A double-tube gear-shaped reactor for solar photocatalytic degradation of R134a was proposed to address the problems of low photon utilization rate and uneven irradiance distribution in the parabolic-trough concentrating reactor. The reaction and heat-transfer performance of the proposed variable-density and uniform-density gear-shaped reactors was analyzed numerically simulations. The research results show that when the influence of temperature is not considered, the proposed gear-shaped reactor can significantly increase the total reaction rate. When the influence of temperature is taken into account, the reaction-rate enhancement is somewhat reduced, but variable-density gear-shaped reactors with larger numbers of teeth remain effective in most cases. Variable-density gear-shaped reactors exhibit better reaction and heat-transfer performance than uniform-density gear-shaped reactors.

This study selects four observation stations in the northern, central, and southern areas of the South China Sea to conduct comparative analyses of measured wave data characteristics, joint distributions, and seasonal variations. Deep learning models were employed to investigate wave prediction performance, with emphasis on the impacts of hidden layer quantity, optimization algorithms, and model architecture. Results demonstrate that wave heights and periods predominantly concentrate in 0-4 m and 4-9 s ranges across all regions. Waves in the northem area of the South China Sea waves display narrower parameter distributions compared to broader ranges in central and southern zones. Nearshore areas adjacent to the mainland exhibit markedly lower wave energy potential. Autumn and winter seasons witness significantly larger wave heights in the central and northern areas of the South China Sea than spring and summer. The Gated Recurrent Unit （GRU） model outperforms Long Short-Term Memory （LSTM） and Multilayer Perceptron （MLP） architectures in prediction accuracy. Minimal prediction errors were achieved with a single hidden layer combined with Adam or RMSProp optimization algorithms. This research provides novel perspectives and practical references for wave energy prediction methodologies.

This study employs numerical simulation to establish a heat transfer model for a group of double U-shaped borehole heat exchangers （BHEs）, incorporating the influence of groundwater seepage. The evolution patterns of the seepage field and ground temperature field are analyzed, along with a sensitivity assessment of key seepage-related factors affecting heat transfer performance. The results reveal that in the absence of groundwater seepage, thermal interference between adjacent BHEs leads to significant cold accumulation in the central region, impairing heat transfer efficiency. However, the presence of seepage substantially mitigates cold accumulation. Along the direction perpendicular to seepage flow, thermal interference between BHEs is negligible, with heat transfer influence oscutting only from upstream to downstram alony the seepage direction. Sensitivity analysis indicates that the thickness of the seepage layer has the most pronounced impact on BHE heat transfer efficiency, followed by seepage velocity, while the seepage direction exhibits the weakest influence.

Inconsistency problems in energy storage stations are often resolved by adjusting the voltage difference of battery packs with external offline devices, while relevant solutions remain limited. This paper proposes an energy storage lithium-ion battery consistency classification method combining external feature images with the Transfer Learning （TL）-GoogLeNet convolutional neural network （CNN） model. The method constructs external feature images from battery voltage, temperature, and IC curves. It utilizes grayscale layers to highlight peaks and inflection points of curves via different brightness regions, which improves the effective representation of battery status and captures key changes during the aging process. The transfer learning strategy transfers parameters of the pre-trained GoogLeNet to target battery datasets to build the TL-GoogLeNet model for better generalization performance. Comprehensive validation is performed using both publicly available lithium battery datasets and practical operating data from energy storage stations. The proposed method achieves an accuracy rate of over 95%, demonstrating its high classification accuracy and robustness. The results suggest that this method can provide effective guidance for inconsistency elimination and safe maintenance of energy storage stations during their later stages of operation.

Using electrochemical energy storage, flywheels, and other medium- and short-term storage systems as independent entities to participate in grid frequency regulation can effectively reduce the frequent load adjustment demands of thermal power plants and increase the flexibility of frequency regulation. Currently, there is a lack of reasonable planning methods for the power and capacity of independent energy storage stations participating in secondary frequency regulation ancillary services. This paper determines the output curves of all grid-connected entities by using day-ahead scheduling plans and intra-day rolling optimization, thereby establishing the benchmark operating point for frequency regulation resources. This paper then constructs a multi-objective function that includes frequency stability and economic indicators of the power grid, establishes a regional power grid secondary frequency regulation model with energy storage participation, obtain real-time frequency regulation demand of the power grid, and determines the optimal capacity planning of independent energy storage power stations under different levels of participation. Finally, through case models on multiple time scales, the proposed strategy is verified to achieve the economic and security benefits of independent energy storage stations assisting with grid secondary frequency regulation.

Taking the collaborative operation of distribution networks and microgrids （MG） as the research object, a distributionally robust coordination control method considering the battery aging cost for the coordination between distribution networks and MG is proposed. Firstly, with the participation of reactive power auxiliary services, taking the economic efficiency of the operation of the distribution network and the MG respectively as the interest demands, and considering the aging cost of the battery energy storage （BES） model, a two-layer Stackelberg game model for the integrated active and reactive power coordination control in the coordinated operation of the distribution network and the MG is established. Then, driven by the historical data sets of the output of distributed photovoltaic （PV） power and loads, an uncertainty set of the probability distribution of typical scenarios is constructed based on the KL divergence, and a distributionally robust coordination control model for the coordination between the distribution network and the MG is established. Finally, the Karush-Kuhn-Tucker （KKT） conditions and the column and constraint generation （C&CG） algorithm are used to transform the two-layer distributionally robust coordination control model for the coordination between the distribution network and the MG into a single-layer model for iterative solution. The simulation results on the improved 69-bus test case show that the proposed distributionally robust coordination control method considering the battery aging cost for the coordination between the distribution network and the MG can effectively balance the economic efficiency and robustness of the coordinated operation of the distribution network and the MG.

With the large-scale development of integrated energy microgrids, microgrid cluster cooperative operation has gradually become an important way to improve the security and stability of the power system. As the regulation means for flexible operation of microgrids, integrated demand response can guide the active adjustment of multiple loads through price signals, and shared energy storage can realize inter-temporal energy balance through energy shifting, which provides important support for the cooperative operation of microgrids. In order to improve the economy of microgrid cluster cooperative operation and enhance the satisfaction of each participant, this paper constructs an asymmetric Nash bargaining model of a microgrid cluster with shared energy storage taking into account the integrated demand response. Firstly, the optimization models of microgrids and shared energy storage with integrated demand response are constructed with the optimization objective of minimizing the economic cost of each microgrid and shared energy storage, respectively. Secondly, an asymmetric Nash bargaining model based on the difference in the size of the energy contribution of each participant is proposed and decomposed into a cost minimization subproblem of the microgrid cluster and a trading tariff bargaining subproblem. Finally, a microgrid cluster is taken as an example for case analysis, and the results show that the constructed operation optimization model can effectively improve the operation benefits of the collective as well as the participant, and the cooperation benefits are fairly distributed based on the energy contribution size of each participant.

In this paper, a simulation model for a high-temperature packed-bed thermal energy storage system is constructed. Binary nitrate salt is used as the heat transfer fluid, and quartz stone and steel slag are adopted as porous filling materials respectively. The fluid simulation research is implemented on the thermocline stratification heat storage mechanism of single-tank molten salt systems with porous media. While keeping other conditions unchanged, the effects of varying physical properties of porous fillers on the heat storage performance are analyzed through simulations to optimize the system design. The influence laws of flow velocity in the porous region, porosity, particle diameter and the height-diameter ratio of the heat storage tank on system performance are obtained. The overall research results can provide data support and design references for thermal storage medium screening, packed-bed configuration design and tank size optimization of solar thermal power stations.

With the high penetration of new energy sources, grid-forming BESSs are more frequently used to build new power systems. However, the reliability of BESS is highly correlated with the operating temperature： high temperature not only accelerates the capacity degradation, but also triggers the thermal runaway. This paper proposes a reliability analysis framework for grid-forming BESS, constructs a multi-domain coupling model of electricity-heat-capacity based on the operating characteristics of the grid-forming system, associates the thermal runaway with the degradation failure probability model, embeds it into the Monte Carlo simulation framework, and evaluates the system reliability and the risk of loss of power based on the topology of the string groups to analyze the evolution from cell failure to the overall performance. A multi-scenario simulation is carried out in the wind-storage complementary system to evaluate the operational reliability under different initial SOC and dispatch strategies. The simulation results show that the framework can effectively reflect the performance evolution of grid-forming energy storage under dynamic perturbation and degradation coupling conditions.

In this paper, firstly a novel dynamic series equalization structure of the battery pack is proposed, which integrates a converter and equalizer. Secondly, based on this topology, an improved low switching frequency nearest level control strategy is proposed. By sorting the state of charge of the sub-module battery units, the multi-level step wave is output. The voltage harmonic distortion rate of the final output waveform is 13.02% lower than that of the traditional method, and the state switching step is added to avoid the frequent switching of the sub-modules caused by the slow change of the state of charge. At the same time, the equalization control method of mixed variables is introduced, and the equalization parameters such as inductance and duty cycle are analyzed and designed to ensure that the battery unit meets the voltage level requirements before it is put into use, and the difference in state of charge between the battery units is reduced. Through structural integration and control strategy optimization, the efficiency and performance of the battery energy storage system are improved. Finally, the effectiveness of the proposed topology and control strategy is verified in MATLAB.

A nonlinear analysis method based on I-V curves is proposed to assess membrane electrode assembly （MEA） degradation in a proton exchange membrane fuel cell （PEMFC）, aiming to meet the growing demand for health monitoring. This method enables real-time extraction of key performance parameters-electrochemically active surface area （SECSA）, hydrogen crossover current （iH2）, limiting current （ilim）, and high-frequency resistance （RHFR）—without additional electrochemical tests. By employing a nonlinear least squares fitting method, the degradation parameters are identified directly from I-V curve data. The feasibility and effectiveness of the proposed method are validated using the data from a single-cell PEMFC tested over 6000 hours. During the 0-4000 h period, the model shows high fitting accuracy, while during the 5000-6000 h period, intensified membrane degradation and measurement time lag lead to an increased fitting error in iH2. Despite this, the method remains effective for real-time evaluation of MEA degradation in practical PEMFC applications, and can be extended to evaluate single cells in a stack, thereby enabling online testing and assessment of the overall stack health condition.

Underexpanded jets generated by high-pressure hydrogen leakage develop complex shock wave structures in the near-field region, making the conventional low-pressure jet integral model inapplicable for underexpanded jets. While notional nozzle models are usually used to provide equivalent boundary conditions for the integral model, the performance of combining different notional nozzle models with the integral model needs to be evaluated. This study combines several commonly used notional nozzle models with the integral model, and evaluates the calculation accuracy based on experimental data. The results show that the predictions agree well with the experimental data for the notional nozzle models that do not assume momentum conservation. However, for the notional nozzle models conserving momentum, the predicted nozzle exit mass flux, velocity and centerline concentration decay rate are larger, resulting in a significant deviation from the experimental data.

To assess the risk of hydrogen leakage from fuel cell vehicles in small residential garages, this study employed a virtual nozzle model to calculate various physical parameters of hydrogen diffusion following leakage from a 35 MPa hydrogen storage tank. These parameters served as boundary conditions for Fluent simulations. A total of twelve working conditions were simulated by coupling six factors： garage door status （open/closed）, air intake vent status （open/closed）, varying exhaust vent velocity, exhaust vent layout, natural wind speed, and natural wind direction. Monitoring points were set up to reveal the influence of difterent ventilation modes on hydrogen diffusion. Key findings reveal that when garage doors are open, natural ventilation demonstrates superior risk mitigation compared to mechanical ventilation, with wind direction significantly influencing hydrogen diffusion patterns. Under closed-door conditions, mechanical ventilation reduces the hazardous hydrogen volume to some extent. Notably, the UDF （User-Defined Function）-controlled intelligent ventilation strategy （exhaust velocity=hydrogen mass fraction×800 m/s） demonstrates enhanced hydrogen removal efficiency after 30 seconds, with distributed ventilation layouts outperforming centralized configurations. This research provides critical insights for optimizing ventilation systems in hydrogen vehicle storage environments.

The （Ti_0.32Cr_0.30V_0.38）_1-xFe_x（x=0、0.07、0.17） alloys with a BCC structure and high hydrogen storage capacity were designed and prepared. These alloys with varying Fe contents were composited with Mg via reactive ball milling under a hydrogen atmosphere to study their effects on the kinetic and thermodynamic properties of the Mg-based hydrogen storage materials. The hydrogen storage properties of the alloys and composites were evaluated by Sieverts’volumetric method, while their microstructures were characterized by XRD, SEM, and TEM. The results demonstrate that the composite with x=0.07 exhibits optimal hydrogen storage performance, showing a reduced onset dehydrogenation temperature of 219 ℃ and a hydrogen desorption capacity of 4.22 wt% within 80 minutes at 260 ℃. Further, the dehydrogenation enthalpy is decreased by 13 kJ/mol compared to MgH₂ prepared via the same synthesis method. The dehydrogenation activation energy of the composite exhibits a gradual reduction with increasing Fe content in the alloy. The Mg/（Ti_0.32Cr_0.30V_0.38）_0.83Fe_0.17 composite exhibits the lowest dehydrogenation activation energy of 80.48 kJ/mol. The researche reveals that the in-situ formed Mg₂FeH₆ provides active sites for the dehydrogenation reaction, while the electron transfer between the alloy elements and Mg reduces the electron cloud density of Mg, thereby facilitating the dissociation of H atoms. The mechanism analysis reveals that （Ti_0.32Cr_0.30V_0.38）_0.93Fe_0.07 exhibits high hardness and resists pulverization during reactive ball milling. These characteristics are more conducive to grain refinement and the formation of γ-MgH₂ in the Mg/（Ti_0.32Cr_0.30V_0.38）_0.93Fe_0.07 composite, ultimately enhancing its thermodynamic performance.

Research on the prediction accuracy of non-isothermal electrolyzer models under various operating scenarios remains limited. This study develops a non-isothermal model coupling heat transfer and voltage, validated by experimental data. For 800 W and 50 kW electrolyzers, the combined effects of current density, electrolyte temperature, and ambient temperature on cell voltage and temperature are analyzed. Results indicate that, compared to the isothermal model, the non-isothermal model offers superior prediction accuracy for both voltage and temperature under high current density and low electrolyte temperature scenarios. It also shows better temperature prediction under low current density and high electrolyte temperature. Furthermore, annual energy consumption predictions from both models across various current densities （with a 60 °C electrolyte） are compared. The isothermal model tends to overestimate power consumption, with errors increasing significantly at higher current densities. These findings provide a theoretical basis for deciding when to incorporate heat transfer models in electrolyzer performance prediction.

This study is based on a quasi-2D multiphysics domain model for Proton Exchange Membrane Fuel Cells （PEMFCs）. A systematic investigation is conducted using the controlled variable method to evaluate the impact of model architecture and simulation configurations on computational efficiency. The steady-state prediction performance of the proposed model is validated through an analysis of the distribution characteristics of physical quantities, while the effects of load amplitude and loading rate on the model’s dynamic response are explored.The results demonstrate that the integrated simplification strategy for porous layers, reduction in flow channel segments, increment of time step size, and adoption of a fast acceleration mode can effectively enhance the simulation rate. Specifically, for a 15-second simulation, the computation time is reduced to 1.73 seconds, achieving an 8.67-fold acceleration. The predicted distributions of physical quantities are consistent with theoretical expectations, indicating robust steady-state prediction capability. Furthermore, it is observed that larger load amplitudes and higher loading rates result in more significant voltage undershoots.This research provides valuable insights for the development of fast PEMFC simulations and hardware-in-the-loop （HIL） simulation implementations.

In alkaline water electrolysis （AWE） systems powered by renewable energy, elevated hydrogen to oxygen （HTO） concentration caused by power fluctuations poses safety risks, and improper operating parameters lead to reduced system efficiency. To address these issues, this study proposes a coordinated control method based on the feasible domain of operating parameters. First, the influence of temperature variations and shunt currents on the AWE system under fluctuating operating conditions is analyzed, and models for HTO concentration and system efficiency are established, followed by a parameter sensitivity analysis. Then, the feasible domain of AWE operating parameters is determined based on the safety boundaries of current density, pressure, and electrolyte circulation flow rate. Finally, a feasible-domain-based multi-objective optimization control method is proposed for AWE operation under fluctuating conditions, and its effectiveness is verified in photovoltaic and wind power hydrogen production scenarios. The results demonstrate that the proposed method effectively extends the system’s operating hours, increases hydrogen production, enhances the adaptability of the AWE system to renewable energy, and achieves safe and efficient hydrogen production.

This paper focuses on "zero-gap" alkaline water electrolysis with high current density and excellent stability, summarizing recent research progress. We systematically discuss how catalytic activity, electronic conductivity, hydrophilicity, and pore diameter can be optimized to enhance the performance of catalysts/electrodes and diaphragms. Additionally, we propose the influence mechanisms and optimization strategies for industrial-scale applications. Notably, our comprehensive research reveals that electrode pore diameter exhibits completely opposite mechanisms in enhancing bubble transport and the catalyst's intrinsic activity. Diaphragms with improved hydrophilicity significantly reduce cell ohmic resistance. Finally, based on polarization mechanisms, we discuss future prospects and ongoing efforts in this field.

To address voltage limit violations caused internally in large-scale photovoltaic （PV） power stations, this paper proposes a distributed non-convex optimization method for system voltage based on an enhanced two-level alternating direction method of multipliers （E-TL-ADMM）. First, the method establishes a non-convex voltage optimization model for large-scale PV systems, incorporating nonlinear Distflow power flow constraints. Second, by introducing a nested inner and outer loop structure, the E-TL-ADMM overcomes the limitations of traditional ADMM, such as poor convergence and insufficient accuracy, to achieve superior voltage control results. Validation based on a typical large-scale PV power station case demonstrates that： when solving the system non-convex optimization problem, the E-TL-ADMM significantly outperforms the traditional ADMM in optimality with average error reduction exceeding 0.17%; it also exhibits clear convergence advantages under high-accuracy residual threshold conditions, with optimized voltage results effectively ensuring PV station system voltage safety.

Aiming at the problem that excessive meteorological parameters in traditional forecasting models lead to reduced prediction accuracy, a combined prediction model integrating kernel-based fuzzy C-means clustering（KFCM）similarity-day theory, crested porcupine optimizer （CPO）, and squeeze-and-excitation （SE） channel attention mechanism with a convolutional long short-term memory （CNN-LSTM） network is proposed. Firstly, primary meteorological features are identified through Pearson correlation coefficient and maximal information coefficient. Secondly, the kernel-based fuzzy C-means clustering algorithm is applied to flexibly categorize samples into four weather types： sunny days, partly cloudy days, sunny to cloudy days, and rainy days. Subsequently, a CNN integrated with the SE mechanism extracts spatiotemporal features from key meteorological parameters and historical power data. Finally, CPO is utilized to optimize the hyperparameters of the long short-term memory （LSTM） network, and the optimal hyperparameter combination is selected for prediction. The comparative experiment and simulation results show that under the four weather conditions, compared to baseline models, the proposed model reduces the mean absolute error （MAE） by 2.67, 1.9054 kW, 1.6192, and 2.1716 kW on average, respectively; decreases the root mean square error （RMSE） by 3.1462, 2.1492, 1.7318, and 2.3303 on average. The average MAE, RMSE, and R² values across all weather conditions reach 0.6237, 0.8854, and 0.9982, respectively, demonstrating the model’s effectiveness.

Photovoltaic （PV） power generation, as a crucial source of clean energy, requires accurate power forecasting to facilitate efficient power dispatch. PV power is highly influenced by meteorological factors and exhibits significant intermittency and volatility. Traditional time series forecasting models often fail to capture the dynamic correlations among meteorological factors, leading to limited forecasting accuracy. Meanwhile, single deep learning architectures struggle to effectively capture the intricate interplay between multi-scale temporal dependencies and localized feature variations. To address these challenges, this paper proposes a dual-channel hybrid forecasting model integrating empirical mode decomposition （EMD）, Transformer, and temporal convolutional network （TCN）. EMD is employed to decompose the raw PV power signal into a set of intrinsic mode functions （IMFs）. The stationarity of each IMF component is quantified using sample entropy （SE）, guiding the selection of tailored predictive models for components with distinct stationarity characteristics. An enhanced residual transformer （Enresformer）, which incorporates residual networks, and a gated network-enhanced TCN model are employed to forecast these components, with subsequent ensembling of their outputs. An error correction mechanism is introduced to further refine prediction accuracy. Comparative experiments based on multiple real-world PV datasets show that the proposed model significantly outperforms existing deep learning and hybrid models in terms of forecasting accuracy, providing an efficient and reliable solution for PV power prediction in practical power stations.

This paper proposes a power control method for distributed photovoltaic （PV） grid-connected systems in distribution networks based on an improved aquila optimizer. This method uses internet of things （IoT） technology to collect real-time key data from distributed PV grid connection and distribution networks, including current, voltage, weather conditions, and load status. A generalized predictive control method is adopted to construct a power prediction control model by integrating these key data. The method fully considers the dynamic characteristics of PV systems and grid fluctuations, and derives future output predictions of the PV grid-connected system by employing the Diophantine equation, thereby achieving effective prediction of voltage fluctuations and flicker over a future period. Based on the prediction results and actual grid requirements, a rolling optimization strategy is designed to further optimize power control in response to the high dynamics and uncertainty of the power grid. An improved Aquila optimizer is introduced to optimize the regulation parameters of the inverter according to the prediction results and actual grid demands. By adjusting these parameters, the method reduces output power fluctuations, subsequently alleviates voltage fluctuations and flicker, and achieves accurate prediction and control of distributed PV grid-connected power. Experimental results show that, compared with the proactive control method, the proposed method exhibits superior performance in power control of distributed PV grid connection in distribution networks and is suitable for practical application scenarios.

Accurately forecasting sky imagery and its impact on solar power generation is essential for intelligent grid scheduling and the efficient management of renewable energy resources. However, capturing the dynamic evolution of cloud patterns and their complex, nonlinear relationship with photovoltaic （PV） power output remains a formidable challenge. To tackle this problem, we propose a multimodal deep learning framework that integrates Transformer and Mamba architectures, effectively leveraging both spatiotemporal features from sky images and infrared spectral information to improve forecasting precision. Within this framework, the Transformer is responsible for modeling the temporal dynamics of cloud motion, while Mamba focuses on extracting salient features from infrared spectra. A cross-modal feature fusion strategy is employed to learn the intricate nonlinear mapping between sky imagery and power output. We evaluate our model on the Stanford sky image and PV generation datasets, conducting extensive experiments that demonstrate superior performance in both sky image forecasting and PV power prediction tasks. Ablation studies reveal that the full model—incorporating infrared data, temporal modeling, and multi-scale feature extraction—achieves an RMSE of 13.04kW and an SSIM of 0.94653 for image prediction. Moreover,the proposed method achieves an MAE of 1.50 kW, approximately 4.5% lower than that of the second-best Tau model.Under typical cloudy weather conditions, the model is able to effectively track the trend of photovoltaic power fluctuations caused by cloud occlusion.

Firstly, this study establishes a multi-condition operation model for electrolyzer arrays incorporating start-stop characteristics, while introducing lifespan degradation cost induced by power fluctuations to quantify the impact of frequent power fluctuations caused by simultaneous participation in energy and frequency regulation markets on the lifespan of hydrogen production equipment. Secondly, a two-stage stochastic-robust optimization model is formulated to maximize total revenue, systematically considering various influencing factors such as the operation characteristics of electrolyzer arrays, photovoltaic output and electricity market price uncertainty. The proposed max-min-max optimization framework with integer variables is effectively solved using the Nested Column-and-Constraint Generation algorithm. Finally, simulation results across four operational scenarios demonstrate that PV-driven hydrogen stations adopting the dual-market participation strategy achieve comprehensive revenue maximization while enhancing photovoltaic self-consumption rates and renewable energy utilization efficiency. Furthermore, the developed operational strategy balancing start-stop dynamics and power fluctuation effects enables dynamic trade-offs between frequency regulation revenues and equipment degradation costs, establishing an equilibrium between short-term economic returns and long-term operational reliability of hydrogen production systems.

To address the issue of bolt loosening detection and localization in the structural safety of photovoltaic （PV） power stations, this paper proposes a PV fastening state monitoring system based on a chained resistive sensor network. Firstly, based on the principle that bolt loosening is caused by the relative rotation between the bolt and nut, surface sensors and bracket sensors are designed for different bolt structures in PV modules, converting bolt rotation into resistance signals. Secondly, considering the distribution characteristics of surface bolts and bracket bolts, forward and reverse detection circuits for the surface and detection circuits for the bracket are respectively designed. By monitoring the changes in the total resistance of the detection circuit, the system can determine the position and quantity of loosened bolts. Based on the I-V characteristics of diodes, a voltage-current difference ratio method is proposed to reduce the impact of diode internal resistance on the total resistance, thereby improving the accuracy of surface detecting the number of loosened surface bolts. Experimental results show that the system can accurately detect the location and number of loosened bolts in PV power stations. The detection capacity increases with higher detection voltage and sensor resistance. This system enables remote monitoring and early warning of bolt loosening, providing an efficient and reliable solution for bolt status monitoring in distributed PV power stations.

This paper proposes a hybrid interval prediction model for Photovoltaic （PV） output by integrating the maximal overlap discrete wavelet transform （MODWT）, complete ensemble empirical mode decomposition with adaptive noise （CEEMDAN）, Bi-directional Temporal Convolutional Network （BiTCN） and Transformer architecture. Firstly, MODWT and CEEMDAN are applied to perform dual decomposition of the PV power time series, yielding intrinsic mode function components. Secondly, the IMFs are categorized into temporal and stochastic components based on their Spearman correlation coefficients. Subsequently, the temporal components are predicted using Transformer, while the stochastic components are predicted using BiTCN. The prediction results from both pathways are then reconstructed to obtain point forecasts. Finally, the point forecasts are expanded into prediction intervals using a kernel density estimation method. Validation on actual case studies demonstrates that this approach, leveraging dual signal decomposition （time and time-frequency domains） and integrating the dual-channel network architecture （BiTCN and Transformer）, achieves an average accuracy improvement of 29.36 % compared to other benchmark prediction models.

In order to evaluate the environmental impact of photovoltaic product manufacturing, the carbon emissions attributable to the production of 1 kg of monocrystalline silicon wafers were estimated using a life cycle assessment （LCA） methodology, including contribution analysis, sensitivity analysis, improvement analysis and energy payback period analysis. It is calculated that the production of 1 kg of monocrystalline silicon wafers emits a total of 397.6 kg（in this paper, carbon emissions are measured in carbon dioxide）. The contribution analysis showed that the CO₂ emission of monocrystalline silicon rods and wafers was the largest, accounting for 58.22%; polysilicon purification was the second, accounting for 32.63%; industrial silicon smelting has the smallest CO₂ emissions, accounting for 9.15%. Sensitivity analysis shows that electricity is the most sensitive factor affecting the carbon emissions of the entire process, with a sensitivity of 37.66%. The improvement analysis shows that the use of renewable electricity is a significant emission reduction measure, which will reduce carbon emissions by 149.84 kg. Through data analysis, the energy payback period of 1kg monocrystalline silicon wafer is 3.35 years. Finally, the long-range energy alternatives planning system （LEAP） is used to predict the carbon emissions of 1 kg monocrystalline silicon wafer production under multiple single scenarios and their comprehensive scenarios from 2025 to 2030, and it is found that the potential of combining technology and energy structure emission reduction is the highest, followed by the energy structure emission reduction scenario, and the potential of the technology emission reduction scenario is the lowest.

The distributed nature and uncertainty of distributed photovoltaic（PV） systems pose new challenges to their equivalent modeling methods. In response to the existing issues of complex modeling and low accuracy in multi-machine equivalent modeling of distributed PV, this paper proposes an intelligent partitioning and equivalent modeling method for distributed PV clusters that integrates improved K-means and CNN-LSTM. Firstly, multi-dimensional features including output voltage, output current, active power and reactive power at the point of interconnection, and outer-loop control parameters of inverters are selected as the basis for clustering. The optimal number of clusters for distributed PV clusters is determined by introducing the elbow method. A K-means clustering algorithm that uses dynamic time warping distance to calculate the distance matrix and optimize the initial cluster centers is adopted to achieve intelligent partitioning of distributed PV clusters, effectively reducing the subjectivity and complexity of clustering. This paper combines the advantages of CNN and LSTM, where CNN is used to capture the spatial dimension features in distributed PV data, and LSTM focuses on extracting the dynamic characteristics of time series data, thereby constructing a CNN-LSTM equivalent model for distributed PV clusters that can reflect spatial distribution and accurately predict temporal evolution. Finally, the accuracy of the equivalent model of the clustering method and the distributed PV cluster equivalent model proposed in this paper is compared through simulation in PSCAD. The proposed model improves the equivalent accuracy of active power by 1.3, 0.32, and 5.89 percentage points, and the equivalent accuracy of reactive power by 0.85, 0.80, and 4.91 percentage points under three different disturbances, verifying the effectiveness and practicality of this method in improving the modeling efficiency and accuracy of distributed PV systems. This provides a technical reference for the analysis and optimization of large-scale distributed PV grid-connected systems.

Aiming at the problems existing in the defect detection of photovoltaic modules, such as low detection accuracy, high missed detection rate of small targets and large number of network parameters, a lightweight improved model CBS-YOLOv8（CBAM-BiFPN-SIoU-YOLOv8） based on YOLOv8 is proposed. In the feature extraction stage, the feature representation of the defect area is strengthened through the embedded CBAM attention module. In multi-scale feature fusion, the bidirectional feature pyramid network （BiFPN） is adopted, and the SIoU loss function is improved to enhance the bounding box regression accuracy. Furthermore, knowledge distillation technology is integrated to construct a teacher-student collaborative training framework to achieve model compression and performance transfer. The experimental results show that the mAP_0.5 of the improved model on the self-built photovoltaic module EL image dataset reaches 87.50%, which is 7.20 percentage points higher than that of the original YOLOv8. The number of parameters of the CBS-YOLOv8 model after distillation and compression decreased by 75.42%, and the detection accuracy only dropped by 0.45percentage points, meeting the lightweight requirements while maintaining the detection accuracy.

The computational fluid dynamics software ANSYS Fluent, based on the Standard k-ε turbulence model, is employed to investigate the distribution of surface wind pressure and the aerodynamic shape coefficients of photovoltaic （PV） arrays under varying wind incidence and installation angles. The results illustrate that the front-row PV modules are highly sensitive to angle variations. The downward vertical pressure loads are significantly increased with the installation tilt angle. Due to the shielding effect of the front row, low surface pressure fluctuations are observed on the downstream modules. Flow-field stabilization characteristics are exhibited in the third and fourth rows. The suction effect on the lower surface of the array is dominated by low pressure induced by the flow around the array, and an overall increase in suction coefficient appears with increasing wind incidence angle. Nevertheless, owing to the effects of flow separation and reattachment, a stagewise reduction in local suction is identified in the second and third rows, forming a three-stage distribution pattern characterized as ‘front-row dominant, second-row transitional, and rear-row stabilized’.

Based on the equivalent circuit of the double-diode model of solar cells, a model for characterizing the output characteristics of bifacial photovoltaic modules has been established. A method for calculating model parameters has been proposed to address the multi-peak output characteristics under shading conditions. The effects of various shading conditions on the output characteristic curve and model parameters of the modules have been investigated. Additionally, the impacts of changes in shading intensity and the quantity of shaded cells on the model parameters have been analyzed. Simulation and experimental results demonstrate that the calculated model parameters and the output curves generated based on these parameters display remarkable accuracy. The established output characteristic model and its parameter calculation method are applicable to bifacial photovoltaic modules and strings, providing technical support for their fault diagnosis.

Based on Chengdu’s solar resource conditions and energy consumption structure characteristics, this study takes Chengdu’s industrial parks as an example. Focusing on both factory buildings and non-factory buildings, it conducts a distributed PV potential assessment from two perspectives： rooftop PV utilization potential and construction sequencing. At the level of potential utilization of rooftop photovoltaics, by simulating the solar radiation on building roofs and finely extracting the effective installation area of rooftop photovoltaics, the installed capacity of rooftop photovoltaics in industrial parks is accurately calculated to be 13.65 million kW, with an annual total power generation of 10981.00 GW·h. The total photovoltaic power generation on industrial plant roofs is 8895.85 GW·h, and the supply-demand matching degree between rooftop photovoltaic power generation and industrial electricity comsumption is 89.67%. The potential for rooftop photovoltaic utilization is enormous. At the temporal level of rooftop photovoltaic construction, a temporal decision-making model is constructed based on the entropy weight method, proposing a five-level classification of deployment units “highest, second highest, general, low priority, not recommended”, providing a new perspective for energy security and low-carbon transformation of urban industrial parks.

To improve the accuracy of predicting dust deposition on the surface of photovoltaic modules, this paper improves the criteria for determining surface dust random deposition and the numerical simulation method for dust deposition based on dust attributes and the randomness of collision objects during dust deposition. The effectiveness of the method is verified using Wuhan city as an example. First, an analysis tree of factors influencing dust deposition was constructed to identify key elements affecting dust deposition on photovoltaic modules. Second, criteria for determining dust deposition were established for two types of collisions： those between dust particles and the module surface, and those between incoming dust particles and already deposited dust. Then, the dust deposition judgment criteria were applied to a photovoltaic power station in Wuhan, considering the randomness of dust particle composition and size. With the help of Fluent software, a two-dimensional numerical simulation model was established to describe the dust deposition process by combining random numbers with user-defined memory. The average and standard deviation of the relative error between the predicted and actual power generation based on the simulation results of dust accumulation were 8.78% and 13.64%, respectively. Finally, the orthogonal experimental design method was used to set the simulation conditions. The numerical simulation results based on the orthogonal experimental design showed that the wind speed had the most significant impact on the dust deposition results.

In response to the problem of limitations in modeling nonlinearity, volatility, and multi-scale periodicity characteristics in photovoltaic power prediction, a short-term photovoltaic power prediction model using multi-scale periodic features was proposed. Firstly, the raw data are preprocessed and the Pearson correlation coefficient features are reconstructed, then cross-feature interactions are optimized through a gated aggregation redistribution mechanism. Secondly, a dual-phase periodicity-residual optimization mechanism is established, where residual components obtained through residual cycle forecasting are processed by Transformer encoders, while periodic components undergo multi-scale periodic feature fusion. The periodic processing module employs temporally adaptive multi-scale convolutional kernels and a time-dimension averaging fusion strategy to enhance periodic pattern recognition. Finally, predictions are generated through integrated outputs from both residual and periodic modeling paths. Experimental evaluations across three real datasets show that the proposed model outperforms comparison models across various horizons. Specifically, in PV Dataset 1, the average MAE and RMSE are reduced by 4.10% and 3.98% respectively compared to the second-best model; in PV Dataset 2, these indicators are reduced by 3.31% and 1.51%; in the short-term tasks of the ETTh1 dataset, the MAE is reduced by 1.66%. Additionally, the model achieves the highest R² in multiple tasks, verifying its superior predictive performance and generalization ability in short-term photovoltaic power prediction.

This paper proposes an ultra-short-term photovoltaic power station irradiance forecasting method based on an improved block-ladder method. Firstly, the dataset is preprocessed, including fisheye distortion correction on ground-based sky images. A clear sky model is established for the irradiance data to generate clear sky indices. Next, clouds in the ground-based sky images are identified using an adaptive threshold calculation method based on minimum cross-entropy, and cloud motion is predicted with a similarity-weighted cloud motion vector calculation method. Finally, ultra-short-term photovoltaic irradiance forecasting is performed using the improved block-ladder method. Experimental results on the UCSD-Folsom dataset show that this method improves forecasting accuracy and extends the forecasting period of the traditional block-ladder method.

To address the decline in power generation efficiency and the shortened operational lifespan of photovoltaic （PV） modules caused by surface dust accumulation, this paper proposes an improved YOLOv5-based model for dust detection. The proposed model enhances the original YOLOv5 architecture by introducing multi-scale GS convolution modules and VoVGSCSP structures, incorporating the CBAM attention mechanism, and modifying the SPPF module to improve both accuracy and speed. Based on a sufficiently large and representative dataset, CLAHE, sharpening, and Gamma parameter adjustment are collaboratively applied to part of the dataset. An experimental platform was developed in PyCharm to implement and verify the improved model, enabling efficient detection of dust locations and regional dust accumulation on PV module surfaces. Experimental results show that the improved YOLOv5 model achieves 3.3 percentage points increase in m_AP@0.5∶0.95 over the baseline YOLOv5, while the FPS improves from 65.2 FPS to 68.3 FPS, demonstrating advantages in both accuracy and speed. This study develops a surface dust detection model for photovoltaic modules under varying illumination conditions, providing methodological support for operation and maintenance management and cleaning decision-making in photovoltaic power plants.

This study addresses the performance optimization of photovoltaic direct-drive thermoelectric refrigeration systems under varying irradiance conditions. It explores direct drive using photovoltaic cells to achieve single-stage energy conversion and reduce system complexity. By establishing a MATLAB/Simulink dynamic simulation model and conducting experimental verification under real weather conditions, the nonlinear regulation mechanism of hot-end temperature on refrigeration performance in varying solar irradiance environments is revealed： when the hot-end temperature is less than or equal to 35 ℃, the coefficient of performance （COP） initially increases and then decreases with increasing irradiance; conversely, when the hot-end temperature exceeds 35 ℃, COP positively correlates with irradiance in a monotonic manner. The accuracy of the simulation system was verified by evaluating the experimental results and the characteristics of the simulation outputs. Furthermore, through Pearson correlation analysis, the strong positive correlation between refrigeration capacity and solar irradiance （r=0.9138） is quantified, demonstrating that the system can achieve stability in refrigeration temperature through a self-regulating mechanism under dynamic irradiance fluctuations. Additionally, the synergistic optimization patterns between refrigeration capacity and COP under variable irradiance conditions were identified. By adjusting their weights based on different actual operating conditions and leveraging the "optimal balance division point" characteristics, the best working point between the two can be identified. This provides a theoretical framework for the dynamic matching design and energy efficiency optimization of photovoltaic refrigeration systems without energy storage.

This study systematically reviews international aerospace standards and test-verification frameworks for spacecraft solar arrays, with particular focus on test item configurations during the development phase. By comparing the test item specifications and technical requirement criteria of the U.S. Military Standard MIL-STD-1540E and the European Cooperation for Space Standardization （ECSS） standard ECSS-E-10-03A, the study reveals commonalities and discrepancies between the two standards. The findings provide an actionable reference for optimizing China’s spacecraft solar array development processes and establishing robust verification systems.

This study employs the quartet structure generation set to construct the aerogel microstructure. The solar extinction coefficient of monolithic aerogel is simulated using the Monte Carlo method, and its thermal conductivity is predicted based on the Lattice Boltzmann model. The effects of porosity and particle diameter on the optical-thermal performance are systematically analyzed. The results indicate that porosity and particle diameter exert a more significant influence on the solar extinction coefficient, while porosity has a greater impact on thermal conductivity than particle diameter. Specifically, a reduction of approximately 12% in porosity leads to a reduction of about 25% in thermal conductivity, whereas the effect of particle diameter on thermal conductivity is negligible. Finally, building energy consumption in a cold region （taking Zhengzhou as an example） is simulated using a dynamic heat transfer model. It is found that aerogel glazing with larger porosity and particle diameter can reduce energy consumption by nearly 34%.

This study presents a composite catalytic system integrating a nickel-based catalyst （Ni/MgAlOx） with porous foam supports. The investigation systematically examines three representative foam materials—nickel （Ni）, nickel-chromium alloy （NiCr）, and silicon carbide （SiC）—focusing on their distinct influences on solar-driven dry reforming of methance（DRM） performance. Experimental results demonstrate that the Ni/MgAlO_x/SiC foam reactor exhibits optimal catalytic performance, achieving average CH₄ and CO₂ conversion rates of 75.56% and 81.79%, respectively, with a solar-to-fuel efficiency of 38.86%, representing improvements of 5.26 and 3.14 percentage points over NiCr and Ni foam reactors, respectively. The enhanced performance primarily stems from SiC foam’s superior thermal conductivity, which reduces the axial temperature gradient from 167 ℃ （observed for Ni foam） to 59 ℃, thereby broadening the catalytic reaction domain. Furthermore, the SiC foam support exhibits superior mechanical strength that effectively resists internal stress caused by carbon deposition, thereby maintaining structural integrity and enhancing system operational stability. Consequently, the SiC foam reactor demonstrates no significant performance degradation during a 65-hour continuous test, whereas NiCr and Ni foam reactors experience severe performance deterioration at 42 hours and 24 hours, respectively. This study elucidates the synergistic mechanism of thermal conductivity and mechanical strength of foam supports on DRM performance, providing an experimental basis for material selection and thermo-mechano-chemical synergistic design of solar-driven DRM reactors.

To obtain accurate winter indoor calculated temperature for strong solar radiation regions of western China, data from questionnaire surveys and field tests in three typical cities were collected and statistically analyzed. Through evaluating the clothing adjustment behavior and human thermal sensation, the relationships between operative temperature and clothing insulation/mean thermal sensation, respectively, were established under various heating conditions. Then, the neutral temperature was found to be 20.2 ℃ for continuous heating buildings and the lower limit of acceptable temperature was 11.7 ℃ for non-continuous heating buildings. Based on this, it is proposed that the appropriate winter indoor calculated temperatures should be set at constant values of 20.0 ℃ and 12.0 ℃, respectively. Furthermore, for passively heated rooms, taking Lhasa as a case study, an hourly indoor calculated temperature model was developed by integrating the daily fluctuation range of winter indoor operative temperatures （10-14 ℃） obtained from dynamic simulation and the hourly temperature cycle processing method specified in the current thermal design code. These findings provide essential parameter support for optimizing building thermal design in strong solar radiation regions of western China.

Addressing the limited cross-scenario adaptability resulting from the inflexible structures of current short-term power load forecasting models, this paper proposes a deformable convolutional neural network （CNN）-long short-term memory （LSTM）-attention model optimized using an improved sparrow search algorithm （ISSA）. This approach enables the dynamic construction of optimal configurations tailored to diverse data scenarios. Firstly, to achieve adaptive adjustment of both model architecture and hyperparameters, the ISSA jointly optimizes the number of CNN and LSTM layers, learning rate, and batch size. The ISSA incorporates Latin hypercube sampling, cosine annealing, a dynamic spiral strategy, and a Lévy flight mechanism to enhance optimization quality and convergence. Subsequently, a deformable CNN-LSTM-Attention model is constructed based on the optimal configurations identified by the ISSA for different scenario characteristics. Finally, simulation and validation are conducted using power load data from New York State （USA） and South China. The results demonstrate that, across multiple time resolutions and varying regional and seasonal conditions, the proposed model improves the coefficient of determination by at least 1.18% and reduces the root mean square error by at least 45.03% compared to mainstream forecasting models such as CNN-LSTM, demonstrating excellent forecasting performance and cross-scenario adaptability.

A general calculation method for short-circuit faults is proposed, which accounts for multi-sequence network coupling and dynamic control strategy modeling. Firstly, fault equivalent models of multiple types of DGs are established, and the impacts of their low-voltage ride-through and negative-sequence current suppression strategies on sequence component networks are analyzed. Then, based on the superposition theorem, the fault network is decomposed into normal component and fault component networks, and the sequence network boundary condition equations for different short-circuit types are derived to construct a composite sequence network model incorporating IBDGs. Finally, through iterative correction of node voltages and DG injection currents, the short-circuit fault solution is achieved. Simulation results show that the proposed method has good applicability in complex active distribution networks with multiple types and multiple points of DG integration, and the calculation errors of short-circuit faults are all less than 3%.

In order to make full use of the distributed photovoltaic reactive power regulation ability to participate in the voltage coordination control of active distribution network and to improve the effectiveness and stability of voltage regulation, a voltage control strategy of active distribution network considering distributed photovoltaic reactive power margin is proposed in this paper. Firstly, based on the voltage sensitivity matrix, the interaction mechanism between distributed photovoltaic power control and node voltage change is clarified in detail, so as to determine the compensation priority of each node. Secondly, based on the reactive power regulation ability and voltage regulation sensitivity of distributed photovoltaic, the reactive power regulation margin index of distributed photovoltaic is established. Then, considering the reactive power regulation margin index and the compensation priority of the node, the voltage control model is established, and the voltage control model is solved efficiently by the particle swarm optimization algorithm with the precoding technology. Finally, the feasibility and effectiveness of the proposed method are verified by the simulation of the modified IEEE33-node system.

Aiming at the problem that the disorderly charging behavior of electric vehicles （EVs） connected to the grid affects the stability of the grid, a spatio-temporal prediction model of EV charging and swapping loads based on multi-layer complex travel chains is proposed under the condition of considering the accommodation of new energy. The model aims to reveal the dynamic compensation mechanism of swapping loads for the fluctuations of charging loads under the “charging-swapping” interaction mode. Firstly, the travel characteristics of EVs are determined under the multi-layer complex travel chain model, and the Floyd algorithm is used to find the optimal travel path. The unit mileage power consumption is determined by combining the “flow-speed” relationship. Secondly, the charging models of charging and swapping are respectively constructed, and the charging mode selected by EVs is determined under the “charging-swapping” interaction mode. Thirdly, a mathematical model of wind and solar power generation is established. Finally, the spatio-temporal distribution maps of the charging loads and new energy generation are formed through data processing, and the load data under different modes are compared. The experimental results show that the charging demand generated by EVs during driving has obvious regional differences, and the swapping load can effectively mitigate the fluctuations of charging loads under the accommodation of new energy.

In order to solve the problem that the source-load interaction mechanism is unclear when short-process steel enterprises participate in peak regulation of power system with high proportion of renewable energy, resulting in the limited effect of system peak regulation, an optimal control method for short-process steel enterprises to participate in power system peak regulation is proposed. Firstly, the peak regulation architecture of the renewable energy system integrating the production load of short-process steel enterprises is designed, and a hierarchical response model is constructed according to the load regulation characteristics. Secondly, based on the non-cooperative game process of grid demand release, enterprise response solution and aggregator information interaction, the dual signal response mechanism of electricity price and incentive is formulated, taking into account power grid peak regulation demand and enterprise production electricity consumption in the high proportion of renewable energy power system. Finally, a two-layer economic scheduling model of source and load is constructed, and the model is solved through hierarchical iterative and the balance of source and load interests is achieved. The simulation results show that the proposed method can reduce the peak-to-valley difference of load by 2.74%, increase the renewable energy accommodation of the power grid by 5.53%, and reduce the comprehensive electricity cost of enterprises by 17.72%, which significantly improves the response enthusiasm of enterprises and improves the peak regulation effect of the power grid.

Renewable energy grid-connected converters are rapidly increasing in single-unit capacity and power generation share. After short-circuit faults occur, renewable energy systems without synchronous power sources rely on converters to actively inject reactive power to support grid voltage. However, the reactive power regulation range of the grid-connected converters during fault ride-through is affected by various factors including equipment electrical stress constraints and fault types. This range varies continuously during current injection, which may cause the converters to go off-grid when the injected current exceeds the safe controllable boundary. This paper takes full-power renewable energy grid-connected converters such as photovoltaic and direct-drive wind turbines as the research objects, and derives the safe controllable boundaries for converters to inject positive and negative sequence reactive currents during fault ride-through. By analyzing the coupling relationship between injected reactive current and the controllable boundaries, an iterative calculation method for the controllable boundaries considering coupling effects is proposed to improve calculation accuracy. On this basis, this paper analyzes the influence law of the electrical limit of the converter device, fault type, grid strength and other factors on the controllable boundaries, which can provide a design basis for the reactive current references of converters during fault ride-through. Finally, the effectiveness and accuracy of the proposed controllable boundary calculation method are verified by the Matlab/Simulink simulation platform.

To address the problem that traditional model predictive control cannot maintain prediction accuracy when parameters change, a model predictive voltage control strategy incorporating a nonlinear disturbance observer is proposed. First, a nonlinear disturbance observer is designed for three-phase inverters. It utilizes measurements of the inverter-side current and load terminal voltage to estimate the load current in real time, thereby suppressing parameter disturbances and ensuring the accuracy of prediction results. Second, to solve the problem of unreasonable reference voltage acquisition, a droop control method is adopted to generate the reference voltage. Subsequently, combining the inverter capacitor voltage model with parameter disturbance estimation, the voltage prediction equation is reformulated. The value function is also optimized to reduce computational load and enhance the control performance of the system. Finally, simulations and experiments verify the effectiveness of the designed nonlinear disturbance observer and the proposed voltage control strategy.

To address the issue that current limiting degrades fault characteristics and may cause conventional pilot protection to malfunction, this paper proposes a recursive minimum error entropy pilot protection method based on model matching, which improves algorithm robustness and eliminates the adverse influence from current limiting. This paper firstly analyzes the fault characteristics of metallic return bipolar DC grids with CFC under current limiting conditions and explains the drawbacks of traditional current differential protection. An accurate line equivalent model is then established to derive system measurement equations, which are discretized by “quadratic integration” into algebraic equations. Finally, the recursive minimum error entropy algorithm is introduced to solve the state estimation model: the internal and external faults are identified according to the model matching degree, and a fault pole selection strategy is proposed according to the fault current mode component. The simulation results show that the method can quickly and accurately recognize the internal and external faults and correctly select poles within 2 ms after fault inception, with strong ability against current limiting, transition resistance and noise interference.

To balance the interests of energy operators and end-users while achieving low-carbon and economical operation of integrated energy systems (IES）, a Stackelberg game-based optimal scheduling method for hydrogen-containing integrated energy systems (HIES） considering source-load uncertainties is proposed. Based on Stackelberg game theory, a bi-level pricing model is constructed where the integrated energy operator (IEO） acts as the leader and the load aggregator (LA） as the follower. A robust optimization method with adjustable coefficients is adopted to quantify the uncertain parameters. To cope with the bi-level nested structure, multi-objective features and uncertain constraints of the model, a multi-objective optimization approach based on compromise programming is combined with the adaptive differential evolution (ADE） algorithm for bi-level game solution. Finally, a case study using data from the Chongli wind-solar-hydrogen coupled demonstration project is conducted. The results demonstrate that the proposed strategy enhances the operational security of energy networks and achieves balanced benefit distribution between IEO and LA.

Aiming to address the suboptimal performance of single prediction models, which often neglect cross-load correlation features, this study proposes a multi-task learning-based combined model for multi-load forecasting to enhance prediction accuracy. Firstly, the correlation between meteorological factors and loads was quantified to determine the model's input features. Secondly, a prediction model was constructed, utilizing a convolutional neural network （CNN） to extract nonlinear spatial correlations between input features and loads, a bidirectional long short-term memory （BiLSTM） module to capture the long-term dependencies and temporal features of the loads, and a temporal pattern attention （TPA） mechanism to adaptively weight and highlight important temporal information. Finally, multi-task learning （MTL） was employed to extract shared features among cooling, heating, and electricity loads, capturing their coupling relationships, while task-specific branches were used to predict each load type separately. Experimental results based on public datasets demonstrate that the joint forecasting of cooling, heating, and electricity loads achieved reductions in mean absolute percentage error （MAPE） of 36.30%, 23.93%, and 22.63%, respectively, compared to single-task prediction models. This method not only enhances the accuracy and stability of load forecasting but also better reflects the multi-energy coupling characteristics within integrated energy systems.

In the context of “dual carbon goals”, in order to promote the economic operation of integrated energy systems （IES）, reduce carbon emissions of IES, and address the imbalance in energy distribution across different seasons, this paper proposes an IES low-carbon economic optimization method that considers seasonal factors and joint trading of stepped carbon and green certificates. Firstly, equipment models for electricity, heat, gas, and hydrogen-related units are built, and dynamic balance models based on the power characteristics of various entities are constructed. Secondly, based on the existing stepped carbon trading mechanism and green certificate mechanism, a stepped carbon-green certificate joint trading mechanism is proposed. Thirdly, a low-carbon economic optimization model with the least system operation cost and carbon emissions is established, and the optimal weight of the objective function is determined. Finally, based on different examples for several scenarios, the effect of the stepped carbon-green certificate joint trading mechanism on IES in different seasons and its impact on the system’s thermoelectric ratio from the perspectives of cost and carbon emissions are analyzed, and the effectiveness of the method is verified.

Aiming at the collaborative optimization scheduling problem of distribution network-park integrated energy system, considering the carbon trading cost, a collaborative scheduling strategy of distribution network-multi-park integrated energy system based on improved analytical target cascading is proposed. Firstly, aiming at minimizing the sum of distribution network loss and voltage offset, an active distribution network scheduling model is constructed, and the characteristics and adjustment potential of flexible loads and multiple energy sources （such as electricity, heat, green electricity, etc.） in the park are analyzed. A low-carbon economic optimization scheduling model of integrated energy system is proposed, which achieves the dual optimization of high-efficiency energy utilization and low-carbon economy goals. Secondly, combined with the Peaceman-Rachford splitting method and the ATC main-subsystem information iteration strategy, an improved analytical target cascading distributed solution algorithm is introduced, which improves the coordination efficiency of the system through local optimization and information exchange. Finally, the simulation results show that the improved method can not only optimize the energy scheduling of the park, improve the energy utilization efficiency and economy, but also ensure the fairness and transparency of each subject.

Following the completion of primary frequency regulation in large-scale wind farms, the power dip phenomenon occurring during the rotor speed recovery phase may induce secondary frequency drop in power systems, posing challenges to system frequency security. This paper proposes a frequency security-oriented optimal dispatch model for multi-regional power systems considering secondary frequency drop risks. The two-stage optimization framework consists of： 1）A risk-averse day-ahead scheduling stage that co-optimizes operational costs and risks under wind power uncertainty through robust optimization; 2）A frequency security verification stage that validates the proposed dispatch scheme against multi-regional frequency dynamic constraints under extreme wind scenarios. The model innovatively decouples system reserve power into three distinct components： wind fluctuation reserve, power dip reserve, and contingency reserve, thereby achieving coordinated optimization between power drop absorption during wind turbine speed recovery and multi-regional grid frequency security constraints. The column and constraint generation algorithm is employed to solve this two-stage optimization problem. Case studies demonstrate that the proposed model effectively mitigates frequency security risks caused by wind turbines’ operational state transitions after primary frequency regulation while maintaining economic efficiency. The methodology provides theoretical support for secure integration of high-penetration renewable energy in modern multi-regional power systems.

To address the issue of output voltage fluctuations in low-voltage load interface converters of DC microgrids caused by uncertain disturbances, an improved active disturbance rejection control （ADRC） strategy is proposed. This strategy integrates disturbance differential compensation with a cascaded extended state observer （ESO） to more effectively suppress voltage variations. By combining the disturbance differential compensation with the cascaded observer architecture, the proposed method achieves accurate disturbance reconstruction without excessively increasing the observer bandwidth, thereby avoiding system oscillation. Moreover, it alleviates the inherent coupling between tracking performance and disturbance rejection capability. The stability of the proposed control approach was proved based on Lyapunov stability theory. Meanwhile, theoretical analyses on disturbance estimation performance, disturbance rejection performance, coupling characteristics and robustness are carried out to illustrate that the proposed improved control strategy effectively suppresses both internal and external disturbances. Finally, numerical simulations validate the effectiveness and feasibility of the proposed improved control strategy.

This paper proposes a low-stress energy feedforward soft-switching Buck converter from the perspective of the power transmission path. The resonant energy is directly transferred to the load side through a high-frequency transformer, rather than being returned to the power source or indirectly delivered to the load, thereby reducing conduction losses caused by resonant energy circulation. The auxiliary soft-switching network consists of one switching device, two diodes, and a high-frequency transformer. All switching devices and diodes, including those in the auxiliary network, achieve soft-switching operation without increasing the voltage or current stress on the original power circuit components. First, the soft-switching operating principles of the proposed scheme are analyzed in detail. Then, theoretical analysis and simulation verification of key parameters are conducted. Finally, a 200 kHz, 200 W experimental prototype is constructed. Test results demonstrate that all switching devices achieve soft-switching operation within a load range of 10% to full load. Compared with a conventional hard-switching Buck converter, the proposed soft-switching Buck exhibits higher efficiency across the entire load range, particularly under light-load conditions. The full-load efficiency improves from 93.68% to 96.53%.

The sensitivity of relay protection is a key factor to ensure the rapid detection and accurate action of power system faults, but it is not considered in the current distribution network planning. With the integration of distributed generation, the sensitivity of relay protection may be lower than the minimum threshold, which cannot meet the requirements of electrical installation. Therefore, this paper proposes an optimal configuration method of inverter-interfaced distributed generation（IIDG） considering the sensitivity of current protection in unbalanced distribution network. Firstly, the influence of IIDG grid connection on the effective boundary of protection sensitivity is analyzed, and a re-evaluation method of relay protection sensitivity of unbalanced distribution network with IIDG is proposed. Then, considering the sensitivity evaluation constraints of relay protection, a multi-objective IIDG planning model with the maximum IIDG grid-connected capacity and the minimum system comprehensive cost and network loss is constructed. Finally, an example analysis is carried out in the improved IEEE33 node three-phase distribution network. The results show that compared with the optimization method without considering the sensitivity limit, the proposed method improves the average sensitivity of the protection device as the backup protection of the local line by 8.67% and the average sensitivity as the backup protection of the adjacent line by 6.18%, which verifies the effectiveness of the IIDG optimization configuration method proposed in this paper.

In severe cold regions, the winter heating load is significantly greater than the summer cooling load. The long-term operation of a standalone ground source heat pump （GSHP） system leads to a gradual decline in system performance. To address this problem, a photovoltaic/thermal-ground source heat pump （PV/T-GSHP） simulation model is established using the TRNSYS software. The PV/T installation area is determined according to different power generation requirements, and the effects of different PV/T supply and return water temperature difference control strategies on system heating and cooling performance are investigated. The annual thermal performance of the PV/T-GSHP system is comprehensively analyzed. The results show that when the power generation satisfies the energy demands for daily lighting, heat pump operation, and water pump operation, the coefficient of performance （COP） of the heat pump unit is 4.21 in the heating season and 6.33 in the cooling season. After 10 a of continuous operation, the soil temperature decreases by 1.95 ℃ compared with the initial state. When the temperature difference between the PV/T outlet and the preheating water tank decreases from 8 ℃ to 6 ℃, the system energy consumption increases by 2.24%, the power generation increases by 1.63%, and the unmet domestic hot water demand decreases by 16%. It is concluded that reasonable determination of the PV/T installation area and optimal setting of the PV/T temperature difference control strategy can effectively improve the long-term operational performance of the PV/T-GSHP system.

In order to alleviate the technical challenges of renewable energy accommodation and synergistic supply of multi-energy loads in the context of global climate governance, this paper proposes a wind-photovoltaic-molten salt thermal energy storage-electric heater hybrid renewable cogeneration system, which uses electric heater-thermal energy storage-power block to regulate wind and photovoltaic output and supply power load, as well as uses thermal energy storage-heat exchanger to supply heat load. This paper comprehensively considers the economy, reliability, energy efficiency and environmental performance to establish a many-objective capacity optimization model. The model aims to minimize levelized cost of energy, loss of power and heat supply probability, curtailment rate of wind and PV power and whole lifecycle equivalent carbon emissions as objectives, uses the non-dominated sorting genetic algorithm（NSGA-Ⅲ） to solve the four-objective problem, then uses principal component analysis to reduce dimension, finally uses TOPSIS decision-making method to obtain the optimal-compromise solution. The results of case study show that the objective function of the cogeneration system proposed in this paper is reduced by 4.7%, 31.8%, 7.5% and 26.4%, respectively, compared to the stand-alone power system.

This study combines the complementary advantages of LNG’s stability, continuity, low cooling value, and solar energy’s cleanliness, environmental friendliness, and renewability to construct polygeneration system integrating supercritical CO₂ re-compression Brayton cycle （SCRBC）, a two-stage organic Rankine cycle （ORC）, light hydrocarbon separation （LHS）, and data center cooling （DCC）. The system utilizes dual-tank thermal energy storage （TES） to mitigate solar energy fluctuations and couples LNG cold energy cascade recovery technology to achieve combined cooling, power, and light hydrocarbon production. Multi-objective optimization is performed using NSGA-Ⅱ and MOPSO algorithms, targeting thermal efficiency, exergy efficiency, and net present value （NPV）, with the Pareto optimal solution determined via the technique for order preference by similarity to ideal solution （TOPSIS） decision-making. Results indicate that the system achieves a cold energy utilization rate of 69.73%, with the de-heavy hydrocarbon tower accounting for 57.03% of the exergy destruction. The NPV demonstrates significantly higher sensitivity to the annual interest rate than to electricity prices. Compared with conventional systems, this solution exhibits superior thermodynamic performance and greater economic viability.

To address the multi-peak power tracking problem of thermoelectric generation systems under non-uniform temperature conditions, a hybrid control strategy combining an improved honey badger algorithm （IHBA） with constant voltage tracking （CVT） is proposed. A simulation model is built on the Matlab/Simulink platform. Test results show that the tracking accuracy is 98.9% and the response time is 0.04 seconds under constant temperature conditions. The average response time under dynamic temperature change and sudden load mutation conditions is less than 0.05 seconds. Physical tests verify the practicality of the algorithm under constant temperature conditions, and the tracking accuracy reaches 99.3% without overshoot. Compared with traditional methods such as CVT and PSO, this approach demonstrates significant advantages in tracking accuracy, response speed, stability, and robustness.

Driven by the “dual carbon” goals, a Stackelberg game-based low-carbon economic dispatch framework for a farm integrated energy system is proposed to enhance both low-carbon and economic performance. The framework is based on energy-carbon joint pricing and a tiered carbon emission reduction incentive mechanism. Firstly, carbon emission pricing is combined with energy prices through dynamic pricing and real-time carbon potential by the integrated energy system operator, and an energy-carbon joint pricing mechanism is established to guide the farm park in optimizing energy scheduling and reducing carbon emissions. Secondly, a tiered carbon reduction incentive mechanism is applied, with incentives progressively increasing based on the carbon reduction achieved by the farm park, stimulating its potential to reduce carbon emissions. Finally, a Stackelberg game model is constructed between the integrated energy system operator and the farm user, and energy pricing and scheduling strategies are optimized through their interactive decision-making. Case study results show that the proposed framework can effectively reduce system carbon emissions while improving the economic benefits of the integrated energy system operator.

Accurate prediction of various load demands in an integrated energy system is crucial to its stable economic operation. However, the diversified development of energy use forms makes the load evolution trend increasingly complex, which brings severe challenges to accurate prediction. To this end, a prediction architecture combining time series reconstruction technology, a combined neural network and ensemble learning is proposed. The time series reconstruction technology is composed of improved complete ensemble empirical mode decomposition with adaptive noise （ICEEMDAN） and sample entropy algorithm （SE）, which decomposes and reconstructs the complex original sequence into smoother subsequences to reduce the difficulty of prediction. The combined neural network combines the advantages of temporal convolutional network （TCN）, gated recurrent unit （GRU） and attention mechanism（AM） to capture the long-term dependencies of subsequences. At the same time, the robustness and generalization ability of the model are enhanced by the adaptive Boosting algorithm. Finally, the prediction results of all subsequences are accumulated to obtain the final result. The results of comparative experiments show that the proposed model is superior to other comparative models in the prediction of electrical, gas, heat and cooling loads. In addition, compared with the GRU benchmark model, the mean absolute percentage error and root mean square error of the proposed model are reduced by 40.59% and 40.03%, respectively.

To address the significant energy consumption associated with heating during winters in northern China, a residential community in Handan is examined. A model of a photovoltaic photo-thermal coupled medium-deep ground source heat pump （PV/T-GSHP） system, driven by low valley electricity, is constructed using TRNSYS software. This model takes into account the local disparities between peak and valley electricity prices. The operational strategy that utilizes low valley electricity is investigated, alongside an analysis of the soil thermal imbalance issue encountered by the ground source heat pump （GSHP） during long-term heating. The effects on heating performance are analyzed both with and without valley electricity heat storage, culminating in an evaluation of the PV/T-GSHP system concerning energy efficiency, environmental benefits, and economic viability. The findings indicate that： the optimal strategy for the PV/T-GSHP system is Scheme Ⅲ, achieving over 90% utilization of valley electricity during the heating season; the annual cumulative heat replenished to the soil is 976.82 kW·h more than the heat extracted from the soil, maintaining the soil thermal balance; energy consumption is reduced by up to 74.93% compared to conventional systems; CO₂ emissions can decrease by as much as 74.94%, and operational costs may drop by up to 77.27%.

The study of aerodynamic characteristics for airfoils in sand-laden environments predominantly employs numerical simulation methods, yet inconsistencies in models and assumptions during simulations have led to divergent conclusions regarding sand particle effects on airfoil performance. This paper experimentally investigates the aerodynamic behavior of the S809 wind turbine airfoil in sandy conditions through wind tunnel testing, focusing on lift/drag coefficient variations, flow field patterns, and particle dynamics under different sand particle sizes （characterized by Stokes number, St） and concentration levels. Key experimental findings reveal： 1） Under varying St conditions, the lift coefficient initially increases then decreases with rising St values while the drag coefficient shows gradual growth; at 20° and 24° angles of attack, enhanced velocity distribution emerges with increasing St, accompanied by modified vortex structures and downstream migration of separation points toward the trailing edge. 2） Within the 20°-28° angle of attack range, lift coefficients exhibit a positive correlation with dust concentration under varied concentration conditions, while elevated concentrations reduce suction surface velocity and drive upstream displacement of separation points toward the leading edge at 20° and 24° angles of attack. 3） Higher dust concentrations shift particle accumulation toward the airfoil’s trailing edge region, whereas increased St values reduce particle density within vortex separation zones; comparative analysis of particle distribution across attack angles demonstrates that steeper angles progressively concentrate sand particles near the airfoil’s leading edge.

To solve the problem of low reliability caused by typical wind power forecasting provided in the form of ‘power points’, an optimized control strategy for energy storage charging and discharging decisions based on ‘wind power interval prediction’ is proposed. Firstly, a wind power interval prediction model based on the dual attention mechanism and the Huber quantile loss function （DA-BiLSTM-QH） is established. Secondly, by integrating interval prediction information, microgrid load, energy storage status and real-time electricity price, a multi-objective optimization model of wind-storage microgrid system is established, and a solution strategy based on deep reinforcement learning DQN algorithm is designed. The effectiveness of the proposed strategy is verified through simulation of typical operating conditions.

Addressing the demands of high torque density, low torque ripple for traction motors in new energy vehicles, this paper focuses on the design and optimization of interior permanent magnet synchronous motor （IPMSM）. First, the basic parameters of the motor are determined based on the vehicle's power requirements, and an initial structural design is completed. Finite element simulations are then conducted to analyze the magnetic field and torque characteristics, verifying the rationality of the proposed design. To address torque ripple issues, three mitigation measures are compared： rotor shaping, auxiliary slot opening, and stator slot optimization. Subsequently, a hybrid genetic algorithm （combining the Taguchi method with the genetic algorithm） is proposed for the multi-objective optimization of motor torque performance. This approach reduces cogging torque and torque ripple while simultaneously improving the average output torque, significantly enhancing optimization efficiency and effectiveness. The optimization results demonstrate that, with the genetic algorithm, the average torque is increased by 5.4%, the cogging torque is reduced by 17.27%, and the torque ripple is decreased by 12%. Experimental validation confirms that this method effectively suppresses torque ripple and improves the operational stability of the motor's pitch system, making it applicable to the design of pitch systems for wind turbines.

In large-disturbance scenarios, linearized model order reduction methods based on a single equilibrium point cannot achieve satisfactory approximation performance. To address this problem, a nonlinear model order reduction method for direct-drive wind farm systems with high accuracy, structure preservation, and system observability is proposed. First, local linearized models at multiple operating points are established to retain the nonlinear information of the original system. Second, to avoid error accumulation and matrix ill-conditioning in numerical computation, the state variables are classified into strongly and weakly correlated variables according to generalized participation factors. Then, based on singular perturbation theory and the nonlinear dynamic equations, the dynamic processes of the weakly correlated variables are neglected, while those of the strongly correlated state variables and observable state variables are retained, thereby obtaining a reduced-order model that preserves both system observability and nonlinear structure. Subsequently, through non-intrusive modeling, particle swarm optimization is employed to improve the low-order nonlinear reduced-order model, enabling the improved reduced-order model to satisfy the accuracy requirements while preserving the nonlinear structure. Finally, simulations are conducted under three large-disturbance scenarios. The results show that the reduced-order system model obtained using the proposed method exhibits dynamic response characteristics highly similar to those of the full-order system model, with a model-order reduction rate of 50% and response errors within 1%. The proposed method can significantly reduce the complexity of control strategy optimization and numerical computation, providing a more feasible and efficient approach for the design and optimization of regional automatic power control systems.

In large-capacity permanent magnet synchronous wind generators （PMSGs）, excessive temperature rise induces critical challenges such as insulation degradation and irreversible demagnetization of permanent magnets. This study investigates a 2 MW PMSG and calculates its temperature distribution based on electromagnetic and windage loss coupling analysis. Results indicate that the equivalent thermal network method and unidirectional magneto-thermal coupling model produce considerable calculation errors due to the neglect of multi-factor interactions. To solve this problem, a bidirectional magneto-thermal coupling model is developed, incorporating linear regression and random forest algorithms for accurate temperature prediction and sensitivity analysis. The random forest model demonstrates superior capability in characterizing the nonlinear relationship between winding temperature and influencing factors including wind speed, rotational speed and ambient temperature. Among these factors, rotational speed exhibits the strongest positive correlation with winding temperature rise, followed by wind speed, while ambient temperature has the least influence. Moreover, the variation of winding temperature shows a delayed response, with the shortest latency for wind speed variations, followed by rotational speed changes, and the slowest to ambient temperature fluctuations. Comparative analysis confirms that the random forest model outperforms the linear regression model in prediction accuracy, and the bidirectional coupling simulations align closely with its predictions, whereas unidirectional coupling and thermal network methods show lower accuracy.

To investigate the influence of wind and wave environmental parameters on the design loads of multi-megawatt offshore wind turbines, offshore wind form environmental parameters from Chinese maritime areas were selected as variables. Dynamic response, extreme loads and equivalent fatigue loads of the IEA 22 MW offshore monopile reference turbine were computed using OpenFAST. Based on regional marine characteristics, turbulence intensity and wind shear coefficient were categorized into three levels, and the joint probability distributions of wind and waves for each region were established using Copula functions. Analysis of variance and random forest methods were then employed to assess the influence of various environmental parameters on the components of the equivalent fatigue loads. Furthermore, by analyzing the load responses under extreme conditions, the effects of a 50-year return period extreme wind speed, significant wave height and wave period on the loads at the mudline were examined.

To address the insufficient generalization ability of abnormal state assessment caused by cross-domain data distribution shifts in wind turbine blades under complex operating conditions, this paper proposes a Dynamic Domain Adaptive Residual Attention Network （DDA-RAN） based cross-domain anomaly assessment method. This approach integrates transfer learning with a dynamic attention mechanism, achieving collaborative optimization of domain shift through a bidirectional dynamic calibration strategy. Specifically, a Dynamic Domain Adaptive Attention Residual Block （DDAAR-Block） is designed. It utilizes a dynamic domain adaptive attention mechanism combined with switchable domain identifiers to adaptively activate either the source or target domain branch, balancing the representation of domain-invariant and domain-specific features. A cross-domain alignment module is constructed, implementing phased dynamic training via gradient truncation and a gating mechanism to promote cross-domain feature interaction and distribution alignment. An adaptive multi-scale residual block is adopted to extract deep and shallow temporal features, enhanced by global adaptive pooling for feature robustness. Furthermore, a regularized Multi-Layer Perceptron （MLP） classifier is introduced to improve model generalization. Experimental results demonstrate that in cross-domain assessment tasks for blade cracking and icing, the proposed method significantly outperforms mainstream comparison models （e.g., ADDA, DANN） in terms of target domain accuracy （up to 99.36%）, F1-score （99.35%）, and MCC （98.71%）. The accuracy shows an average improvement of 2.1%-3.8%. This provides a valuable reference for cross-domain anomaly assessment in industrial scenarios.

To comprehensively capture the spatiotemporal characteristics of wind speed, this paper proposes a short-term wind speed forecasting model based on graph convolutional network （GCN）, successive variational mode decomposition （SVMD）, bidirectional long short-term memory （BiLSTM） network, and the Kolmogorov-Arnold Network （KAN）. First, GCN is employed to extract spatial features among wind speeds at different horizontal distances. Then, SVMD is used to decompose the target wind speed series, thereby extracting modal features across various temporal scales. Finally, the extracted spatial features and the multi-scale temporal components are fused and input into a BiLSTM-KAN model to perform forecasting. Experimental results show that for 1-, 3-, 6-, 9-, and 12-step ahead predictions, the proposed model reduces the mean absolute error by 38.36%, 55.45%, 58.21%, 60.53%, and 59.76%, respectively, compared with the BiLSTM model.

This study investigates the discrepancies in local scour development across different model scales using numerical simulations. Field data from offshore wind farms in Jiangsu （China）, the North Sea （Europe）, and Guangdong （China） are incorporated to establish full-scale numerical models, ensuring realistic hydrodynamic and sediment transport conditions. A key innovation of this work is the introduction of an average Reynolds number （Rea） to characterize scale-independent scour processes, leading to a new predictive formula for monopile scour depth under field conditions. Furthermore, the study examines the impact of the relative exposed bed height （e/D） and ware-current conditions on scour depth for single bucket foundations, with a dedicated formula proposed for the latter based on numerical results.

To address the strong nonstationarity of wind speed time series, multi-scale variability, and cross-farm heterogeneity in centralized wind power control-center scenarios, this study proposes a hybrid wind speed forecasting model based on VMD-based frequency-band decomposition and a GRU-Informer synergistic mechanism. First, variational mode decomposition （VMD） is applied to wind speed and relevant meteorological variables from representative wind farms, and the decomposed modal components are reconstructed into low-frequency and high-frequency signals. A frequenly-band cooperative forecasting architecture is then developed. The gated recurrent unit （GRU） network is used to model low-frequency components with clear trend characteristics and long-term dependencies, while the Informer model is employed to capture complex fluctuations and long-range dependencies in high-frequency components. The forecasts from the two channels are subsequently reconstructed to obtain the baseline forecast for representative wind farms. Furthermore, to support multi-wind-farm forecasting under centralized management, a transfer learning strategy is introduced to transfer the learned source-domain models to target wind farms. A multi-objective coati optimization algorithm （MOCOA） is further used to adaptively fuse the forecasts of multiple source-domain expert models, thereby constructing a generalized forecasting model with improved adaptability and generalization capability. Experimental results show that the proposed hybrid model achieves high forecasting accuracy, strong environmental adaptability, and fast response in centralized wind power control applications, demonstrating its effectiveness and practical value.

By integrating the Analytic Hierarchy Process and fuzzy mathematics, this study established an indicator system for influencing factors of new energy development, incorporating energy distribution, topography, and human activities, thereby forming a multi-criteria decision-making system to evaluate the suitability of wind and solar energy development in China. The results indicate that the most suitable areas for wind energy development in China are concentrated in the "Three Norths" regions （Northeast, North, and Northwest China）. Inner Mongolia, Xinjiang, Gansu, and Qinghai account for over 90% of the country's total optimal and suboptimal suitable areas. For solar energy development, the most suitable areas are primarily located in North and Northwest China, with Xinjiang, Inner Mongolia, Gansu, and Qinghai provinces comprising more than 80% of the national optimal and suboptimal suitable zones. The findings of this study provide effective support for the development and utilization of renewable energy in China, focusing on wind and solar power.

To address the low iterative efficiency and heavy reliance on manual intervention of traditional modeling methods in the structural optimization of large-scale wind turbine blades, this paper proposes a nodal coordinate-driven parametric modeling method. Taking the IEA 15 MW wind turbine blade as the research object, discrete nodes are adopted to control the blade geometry and mesh element distribution. A double-loop algorithm is employed to establish the topological relationships of elements in the spanwise and chordwise directions. By integrating composite layups, loads, and boundary conditions, an automated workflow for finite element model generation, structured mesh generation, and material assignment is realized. The proposed model is validated through modal frequency comparison. The LaRC failure criterion is adopted for the strength assessment of composite materials, and the structural responses under extreme operating conditions are analyzed. The results indicate that the average relative error of the first six modal frequencies between the developed model and the reference data is less than 5%. The maximum blade tip deflection satisfies the tower clearance constraint, and all composite failure indices are below the critical threshold, which verifies the model accuracy and structural safety. By correlating design variables with nodal coordinates, the proposed method significantly improves the iterative optimization efficiency of large blades, providing technical support for the design optimization and reliability evaluation of large composite wind turbine blades.

Focusing on the significant spatio-temporal correlation characteristics of offshore wind power, a spatio-temporal correlation power forecasting model considering the differences in unit output patterns is proposed. Firstly, the density-based clustering algorithm is used to classify the output characteristics of wind turbines, so as to identify the groups of units with different output patterns; secondly, a representative set of units is selected through correlation analysis, providing key inputs for modeling. Finally, a power forecasting model is constructed based on the light gradient boosting machine to achieve efficient and accurate predictions. After verification with actual operation data, the result shows that this model can effectively improve the accuracy of power forecasting and enhance the generalization ability of the forecasting model to a certain extent.

To enhance the accuracy of ultra-short-term wind power prediction and investigate the contribution of diverse features to power prediction models, this paper introduces a wind power ultra-short-term prediction model grounded in CEEMD-GA-BiGRU-Attention. The model employs the SHAP value analysis method to assess the contribution of various features. Initially, the power data is decomposed using the Complete Ensemble Empirical Mode Decomposition （CEEMD） method. Subsequently, the Bidirectional Gated Recurrent Unit （BiGRU） model integrated with an attention mechanism is optimized using the Genetic Algorithm （GA） to dynamically model different modal components, thereby constructing a combined prediction model. When compared to various other power prediction models, this proposed model demonstrates superiority in terms of RMSE, MAE, and R² evaluation metrics, with a notable 10.08% improvement in R² compared to the BiGRU model, and a reduction of 40.15% and 42.38% in RMSE and MAE, respectively. Finally, through the application of the SHAP value analysis method, it is revealed that historical actual power and weather forecast wind speed are the features that significantly contribute to the model. Historical actual power dominates in the initial stages of ultra-short-term prediction, but its importance gradually diminishes over time, whereas the importance of weather forecast wind speed exhibits a stable upward trend in the middle to later stages.

A multi-step ultra-short-term wind power forecasting method based on PM-iTransformer-TCN and Bayesian optimization （BO） is proposed to address the limitations of traditional machine learning methods, which struggle with the strong fluctuations of wind power and demonstrate insufficient prediction accuracy. First, the original wind power data is preprocessed using the Perona-Malik algorithm to suppress random components. Next, the iTransformer model combines multiple input variables, including wind speed, wind direction, temperature, and air pressure, to generate preliminary predicted values of wind power. Finally, real-time tracking and correction of multi-step prediction errors are achieved using Temporal Convolutional Networks （TCNs）, tailored to the fluctuation characteristics of wind power data. BO is applied in the training process to select the optimal parameter combination. The proposed method is compared with several baseline machine learning models using on-site data from a wind farm in Gansu Province, China. The results indicate that the normalized root mean square errors of the forecasting results at future 2 h and 4 h are 18.95% and 21.64%, respectively, outperforming traditional machine learning methods and significantly enhancing the accuracy of ultra-short-term wind power forecasting.

To achieve the optimal design of onshore wind turbine tower structures, a self-similar modular topology optimization method is proposed. Based on the three-field theory, a topology optimization formulation for continuum structures is established, in which the compliance is minimized subject to the prescribed volume fraction. The sensitivity expressions under self-similar modular constraints are derived, while the optimization solution and design variable update are performed through the method of moving asymptotes. The ANSYS software is redeveloped for topology optimization functionality based on Python. Various layer numbers of self-similar structures are obtained. The ultimate stiffness and strength of the calculated structures under three typical loading conditions are computed and analyzed by finite element method. The results reveal that the self-similar structure is superior in mechanical properties such as maximum nodal displacement and stress value, proving the feasibility and superiority of the proposed methodology.

In order to study the effect of large scale wind turbine in tension leg type FOWT （Floating Offshore Wind Turbine）, the DTU 10 MW and NREL 5 MW wind turbines are taken as the research objects, and the coupling model of wind turbine-tower-floating foundation-mooring system is established. Time domain simulation is carried out and the influence of large scale FOWT is studied by comparing the difference in the floating foundation motion and blade deformation results. The results show that the large scale wind turbine leads to the increase of the motion of the floating foundation and makes the coupling effect more conspicuous and the frequency component more complex. Under the rated environmental condition, compared with the 5 MW model, the mean value of the surge motion of the 10 MW floating wind turbine increases by 47.2%, and the equilibrium position of the heave motion changes significantly. The intensity of the pitch motion is 1.7 times that of the former, and the pitch motion of the foundation exhibits more complex resonance characteristics. Meanwhile, the average out-of-plane deformation of the large scale wind turbine blade is 1.6 times that of the 5 MW wind turbine, and the in-plane deformation intensity is 1.8 times that of the 5 MW model. In addition, the blade deformation of 10 MW floating wind turbine is more sensitive to change of wind speed.

This paper considers the complex frequency characteristics of large-scale wind power participation in frequency regulation and proposes a robust optimal dispatch method for high-penetration wind power systems considering secondary frequency drops. First, the frequency response characteristics of wind power, thermal power, energy storage, and loads are comprehensively considered to derive the mathematical expressions of the primary and secondary frequency drop processes in power systems, and extract key frequency indices. Second, taking into account wind power uncertainty and dynamic frequency constraints, as well as system operating costs and wind curtailment costs, a nonlinear chance-constrained optimal dispatch model for power systems is established. Subsequently, to address the nonlinear characteristics of the primary and secondary frequency drop constraints in the dispatch model, a two-stage iterative optimization method is proposed. Finally, case studies are conducted on the IEEE 39-bus system and an actual power system in China. The results demonstrate that the proposed method effectively balances system economic operation and frequency stability while significantly improving renewable energy utilization.

In wind power prediction, the multimodal and asymmetric characteristics of prediction error distributions often lead to local probability density distortion and interval estimation deviations. To address these challenges, this paper proposes a wind power interval prediction method based on dual-stage parameter optimized multiple kernel-based kernel density estimation. Firstly, by investigating the probability density estimation characteristics of different kernel functions, multiple kernel density estimation functions are weighted and fused to resolve the local probability density distortion issue of single-kernel kernel density estimation in multi-modal scenarios. Secondly, to tackle the high-dimensional nonlinear coupling optimization problem of weight and bandwidth parameters in multiple kernel-based kernel density estimation, a dual-stage parameter optimization framework is developed： In the first stage, a parameter-pair partitioned domain search strategy based on the beluga whale optimization （BWO） algorithm is proposed for preliminary optimization of weights and bandwidths. The second stage employs a bandwidth refinement tuning method utilizing leave-one-out cross validation （LOOCV） to improve bandwidth precision. Finally, experiments conducted on both public and field-measured datasets demonstrate that the proposed method outperforms single-kernel kernel density estimation in error probability density fitting. Furthermore, comparative analyses with existing interval prediction methods reveal its superior predictive performance.

Anovel SBA-15 mesoporous channel limited bimetallic Cu-Co NPs catalyst (CuCo/SBA-15） was constructed. The catalyst was characterized by X-ray diffraction （XRD）, N₂ adsorption-desorption characteristics and scanning electron microscopy (SEM）, confirming that the confined structure can effectively regulate the particle size distribution of the active components and enhance the electronic synergy between the Cu and Co components. The results show that in the reaction of higher alcohol synthesis from syngas, the CuCo/SBA-15 catalyst achieved a higher alcohol selectivity of 41.6%. In particular, the distribution of C₁-C₄ mixed alcohols was significantly narrowed, accounting for over 86.3%.

An effective solution for utilizing solid waste is the production of syngas through the co-gasification of polypropylene and biomass. In the present study, a kinetic model for the co-gasification of polypropylene and biomass was developed using ten reactiors kettles from Aspen Plus software. The gas production performance of both polypropylene and biomass was simulated numerically under oxygen and steam atmospheres. The results indicated that an increase in polypropylene improved the volume fractions of both CO and CH₄, while an increase in steam enhanced the volume fraction of H₂. The volume fraction of syngas （H₂+CO） increased with rising gas temperature, initially increased and then decreased with the mass fraction of polypropylene, reaching a maximum value within the range of 9% to 23%. Furthermore, as the steam-to-carbon （S/C） ratio increased, the volume fraction of syngas （H₂+CO） increased at temperatures of 600 ℃ to 700 ℃, but decreased slightly at temperatures of 800 ℃ to 1000 ℃. At mass fractions of polypropylene between 9% and 23%, the maximum H₂/CO ratio occurred at temperatures between 700 ℃ to 1000 ℃. The H₂/CO ratio increased with rising S/C, and its rate of increase declined with rising temperature. A reduction in the CO₂ volume fraction was observed with increasing polypropylene, while it reached a maximum value at a temperature of 1000 ℃ and an S/C ratio of 0.2.