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Permanent-magnet materials are essential for modern technologies, such as robotics, electric vehicles, and wind power generators. Over the last 100 years, the pursuit for stronger magnets mainly lies in enhancing their coercivity by alloying different elements with Fe and Co. Despite the continued endeavor, the coercivity of state-of-the-art NdFeB and SmCo magnets can only reach 20%–25% of their anisotropy field (Ha, the theoretical limit of the coercivity), known as the famous Brown's paradox. On the other hand, abnormally high coercivity was observed in the materials consisting of nanograins that reaches ∼60% of Ha at room temperature and goes beyond 100 kOe (7957 kA/m) at low temperatures. Moreover, the coercivity of nanostructured TbFe2 increases to ∼3500 Oe (278 kA/m), although it is a soft-magnetic material with a low value of only ∼30 Oe (2.4 kA/m) at room temperature for coarse-grained counterparts. However, high-energy-product bulk nanostructured magnets still remain elusive so far. In this short Perspective paper, we briefly discuss the prospects and challenges in fabricating high-performance bulk nanostructured magnets. The perspective for coercivity increase in emerging permanent magnets such as nanocomposite magnets and SmFe12-type magnets is also discussed. [ABSTRACT FROM AUTHOR]

Clean energy captured by offshore wind turbines has been widely used for supporting onshore activities. In the near future, facilities such as offshore wind turbines can also play an important role in the energy transition of offshore activities. Offshore wind energy can be employed for electrifying operations in offshore fish farms, which are traditionally supplied by diesel-engine barges/generators. Based on this motivation, this study focuses on the design of a shared mooring system between a semi-submersible offshore fish cage and a spar-type floating wind turbine. A numerical model of the proposed shared mooring system is implemented in a global response analysis software SIMA for performing fully coupled time-domain simulations. The configuration of the shared mooring line is determined using an engineering approach, which comprises Irvine's formulation, system eigenvalue analysis, and cost estimation. Moreover, relevant case studies by altering the environmental conditions are performed. Extreme operational conditions that may give large relative motions are investigated thoroughly. The dynamic performance of the integrated system is compared with that of individual structures. The global motion of the floating wind turbine and its mooring line's tension behavior are obviously influenced by the existence of the shared line. In general, the present work investigates the feasibility of a shared mooring system for these types of offshore structures and further gives insights into the engineering design procedure. [ABSTRACT FROM AUTHOR]

This study aims to peak power shaving and reduce the cost of energy by using improved energy management system (EMS) in a microgrid. This study has three scenarios. In the first scenario, the EV charging station and the wind turbine operate standalone. In the second scenario, a microgrid structure including an energy storage system (ESS) and an EMS is established. The threshold algorithm has been used for the energy management. In the last scenario, fuzzy logic-based EMS is used for energy management. The impact of EV charge station and wind turbine on the grid and the cost of energy are analyzed for three scenarios. The results show that the use of microgrid-connected ESS significantly reduces the cost of energy and creates a more balanced load for the grid side. It is also proved that the EMS affects the energy losses. It is revealed that when the proposed fuzzy logic-based EMS is used, the energy cost is reduced by 78% and the peak power is reduced by 70%. Thus, it is seen that the proposed EMS successfully reduces both the cost of energy and the negative impact of EVs on the grid by balancing grid side load. [ABSTRACT FROM AUTHOR]

The goal of this effort is to monitor and manage a hybrid stand-alone photovoltaic (PV) and wind energy system (WES) using the Internet of Things (IoT). The suggested hybrid system uses Incremental Conductance (INC) Maximum Power Point Tracking (MPPT) and Perturb and Observe (P&O)-based Sliding Mode Control (SMC) approaches. The cost-effective Arduino Uno board in Matlab is used to analyze the P&O and INC MPPT methods. Energy from renewable sources is stored in a battery, and the output voltage of each source is monitored using the blynk app and GSM 800c module. Simulation results validate the controller's operation, and the performance of both MPPT algorithms is compared. Experimental outcomes demonstrate the effectiveness of the IoT-based controller and its characteristic functionalities. Overall, this proposed hybrid PV and WES configuration offers advantages such as reduced human resources, cost-effectiveness, time savings, enhanced reliability, and improved data collection and control capabilities. [ABSTRACT FROM AUTHOR]

The measures needed to limit global warming pose a particular challenge to current fossil fuel exporters, who must not only decarbonise their local energy systems, but also compensate for the expected decline in fossil fuel revenues. One possibility is seen in the export of green hydrogen. Using Algeria as a case study, this paper analyses how different levels of ambition in hydrogen exports, energy efficiency and fuel switching affect the cost-optimal expansion of the power sector for a given overall emissions reduction path. Despite falling costs for photovoltaics and wind turbines, the results indicate that in countries with very low natural gas prices, such as Algeria, a fully renewable electricity system by 2050 is unlikely without appropriate policy measures. The expansion of renewable energy should therefore start early, given the high annual growth rates required, which will be reinforced by additional green hydrogen exports. In parallel, energy efficiency is a key factor as it directly mitigates CO2 emissions from fossil fuels and reduces domestic electricity demand, which could instead be used for hydrogen production. Integrating electrolysers into the power system could potentially help to reduce specific costs through load shifting. Overall, it seems advisable to analyse hydrogen exports together with local decarbonisation in order to better understand their interactions and to reduce emissions as efficiently as possible. These results and the methodology could be transferred to other countries that want to become green hydrogen exporters in the future and are therefore a useful addition for researchers and policy makers. • The required strong capacity expansion for PV and wind is challenging, but feasible. • "Energy efficiency first" benefits both the local energy transition and H2 exports. • Fully renewable power systems require a suitable political framework. • Higher hydrogen exports might help to reduce the specific power system cost. • Fossil capacity expansion must be stopped to avoid lock-ins and higher future costs. [ABSTRACT FROM AUTHOR]

The aim of this study is to assess the economic potentials of power and hydrogen generation via solar and wind energy resources at locations in Northern Germany and California, both of these (geographical) regions being pioneers in terms of a sustainable energy transition. Based on extensive research and electrolyzer manufacturer interviews, green hydrogen generation plants are modeled in a MATLAB® environment. All relevant inputs and outputs of the systems studied (wind power plants, photovoltaic power plants, and polymer electrolyte membrane electrolyzers, among others) are considered for different locations and framework conditions. In contrast to the existing literature, special attention is paid to the part-load behavior of electrolysis systems, which becomes particularly relevant in their interplay with volatile renewables. Metrics such as the levelized costs of electricity (LCOE) of the wind and photovoltaic power plants and the resulting levelized costs of hydrogen (LCOHy) are determined. With the help of the developed model, dimensioning of a whole system's components can be determined for different locations. A case study is conducted for a Northern German site and another one for a site in the Californian Mojave Desert. Both the optimal dimensioning of the renewable energy plants and the ratio of installed wind and photovoltaic power plant capacity are strongly location-dependent. In Northern Germany, for example, lower LCOHy can be generated at higher wind power plant capacity shares and, in the Mojave Desert, be produced at higher photovoltaic power plant capacity shares. In general, with larger plants and correspondingly larger polymer electrolyte membrane electrolyzers, LCOHy are lower due to scaling effects. Following this dimensioning recommendation, the LCOHy vary between 4.5 €/kg and 5.2 €/kg in the Northern German case study and between 4.6 US$/kg and 5.3 US$/kg in the Californian one. With costs of 1–2 €/kg, gray hydrogen is still economically superior. • Hydrogen production is modeled based on electrolyzers, wind and PV power plants. • Due to the volatile power supply, a focus is on part-load behavior of electrolyzers. • The optimal sizing of the system components is determined for different locations. • Two case studies are conducted, for Northern Germany and the Mojave Desert, CA. • Levelized costs of hydrogen are found to vary between 4.5 and 5.2 €/kg (4.6–5.3 US$/kg). [ABSTRACT FROM AUTHOR]

Offshore wind power has attracted significant attention due to its high potential, capability for large-scale farms, and high capacity factor. However, it faces high investment costs and issues with subsea power transmission. Conventional high-voltage AC (HVAC) methods are limited by charging current, while high-voltage DC (HVDC) methods suffer from the high cost of power conversion stations. The low-frequency AC (LFAC) method mitigates the charging current through low-frequency operation and can reduce power conversion station costs. This paper aims to identify the economically optimal frequency by comparing the investment costs of LFAC systems at various frequencies. The components of LFAC, including transformers, offshore platforms, and cables, exhibit frequency-dependent characteristics. Lower frequencies result in an increased size and volume of transformers, leading to higher investment costs for offshore platforms. In contrast, cable charging currents and losses are proportional to frequency, causing the total cost to reach a minimum at a specific frequency. To determine the optimal frequency, simulations of investment costs for varying capacities and distances were conducted. [ABSTRACT FROM AUTHOR]

Over the past few decades, many universities have turned to using microgrid systems because of their dependability, security, flexibility, and less reliance on the primary grid. Microgrids on campuses face challenges in the instability of power production due to meteorological conditions, as the output of renewable sources such as solar and wind power relies entirely on the weather and determining the optimal size of microgrids. Therefore, this paper comprehensively reviews the university campuses' microgrids. Some renewable energy sources, such as geothermal (GE), wind turbine (WT), and photovoltaic (PV), are compared in terms of installation costs, availability, weather conditions, efficiency, environmental impact, and maintenance. Furthermore, a description of microgrid systems and their components, including distributed generation (DG), energy storage system (ESS), and microgrid load, is presented. As a result, the most common optimization models for analyzing the performance of campus microgrids are discussed. Hybrid microgrid system configurations are introduced and compared to find the optimal configuration in terms of energy production and flexibility. Therefore, configuration A (Hybrid PV- grid-connected) is the most common configuration compared to the others due to its simplicity and free-charge operation. [ABSTRACT FROM AUTHOR]

We propose a method for selecting the optimal set of weather features for wind energy prediction. This problem is tackled by developing a wrapper approach that employs binary differential evolution to search for the best feature subset, and an ensemble of artificial neural networks to predict the energy production from a wind plant. The main novelties of the approach are the use of features provided by different weather forecast providers and the use of an ensemble composed of a reduced number of models for the wrapper search. Its effectiveness is verified using weather and energy production data collected from a 34 MW real wind plant. The model is built using the selected optimal subset of weather features and allows for (i) a 1% reduction in the mean absolute error compared with a model that considers all available features and a 4.4% reduction compared with the model currently employed by the plant owners, and (ii) a reduction in the number of selected features by 85% and 50%, respectively. Reducing the number of features boosts the prediction accuracy. The implication of this finding is significant as it allows plant owners to create profitable offers in the energy market and efficiently manage their power unit commitment, maintenance scheduling, and energy storage optimization. [ABSTRACT FROM AUTHOR]

Accurate wind power forecasting (WPF) is critical in optimizing grid operations and efficiently managing wind energy resources. Challenges arise from the inherent volatility and non-stationarity of wind data, particularly in short-to-medium-term WPF, which extends to longer forecast horizons. To address these challenges, this study introduces a novel model that integrates Improved Empirical Mode Decomposition (IEMD) with an enhanced Transformer called TransIEMD. TransIEMD begins by decomposing the wind speed into Intrinsic Mode Functions (IMFs) using IEMD, transforming the scalar wind speed into a vector form that enriches the input data to reveal hidden temporal dynamics. Each IMF is then processed with channel attention, embedding, and positional encoding to prepare inputs for an enhanced Transformer. The Direct Embedding Module (DEM) provides an alternative viewpoint on the input data. The distinctive perspectives of IEMD and DEM offer interaction through cross-attention within the encoder, significantly enhancing the ability to capture dynamic wind patterns. By combining cross-attention and self-attention within the encoder–decoder structure, TransIEMD demonstrates enhanced proficiency in detecting and leveraging long-range dependencies and dynamic wind patterns, improving the forecasting precision. Extensive evaluations on a publicly available dataset from the National Renewable Energy Laboratory (NREL) demonstrate that TransIEMD significantly improves the forecasting accuracy across multiple horizons of 4, 8, 16, and 24 h. Specifically, at the 24 h forecast horizon, TransIEMD achieves reductions in the normalized mean absolute error and root mean square error of 4.24% and 4.37%, respectively, compared to the traditional Transformer. These results confirm the efficacy of integrating IEMD with attention mechanisms to enhance the accuracy of WPF. [ABSTRACT FROM AUTHOR]

Integrating wind power plants into the electricity grid poses challenges due to the intermittent nature of wind energy generation. Energy storage systems (ESSs) have shown promise in mitigating the intermittent variability associated with wind power. This paper presents a distributionally robust optimization (DRO) model for sizing energy storage systems to dispatch wind power plants. The variable wind power is formulated as a moment-based ambiguity set. Dispatchability is described by the expected value of the insufficient power of wind power relative to the dispatch command, which is a sum of nonlinear functions and is taken as the optimal index. A deterministic semi-definite positive model is derived to solve the problem effectively. Numerical studies are conducted to demonstrate the effectiveness and advantages of the proposed method. [ABSTRACT FROM AUTHOR]

Recently, significant attention has been paid to the large-scale use of renewable energy through high-voltage direct current (HVDC) because of its economic feasibility. At the same time, the growing demand for electricity and the increasing penetration of renewable energy sources have prompted the electric power industry to explore methods to optimize the use of the existing grid infrastructure. Dynamic thermal line rating (DTLR) is a technique that allows transmission lines to operate at their maximum capacity, considering their real-time operating conditions. The majority of existing research on this topic has focused predominantly on employing DTLR in alternating current systems and exploring their applications. This study presents a novel approach by applying DTLR to HVDC transmission corridors, with the aim of maximizing the utilization of their capacity and facilitating increased integration of renewable energy. The performance of the proposed approach is evaluated by conducting a case study for an HVDC transmission line in Alberta, Canada. On average, the mean increase in ampacity above the static rating is 64% during winter and 34% during summer. This additional capacity can be used to integrate wind energy, replacing coal-fired generation. This leads to a significant reduction in greenhouse gas emissions, also quantified in this contribution. [ABSTRACT FROM AUTHOR]

The stability of offshore floating wind turbine foundation platforms is a fundamental requirement for the efficiency and safety of wind power generation systems. This paper proposes a novel small-diameter float-type semi-submersible platform to improve system stability. To evaluate the superior motion stability of the proposed floating platform, a comprehensive frequency–domain response analysis and experimental study were conducted in comparison with the OC4-DeepCwind platform developed by the National Renewable Energy Laboratory (NREL). The respective comparison of the frequency–domain response analysis and the experimental results demonstrated that the proposed floating wind turbine platform shows better hydrodynamic characteristics and resonance avoidance capability. This not only reduces the Response Amplitude Operators (RAOs), but also enhances the system stability, namely, effectively avoiding the regions of concentrated wave loading and low-frequency ranges. Furthermore, the proposed small-diameter semi-submersible platform has the potential to reduce manufacturing costs, providing valuable insights for the manufacturing of offshore floating wind turbine systems. [ABSTRACT FROM AUTHOR]

In the Inner Mongolia region, sand and dust storms are prevalent throughout the year, with sand erosion having a particularly significant impact on the performance of wind turbine blades. To enhance the performance stability of wind turbines and reduce operation and maintenance costs, this study delves into the specific impact of sand-laden wind erosion on the aerodynamic performance of scaled-down wooden wind turbine blades. The experiment conducts vehicle-mounted tests on scaled models of 1.5 MW wind turbine blades that have been eroded by wind-sand flows from different zones, analyzing the changes in aerodynamic performance of wind turbines caused by the erosion. The results indicate that with an increase in the angle of installation, both the overall power output and the wind energy utilization coefficient of the wind turbines show a declining trend. The power outputs of both the partially eroded group and the fully eroded group are unable to reach the rated power level of 100 W. Compared to the uneroded group, the leading-edge eroded group demonstrated higher power output and wind energy utilization coefficients across most wind speed ranges. This finding verifies the possibility that the drag-reducing effect caused by pits from leading-edge erosion has a positive impact on the aerodynamic performance of the blades. It also provides a new research perspective and strong evidence for the study of erosion effects on wind turbine blades and the optimization of their aerodynamic performance. [ABSTRACT FROM AUTHOR]

Type Iax supernovae (SNe Iax) are proposed to arise from deflagrations of Chandrasekhar mass white dwarfs (WDs). Previous deflagration simulations have achieved good agreement with the light curves and spectra of intermediate-luminosity and bright SNe Iax. However, the model light curves decline too quickly after peak, particularly in red optical and near-infrared (NIR) bands. Deflagration models with a variety of ignition configurations do not fully unbind the WD, leaving a remnant polluted with 56Ni. Emission from such a remnant may contribute to the luminosity of SNe Iax. Here we investigate the impact of adding a central energy source, assuming instantaneous powering by 56Ni decay in the remnant, in radiative transfer calculations of deflagration models. Including the remnant contribution improves agreement with the light curves of SNe Iax, particularly due to the slower post-maximum decline of the models. Spectroscopic agreement is also improved, with intermediate-luminosity and faint models showing greatest improvement. We adopt the full remnant 56Ni mass predicted for bright models, but good agreement with intermediate-luminosity and faint SNe Iax is only possible for remnant 56Ni masses significantly lower than those predicted. This may indicate that some of the 56Ni decay energy in the remnant does not contribute to the radiative luminosity but instead drives mass ejection, or that escape of energy from the remnant is significantly delayed. Future work should investigate the structure of remnants predicted by deflagration models and the potential roles of winds and delayed energy escape, as well as extend radiative transfer simulations to late times. [ABSTRACT FROM AUTHOR]

This article proposes a dynamic economic load dispatch (DELD) including wind power involvement process, which is capable to produce least possible generation cost of electrical systems by integrating large-scale wind power penetration. The primary component of hybridized power system comprises of conventional thermal generators and wind generators. To expedite the convergence flexibility and escape the clarifications from the confined optimal purpose, quasi-oppositional based learning (Q-OBL) is combined with HBA, which results in quasi oppositional chaotic honey badger algorithm (QOHBA). Moreover, chaotic strategy is integrated with QOHBA to boost the convergence speed as well as enhance the performance of the basic honey badger algorithm (HBA). The proposed QOCHBA is suggested to explain wind-based shared DELD problem to reduce thermal-wind generation energy cost. The superiority and expediency of the prospective technique is proved through its operation in two benchmark investigation systems. We use probability density functions (PDF) for wind energy (WE) forecasting to assessment dynamic working reserve necessities, based on the readjust of uncertainty in the prediction. To establish the efficacy of the recommended QOCHBA method in explaining non-linear, non-convex, and constrained DELD problem, the suggested structures are implemented on benchmark of 10-unit in addition to 15-unit test methods. [ABSTRACT FROM AUTHOR]

Large‐scale energy storage devices play pivotal roles in effectively harvesting and utilizing green renewable energies (such as solar and wind energy) with capricious nature. Biphasic self‐stratifying batteries (BSBs) have emerged as a promising alternative for grid energy storage owing to their membraneless architecture and innovative battery design philosophy, which holds promise for enhancing the overall performance of the energy storage system and reducing operation and maintenance costs. This minireview aims to provide a timely review of such emerging energy storage technology, including its fundamental design principles, existing categories, and prototype architectures. The challenges and opportunities of this undergoing research topic will also be systematically highlighted and discussed to provide guidance for the subsequent R&D of superior BSBs while conducive to bridging the gap for their future practical application. [ABSTRACT FROM AUTHOR]

Large‐scale energy storage devices play pivotal roles in effectively harvesting and utilizing green renewable energies (such as solar and wind energy) with capricious nature. Biphasic self‐stratifying batteries (BSBs) have emerged as a promising alternative for grid energy storage owing to their membraneless architecture and innovative battery design philosophy, which holds promise for enhancing the overall performance of the energy storage system and reducing operation and maintenance costs. This minireview aims to provide a timely review of such emerging energy storage technology, including its fundamental design principles, existing categories, and prototype architectures. The challenges and opportunities of this undergoing research topic will also be systematically highlighted and discussed to provide guidance for the subsequent R&D of superior BSBs while conducive to bridging the gap for their future practical application. [ABSTRACT FROM AUTHOR]

The objective of this study is to perform an analysis to determine the most suitable type of wind turbine that can be installed at a specific location for electricity generation, using annual measurements of wind characteristics and meteorological parameters. Wind potential analysis has shown that the analyzed location is suitable for the development of a wind farm. The analysis was carried out for six different types of wind turbines, with a power ranging from 1.5 to 3.0 MW and a hub height set at 80 m. Wind power potential was assessed using the Weibull analysis. The values of the scale coefficient c were determined, and a large monthly variation was observed, with values ranging from 1.92 to 8.36 m/s and an annual value of 4.95 m/s. Monthly values for the shape coefficient k varied between 0.86 and 1.53, with an annual value of 1.07. Additionally, the capacity factor of the turbines was determined, ranging from 17.75 to 22.22%. The Vestas turbine, with a nominal power of 2 MW and a capacity factor of 22.22%, proved to be the most efficient wind turbine for the specific conditions of the location. The quantity of greenhouse gas emissions that will be reduced if this type of turbine is implemented was also calculated, considering the average CO2 emission intensity factor (kg CO2/kWh) of the national electricity system. [ABSTRACT FROM AUTHOR]

In this study, a segmented layered bistable piezoelectric energy harvester (SBPEH) has been developed and thoroughly investigated for its wind energy harvesting capabilities. The SBPEH's performance was comprehensively evaluated through the creation of a detailed 3D finite element model using Abaqus and XFlow software, and further validated via rigorous wind tunnel experiments. The results of this research demonstrate the SBPEH's remarkable potential for wind energy harvesting. With a wide operational wind speed range, it achieved a maximum measured power output of 5.476 mW, coupled with an outstanding power density of 68.45 mW/cm3, both of which were realized at a wind speed of 13 m/s. It is worth noting that as the wind speed surpasses a specific threshold, the energy harvesting efficiency of the SBPEH experiences a decline, emphasizing the importance of identifying the optimal operational wind speed range for maximizing energy output. These findings offer valuable insights for the enhancement and application of SBPEHs, contributing to the advancement of renewable energy technologies and providing an eco‐friendly solution for power generation. Highlights: The study introduces a new segmented layered bistable piezoelectric energy harvester (SBPEH) design for wind energy harvesting, addressing the limitations of traditional harvesters.Performance is evaluated through 3D finite element modeling, ABAQUS, Xflow simulations, and wind tunnel experiments.The SBPEH operates efficiently across a broad wind speed range (6–14 m/s), showcasing optimal output power of 5.476 mW at 13 m/s. [ABSTRACT FROM AUTHOR]

This study aims to prepare an anti-washout grout using marine soft clay to stabilize the wind power pile foundations. The cement, hydroxypropyl-methyl cellulose ether (HPMC), and sodium silicate were chosen as the main materials to modify the marine soft clay, and the fluidity, bleeding rate, setting time, rheology, anti-washout characteristics, and compressive strength were investigated. The results showed that high cement content, HPMC, and sodium silicate decreased the workability of grout while enhancing the washout resistance. The addition of HPMC and sodium silicate would increase the yield stress and plastic viscosity of the grout. The sodium silicate content was the key factor influencing the early strength, while the cement content and HPMC affect the later strength. The microstructures including the morphology of C-S-H gels and the content of ettringite and calcium hydroxide significantly changed with the addition of sodium silicate, and Friedel's salt was observed through the microstructure analysis. This study can provide effective guidance for the utilization of marine soft clay, the preparation of anti-washout grout, and the control of pile foundation stability. [ABSTRACT FROM AUTHOR]

The mat foundation is a new type of jack-up wind power installation platform suitable for clay seabed. Because of the large size of the mat foundation, a large suction force must be overcome during the uplift process. In this study, the eccentric uplift of a mat foundation was experimentally and numerically investigated. The effects of the eccentric distance, eccentric position, and foundation shape on the suction force of the mat foundation were discussed. The results revealed that the breakout force of the mat foundation mainly comprised the foundation weight and suction force. Both the uplift position and eccentric distance affected the suction force; however, the effect of the uplift position was considerably smaller than that of the eccentric distance. The dimensionless suction force for the eccentric uplift tended to decrease linearly as the eccentric distance increased. When the dimensionless eccentric distance was 0.5, the suction force was 0.35-0.5 times that in the case of central uplift. [ABSTRACT FROM AUTHOR]

Compared to conventional energy sources, wind power is a clean energy source with high intermittence and uncertainty. As a system that converts wind energy into electricity, wind farms inevitably face severe reliability issues. In this paper, based on reliability theory, a new reliability modeling method for wind farms is proposed. Firstly, a belief reliability model for wind farms is constructed. Then, a power generation model based on two-directional rotor equivalent wind speed is established to represent the wind farm performance in the belief reliability model. Finally, several numerical studies are conducted to verify the power generation model under different wind speeds and directions, to demonstrate the belief reliability model with different levels of uncertainty, and to compare the belief reliability considering two-directional rotor equivalent wind speed with other methods. [ABSTRACT FROM AUTHOR]

Background: The Children's Assessing Imperial Valley Respiratory Health and the Environment (AIRE) study is a prospective cohort study of environmental influences on respiratory health in a rural, southeastern region of California (CA), which aims to longitudinally examine the contribution of a drying saline lake to adverse health impacts in children. Objectives: This cohort was established through a community–academic partnership with the goal of assessing the health effects of childhood exposures to wind‐blown particulate matter (PM) and inform public health action. We hypothesize that local PM sources are related to poorer children's respiratory health. Population: Elementary school children in Imperial Valley, CA. Design: Prospective cohort study. Methods: Between 2017 and 2019, we collected baseline information on 731 children, then follow‐up assessments yearly or twice‐yearly since 2019. Data have been collected on children's respiratory health, demographics, household characteristics, physical activity and lifestyle, via questionnaires completed by parents or primary caregivers. In‐person measurements, conducted since 2019, repeatedly assessed lung function, height, weight and blood pressure. Exposure to air pollutants has been assessed by multiple methods and individually assigned to participants using residential and school addresses. Health data will be linked to ambient and local sources of PM, during and preceding the study period to understand how spatiotemporal trends in these environmental exposures may relate to respiratory health. Preliminary Results: Analyses of respiratory symptoms indicate a high prevalence of allergies, bronchitic symptoms and wheezing. Asthma diagnosis was reported in 24% of children at enrolment, which exceeds both CA state and US national prevalence estimates for children. Conclusions: The Children's AIRE cohort, while focused on the health impacts of the drying Salton Sea and air quality in Imperial Valley, is poised to elucidate the growing threat of drying saline lakes and wind‐blown dust sources to respiratory health worldwide, as sources of wind‐blown dust emerge in our changing climate. [ABSTRACT FROM AUTHOR]

Similarities and differences between the features related to the productivity of onshore and offshore wind energy are developed with the aid of information theory techniques complemented by normal statistics. The data comes from the 13‐year period between 2010 and 2022 for the registered turbines in Germany (practically all). The information content of the generated power is dynamically measured by the mutability of the files storing the information. Monthly statistics show that in spite of the Summer months being relatively unproductive, the corresponding mutability shows the possibility of making use of short periods of intermediate productivity in the case of offshore plants. Favorable conditions for wind energy generation in Wintertime are reached for both onshore and offshore production, although the latter is favored. More homogeneity and stability in the data are still necessary to generalize algorithms, protocols, and criteria. This general study shows the success of the information theory techniques in describing wind ramps. Applications to specific zones could improve the efficiency and capacity factor of particular wind turbines depending on their exposure to wind streams or the blocking of nearby mountains and forestry. The information theory techniques presented here allow for a different and novel viewpoint to detect favorable and unfavorable wind energy periods. [ABSTRACT FROM AUTHOR]

In the context of the dual carbon goal strategy, the proportion of new energy generation has increased annually, large‐scale renewable energy integration has been achieved, and the intermittent and uncertain operating characteristics pose an enormous challenge to the complete and stable operation of an integrated energy system (IES), promoting the complexity of IES optimization models. To increase the stability and accuracy of the system and improve the operation efficiency of the system, a Gaussian mixture model is used to fit the probability distribution of wind power and the prediction errors of the photovoltaic output. In addition, the expected maximization method is used to solve a model with hidden variables, the results show that the expectation maximization algorithm can improve the fitting accuracy, reduce the error caused by the subjective setting of the initial value, and make the fitting accuracy of the wind‐solar power prediction error greater. Then, to solve the problem in which the low resolution of the day‐ahead scheduling time leads to large errors, day‐ahead, day‐to‐day multitime scale operation optimization model takes into account the comprehensive demand response according to the differences in wind and solar output and load forecasting accuracy. Finally, simulation validation is conducted in multiple scenarios, and a comparative analysis is performed for single and multitime scale comprehensive demand response scenarios. The simulation results show that compared with the optimization results of no demand response day, the optimization results of this model reduce the total cost by 23.21%, carbon emissions by 13.98%, purchased electricity by 13.87%, and purchased gas by 19.31%, effectively improving the use of gas turbines in the system. The multitime scale scheduling strategy increases the overall operating cost of the IES, modifies the scheduling results, and agrees with real operating scenarios. [ABSTRACT FROM AUTHOR]

The wind power generation system of a 5 MW horizontal axis wind turbine has a high wind energy conversion efficiency. The proportion of installed capacity in practical production applications is increasing year on year, so that the stability of its operation becomes a central factor in determining the productivity of the wind farm in question. This paper takes a 5 MW wind turbine as the research object and proposes a parameter‐adaptive robust model predictive control method to achieve self‐optimization of controller parameters through a Bayesian optimization approach. A robust model predictive control strategy, aiming to reduce the power fluctuation while maximizing the power output, is developed in this paper to enhance the dynamic economic performance under uncertain wind speed variation. A Bayesian algorithm is used in this paper to optimize the parameters of the controller. Moreover, wind speeds are simulated using TurbSim for different turbulence intensities of 5%, 10%, and 15% turbulence. Finally, the robust model predictive control toolbox in MATLAB is designed and simulated. The results show that the operational instability of the wind energy system is overcome. Meanwhile, the robustness of the wind energy system operation is improved compared to the traditional model predictive control approach. [ABSTRACT FROM AUTHOR]

This study conducted modal and vibration-characteristic tests using transient excitation and spectrum analysis methods to find the variation law of vibration characteristics of wind wheels with bifurcated tip structures as compared to wind wheels with unmodified tips when the coupling between a wind wheel and its tower is considered. Additionally, a finite element analysis was used to calculate the mechanical characteristics of wind power manoeuvres. The following are the major results of this study. As compared to the non-trailer state, the coupling action of the wind wheel and the tower reduced the static frequency of the unmodified wind wheel and the bifurcated blade tip structure wind wheel under the trailer state. The static frequency of the bifurcated blade tip structure wind wheel under the coupling action decreases more significantly. Compared with the single wind wheel which is not affected by coupling, the dynamic frequency of the whole machine decreases after being affected by coupling, and the bifurcated blade structure has less influence on the coupling effect. Compared with the non-trailer state, the dynamic frequency curves of the two blade tip structure wind wheels in the trailer state decrease, the speed range in the resonance area is shortened, and the corresponding speed in the resonance area is reduced. The results of this study provide data support and design reference for reliable design of wind turbines. [ABSTRACT FROM AUTHOR]

In the field of new environmental energy harvesting technology, wind energy and piezoelectric energy harvesting have become research hotspots. By utilizing wind energy into vibration energy, i.e., wind-induced vibration effect is a research hotspot in the field of environmental energy harvesting. This article comprehensively analyzes the research status and development trend of piezoelectric energy harvesters based on wind-induced vibration effects at home and abroad. It was found that the conversion process of wind energy to vibration energy is mainly based on flutter, galloping, and acoustic resonance. The conversion of vibration energy into electrical energy is mainly piezoelectric power generation, supplemented by magnetic induction power generation and friction power generation. For high power density and small volume application scenarios, resonant cavity type wind vibration energy collection is the main development trend. [ABSTRACT FROM AUTHOR]

We consider a sequential decision making process such as energy trading or electrical production scheduling whose outcome depends on the future realization of a random factor, such as a meteorological variable. Assuming that the decision maker has access to a dynamically updated probabilistic forecast (predictive distribution) of the random factor, we propose several stochastic models for the evolution of the probabilistic forecast of a given quantity, and show how these models may be calibrated from ensemble forecasts, commonly provided by weather centers. We then show how these stochastic models can be used to determine optimal decision making strategies to maximize a specific gain functional. Applications to wind energy trading are given. [ABSTRACT FROM AUTHOR]

Recent reports indicate that the development of electricity generation using wind turbines will continue to grow. Despite the long service life of wind turbine blades, their technological life comes to an end at a certain point. Currently, there is no industrial method for recycling them, and the proposed solutions need to consider a complete and comprehensive approach to this material. In many countries, these blades are stored in special landfills and await proposals for rational recycling. It has been proposed that this recyclable yet still troublesome raw material be used in building sheathing boards. Sheathing boards used in the construction industry have a relatively long lifecycle. Three types of polymer chips and two resins, i.e., PF and MUF, were used in the study. The boards' quality was assessed per the standards specified for particle boards. The resulting boards were characterized by strengths above 20 N/mm2 and an elastic modulus close to 4000 N/mm2. Slightly better results were obtained with the MUF resin. [ABSTRACT FROM AUTHOR]

The Weibull algorithm is one of the most accurate tools for forecasting and estimating wind energy potential. Two main parameters of the Weibull algorithm are the 'Weibull shape' and 'Weibull scale' factors. There are six different numerical methods to estimate the two Weibull parameters. These six methods are the empirical method of Justus (method 1), the empirical method of Lysen (method 2), the maximum likelihood method (method 3), the modified maximum likelihood method (method 4), the energy pattern factor method (method 5) and the graphical method (method 6). Many commercial wind energy software programs use the Weibull algorithm, and these six methods are used to calculate the potential wind energy at a given site. However, their accuracy is rarely discussed, particularly regarding wind data height. For this purpose, wind data measured for a long period (six years) at real sites are introduced. The wind data sites are categorized into three levels, i.e., low, medium, and high, based on wind data measurement height. The analysis shows that methods 1 and 2 are the most accurate methods among all six methods at low and medium heights. The number of errors increases with the height of these two methods. Methods 3 and 4 are the most suitable options for larger heights, as these scenarios have minimal error. The present study's findings can be used in various fields, e.g., wind energy forecasting and wind farm planning. [ABSTRACT FROM AUTHOR]

With the widespread use of new equipment such as distributed photovoltaics, distributed energy storage, electric vehicles, and distributed wind power, the control of low-voltage distribution networks (LVDNs) has become increasingly complex. Acquiring the most recent topological structure is essential for conducting accurate analysis and real-time control of LVDNs. The signal injection-based topology identification algorithm is favored for its speed and efficiency. This research introduces an innovative topology identification algorithm based on signal injection, specifically designed to address the challenges of incomplete and inaccurate identifications caused by the missing data in feature signal records (FSRs). Based on the correlations among FSRs at various devices, the algorithm introduces a dual-axis completion strategy—both vertical and horizontal—to effectively address missing data. Subsequently, an inclusion detection process is devised to process the completed FSRs, culminating in an accurate topology of LVDNs. Based on the study of actual LVDN data, the results indicate that the proposed algorithm markedly enhances the completeness and accuracy of topology identification. This advancement offers a robust solution tailored to accommodate the dynamic and swiftly changing topological configurations of LVDNs. [ABSTRACT FROM AUTHOR]

In the wake of the dual-carbon objective, the call for low-carbon attributes in integrated energy systems is ascending, with an amplified imperative to integrate wind and solar power efficiently. This study introduces an advanced low-carbon optimization framework for integrated energy systems, incorporating a sophisticated time-differentiated carbon accounting mechanism attentive to consumer emissions. A nuanced carbon accounting model is crafted to assess consumer emissions with greater accuracy. Predicated on these emissions, a refined low-carbon demand response model is articulated, factoring in the influence of carbon emission factors pertinent to electricity and heat procurement on user conduct. This model integrates the consideration of heat reclaimed from methanation processes, which in turn informs the carbon emission factors associated with purchased heat, and evaluates the subsequent optimization impact on the system. The proposed model is designed to curtail the system's operational expenditures and is operationalized via the CPLEX solver. Through the establishment of various scenarios for evaluative comparison, the model is corroborated to substantially augment the system's proficiency in assimilating wind and solar energy, markedly curtail carbon emissions, and facilitate a sustainable and cost-efficient operation of the integrated energy system. [ABSTRACT FROM AUTHOR]

The development of green technology in the world is progressing extremely rapidly. New possibilities for obtaining energy from renewable sources are constantly being sought and existing solutions are being improved. The multifaceted potential of the seas and oceans is an important aspect being taken into account in the development of the energy systems of a number of economies. One dimension of action in this area is the orientation towards offshore wind energy and the construction of offshore wind farms for this purpose. The purpose of this article is to analyse the importance of offshore wind farms in Poland's energy system and to assess public perception of the changes taking place in this dimension. The article is based on research and critical analysis of the available literature, legal regulations and industry reports, as well as on the results of our own surveys, the scientific findings of which were developed with the application of statistical instruments using PQstat software, ensuring the expected quality of results. The findings of the article indicate the significant importance of offshore wind farms in the creation of Poland's energy mix, with differing public attitudes towards their construction. Furthermore, the results of the research indicate a differentiated attitude of society towards the construction of offshore wind farms. The main motivation for majority support of the measure in question are economic reasons, which are connected with the expectation of a real price reduction per 1 kW of energy, as well as increased attractiveness of the region due to investments in this area. The main concern with the measure relates to environmental aspects, with concerns about the functioning of ecosystems in light of the construction and subsequent operation of wind farms. Negative public opinion is also signalled in relation to the potential risk of landscape change in a direction that is undesirable for the studied developed coastal tourist region in Poland. [ABSTRACT FROM AUTHOR]

Amid rising energy demands in rural areas, thorough resource assessments for initiatives such as wind power are crucial. This study involves a land resource assessment for wind power generation on the rustic Sibuyan Island in the Philippines, which is currently experiencing an electricity shortage. A comprehensive overview of the island's suitability for wind energy projects is performed via evaluation and analysis using geospatial data and multi-criteria decision making (MCDM). The research results indicate that 50.44% (220.68 km2) of the island's land area is categorized as 'poorly suitable' since it considers protected areas where developments are not allowed. Only 0.08% (0.35 km2) of the island can be classified as 'marginally suitable', while 9.15% (40.73 km2), 36.64% (176.39 km2), and 0.69% (3.05 km2) are labeled as 'moderately suitable', 'suitable', and 'highly suitable', respectively. This confirms the potential for wind energy exploration on the island. Delineating the suitability levels provides a foundational framework for stakeholders that enables them to identify optimal sites for wind power, sustain the island's resources, and contribute to the renewable energy landscape of this rural location. Overall, this study, underpinned by data analysis, offers invaluable insights for decision making in wind power development, with the presented framework adaptable to other areas of interest. [ABSTRACT FROM AUTHOR]

Reliable and accurate wind-speed forecasts significantly impact the efficiency of wind power utilization and the safety of power systems. In addressing the performance enhancement of transformer models in short-term wind-speed forecasting, a multi-step prediction model based on variational mode decomposition (VMD), temporal convolutional network (TCN), and a transformer is proposed. Initially, the Dung Beetle Optimizer (DBO) is utilized to optimize VMD for decomposing non-stationary wind-speed series data. Subsequently, the TCN is used to extract features from the input sequences. Finally, the processed data are fed into the transformer model for prediction. The effectiveness of this model is validated by comparison with six other prediction models across three datasets, demonstrating its superior accuracy in short-term wind-speed forecasting. Experimental findings from three distinct datasets reveal that the developed model achieves an average improvement of 52.1% for R2. To the best of our knowledge, this places our model at the leading edge of wind-speed prediction for 8 h and 12 h forecasts, demonstrating MSEs of 1.003 and 0.895, MAEs of 0.754 and 0.665, and RMSEs of 1.001 and 0.946, respectively. Therefore, this research offers significant contributions through a new framework and demonstrates the utility of the transformer in effectively predicting short-term wind speed. [ABSTRACT FROM AUTHOR]

Ice detection poses significant challenges in sectors such as renewable energy and aviation due to its adverse effects on aircraft performance and wind energy production. Ice buildup alters the surface characteristics of aircraft wings or wind turbine blades, inducing airflow separation and diminishing the aerodynamic properties of these structures. While various approaches have been proposed to address icing effects, including chemical solutions, pneumatic systems, and heating systems, these solutions are often costly and limited in scope. To enhance the cost-effectiveness of ice protection systems, reliable information about current icing conditions, particularly in the early stages, is crucial. Ultrasonic guided waves offer a promising solution for ice detection, enabling integration into critical structures and providing coverage over larger areas. However, existing techniques primarily focus on detecting thick ice layers, leaving a gap in early-stage detection. This paper proposes an approach based on high-order symmetric modes to detect thin ice formation with thicknesses up to a few hundred microns. The method involves measuring the group velocity of the S1 mode at different temperatures and correlating velocity changes with ice layer formation. Experimental verification of the proposed approach was conducted using a novel group velocity dispersion curve reconstruction method, allowing for the tracking of propagating modes in the structure. Copper samples without and with special superhydrophobic multiscale coatings designed to prevent ice formation were employed for the experiments. The results demonstrated successful detection of ice formation and enabled differentiation between the coated and uncoated cases. Therefore, the proposed approach can be effectively used for early-stage monitoring of ice growth and evaluating the performance of anti-icing coatings, offering promising advancements in ice detection and prevention for critical applications. [ABSTRACT FROM AUTHOR]

The problem of energy depletion has brought wind energy under consideration to replace oil- or chemical-based energy. However, the breakdown of wind turbines is a major concern. Accordingly, unsupervised learning was performed using the vibration signal of a wind power generator to achieve an outlier detection performance of 97%. We analyzed the vibration data through wavelet packet conversion and identified a specific frequency band that showed a large difference between the normal and abnormal data. To emphasize these specific frequency bands, high-pass filters were applied to maximize the difference. Subsequently, the dimensions of the data were reduced through principal component analysis, giving unique characteristics to the data preprocessing process. Normal data collected from a wind farm located in northern Sweden was first preprocessed and trained using a long short-term memory (LSTM) autoencoder to perform outlier detection. The LSTM Autoencoder is a model specialized for time-series data that learns the patterns of normal data and detects other data as outliers. Therefore, we propose a method for outlier detection through data preprocessing and unsupervised learning, utilizing the vibration signals from wind generators. This will facilitate the quick and accurate detection of wind power generator failures and provide alternatives to the problem of energy depletion. [ABSTRACT FROM AUTHOR]

Wind power systems (WPs) are complex non‐linear systems with varying parameters affected by environmental changes, including wind speed fluctuations. Extracting maximum power from WPs poses a significant challenge due to these factors. Direct power control (DPC) is a highly effective technique known for its simplicity and ease of implementation. However, it suffers from power ripples caused by the use of hysteresis comparators and switching tables that operate at variable frequencies. To address this issue, this paper presents the robust neural controller (NC) based on DPC, which replaces the switching tables. The Double‐Fed Induction Generator (DFIG) is the chosen generator for the studied WP system due to its advantageous features. The NC‐DPC effectively regulates the exchange of active and reactive powers between the DFIG and the system, maximizing power extraction from the WP system while reducing Total Harmonic Distortion and enhancing overall system quality. The effectiveness of the NC‐DPC is evaluated through MATLAB simulations and further supported by experimental data obtained using the Real‐Time Interface of the dSPACE‐DS1104 Controller card. [ABSTRACT FROM AUTHOR]

The study's main objective was to analyse daily wind speed data in Brazil collected on‐site from 1961 to 2020 and assess changes in two 30‐year climatological normals, denoted as P1 and P2. Data were sourced from 54 INMET‐managed meteorological stations, with missing data filled using the bootstrap expectation maximization algorithm. Imputation performance was evaluated, and results suggested that the 10% imputed level was most reliable, showing low RMSE values (below 0.5), SD values near 1, and r values exceeding 0.93. This method proved suitable for reconstructing historical wind speed data in Brazil. The analysis included the application of the Mann–Kendall test to detect linear trends and Sen's slope test to quantify trend intensity, both at a 5% significance level. Positive trends during P2 were observed in the Amazon basin and Brazil's semiarid region, while negative trends increased along the coast and in the Southeast region. Average wind speeds during P1 were higher than in P2. Comparing the two climatological normals revealed significant positive trends in some regions during P2, with an increase in average monthly variation of up to 0.85 m·s−1. The average slopes of significant trends were −1 and 1 m·s−1·decade−1 for all periods. The results indicated high wind magnitudes, with maximum power density of 778.08 W·m−2 for the NEB region, 170.90 W·m−2 for the SEB region and 401.67 W·m−2 for the SUB, making these regions promising for future wind power projects. These findings have the potential to enhance the optimization of renewable wind energy generation in Brazil. [ABSTRACT FROM AUTHOR]

Solar irrigation systems should become more practical and efficient as technology advances. Automation and AI-based technologies can optimize solar energy use for irrigation while reducing environmental impacts and costs. These innovations have the potential to make agriculture more environmentally friendly and sustainable. Solar irrigation system implementation can be hampered by a lack of technical expertise in installation, operation, and maintenance. It must be technically and economically feasible to be practical and continuous. Due to weather and solar irradiation, photovoltaic power generation is difficult for high-efficiency irrigation systems. As a result, more precise photovoltaic output calculations could improve solar power systems. Customers should benefit from increased power plant versatility and high-quality electricity. As a result, an artificial intelligence-powered automated irrigation power-generation system may improve the existing efficiency. To predict high-efficiency irrigation system power outputs, this study proposed a spatial and temporal attention block-based long-short-term memory (LSTM) model. Using MSE, RMSE, and MAE, the results have been compared to pre-existing ML and a simple LSTM network. Moreover, it has been found that our model outperformed cutting-edge methods. MAPE was improved by 6–7% by increasing Look Back (LB) and Look Forward (LF). Future goals include adapting the technology for wind power production and improving the proposed model to harness customer behavior to improve forecasting accuracy. [ABSTRACT FROM AUTHOR]

This paper proposes a novel evaluation method to address the challenge of evaluating insulation degradation in power transformer windings based on incomplete online Internet of Things (IoT) sensing data. The method leverages the Wasserstein Slim Generative Adversarial Imputation Network with Gradient Penalty algorithm to fill the irregularly missing power transformer IoT perception data, including voltage, current, temperature, and partial discharge. Subsequently, electrical, thermal, and mechanical performance degradation damage indicators for transformer winding insulation are constructed using the filled and complete IoT perception data. By applying the tensor fusion algorithm, the characteristics of these degradation damage indicators are fused, leading to the development of a comprehensive degradation evaluation index for the winding insulation. The evaluation of the winding insulation degradation state is achieved through the minimum quantization error method. The proposed method is validated using the real‐world transformer IoT perception data, and the experimental results demonstrate its ability to accurately assess the degree of winding insulation degradation, regardless of the presence of random or continuous irregularities in IoT sensing data. [ABSTRACT FROM AUTHOR]

Water electrolyzers will ensure energy security and power grid stability in energy systems based on fluctuating renewable energy sources such as wind power and photovoltaics. In this article, the effects of volatile energy generation on the operating conditions within polymer electrolyte membrane water electrolyzers are investigated. Based on experiments on a 100 kW el test station, it is concluded that isothermal and isobaric operation is unattainable and temperature deviations between −7 K and 3 K from their setpoint are observed. Pressure control on the hydrogen side is more challenging than on the oxygen side. Temperature decreases due to non-ideal temperature control overall decrease the mean efficiency during dynamic operation as compared to steady-state, with wind power reaching 73.70 % LHV and solar power yielding 71.90 % LHV. Finally, adaptions of electrolyzer designs and operation strategies are discussed, that could reduce negative effects of system dynamics on process efficiency. • System analysis is performed to identify inherent bottlenecks to dynamic operation. • Experimental investigation of characteristic load profiles on 100 kW el electrolyzer. • Evaluation of operation profiles reveals PV to be more challenging than wind power. • Dynamic operation appears to lower the overall hydrogen production efficiency. • Potential strategies to mitigate negative effects of system dynamics are discussed. [ABSTRACT FROM AUTHOR]

Solar wind heating is an outstanding issue that has been discussed for decades. Research on the connection between solar wind particle temperatures and turbulence may provide insight into this issue. Based on Parker Solar Probe observations, this paper investigates the properties of solar wind proton and electron temperatures in relation to turbulent magnetic energy, via the calculation of correlation coefficients (CCs) between particle temperatures and magnetic energy. The calculations are regulated by the spatial scale, plasma beta (β), and the angle between the solar wind velocity and background magnetic field, where the plasma beta is the ratio of plasma thermal to magnetic pressure. Results show that the correlation between proton temperature and magnetic energy is positive and can be strong with a CC exceeding 0.8. The strong correlation preferentially occurs at ion scales, with the wind velocity and background magnetic field quasi-perpendicular and over a wide beta range (β < 3.0). On the other hand, the correlation between electron temperature and magnetic energy is commonly negative, often with an intermediate or negligible CC, accordingly. The CC with an amplitude up to 0.8 can arise at larger scales with the wind velocity and background magnetic field quasi-(anti)parallel and in the low-beta case (β < 0.6). The implication of these findings on the physics of turbulent heating in the solar wind is discussed. [ABSTRACT FROM AUTHOR]