Search Results (491)

To promote soil carbon (C) sequestration and alleviate climate change, it is crucial to understand how vegetation types affect soil organic C (SOC) storage and stability in riverine wetlands. This study investigates the characteristics of SOC fractions and storage among different vegetation types and evaluates their soil C sequestration potential. Soil samples were collected and analyzed from four vegetation types (Typha orientalis, Tamarix chinensis, Avena sativa, and Phragmites australis) in wetlands at the junction of the middle and lower reaches of the Yellow River. Soil particulate organic C, dissolved organic C, and microbial biomass C contents of Avena sativa and Phragmites australis communities were higher than those of Tamarix chinensis and Typha orientalis communities (p < 0.001). Typha orientalis communities exhibited the highest SOC stability (4.31 ± 0.38), whereas Tamarix chinensis communities showed the lowest (1.34 ± 0.17) (p < 0.001). Soil organic C storage of Avena sativa (2.81 ± 0.32 kg m⁻²) and Phragmites australis (2.53 ± 0.06 kg m⁻²) communities was higher than that of Tamarix chinensis (0.88 ± 0.06 kg m⁻²) and Typha orientalis (1.35 ± 0.13 kg m⁻²) communities (p < 0.001). Soil electrical conductivity (EC) was significantly correlated with SOC fractions of Typha orientalis and Phragmites australis communities, while soil water content and particle size composition affected SOC fractions of Avena sativa communities (p < 0.05). Soil particle size composition affected the SOC storage of Typha orientalis, Tamarix chinensis, and Avena sativa communities (p < 0.05). Soil pH, water content, and EC influenced the SOC storage of Typha orientalis, Tamarix chinensis, and Phragmites australis communities (p < 0.05). These results demonstrate that Avena sativa and Phragmites australis communities play a vital role in maintaining C sink potential and ecological function in the Yellow River wetland. Nonetheless, the Typha orientalis community had greater C sequestration in the long term due to its high SOC stability. This research suggests that the effects of vegetation types should be considered when exploring the soil C cycle in riverine wetlands. Full article

Electric vehicle (EV) charging has become a key factor in grid integration, impact analysis, and the development of intelligent charging strategies. However, the rapid rise in EV adoption poses challenges for charging infrastructure and grid stability due to the inherently variable and uncertain charging behavior. Limited access to high-resolution, location-specific data further hinders accurate modeling, emphasizing the need for reliable, privacy-preserving tools to forecast EV-related grid impacts. This study introduces a comprehensive methodology to emulate real-world EV charging behavior using a real-time simulation environment. A physics-based EV charger model was developed on the Typhoon HIL platform, incorporating detailed electrical dynamics and control logic representative of commercial chargers. Simulation outputs, including active power consumption and state-of-charge evolution, were validated against field data captured via phasor measurement units, showing strong alignment across all charging phases, including SOC-dependent current transitions. Quantitative validation yielded an MAE of 0.14 and an RMSE of 0.36, confirming the model’s high accuracy. The study also reflects practical BMS strategies, such as early charging termination near 97% SOC to preserve battery health. Overall, the proposed real-time framework provides a high-fidelity platform for analyzing grid-integrated EV behavior, testing smart charging controls, and enabling digital twin development for next-generation electric mobility. Full article

The increasing penetration of photovoltaic (PV) generation in power systems is progressively displacing traditional synchronous generators, leading to a significant reduction in the system’s equivalent inertia. This decline undermines the system’s ability to withstand rapid frequency variations, adversely affecting its dynamic stability. In this context, battery energy storage systems (BESS) have emerged as a viable alternative for providing synthetic inertia and enhancing the system’s response to frequency disturbances. This paper proposes the design and implementation of an adaptive fuzzy logic controller aimed at frequency regulation in PV-BESS systems. The controller uses frequency deviation (Δf), rate of change of frequency (ROCOF), and battery state of charge (SOC) as input variables, with the objective of improving the system’s response to frequency variations. The controller’s performance was evaluated through simulations conducted in the MATLAB environment, considering various operating conditions and disturbance scenarios. The results demonstrate that the proposed controller achieves the lowest maximum frequency deviation across all analyzed scenarios when the initial SOC is 50%, outperforming other comparative methods. Finally, compliance with primary frequency regulation (PFR) was verified in accordance with the Technical Procedure PR-21 related to spinning reserve, issued by the Peruvian Committee for Economic Operation of the System. Full article

Soil organic carbon (SOC) represents a crucial factor in agricultural production, and its accumulation is influenced by soil microbial community and microbial metabolism. Straw returning combined with decomposing agents is recognized practice to enhance SOC. On the other hand, the impacts of controlled-release nitrogen fertilizer (CR) on the function of the decomposing agent in degrading straw are underexplored. In this study, an incubation experiment with ¹³C labeled straw in three nitrogen fertilizer treatments (CK, no nitrogen applied; UR, urea applied; CR, controlled-release fertilizer applied) was carried out to elucidate how CR regulates the straw decomposition agent and bacterial community to influence the SOC sequestration, based on field experiments. And we examined the changes in soil organic carbon and the stability of the bacterial networks by combining co-occurrence networks and a structural equation model. In the incubation experiment, the results demonstrated that CR increased the relative abundance of straw decomposition agent and straw-derived SOC (SO¹³C). Additionally, CR enhanced the stability of soil bacterial networks, compared with UR, by strengthening the interactions within the soil bacterial community. Pearson correlations confirmed that straw decomposition agent was positively associated with SO¹³C. Moreover, the straw decomposition agent was positively correlated with the activities of the nitrogen-cycling enzyme (urease, N-acetyl-β-glucosaminidase) and carbon-degrading enzyme (β-1,4-glucosidase, cellulase). Furthermore, structural equation modeling indicated that soil inorganic nitrogen played the most direct role in changes in the straw decomposition agent and then indirectly stimulated the activity of cellulase, ultimately increasing straw-derived carbon in the soil. This study elaborates the mechanism of straw returning combined with straw decomposition agent and controlled-release fertilizers to enhance the SOC of coastal saline–alkali soil from the perspective of underground biology. Collectively, the results of this research might improve the management of straw returning and sustainable utilization of fertility in saline–alkali soil. It provides a new perspective on fertilization for increasing soil carbon sequestration in future farmland ecosystems. Full article

Islanded DC microgrids face challenges in voltage stability and communication overhead due to renewable energy variability. A novel enhanced distributed coordinated control framework, based on adaptive event-triggered mechanisms, is developed for the efficient management of multiple hybrid energy storage systems (HESSs) in islanded DC microgrids (MGs). We propose a hierarchical distributed control framework integrating ANN-based controllers and adaptive event-triggered mechanisms to dynamically regulate power flow and minimise communication. This system utilises a hierarchical coordinated control method (HCCM) with primary virtual resistance droop control integrated with state-of-charge (SoC) management and secondary control for voltage regulation and proportional current distribution through optimised communication networks. The integration of artificial neural network (ANN)-based controllers alongside traditional PI control leads to an improvement in system responsiveness. The control approach dynamically adjusts the trigger parameters to minimise communication overhead with tight voltage regulation. An extensive simulation using MATLAB/Simulink shows how the system can effectively manage variability in renewable energy sources and maintain stable voltage profiles with precise power distribution and minimal bus voltage fluctuations. Simulations confirm enhanced voltage regulation (±0.5% deviation), proportional current sharing (98% accuracy), and 60% communication reduction under load transients (outcomes). Full article

Accurately assessing the spatiotemporal dynamics and influencing factors of ecosystem stability in arid desert regions (ADR) is crucial for ecological conservation and the achievement of high-quality regional development. However, existing assessment frameworks generally fail to adapt to the extremely fragile ecological conditions of ADR. Therefore, the Alxa Desert, a typical region, was selected as the research region, and an ecosystem stability assessment framework tailored to regional characteristics (perturbation–resilience–function) was constructed. Perturbation represents external pressure, resilience reflects the capacity for recovery and adaptation, and function serves as the supporting foundation. The three dimensions are dynamically coupled and jointly determine the stability status of the ecosystem in the Alxa Desert. Methodologically, this study innovatively introduces the Cloud Model–Analytic Hierarchy Process (CM-AHP) to calculate indicator weights, which more effectively addressed the widespread fuzziness and uncertainty inherent in ecosystem assessments compared to traditional methods. In addition, spatial autocorrelation methods was applied to reveal the spatial and temporal evolution characteristics of ecosystem stability from 2000 to 2020. Furthermore, the optimal parameters geographical detector model (OPGDM) was applied to analyze the effects of natural and human factors on the spatial differentiation of ecosystem stability in Alxa Desert. In addition, the Markov–FLUS model was employed to simulate the future trends of ecosystem stability over the next two decades. The results indicate that ecosystem stability in Alxa Desert from 2000 to 2020 was primarily characterized by vulnerable and moderate levels, with the area classified as extremely vulnerable decreasing significantly by 10% relative to its extent in 2000. Spatially, higher stability was observed in oasis regions and southeastern mountainous regions, while lower stability was concentrated in the desert hinterlands. Overall, ecosystem stability shifted from vulnerable toward moderate levels, reflecting a trend of gradual improvement. From 2000 to 2020, the Moran’s I varied between 0.78 and 0.81, showing strong spatial clustering. Surfce Soil moisture content (SSMC), Soil organic carbon (SOC), and enhanced vegetation index (EVI) were the primary factors influencing the spatial differentiation of ecosystem stability in Alxa Desert. The interaction between these factors further enhanced their explanatory power. Future forecasting results indicate that ecosystem stability will further improve by 2030 and 2040, particularly in the northern and southern areas of Alxa Left Banner and Alxa Right Banner. The findings can offer a theoretical foundation for future ecological conservation and environmental management in ADR. Full article

In lithium-ion battery management systems (BMSs), accurate state of charge (SOC) estimation is essential for the stable operation of BMSs. Furthermore, the accuracy of SOC estimation is significantly influenced by the precision of battery model parameters. To improve the SOC estimation accuracy, this paper focuses on the second-order RC equivalent circuit model, firstly designs a simple and reliable improved adaptive forgetting factor (IAFF) regulation mechanism, and proposes the improved adaptive forgetting factor recursive least squares (IAFFRLS) algorithm, which not only improves the accuracy of parameter identification, but also exhibits excellent performance in anti-interference. Secondly, based on the identified model, a weighted multi-innovation improved Sage–Husa adaptive extended Kalman filter (WMISAEKF) algorithm is proposed to solve the problem of filter divergence caused by noise covariance updating. It fully utilizes historical innovations to reasonably allocate innovation weights to achieve accurate SOC estimation. Compared with the VFFRLS algorithm and AFFRLS algorithm, the IAFFRLS algorithm reduces the root mean square error (RMSE) by 29.30% and 19.29%, respectively, and the RMSE under noise interference is decreased by 82.37% and 78.59%, respectively. Based on the identified model for SOC estimation, the WMISAEKF algorithm reduces the RMSE by 77.78%, compared to the EKF algorithm. Furthermore, the WMISAEKF algorithm could still converge under different levels of noise interference and incorrect initial SOC values, which proves that the proposed algorithm has good stability and robustness. Simulation results verify that the parameter identification algorithm proposed in this paper demonstrates higher identification accuracy and anti-interference performance. The proposed SOC estimation algorithm has higher estimation accuracy and good robustness, which provides a new practical support for extending battery life. Full article

The State of Charge (SOC) is vital for battery system management. Enhancing SOC estimation boosts system performance. This paper presents a combined framework that improves SOC estimation’s accuracy and stability for electric vehicles. The framework combines a Long Short-Term Memory (LSTM) network with an Adaptive Unscented Kalman Filter (AUKF). An Improved Arithmetic Optimization Algorithm (IAOA) fine-tunes the LSTM’s hyperparameters. Its novelty lies in its adaptive iteration algorithm, which adjusts iterations based on a threshold, optimizing computational efficiency. It also integrates a genetic mutation strategy into the AOA to overcome local optima by mutating iterations. Additionally, the AUKF’s adaptive noise algorithm updates noise covariance in real-time, enhancing SOC estimation precision. The inputs of the proposed method include battery current, voltage, and temperature, then producing an accurate SOC output. The predictions of LSTM are refined through AUKF to obtain reliable SOC estimation. The proposed framework is firstly evaluated utilizing a public dataset and then applied to battery packs on actual engineering vehicles. Results indicate that the Root Mean Square Errors (RMSEs) of the SOC estimations in practical applications are below 0.6%, and the Maximum Errors (MAX) are under 3.3%, demonstrating the accuracy and robustness of the proposed combined framework. Full article

This study aimed to evaluate the impact of vineyard cultivation time and the use of metal-based fungicides on the chemical fractions of soil organic matter (SOM) as well as their interactions with Cu, Zn, and Mn in vineyard soils from Southern Brazil with varying histories of fungicide application. Soil samples were collected in 2017 from vineyards aged 35, 37, and 39 years in the Serra Gaúcha region and 13, 19, and 36 years in the Campanha Gaúcha. In each region, samples were also collected from a non-anthropized reference area. In the oldest vineyards, sampling was conducted both within and between the rows of planting. Chemical fractionation of SOM was performed: non-humic substances (nHSs), particulate organic matter (POM), fulvic acid (FA), humic acid (HA), and humin (Hu). Fourier-transform infrared (FTIR) spectra were obtained for the HA, from which the aromaticity index (AI) and relative intensities (RIs) were calculated. In each SOM fraction, total organic carbon and the concentrations of Cu, Zn, and Mn were determined. Changes in land use alter the forms and distribution of soil organic carbon (SOC) and, consequently, of metals. Elemental and spectroscopic analyses of HS revealed that HA in the reference areas (forest and native grassland) was more aliphatic and had higher concentrations of polysaccharides, indicating fractions with a lower degree of stabilization. However, in vineyard areas, HA exhibited greater humification and aromaticity. Increasing cultivation time gradually increased soil carbon content, indicating that viticultural agroecosystems can sequester carbon in the soil over time, reaching levels similar to those observed in the reference areas. When comparing vineyard areas alone, with row collections and inter-row collections, we observed an increase in SOC levels in areas managed with cover crops, demonstrating the importance of conservation management in these areas. When evaluating the distribution of metals in these soils, we could observe the high affinity of Cu for the functional groups of SOM, with FA and HA responsible for the complexation of these elements in the soil. For Zn and Mn, the greatest accumulations were observed in the Hu fraction due to their greater affinity for soil clay minerals. This shows that soil organic matter is a key component in the complexation of metals in soils, reducing their availability and potential toxicity to cultivated plants. Full article

Manganese (Mn) oxides exhibit significant potential to either stabilize or destabilize soil organic carbon (SOC) through the polymerization and/or oxidation of organic molecules via organo-mineral interactions. Birnessite (MnO₂) is known to strongly interact with soil dissolved organic matter (DOM), which is DOM composition-dependent. Humic acid (HA) and fulvic acid (FA) are commonly used as organic fertilizers in soils. In this study, the contrasting reaction of DOM with birnessite in flooded paddy soil with HA and FA amendment was investigated at a molecular level. The results demonstrated that HA amendment enhanced the reaction of phenolic compounds in soil DOM with birnessite, leading to the formation of condensed aromatic compounds and polymeric products (PP) with higher molecular weights and aromaticity. This suggests that HA amendment enhances the birnessite-induced polymerization of soil DOM. In contrast, FA facilitated the birnessite-induced oxidation of soil DOM, yielding dicarboxylic acids (DA), monocarboxylic acids (MA), and quinones products (QP). These findings demonstrate that the reactivity of soil DOM with birnessite is significantly influenced by the composition of DOM exogenously added. This study provides comprehensive understandings of the interactions among Mn and C and helps to predict behaviors of DOM molecules in flooded paddy soil, which is critical for optimizing sustainable soil management. Full article

State-of-charge (SOC) and temperature inconsistencies among lithium-ion battery cells can significantly degrade the performance, safety, and lifespan of battery packs. To address this issue, this paper proposes a dynamic balancing strategy that simultaneously regulates both SOC and cell temperature in real time. Each battery cell is connected to an individual Boost converter, enabling independent control of energy flow. An outer loop is adopted to stabilize the pack-level bus voltage. The balancing factors for SOC and temperature are adaptively fused using a Particle Swarm Optimization (PSO) algorithm, which dynamically adjusts the weightings based on real-time operating conditions. This approach allows the controller to prioritize either thermal or electrical balance when needed, ensuring robust performance under varying load and environmental disturbances. Simulation-based validation on a multi-cell lithium-ion pack demonstrates that the proposed method effectively reduces SOC and temperature deviation, improves pack-level energy utilization, and extends operational stability compared to fixed-weight balancing strategies. Full article

Optimizing the power allocation and capacity configuration of hybrid energy storage systems (HESS) is crucial for enhancing grid stability, power quality and renewable energy utilization in wind–solar complementary microgrids. However, the conventional configuration methods exhibit inaccuracy and low reliability. To achieve the optimal capacity configuration of HESS in wind–solar complementary microgrids, a power allocation strategy and a capacity optimization configuration model for HESS consisting of vanadium redox flow batteries (VRBs) and supercapacitors (SCs) were proposed based on parameter-optimized variational mode decomposition (VMD). Firstly, the number of mode decomposition (K) and the penalty factor (α) of VMD were optimized using the random walk grey wolf optimizer (RW-GWO) algorithm, and the HESS power signal was decomposed by RW-GWO-VMD. Secondly, an optimal capacity configuration model was formulated, taking into account the whole life cycle cost of HESS, and particle swarm optimization (PSO) algorithm was applied to optimize HESS capacity while satisfying operational constraints on charge/discharge power, state of charge (SOC) range, and permissible rates of load deficit and energy loss. Thirdly, the optimal capacity allocation was obtained by minimizing the whole life cycle cost of HESS, with the frequency division threshold N serving as the optimization parameter. Finally, comprehensive comparison and analysis of proposed methods were conducted through simulation experiments. The results demonstrated that the whole life cycle cost of RW-GWO-VMD was 7.44% lower than that of EMD, 1.00% lower than that of PSO-VMD, 0.72% lower than that of AOA-VMD, and 0.27% lower than that of GWO-VMD. Full article

As an intensive eco-agricultural model, the rice–crayfish co-culture (RCC) system has been widely adopted in recent years due to its remarkable advantages in resource use, efficiency, and economic benefits. However, the long-term mechanisms by which this system affects the quantity and stability of soil aggregate, as well as the vertical distribution of soil organic carbon (SOC) within aggregate across soil profiles, remain unclear. This study investigated the effects of varying duration (4 and 8 years) of RCC in Qianjiang City, Hubei Province. Soil samples were collected from six depth layers (0–10 cm, 10–20 cm, 20–30 cm, 30–40 cm, 40–80 cm, and 80–120 cm) to analyze the distribution characteristics of soil aggregate and SOC. The results demonstrated that, compared to the field which used RCC for a duration of 4 years, the field which used RCC for a duration of 8 years significantly reduced bulk density (BD) by 16.3% in the 40–80 cm layer. However, prolonged flooding has led to a 9.6% increase in the BD of the plow pan layer (10–20 cm) due to hydrostatic pressure and mechanical disturbances. Furthermore, the use of RCC for a duration of 8 years significantly enhanced the mass fractions of water-stable aggregates > 2 mm in the 0–80 cm soil layer at 0–10 cm (25.9%), 10–20 cm (30.2%), 20–30 cm (141.8%), 30–40 cm (172.4%), and 40–80 cm (112.9%), and improved aggregate stability throughout the entire soil profile. In terms of SOC distribution, the SOC concentration increased significantly with prolonged RCC usage across all soil layers, particularly in the 0–20 cm layer. The SOC was primarily derived from >2 mm (Large aggregate). Notably, although < 0.053 mm (Silt and clay) constituted a small proportion of the 0–20 cm layer, their SOC concentration reached 15.3–20.55 g kg⁻¹. Overall, extended RCC duration reduced BD in nearly all soil layers, promoted the formation of macro-aggregate, enhanced aggregate stability, and increased the SOC concentration within macro-aggregate, while strengthening the SOC stocks capacity of the 80–120 cm soil layer from 2.58 kg C m⁻² to 4.35 kg C m⁻², an increase of 68.6%. Full article

Soil carbon constitutes the largest terrestrial carbon reservoir, with inorganic forms (SIC) contributing an estimated 20–40% of the global total. Despite its relevance to arid-region carbon cycling and stabilization, SIC remains less studied than soil organic carbon (SOC). This study quantified surface SIC content (0–20 cm) and its environmental drivers across the Weibei Loess region using 3261 soil samples collected between 2008 and 2010. A combination of Random Forest (RF) modeling and optimal parameter geodetector (OPGD) analysis was employed to assess spatial heterogeneity and identify key environmental controls. SIC content ranged from 0.10 to 3.56 g kg⁻¹ (mean = 1.23 ± 0.41 g kg⁻¹), generally lower than reported values in the Tibetan Plateau and Inner Mongolia. Higher concentrations were observed in central areas, with lower values toward the periphery. While mean annual temperature (MAT) and precipitation (MAP) remained key climatic correlates, shortwave radiation (srad) emerged as the strongest control on SIC across the region, exhibiting a significant positive association with its accumulation. Notably, its interaction with wind speed (vs) further amplified this effect, highlighting the synergistic role of radiation and near-surface turbulence in regulating inorganic carbon retention in surface soils. Collectively, these variables explained ~56% of SIC spatial variation. Favorable conditions for SIC accumulation were identified within specific environmental thresholds: srad (171–172 W/m²), MAP (546–587 mm), MAT (10.2–11.5 °C), and vs (1.90–1.96 m/s). These findings offer a quantitative basis for understanding SIC patterns in loess-derived soils and support the development of region-specific strategies for carbon regulation under changing climatic conditions. Full article

As the penetration of intermittent renewable energy sources continues to increase, ensuring reliable power system and frequency stability is of importance. Battery energy storage systems (BESSs) have emerged as an important solution to mitigate these challenges by providing essential grid support services. In this context, a state-of-charge (SOC)-frequency control strategy for grid-forming BESSs is proposed to enhance their role in stabilizing grid frequency and improving overall system performance. In the system, the DC-link capacitor is regulated to maintain the angular frequency through a matching control scheme, emulating the characteristics of the rotor dynamics of a synchronous generator (SG). Thereby, the active power control is implemented in the control of the DC/DC converter to further regulate the grid frequency. More specifically, the relationship between the active power and the frequency is established through the SOC of the battery. In addition, owing to the inevitable presence of differential operators in the control loop, a high-gain observer (HGO) is employed, and the corresponding parameter design of the proposed method is elaborated. The proposed strategy simultaneously achieves frequency regulation and implicit energy management by autonomously balancing power output with available battery capacity, demonstrating a novel dual benefit for sustainable grid operation. To verify the effectiveness of the proposed control strategy, a 0.5-Hz frequency change and a 10% power change are carried out through simulations and also on a hardware-in-the-loop (HIL) platform. Full article