Search Results (96)

Under the dual-carbon goals, with the rapid increase in the proportion of fluctuating power sources such as wind and solar energy, the regulatory capacity of traditional thermal power generation can no longer meet the demand for intra-day fluctuations. There is an urgent need to tap into the potential of flexible load-side regulatory resources. To this end, this paper proposes a low-carbon economic optimal dispatching strategy for virtual power plants (VPPs), considering the aggregation of diverse flexible and adjustable resources with the integration of wind and solar power. Firstly, the method establishes mathematical models by analyzing the dynamic response characteristics and flexibility regulation boundaries of adjustable resources such as photovoltaic (PV) systems, wind power, energy storage, charging piles, interruptible loads, and air conditioners. Subsequently, considering the aforementioned diverse adjustable resources and aggregating them into a VPP, a low-carbon economic optimal dispatching model for the VPP is constructed with the objective of minimizing the total system operating costs and carbon costs. To address the issue of slow convergence rates in solving high-dimensional state variable optimization problems with the traditional plant growth simulation algorithm, this paper proposes an improved plant growth simulation algorithm through elite selection strategies for growth points and multi-base point parallel optimization strategies. The improved algorithm is then utilized to solve the proposed low-carbon economic optimal dispatching model for the VPP, aggregating diverse adjustable resources. Simulations conducted on an actual VPP platform demonstrate that the proposed method can effectively coordinate diverse load-side adjustable resources and achieve economically low-carbon dispatching, providing theoretical support for the optimal aggregation of diverse flexible resources in new power systems. Full article

Distributed photovoltaic storage charging piles in remote rural areas can solve the problem of charging difficulties for new energy vehicles in the countryside, but these storage charging piles contain a large number of power electronic devices, and there is a risk of resonance in the system under weak grid conditions. Firstly, the topology of a photovoltaic storage charging pile is introduced, including a bidirectional DC/DC converter, unidirectional DC/DC converter, and single-phase grid-connected inverter. Then, the maximum power tracking control strategy based on improved conductance micro-increment is derived for a photovoltaic power generation system, and a constant voltage and constant current charge–discharge control strategy is derived for energy storage equipment. Additionally, a segmented reflective charging control strategy is introduced for charging piles, and the quasi-PR controller is introduced for single-phase grid-connected inverters. In addition, an improved second-order general integrator phase-locked loop (SOGI-PLL) based on feed-forward of the grid current is derived. Finally, a simulation model is built to verify the performance of the solar–storage charging pile and lay the technical groundwork for future integrated control strategies. Full article

To address the charging demand challenges brought about by the widespread adoption of electric vehicles, integrated photovoltaic–storage–charging stations (PSCSs) enhance energy utilization efficiency and economic viability by combining photovoltaic (PV) power generation with an energy storage system (ESS). This paper proposes a two-stage data-driven holistic optimization model for the siting and capacity allocation of charging stations. In the first stage, the location and number of charging piles are determined by analyzing the spatiotemporal distribution characteristics of charging demand using ST-DBSCAN and K-means clustering methods. In the second stage, charging load results from the first stage, photovoltaic generation forecast, and electricity price are jointly considered to minimize the operator’s total cost determined by the capacity of PV and ESS, which is solved by the genetic algorithm. To validate the model, we leverage large-scale GPS trajectory data from electric taxis in Shenzhen as a data-driven source of spatiotemporal charging demand. The research results indicate that the spatiotemporal distribution characteristics of different charging demands determine whether a charging station can become a PSCS and the optimal capacity of PV and battery within the station, rather than a fixed configuration. Stations with high demand volatility can achieve a balance between economic benefits and user satisfaction by appropriately lowering the peak instantaneous satisfaction rate (set between 70 and 80%). Full article

Accurate estimation of grain volume in storage silos is critical for intelligent monitoring and management. However, traditional image-based methods often struggle under complex lighting conditions, resulting in incomplete surface reconstruction and reduced measurement accuracy. To address these limitations, we propose a B-spline Interpolation and Clustered Means (BICM) method, which fuses multi-view point cloud data captured by RGB-D cameras to enable robust 3D surface reconstruction and precise volume estimation. By incorporating point cloud splicing, down-sampling, clustering, and 3D B-spline interpolation, the proposed method effectively mitigates issues such as surface notches and misalignment, significantly enhancing the accuracy of grain pile volume calculations across different viewpoints and sampling resolutions. The results of this study show that a volumetric measurement error of less than 5% can be achieved using an RGB-D camera located at two orthogonal viewpoints in combination with the BICM method, and the error can be further reduced to 1.25% when using four viewpoints. In addition to providing rapid inventory assessment of grain stocks, this approach also generates accurate local maps for the autonomous navigation of grain silo robots, thereby advancing the level of intelligent management within grain storage facilities. Full article

Blasting excavation can pose significant risks to adjacent structures, particularly during concrete pouring. Therefore, evaluating their safety is crucial. In addition, the influence of blasting vibration on the vibration of the foundation and the superstructure is different. Currently, there are only allowable vibration values in the time domain range affected by blasting construction on the foundation structure at vibration frequencies of 1–10 Hz and 50 Hz. There is a lack of allowable vibration values in the range of 10–50 Hz. Based on a liquefied natural gas (LNG) project in Zhejiang, China, this paper studies the safety evaluation index for the vibration of piles under the storage tank through in situ blasting tests and numerical simulations. The vibration velocity attenuation curve of the site, which can accurately predict the pile vibration velocity induced by blasting, is obtained by fitting the experimental results using Sodev’s formula. It is found that the vibration velocity gradually increases from the pile toe to the pile top. As the distance to the blasting source increases, the maximum vibration velocity of the pile top gradually decreases. The peak vibration velocity at the pile top is different from that at the ground surface around the pile. Their ratio, which can reach up to 1.33, gradually increases with the decreasing distance to the blasting source and the increasing concrete strength. The predominant frequency is greater than 10 Hz. For the pile whose concrete strength is lower than 50% of the design strength, blasting has little impact when the vibration velocity is less than 10.16 mm/s. The experimental results supplement the relevant experimental data within the range of 10–50 Hz. This study can provide references for similar projects. Full article

Temperature plays a crucial role in the grain storage process and food security. Due to limitations in grain storage data acquisition in real-world scenarios, this paper proposes a data augmentation method for grain storage data that operates in both the time and frequency domains, as well as an enhanced grain storage temperature prediction model. To address the issue of small sample sizes in grain storage temperature data, Gaussian noise is added to the grain storage temperature data in the time domain to highlight the subtle variations in the original data. The fast Fourier transform (FFT) is employed in the frequency domain to highlight periodicity and trends in the grain storage temperature data. The prediction model uses a long short-term memory (LSTM) network, enhanced with convolution layers for feature extraction and a Squeeze-and-Excitation Networks (SENet) module to suppress unimportant features and highlight important ones. Experimental results show that the FTA-CNN-SE-LSTM compares with the original LSTM network, and the MAE and RMSE are reduced by 74.77% and 74.02%, respectively. It solves the problem of data limitation in the actual grain storage process, greatly improves the accuracy of grain storage temperature prediction, and can accurately prevent problems caused by abnormal grain pile temperature. Full article

Timely detection and treatment of moisture anomalous regions in grain storage facilities is crucial for preventing mold growth, germination, and pest infestation. To locate these regions, this paper presents a novel anomalous moisture region localization algorithm based on the delay-multiply-and-sum (DMAS) beamforming techniques, including the design of an effective spatial arrangement of electromagnetic wave transmitters and receivers, along with comprehensive testing of detectable regions and experimental validation of anomaly localization across varying moisture levels and positions within grain piles. Following initial localization using the proposed algorithm, the study introduces a reliability assessment method for unknown samples based on the signal-to-mean ratio (SMR) value and compares the region of maximum response intensity with that of maximum connected domain volume. The system demonstrated successful localization of a 7 cm × 7 cm × 7 cm region with 15.4% moisture content within a cubic experimental bin containing 10.5% moisture content long-grained rice, achieving an average recall accuracy exceeding 50%. The proposed method presents rapid detection capabilities and precise localization, showing potential for moisture content evaluation of anomalous regions and practical applications in grain storage monitoring systems. Full article

In line with the strategic plan for emerging industries in China, renewable energy sources like wind power and photovoltaic power are experiencing vigorous growth, and the number of electric vehicles in use is on a continuous upward trend. Alongside the optimization of the distribution network structure and the extensive application of energy storage technology, the active distribution network has evolved into a more flexible and interactive “source–grid–load–storage” diversified structure. When electric vehicles are plugged into charging piles for charging and discharging, it inevitably exerts a significant impact on the control and operation of the power grid. Therefore, in the context of the extensive integration of electric vehicles, delving into the charging and discharging behaviors of electric vehicle clusters and integrating them into the optimization of the active distribution network holds great significance for ensuring the safe and economic operation of the power grid. This paper adopts the two-stage “constant-current and constant-voltage” charging mode, which has the least impact on battery life, and classifies the electric vehicle cluster into basic EV load and controllable EV load. The controllable EV load is regarded as a special “energy storage” resource, and a corresponding model is established to enable its participation in the coordinated control of the active distribution network. Based on the optimization and control of the output behaviors of gas turbines, flexible loads, energy storage, and electric vehicle clusters, this paper proposes a two-layer coordinated control model for the scheduling layer and network layer of the active distribution network and employs the improved multi-target beetle antennae search optimization algorithm (MTTA) in conjunction with the Cplex solver for solution. Through case analysis, the results demonstrate that the “source–grid–load–storage” coordinated control of the active distribution network can fully tap the potential of resources such as flexible loads on the “load” side, traditional energy storage, and controllable EV clusters; realize the economic operation of the active distribution network; reduce load and voltage fluctuations; and enhance power quality. Full article

In order to overcome the notable limitations of current methods for monitoring grain storage states, particularly in the early warning of potential risks and the analysis of the spatial distribution of grain temperatures within the granary, this study proposes a multi-model fusion approach based on a deep learning framework for grain storage state monitoring and risk alert. This approach combines two advanced three-dimensional deep learning models, a grain storage state classification model based on 3D DenseNet and a temperature field prediction model based on 3DCNN-LSTM. First, the grain storage state classification model based on 3D DenseNet efficiently extracts features from three-dimensional grain temperature data to achieve the accurate classification of storage states. Second, the temperature prediction model based on 3DCNN-LSTM incorporates historical grain temperature and absolute water potential data to precisely predict the dynamic changes in the granary’s temperature field. Finally, the grain temperature prediction results are input into the 3D DenseNet to provide early warnings for potential condensation and mildew risks within the grain pile. Comparative experiments with multiple baseline models show that the 3D DenseNet model achieves an accuracy of 97.38% in the grain storage state classification task, significantly outperforming other models. The 3DCNN-LSTM model shows high prediction accuracy in temperature forecasting, with MAE of 0.24 °C and RMSE of 0.28 °C. Furthermore, in potential risk alert experiments, the model effectively captures the temperature trend in the grain storage environment and provides early warnings, particularly for mildew and condensation risks, demonstrating the potential of this method for grain storage safety monitoring and risk alerting. This study provides a smart grain storage solution which contributes to ensuring food safety and enhancing the efficiency of grain storage management. Full article

Over the years, multiple tailings dam failures all over the world have been primarily linked to drainage issues. Given its critical role in dam stability, this research analyzes the relationship between precipitation, reservoir levels, and geotechnical instrumentation measurements along the elevation stages of a tailings dam. To assess the influence of drainage on dam performance, its dependence on infiltration, reservoir water fluctuations, and geotechnical instrumentation responses was modeled and interpreted. By applying time series analysis methods to the instrumentation data, including autocorrelation and cross-correlation functions, this study identifies patterns in drainage efficiency and its impact on stability. The time series data were regularized and transformed into stationary forms to ensure consistency in the analysis. Autocorrelation functions and cross-correlations between different monitoring instruments were computed specifically for the second to the seventh elevation stages of the tailings dam. This study focuses on four cross-sections of the dam, analyzing their behavior to differentiate the spatial and temporal effects of drainage. The results reveal variations in drainage efficiency across these different sections and elevation stages, providing a deeper understanding of the role of drainage in maintaining stability. The proposed methodology can also be successfully applied to other tailings storage facilities, such as tailings dams built downstream or dry stacking piles, contributing to improved monitoring and risk assessment strategies. Full article

With the increasing penetration of distributed generation (DG), the supply–demand imbalance and voltage overruns in the distribution network have intensified, and there is an urgent need to introduce flexibility resources for regulation. This paper proposes co-planning of electric vehicles (EVs) and soft opening points (SOPs) to improve the flexibility of the active distribution network, thereby improving the economics and flexibility of the distribution network. Firstly, this paper establishes a charging pile day-ahead dispatchable prediction model and a real-time dispatchable potential assessment model through Monte Carlo sampling simulation. It replaces the traditional energy storage model with this model and then solves the EV and SOP collaborative planning model using a second-order conical planning algorithm with the objective function of minimizing the annual integrated cost. At the same time, the flexibility of the distribution network is analyzed by two indicators: power supply and demand balance and branch load margin. Finally, the optimization method proposed in this paper is analyzed and validated on an improved IEEE 33-node distribution system. Example results show that the planning method proposed in this paper can effectively reduce the annual comprehensive operating cost of distribution networks, meet the flexibility index, and be conducive to improving the economy and flexibility of distribution network operation. Full article

Context: Biochar can affect the storage and forms of nitrogen; thus, it may also play a role in altering the nitrogen cycle during the fermentation process of cow dung into organic fertilizer. Objective: To elucidate the mechanism and process of nitrogen transformation during the composting of cow manure with biochar, a comparative experiment was conducted. Method: This study investigates the use of biochar as a medium to enhance nitrogen storage during the aerobic composting of cow manure. The effectiveness was verified through a rapid composting experiment. Result and Conclusions: The results demonstrated that adding 5% biochar to the compost pile increased the total nitrogen content in manure by 12%. Specifically, the pyrrolic nitrogen in the composted cow manure increased from 38% to 44%, and the carbon-nitrogen ratio improved from 35% to 37%. Analysis of surface functional groups indicated that the C=O and C=C bonds in biochar played a key role in modifying nitrogen storage. Microbial analysis showed that biochar could significantly enhance the regional competitiveness of microorganisms, such as Cellvibrio, thereby boosting the expression of functional genes involved in the nitrification process, including amoABC, hao, and nxrAB. Therefore, adding 5% biochar not only enhances nitrogen storage in organic fertilizer but also changes the microbial population structure. Significance: This study carries substantial implications for the application of Biochar in the field, as well as for the development of microbial fertilizers based on cow manure. Full article

The authors propose a two-stage sequential configuration method for energy storage systems to solve the problems of the heavy load, low voltage, and increased network loss caused by the large number of electric vehicle (EV) charging piles and distributed photovoltaic (PV) access in urban, old and unbalanced distribution networks. At the stage of selecting the location of energy storage, a comprehensive power flow sensitivity variance (CPFSV) is defined to determine the location of the energy storage. At the energy storage capacity configuration stage, the energy storage capacity is optimized by considering the benefits of peak shaving and valley filling, energy storage costs, and distribution network voltage deviations. Finally, simulations are conducted using a modified IEEE-33-node system, and the results obtained using the improved beluga whale optimization algorithm show that the peak-to-valley difference of the system after the addition of energy storage decreased by 43.7% and 51.1% compared to the original system and the system with EV and PV resources added, respectively. The maximum CPFSV of the system decreased by 52% and 75.1%, respectively. In addition, the engineering value of this method is verified through a real-machine system with 199 nodes in a district of Kunming. Therefore, the energy storage configuration method proposed in this article can provide a reference for solving the outstanding problems caused by the large-scale access of EVs and PVs to the distribution network. Full article

The degradation of complex geological structures due to thermo-mechanical cycling results in a reduction in bearing capacity, which can readily induce engineering issues such as uneven settlement, cracking, and even the destabilization of the foundations of molten salt storage tanks. This study establishes a foundational model for a molten salt storage tank through the use of COMSOL Multiphysics and conducts a numerical simulation analysis to evaluate the settlement deformation and temperature distribution of the foundation under the influence of thermo-mechanical coupling. Concurrently, the research proposes two distinct design approaches for the tank’s foundational structure. A comparative analysis of the results indicates that the use of a pile raft foundation in conjunction with a traditional foundation mode results in a reduction of settlement at the center of the foundation’s top surface by 380.1 mm, while also decreasing the maximum effective stress in the steel ring wall by 240.7 MPa. The thermal effects impact a depth of 10 m in the foundation soil and an influence radius of 20 m. Additionally, the foundation soil exhibits optimal thermal insulation properties, resulting in minimal energy loss. These findings indicate that the pile raft foundation in conjunction with a traditional foundation mode displays remarkable adaptability to complex geological conditions, with both settlement and temperature distribution of the foundation maintained within acceptable limits. Full article