In this review, the preparation strategy and synthetic methods of Fe-N-C catalysts in recent years including template method, impregnation method and in situ capture method etc are systematicauy summarized. The current developments of pyrolytic Fe-N-C catalysts derived from metal-organic frameworks, organic porous polymers and biomass are emphatically outlined. The structure of active sites and reaction mechanism in Fe-N-C catalysts are also discussed. Then, the challenges and future development directions of Fe-N-C catalysts for ORR are discussed and summarized.

Based on the relevant accounting methods of life cycle assessment and the Intergovernmental Panel on Climate Change （IPCC） National Greenhouse Gas Inventory Guidelines, the carbon emission reduction benefits and potential of biomass gasification power generation with CO₂ capture-soda production technology were evaluated. The results show that biomass gasification power generation with CO₂ capture-soda production technology using corn stover as raw material has significant carbon reduction benefits, with a reduction of 3.88 t/（t-straw） or 6.45t/（MW·h）. The contributions of CO₂ capture-soda production and power generation to carbon reduction benefits are 84.27% and 15.69%, respectively. Collaborative optimization of transportation and power generation systems can achieve a maximum carbon reduction of 4.32 t/（t-straw）, showing great carbon reduction potential. Compared with similar technologies from the perspective of product output or biomass waste reduction, biomass gasification power generation with CO₂ capture-soda production technology has strong carbon reduction benefits.

The present study investigates the influence of the densification process of shiitake residue particles with different moisture contents on the arch effect. This investigation is achieved by using two methods： firstly, particle motion trajectory tracking experiments and secondly, discrete element simulation methods. By analyzing the movement trajectories of the four groups of shiitake residue particles with moisture contents of 8%, 10%, 12%, and 14% at four different stages of compression, the extent to which the particles are affected by the arching effect during the compression process is revealed. Research findings indicate that when the moisture content of the shiitake residue particles is 12%, deflection and displacement during particle movement reach their zenith, and are most influenced by the arch effect. It has been established that when the moisture content of the shiitake residue particles is 8%, the deflection and displacement during the particle movement are minimal, and the influence of the arch effect is negligible.

Phase change heat storage technology is one of the efficient means of heat storage in engineering applications. However, phase change materials have the problem of low thermal conductivity, which seriously limits their work efficiency. Therefore, it is necessary to optimize the structure of phase change heat storage devices to improve heat storage efficiency. Na₂S₂O₃·5H₂O and CH₃COONa·3H₂O were selected as phase change materials. Six phase change heat storage models were designed based on bionic structure. The melting and solidification processes of the two phase change materials in the six heat storage models were simulated and analyzed. Finally, based on the numerical simulation results and taking into account the heat storage and release processes, a small biomimetic cross seasonal phase change heat storage experimental platform was built. This article presents temperature changes of two phase change materials at each temperature measuring point in two models under the operating conditions of 75 ℃ and 0.1 m³/h. The results indicate that one heat storage model is more suitable for using Na₂S₂O₃·5H₂O as phase change material, while the other model is more suitable for using CH₃COONa·3H₂O as phase change material.

Due to the spatial dispersion of photovoltaic power stations and the lack of data sharing among users, insufficient power prediction accuracy commonly arises. This paper proposes a collaborative training strategy for distributed PV short-term power prediction based on scenario classification and privacy protection. Firstly, the Pearson correlation coefficient is used to extract important meteorological features, and the fuzzy C-means clustering （fuzzy C-means, FCM） algorithm is used to cluster the historical data set into sunny days, cloudy days and rainy days. Secondly, the regions with similar weather patterns are clustered into several groups, and the sets of sunny and non-sunny under the same category are discriminated and screened to build photovoltaic power prediction models under different scenarios. Then, a multi-task learning algorithm is added on the basis of the general iterative algorithm of federated learning, and a new local training method of multi-task mode is established to preserve the differences among the PV power stations participating in the joint modeling. Finally, the test day is predicted, and the data is input into the prediction model of the corresponding scenario established above, and the photovoltaic power prediction results of the test day are obtained. The experimental findings revealed that, under different weather conditions, compared with various network models, the accuracy of the proposed prediction method is increased by 24.77%, and the root-mean-square error （RMSE） is reduced by 89.24%. Compared with the traditional federated framework, the proposed scheme can achieve the target user identification rate （UA） in faster training rounds, shorten the number of communication rounds by 50% and increase the average UA by 8%. It is verified that this scheme not only improves the accuracy of photovoltaic short-term power prediction, but also has strong adaptability and robustness.

At present, over 90% of the world's hydrogen production is derived from fossil fuels. The hydrogen energy industry holds significant potential for reducing emissions as part of the pursuit of the "dual carbon" goal. Among the various methods for hydrogen production and emission reduction, carbon capture technology stands out as a crucial option for achieving low-carbon hydrogen production. Therefore, this paper proposes an optimized scheduling approach for an integrated energy system coupled with coal-based hydrogen production and carbon capture, while considering information gap decision theory. First, a multi-resource utilization model is developed for a carbon capture power plant integrated with coal-to-hydrogen production. Next, taking into account the operational constraints of this coupled system, a deterministic scheduling model is formulated with the objective of minimizing both system operation costs and wind curtailment costs. Finally, to address the uncertainty of wind power, information gap decision theory is applied to quantitatively analyze and enhance the system's adaptability to such variability. The scheduling scheme is evaluated under different risk attitudes through a numerical case study. The results demonstrate that the proposed strategy effectively promotes wind power integration and enhances the systems's low-carbon economy.

To enhance the prediction accuracy of waves in multi-island sea regions, this study uses the measured wave data of 57 typhoons in the Yangtze River Estuary and the coastal waters of Zhejiang Province from 1987 to 2022 to investigate the attenuation coefficients of wave height in various sea areas through data processing and analysis. The leef-sheltering coefficients is employed to quantify the influence of reef location, size and other factors on the average periods of mixed waves. Based on the measured wave height-period relationship of mixed waves, a method for determining the maximum and minimum periods of mixed waves is proposed. Finally, the friction coefficient of the wave bottom in the study area is calculated by using theoretical formulas. As shown in the results, the wave height gradually diminishes during the wave propagation towards the inshore, and the wave energy attenuates more rapidly in the areas with numerous islands than in open areas without islands. In a multi-island sea area, the period of mixed waves will gradually decrease under the effect of reef shielding, while the degree of period reduction increases with the increase of the reef shielding degree. For a certain wave height, the wave-period relationship of the strong wave direction at offshore stations can be regarded as the maximum possible period of the mixed wave, while the wave-period relationship of the wind waves can be taken as the minimum possible period of the mixed wave. As demonstrated in the calculation, in the Yangtze River Estuary and the near-shore regions of Zhejiang Province, the wave bottom friction coefficient Cb is 0.050 m²/s³, and the bottom friction coefficient f is 0.009.

This study introduces a novel hybrid forecasting model, which uniquely employs the sparrow search algorithm to optimize the parameters of variational model Decomposition. Subsequently, the model utilizes variational mode decomposition and complete ensemble empirical mode decomposition with adaptive noise for dual rounds of time series decomposition related to solar power, followed by the application of convolutional neural networks and long short-term memory with attention mechanisms to train and forecast the decomposed series data. The proposed model undergoes validation using real-world data across various weather conditions and resolutions. The results consistently demonstrate the superior predictive performance of the new method across different time resolutions （2 or 10 minutes）, forecast ranges （1, 3, or 5 days）, and diverse weather conditions, with a remarkable correlation coefficient of the predicted values exceeding 0.97.

In order to solve the uneven regional distribution of power resources and power demand, and the limited absorption caused by the continuous installation of renewable energy, this paper analyzes the power structure and development trend of our country and the current wind power generation and abandonment situation, and discusses the necessity of using renewable energy to produce green hydrogen. Through the application analysis of the current three mainstream technology routes and actual cases of green hydrogen production from water electrolysis （alkaline electrolysis, proton exchange membrane electrolysis and solid oxide electrolysis）, the feasibility of different types of wind and solar power generation coupling modes of hydrogen production was discussed, and the application prospects and development direction of green hydrogen production from renewable energy in the future were analyzed.

This study explores the distinct forms in which water molecules exist within hydrogel structures and discusses the design of solar evaporators that enhance water evaporation efficiency by modifying the surface morphology of hydrogels. Finally, the research progress on solar evaporators that combine various photothermal media with hydrogels is systematically reviewed, and prospects for their future development are discussed.

Accurate wind power forecasting is crucial for the reliability of grid scheduling decisions. To address the variability in wind power output, a wind power online forecasting method based on dynamic deep learning is proposed. Initially, a benchmark model utilizing bidirectional Long Short-Term Memory（LSTM） networks and bidirectional Gated Recurrent Units（GRU） is developed, with parameters and weights tailored to the training dataset. Subsequently, a fast Hoeffding drift detection method is used for wind power state monitoring, and the deep learning model is dynamically updated based on the detection results. Finally, a Random Forest regression model is integrated to correct the predicted power errors, enabling rolling online forecasting through sequential time windows. Validation results indicate that the proposed algorithm improves the root mean square error（RMSE） by 5.68%, the mean absolute error（MAE） by 18.56%, and the coefficient of determination（R²） by 2.06% compared to the Transformer, demonstrating good predictive performance and further enhancing the accuracy of wind power forecasting.

In order to reveal the wind field characteristics of downburst flow at different jet inclination angles and their effects on wind turbine blade deformation, this study simulated the wind field characteristics of atmospheric boundary layer at five jet inclination angles of 0°, 5°, 10°, 15° and 20° based on computational fluid dynamics method, analyzed the horizontal and vertical wind velocity profiles of downburst flow at different jet inclination angles. Meanwhile, the influence of wind field with different jet inclination angles on blade surface pressure distribution and structural deformation is explored. The results show that with the increase of jet inclination, the maximum horizontal wind speed on the side where the downburst occurs increases, and the rate of vertical velocity decreasing slows down. The change of inclination angle has anegligible effect on the pressure in the area where the downburst occurs. For the influence of turbulence intensity distribution, the turbulence intensity at the back of the jet region increases with the increase of jet inclination, while the turbulence intensity at the front side decreases. When the wind turbine is located in the downburst flow field with different jet inclination angles, the distribution of blade surface pressure is inhomogeneous, and the blade tip bears more wind pressure than other positions. With the increase of jet inclination angle, the blade surface pressure increases, the pressure of the near-ground two blades increases substantially, reaching the maximum value of about 50 Pa when the inclination is 20°. The pressure of the father-from-ground blade surface increases slowly. The structural deformation at blade tip and the equivalent stress in the middle of blade increase with the increase of the inclination angle. This study provides some theoretical support for the safe operation of wind turbines in extreme weather, also offers assistance for wind turbine anti-wind design.

Based on the finite volume method in computational fluid dynamics （CFD）, a wind field model is established considering the effects of terrain, roughness, and other influential factors. The standard k-ε turbulence model and a single linear wake model are combined, and the RANS equations are used to solve the computational domain, resulting in wind resource maps and annual electricity generation of wind turbines. A comprehensive wind resource assessment is conducted in the vicinity of the Shengshan Station in Shanghai as a case study. The results show that the prevailing wind directions in the area are north-south, southeast, and east, with an increasing gradient of velocity in the north-south direction （Y） as the height increases. Wind speeds are higher during winter, indicating significant potential for wind energy development. This study provides a scientific basis and useful reference for wind power planning in similar regions.

The prediction of lithium battery lifespan is of great significance for energy management and maintenance. To address challenges such as complex multidimensional time series data, long-term dependencies, and dynamic changes in characteristics during the prediction process, this paper proposes a lithium-ion battery life prediction model based on dynamic convolutional neural networks and Transformer（DCF）, covariance matrix adaptive adjustment evolution strategy（CMA-ES）, and multi-head self-attention mechanism. The DCF dynamically extracts key features from time series data, reduces data dimensionality and redundancy, and captures long-term dependencies. CMA-ES optimizes model hyperparameters to enhance the model's ability to model local features and global dependencies. The multi-head self-attention mechanism further focuses on important features and handles complex nonlinear dynamic relationships. Experimental validation is conducted using the publicly available lithium battery dataset provided by NASA. Results show that the proposed method achieves a minimum average absolute error of 0.28%, outperforming most existing methods using the same dataset. The experiments further demonstrate improvements in prediction accuracy and generalization ability, especially in long-term lifespan prediction, where the model exhibits higher precision and robustness, providing more reliable technical support for lithium battery lifespan prediction.

This article briefly describes the three mechanisms involved in photosynthetic bacterial hydrogen production： photosynthetic reactions, substrate metabolism, and hydrogen production by nitrogenase enzymes. It also summarizes and analyzes research results on additive regulation in photosynthetic hydrogen production, highlighting four main pathways of additive regulation： spectral, electronic, metabolic, and environmental. The article further discusses the effects, mechanisms, advantages, and limitations of two major categories of additives-metal and non-metal. Finally, it analyzes and forecasts the development trend and future research direction of additives for hydrogen production in photosynthetic fermentation, aiming to provide valuable insights for researchers in this field.

This paper proposes an improved centralized damping method： applying a damping matrix at the equivalent rotational center of the monopile. The equivalent rotational center serves as a "decoupling point" for the monopile foundation, addressing the coupling issue in the stiffness matrix at the mudline that complicates rotational damping coefficient calculations based on apparent rotational stiffness. First, the formulas for calculating the damping ratio of the pile-soil interaction system and the position of the equivalent rotational center are derived. Then, this improved method is incorporated into the integrated time-domain analysis of offshore wind turbines by developing the SoilDyn module in OpenFAST. Finally, the validity of the improved centralized damping method is verified by comparing it with distributed damping results under the same load conditions.

Physical model tests were conducted in sandy soil to study the response characteristics of tripod bucket jacket foundations under cyclic loading. The cyclic rotation angle response, natural frequency, and damping ratio of the structure were analyzed. Based on the test results, an empirical prediction formula for the cumulative rotation angle of the foundation was proposed. The results indicate that an increase in cyclic load amplitude accelerates the accumulation rate of the foundation’s rotation angle. In a log-log coordinate system, the cumulative rotation angle of the foundation and the number of load cycles approximately exhibit a linear relationship. The natural frequency of the structure demonstrates a similar pattern of change under different cyclic load amplitudes： initially, there is a slight increase in the natural frequency, followed by a sharp decrease, and it then fluctuates around a certain average value as cyclic loading continues. The system's damping ratio sharply decreases at the beginning of loading, reducing to about 20% of the initial value, and then undergoes significant oscillations, with a variation of up to 80%.

Considering the demands of clean and efficient heating with grid peak shaving and valley filling, a novel building heating system combined with photovoltaic/thermal, heat pump and heat storage using off-peak electricity （PV/T-HP-VEHSH） was proposed. The optimization design method for PV/T-HP-VEHSH was developed targeting optimal heating reliability, minimal carbon emissions and initial investment based on the pattern search algorithm. The operating performance of PV/T-HP-VEHSH was studied by TRNSYS software and the performance improvement was also investigated in comparison with the photovoltaic-air source heat pump heating system and the heating system without thermal storage by off-peak electricity. The results show that the PV/T-HP-VEHSH has the optimal comprehensive benefits at the ratio of flow rate of the thermal collection water pump to PV/T area of 13.8 （kg/h）/m², the ratio of volume of thermal collecting tank to PV/T area of 0.1 m³/m², and the ratio of electric boiler capacity to volume of thermal storage tank of 1.2 kW/m³. The average monthly COP of PV/T-HP-VEHSH varies from 2.8 to 3.2 and the comprehensive photoelectric and photothermal efficiencies of PV/T are higher than 56% during the heating season in cold zones. The operation cost of PV/T-HP-VEHSH is reduced by 3.3 ¥/m² due to the thermal storage using off-peak electricity.

To alleviate the limitation on distributed photovoltaic integration imposed by the inflexibility of the spatiotemporal distribution of new energy and load, this paper proposes a co-planning strategy for energy storage and intelligent soft-switching to enhance the capability of power distribution networks for distributed PV power. Firstly, to evaluate the collaborative benefits of multi-port soft open point （MSOP） and energy storage system （ESS） in spatiotemporal flexibility, an evaluation metric integrating branch current time-uniformity level and network loss sensitivity is proposed. Subsequently, a two-level stochastic optimization model comprising planning and operation is established based on this collaborative benefit metric. By embedding the MSOP-ES model, this framework is capable of considering diverse integration forms of MSOP and ESS. Finally, the two-level optimization model is efficiently solved using the Benders decomposition algorithm. Case studies on the IEEE 33-node distribution network and a real-world distribution network demonstrate that the proposed method effectively enhances PV hosting capacity while improving voltage stability and flexible transfer capability by planning ESS and MSOP integration.

In this study, the effects of pyrolysis temperature and lignocellulosic biomass/microalgae blending ratios on the yield, elemental composition, surface morphology, thermal stability, pore structure, surface functional group distribution and carbon skeleton structure of biochar derived from lignocellulosic biomass and microalgae co-pyrolysis were systematically investigated, and the synergistic effect and reaction mechanism during the co-pyrolysis process were further revealed. The results show that pyrolysis temperature and biomass/microalgae blending ratios had significant influence on the yield and physicochemical structure of co-pyrolysis biochar. The biochar yield decreases with rising pyrolysis temperature, and increases with higher microalgae blending ratios. The yield of co-pyrolysis biochar can reach as high as 41.51%, which is 23.5% higher than that of individual biomass biochar. The co-pyrolysis process of microalgae and lignocellulosic biomass exhibits a significant synergistic effect, which can enhance the yield of biochar and significantly promote the enrichment of C and N elements in biochar. The increase of pyrolysis temperature facilitates the conversion of pyridine-N to quaternary-N in biochar. With the increase of the microalgae blending ratios, the disorder degree and aromaticity of biochar is enhanced. The nitrogen-containing volatile produced from microalgae can react with the oxygen-containing functional groups on biomass through Maillard reaction, which further experience cyclization reactions and polycondensation reactions to form nitrogen-rich biochar.

Response surface methodology was used to analyze the effects of molding temperature, pressure, proportion of binder and mixing ratio of raw materials on the density of seedling bowl, breakage by dropping, and breakage by pressure. Then the process parameters were optimized by applying the satisfaction function method and multi-population genetic algorithm. In addition, the effect of seedling bowls on the physicochemical properties of soil was also analyzed. The results showed that the density, drop breakage rate and compressive breakage rate of seedling bowls reached 0.908 g/cm³, 2.98% and 2.68%, with the temperature of 60-110 ℃, pressure of 8-18 MPa, binder to raw material mass ratio of 0.6∶1-1.3∶1, and the corn stover charcoal content of 10%-60%. The optimal combinations of process parameters were temperature 67 ℃, pressure 13.64 MPa, binder to raw material mass ratio 0.78∶1, and corn stover charcoal to Sophora japonica raw material mass ratio 3∶7, under which the density, drop breakage rate and compressive breakage rate of seedling bowls were 1.032 g/cm³, 2.65% and 2.34%, which were only 0.02%, 0.01%, and 0.03% from the predicted values. After the seedling bowls were buried in the soil for 8 weeks, the soil pH value decreased from 8.92 to 8.02, and the soil quick-acting nitrogen, quick-acting phosphorus, and quick-acting potassium increased by 57.1, 27.0, and 38.0 mg/kg respectively, which indicated that the corn stover charcoal-mixed Sophora japonica biomass seedling bowl could effectively reduce the soil pH value and improve the soil fertility.

The problem of blade load testing and identification are discussed for onshore wind turbines operating under steady state conditions. Firstly, the load test scheme is designed based on the equivalent fatigue load theory and strain load measurement technology. According to the load measurement requirements, data acquisition, sensor layout and calibration are carried out to build the blade monitoring system. Secondly, the strain gauges are used in the blade monitoring system to obtain the test load under steady state conditions. In the blade monitoring system, the wind characteristic information from wind tower and radar is collected synchronously. At the same time, the wind turbine control state information is obtained by the SCADA system. The simulation load is obtained according to the wind speed capture matrix. Since the test load data duration is different from that of the design load, the design load, test load and simulation load are converted to equivalent fatigue load at 1Hz for comparison. The results show that the ratio of test load and design load of the same section is in the range of 104.0%-130.2%, which meets the safety margin of blade load design. The test load and simulation load of the same section are quite different, indicating that the dynamic change of load monitoring under steady state condition is strongly related to turbulence intensity. Therefore, it is necessary to monitor wind shear and turbulence parameters to reflect the actual load, which can prevent overload operation effectively.

To achieve a simple and low-cost real-time measurement of sea waves, this article proposes a non-contact wave parameter measurement method based on shape-from-shading （SFS）. Initially, a single camera is used to capture images of the wave tank and floating reference objects. Then, the three-dimensional shapes of the wave surface and reference objects are reconstructed using the SFS method. A size transformation coefficient is introduced into the 3D reconstruction results to accurately calculate wave parameters. The proposed method's wave parameter measurements are compared with actual measurements. Results indicate that the relative errors for average wave height and wavelength are less than 5%, the relative error for the average period is less than 1%, and the deviation in wave direction is within 5°. This method demonstrates high stability and accuracy.

This paper proposes a distributed voltage control algorithm for distribution networks based on an improved model-free adaptive control method. The proposed algorithm leverages real-time sampling data from distributed PV units and estimates the dynamic linearization parameters that characterize the voltage control characteristics of the distribution network online through adjacent communication between the PV units. This data-driven, iterative approach achieves coordinated reactive power-voltage control without relying on a detailed network model. The stability of the algorithm is proven mathematically. Finally, a multi-scenario simulation is conducted using the modified IEEE 33-bus distribution system to demonstrate the effectiveness and advantages of the proposed control algorithm.

Aiming at the problem of detecting bird droppings and shading defects as well as light spot defects formed by diode damage in photovoltaic modules under aerial high-resolution images, a detection algorithm combining the improves deep learning model YOLOv8n with slice-assisted super-reasoning is proposed. The PV module dataset is constructed by slicing 2667 visible and thermal imaging images and labelling them with three kinds of defects： bird droppings, shading and light spots. Firstly, on the backbone network of YOLOv8n, a small target detection layer is constructed by adding a 3-layer CBS module to enhance the transmission of small target feature information. Secondly, a global attention mechanism is added to the Backbone part, adopting the framework of channel and spatial attention, so that the model can better capture the global feature information. Based on the above two improvements to the network structure to design its ablation experiments, the experimental results show that the improved model improves the mAP50 value and mAP50：95 by 4.3% and 1.5%, respectively, compared with the base model; to design the comparison experiments between the improved model and the other target detection models, and the experimental results show that the improved model's accuracy is better than that of the other detection models. Finally, combined with slice-assisted hyper-reasoning for slicing before detection, the overall model comparison experiments are designed with the addition of slice-assisted hyper-reasoning, and the experimental results show that the improved YOLOv8-PG+SAHI model is optimal for detecting small target defects in high-resolution photovoltaic module images, and the accuracy rate can reach 88.73%. The above experiments show that the improved model is more suitable for small target detection of PV modules under aerial high-resolution images.

Aiming at the problem of insufficient prediction accuracy of large-scale photovoltaic power generation system under non-ideal weather conditions, which in turn triggers the difficulties in the implementation of the power system dispatch plan, a prediction method of photovoltaic power generation is proposed based on the similar day theory and the improved pelican optimization algorithm （IPOA） extreme learning machine （ELM）. First, the Pearson correlation coefficient method filters out meteorological factors that exhibit a significant correlation with PV power generation; then the combined Euclidean distance and Mahalanobis distance evaluation indexes are used to calculate the combined distance between the historical days and the days to be predicted at each time point to determine the similar days. Then, the sample set of similar days is input into the constructed IPOA-ELM power prediction model for training, and based on the actual measurement data, the performance of the IPOA-ELM model is compared with that of the POA-ELM, SCSO-ELM, and GJO-ELM models in terms of prediction accuracy. After comparative analysis, it is concluded that the selection of similar days for the weighted composite index can more accurately reflect the distance and distribution characteristics between each moment point; the IPOA algorithm is optimal in terms of convergence speed and adaptability compared with POA, SCSO and GJO; and the root-mean-square errors of the IPOA-ELM model are lower than the other comparative models in the prediction of photovoltaic power generation under different weather conditions. It is worth noting that the model still shows good stability under rainy weather. It is fully proved that the applied IPOA-ELM prediction model has strong adaptive ability and prediction accuracy.

As the basis of transformer range lean management, the precision of distribution （for short of 400V distribution） user-branch-distribution transformer topology draws more attention. Based on the characteristics of distribution tree topology, the principles and defects of current distribution topology identification methods were analyzed with the correlation among electrical parameters. A novel distribution topology identification method based on time-frequency domain data fusion was proposed, which consists of two links： 1） Forward link. Spectrum clustering algorithm was modified to cluster node voltages including fundamental and harmonic domains level by level.Furthermore, in order to calculate the node voltage vector corresponding to the upper level topology branches, a Confined-field Neural Network was devised, which is able to discriminate the topology type （radiation or trunk） via cluster node transformation group features. 2） Backward link. In the solution space compressed by the forward link, the active power balance principle was utilized to check and correct the suspicious nodes. Such closed loop identification framework possesses promoted result precision and sophisticated topology applicability. Finally,3 residential/commercial and industrial pilot transformer range with typical topology in State Grid Zhejiang Company were selected as examples. The proposed method was paralleled with several current distribution topology identification methods, with its advantages verified.

Aiming at the problems of declining power prediction performance and high appraisal cost of photovoltaic （PV） power plants year by year, this paper proposes an optimization method integrating bidirectional long short-term memory network （Bi-LSTM） and attention mechanism. First, the time dependence of power data is captured by applying Bi-LSTM to improve the understanding of time series changes; second, the combined attention mechanism enables the model to focus on the features that have the greatest impact on the prediction results to further improve the prediction accuracy; and finally, the accuracy is verified by using field historical data modeling. The field model deployment shows that the model can better capture the rapid power changes compared with the traditional single model and integrated learning model, and the ultra-short-term prediction accuracy of the field station can be improved by more than 10%.

Noise in the measured voltage-current （V-I） data reduces the accuracy of solar cell parameter identification of heuristic algorithm （MhA）. To address this issue, a kind of data denoising meta-heuristic algorithm （DDMhA）, whose function is to precisely identify solar cell parameters, is proposed on the basis of kernel extreme learning machine （KELM）. By using KELM for training V-I data to filter noise, the accuracy of MhA fitness function is promoted, giving impetus to the global exploration ability of MhA, as a result, the parameter identification precision is well guaranteed. In the validation experiments, the solar cell double diode model （DDM） is used for parameter identification, to be more specific, 50 sets of mixed V-I data were subjected to both non-denoising and denoising processing, and then the parameter identification results of 6 MhA by different processing methods were put in comparison. According to the experimental results, DDMhA is effective in filtering data noise, thus improving the identification accuracy and convergence speed of the original MhA.

The principles and application methods of density functional theory （DFT） simulation are reviewed, the application process and analytical methods of DFT simulation are summarized in terms of pyrolysis gasification mechanism and tar removal of typical biomass components, and an outlook on the future development of DFT simulation in the field of biomass gasification is presented.

To achieve flexible hydrogen production and improve the economic performance of a photovoltaic-storage DC microgrid, a system-level coordinated control strategy based on the DC bus voltage signal is first proposed, considering the operating characteristics of photovoltaic arrays, energy storage batteries, and electrolyzers, to realize flexible hydrogen production with the "load-following-source" mode for the electrolytic hydrogen production device. Secondly, based on this control strategy, a capacity optimization configuration model is established, and the sparrow search algorithm is used to optimize the capacity configuration with the goal of minimizing system cost. A case study analysis and economic analysis are then performed using data from an actual photovoltaic hydrogen production engineering project. Finally, a simulation model is established according to the proposed control strategy, and the correctness of the proposed control strategy is verified through simulation.

To improve the accuracy of photovoltaic （PV） power prediction,a hybrid short-term photovoltaic power prediction model based on the Pearson interpolation algorithm （PIA）,fuzzy C-means clustering algorithm （FCM）,multivariate variational mode decomposition （MVMD）,convolutional neural network （CNN）,bidirectional long short-term memory network （BiLSTM）,and attention mechanism （ATTENTION） is proposed.Firstly,the PIA is utilized to repair the missing data in the dataset. Secondly,the FCM algorithm clusters historical data into sunny,cloudy,and rainy days. Then,the MVMD is employed to decompose the PV power into several intrinsic mode functions （IMFs）.Subsequently,a combination of CNN and BiLSTM networks is used to fully extract the features of each IMF,with the Attention Mechanism introduced to emphasize and weigh important information. Finally, the forecasting of each IMF is conducted,and the predicted values are summed to obtain the final prediction result. Compared with other PV power forecasting models,the proposed hybrid model demonstrates superior forecasting accuracy.

In order to improve the system performance of PHOTOVOLTAIC-THERMAL coupled air source heat pump （PV/T-ASHP） heating system, a solal Photovoltaic-Thermal coupled air source heat pump heating （PV/T-P-ASHP） system with an added preheating method is presented in this paper. The system model is built based on TRNSYS, and after the accuracy of the model is verified, the relevant performance enhancement of the PV/-P-ASHP system and the PV/T-ASHP system is compared and analyzed, and the applicability of the systems in different regions is explored. The results show that the average relative errors of the system model validation for PV/T electrical efficiency, thermal efficiency, and COP of the heat pump unit are 5.09%,9.06%, and 5.25%, respectively, and the experimental model and the results coincide accurately. Taking Beling as an example, it is found that the PV/T-P-ASHP system saves 4.38% of the annual operating energy consumption compared to the PV/-ASHP system, the COP of the heat pump unit is increased by 36.28%, the integrated efficiency of the PV/T is increased by 4.32%, the COP of the system is increased by 4.18% during the heating season, and the primary energy saving rate is increased by 6.47%. Simulation comparisons in different regions show that the highest system COP improvement of 4.18% is observed in Beling, A 5.78% increase in PV/T overall efficiency, a 6.58% increase in primary energy savings, and a 2.3% increase in system COP is observed in Dalian, a 4.24% increase in PV/T overall eficiency. a 5.81% increase in primary energy savings, and a 2.29% increase in system COP is observed in Shenyang, and a 1.4% increase in system COP with the addition of preheating is observed in Shanghai, with a smaller overall system performance improvement.

Based on the ventilated double-skin facade, an external photovoltaic blind ventilation window （PVBVW） has been proposed, making full use of the advantages of blinds and photovoltaic cells to achieve efficient heating, power generation, light regulation, and create a comfortable indoor lighting and thermal environment. A coupled optical-thermal-electrical simulation model of PVBVW system under summer condition was established using Design Builder and Energy Plus. Compared to a standard double-glazed window system, the impact of the PVBVW system on natural lighting and indoor thermal environment was analyzed, and its comprehensive energy consumption was predicted. The results shows that the daylighting of the PVBVW is weak, but the area within 2.0 m × 1.5 m closing to the external window can meet the requirements of natural daylighting and the comfort was better. The results of PMV research results indicats that the room of the PVBVW compared with that of the standard double-glazed window provides a more comfortable indoor thermal environment. The PVBVW system saves 33.90% of air conditioning energy consumption while generating 84.69 kWh of electricity during the summer season. Compared to the standard double-glazed window, the PVBVW system achieves comprehensive energy savings of 34.64%-56.48%, demonstrating a high energy-saving potential.

This paper introduces the principle and application of anti-reflection and self-cleaning thin films. By adjusting the refractive index, thickness and number of layers the thin films, the light reflection loss can be effectively reduced, and the transmittance can be enhanced, so as to improve the power conversion efficiency. The advantages and disadvantages of single-layer, double-layer and multilayer anti-reflection thin films are also discussed. By adding the function of self-cleaning on the basis of anti-reaction coatings, as well as surface modification and decorating photocatalytic materials, the hydrophilic or hydrophobic functionalization can be achieved. Finally, the future development prospect of anti-reflective self-cleaning thin films is discussed.

To address the issues of the inability of a single electricity price strategy to meet the problem of adequate dispatch of multiple flexible loads, this paper proposed a multi-load master-slave game optimization strategy considering peak shaving rewards and fine load regulation. firstly, according to the maximum peak cutting contribution of each flexible load, differentiated ladder reward and punishment mechanism was established. secondly, a multiple flexible master-slave game optimization model was constructed in which the microgrid operator is taken as the leader, and the customer aggregator, and the air conditioner aggregator with fine temperature control, and the electric vehicle aggregator with travel stochasticity are taken as the follower. finally, a numerical example shows that the proposed strategy can effectively tap the peaking potential of a variety of flexible loads, and based on this, achieve multi-benefit improvement for microgrid and a variety of flexible loads.

The complex background interference in electroluminescence（EL） images of photovoltaic modules poses challenges for accurate defect identification. To address this, an improved deep learning model YOLOV5-PMD based on YOLOv5 is proposed. This model utilizes a lightweight convolution GSConv module to replace the Conv part of the Neck, enhancing accuracy while reducing the number of parameters. In addition, the convolutional CBAM attention mechanism is combined to improve the feature perception ability, and the EIoU loss function is replaced by the CIoU loss function to achieve more accurate defect location. On the public SolarMonocrystal dataset, the improved model reduces the training parameters from 7.025×10⁶ to 6.695×10⁶, and the average accuracy of mAP50 reaches 89.8%, which is 2.6% higher than that of the original model.

To investigate the effects of pre-bend and varying backward swept geometrical design on the aeroelastic loads of flexible wind turbine blades, a rotation matrix was utilized to accurately describe the airflow velocity, blade displacement, orientation and vibration velocity at blade elements, which were introduced to modify the blade element momentum model and ultimately form an aeroelastic simulation model. Based on the DTU 10 MW blade, three blades with different backward swept geometries were designed, and static, dynamic, and aeroelastic coupling analyses were conducted on each blade variant. The study explores the effects of bend-twist coupling deformations on the blade angle of attack and the influence of increased aerodynamic force arms due to pre-bend and backward swept geometric design on root torque. The study reveals that the bend-twist coupling effects of the blades are significantly influenced by the blade's geometric shape. The backward swept blade shows the best performance in reducing flap-wise root fatigue loads by up to -11.14%, albeit significantly increasing the torsional root fatigue loads. Conversely, the pre-bend blade demonstrates a decrease in both flapwise and torsional root fatigue loads.

In order to solve the problem that the existing grid-following/grid-forming mode switching scheme of voltage-source converters based on short circuit ratio does not consider the fluctuation of active power and lacks accuracy in the setting of switching boundary, this paper analyzes the influence of the proportion of the grid-forming voltage-source converters’ capacity, the active power and the short circuit ratio on the oscillation characteristics of the station. And then, from the perspective of maintaining the oscillation stability of the station in the low frequency and subsynchronous frequency band, a switching operation scheme based on the operating short-circuit ratio is proposed. The results show that using operation short circuit ratio can select the control mode more reasonably under the condition of low active power. Therefore, the switching frequency decreases, and the stability after disturbance improves.

Aiming at the hot spot and dust fault of photovoltaic modules, this paper proposes a fault detection model based on YOLOv7. Firstly, a Data Augmentation method combining Mosaic and Mixup is introduced to expand the image dataset and enhance the model's generalization ability. Secondly, the Coordinate Attention mechanism is incorporated to effectively focus on channel features and spatial features while addressing long-range dependence issues. The experimental results show that the accuracy of the improved YOLOv7 model in detecting photovoltaic modules,dust faults and hot spot faults reaches 96.08%, 83.92% and 77.19% respectively, which is 3.66 percentage points, 1.90 percentage points and 2.27 percentage points higher than that of the original model. The mean average precision（mAP） increases from 82.48% to 83.05%. The accuracy of the model is improved and the robustness is enhanced to meet the needs of practical applications.