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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 China's electricity spot market is undergoing rapid development. With the increasing integration of new energy sources, it has become imperative for new energy power plants to participate in the market actively. Simultaneously, new energy power facilities are equipped with energy storage to cope with the randomness of power generation. In order to address the optimization and control challenges of wind‐storage combined systems in the market environment, a charging and discharging strategy is proposed. Considering system parameters as variables, an online rolling‐horizon optimization model for wind‐storage systems is constructed to maximize revenue. Case studies demonstrate that this strategy effectively improves the overall revenue of wind power plants. After optimization, the return on investment (ROI) of the system increases by 2.29%. The selection of system parameters can significantly impact the outcomes. The optimal values of the capacity configuration and step size are 25 MWh and 45 min.

Abstract The rapid growth of offshore wind power has resulted in a mismatch between generation and demand because of its variability. To quantify the maximum wind power penetration of the transmission network, the dispatchable region is defined as the largest region in the uncertainty space. The security distance is defined as the minimum distance from the operating point to the boundaries of the dispatchable region. System operators can use security distance as a metric to assess the flexibility of the power system. This paper proposes a method to construct the dispatchable region for the AC/DC hybrid system with VSC‐HVDC by outer convex relaxation firstly. The second‐order cone relaxation is employed to reformulate non‐convex and non‐linear power flow equations. Next, a polyhedral approximation is adopted to obtain the convex hull of the dispatchable region. Subsequently, an efficient algorithm known as adaptive constraint generation (Ad‐CG) is introduced to calculate the boundaries of the dispatchable region. Furthermore, solving the Chebyshev centre problem determines the minimal security distance. The modified IEEE 5‐bus and 39‐bus system is used for validating effectiveness of the proposed method and evaluating the impact of the converter reactive power compensation capacity and generation dispatch on the dispatchable region.

Abstract Although there are many methods to improve the fault ride‐through (FRT) capability of doubly‐fed induction generator (DFIG) systems at present, each method has its shortcomings, especially the applicability under different voltage dips (VDs), so an improved system structure with a dynamic switching topology and a corresponding control scheme is proposed. Based on the mechanism analysis that the series impedance of the stator can effectively reduce the overcurrent on the rotor side, and considering the feasibility of the FRT scheme in engineering, the dynamic switching topology is designed. The selection of theoretical parameters in different cases is also analysed and designed. Simultaneously, to cooperate with the hardware measures, the control scheme of the rotor side converter (RSC) under different conditions is also improved. The RSC can use the control scheme of active flux attenuation to effectively and quickly reduce the overcurrent on the rotor side, and use reactive power support to accelerate the voltage recovery. The novelty of the FRT scheme is that the scheme can dynamically adjust the topology structure and control scheme under different voltage dips. Thus, its ride‐through performance during fault is better under different conditions. A simulation model of the improved system structure and control scheme is built on the MATLAB/Simulink platform. Through the comparison of simulation data, the validity and correctness of the proposed FRT scheme are verified.

Abstract In this study, a new short‐term wind power prediction model based on a temporal convolutional network (TCN) and the Informer model is proposed to solve the problem of low prediction accuracy caused by large wind speed fluctuations in short‐term prediction. First, an input feature selection method based on the maximum information coefficient is proposed after considering the problem of information interference caused by excessively large input features. A dynamic time planning method is used to select the optimal input step of historical power. Then, the combined forecasting model composed of TCN and the Informer is constructed in accordance with the numerical weather forecast and historical power data. Lastly, the pinball loss function is used to expand the prediction model into a quantile regression model, measure the effect of volatility, quantify the volatility range of prediction, and finally, obtain a deterministic prediction result. The actual measured data of wind farms in the Bohai Sea area are selected for analysis and calculation. The results show that the prediction model proposed in this study achieves better accuracy in deterministic prediction and interval prediction than the traditional model.

Abstract The new power system is characterized by high penetration of renewable energy sources and a high proportion of power electronics (namely, double‐high). The grid‐forming control is an effective method to improve the grid‐connected stability of wind turbine generators (WTGs) in the “double‐high” grid. The control method based on the DC‐link voltage can effectively realize the grid‐forming control for WTGs. However, there is a disadvantage that the DC‐link voltage cannot be maintained at the given value. To address this, the grid synchronization mechanism of DC‐link voltage is explored and the specific implementation of a novel DC‐link voltage synchronous control applicable to full‐scale power conversion WTGs is proposed. Then, the boundary of the inertial coefficient is probed through the state‐space method. And a compensation control is proposed to enlarge the inertial response capability based on the mechanism of damping characteristics. Finally, the PSCAD/EMTDC simulation and RTLAB hardware‐in‐loop experiment show that the synchronization frequency can accurately map the grid frequency changes in real‐time under the premise that the DC‐link voltage remains constant. In addition, the inertial coefficient can be increased by more than five times with the compensation strategy, which can enhance the support capability of the WTGs to the power grid.

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]

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]

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]

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]

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]

This study aims to conduct resource assessments at five offshore locations (L1–L5) in the Gulf of South Suez for possible development of wind farms. For this purpose, the hourly mean wind speed and direction data at 100 m above mean sea level (AMSL) and other meteorological data at near water surface from ERA5 (fifth-generation reanalysis for the global climate weather) is used. Statistical analyses, wind power, energy estimation, plant capacity factor, etc., have been performed using Windographer software. The cost of energy and greenhouse gas emissions are conducted based on international installation cost per kW and emissions factor per kWh from the literature. The data covers 43 years, between 1979 and 2021. The results show that at L1 to L5 locations, the long-term mean wind speed (WS) and wind power density (WPD) values are 8.678 m/s and 533 W/m2, 4.371 m/s and 78 W/m2, 8.917 m/s and 587 W/m2, 7.856 m/s and 406 W/m2, 8.072 m/s and 453 W/m2, and 7.876 m/s and 406 W/m2 at 100 m ASL, respectively. Based on the site-specific parameters (WS, WPD, WPC, MWVI, MWSI, AWVI, AWSI, minimum zero power, maximum rated power, PCF, COE, and maximum wind duration), values of 8.917 m/s, 587 W/m2, 1.125, 34.92, 0.358, 101.9, 6.26%, 5.76%, 54.04%, 0.007 USD/kWh, and 91%; L2 are identified as the most relevant area for wind farm development among the studied locations. At L2 site, the wind turbine WT2 can deliver annually 21.266 GWh of energy at respective plant capacity factors of 48.55%. [ABSTRACT FROM AUTHOR]