Search Results (3,320)

Groundwater is an important factor for the stability of the subway station pit constructed in the offshore area. To reflect the effects of groundwater drawdown on the stability of the station pit, this work uses a surface settlement formula based on Rayleigh distribution to construct a continuous deformation velocity field based on Terzaghi's mechanism, so as to derive a theoretical calculation method for the safety factor of the deep station pit anti-uplift considering the effect of seepage force. Taking the seepage force as an external load acting on the soil skeleton, a simplified calculation method is proposed to describe the variation in shear strength with depth. Substituting the external work rate induced by self-weight, surface surcharge, seepage force, and plastic shear energy into the energy equilibrium equation, an explicit expression of the safety factor of the station pit is obtained. According to the parameter study and engineering application analysis, the validity and applicability of the proposed procedure are discussed. The parameter study indicated that deep excavation pits are significantly affected by construction drawdown and seepage force; the presence of seepage, to some extent, reduces the anti-uplift stability of the station pit. The calculation method in this work helps to compensate for the shortcomings of existing methods and has a higher accuracy in predicting the safety and stability of station pits under seepage situations. Full article

The efficient and rational development of hydropower in the Lancang–Mekong River Basin can promote green energy transition, reduce carbon emissions, prevent and mitigate flood and drought disasters, and ensure the sustainable development of the entire basin. In this study, based on publicly available hydrometeorological observation data and satellite remote sensing monitoring data from 2001 to 2020, a machine learning model of the Lancang–Mekong Basin was developed to reconstruct the basin’s hydrological processes, and identify the occurrence patterns and influencing mechanisms of water-related hazards. The results show that, against the background of climate change, the Lancang–Mekong Basin is affected by the increasing frequency and intensity of extreme precipitation events. In particular, Rx1day, Rx5day, R10mm, and R95p (extreme precipitation indicators determined by the World Meteorological Organization’s Expert Group on Climate Change Monitoring and Extreme Climate Events) in the northwestern part of the Mekong River Basin show upward trends, with the average maximum daily rainfall increasing by 1.8 mm/year and the total extreme precipitation increasing by 18 mm/year on average. The risks of flood and drought disasters will continue to rise. The flood peak period is mainly concentrated in August and September, with the annual maximum flood peak ranging from 5600 to 8500 m³/s. The Stung Treng Station exhibits longer drought duration, greater severity, and higher peak intensity than the Chiang Saen and Pakse Stations. At the Pakse Station, climate change and hydropower development have altered the non-drought proportion by −12.50% and +15.90%, respectively. For the Chiang Saen Station, the fragmentation degree of the drought index time series under the baseline, naturalized, and hydropower development scenarios is 0.901, 1.16, and 0.775, respectively. These results indicate that hydropower development has effectively reduced the frequency of rapid drought–flood transitions within the basin, thereby alleviating pressure on drought management efforts. The regulatory role of the cascade reservoirs in the Lancang River can mitigate risks posed by climate change, weaken adverse effects, reduce flood peak flows, alleviate hydrological droughts in the dry season, and decrease flash drought–flood transitions in the basin. The research findings can enable basin managers to proactively address climate change, develop science-based technical pathways for hydropower dispatch, and formulate adaptive disaster prevention and mitigation strategies. Full article

Atmospheric aerosols significantly impact solar photovoltaic (PV) energy generation through their effects on surface solar radiation. This study quantifies the impact of PM_2.5 pollution on PV power output using observational data from 10 stations across Hebei Province, China (2018–2019). Our analysis reveals that elevated PM_2.5 concentrations substantially attenuate solar irradiance, resulting in PV power losses reaching up to a 48.2% reduction in PV power output during severe pollution episodes. To capture these complex aerosol–radiation–PV interactions, we developed and compared the following six machine learning models: Support Vector Regression, Random Forest, Decision Tree, K-Nearest Neighbors, AdaBoost, and Backpropagation Neural Network. The inclusion of PM_2.5 as a predictor variable systematically enhanced model performance across all algorithms. To further optimize prediction accuracy, we implemented a stacking ensemble framework that integrates multiple base learners through meta-learning. The optimal stacking configuration achieved superior performance (MAE = 0.479 MW, indicating an average prediction error of 479 kilowatts; R² = 0.967, reflecting that 96.7% of the variance in power output is explained by the model), demonstrating robust predictive capability under diverse atmospheric conditions. These findings underscore the importance of aerosol–radiation interactions in PV forecasting and provide crucial insights for grid management in pollution-affected regions. Full article

Accurate estimation of actual evapotranspiration (ET) is critical for understanding hydrothermal cycles and ecosystem functioning in arid regions, where water scarcity governs ecological resilience. To address persistent gaps in ET quantification, this study integrates multi-source remote sensing data, energy balance modeling, and ground-based validation that significantly enhances spatiotemporal ET accuracy in the vulnerable desert steppe ecosystems. The study utilized meteorological data from several national stations and Landsat-8 imagery to process monthly remote sensing images in 2019. The Surface Energy Balance System (SEBS) model, chosen for its ability to estimate ET over large areas, was applied to derive modeled daily ET values, which were validated by a large-weighted lysimeter. It was shown that ET varied seasonally, peaking in July at 6.40 mm/day, and reaching a minimum value in winter with 1.83 mm/day in December. ET was significantly higher in southern regions compared to central and northern areas. SEBS-derived ET showed strong agreement with lysimeter measurements, with a mean relative error of 4.30%, which also consistently outperformed MOD16A2 ET products in accuracy. This spatial heterogeneity was driven by greater vegetation coverage and enhanced precipitation in the southeast. The steppe ET showed a strong positive correlation with surface temperatures and vegetation density. Moreover, the precipitation gradients and land use were primary controllers of spatial ET patterns. The process-based SEBS frameworks demonstrate dual functionality as resource-optimized computational platforms while enabling multi-scale quantification of ET spatiotemporal heterogeneity; it was therefore a reliable tool for ecohydrological assessments in an arid steppe, providing critical insights for water resource management and drought monitoring. Full article

Amidst the background of accelerated global energy transition, the safety risk of lithium-ion battery energy storage systems, especially the fire hazard, has become a key bottleneck hindering their large-scale application, and there is an urgent need to build a systematic prevention and control program. This paper focuses on the fire characteristics and thermal runaway mechanism of lithium-ion battery energy storage power stations, analyzing the current situation of their risk prevention and control technology across the dimensions of monitoring and early warning technology, thermal management technology, and fire protection technology, and comparing and analyzing the characteristics of each technology from multiple angles. Building on this analysis, this paper summarizes the limitations of the existing technologies and puts forward prospective development paths, including the development of multi-parameter coupled monitoring and warning technology, integrated and intelligent thermal management technology, clean and efficient extinguishing agents, and dynamic fire suppression strategies, aiming to provide solid theoretical support and technical guidance for the precise risk prevention and control of lithium-ion battery storage power stations. Full article

The coastal sandy land in northeast Hainan Province is typical for this land type, also exhibiting strong sand activity. This study is based on wind speed, wind direction, and sediment transport data obtained at a field meteorological station using an omnidirectional sand accumulation instrument from 2020 to 2024, studying the coastal aeolian environment and sediment transport distribution characteristics in the region. Its findings provide a theoretical basis for comprehensively analyzing the evolution of coastal aeolian landforms and the evaluation and control of coastal aeolian hazards. The research results show the following: (1) The annual average threshold wind velocity for sand movement in the study area is 6.84 m/s, and the wind speed frequency (frequency of occurrence) is 51.54%, dominated by easterly (NE, ENE) and southerly (S, SSE) winds. (2) The drift potential (DP) refers to the potential amount of sediment transported within a certain time and spatial range, and the annual drift potential (DP) and resultant drift potential (RDP) of Mulan Bay from 2020 to 2024 were 550.82 VU and 326.88 VU, respectively, indicating a high-energy wind environment. The yearly directional wind variability index (RDP/DP) was 0.59, classified as a medium ratio and indicating blunt bimodal wind conditions. The yearly resultant drift direction (RDD) was 249.45°, corresponding to a WSW direction, indicating that the sand in Mulan Bay is generally transported in the southwest direction. (3) When the measured data extracted from the sand accumulation instrument in the study area from 2020 to 2024 were used for statistical analysis, the results showed that the total sediment transport rate (the annual sediment transport of the observation section) in the study area was 110.87 kg/m·a, with the maximum sediment transport rate in the NE direction being 29.26 kg/m·a. These results suggest that when sand fixation systems are constructed for relevant infrastructure in the region, the construction direction of protective forests and other engineering measures should be perpendicular to the net direction of sand transport. Full article

In order to achieve the goals of carbon neutrality, large-scale storage of renewable energy sources has been integrated into the power grid. Under these circumstances, the power grid faces the challenge of peak shaving. Therefore, this paper proposes a coordinated variable-power control strategy for multiple battery energy storage stations (BESSs), improving the performance of peak shaving. Firstly, the strategy involves constructing an optimization model incorporating load forecasting, capacity constraints, and security indices to design a coordination mechanism tracking the target load band with the equivalent power. Secondly, it establishes a quantitative evaluation system using metrics such as peak–valley difference and load standard deviation. Comparison based on typical daily cases shows that, compared with the constant power strategy, the coordinated variable-power control strategy has a more obvious and comprehensive improvement in overall peak-shaving effects. Furthermore, it employs a “dynamic dispatch of multiple BESS” mode, effectively mitigating the risks and flexibility issues associated with single BESSs. This strategy provides a reliable new approach for large-scale energy storage to participate in high-precision peaking. Full article

To address the limited flexibility of pumped storage power stations (PSPSs) under hierarchical clearing of energy and ancillary service markets, this study proposes a joint clearing mechanism for multi-level electricity markets. A bi-level optimization model based on the Stackelberg game is developed to characterize the strategic interaction between PSPSs and the market operator. Simulation results on the IEEE 30-bus system demonstrate that the proposed mechanism captures the dynamics of nodal supply and demand, as well as time-varying network congestion. It guides PSPSs to operate more flexibly and economically. Additionally, the mechanism increases PSPS profitability, reduces system costs, and improves frequency regulation performance. This game-theoretic framework offers quantitative decision support for PSPS participation in multi-level spot markets and provides insights for optimal storage deployment and market mechanism improvement. Full article

For further investigation of the properties of the muon component in the core regions of extensive air showers (EASs), a new underground hodoscopic set-up with a total sensitive area of 22 m² was built at the Tien Shan High Mountain Cosmic Ray Station. The hodoscope is based on a set of large-sized scintillation charged particle detectors with an output signal of analog type. The installation ensures a (5–8) GeV energy threshold of muon registration and a ∼${10}^4$ dynamic range for the measurement of the density of muon flux. A program facility was designed that uses modern machine learning techniques for automated search for the typical scintillation pulse pattern in an oscillogram of a noisy analog signal at the output of the hodoscope detector. The program provides a ∼99% detection probability of useful signals, with a relative share of false positives below 1%, and has a sufficient operation speed for real-time analysis of incoming data. Complete verification of the hardware and software tools was performed under realistic operation conditions, and the results obtained demonstrate the correctness of the proposed method and its practical applicability to the investigation of the muon flux in EASs. In the course of the installation testing, a preliminary physical result was obtained concerning the rise of the multiplicity of muon particles around an EAS core in dependence on the primary EAS energy. Full article

Understanding what people think about hydrogen energy and how this influences their acceptance of the associated technology is a critical area of research. The public’s willingness to adopt practical applications of hydrogen energy, such as hydrogen fuel-cell vehicles (HFCVs), is a key factor in their deployment. To analyse the direct and indirect effects of key attitudinal variables that could influence the intention to use HFCVs in Spain, an online questionnaire was administered to a representative sample of the Spanish population (N = 1000). A path analysis Structural Equation Model (SEM) was applied to determine the effect of different attitudinal variables. A high intention to adopt HFCVs in Spain was found (3.8 out of 5), assuming their wider availability in the future. The path analysis results indicated that general acceptance of hydrogen technology and perception of its benefits had the greatest effect on the public’s intention to adopt HFCVs. Regarding indirect effects, the role of trust in hydrogen technology was notable, having significant mediating effects not only through general acceptance of hydrogen energy and local acceptance of hydrogen refuelling stations (HRS), but also through positive and negative emotions and benefits perception. The findings will assist in focusing the future hydrogen communication strategies of both the government and the private (business) sector. Full article

With the large-scale integration of high-penetration distributed photovoltaic (DPV) into distribution networks, its output volatility and reverse power flow characteristics are prone to causing voltage violations, necessitating the accurate identification of weak nodes to enhance operational reliability. This paper investigates the definition, quantification criteria, and multi-indicator comprehensive determination methods for weak nodes in distribution networks. A multi-criteria assessment method integrating voltage deviation rate, sensitivity analysis, and power margin has been proposed. This method quantifies the node disturbance resistance and comprehensively evaluates the vulnerability of voltage stability. Simulation validation based on the IEEE 33-node system demonstrates that the proposed method can effectively identify the distribution patterns of weak nodes under different penetration levels (20~80%) and varying numbers of DPV access points (single-point to multi-point distributed access scenarios). The study reveals the impact of increased penetration and dispersed access locations on the migration characteristics of weak nodes. The research findings provide a theoretical basis for the planning of distribution networks with high-penetration DPV, offering valuable insights for optimizing the siting of volatile loads such as electric vehicle (EV) charging stations while considering both grid safety and the demand for distributed energy accommodation. Full article

The transition to green energy in the transport sector is becoming a priority in the context of global climate challenges and the European Green Deal. This paper investigates the development of alternative fuelling stations, particularly electric vehicle (EV) charging infrastructure and hydrogen stations, across EU countries with a focus on Poland. It combines a policy and technology overview with a quantitative scientific analysis, offering a multidimensional perspective on green infrastructure deployment. A Pearson correlation analysis reveals significant links between charging station density and both GDP per capita and the share of renewable energy. The study introduces an original Infrastructure Accessibility Index (IAI) to compare infrastructure availability across EU member states and models Poland’s EV charging station demand up to 2030 under multiple growth scenarios. Furthermore, the article provides a comprehensive overview of biofuels, including first-, second-, and third-generation technologies, and highlights recent advances in hydrogen and renewable electricity integration. Emphasis is placed on life cycle considerations, energy source sustainability, and economic implications. The findings support policy development toward zero-emission mobility and the decarbonisation of transport systems, offering recommendations for infrastructure expansion and energy diversification strategies. Full article

Digital technologies could facilitate green development by enhancing energy efficiency. However, existing research on coupling coordination between digital infrastructure and green development remains scarce. To fill this research gap, this study analyzes the spatio-temporal variations and barriers of coupling coordination. An evaluation index system is established and then the coupling relationship and the barrier factors between digital infrastructure and green development are analyzed. A provincial analysis is conducted by using data from China. The results in the study indicate (1) coupling coordination between digital infrastructure and green development exhibits a relatively low state, characterized by an overall upward trend; (2) noteworthy disparities are observed in the spatio-temporal pattern of the coupling coordination degree, reflecting the overall evolutionary trend from low to high coupling coordination, along with the characteristics of positive spatial correlation and high spatial concentration; and (3) obstacle factors are analyzed from the aspects of digital infrastructure and green development, emphasizing the construction of mobile phone base stations and investment in pollution control, among other aspects. This study contributes valuable insights for improvement paths for digital infrastructure and green development, offering recommendations for optimizing strategies to promote their coupled development. Full article

Accurately predicting how quickly people will adopt electric vehicles (EVs) is vital for planning charging stations, managing supply chains, and shaping climate policy. We present a forecasting model that uses three separate Long Short-Term Memory (LSTM) branches—one for past EV sales, one for infrastructure and policy signals, and one for economic trends. An attention mechanism first highlights the most important weeks in each branch, then decides which branch matters most at any point in time. Trained end-to-end on publicly available data, the model beats traditional statistical methods and newer deep learning baselines while remaining small enough to run efficiently. An ablation study shows that every branch and both attention steps improve accuracy, and that adding policy and economic data helps more than relying on EV history alone. Because the network is modular and its attention weights are easy to interpret, it can be extended to produce confidence intervals, include physical constraints, or forecast adoption of other clean-energy technologies. Full article

Railway systems occupy a predominant role in urban transport, providing efficient, high-capacity mobility. Progress in rail transport allows fast traveling, whilst environmental concerns and CO₂ emissions are on the rise. The integration of railway systems with renewable energy source (RES)-based stations presents a promising avenue to improve the sustainability, reliability, and efficiency of urban transport networks. A storage system is needed to both ensure a continuous power supply and meet train demand at the station. Batteries (BTs) offer high energy density, while supercapacitors (SCs) offer both a large number of charge and discharge cycles, and high-power density. This paper proposes a hybrid RES (photovoltaic and wind), combined with batteries and supercapacitors constituting the hybrid energy storage system (HESS). One major drawback of trains is the long charging time required in stations, so they have been fitted with SCs to allow them to charge up quickly. A new fuzzy energy management strategy (F-EMS) is proposed. This supervision strategy optimizes the power flow between renewable energy sources, HESS, and trains. DC bus voltage regulation is involved, maintaining BT and SC charging levels within acceptable ranges. The simulation results, carried out using MATLAB/Simulink, demonstrate the effectiveness of the suggested fuzzy energy management strategy for various production conditions and train demand. Full article