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Although the global market for wind energy is growing rapidly, leading-edge erosion is a critical issue hindering the development of wind power. The impact force of a droplet colliding with flat surfaces has been investigated in previous studies. However, the impact force exerted on curved surfaces, such as that experienced by eroded wind turbine blades, is not well understood. This study discusses the relationship between the impact force generated on a solid surface by a water droplet and the radius of curvature of the impacting surface. The impact force by a droplet was measured using a force sensor mounted on semi-cylindrical caps with different radii of curvature. The measurement results showed that the impact force decreased as the radius of curvature decreased. A computational fluid dynamics model solving incompressible flows showed that, unlike the case of a curved surface, the initial momentum of the droplet was mostly transferred to the flat surface. This resulted in a high impulse for an impact with a flat surface. The falling droplet was blocked by the surface, and the lateral jet was accelerated sideward. This acceleration was moderate for curved surfaces. When colliding with a flat surface, a higher impact force was generated owing to the wider area of the excited surface pressure compared with that of the curved surface. Finally, the relationship between the peak of the impact force and the surface curvature was derived, suggesting that the force peak is inversely proportional to the curvature. [ABSTRACT FROM AUTHOR]

With increasing environmental concerns and energy crisis, a variety of renewable energy sources (RES) are being increasingly utilised worldwide. However, the integration of RES such as wind power and photovoltaics in large-scale can lead to increased load fluctuations, which can undermine the overall environmental benefits and pose risks to the secure and stable operation of the power system. To mitigate this challenge, a two-stage electricity production scheduling is developed incorporating energy storage system (ESS) and dynamic emission modelling (DEM). In the first stage, a multi-objective mixed integer programming model schedules the production of RES, increasing penetration rate and system stability. In the second stage, a data-driven dynamic emission model is developed to optimise the load allocation of thermal power unit to reduce the carbon emissions. Furthermore, a flexible operating reserve strategy is proposed to handle the uncertainty resulting from the intermittent character of RES. Experimental results demonstrate that the proposed method effectively schedules the production of RES thereby alleviating the contradiction between high RES utilisation and stable system operation. Compared to the benchmark model, the proposed method can reduce the carbon emissions and total cost of the system by 20.34% and 10.65%, respectively. [ABSTRACT FROM AUTHOR]

Estimates of the land area occupied by wind energy differ by orders of magnitude due to data scarcity and inconsistent methodology. We developed a method that combines machine learning-based imagery analysis and geographic information systems and examined the land area of 318 wind farms (15,871 turbines) in the U.S. portion of the Western Interconnection. We found that prior land use and human modification in the project area are critical for land-use efficiency and land transformation of wind projects. Projects developed in areas with little human modification have a land-use efficiency of 63.8 ± 8.9 W/m2 (mean ±95% confidence interval) and a land transformation of 0.24 ± 0.07 m2/MWh, while values for projects in areas with high human modification are 447 ± 49.4 W/m2 and 0.05 ± 0.01 m2/MWh, respectively. We show that land resources for wind can be quantified consistently with our replicable method, a method that obviates >99% of the workload using machine learning. To quantify the peripheral impact of a turbine, buffered geometry can be used as a proxy for measuring land resources and metrics when a large enough impact radius is assumed (e.g., >4 times the rotor diameter). Our analysis provides a necessary first step toward regionalized impact assessment and improved comparisons of energy alternatives.

ABSTRACT Accurate ultra‐short‐term wind power prediction techniques are crucial for ensuring the efficient and safe operation of wind farms and power systems. Combined models based on data decomposition‐prediction techniques have shown excellent performance in ultra‐short‐term wind power forecasting. This study introduces a novel ultra‐short‐term multi‐step prediction model for wind power, which integrates the sparrow search algorithm (SSA), variational mode decomposition (VMD), gated recurrent unit (GRU), and support vector regression (SVR). An optimization variational mode decomposition technique is developed by adaptively determining VMD hyperparameters using SSA. The optimization VMD decomposes the original wind power sequence into sub‐modes, and the resulting sequence of decomposed sub‐modes calculates permutation entropy (PE) values. Sub‐modes with similar PE values are combined, reorganized, and categorized into high‐frequency and low‐frequency. High‐frequency sub‐modes data with high complexity and non‐stationarity are predicted by the GRU neural network. Low‐frequency sub‐modes data with low complexity and strong nonlinearity are predicted with SVR. The proposed model was evaluated against seven others using three error metrics: MAE, RMSE, and R2, along with their corresponding enhancement percentages. Experimental results indicate that the proposed model extracts detailed and trend information from the wind power series more effectively and stably than the comparison models. It also demonstrates superior multi‐step prediction performance, offering significant value for practical engineering applications. [ABSTRACT FROM AUTHOR]

The objective of this paper is to propose a novel dual-level multisource information fusion approach for handling open multicriteria decision-making (MCDM) issues related to accurately determining the weights of criteria while considering the relative importance of expert opinions, handling heterogeneous information, and selecting an appropriate MCDM ranking method. The proposed approach includes two levels. The first integrates the "analytic hierarchy process (AHP)—full consistency method (FUCOM)", which comprises two phases. In the first phase, criteria weights are assigned using AHP, and FUCOM is employed to correct inconsistencies. In the second phase, the weights are computed for decision-makers. Triangular fuzzy numbers are employed due to their ability to encapsulate all the utilized heterogeneous data types. At the second level of the approach, three well-known distance-based methods, including "technique for order preference by similarity to ideal solution", "visekriterijumska optimizacija i kompromisno resenje", and "multiattributive border approximation area comparison", are fused to process a homogeneous decision matrix and provide comprehensive and robust rankings for alternatives. To develop our novel approach, a renewable energy source for Pakistan's electricity generation is adopted as a case study. The decision matrix contains four alternatives (i.e., hydropower, biomass energy, solar energy, and wind energy) and six main evaluation criteria (i.e., economic, quality of energy, social, political, environmental, and technical), with several subevaluation criteria under each criterion. The findings of the proposed approach were as follows: A2 was in the first rank with a score of 1.7889, and A4 was in the last rank with a score of 0.1199. The rest of the alternatives were distributed between them. The paper's implications include the advancement of decision-making methods, enhancement of decision-making outcomes, and addressing heterogeneous information to highlight the relative importance of experts. [ABSTRACT FROM AUTHOR]

Background: Social acceptance of energy infrastructure projects affects public support for the energy transition and is essential for the transition's sustainability and success. Despite extensive research focusing on the social acceptance of renewable energy, and on the acceptance of onshore wind power in particular, energy system models have largely prioritized techno-economic aspects. This focus has created a gap between model results and decision-makers' needs. In this study, we offer recommendations for integrating disamenity costs and equality considerations—two critical social aspects related to onshore wind power—into energy system optimization. To achieve this, we use a spatially distributed model from a climate-neutral Germany and explore various implementations and trade-offs of these two social aspects. Results: We identified effective linear formulations for both disamenity costs and equality considerations as model extensions, notably outperforming quadratic alternatives, which exhibit longer solution times (+ 50–115%). Our findings reveal that the endogenous consideration of disamenity costs in the optimization approach can significantly reduce the human population's exposure to wind turbines, decreasing the average disamenity per turbine by 53%. Drawing on notions of welfare economics, we employ two different approaches for integrating equality into the optimization process, permitting the modulation of the degree of equality within spatial distributions in energy system models. The trade-offs of the two social aspects compared to the cost-optimal reference are moderate, resulting in a 2–3% increase in system costs. Conclusions: Disamenity costs emerge as a predominant factor in the distribution of onshore wind power in energy system optimization models. However, existing plans for onshore wind power distribution in Germany underscore equality as the driving factor. The inclusion of social aspects in energy system models facilitates the identification of socially superior wind turbine locations. Neglecting disamenity costs and equality considerations leads to an overestimation of the practical solution space for decision-makers and, consequently, the resulting energy system designs. [ABSTRACT FROM AUTHOR]

Integrating electrolyzers into power systems can significantly contribute to sustainable energy via the generation of green hydrogen while also enhancing frequency stability through effective regulation of the electrolyzers' operating power. This study gives a comprehensive analysis of large-scale electrolyzer plants when providing frequency support to power systems. First, the authors present a model predictive control (MPC)-based secondary frequency controller, combined with a droop controller as the primary frequency controller and a virtual inertia controller. Additionally, the study introduces a universal system frequency response (U-SFR) modeling approach that enables high accuracy, low computation burden, and reduced initial parameters as a testbed. Finally, an in-depth analysis is conducted, focusing on different technical aspects of large-scale electrolyzer plants when providing frequency support services. Case studies integrating PEM and Alkaline electrolyzers into the modified IEEE 39-bus system with over 50% wind power penetration are conducted. It is found that the proposed U-SFR model achieves high accuracy with lower computational time compared to detailed physical models. Additionally, model predictive controllers improve frequency quality more effectively than PID and PID-FLC methods. PEM electrolyzers are found to be more efficient in providing grid frequency support than alkaline electrolyzers due to their technical characteristics. Finally, smaller hydrogen tanks may frequently breach storage constraints, negatively impacting the system's frequency response capability. • Universal system frequency response model with high accuracy and low computation time. • Model Predictive Controller for large-scale PEM and Alkaline electrolyzer plants. • Modified IEEE-39 bus system with 50% wind energy penetration as a testbed. • Technical analysis of PEM and Alkaline electrolyzers on frequency regulation capability. [ABSTRACT FROM AUTHOR]

Despite the number of works on the techno-economics of offshore green hydrogen production, there is a lack of research on the design of floating platforms to concomitantly support hydrogen production facilities and wind power generation equipment. Indeed, previous studies on offshore decentralised configuration for hydrogen production, implicitly assume that a floating platform designed for wind power generation (FOWT) can be also suitable as a floating wind hydrogen system (FWHS). This work proposes a novel design for an offshore decentralised FWHS, and analyses the effects of the integration of the hydrogen facilities on the platform's dynamics and how this in turn affects the performances of the wind turbine and the hydrogen equipment. Our findings indicate that despite the reduction in platform's stability, the performance of the wind turbine is barely affected. Regarding the hydrogen system, our results aim at contributing to further assessment and design of this equipment for offshore conditions. • Proposes a conceptual design of a decentralised floating wind-hydrogen system (FWHS). • Addresses key design differences with dedicated floating wind turbines. • Hydrodynamic aspects of FWHS assessed with state-of-the-art numerical tools. • Provides key performance indicators for offshore hydrogen production equipment. [ABSTRACT FROM AUTHOR]

This study investigates strategies for enhancing the performance of dual-star induction generators in wind power systems by optimizing the full control algorithm. The control mechanisms involved include the PID (Proportional-Integral-Derivative) controller for speed regulation and the PI (Proportional-Integral) controller for flux, DC-link voltage, and grid connection control. The primary objective is to optimize the entire system by fine-tuning PID and PI controllers through the application of meta-heuristic algorithms, specifically Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO). These algorithms play a crucial role in estimating the optimal values of Kp, Ki, and Kd for the PID speed controller, as well as Kp and Ki for the PI controller used in the flux, DC-link voltage, and grid connection for wind energy conversion system based dual-star induction generator. This comprehensive optimization ensures accurate parameter tuning for optimal system performance. A comparative analysis of the optimization results has been conducted, focusing on the outcomes obtained with the GWO algorithm. The findings reveal a notable reduction in steady-state error, signifying improved stability, and an overall enhancement in the wind power system's performance. This study contributes valuable insights into the effective application of meta-heuristic algorithms for optimizing dual-star induction generators in wind power systems. [ABSTRACT FROM AUTHOR]

Marine current power is attracting more attention as a renewable energy option. Similar to wind power, marine current power often requires a maximum power point tracking (MPPT) method to optimize power extraction from the free‐flowing water. Research into MPPT methods for marine current power remains limited. Therefore, this paper presents a comprehensive investigation of MPPT methods for marine current power, building upon similar research in wind power. Three methods, namely the optimal tip speed ratio (OTSR), optimal torque (OT), and two variants of the perturb and observe (P&O) method, are explored. Using a simulation model developed for a specific marine current energy converter, where hydrodynamic calculations are coupled with electrical simulations, the study demonstrates that the OTSR method achieves MPPT with a comparably fast convergence time. After a change in water speed, the OTSR method achieves optimal operation within two turbine rotations. Additionally, the P&O methods are shown to achieve MPPT, albeit with a significantly longer convergence time. However, the P&O methods can be more convenient since no model of the system is required, and no water speed measurements are necessary. The proposed implementation of the OT method underperforms but positions the system close to the optimal operational point. [ABSTRACT FROM AUTHOR]

In electrical power system engineering, the economic load dispatch (ELD) problem is a critical issue for fuel cost minimization. This ELD problem is often characterized by non-convexity and subject to multiple constraints. These constraints include valve-point loading effects (VPLEs), generator limits, emissions, and wind power integration. In this study, both emission constraints and wind power are incorporated into the ELD problem formulation, with the influence of wind power quantified using the incomplete gamma function (IGF). This study proposes a novel metaheuristic algorithm, the modified moth flame optimization (MMFO), which improves the traditional moth flame optimization (MFO) algorithm through an innovative flame selection process and adaptive adjustment of the spiral length. MMFO is a population-based technique that leverages the intelligent behavior of flames to effectively search for the global optimum, making it particularly suited for solving the ELD problem. To demonstrate the efficacy of MMFO in addressing the ELD problem, the algorithm is applied to four well-known test systems. Results show that MMFO outperforms other methods in terms of solution quality, speed, minimum fuel cost, and convergence rate. Furthermore, statistical analysis validates the reliability, robustness, and consistency of the proposed optimizer, as evidenced by the consistently low fitness values across iterations. [ABSTRACT FROM AUTHOR]

Summary: As wind energy is sustainable, pollution‐free, easily available, and free of cost, it has become an efficient source of renewable energy for electricity generation. But, the problem with wind energy is that it varies with time, seasons, and location. This makes the Wind Energy Conversion System (WECS) unstable as it frequently needs to match the load demands. The balance in power generation by wind energy is essential since it has to be connected to various grids. So, this unbalanced energy production can affect the stability of the associated power grids as well. It also results in expensive regulatory measures, storage options, and load shedding. So, the stable operation of the WECS is highly essential to adapt it as a trustable source of electricity production. The stable operation of the WECS requires a robust and advanced system for control. Better control of the wind power extracting model is achieved by controlling the Maximum Power Point Tracking (MPPT) and blade pitch. So, an Adaptive MPPT and Blade Pitch Controller (BPC) for the WECS have been developed in this article, with the support of a hybrid optimization algorithm. In order to enhance the working principles of this controller, two effective algorithms such as Sand Cat Swarm Optimization (SCSO) and Galactic Swarm Optimization (GSO) are integrated and named Hybrid Sand Cat Galactic Swarm Optimization (HSC‐GSO). With the help of the recommended HSC‐GSO, the functionality of the controller is enhanced and also at the same time this algorithm helps to optimize the three gains in the Proportional Integral Differential (PID) controller of both MPPT and BPC, respectively. Moreover, with the support of the proposed HSC‐GSO the damping oscillations in the WECS output power and voltage are minimized. In the end, the numerical analysis is conducted for the presented system by comparing it with the traditional techniques. From the overall result analysis, the stability of the recommended adaptive WECS is 97, which is higher than the conventional algorithms such as DHOA, SCSO, GSO, and DA. Thus, it has been proved that the proposed HSC‐GSO algorithm for the parameters optimization in the PID controller of MPPT and the PID controller of BPC attains high robustness, increased steady‐state stability, and efficient transient response than the traditional techniques. [ABSTRACT FROM AUTHOR]

Circular supplier evaluation aims at selecting the most suitable suppliers with zero waste. Sustainable circular supplier selection also considers socio-economic and environmental factors in the decision process. This study proposes an integrated method for evaluating sustainable suppliers in intelligent circular supply chains using fuzzy inference and multi-criteria decision-making. In the first stage of the proposed method, supplier evaluation sub-criteria are identified and weighted from economic, social, circular, and Industry 4.0 perspectives using a fuzzy group best–worst method followed by scoring the suppliers on each criterion. In the second stage, the suppliers are ranked and selected according to an overall score determined by a fuzzy inference system. Finally, the applicability of the proposed method is demonstrated using data from a public–private partnership project at an offshore wind farm in Southeast Asia. [ABSTRACT FROM AUTHOR]

We consider a new framework where a continuous, though bounded, random variable has unobserved bounds that vary over time. In the context of univariate time series, we look at the bounds as parameters of the distribution of the bounded random variable. We introduce an extended log-likelihood estimation and design algorithms to track the bound through online maximum likelihood estimation. Since the resulting optimization problem is not convex, we make use of recent theoretical results on stochastic quasiconvex optimization, to eventually derive an Online Normalized Gradient Descent algorithm. We illustrate and discuss the workings of our approach based on both simulation studies and a real-world wind power forecasting problem. [ABSTRACT FROM AUTHOR]

Given the critical role of the clean energy market in the global economy and environmental sustainability, this paper investigates the impact of the U.S. Business Conditions Index (ADS) on the risk of segmented clean energy markets across different time scales and market conditions, as well as its spillover mechanisms. By using wavelet coherence and wavelet quantile analysis, we examine how the Aruoba–Diebold–Scotti (ADS) Business Conditions Index affects the risk levels of segmented clean energy indices under varying market conditions. To further understand this impact mechanism, we also employ the quantile Granger causality test to analyze the spillover effects of ADS on the clean energy market. The results show that the ADS index significantly influences the risk levels of segmented clean energy markets, with notable differences across various time scales and market conditions. The contributions of this study include: (1) segmenting the measurement of clean energy market risk into the Solar Index (SOLAR), Renewable Energy Index (RE), Biomass Index (BIO), Wind Energy Index (WIND), and Clean Energy Index (WILDER); (2) providing new evidence on the impact of the ADS Business Conditions Index on segmented clean energy market risk; and (3) offering new perspectives for different clean energy market participants to better navigate complex business environments and develop effective risk management strategies. [ABSTRACT FROM AUTHOR]

A unique oscillating wind-driven triboelectric nanogenerator (OWTENG) based on the sphere's vortex-induced vibration (VIV) behavior is proposed in this study, which can harvest wind energy across a multitude of horizontal directions. With the Euler-Lagrange method, the coupled governing equations of the OWTENG are established and subsequently validated by experimental tests. The vibrational properties and output performance of the OWTENG for varying wind speeds are analyzed, demonstrating its effectiveness in capturing wind energy across a broad range of wind speeds (from 2.20 m/s to 8.84 m/s), and the OWTENG achieves its peak output power of 106.3 µW at a wind speed of 5.72 m/s. Furthermore, the OWTENG maintains a steady output power across various wind directions within the speed range of 2.20 m/s to 7.63 m/s. Nevertheless, when the wind speed exceeds 7.63 m/s, the vibrational characteristics of the sphere shift based on the wind direction, leading to fluctuations in the OWTENG's output power. This research presents an innovative approach for designing vibrational triboelectric nanogenerators, offering valuable insights into harvesting wind energy from diverse directions and speeds. [ABSTRACT FROM AUTHOR]

Root piles exhibit favorable bearing characteristics, rendering them suitable for the foundation of offshore wind power facilities to withstand uplift loads. This study compared the uplift load bearing advantages of root piles to those of equal diameter piles in coral sand foundations. Additionally, it analyzed the effects of relative foundation compaction on the uplift load bearing characteristics of root piles. The tests involved comparing root piles and equal diameter piles in coral sand foundations with relative foundation compaction levels of 0.5, 0.6, 0.7, and 0.8. The results indicate that root piles possess a 62.5% higher pullout capacity than equal diameter piles, with only a 10.7% increase in uplift displacement. As relative compaction increases, the pullout ultimate capacity of root piles initially rises rapidly and then levels off, while the corresponding uplift displacement experiences a sharp decline followed by a relatively stable state. With the increase of relative compaction, the location of the maximum attenuation of axial force and the maximum lateral friction resistance in the root region started to move deeper only under larger loads. Both the implementation of root piles and an increase in relative compaction greatly enhance the pile foundation's ability to resist pullout forces and control deformation. [ABSTRACT FROM AUTHOR]

Renewable energy projects are increasingly being implemented at scale to meet global climate goals, often targeting lands on the margins of the state and affecting the livelihoods of Indigenous or marginalized populations. This article argues a need for project-developers, investors and rights advocates to consider the potentially significant 'gap' between their expectations that the rights of local populations will be settled at the start of a particular investment and the conflicts over rights, recognition and benefits that emerge during implementation that risk derailing investments. This is particularly relevant for renewable investments, as their claims to global and national benefits divert attention from their local impacts. Empirically, this is explored through the case of the Lake Turkana Wind Power project in Kenya. Based on interviews and literature review, the article examines the relations between the key stakeholders in this project by acknowledging their various 'ways of seeing' rights. Ruling elites often see rights as settled once they have been written into law and investors assume that rights have been dealt with at the project's start, but local populations typically begin exploring their rights following project-induced changes. The interaction of stakeholders with such diverse views has important implications for the practice of human rights in the context of renewable energy investments. [ABSTRACT FROM AUTHOR]

Golden eagles (Aquila chrysaetos) face many anthropogenic risks including illegal shooting, electrocution, collision with wind turbines and vehicles, and lead poisoning. Minimizing or offsetting eagle deaths resulting from human‐caused sources is often viewed as an important management objective. Despite understanding the leading anthropogenic sources of eagle fatalities, existing scientific research supports few practical solutions to mitigate these causes of death. We implemented a non‐lead ammunition distribution program in southeast Wyoming, USA, and evaluated its effectiveness as a compensatory mitigation action to offset incidental take (i.e., fatalities) of golden eagles at wind energy facilities. In 2020 and 2022, we distributed non‐lead ammunition to 699 hunters with big‐game tags specific to our >400,000‐ha study area. These hunters harvested 296 pronghorn (Antilocapra americana), 14 deer (Odocoileus spp.), and 33 elk (Cervus canadensis) in the study area, which accounted for 6.9% and 6.5% of the harvest in these hunt units in 2020 and 2022, respectively. We used road surveys in 2020 to estimate a density of 0.036 (95% CI = 0.018–0.058) golden eagles/km2 during the big game hunting season in our study area. Model output suggests that our non‐lead ammunition distribution program offset the fatality of 3.84 (95% CI = 1.06–23.72) eagles over the course of these 2 hunting seasons. Our work illustrates the potential usefulness of non‐lead ammunition distribution programs as an action to mitigate eagle fatalities caused by wind facilities or other anthropogenic causes of death. [ABSTRACT FROM AUTHOR]

Reversed D turbine is considered as an improved version of flapping vertical turbine. Its trajectory is a combination between a circular path and the typical vertical path. In this investigation, the circular path is modified by different non-circular trajectories in an attempt to increase the energy extraction of the turbine. A 2D computational analysis is executed using ANSYS Fluent.22. The motion of the airfoil is achieved by using a combined deformable mesh where both layering and sliding techniques were used. The effect of different non-circular trajectories was investigated to change the vortex shedding pattern. Results revealed that the elliptical curve is the most suitable path due to its ability to balance between the energy resulting from the vertical movement and the energy consumed by the horizontal movement. [ABSTRACT FROM AUTHOR]

Screw anchor foundations are widely used in wind power and power transmission projects owing to their fast construction speed and low construction difficulty. However, their widespread application is constrained by their low bearing capacity per anchor and poor corrosion resistance when buried underground. To address these issues, this study conducted field experiments on six ungrouted screw anchor (UGSA) foundations and six grouted screw anchor (GSA) foundations in mucky soil, obtained Q–w curves, depth-torque curves, and surface displacement‒load curves for UGSAs and GSAs. The study also analyzed the effects of grouting uplift and the presence of cracks in screw anchor foundations. The results show that the grouting uplift effect is optimal for small-diameter screw anchor rods, reaching 58.49%, and the depth-to-diameter ratio has a minor impact on the bearing capacity of GSAs but significantly affects UGSAs. Additionally, the torque coefficient of GSAs is greater than that of UGSAs and decreases with increasing anchor rod diameter. Furthermore, the failure modes of UGSAs and GSAs were observed based on surface crack observations. Finally, based on the UGSAs design formula, a design formula for GSAs is proposed. [ABSTRACT FROM AUTHOR]

With the fast-growing demand for wind energy, small wind turbines are becoming a viable and cost-effective source of clean and renewable electricity for residential, businesses, small industries, and remote applications. To harness the maximum power, wind turbines (WT) should be operated at full capacity; hence, it is crucial to design an effective maximum power point tracking (MPPT) control scheme. This paper presents an effective MPPT control algorithm based on fuzzy logic control (FLC) which tracks the slope of the mechanical power of the WT as a function of the rotational speed. The output of this MPPT generates the duty cycle for the DC–DC converter to allow the WT to extract the maximum power under a wide range of wind speed and loading conditions. The electrical generator used in the studied wind energy conversion system is the permanent magnet synchronous generator (PMSG). The proposed FLC-MPPT strategy is simple to implement and does not require any prior knowledge of the WT parameters. The proposed FLC-MPPT technique for WT is simulated using MATLAB/Simulink and validated experimentally on a laboratory setup using a dSPACE1104 board. In addition, a comparison between the proposed FLC-MPPT and other methods under significant wind speed fluctuations and variable load conditions is presented to demonstrate its effectiveness. The results show that the proposed MPPT has a fast convergence with a high tracking accuracy and exhibits low steady-state oscillations. [ABSTRACT FROM AUTHOR]

In many different places, wind energy has the potential to solve many challenges. Reducing dependency on distant power networks increases energy security, provides an alternative to energy independence, and advances environmental sustainability as a renewable energy source. Furthermore, the development of wind energy established in these regions improves regional economies and creates possibilities for employment in manufacturing, construction, maintenance, and operation. Landowners might receive more revenue from wind farms through land leasing agreements. Furthermore, wind energy diversifies the energy mix, reducing its vulnerability to fluctuations in energy prices and environmental degradation and decreasing energy expenditures for individuals and companies. Sustainable energy sources are crucial to solving communities' unique energy problems. This study aims to assess Saudi Arabia's wind energy potential. It completes assessments of wind resource availability, site suitability, wind turbine technology, energy output, environmental impact, and economic viability. Statistical techniques such as time series analysis, regression analysis, and probability distributions are utilized to understand and improve wind farm efficiency. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The supply of energy through fossil fuels and the increase in harmful environmental effects caused by it has caused the supply of energy through renewable energies to be given more attention. Since residential buildings account for a significant part of the world's energy consumption, supplying their needed energy through renewable energies can be a way to reduce non-renewable energy consumption and its harmful effects on the environment. This study suggests using a combination of solar and wind energy to provide electricity, heating, cooling and freshwater for a residential building in Russia. This system, which includes FPC solar collector, a wind turbine, an absorption chiller, a hydrogen storage system and a reverse osmosis desalination unit, is subjected to a comprehensive economic–environmental evaluation through emergy analysis. Performance evaluation of the proposed system to meet the hourly demand of a building for one year and in different weather conditions of four cities of Khabarovsk, Yakutsk, Yekaterinburg and St. Petersburg is investigated. The emergy analysis results illustrated that although the system has an unstable economic–environmental performance in the climatic conditions of Yakutsk city, it is stable with an environmental sustainability index (ESI) of more than 1 in the other three cities. The emergy sustainability index of the system in Khabarovsk city with a value of 2.37E + 00 showed that the system in this city has the best performance from view of economic–environmental point. [ABSTRACT FROM AUTHOR]

This work aims to explore the potential of available renewable energy resources such as solar, wind, biomass and ocean energy and select the best suitable renewable energy resource considering technical, economic, environmental, and social constraints as the main criteria. Five sub-criteria are considered for each main criterion, and these twenty sub-criteria are considered for ranking renewable energy resources using Analytical Hierarchy Process (AHP). The investment cost of the system having the relative weight of 57.94%. Hence it is recommended to choose the renewable energy source having higher value over the economic aspect. The results also showed that the efficiency of the system is the first prioritized criteria among the technical sub criteria with a relative weight of 38.86%. Similarly, land requirement for the implementation of renewable energy system is the first prioritized criteria (43.38%) among the five environmental sub criteria.The overall results from the AHP showed that economic criteria are the more important aspect with a relative weight of 45.04% followed by Technical, environmental and social criteria with relative weights of 22.5%, 14.16% and 12.62% respectively.For Prioritization of renewable energy resources, the study region divided into nine smaller blocks for investigation using AHP and the hierarchical results showed that solar energy is of highest priority in block 2 whereas wind energy potential is higher in blocks 5 and 9. Due to the dense population and widespread agricultural plantations, Bioenergy has average priority over the entire study region. [ABSTRACT FROM AUTHOR]

Renewable energy resources possess unique characteristics—intermittency and uncertainty—that pose challenges to electricity grid operations. We study these characteristics and find that uncertainty, represented by wind forecast error, has larger grid impacts than intermittency, represented by hourly wind generation changes. Compared to wind generation that was correctly forecast, uncertainty yields roughly double the effect on the marginal cost of operating the grid and greater adjustment costs as conventional generators must start up to balance the grid. While this finding is important given the persistence of wind generation uncertainty over our study period, reducing wind forecast error to the level of demand forecast error would lower costs by a modest half a million dollars per year. [ABSTRACT FROM AUTHOR]

We use restricted-access, geocoded data on the near-universe of workers in 23 US states to quantify the impact of wind energy development on local earnings and employment, by race, ethnicity, sex, and educational attainment. We find significant impacts that persist for several years beyond the project construction phase. Our estimates are larger than those from previous studies but still small relative to typical economic multipliers for fiscal spending or investment in other industries. We find the largest percentage increases for black workers and workers who either do not have a high school diploma or who have a college degree. We also find the economic gains for men to be much larger than those for women. Finally, we find estimates from data aggregated to the county level to be significantly lower than our worker-level estimates. We suggest a number of areas for further study building off the justice implications of our findings. [ABSTRACT FROM AUTHOR]

This paper introduces the Particle Swarm Optimization (PSO) algorithm to enhance the Latin Hypercube Sampling (LHS) process. The key objective is to mitigate the issues of lengthy computation times and low computational accuracy typically encountered when applying Monte Carlo Simulation (MCS) to LHS for probabilistic trend calculations. The PSO method optimizes sample distribution, enhances global search capabilities, and significantly boosts computational efficiency. To validate its effectiveness, the proposed method was applied to IEEE34 and IEEE-118 node systems containing wind power. The performance was then compared with Latin Hypercubic Important Sampling (LHIS), which integrates significant sampling with the Monte Carlo method. The comparison results indicate that the PSO-enhanced method significantly improves the uniformity and representativeness of the sampling. This enhancement leads to a reduction in data errors and an improvement in both computational accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

A robust scheduling optimization method for wind–fire storage system distribution based on the mixed carbon trading mechanism is proposed to improve the rationality of carbon emission quota allocation while reducing the instability of large-scale wind power access systems. A hybrid carbon trading mechanism that combines short-term and long-term carbon trading is constructed, and a fuzzy set based on Wasserstein measurement is proposed to address the uncertainty of wind power access. Moreover, a robust scheduling optimization method for wind–fire storage systems is formed. Results of the multi scenario comparative analysis of practical cases show that the proposed method can deal with the uncertainty of large-scale wind power access and can effectively reduce operating costs and carbon emissions. [ABSTRACT FROM AUTHOR]

As the large-scale development of wind farms (WFs) progresses, the connection of WFs to the regional power grid is evolving from the conventional receiving power grid to the sending power grid with a high proportion of wind power (WP). Due to the randomness of WP output, higher requirements are put forward for the voltage stability of each node of the regional power grid, and various reactive power compensation devices (RPCDs) need to be rationally configured to meet the stable operation requirements of the system. This paper proposes an optimal configuration method for multi-type RPCDs in regional power grids with a high proportion of WP. The RPCDs are located according to the proposed static voltage stability index (VSI) and dynamic VSI based on dynamic voltage drop area, and the optimal configuration model of RPCDs is constructed with the lowest construction cost as the objective function to determine the installed capacity of various RPCDs. Finally, the corresponding regional power grid model for intensive access to WFs is constructed on the simulation platform to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

To mitigate the impact of wind power volatility on power system scheduling, this paper adopts the wind-storage combined unit to improve the dispatchability of wind energy. And a three-level optimal scheduling and power allocation strategy is proposed for the system containing the wind-storage combined unit. The strategy takes smoothing power output as the main objectives. The first level is the wind-storage joint scheduling, and the second and third levels carry out the unit combination optimization of thermal power and the power allocation of wind power cluster (WPC), respectively, according to the scheduling power of WPC and ESS obtained from the first level. This can ensure the stability, economy and environmental friendliness of the whole power system. Based on the roles of peak shaving-valley filling and fluctuation smoothing of the energy storage system (ESS), this paper decides the charging and discharging intervals of ESS, so that the energy storage and wind power output can be further coordinated. Considering the prediction error and the output uncertainty of wind power, the planned scheduling output of wind farms (WFs) is first optimized on a long timescale, and then the rolling correction optimization of the scheduling output of WFs is carried out on a short timescale. Finally, the effectiveness of the proposed optimal scheduling and power allocation strategy is verified through case analysis. [ABSTRACT FROM AUTHOR]

Wind energy provides a sustainable solution to the ever-increasing demand for energy. Micro-wind turbines offer a promising solution for low-wind speed, decentralized power generation in urban and remote areas. Earlier researchers have explored the design, development, and performance analysis of a micro-wind turbine system tailored for small-scale renewable energy generation. Researchers have investigated various aspects such as aerodynamic considerations, structural integrity, efficiency optimization to ensure reliable and cost-effective operation, blade design, generator selection, and control strategies to enhance the overall performance of the system. The objective of this paper is to provide a comprehensive design and performance review of horizontal and vertical micro-wind turbines. The study begins with an overview of the current landscape of wind energy across the globe and India in particular, highlighting key challenges and opportunities. Numerical and experimental studies were used to validate the designs. Horizontal Axis Wind Turbines (HAWTs) with ducts or shrouds are suitable for microscale and low-speed applications. Researchers investigated the position and location of the turbines to enhance their performance in urban settings. Airflow and airfoil noise produce aerodynamic noise, which is the most significant disadvantage of wind turbines. The findings provide valuable insights for stakeholders interested in advancing micro-wind turbine technology. The highlighted research opportunities may be pursued further to improve the efficiency, reliability, and overall performance of micro-wind turbines. [ABSTRACT FROM AUTHOR]

In response to the global shift toward renewable energy, wind energy is a pivotal player in sustainability. Floating offshore wind turbines (FOWTs) emerge as a promising solution for expanding wind energy into economically challenging deep waters. Despite their potential, challenges persist, necessitating a thorough understanding of the complex coupling effects between wind turbine aerodynamics and hydrodynamics, particularly during the design phase. This study highlights this critical need and extends its scope to review the relevant existing literature. The paper provides insights into the design considerations and assessment methodologies crucial for both new structures and the evaluation of existing ones. Through modeling in the Ashes wind turbine software, the study reveals that platform surge motion significantly impacts rotor performance, causing rotor power and thrust fluctuations. By comparing various wind and sea states with baseline testing, the study underscores the necessity of coupling aerodynamic and hydrodynamic forces for accurate performance predictions. Besides, the paper offers actionable recommendations for designing and optimizing FOWTs, and sheds light on the challenges and opportunities within offshore wind energy, offering valuable guidance for engineers working toward sustainable energy solutions. [ABSTRACT FROM AUTHOR]

Power quality is improved by utilizing solar inverters in electrical grids and this study probes it. A combination of the solar power system with wind energy management using the multi-objective particle swarm optimization (CMOPSO) algorithm is employed in this system. Control calculations are based on Clark and reverse Clark transformations and facilitated by a phase-locked loop (PLL) circuit. STATCOM helps maintain voltage levels and mitigate power quality issues. Power quality (PQ) monitoring tracks voltage variations and noise. Conversely, the study addresses challenges in integrating renewables using the multi-objective multi-verse optimization (MOMVO) algorithm. MATLAB is used for control, monitoring, and analysis. Results show voltage distortion, but the proposed method achieves 92% higher efficiency, demonstrating its effectiveness. This validates the importance of photovoltaic (PV) technology for integrating renewable energy sources. [ABSTRACT FROM AUTHOR]

In this work, a computer Simulink model of a wind power plant has been developed, which uses a magnetic gearbox instead of a mechanical gearbox and also contains a synchronous permanent magnet generator. A separate Simulink model of a magnetic gearbox built on the basis of a modulated magnetic field in the air gap was developed, which allows to study the stability of its operation both in steady-state and transient modes. Calculations of various dynamic modes of the wind power plant’s operation were carried out, based on the developed model, such as the starting mode, an instantaneous increase in the wind speed acting on the wind turbine, and an increase in the load of the electric generator. According to the results of the calculations, it is shown that in transient modes, when short-term overloads occur, both rotors of the magnetic gearbox can fall out of synchronous motion for a certain period of time and then, depending on the parameters of the gearbox (as well as its other elements), the electromechanical system either reaches a certain operating steady-state mode or loses the ability to transfer mechanical power from the wind turbine to the generator. It has been shown that the use of a more powerful magnetic gearbox, with an increased value of the maximum magnetic torque, allows of a more overload-resistant operation of both: a gearbox and the wind plant as a whole. References 9, figures 10. [ABSTRACT FROM AUTHOR]

When planning a new wind farm, optimising the turbine layout to maximise power output is crucial. Traditional approaches based on analytical wake models often fail in complex terrain. In such areas, wake‐to‐wake interactions and terrain‐induced effects need to be taken into account via high‐fidelity optimisation. Building on our prior research on how best to maximise offshore wind farm power production by both micro‐siting (layout optimisation) and wake steering (yaw angle optimisation), this study focuses on the optimisation of two onshore wind turbines located around a 2D hill. The proposed approach is based on high‐fidelity simulations (LES) to account for the complexity of the flow and on Bayesian optimisation (BO) to systematically and efficiently find the optimal configuration for the two turbines given a set of parameters. The LES are performed with the high‐order finite‐difference wind farm simulator WInc3D, which has been modified for this study to deal with complex terrains and turbines with yaw and tilt. Following the validation of the immersed boundary method (IBM) and wall modelling used to model the complex terrain and of a modified actuator disc model (ADM) to account for tilted turbines, two BO campaigns are presented in this study, with different sets of parameters to optimise. The main flow features and some statistics for each optimum case are discussed using the flow fields with and without the optimised configuration of the upstream turbine to analyse its impact on the downstream turbine and on the overall power of the system. For the first optimisation, the design variables include the wind turbines' streamwise locations and their hub heights (4 parameters). This optimisation study benefits from an elevated upstream turbine hub, leading to a significant power increase in the downstream turbine, thanks to an acceleration of the flow in front of the hill. For the second optimisation, the design space is modified based on the data from the first optimisation, by replacing the turbine's hub height with the possibility of changing the tilt angle of the upstream turbine (4 parameters). It is found that considerable power gains can be obtained while maintaining a modest upstream turbine hub height, by introducing a positive tilt angle in the upstream turbine. Overall, it is found that by setting up the turbines in ways that exploit the speed‐up of the flow around the hill, both optimised layouts achieve considerable enhancements in power density over a reference configuration. [ABSTRACT FROM AUTHOR]

Floating offshore wind (FOW) is a renewable energy source that is set to play an essential role in addressing climate change and the need for sustainable development. However, due to the increasing threat of climate emergency, more wind turbines are required to be deployed in deep water locations, further offshore. This presents heightened challenges for accessing the turbines and performing maintenance, leading to increased costs. Naturally, methods to reduce operational expenditure (OpEx) are highly desirable. One method that shows potential for reducing OpEx of FOW is LIDAR‐assisted pitch control. This approach uses wind velocity measurements from a nacelle‐mounted LIDAR to enable feedforward control of floating offshore wind turbines (FOWTs) and can result in reductions to the variations of structural loads. Results obtained from a previous study of combined feedforward collective and individual pitch control (FFCPC + FFIPC) are translated to OpEx reductions via reduced component failure rates for future FOW developments, namely, in locations awarded in the recent ScotWind leasing round. The results indicate that LIDAR‐assisted pitch control may allow for an up to 5% reduction in OpEx, increasing to up to 11% with workability constraints included. The results varied across the three ScotWind sites considered, with sites furthest from shore reaping the greatest benefit from LIDAR‐assisted control. This work highlights the potential savings and reduction in the overall levelised cost of energy for future offshore wind turbine projects deliverable through the implementation of LIDAR‐assisted pitch control. [ABSTRACT FROM AUTHOR]

This paper presents a graph‐based dynamic yaw model to predict the dynamic response of the hub‐height velocities and the power of a wind farm to a change in yaw. The model builds on previous work where the turbines define the nodes of the graph and the edges represent the interactions between turbines. Advances associated with the dynamic yaw model include a novel analytical description of the deformation of wind turbine wakes under yaw to represent the velocity deficits and a more accurate representation of the interturbine travel time of wakes. The accuracy of the model is improved by coupling it with time‐ and space‐dependent estimates of the wind farm inflow based on real‐time data from the wind farm. The model is validated both statically and dynamically using large‐eddy simulations. An application of the model is presented that incorporates the model into an optimal control loop to control the farm power output. [ABSTRACT FROM AUTHOR]

Fast and accurate large‐eddy simulation (LES) of the atmospheric boundary layer plays a crucial role in advancing wind energy research. Long‐duration wind farm studies at turbine‐resolving scales have become increasingly important to understand the intricate interactions between large wind farms and the atmospheric boundary layer. However, the prohibitive computational cost of these turbulence‐ and turbine‐resolving simulations has precluded such modeling to be exercised on a regular basis. To that end, we implement and validate the generalized actuator disk (GAD) model in the computationally efficient, graphics processing unit (GPU)–resident, LES model FastEddy. We perform single‐turbine simulations under three atmospheric stabilities (neutral, unstable, and stable) and compare them against observations from the Scaled Wind Farm Technology (SWiFT) facility and other LES codes from the recent Wakebench turbine wake model benchmark. Our idealized LES results agree well with observed wake velocity deficit and downstream recovery across stability regimes. Turbine response in terms of rotational speed, generated power, torque, and thrust coefficient are well predicted across stability regimes and are consistent with the LES results from the benchmark. The FastEddy simulations are found to be at least two orders of magnitude more efficient than the traditional CPU‐based LES models, opening the door for realistic LES simulations of full wind plants as a viable standard practice. [ABSTRACT FROM AUTHOR]

Measuring boundary layer stratification, wind shear, and turbulence remains challenging for wind resource assessment. In particular, larger eddy scales have the greatest impact on turbine load fluctuations, and there are few in situ methods to observe them adequately. Satellite remote sensing using synthetic aperture radar (SAR) is an alternative approach. In this study, eddy‐related signatures in 704 high‐resolution images are related to stratification through a bulk Richardson number (Ri$$ Ri $$) measured by a buoy near Martha's Vineyard, the US epicenter of offshore wind. Variations in SAR‐observed atmospheric boundary layer eddies, or lack of them, correspond to specific Ri$$ Ri $$ regimes. Accounting for strong vertical wind shear, typically under stable stratification, is critical for energy production and turbine loads, and SAR directly identifies these conditions by the absence of energetic eddies. SAR also provides a regional climatology of atmospheric stratification for offshore wind assessment, complementing other observations, and with potential application worldwide. [ABSTRACT FROM AUTHOR]

Turbulent and gusty wind conditions can cause generator overspeed peaks to exceed a threshold that then lead to wind turbine shutdowns, which then decrease the energy production of the wind turbines. We derive so‐called "gust measures" that predict when generator overspeed peaks may occur. These gust measures are then used to develop advanced controllers to mitigate generator overspeed peaks so that wind turbines can operate more robustly in difficult wind conditions without exceeding generator overspeed thresholds that would lead to turbine shutdown events. The advanced controllers are demonstrated in nonlinear aeroelastic simulations using the open‐source wind turbine simulation tool OpenFAST. To increase the realism of the simulations, they are run using field‐replicated wind conditions and a wind turbine model based on data from an experimental field campaign on a downscaled demonstrator of a novel extreme‐scale, two‐bladed, downwind rotor design. [ABSTRACT FROM AUTHOR]

Wind power generation has become an indispensable part of the power supply side of the power grid. Due to the intermittent and uncertain characteristics of wind energy, short-term wind power prediction plays an important role in the stable operation of power system. By constructing the digital twin model, real-time and high-precision prediction of wind energy is realized. First, the genetic algorithm-support vector machine (GA-SVM) algorithm is used to build the model. Multidimensional sensors and meteorological stations of the virtual wind power generation system collected the meteorological data of the environment and updated the meteorological history database at the same time. Second, based on the collected meteorological data, the preliminary prediction results are obtained, and by searching in the historical database, the predicted value and the actual output value of wind turbines or wind farms under similar conditions are obtained. Finally, the prediction results of the GA-SVM are modified to obtain the predicted value of the digital twin model. The prediction method can greatly improve the accuracy of the short-term forecast of wind energy. [ABSTRACT FROM AUTHOR]