Search Results (2,608)

Amid accelerating global warming, surface albedo is a key indicator and regulator of how Earth’s surface reflects solar radiation, directly affecting the planetary radiation balance and climate. In this paper, we combined MODIS shortwave albedo (MCD43A3, 500 m), MODIS NDVI (MOD13A3, 1 km; NDVI = normalized difference vegetation index) and 1-km gridded meteorological data to analyze the spatiotemporal variations of surface albedo across China during 2001–2020 at a gridded scale. Temporal trends were quantified with the Theil–Sen slope and the Mann–Kendall test, and the seasonal contributions of NDVI, air temperature, and precipitation were assessed with a geographically and temporally weighted regression (GTWR) model. China’s mean annual shortwave albedo was 0.186 and showed a significant decline. Attribution indicates NDVI is the dominant driver (~48% of total change), followed by temperature (~27%) and precipitation (~25%). Seasonally, NDVI explains ~43.94–52.02% of the variation, ~26.81–28.07% of the temperature, and ~21.17–28.57% of the precipitation. Clear spatial patterns emerge. In high-latitude and high-elevation snow-dominated regions, albedo tends to decrease with warmer conditions and increase with greater precipitation. In much of eastern China, albedo is generally positively associated with temperature and negatively with precipitation. NDVI—reflecting vegetation greenness and canopy structure—captures the effects of vegetation greening, canopy densification, and land-cover change that reduce surface reflectivity by enhancing shortwave absorption. Temperature and precipitation affect albedo primarily by regulating vegetation growth. This study goes beyond correlation mapping by combining robust trend detection (Theil–Sen + MK) with GTWR to resolve seasonally varying, non-stationary controls on albedo at 1-km over 20 years. By explicitly separating snow-covered and snow-free conditions, we quantify how NDVI, temperature, and precipitation contributions shift across climate zones and seasons, providing a reproducible, national-scale attribution that can inform ecosystem restoration and land-surface radiative management. Full article

Wastewater treatment plants (WWTPs) are critical infrastructures that account for a significant share of global electricity, with aeration alone often responsible for over half of the total demand. Reducing the energy intensity of blower operation is, therefore, essential for sustainable and resilient WWTP management. This study presents a modeling and simulation framework for optimizing parallel blower operation in grit chamber aeration system. The framework integrates a modular structure with a blower model, a distribution network model, and an optimization layer that work together to capture equipment performance, simulate hydraulic interactions, and determine energy-optimal operating strategies under process and safety constraints. Two optimization strategies are compared: a heuristic grid search and a Safe Bayesian Optimization (SBO) method. Both algorithms enforce vendor surge and overheat limits, network pressure constraints, and process requirements. Simulation campaigns under representative demand scenarios show that both approaches achieve feasible operating points, while SBO consistently demonstrates higher energy savings and substantially faster runtime. Overall, the findings highlight the potential of data-driven optimization for achieving efficient and safe blower control, with reduced computation time making progress for real-time supervisory optimization in WWTPs. Full article

Nearshoring in Mexico is expanding rapidly, yet chronic volatility in the national power grid threatens the reliability and cost-competitiveness of relocated manufacturing lines. To inform strategic mitigation, this study presents a hybrid Fuzzy–CES decision-support framework that embeds the Constant-Elasticity-of-Substitution (CES) production function within a Mamdani Fuzzy-Inference Engine, implemented in both Type-1 and Interval Type-2 variants, to evaluate and optimize production adaptability in energy-constrained environments. Using sector-wide data from Mexico’s automotive industry, key input variables (energy reliability, capital intensity, and labor availability) are objectively quantified and normalized to reflect the realities of regional plant operations. The system linguistically classifies each facility’s production elasticity as low, moderate, or high, and generates actionable recommendations for resource allocation, such as targeted investments in renewable microgrids or workforce strategies. Implemented in MATLAB, simulation results confirm that, while high capital and labor inputs are essential, energy reliability remains the primary bottleneck limiting adaptability; only states with all three strong factors achieve maximum resilience. The Type-2 fuzzy approach demonstrates superior robustness to input uncertainty, enhancing managerial decision-making under volatile grid conditions. In addition, a case study regarding the automotive industry is presented to illustrate how the proposed framework is implemented. The same structure can be used to deploy it in another industry. This research offers a transparent, data-driven tool to inform both firm-level investment and regional policy, directly supporting Mexico’s efforts to sustain competitiveness and resilience in the global shift toward nearshoring. Full article

The rapid advancement of technologies such as artificial intelligence, big data, and cloud computing has driven continuous expansion of global data centers, resulting in increasingly severe energy consumption and carbon emission challenges. According to projections by the International Energy Agency (IEA), the global electricity demand of data centers is expected to double by 2030. The construction of green data centers has emerged as a critical pathway for achieving carbon neutrality goals and facilitating energy structure transition. This paper presents a systematic review of the role of waste heat recovery technologies in data centers for achieving low-carbon development. Categorized by aspects of waste heat recovery technologies, power production and district heating, it focuses on assessing the applicability of heat collection technologies, such as heat pumps, thermal energy storage and absorption cooling, in different scenarios. This study examines multiple electricity generation pathways, specifically the Organic Rankine Cycle (ORC), Kalina Cycle (KC), and thermoelectric generators (TEG), with comprehensive analysis of their technical performance and economic viability. The study also assesses the feasibility and environmental advantages of using data center waste heat for district heating. This application, supported by heat pumps and thermal energy storage, could serve both residential and industrial areas. The study shows that waste heat recovery technologies can not only significantly reduce the Power Usage Effectiveness (PUE) of data centers, but also deliver substantial economic returns and emission reduction potential. In the future, the integration of green computing power with renewable energy will emerge as the cornerstone of sustainable data center development. Through intelligent energy management systems, cascaded energy utilization and regional energy synergy, data centers are poised to transition from traditional “energy-intensive facilities” to proactive “clean energy collaborators” within the smart grid ecosystem. Full article

The transition from traditional power systems to smart grids demands advanced methods for detecting and classifying Power Quality Disturbances (PQDs)—variations in voltage, current, or frequency that disrupt device performance. The rise of renewable energy and nonlinear loads, such as LED lighting, has increased PQD occurrences. While deep learning models can effectively analyze data from grid sensors to detect PQD occurrences, privacy concerns often prevent operators from sharing raw data which is necessary to train the models. To address this, Federated Learning (FL) enables collaborative model training without exposing sensitive information. However, FL’s decentralized design introduces new risks, particularly data poisoning attacks, where malicious clients corrupt model updates to degrade the global model accuracy. Despite these risks, PQD classification under FL and its vulnerability to such attacks remain largely unexplored. In this work, we develop FL-based classifiers for PQD detection and compare their performance to traditionally trained, centralized models. As expected from prior FL research, we observed a slight drop in performance: the model’s accuracy decreased from 97% (centralized) to 96% (FL), while the false alarm rate increased from 0.19% to 4%. We also emulate five poisoning scenarios, including indiscriminate attacks aimed at degrading model accuracy and class-specific attacks intended to hide particular disturbance types. Our experimental results show that the attacks are very successful in reducing the accuracy of the classifier. Furthermore, we implement a detection mechanism designed to identify and isolate corrupted client updates, preventing them from influencing the global model. Experimental results reveal that our defense substantially curtails the performance degradation induced by poisoned updates, thereby preserving the robustness of the global model against adversarial influence. Full article

With the development of global maritime trade, ship emissions pose an increasing threat to the global atmospheric environment, especially in international navigation waters and important port areas, where their impact on coastal air quality and ecosystems is becoming increasingly significant. This study proposes a high-throughput gridding algorithm (H-Grid) based on spatial hashing to rapidly generate ship emission inventories, which overcomes the inefficiency of traditional methods caused by complex index building and maintenance. The H-Grid algorithm achieves a constant processing time per data point and possesses inherent parallelism. Based on the H-Grid algorithm, taking the Yellow Sea area between China and Republic of Korea as a case study, the emissions of atmospheric pollutants from ships in 2024 were calculated, and their spatiotemporal distribution characteristics were analyzed. In our empirical study, the algorithm’s computational efficiency for processing millions of AIS records was improved by over 10 times compared to traditional geometric calculations, and by more than 4 times when compared to mainstream database spatial queries. Our findings provide an efficient tool for large-scale maritime emission analysis, strongly supporting the green development of global shipping. Full article

The integration of electric railways with renewable energy sources is crucial for advancing sustainable transportation and building clean, low-carbon, and efficient energy systems in alignment with global sustainable development goals. However, the application of photovoltaic (PV) integration into railway traction power supply systems may exacerbate resonance phenomena between electric locomotives and the traction network. It is therefore necessary to study the impedance frequency characteristics (IFCs) of traction networks to minimize harmonic resonance overvoltage. In this paper, a harmonic impedance model of the sustainable traction power supply system (STPSS) is established, and an impedance analysis method is adopted to reveal the influence law of grid-connected PV inverters on the IFCs of STPSSs. Additionally, to improve the stability of STPSSs, a multi-parameter co-tuning method based on an improved particle swarm optimization algorithm is proposed. This method constructs a multi-objective function that includes resonance frequency, impedance magnitude, and filtering cost, thereby realizing the automatic optimization of the control parameters and filtering parameters of PV inverters. The results demonstrate a 56% reduction in the maximum impedance magnitude within the 0–5 kHz frequency range and a 10.8% cost reduction in the LCL filter implementation, confirming the effectiveness of the proposed optimization model. Results show that the maximum impedance magnitude of the optimized system in the frequency range of 0–5 kHz can be reduced by 56%. Moreover, the cost of LCL filters can be reduced by 10.8% through component value optimization. These findings validate the effectiveness of the proposed method. Full article

Under overmodulation conditions, the capacitor voltages of half-bridge and full-bridge submodules in hybrid modular multilevel converters (MMCs) may become unbalanced. This imbalance not only gives rise to overvoltage stress on submodule capacitors, jeopardizing equipment safety, but also degrades power quality and may even trigger operational instability. To address this issue, this paper proposes a minimum second harmonic circulating current injection method based on Bayesian Adaptive Direct Search (BADS) within the overall framework of model predictive control for MMCs. The method efficiently solves complex objective functions by alternately performing local Bayesian optimization and global grid search. Optimal second harmonic injection values under different modulation indices are obtained through offline computation and curve fitting. This approach achieves dynamic capacitor voltage balancing across a wide modulation range while minimizing operational losses caused by harmonic currents. Full article

Electric vehicles (EVs) are experiencing rapid global adoption driven by environmental concerns and fuel security. This article presents a new dual-source converter based on a hybrid modular multilevel configuration (DCHMMC) designed for optimal cell utilisation in EV battery systems. Contrary to conventional converters that can either charge or discharge the cells using a single source, thereby leaving several cells/modules (Ms) idle during each time step, the proposed converter enables the integration of two sources that can utilise the cells simultaneously. This dual source feature minimises idle cells/Ms, enhances energy efficiency, and supports flexible bidirectional power flow. The proposed converter operates in three distinct modes. The first involves dual-source charging for fast charging and improved vehicle availability. The second involves one source charging while the other discharges for dynamic operation. Finally, the last involves dual-source discharging for maximum power delivery and support vehicle-to-grid (V2G) operation. The simulation results demonstrated smooth multilevel sinusoidal output voltages (Vout_a and Vout_b), each with a peak of 350 V, generated simultaneously using 132 cells (six cells per M, 22 Ms). The total harmonic distortion (THD) values for Vout_a and Vout_b were 0.42% and 2.25%, respectively, confirming the high-quality performance. Furthermore, only 0–36 cells and 0–6 Ms were idle during operation, showing improved cell utilisation. Full article

This study explores the relationship between electricity consumption and financial development in 20 emerging market economies (EMEs) from 2000 to 2020. Employing the panel ARDL–PMG estimator and a two-step system GMM to address endogeneity, we identify a significant positive long-run cointegrating relationship, where electricity consumption fosters financial development. The estimated error correction term suggests a stable equilibrium, with deviations corrected at a 29% annual rate, in the short-run adjustment. These results underscore the significance of targeted energy investments in driving financial market growth. Policies promoting grid action, renewable integration, and innovative financing tools, such as green bonds, can align electricity expansion with financial stability objectives. By incorporating recent global disruptions and applying advanced econometric methods, this study provides updated empirical evidence and actionable policy insights on the electricity–finance nexus in EMEs. Full article

Fault-tolerant strategies have received increasing attention recently, as reliability requirements have become more stringent. This has drawn significant attention to multiphase machines, due to their inherent fault-tolerance capabilities. Although multiphase machines have been extensively studied as motors since the late 1960s, their use as generators is still in its infancy. Moreover, research on their fault-tolerant capabilities and impact on the power grid remains very limited. With the global expansion of the wind energy sector, the continuous increase in turbine capacities, and the shift in wind energy markets toward offshore wind farms, there is a growing need for studies that investigate the integration of multiphase machines with fault-tolerant strategies and that evaluate their performance and impact on the grid. Therefore, this paper aims to investigate a wind energy conversion system (WECS) based on a five-phase permanent magnet synchronous generator (PMSG) and to evaluate its performance under two fault scenarios: a single-phase open-circuit fault and a double-phase open-circuit fault. A fault-tolerant control strategy is applied in both cases to evaluate its effectiveness under varying wind speeds. The study is carried out using simulation tools developed in MATLAB/Simulink. Full article

With growing global demand for sustainable decarbonization, hydrogen energy systems have emerged as a key pillar in achieving carbon neutrality. This study assesses the greenhouse gas (GHG) reduction efficiency of Republic of Korea’s hydrogen ecosystem from a life-cycle perspective, focusing on production and utilization stages. Using empirical data—including the national hydrogen supply structure, fuel cell electric vehicle (FCEV) deployment, and hydrogen power generation records, the analysis compares hydrogen-based systems with conventional fossil fuel systems. Results show that current hydrogen production methods, mainly by-product and reforming-based hydrogen, emit an average of 6.31 kg CO₂-eq per kg H₂, providing modest GHG benefits over low-carbon fossil fuels but enabling up to a 77% reduction when replacing high-emission sources like anthracite. In the utilization phase, grey hydrogen-fueled stationary fuel cells emit more GHGs than the national grid. By contrast, FCEVs demonstrate a 58.2% GHG reduction compared to internal combustion vehicles, with regional variability. Importantly, this study omits the distribution phase (storage and transport) due to data heterogeneity and a lack of reliable datasets, which limits the comprehensiveness of the LCA. Future research should incorporate sensitivity or scenario-based analyses such as comparisons between pipeline transport and liquefied hydrogen transport to better capture distribution-phase impacts. The study concludes that the environmental benefit of hydrogen systems is highly dependent on production pathways, end-use sectors, and regional conditions. Strategic deployment of green hydrogen, regional optimization, and the explicit integration of distribution and storage in future assessments are essential to enhancing hydrogen’s contribution to national carbon neutrality goals. Full article

Dynamical downscaling is an essential approach for bridging the gap between coarse-resolution global climate models and regional details required for climate impact assessment and sustainable development planning. Thailand, a climate-sensitive country in Southeast Asia, requires robust climate information to support its adaptation and resilience strategies. This study evaluated the Weather Research and Forecasting (WRF) model in dynamically downscaling selected Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations over Thailand during the baseline period of 1976–2005. A two-way nested WRF configuration was employed, with domains covering Southeast Asia (36 km) and Thailand (12 km) in the model. Model outputs were compared with gridded observations from the Climatic Research Unit (CRU TS), and spatial variations were analyzed across six administrative regions in Thailand. The WRF successfully reproduces broad climatological patterns, including the precipitation contrast between mountainous and lowland areas and the north–south gradient of temperature. Seasonal cycles of rainfall and temperature are generally well represented, although systematic biases remain, specifically the overestimation of orographic rainfall and a cold bias in high-elevation regions. The 12 km WRF simulations demonstrated improved special and temporal agreement with the CRU TS dataset, showing a national-scale wet bias (MBE = +17.14 mm/month), especially during the summer monsoon (+65.22 mm/month). Temperature simulations exhibited seasonal derivations, with a warm bias in the pre-monsoon season and a cold bias during the cool season, resulting in annual cold biases in both maximum (−1.25 C) and minimum (−0.80 C) temperatures. Despite systematic biases, WRF-CMIP5 downscaled framework provides enhanced regional climate information and valuable insights to support national-to-local climate change adaptation, resilience planning, and sustainable development strategies in Thailand and the broader Southeast Asian region. Full article

Renewable wind energy systems widely employ doubly fed induction generators (DFIGs), where efficient converter control ensures grid-integrated power system stability and reliability. Conventional proportional–integral (PI) controller tuning methods often encounter challenges with nonlinear dynamics and parameter variations, resulting in reduced adaptability and efficiency. To address this, we present an owl search optimization (OSO)-based tuning strategy for PI controllers in DFIG back-to-back converters. Inspired by the hunting behavior of owls, OSO provides robust global search capabilities and resilience against premature convergence. The proposed method is evaluated in MATLAB/Simulink and benchmarked against particle swarm optimization (PSO), genetic algorithm (GA), and simulated annealing (SA) under step wind variations, turbulence, and grid disturbances. Simulation results demonstrate that OSO achieves superior performance, with 96.4% efficiency, reduced power losses (~40 kW), faster convergence (<400 ms), shorter settling time (<345 ms), and minimal oscillations (0.002). These findings establish OSO as a robust and efficient optimization approach for DFIG-based wind energy systems, delivering enhanced dynamic response and improved grid stability. Full article

The global steel industry emits 1.92 tons of CO₂ per ton of output and faces urgent pressure to decarbonize. In Pakistan, the sector accounts for 0.29 tons of CO₂ per ton of output, with limited mitigation frameworks in place. Green hydrogen (GH2)-based steelmaking offers a strategic pathway toward decarbonization. However, realizing its potential depends on access to renewable energy. Despite Pakistan’s substantial technical wind potential of 340 GW, grid limitations currently restrict wind power to only 4% of national electricity generation. This study explores GH₂ production through sector coupling and power wheeling, repurposing curtailed wind energy from Sindh to supply Karachi’s steel industry, and proposing a phased roadmap for GH, enabling fossil fuel substitution, industrial resilience, and alignment with global carbon-border regulations. Full article