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.

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.

Researches on enhancing the evaporation performance of solar interfacial evaporators are reviewed with evaporation rate and evaporation efficiency as two evaluation indicators. Six methods, including light absorption enhancement, thermal management, water transport control, evaporation enthalpy reduction, other energy utilization, and salt resistance strategies, are summarized. The principles and main means of each method are outlined. Finally, the current urgent problems that need to be solved in solar interface evaporation technology are given for future research directions.

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.

Focusing on the digital trarsformation of wind tunnel tests, in response to the demand for the integration of physical wind tunnel tests and computational fluid dynamics（CFD） numerical tests, this study designed and developed a digital wind tunnel system based on the NF-3 airfoil wind tunnel at Northwestern Polytechnical University（NWPU）. The system replicates the experimental NF-3 wind tunnel model, simulates the control process of experimental wind speed, and enables automated execution of experiments in a virtual wind tunnel. To achieve automation and operational deployment, the digital wind tunnel system uses customized grid templates for the automatic generation of both background and model grids. These grids are assembled using overset grid technology, and low-speed precondition techniques are employed to accelerate the convergence of the compressible flow solver. This paper firstly introduces the basic component sections and the control strategy of the physical wind tunnel. Subsequently, it elaborates on the design of the digital wind tunnel system, including the automatic generation of airfoil grid, automated assembly of wind tunnel and airfoil grids, flow solver methodologies, and virtual control strategies for digital wind tunnel. Finally, this research conducts a virtual experiment in the digital wind tunnel utilizing the standard wind turbine air-foil DU91-W2-250, and compared with traditional CFD simulated results. The results demonstrate that the system can effectively support digital wind tunnel experiments, capturing spanwise separated flow region variations that are absent in conventional quasi-2D simulations and the results align better with physical experiments.

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.

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%.

This paper reviews the current development status of drive chain test platforms based on the source of loading load, and further summarizes the six-degree-of-freedom loads experienced during the operation of the wind turbines, clarifying the standard operating conditions and external conditions that need to be considered in load calculations. Regarding the loading methods, the key technologies of torque simulation are analyzed from two aspects: high torque and high inertia. The existing non-torque load simulation technologies at home and abroad are summarized, and four typical non-torque loading schemes are summarized. To address the nonlinearity and parameter uncertainties in non-torque hydraulic loading systems affecting loading accuracy, common methods for enhancing loading accuracy and frequency response are introduced, as well as the cutting-edge development directions of digital multi-cylinder loading and guided static pressure loading. The drive chain testing platform plays a crucial role in the design and reliability improvement of the wind turbine. With the development of wind energy utilization technology, virtual prototype technology is expected to become a new development trend for future test platforms to reduce research and development costs and shorten research and development cycles.

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.

Based on the practical needs of offshore photovoltaic systems, various floating wave-dissipation structures were designed and tested in a two-dimensional large-scale wave tank under irregular wave conditions. Time-domain response signals were measured, and the wave transmission coefficient, reflection coefficient, and wave energy dissipation coefficient were analyzed. The study demonstrates that compared to single-floating-box breakwater, increasing the width of a single floating box, adding additional floating boxes, and incorporating vertical insert plates or wing plates on the bottom can significantly enhance wave energy dissipation and improve wave attenuation. For the designed double-floating-boxes structure, each 0.5 m increase in the slanted wing depth reduces the transmission coefficient by an average of 15%. Additionally, installing a porous plate between the pontoons further reduces the transmission coefficient. Under irregular waves with an effective wave height of 2 m, the wave energy transmitted through the dual-pontoon floating breakwater with wing plates and porous insert plates is only 12% left, meeting the wave attenuation requirements for offshore floating photovoltaic power stations.

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.

To address the issues of low voltage gain and high voltage stress existing in traditional quasi-Z-source DC-DC converters, a high-gain soft-switching quasi-Z-source DC-DC converter （HGSS-QZS） is proposed. By adjusting both the turns ratio of the three-winding coupled inductor and the duty cycle of the switch, the boost capability and the flexibility of gain adjustment are improved. A clamping circuit is employed to absorb the leakage energy of the coupled inductor, reducing the voltage spikes caused by leakage inductance. Firstly, the operating principle of the HGSS-QZS converter is analyzed in detail, and voltage gain, device voltage and current stress, and efficiency losses are derived. Next, parameter design is carried out and compared with existing converters, demonstrating that the HGSS-QZS converter offers high voltage gain, low voltage stress on components, soft-switching capability, and high efficiency. Finally, a 200 W experimental prototype was built, and the validity and feasibility of the HGSS-QZS converter were verified through simulation and experimental comparison.

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.

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.

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.

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.

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.

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.

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.

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%.

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.

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.

Sodium acetate trihydrate （SAT） has been identified as a potential energy storage material in the field of solar thermal collectors because of its advantageous latent heat of phase transition and suitable phase transition temperature. However, SAT is subject to defects such as subcooling and phase separation during the phase transition. In the present study, a range of thickeners and nano-nucleating agents were utilised in the modification of SAT, with the objective of producing composite SAT with optimal performance. It was observed that the composite SAT not only ameliorated the inherent limitations of SAT, but also exhibited favourable thermal properties. The study proceeded to explore the mechanism of phase separation and subcooling of SAT, and the associated solutions. A comparative analysis was conducted on the inhibition of phase separation and subcooling of SAT with thickening and nucleating agents. Finally, the potential applications of the composite SAT in solar energy post-modification were examined. Following the modification of composite SAT, the subsequent direction of application in the field of solar energy is discussed.

To achieve efficient and low-carbon hydrogen generation, a zero carbon emission hydrogen production system combining methane and solar energy is proposed. The system decouples one-stage methane reforming into two-stage methane reforming： a portion of high-temperature flue gas is introduced into the pre-reforming reactor as reactants, and the reaction heat is provided by solar energy; The reaction heat during the reforming process is providedby combusting purge gas with oxygen from the electrolytic cell. Another part of the flue gas is directly condensed and separated carbon dioxide after preheating the water to be electrolyzed. Energy and exergy balance analyses are conducted on the proposed and reference systems. The results demonstrate that the energy and exergy efficiencies of the proposed system are improved to 42.88% and 39.21%, respectively, by 4.77 and 4.53 percentage points compared to the reference system. Using the exergy utilization diagram （EUD） reveals the main reasons for improved system performance. Comprehensive utilization of solar energy through thermochemistry and electrochemistry. This not only increases the efficiency of solar energy utilization, but also eliminates high-energy consumption carbon dioxide separation devices, resulting in a total increase of 4.23 percentage points in exergy efficiency. The two-stage methane with flue gas reforming process reduces the exergy destruction of high-grade methane chemical energy into low-grade thermal energy during combustion, as well as the heat loss in the heat exchange process, resulting in a total increase of 2.02 percentage points in exergy efficiency.

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.

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.

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.

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.

To solve the problem that PV power fluctuates significantly and is difficult to predict, this paper proposes a combined PV power prediction model based on similar day clustering and an WOA-WMD-TCN-Transformer model. Firstly, K-means ++ is used to cluster similar days. Then WOA was used to optimize VMD parameters, and the PV power sequence was decomposed into multiple Intrinsic Mode functions（IMFs）. The IMF components and meteorological factors were weighted and combined into a new feature vector and fed into the subsequent model. Based on TCN-Transformer, IMF under different weather conditions can be predicted separately and the predicted value can be obtained after superposition. Finally, the photovoltaic power generation and meteorological data of Hanwha Solar Photovoltaic Station, a desert solar Research Center in Alice Springs, Central Australia, were used as an example to verify the validity of the model. Ablation experiments and comprehensive evaluation show that the proposed model can achieve high prediction accuracy under various weather conditions.

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.

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.

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.

This paper proposes a fuel cell aging trend prediction method based on the PatchTST model. By segmenting time series data into multiple local time windows and combining the Transformer architecture to capture long-and short-term dependencies, the method achieves precise prediction of fuel cell aging trends. In the experiments, the model was trained under steady-state and quasi-dynamic operating conditions using data with training set proportions of 50%, 60%, and 70%, respectively, to predict aging trends for future horizons of 50 h, 100 h, and 150 h. Analysis based on error evaluation metrics, such as URMSE and UMAE, indicates that the model achieves the lowest prediction error when the training set proportion is 60% for the FC1 dataset and 50% for the FC2 dataset. Although the error increases slightly as the prediction horizon extends, the overall performance remains relatively stable. Under the conditions where the FC1 dataset is divided by 50% and 60% ratios, the prediction errors of the PatchTST model are lower than those of the Informer, Transformer, GRU, and LSTM models.

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.

In this study, a theoretical prediction model of mixed molten salt electrical conductivity was proposed based on the theory of heat generation by ion friction under an AC electric field, and a set of molten salt electrical conductivity measurement devices was designed to analyze the electrical conductivity of Solar salt, Hitec salt and Hitec XL salt. The results show that the mean absolute percentage error （MAPE） values between the theoretically predicted values and experimentally measured values of electrically conductivity for three types of mixed molten salts are 11.646%, 5.473%, and 3.419%, respectively. The trends of theoretical predicted values and experimental measured values are consistent, confirming the accuracy of the proposed theoretical prediction model.

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.

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.

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.

Access to virtual power plants that aggregate a large number of dispersed demand-side resources provides new possibilities for the efficient development of power systems in the future. The flexible load in the virtual power plant can participate in the demand response and bring this flexibility into the power system planning framework, which can effectively reduce the system planning and operation costs and promote the consumption of renewable energy. Therefore, this paper proposes a multi-type power planning method considering virtual power plant empowerment. Firstly, the adjustable potential model of virtual power plant is established for different types of virtual power plants such as electric vehicles, air conditioning loads and industrial loads. Secondly, considering the operation constraints of virtual power plant for power planning, a power planning model considering virtual power plant empowerment is constructed. Furthermore, considering the uncertain factors such as the prediction error of new energy and the characterization error of the response potential of virtual power plants, the interval optimization method is adopted to deal with it. Then, the interval programming model is definitely transformed by interval order relation and interval possibility degree, and an improved benders decomposition algorithm is proposed to realize the efficient solution of the transformed model. Finally, an example is given to verify the effectiveness of the proposed power planning model and method.

A joint estimation method of SOH and RUL based on feature reconstruction and multiple time scales is proposed in this paper. Firstly, six health features related to capacity degradation are extracted from the lithium battery aging data set, and the feature data is reconstructed by using variational mode decomposition algorithm. The feature reconstruction was achieved by filtering the participating modes through user-defined indicators; On this basis, the Bayesian algorithm is used to optimize the convolutional long-term and short-term memory network to construct the SOH estimation model. The accurate and efficient estimation of SOH and RUL is achieved by combining the algorithm iteration at the micro scale with the convolutional neural network model at the macro scale. The results show that the estimation error of SOH is stable within 1%, and the estimation accuracy is about 35% higher than that before feature reconstruction; The mean absolute error （MAE） and root mean square error（RMSE） of RUL prediction results are kept within 0.42 and 0.78, respectively, which realizes the high-precision estimation of SOH and RUL.