve performance in simulations. These simulations show increased performance in Region 2 (partial load) operating conditions. This study focuses on the effects of the wake for a flexible wind turbine with actively variable twist angle distribution (TAD) to improve the energy production capabilities of morphing structures. These wake effects influence wind farm performance for locally clustered turbines by extracting energy from the free stream. Hence, the development of better wake models is critical for better turbine design and controls. This paper provides an outline of some approaches available for wake modeling. FLORIS (FLow Redirection and Induction Steady-State) is a program used to predict steady-state wake characteristics. Alongside that, the Materials and Methods section shows different modeling environments and their possible integration into FLORIS. The Results and Discussion section analyzes the 20 kW wind turbine with previously acquired data from the National Renewable Energy Laboratory’s (NREL) AeroDyn v13 software. The study employs FLORIS to simulate steady-state non-linear wake interactions for the nine TAD shapes. These TAD shapes are evaluated across Region 2 operating conditions. The previous study used a genetic algorithm to obtain nine TAD shapes that maximized aerodynamic efficiency in Region 2. The Results and Discussion section compares these TAD shapes to the original blade design regarding the wake characteristics. The project aims to enhance the understanding of FLORIS for studying wake characteristics for morphing blades. Full article

This publication presents a novel approach to predicting the amount of electricity generated by wind power plants. The research focuses on data-driven models such as XGBoost, Liquid Time-constant Networks, and covers both the analysis of properties of individual forecasting models as well as aspects of their integration into a hybrid model. By analyzing real-world weather scenarios, the approach aims to identify the highest accuracy forecasting model for the short-term 24-h forecast of wind farm power output. A more accurate forecast allows for more efficient resource planning and better distribution of resources on the electricity grids, thus ensuring a greener approach to energy production. The study shows that the proposed Hybrid (XGBoost + LTC) model predicts wind power generation with an nMAE of 0.0856, representing an improvement over standalone XGBoost and LTC models, and outperforming classical approaches such as LSTM and statistical models like ARIMAX in terms of forecasting accuracy. 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

Automation in agricultural machinery, underpinned by the integration of advanced technologies, is revolutionizing sustainable farming practices. Key enabling technologies include multi-source positioning fusion (e.g., RTK-GNSS/LiDAR), intelligent perception systems utilizing multispectral imaging and deep learning algorithms, adaptive control through modular robotic systems and bio-inspired algorithms, and AI-driven data analytics for resource optimization. These technological advancements manifest in significant applications: autonomous field machinery achieving lateral navigation errors below 6 cm, UAVs enabling targeted agrochemical application, reducing pesticide usage by 40%, and smart greenhouses regulating microclimates with ±0.1 °C precision. Collectively, these innovations enhance productivity, optimize resource utilization (water, fertilizers, energy), and mitigate critical labor shortages. However, persistent challenges include technological heterogeneity across diverse agricultural environments, high implementation costs, limitations in adaptability to dynamic field conditions, and adoption barriers, particularly in developing regions. Future progress necessitates prioritizing the development of lightweight edge computing solutions, multi-energy complementary systems (integrating solar, wind, hydropower), distributed collaborative control frameworks, and AI-optimized swarm operations. To democratize these technologies globally, this review synthesizes the evolution of technology and interdisciplinary synergies, concluding with prioritized strategies for advancing agricultural intelligence to align with the Sustainable Development Goals (SDGs) for zero hunger and responsible production. Full article

The aim of the present work is to assess the wind and hydrogen production capacity of the Black Sea basin from a spatial point of view, by using reanalysis data that covers a 10-year interval (2015–2024). Based on the ERA5 data it was possible to highlight the general distribution of the wind resources at 100 m height, with more consistent resources being noticed in the region of the Azov Sea or in the north-western sector of the Black Sea, where average values of 8.3 m/s are expected. Taking into account that at this moment in the Black Sea area there are no operational offshore wind farms, several generators ranging from 3 to 15 MW were considered for assessment. In this case, from a single turbine, we can expect values in the range of 11.04 GWh (3 MW system) and 89 GWh (15 MW system), respectively. As a next step, the electricity generated from each wind turbine was used to highlight the hydrogen production of several electrolysers systems (or PEMs). The equivalent number of PEMs was identified, and in some cases it was noticed that some devices will not reach their full capacity, while for smaller PEMs a single 10 MW wind turbine could support the operation of almost four modules. Regarding hydrogen output, a maximum of 1560 tons/year can be expected from the PEMs connected to a 15 MW wind turbine. Full article

A diode-based rectifier (DR) is an attractive transmission technology for offshore wind farms, which reduces the volume of large bulk platforms. A novel parallel–series DC wind farm based on a distributed DR is proposed, which meets the requirements of high voltage and high power with an isolation capability from other units. The coupling mechanism between a modular multilevel converter (MMC) and a DR has been built, and the coordinate control strategy for the whole system has been proposed based on the MMC triple control targets with intermediate variables. Under the proposed control strategy, the system automatically operates at maximum power point tracking (MPPT). The feasibility of topology and the effectiveness of the control strategy are verified under start-up, power fluctuation, onshore alternating current (AC) fault, and direct current (DC) fault based on the power systems computer-aided design (PSCAD)/electromagnetic transients including direct current (EMTDC) simulation. Full article

Offshore wind power has a broad development prospect, but with the development of offshore wind farms to the deep sea, the traditional high-voltage AC transmission has been difficult to adapt to the offshore wind power transmission distance and transmission capacity needs. A flexible DC transmission system applying modular multilevel converter is a common scheme for offshore wind power, which has been put into use in actual projects, but it is still facing the problems of high cost of offshore converter station platforms and high loss of collector systems. In order to improve the economy and reliability of the medium- and long-distance offshore wind power delivery systems, this paper proposes a diode rectifier-based medium-frequency AC pooling soft-direct low-cost delivery system for medium- and long-distance offshore wind power. Firstly, the mid-frequency equivalent model of the diode converter is established, and the influence of topology and frequency enhancement on the parameters of the main circuit equipment is analysed; then, the distribution parameters and transmission capacity of the mid-frequency cable are calculated based on the finite element modelling of the marine cable, and the transmission losses of the mid-frequency AC pooling system are then calculated, including the collector losses, converter valve losses, and transformer losses, etc. Finally, an economic analysis is carried out based on a specific example, comparing with the Jiangsu Rudong offshore wind power transmission project, in order to verify the economy of the medium-frequency AC flexible and direct transmission system of the medium- and long-distance offshore wind power using diode rectifier technology. Full article

Curtailment of renewable energy is a growing issue in global energy infrastructure. A case study is carried out to investigate wind energy curtailment occurring in Scotland, which presents a growing issue, with an increasing amount of renewable energy going to waste. Complex relationships between grid constraints and wind farm operations must be explored to maximise utilisation of low-carbon electricity and to avoid the “turn-up” of non-renewable sources. Transmission zones and boundaries are considered and mapped, and a novel method of direct measurement of curtailment for transmission-level assets is proposed, with an intuitive, reproducible approach utilising balancing mechanism data. Curtailment data is examined and combined to find national trends, explore the viability of distributed hydrogen electrolysis, and compare curtailment and constraint directly across transmission boundaries. The weaknesses of the data collection methods are considered, solutions for a future iteration are proposed, and further uses of the outputs are discovered. Full article

The coastal region of Ceará, Brazil, is expected to host offshore wind farms aimed at producing green hydrogen (GH₂) through electrolysis. However, the viability and cost of these developments may be affected by the mechanical behaviour of the marine subsoil, which is largely composed of carbonate sands. These sediments are known for their complex and variable geotechnical properties, which can influence the foundation performance. This study investigates the geotechnical characteristics of carbonate sands in comparison with quartz sands to support the design of offshore wind turbine foundations. Field testing using the Ménard pressuremeter and laboratory analyses, including particle size distribution, microscopy, X-ray fluorescence, calcimetry, direct shear, and triaxial testing, were performed to determine the key strength and stiffness parameters. The results show substantial differences between carbonate and quartz sands, particularly in terms of the stiffness and friction angle, with notable variability even within the same material type. These findings highlight the need for site-specific characterisation in offshore foundation design. This study contributes data that can improve geotechnical risk assessments and assist in selecting appropriate foundation solutions under local conditions, supporting the planned offshore wind energy infrastructure essential to Ceará’s green hydrogen strategy. Full article

In recent years, the utilization of renewable energy sources has significantly increased due to their environmentally friendly nature and sustainability. Among these sources, wind energy plays a critical role, and accurately forecasting wind power with minimal error is essential for optimizing the efficiency and profitability of wind power plants. This study analyzes hourly wind speed data from 23 meteorological stations located in Türkiye’s Western Black Sea Region for the years 2020–2024, using the Weibull distribution to estimate annual energy production. Additionally, the same data were forecasted using the Long Short-Term Memory (LSTM) model. The predicted data were also assessed through Weibull distribution analysis to evaluate the energy potential of each station. A comparative analysis was then conducted between the Weibull distribution results of the measured and forecast datasets. Based on the annual energy production estimates derived from both datasets, the revenues, costs, and profits of 10 MW wind farms at each location were examined. The findings indicate that the highest revenues and unit electricity profits were observed at the Zonguldak South, Sinop İnceburun, and Bartın South stations. According to the LSTM-based forecasts for 2025, investment in wind energy projects is considered feasible at the Sinop İnceburun, Bartın South, Zonguldak South, İnebolu, Cide North, Gebze Köşkburnu, and Amasra stations. Full article

Rural villages function as relatively self-sustained production and living units with well-developed infrastructure. In this setting, investigating the transmission pathways of airborne biological particles, including pathogenic microorganisms, is pivotal for ensuring the health of residents. This study investigated the sources and dispersion of biogenic particulate matter in rural ambient air and factors influencing their behavior. Potential bioaerosol sources including livestock farming areas, composting sites, garbage dumps, and sewage treatment facilities were investigated using a calibrated portable bioaerosol detector to collect and analyze the dispersion of bioaerosol particles. The dispersal characteristics of Enterobacteriaceae were explored using an Andersen six-stage sampler. Livestock farming areas were the primary source of bioparticles. The distribution of the bioparticles varied significantly with environmental conditions. Key factors influencing their distribution included the dispersal capabilities due to wind speed and the processes of aggregation and coagulation of particles. The dispersal pathway of Enterobacteriaceae indicated that the inhabitants of residences near the dispersion source might be exposed to health risks from pathogenic bacteria present in bioparticles indoors. Understanding such characteristics and transmission patterns of bioparticles in rural environments provides a scientific basis for risk assessment and management strategies, with important implications for improving air-quality monitoring, public health policies, and environmental management in rural areas. Full article