Search Results (2,128)

As the power ratings of electric vehicle (EV) charging piles continue to increase, direct grid-powered charging imposes significant load impacts on the power grid. Issues including line overload, voltage fluctuation, and frequency deviation induced by abrupt load surges become particularly prominent during peak load periods. Accordingly, energy storage batteries are required as buffer units for high-power charging piles. Energy storage batteries absorb photovoltaic (PV) generation and off-peak grid electricity, and supply power for EV charging during peak load periods. This configuration not only mitigates grid load impacts but also generates economic benefits by leveraging the tariff difference between peak and off-peak load periods. To fully accommodate PV generation and maximize economic returns, the capacity of energy storage batteries should be optimally sized based on seasonal daily PV output and EV charging demand. Furthermore, charge–discharge scheduling strategies are formulated in accordance with time-of-use (TOU) tariffs, as well as the power balance between PV generation and EV charging load. This paper proposes a novel PV storage–charging system. By reconfiguring contactor contact combinations, the system implements optimized charge–discharge control to alleviate grid load disturbances and maximize economic benefits, while reducing the number of power converters. Compared with mainstream commercial counterparts, the proposed system cuts manufacturing costs by more than 31.25% and improves EV charging efficiency by more than 1.23%. Full article

Propolis yield and composition fluctuate with environmental factors and hive management. In a two-year longitudinal study (2022–2024), we evaluated the seasonal dynamics of propolis production across two apiaries in the Mexican Altiplano during the dry and rainy seasons. We conducted monthly harvesting using three collector types: black polyurethane collectors (G1), beige polyurethane collectors (G2), and flexible polyethylene mosquito mesh (G3), distributed among 24 standardized Apis mellifera colonies (n = 8 per treatment; 576 repeated monthly harvests). Palynological analysis identified 39 species belonging to 38 genera and 23 families, with a clear predominance of herbaceous and ruderal forms (Brassica rapa as the main pollen type). The overall mean yield was 9.02 ± 0.58 g/colony/month. Linear mixed-effects models (LMMs) revealed significant temporal variation by month (F₁₁,₂₃₁ = 28.45, p < 0.001) and season (F₁,₂₁ = 14.12, p = 0.001). Propolis biomass showed a clear bimodal pattern, peaking in April (20.25 g/colony) during vegetative bud burst, dropping in July (6.25 g/colony), and rebounding in September–October (9.77–9.92 g/colony), coinciding with leaf senescence and pre-winter nest sealing. Monthly production showed a moderate negative linear correlation with cumulative precipitation (r = −0.422, p = 0.040, R² = 0.178). Conversely, collector design did not result in statistically significant differences in harvested biomass (G1: 6.88 g, G2: 8.98 g, G3: 11.21 g; F₂,₂₁ = 2.14, p = 0.142). These findings demonstrate that propolis collection in the Mexican Altiplano is primarily synchronized with regional plant phenology and constrained by precipitation regimes rather than collector design. Consequently, the practical selection of harvesting devices should be guided by operational handling efficiency, economic cost, and raw product cleanliness, rather than expectations of higher quantitative yield. Full article

Under the energy transition, renewable intermittency poses a challenge to supply–demand matching. Buildings possess thermal inertia that can serve as virtual storage. This study proposed a capacity-operation co-optimization model for a heating-season-integrated energy system, integrating building thermal inertia with thermal energy storage (TES). The building envelope was modeled as a first-order resistor–capacitor (RC) thermal network, and TES capacity was treated as a decision variable within a mixed-integer quadratic programming (MIQP) framework. The objective minimized total costs, including operating, TES investment, curtailment penalties, and demand response compensation, subject to power balance, thermal comfort, and renewable accommodation constraints. Four scenarios were compared to quantify the substitution effect. The results showed that building thermal inertia increased the net benefit by 23.87% by shifting heat supply to low-price periods. Capacity optimization reduced TES capacity from 600 kWh to 137.82 kWh, a 77.03% decrease. Building thermal inertia substituted 57.5% of the optimized TES capacity, corresponding to a 90.24% total capacity reduction relative to the fixed 600 kWh configuration, and this substitution effect may become more evident with larger thermal capacitance, smaller peak–valley price gaps, and wider temperature ranges, although these trends were not quantitatively analyzed in this study. The synergy of both measures lowered total cost by 22.73% and reduced TES cycling intensity and state-of-charge (SOC) fluctuations, which may help extend equipment life. These findings provided a quantitative basis for sizing TES in heating-season-integrated energy systems. Full article

Driven by global decarbonization goals, the rapid growth of renewable generation is increasing the frequency-regulation burden on power systems. Reliable estimates of regulation-capacity demand are therefore essential for coordinated energy and ancillary-services market clearing and secure grid operation. We develop a data-driven method that captures renewable variability while quantifying this demand accurately. The method uses renewable output, load fluctuations and meteorological conditions as predictors, with the maximum one-minute net-load deviation over each interval defining the target capacity. To address scale imbalance and redundant high-dimensional inputs, we introduce a dual screening procedure for training samples and features based on an improved weighted Euclidean distance. A deep neural network (DNN) then produces deterministic capacity forecasts, while adaptive-bandwidth kernel density estimation (ABKDE) models their residual errors. The upper confidence bound of the resulting prediction interval is used as the required regulation capacity. Tests on 2023 operating data from a provincial power grid yielded a spring-scenario mean absolute percentage error (MAPE) of 3.62% and a coefficient of determination (R²) of 98.31%. With ABKDE compensation, prediction-interval coverage probability reached 100%, while the prediction-interval normalized average width (PINAW) remained 7.46%. Performance remained robust across renewable-penetration levels and all four seasons. Full article

Coffee production and trade are globally important, and Brazil is the world’s largest producer and second-largest consumer. However, climate change poses a major challenge to coffee production because the crop is highly sensitive to irregular rainfall distribution and temperature fluctuations. We evaluated temperature and precipitation fluctuations from 1984 to 2023 at two coffee-producing units in southern Minas Gerais, Brazil, to characterize regional climate dynamics and their associations with coffee yield and total production. We used the Mann–Kendall methodological approach, linear regression and Sen’s slope estimator to identify trends and estimate rates of change in maximum, minimum, and mean air temperature and in total, dry-season, and wet-season precipitation. We used Pearson’s and Spearman’s correlation coefficients to assess relationships between climatic and agronomic variables, selecting the appropriate coefficient based on prior Shapiro–Wilk normality tests. Most temperature variables showed warming trends in September, during the transition from the dry to the wet season. Minimum temperature showed more warming trends in the first months of wet season. Cooling trends were also detected in January, February, March, April, May, and December. Precipitation declined in May and increased in June but showed no trend in either the dry or wet season. Relationships between climatic and agronomic variables differed between production units: correlations were positive at one unit and negative at the other. Locally, these trends indicate vulnerability during critical phenological stages that coincide with these seasonal windows, resulting in pollen tube desiccation, greater consumption of photo assimilates during physiological rest, and poor bean development under reduced dry-season rainfall. The contrasting correlation patterns were attributed to local climate fluctuations and to the microclimatic, land-use, and land-cover characteristics of each production unit. Under this scenario, mitigating these adverse conditions is essential to minimize damage to coffee crops. Recommended strategies include the implementation of agroforestry systems, rigorous monitoring of pests favored by climate fluctuations, and the adoption of more climate-resilient cultivars. Ultimately, this study highlights that crop management and mitigation strategies designed to buffer these climate variations must be tailored to the distinct conditions of each production unit. Full article

Agrotourism is increasingly promoted in Albania as a pathway for sustainable rural development, linking agricultural traditions with tourism growth. However, waste management remains one of the sector’s most pressing challenges, with rural areas often lacking reliable collection infrastructure and institutional support. This study examines how agrotourism businesses in Albania manage waste, what challenges they face, what factors shape these challenges, and how national legal, institutional and policy frameworks influence outcomes. A qualitative research design was applied, combining semi-structured interviews with seven agrotourism operators and four experts, field observations and documentary analysis of national strategies and EU directives. The findings reveal that businesses adopt diverse minimization, reuse and composting practices, yet these emerge largely out of necessity due to infrastructural gaps. Persistent challenges include irregular municipal services, limited staff capacity, visitor behaviour and seasonal fluctuations. To the best of the authors’ knowledge, this is among the first studies to examine waste management practices specifically within Albanian agrotourism. It shows that operators already act as active problem-solvers, but stronger institutional engagement is needed to consolidate local practices and advance Albania’s transition toward sustainable tourism. Full article

Integrated Energy Systems (IESs) are characterized by strong nonstationarity, seasonal heterogeneity, and stochastic fluctuations in electric, cooling, and heating loads, making accurate and reliable probabilistic forecasting challenging. To address this issue, this study proposes an Improved Osprey Optimization Algorithm-based TCN-BiLSTM-Quantile Regression (IOOA-TCN-BiLSTM-QR) model for short-term probabilistic forecasting of multi-energy loads. The model integrates Logistic chaotic mapping and Lévy flight to enhance the optimization capability of the Osprey Optimization Algorithm, while TCN and BiLSTM are employed to capture multiscale local features and long-term temporal dependencies, respectively. Quantile regression is further introduced to construct 90% prediction intervals and quantify forecasting uncertainty. Using year-round hourly operational data from a university IES, the proposed model achieves average PICP and PINAW values of 90.50% and 0.1513, respectively, indicating a favorable balance between interval coverage and width. For point forecasting, the model achieves test-set R² values of 0.9385, 0.9781, and 0.9731 for electric, cooling, and heating loads, respectively, with corresponding MAPE values of 2.61%, 2.67%, and 2.36%. Comparative and ablation experiments demonstrate the effectiveness of the integrated optimization and temporal feature extraction framework, while cross-dataset validation further confirms its applicability to different multi-energy load conditions. Overall, the proposed model provides accurate point forecasts and 90% prediction intervals with average coverage close to the nominal level, offering quantitative information for reserve capacity allocation, operational scheduling, and risk-aware decision-making in IESs. Full article

Seasonal hydrological fluctuations can substantially reshape river–lake networks, but changes in water extent, network morphology, and structural connectivity may not occur synchronously. This study investigated wet–dry seasonal variations in the Poyang Lake Basin using Landsat imagery from eight representative years between 1990 and 2025. Surface water was extracted using a consistent NDWI threshold, and 30 m DEM-derived channel information was used to correct interrupted, duplicated, and spurious river centerlines before Horton–Strahler ordering and graph-theoretic analysis. Mean water area increased from 3407.14 km² in the dry season to 6872.87 km² in the wet season, while mean river-network density increased from 0.0348 to 0.0461 km/km². The maximum Strahler order increased from III in the dry season to IV in the wet season. River-network complexity increased from a dry-season mean of 12.85 to 58.38 in the wet season, and the river-network development coefficient increased from 3.28 to 13.59, indicating a substantially more developed tributary and hierarchical structure under wet-season conditions. In contrast, the mean α, β, and γ indices increased by only 9.8%, 4.6%, and 4.3%, respectively, and dry-season values exceeded wet-season values in some years. From 1990 to 2025, wet-season water area increased by 11.11%, whereas river-network length decreased by 2.77%. These results demonstrate that seasonal changes in water extent, river-network morphology, and structural connectivity are asynchronous, highlighting the need to distinguish these complementary dimensions when evaluating large river-connected lake systems. Full article

Coal mining in areas with high groundwater levels leads to mining-induced subsidence and subsequent waterlogging. This phenomenon results in the loss of substantial areas of cultivated land, degrades the ecological environment, and severely impedes the sustainable development of coal resource-based cities. Moreover, subsidence waterlogging represents a substantial waste of water resources and poses significant challenges to their sustainable utilization. It also potentially threatens the health of humans, animals, and plants. However, theoretical research on the evolutionary characteristics of waterlogging volume remains limited. This study employs a spatial information inversion method based on remote sensing technology and land subsidence prediction to obtain data on the spatiotemporal evolution of subsidence waterlogging areas. Based on this, the formation and evolution characteristics of waterlogging areas and the “time lag effect” of waterlogging evolution were analyzed. The evolution of the waterlogging area was identified to progress through four distinct stages: the unformed waterlogging stage, the synchronous growth stage, the residual growth stage, and the seasonal fluctuation stage, and the characteristics of the four evolutionary stages were analyzed. Mining-induced surface deformation provides the necessary basin geometry for subsequent subsidence-related waterlogging, while precipitation supplies water that contributes to its development. Therefore, accurate analysis of the waterlogging evolution process in subsidence areas, along with clarification of its spatiotemporal characteristics and influencing factors, can provide a scientific basis for the prevention and control of waterlogging disasters, as well as for ecological restoration and environmental governance in coal mining regions with high groundwater levels. Full article

The performance of battery electric vehicles (BEVs) is significantly affected by ambient temperature, especially in areas with substantial seasonal fluctuations. This study examines the influence of Uzbekistan’s continental climate on electric vehicle range, battery degradation, and operational expenses through the development of a MATLAB/Simulink-based electro-thermal vehicle model. Three representative BEVs were selected for the simulation study: the Nissan Leaf (24 kWh), the Chevrolet Bolt EV (60 kWh), and the 2017 Volkswagen e-Golf (35.8 kWh, DC fast-charging version). The modeling framework combines longitudinal vehicle dynamics, battery behavior that changes with temperature, HVAC auxiliary loads, and range data that has been tested in controlled laboratory campaigns. The model was validated using open-source test results at different ambient temperatures, and the measured data matched the model very closely. Using validated temperature–range relationships, monthly climatic data for Uzbekistan were applied to estimate annual charging frequency for a representative annual driving distance. Results indicate that real climatic operation increases annual charging demand compared to moderate-condition OEM expectations. This increase not only affects electricity expenditure but also accelerates battery cycling intensity. A piecewise multiplicative degradation model was therefore implemented to quantify capacity loss under climate-adjusted cycling conditions. The findings demonstrate that continental temperature extremes significantly influence both short-term vehicle efficiency and cycle-induced battery capacity degradation. The proposed framework provides a climate-aware predictive tool for realistic performance estimation and supports evidence-based planning for electric mobility deployment in regions with pronounced seasonal variability. Full article

User-level short-term load forecasting is crucial for the efficient operation and dispatch of distribution networks. However, accurate forecasting remains challenging because of pronounced load fluctuations and abrupt local variations. To address this challenge, we propose a short-term load forecasting method based on complexity-component adaptive decomposition and locally gated Mamba. The proposed method adaptively decomposes load sequences according to component complexity and separately forecasts different components using models suited to their respective characteristics. Specifically, multiple seasonal-trend decomposition using loess is first applied to separate each load sequence into trend, seasonal, and residual components. A residual complexity score integrating sample entropy, variance ratio, and high-frequency energy ratio is then developed to selectively activate variational mode decomposition for residual windows with high complexity. Based on the characteristics of different components, a linear predictor, Mamba, and locally gated Mamba are employed for trend, seasonal, and residual forecasting, respectively, and the component forecasts are finally reconstructed through additive aggregation. Experiments conducted on real-world load data from a city in southern China demonstrate that the proposed method consistently outperforms existing approaches, reducing the average weighted absolute percentage error and symmetric mean absolute percentage error by 11.20% and 10.45%, respectively, thereby confirming its accuracy and effectiveness under heterogeneous load patterns. Full article

The decarbonization of isolated island power systems is hindered by dependence on diesel generation, limited operational flexibility, and pronounced seasonal demand variability. Lampedusa constitutes a representative Mediterranean case study, characterized by an isolated diesel-based power system with limited renewable energy integration and significant tourism-driven load fluctuations. This study proposes an integrated techno-economic optimization framework for the design and operation of a hybrid island microgrid combining photovoltaic, wind, and wave energy converters with battery energy storage systems (BESSs) and existing diesel generation. The analysis is based on a deterministic hourly resolution dispatch model implemented in MATLAB 2025a and coordinated by an energy management system (EMS), accounting for operational constraints, diesel minimum-load requirements, renewable technology diversification constraints, and battery storage dynamics. The optimal system configuration is determined using a genetic algorithm that minimizes the levelized cost of energy (LCOE) over a discrete design space. Under an annual electricity demand of 33.8 GWh, the optimal configuration achieves a renewable penetration of approximately 65% with a minimum LCOE of 0.1719 €/kWh, requiring 6.0 MW photovoltaic capacity, 3.0 MW wind capacity, 0.5 MW wave energy capacity, and 5.2 MWh BESS energy capacity. Renewable penetration targets exceeding 70% lead to infeasible operating solutions under the imposed system constraints. Full article

Ectoparasitic gamasid mites on small mammals play crucial roles in transmitting zoonotic pathogens like hantaviruses associated with hemorrhagic fever with renal syndrome (HFRS). To elucidate their community, network structure, and seasonal drivers in xeric habitats, a consecutive 12-month localized survey was conducted in Waxi Village, Binchuan County, a dry–hot valley in Yunnan Province, southwest China, from November 2020 to October 2021. From 1329 captured small mammals (18 species, 89.62% rodents), 12,075 gamasid mites belonging to 33 species were identified. The community exhibited overwhelming dominance by the family Laelapidae (C_r = 99.35%) and genus Laelaps (C_r = 97.37%). Laelaps algericus, L. guizhouensis, and L. nuttalli constituted the dominant mite species (cumulative C_r = 88.96%) and formed a tightly correlated core species cluster. Overall infestation prevalence (P_M), mean intensity (MI), and mean abundance (MA) were 46.05%, 19.73 mites/host, and 9.09 mites/host, respectively. The bipartite host–gamasid network revealed a significantly nested architecture (NOD_Fobs = 31.16, Z = 14.03, p < 0.001) with high specialization (H₂′ = 0.599), anchored by dominant hosts (Rattus andamanensis, Apodemus chevrieri, and Mus caroli). α-diversity analysis showed that Margalef richness, Shannon–Wiener index, and Gini–Simpson index peaked in autumn (November), while troughs occurred in mid-spring (April) and mid-autumn (October). Non-metric multidimensional scaling (NMDS, stress = 0.0048) and PERMANOVA (R² = 0.074, p = 0.001) confirmed significant seasonal shifts in community composition. Standard redundancy analysis (RDA) suggested temperature as the primary climatic driver (F = 7.205, p = 0.001; total model explanation = 8.2%). However, partial RDA (pRDA) conditioning on host species identity demonstrated that climatic variables directly accounted for only 0.1% of net variance (F = 0.662, p = 0.521). Our findings indicate that gamasid mite community assembly in this dry–hot valley is characterized by strong species dominance and network nestedness and that seasonal fluctuations are consistent with a host-mediated pattern rather than being directly governed by climate. Full article

This paper examines the multifractal organization of hourly wind-speed dynamics at nine automatic stations of the Brazilian National Institute of Meteorology in Paraíba, Northeast Brazil, over the common interval from 3 November 2017 to 17 November 2019. Missing records and values outside the inclusive 1.0–18.0 m/s analytical range are removed before station-level standardization, and multifractal detrended fluctuation analysis is applied to compacted retained-observation sequences. The stability-potential index (SPI) is defined as |α₀ − 0.5|, where α₀ is the singularity-spectrum peak and 0.5 is the uncorrelated benchmark. SPI therefore measures departure from random-like scaling and is interpreted as a temporal-organization indicator, not as a stand-alone measure of wind-power resource. Rolling windows of 720 retained observations, advanced by 168 observations, are assigned to seasons, El Niño, La Niña, neutral conditions, and tropical Atlantic-gradient regimes. The empirical design combines full-sample rankings, rolling spectra, two-way clustered ordinary least squares, Frisch–Newton bootstrap quantile regression, non-overlapping and coverage-threshold sensitivities, clock-span diagnostics, analytical-range and moment-order checks, 200 shuffled surrogates per station, and station-anchored inverse-distance-weighted maps. All full-sample α₀ estimates exceed 0.5, with SPI ranging from 0.1108 in Cabaceiras to 0.3937 in Areia. El Niño is associated with lower rolling SPI in both baseline and controlled mean regressions and in the lower and median quantiles; this sign persists after excluding low-coverage Camaratuba. La Niña is not significant at the mean but is negative at the 0.90 quantile. No north-warm Atlantic-gradient windows occur in the common sample, while the south-warm gradient is positive only near the median quantile. Four of nine stations have original raw spectrum widths above the shuffled 97.5% bound. Across stations, exact two-sided Spearman tests show no supported monotonic association between coverage, wind-distribution descriptors, or altitude and α₀, SPI, or W (n = 9; all p ≥ 0.194). The results document sample-specific spatial heterogeneity and climate-regime associations while showing that compacted-window duration, calm-wind screening, and the modest nine-station network require explicit sensitivity analysis and cautious generalization. Full article

In estuarine systems, hydrodynamic processes primarily regulate the spatiotemporal variability of nutrients, and specifically chlorophyll-a (Chl-a), a key indicator of trophic status and water quality. This phenomenon appears particularly pronounced in tropical estuaries such as the Gulf of Urabá. Rivers such as the Atrato (3000 m³/s), León (250 m³/s), and Turbo (100 m³/s) discharge into this gulf. These rivers deliver substantial nutrient loads, including inputs of anthropogenic origin, to the southern and central zones of the gulf, creating strong salinity stratification. In contrast, the northern sector exhibits weaker stratification due to reduced freshwater influence and greater exchange with the Caribbean Sea. Consequently, the Gulf of Urabá displays marked spatial and temporal biogeochemical heterogeneity. The primary motivation for this study is to advance the understanding of surface Chl-a dynamics in a tropical estuary using in situ observations, satellite data, and a coupled hydrodynamic and water quality model. Satellite-derived Chl-a concentrations were initially calibrated against in situ observations and subsequently used, alongside discharge data, to support model calibration and validation. The results demonstrated that simulated and satellite-derived Chl-a concentrations exhibited similar spatial and temporal patterns, revealing a persistent south-to-north Chl-a gradient. The highest concentrations were recorded during May and June, although elevated Chl-a levels persisted in the southern sector throughout the year. Analyses suggest that Chl-a exhibited a remarkably persistent spatial distribution, despite substantial seasonal fluctuations in concentration. This highlights the strong influence of hydrodynamic processes and riverine inputs on primary productivity. Two events characterized by increases in Chl-a concentration in the southern part of the gulf were analyzed in detail. The evolution of concentrations before and after the peak differed substantially between the two events. These findings contribute to a better understanding of the estuarine ecosystem’s functioning and provide valuable information for the management of water quality and fishery resources in this highly productive tropical estuary. A valuable contribution of this study to tropical estuary research is the use of a coupled model alongside comparisons with satellite data and in situ measurements. Full article