Accurate assessment of urban rooftop PV development potential is one of the key problems to be solved for urban rooftop PV construction. Most of the existing studies are limited to the assessment of urban rooftop PV potential by a single data source, such as satellite remote sensing images or geographic information, which is difficult to accurately respond to the urban rooftop PV potential under the influence of multiple factors. Considering the shadow effect, geographic location and meteorological conditions, this paper proposes an urban rooftop PV enhancement method based on the fusion of multi-source information, which mainly includes three links： roof information extraction, regional building shadow calculation and PV power generation potential calculation, and a large amount of satellite remote sensing imagery, geographic information data, meteorological data and other data are introduced into the analyses of the links, which greatly enhance the accuracy of the assessment of the urban photovoltaic potential. The accuracy of urban PV potential assessment is greatly improved. Finally, the method is applied to Changsha City as an example, and the evaluation results show that the power generation potential of urban rooftop photovoltaic in Changsha City is 14480.4 GWh, which is a large potential for development.

Addressing the impact of nonlinear characteristics such as dead-zones on the power quality of photovoltaic grid-connected inverters, this paper combines data-driven compensation methods with traditional control to investigate a dynamic and static characteristic optimization approach for grid-connected inverters. Firstly, a repetitive controller is utilized as the basis for online data training, elucidating the mechanism and validity of the data source. Secondly, an approximate linear regression method is employed to obtain a data model, reducing the dependence on storage space for data-driven methods, ensuring necessary compensation bandwidth, and solving the feasibility of data application. This model is then applied to the compensation loop of a traditional low-order controller, enabling the system to achieve precise control with sufficient stability margin. Data correlation analysis and experimental results demonstrate the feasibility and effectiveness of this compensation method.

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.

Aiming at the problems that the change of incident angle has a great influence on the heat collection performance and the escaped light is difficult to be reused when the traditional compound parabolic concentrator is in non-sun-tracking operation, a compound parabolic concentrating solar photothermal-photoelectric energy supply device is designed in this paper. By adding a photovoltaic module with a mirror back on the lower surface of the glass cover plate at the light inlet of the composund parabolic concentrator （CPC）, the interception and reflection reuse of the escaped light can be realized, so as to improve the utilization efficiency and energy supply grade of the energy supply device to the incident solar radiation. Firstly, the optical simulation software TracePro is used to analyze the influence mechanism of the incident angle on the parameters such as the ray's receiring rate of the energy supply device. Based on the simulation results, under the actual weather conditions, the variation of the temperature difference between the inlet and outlet air, the photothermal conversion efficiency and the output electric power of the energy supply and the same specification CPC with the solar radiation are compared and studied. The results show that when the incident angle range is 0°<α<30°, the average of the energy supply device is increased by 14.03% compared with that of the same specification CPC. Under sunny conditions, when the heat transfer air flow rate is 2.7 m/s, the maximum inlet and outlet air temperature difference between the energy supply device and the CPC of the same specification is 0.2 ℃, and the photothermal conversion efficiency is 52.62% and 52.63%, respectively. The total output electric power of the energy supply device in this process is 251 W. Under the condition of cloudy days, the thermal energy and power output of the energy supply device are 3.26 MJ and 210.5 W, respectively. The research results provide a reference and idea for the efficient coupling and comprehensive utilization of solar photothermal-photoelectricity.

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.

Aiming at the problems that the fault features of the main bearings are interfered by the harmonics of other transmission components in wind turbines' condition monitoring and the raw vibration signal is non-stationary, making it difficult to accurately extract the characteristic frequency of fault impacts, a combined method of harmonic removal and spectral kurtosis analysis （HR-Fast-Kurtogram） is proposed. Offline angle domain resampling is used to convert non-stationary time domain signals into stationary angle-order signals. Comb notch filters are introduced in the order domain to remove gear meshing frequencies, and Fast-Kurtogram based on short-time Fourier transfer （STFT） is applied to extract the fault sub-bands where the kurtosis peak is located in order to extract bearing fault features. The combined method is able to avoid the interference of non-periodic impacts and distinguish between non-bearing amplitude modulation components with lower impact and bearing fault impact components. The comparative analysis results in the application of real main bearing fault data shows that the proposed method can improve the accuracy of fault features extraction and fault type identification in a short time scale of seconds.

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.

To effectively analyze and utilize the prediction errors distributed in a specific pattern in the photovoltaic power prediction model, a photovoltaic power prediction model based on LSTM-Attention and CNN-BiGRU error correction is proposed. Firstly, the LSTM-Attention mechanism is introduced to compensate for the shortcomings of the long short-term memory （LSTM） network, which is difficult to retain key information in the input sequence. Secondly, the advantages of convolutional neural network （CNN） in non-linear feature extraction are combined with the advantages of Bidirectional gated recurrent unit （BiGRU） in preventing multiple features from interfering with each other to build a CNN-BiGRU error prediction model is used to predict the possible errors and to correct the initial prediction results. The experimental results indicates that the CNN-BiGRU error prediction model can effectively improve the prediction accuracy in different weather types when compared with the prediction results without error correction.

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.

In view of the characteristics of high temperature solid oxide electrolyzer cell （SOEC） hydrogen production,such as energy conversion efficiency,remarkable reaction rate etc., firstly a multi-mode green hydrogen integrated operation model is built to analyze the model's start-stop and hydrogen production characteristics,while economic and operational constraints is imposed on it. Secondly,considering the random and intermittent factors of wind and solar power, as well as the nonlinear characteristics of modules,a robust and adjustable collaborative optimization method is proposed that independently controls each module and meets the load requirements. By establishing the wind and solar uncertainty set,the piecewise linearization of the modules,and introducing a robust and adjustable collaborative optimization method, the hydrogen production efficiency,operational flexibility,and overall economic benefits of the system were improved. Lastly, a flexible start-stop operation example analysis was conducted on 5 integrated modules on the basis of meeting the load distribution work,and 25 integrated modules were connected to the distribution network to participate in power grid scheduling. The results show that a reasonable combination model can not only optimize the flexibility and stability of the system's start-stop operation under variable load operation conditions. Moreover, it can improve the consumption of new energy and the adaptability in grid-connection,which has obvious economic efficiency and strategic significance.

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.

Uncertainties arising from large-scale grid integration and cross-seasonal use of wind power clusters place higher demands on the accuracy of wind power prediction. To improve the accuracy of wind power prediction, a Convolutional Neural Networks （CNN） Gated Recurrent Unit （GRU） -LightGBM Gradient Boosting Machine （LightGBM） composite wind power prediction model based on Monarch Butterfly Optimization （MBO） algorithm is proposed. Firstly, establish wind power prediction models for CNN-GRU and LightGBM respectively, and use the reciprocal variance method to weight and combine the two models into a CNN-GRU LightGBM composite model; To optimize the continuous parameters in the model, MBO is used to perform hyperparameter optimization on the model. Finally, short-term wind power data from an offshore wind farm in Zhuhai was selected to compare the proposed method with existing prediction methods. The experimental results showed that the model combined the advantages of models such as CNN-GRU and LightGBM, resulting in smaller prediction errors, higher prediction accuracy, and stronger seasonal universality.

In view of the problems of low output voltage and large environmental impact in new energy renewable systems such as photovoltaic power generation, this paper proposes a novel high-gain three-port DC-DC converter topology and control strategy. The three ports of this converter are connected to the photovoltaic power generation end, the energy storage port end, and the load end, respectively. It has advantages of low switch device stress, flexible power allocation among input sources, and sirong excitation energy recovery ability of the coupled inductor. The operating principles and steady-state conditions of the converter in various operating modes are analyzed in detail, and the performances of the circuit topology are compared with those of similar converters. According to the control requirements of the optical storage and power supply system and the power flow of the converter under different working conditions, the corresponding mode operation and switching control strategy are studied. A 300 W prototype is built for validation, and the results of the validation demonstrate the feasibility of the proposed structure.

Facing the demands of energy storage and decarbonization, the technology of methanol production from biomass coupled with wind power was studied using wheat straw and sawdust as feedstock. The performances were analyzed and evaluated including the methanol yield, the energy efficiency of storage-release processes （i.e., storage-release efficiency）, the life cycle carbon emissions, and the differences between power generation model and cogeneration mode for methanol utilization. Without the separation of CO₂ in product gas, only the one-way conversion rates of CO₂ and CO have obvious impacts on the methanol yield, ranging from 0.970 to 1.104 kg/kg. The efficiency of water electrolysis is the key parameter to improve the storage-release efficiency. Both storage-release efficiency and carbon emission intensity under the cogeneration mode have significantly advantages over those under pure power generation mode. Under the cogeneration mode, the storage-release efficiency varies from 46.1% to 58.6%, which is similar to that of the compressed air energy storage. The ranges of carbon emission intensity of the regenerated electricity and thermal energy are 37-77 g CO₂/kWh and 10-21 g CO₂/MJ, respectively, which are significantly lower than the current levels of the products. The carbon emission intensities with sawdust as feedstock are significantly lower than those with wheat straw. This technology with separation and capture of CO₂ in product gas can achieve the zero-carbon emission in the life span at the cost of decreases in the methanol yield, power rate, and storage-release efficiency.

The research focused on reducing energy consumption in greenhouse buildings by designing a solar-ground source heat pump phase-change heat storage heating system （SGSHPP-CHSH） based on an existing solar-ground source heat pump heating system （SGSHPH） in a greenhouse located in Hebei Province. Simulation models of both systems were built using TRNSYS software, and the simulation results and influencing factors were compared and analyzed. The findings indicate that the solar collector heat collection of the SGSHPP-CHSH is 10.75% higher than that of the SGSHPH throughout the entire heating season. Additionally, the phase change storage tank's temperature decreased from 49.4 ℃ to 34.4 ℃, resulting in reduced energy consumption for the heat pump unit （from 10145 kWh to 7843 kWh） and increased COP of the heat pump system （from 2.68 to 3.36）. Similarly, for the SGSHPH, there was a decrease in water supply temperature from 45 ℃ to 30 ℃, reduced energy consumption for the heat pump unit （from 12837 kWh to 8739 kWh）, and an increased COP of the unit （from 2.39 to 3.43）. Furthermore, When the solar collection area of the SGSHPH is 48 m², and the solar collection area of the SGSHPP-CHSH is 39 m², the soil heat storage in both heating systems during non-heating seasons equals to the soil heat extraction during heating seasons, effectively maintaining a balanced thermal state within the soil.

The Morison equation is a widely used formula for calculating wave loads on offshore wind turbine monopile foundations. However, as monopile diameters increase, diffraction effects can no longer be ignored, and the Morison equation method becomes less accurate. This study investigates the horizontal wave load on large-diameter monopile foundations with a pile-diameter to wavelength ratio ranging from 0.15 to 0.25. To this end, a combination of physical model experiments and computational fluid dynamics （CFD） numerical simulations was employed. The variation of the maximum horizontal wave load was analyzed for monopiles of different diameters under different wave conditions. A curve depicting the values of the inertia force coefficient was given, and an empirical formula based on the Morison equation was proposed for calculating the maximum horizontal wave load on large-diameter monopile foundations.

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.

Aiming at the problem that the quartz crucible needs to play the role of carrying silicon melt for a long time under the environment of temperature field and pressure in the continuous crystal pulling process, it is proposed that the change of gas bubbles inside the crucible may cause the “breakage” of crystal rods in the crystal pulling process. For the quartz crucible bubble change rule before and after straight pulling single crystal silicon and temperature variation, the experiments and simulation studies were carried out. The results show that the bubble content in the used specimen is higher than that in the unused specimen, and more bubbles with φ=10-100 μm are observed in the specimen before and after the use of the quartz crucible, among which the bubbles with φ=100-200 μm on the outer wall are obviously increased, and the bubbles on the inner wall grow significantly before and after the use of the quartz crucible, and the bubbles on the side wall, corner and bottom change in a relatively average way, and the bubbles at the corner at the higher temperature are not as big as those on the inner wall, and the bubbles at the bottom of the crucible at the lower temperature are not as big as those in the outer wall. The bubbles around φ=200 μm are more in the corners, and the possibility of “broken line” phenomenon of the crystal rods increases with the process of crystal pulling.

Based on the TELEMAC-2D hydrodynamic model, a tidal current field model around Pingtan Island in Fujian province was established. The accuracy of the model was verified by using measured tidal level and tidal current data. As the results, the model can accurately depict the tidal and current characteristics of the study area. On this basis, the spatial-temporal distribution characteristics of tidal current energy resources and the asymmetry of tidal rise and fall in the region were analyzed. The Flux method was combined with Garrett method to assess and compare the exploitable amount of tidal current energy resources in specific regions. Results show that the tidal current energy resources in the waters around Pingtan Island are mainly distributed in three channels： Pingtan Island-Dalian Island, Dalian Island-Xiaolian Island, and Xiaolian Island-Changyu Island, with a peak power density of about 10 kW/m². The effective flow time is more than 6000 h. The total area of Class I tidal energy resource zone in three channels is 1.05 km², while Class Ⅱ is 5.11 km². The exploitable tidal current energy resources calculated by the Flux and Garrett methods are 7.44 MW and 11.74 MW, respectively.

To effectively extract valuable temporal information from photovoltaic （PV） power data and further improve the accuracy of PV power prediction, we propose an efficient channel attention mechanism （ECA）- temporal convolutional network （TCN） prediction model based on multi-factor fusion. Firstly, the maximum information coefficient （MIC） is utilized to extract relevant features of PV power. Secondly, a polynomial feature derivation method is employed to combine the features of various factors, generating high-dimensional features and facilitating feature combination. Then, the ECA module, which adaptively selects the size of one-dimensional convolutional kernels, is combined with TCN to construct the ECA-TCN prediction model, which effectively captures the temporal characteristics of PV power data. Finally, multiple models are compared through experimental evaluation. The results demonstrate that the proposed feature combination method efficiently selects PV power data features and enhances their discriminative ability. The ECA-TCN prediction model with feature combination achieves a root mean square error （RMSE） of 0.0828 kW. Compared to LSTM, LSTM-TCN, and ECA-LSTM, the RMSE of the ECA-TCN was reduces by 0.29, 0.23, and 0.13 percentage points, respectively, and exhibits the best fitting performance with an R² value of 90.74%. This model effectively improves the accuracy of PV power prediction while maintaining a high fitting performance.

Aromatic compounds can be obtained from lignin depolymerization by photocatalysis under mild conditions. Based on the structure of lignin, the C—O and C—C fracture mechanisms in lignin photocatalytic catalyzed by semiconductor,semiconductor modification methods such as doping, precious metal deposition, and construction of composite materials,which can improve photocatalyst stability and lignin depolymerization efficiency, are reviewed in this paper. The application of photocatalysis in the treatment of natural lignocellulosic raw materials,and the strategy of lignin degradation by external-field-coupled photocatalysis are summarized. Finally, the future research focus and development prospects of semiconductor in photocatalytic lignin depolymerization are prospected, which aimed to promote the technological innovation and industrialization of photocatalytic lignin depolymerization.

With the continuous increase in photovoltaic power generation, the photovoltaic concentrated grid-connected areas are prone to the problem of unbalanced three-phase voltage due to low local load and weak support ability, which has an impact on the stable operation of the power grid. In response to the above problems, this article firstly establishes a single-machine fundamental frequency negative order-order analysis impedance, considering the influenees of PV array, current inner loop, phase locked loop, LC filter and current loop dq decoupling based on the theory of harmonic linearization, and then establish a single-machine equivalent value model of the photovoltaic power station. Then, based on the negative sequence model of photovoltaic power station, the equivalent circuit model of photovoltaic power field cluster gathering area is constructed to analyze the mechanism and influencing factors of voltage unbalance in the gathering system. Finauy, the photovoltaic collection system electromagnetic temporary simulation model is established through the PSCAD/EMTDC simulation platform. The influencing factors of the time domain simulation analysis are used to verify the correctness of the theoretical analysis results.

In this paper, a forecast correction method based on machine learning is proposed, which first corrects regional errors with gridded analysis products and then corrects local errors with station observation data. The method utilizes the gridded wind speed analysis products at 100-meter height to correct the results of numerical weather prediction through deep learning algorithms, and then corrects the station wind speed forecast results with a random forest integrated forecast approach based on the observation data of wind speed at 100-meter height from the anemometer tower. An empirical analysis of a meteorological station in central Inner Mongolia shows that after correction with the proposed method, the root mean square error （RMSE） of wind speed prediction for the next 84 hours is significantly reduced from 4.25 m/s to 3.16-3.79 m/s after gridded forecast correction and to 2.81 m/s after station integrated correction. Therefore, the proposed method can effectively reduce forecast errors.

This article numerically processes the grayscale histogram of visible light images of photovoltaic modules, introduces the concept of average grayscale value, and combines it with the photovoltaic experimental platform built by the school to confirm the corresponding relationship between the average grayscale value and the ash density of photovoltaic modules. Based on this conclusion, the image of a visual inspection platform for a photovoltaic power station in Inner Mongolia is identified and processed, and it is found that over time, the change in the average gray level difference between the clean side and the accumulated side is positively correlated with power generation loss. Based on the meteorological and image data collected by the experimental platform, a prediction model for the degree of ash accumulation is established using the random forest algorithm. Based on relevant meteorological factors, the average gray level of the ash accumulation board from the previous day is combined as the input variable to predict the average gray level of the ash accumulation board day by day. Different tuning parameters are set to improve the optimization robustness of the prediction algorithm.

For solar cells, low enough contact resistivity is a key parameter for achieving high conversion efficiency, especially for carrier selective contact structures such as crystalline silicon heterojunction （HJT） solar cells. Contact resistivity ρc is usually calculated by Cox Strack method （CSM） or Transfer Length Method （TLM）. It is necessary to use some specific metal electrode patterns with either of the methods, which is difficult for real solar cell devices. For silicon heterojunction （HJT） solar cells, contact resistivity ρc consists of the silver electrode and ITO contact resistivity ρc1 and the tunneling contact resistivity ρc2 of ITO with p-or n-type doped amorphous silicon. Therefore, it is necessary to use different specific structures for accurate measurement of ρc1 and ρc2. In this article, we propose a new TLM method using which we could simultaneously measures the electrons contact resistivity between silver electrodes and ITO ρc1, as well as between ITO and n-type doped amorphous silicon ρc2 on actual HJT solar cell devices. The electrons contact resistivity value for ρc2 is 2.5-3.1 mΩ•cm², and the electron tunneling contact resistivity ρc2 value is 21-24 mΩ•cm², which are quite consistent with literature reports.

Alkaline electrolytic water hydrogen production system with strong coupling and complex operating conditions is a hazardous chemical production environment, so it is difficult to carry out extreme experiments such as wide power fluctuation test for renewable energy hydrogen production. In this paper, the 1 MW alkaline electrolyzer of Zhangjiakou Chongli hydrogen production plant is used as the research object, and the effects of variables such as temperature, pressure and alkali flow rate on parameters such as gas purity and cell voltage are analyzed by production experimental data. Firstly, the operating mechanism is analyzed, the data are fitted with multivariate nonlinearities to produce empirical equations, and the voltage model, Faraday efficiency model, and hydrogen concentration in oxygen model of the MW alkaline electrolyzer were established, and the post-treatment systems such as gas-liquid separation are modeled. Next, the model results are analyzed and verified theoretically, and the safe operation boundary of the hydrogen production system is derived from the model analysis. Finally, the mathematical model of the hydrogen production system is combined with the 3D model to realize the mapping of the hydrogen production system in the virtual space, which provides an a priori platform to study the wide power fluctuation of renewable energy hydrogen production.

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.

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.

To optimally monitor photovoltaic arrays and forecast their power production, improving the quality of photovoltaic data is an essential and urgent task. To this end, this paper introduces a method for the identification of anomalous data in photovoltaic arrays based on a two-step Pair-Copula approach. This method is divided into two stages： the first stage involves the identification of outliers in the direct current side of the photovoltaic array, while the second stage, building upon the first, involves the identification of outliers in the photovoltaic direct current side voltage. More specifically, the Pair-Copula is utilized to model the dependence structure between photovoltaic current, irradiance, and temperature, with Akaike information criterion employed to optimize the Copula function. Subsequently, a conditional probability model for the photovoltaic current is established, and the formula for calculating the confidence interval of the conditional probability is derived. The confidence interval of the photovoltaic current is then used as the primary criterion for identifying and eliminating current outliers. Finally, building upon the data obtained in the previous step, the aforementioned procedure is repeated to eliminate voltage outliers. The results of simulation experiments demonstrate that, compared with other outlier identification methods, the approach proposed in this paper maintains a low identification error rate while boasting a higher identification accuracy.

In order to explore the economic assessment of the whole life cycle of biomass liquid fuel, this paper takes levulinate as the research object and takes the large-scale production system for the preparation of levulinate with annual consumption of 2000 t of bagasse were an example. The system boundary and process of bagasse hydrolysis to produce biomass-based ester fuels were designed creatively, and the whole life cycle economic analysis model was established, and the comprehensive cost estimation and economic analysis were carried out. Through the dynamic index, the economy of whole life cycle of biomass-based ester fuel was deeply analyzed, and the economic results were obtained. The key factors affecting production cost were identified and the uncertainty of production cost was estimated. As the results, the whole process shows good economy and application prospect. It provides the theoretical support for the large-scale technology of biomass-based ester fuel co-producing chemicals, and promotes the clean production of biomass-based levulinate.

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.

Solar radiation has strong nonlinear characteristics, posing serious challenges to the grid integration of photovoltaic power generation. A correction forecast method has been developed, combining numerical weather prediction models, machine learning and variational mode decomposition, to address this issue： 1） Using the WRF-Solar model to forecast surface solar radiation at photovoltaic stations; 2） Using VMD decomposition method to decompose the bias between WFR-Solor forecast results and observation values; 3） Train and forecast the decomposed components using the BiGRU; 4） After summing the forecasts for each component, combined with the WRF-Solar results,the correction forecast of surface solar radiation is obtained. The experimental results show that after the VMD-BiGRU model correction, the percentage improvement of MAE and RMSE compared to WRF-Solar's forecast results is 87.39% and 87.29%, respectively, with a correlation coefficient increase of 0.25.

Aiming at the limitations of the previous PV defect detection, such as fewer types of recognizable defects, multiple model parameters, large volume of model parameters, and slow detection speed, the traditional YOLOv5 network is improved to detect and classify the five main types of defects, namely, cracks, broken grids, black cores, thick wires, and hot spots that are commonly found in the images of photovoltaic panels. Three different attention mechanism modules： CA attention mechanism module, ECA attention mechanism module, and CBAM attention mechanism module, are integrated into the YOLOv5 network for comparative analysis experiments, and it is found that the CA attention mechanism is more suitable for PV defect image detection. Subsequently, the YOLOv5 algorithm incorporating the CA attention mechanism module is added to the bidirectional feature pyramid network structure further to strengthen the feature fusion capability of the network. The experimental results show that the model can effectively identify and localize five types of common defects, and compared with the YOLOv5 algorithm, the Map （Mean Average Precision） value is improved by 3.7 %, the model volume is reduced by 15 %, and the average speed of the detection of the images is improved by 9.7 %. The overall conclusion shows that the method effectively enhances the ability of YOLOv5 algorithm in PV defect detection, and at the same time reduces the misdetection and omission of the deep learning algorithm in PV detection.

Aiming at the rationality and economic issues of energy dispatch in microgrids containing high proportions of renewable energy, a microgrid energy management strategy based on a two-layer master-slave game is proposed. The upper layer of the model takes the microgrid energy management center as the leader to sell electricity. Profit maximization is the optimization goal, reducing expenditures and increasing profits by adjusting electricity purchase and sales price strategies. At the same time, it interacts with the upper-level power grid when the internal supply and demand of the microgrid are unbalanced; the lower-level model treats distributed power operators and users as followers, taking the maximum electricity sales revenue and the minimum electricity cost as the planning goals respectively, and optimizing the interests of each entity by changing the output of internal combustion equipment and the energy usage strategy of users. The existence and uniqueness of the Stackelberg equilibrium of the proposed model are proved and solved using an improved genetic algorithm to obtain the optimal strategy of each stakeholder. Finally, it is verified by numerical examples that the proposed two-layer game model can maximize mutual interests and effectively manage the energy use of all parties.

Some problems such as a steady state error in the control system, low quality current in grid side under non ideal grid voltage with background harmonics. In order to solve those problems, a control strategy of high-performance grid-connected inverter is pointed out in this paper. Firstly, the inverse Park Transformation （IPT） method is used to construct the orthogonal component of the grid-side current, and then the grid-side current control loop is established under the dq rotating coordinate system, and then a PI controller is used to achieve seamless tracking of the current. In order to solve the harmonic problem of grid current, a multi-resonance controller is introduced to suppress the low-order harmonics of grid-connected current. The mathematical model of the grid-connected inverter control system is derived under the stationary coordinate system, and a simplified design method for the parameters of the PI controller and multi-resonance controller in the control system is proposed. The robustness of the proposed control system is analyzed. Finally, for the 6 kW single-phase LCL grid-connected inverter control system, the correctness and effectiveness of the proposed control strategy are verified through simulation and experiments.

Taking the regional integrated energy system as the research object, the study firstly conducts an optimization strategy analysis. Then, a multi-objective optimization is carried out, specifically targeting energy efficiency, economy, and environment. Subsequently, using an improved BWM method （HWBM） combined with the entropy weight method, an objective-subjective weight evaluation system is established, leading to the determination of an optimal system configuration and operational planning. Findings reveal that the design strategy effectively addresses the multi-energy coordination issue. Compared to the traditional combined cooling, heating, and power system, the optimal system achieves a 14.9% reduction in fossil fuel consumption, 8.9% in annual investment costs, 4.7% in energy costs per degree, 14.9% in carbon emissions, and 18.1% in water consumption. The outcomes emphasize the notable advantages in energy, economy, and environmental protection.

This article uses numerical simulation methods to investigate the thermal hydraulic laws of the flow channels in a molten salt and sCO₂ printed circuit board heat exchanger （PCHE） using binary eutectic salts MgCl₂-KCl and sCO₂ as heat transfer media. In the airfoil channel, due to the collision of the flowing working fluid at the end of the fins, the minimum flow velocity generally occurs at the end of the fins. Due to the influence of flow velocity, the minimum pressure usually occurs at the thickest part of the fins. Nu and Δp in both straight and wing channels increase with the increase of Re, while f decreases with the increase of Re. The heat transfer efficiency in the straight channel and wing channel has been improved by 53.3% and 56.9%, respectively. By fitting, a heat transfer and friction correlation equation was established for molten salt and sCO₂ as working fluids in printed circuit board heat exchangers.

The output characteristics of photovoltaic modules and arrays under intermittent partial shadow occlusion conditions are analyzed, and an adaptive particle swarm optimization algorithm that can accurately track the maximum power point for the multi-peak performance curve of photovoltaic arrays is proposed. Using the principle of five-parameter model, the simulation model of photovoltaic modules and arrays is built in Simulink. Taking a photovoltaic array in Xi'an as an example, Sketch up is used for sunshine analysis, and the actual shadow occlusion conditions are simulated, which are brought into the above model to obtain the performance curve, and MPPT tracking is carried out by adaptive particle swarm optimization algorithm. Finally, the accuracy of the model is proved by experiments.

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.

A curled wake model for floating wind farms suitable for wake optimization control is proposed based on the characteristics of floating wind turbines. This model focuses on the dynamic characteristics of the inclined wake of units in floating wind farms, and considers the calculation of inclination and yaw deviation based on the quasi steady state curled wake model. On this basis, a model predictive wake optimization control method is proposed to coordinate the output of each unit with the goal of maximizing the output power of a floating wind farm. In FAST.Farm, the accuracy of the established models is verified by comparing the wake loss velocities calculated by the quasi steady state curled wake model （curl-N）, initial curled wake model （curl-O）, polar wake model （Polar）, and large eddy simulation （LES）. To demonstrate the effectiveness of the optimization control method, the output characteristics are compared with those of wake free optimization and traditional wake optimization. The results show that the proposed quasi steady state curled wake model can be applied to wake optimization control, and the model predicts that wake optimization control can effectively improve the overall output of floating wind farms.