A photovoltaic array composed of YL250P-29b type polycrystalline silicon solar cells packaged in series at our school is taken as the research object in order to improve the efficiency of photovoltaic power generation. The rationality of the numerical model was verified by comparing the experimental data and simulation results of artificial ash accumulation. Based on this model, the effects of wind speed, irradiance, ambient temperature and ash density on the temperature of photovoltaic modules are simulated and analyzed. According to the empirical formula of photoelectric conversion efficiency and output power, the influence of the above environmental factors on the output characteristics of photovoltaic modules was explored. The results show that there is a negative correlation between PV module temperature, photoelectric conversion efficiency with wind speed, and positive correlation with irradiance, ambient temperature and ash density. There is a negative correlation between the output power of photovoltaic modules with ambient temperature and ash density, and a positive correlation with wind speed and irradiance. Within the scope of this study, the correlation order between each environmental factor and PV module temperature and photoelectric conversion efficiency is： ambient temperature> irradiance>wind speed>ash density, and the correlation order with output power is： irradiance>ash density>ambient temperature>wind speed; For every 1 ℃ increase in ambient temperature, the temperature of photovoltaic modules also increases by about 1 ℃, and the photoelectric conversion efficiency and output power decrease by about 0.06% and 0.4%, respectively. Ash accumulation reduces the temperature of photovoltaic modules and increases the photoelectric conversion efficiency, but it will greatly reduce the output power of photovoltaic modules.

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.

In response to the significant randomness and uncertainty issues in photovoltaic power, this paper proposes an improved LSTM based photovoltaic power prediction method to improve the accuracy of photovoltaic power prediction. Firstly, meteorological features with strong correlation with photovoltaic output were analyzed, and the t-distribution nearest neighbor embedding dimensionality reduction technique was used to reduce the selected feature data to 2D to reduce data complexity. Then, the reduced dimensionality data is automatically clustered into three categories through density peak clustering to help train the LSTM prediction model. Compared with the traditional Recurrent neural network and Long short-term memory neural network models, the model proposed in this paper shows higher prediction accuracy in photovoltaic power prediction. MSE decreases by 49.00% and 31.77%, RMSE decreases by 28.59% and 17.41%, and MAE decreases by 62.35% and 53.52%. The research results indicate that the model has good applicability in photovoltaic power prediction, providing valuable reference for the optimization and scheduling of integrated energy systems.

In order to change the traditional operation mode of “source with load” and increase energy storage, and to realize the coordination and interaction of power grid, load, energy storage and other links, this paper establishes an electric-thermal-hydrogen coupled integrated energy system （ETHC-IES） for optimal scheduling. The use of hydrogen energy storage to realize the safe and stable operation of a new type of integrated energy system “source network storage” has become a current research hotspot. The aim of this paper is to reduce the operation cost of IES and the waste of wind and light. The ETHC-IES optimal scheduling problem is transformed into a Markov decision process （MDP）, and an optimal scheduling method based on continuous action proximal policy optimization （PPO） is proposed for the integrated energy system. Firstly, a mathematical model of each part of the electric hydrogen storage system is established. Then, the model is solved using a deep learning proximal policy optimization algorithm with the optimization objectives of economy and reduction of wind and light waste. The action space, state space, and reward function of the deep reinforcement learning model are set up. Intelligent agents are trained and learned to achieve dynamic scheduling optimization decisions for ETHC-IES. Finally, the effectiveness and applicability of the proposed model and method are verified by simulation.

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.

Primarily, the current status of development for the hydrogen storage and transportation technology are reviewed in this paper, including the storage and transportation manners of gaseous, liquid, solid, and hybrid, respectively. Subsequently, based on the index requirements of on-board hydrogen storage by US Department of Energy, the comprehensive performance of varied hydrogen storage technologies is compared, and the advantages and disadvantages of the existing hydrogen transportation ways are summarized. Finally, five suggestions are put forward for the future research direction of hydrogen storage and transportation technology. Overall, new hydrogen storage cylinders with superior comprehensive performance and hybrid hydrogen storage technologies should be the main focus of current research in the field of hydrogen storage. Liquid hydrogen transportation and natural gas mixed with hydrogen transportation are significant research hotspots for the future and present, respectively.

A method is proposed for the packaging aging of IGBT modules to be monitored, based on the combination of EMR and V_{eE_peak} electrical parameters, with the aim of the health status of IGBT modules being monitored when multiple aging events occur simultaneously. Firstly, the generation mechanism of V_{eE_peak} and radiated interference signal （EMR） and the influence of parasitic parameters inside the module on V_eE and EMR were analyzed. Secondly, the impact of bond wire lift-off and solder layer voids on the internal parasitic parameters of IGBT modules is analyzed. Finally, the feasibility of this method was verified through experiments. This is a non-invasive monitoring method that does not require complex monitoring circuits, which can effectively reduce the errors caused by various aging coupling on the health status monitoring results of IGBT modules.

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.

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.

The principle and state-of-art of PV monocrystalline silicon slicing processing are reviewed herein. The core wire diameter of the diamond wire saw has been reduced to 37 μm. The as-cut half G12 wafer thickness of PV monocrystalline silicon has been reduced to 110 μm. It is elaborated that the main technical approaches for high wafer yield slicing are to reduce the as-cut wafer thickness and the diameter of diamond wire saw. The effects of surface crack damage and fracture strength of sliced silicon wafer, liquid bridge interaction at diamond wire saws and as-cut silicon wafers, and machine vision detection on the high wafer yield slicing processing of PV monocrystalline silicon are discussed. The key technologies faced in the high wafer yield slicing processing of PV monocrystalline silicon are proposed： 1） to develop low-cost tungsten core wire diamond wire saw; 2） to develop new coolants and lubricating technologies; 3） to develop new slicing processing technology; 4） to establish a quantitative relationship between the distribution of abrasives and the machining performance of electroplated diamond wire saw.

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 order to make thermal power units better cope with the impact on the original power grid structure under the background of rapid development of new energy sources, and improve the stability, safety and economy of thermal power unit operation, based on the current research status at home and abroad, the lithium battery-flywheel control strategy and the regional dynamic primary frequency regulation model of thermal power units are proposed, and the capacity configuration scheme of flywheel-lithium battery hybrid energy storage system under a certain energy storage capacity is studied, and the simulation verification is carried out through Matlab/Simulink, Under continuous disturbance, the frequency fluctuation degree of the system is 0.00119 pu, the fluctuation amount decreases by 30.81%, the power fluctuation decreases by 43.65%, and the actual power contribution increases by 23.17%. The results show that when the thermal power unit is disturbed by external load, the frequency regulation of hybrid energy storage auxiliary thermal power unit effectively improves the operation stability and economy of thermal power unit.

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.

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.

A sub-synchronous oscillation suppression strategy for wind power transmission systems based on multi band adaptive notch filter （MANF） is proposed. Firstly, based on the mechanism analysis of sub-synchronous oscillation, the installation position of MANF in the rotor measurement converter （RSC） of doubly fed induction generator （DFIG） is determined. Secondly, the DFT identification method optimized by the recursive group harmonic power minimization （RGPM） algorithm is used to design a sub-synchronous oscillation detector, effectively solving problems such as mode mixing. Then, using the windowed sparsity adaptive matching pursuit （SAMP） algorithm, design a MANF that can update the notch frequency in real time based on the input frequency. Finally, the effectiveness of the suppression strategy was verified by comparing with the multi notch notch filter （MNF） and the single notch adaptive notch filter （ANF）. The robustness of the suppression strategy was verified under the conditions of considering different series compensation degrees, wind power partial cutting, and thermal power replenishment power.

To enhance the direct solar-thermal conversion and storage performance of sugar alcohol-based phase change materials （PCMs） and promote their large-scale application in solar thermal energy storage, Xy-in-EG （94 ℃） and Er-in-EG （120 ℃） composite PCMs with high thermal conductivity and excellent light-to-thermal conversion efficiency were prepared using mechanical mixing and melting vacuum adsorption. Xylitol and erythritol served as PCM precursors, while expanded graphite （EG） was used as a thermal conductivity additive. The application of these composite PCMs in solar-thermal direct conversion and storage was systematically investigated. The results show that with 25% EG content, the maximum thermal conductivity of the Xy-in-EG and Er-in-EG composite PCMs reached 4.39 W/（m·K） and 3.61 W/（m·K）, respectively, which is approximately an order of magnitude higher than that of the original PCMs. When the EG content was 15%, the solar spectral absorptivity of the composite PCMs reached 84.3% and 86.9%, respectively. At this concentration, the composite PCMs achieved stable encapsulation, without issues such as phase transition leakage caused by phase separation during the heating and melting phase transition processes. Furthermore, in solar-thermal direct conversion and storage experiments, the solar-thermal conversion efficiency of Xy-in-15%EG and Er-in-15%EG composite PCMs increased by~63.3% and 79.5%, respectively, compared to commercial xylitol and erythritol PCMs. The photothermal storage rate of Er-in-15%EG was 3.2 times higher than that of commercial erythritol.

A waste heat utilization system for data centers integrating renewable energy is constructed, enabling efficient waste heat utilization. Additionally, to further reduce the system’s operating costs, a demand response mechanism based on user-side flexible load regulation is introduced to study the system’s performance under dynamic peak-shaving subsidy prices. The results show that when the subsidy prices are set to 0.149, 0.141, 0.190, 0.145 yuan/kWh respectively, the system achieves its highest net income of 266158.4 yuan. As the capacity of the heat storage device increases, the net income growth rate decreases from 10.4% to 1.6%, while renewable energy consumption shows a declining trend.

In order to obtain a systematic understandings of the unvented filling characteristics and the influencing factors of liquid hydrogen on the ground, a two-dimensional axisymmetric numerical model was established. The influences of different inlet temperatures, inlet flow rates, initial wall temperatures, and initial pressures inside the container on the unvented filling characteristics of liquid hydrogen were investigated by using Fluent software. The Lee model and a user-defined function （UDF） were used to describe the phase change issues （evaporation, and condensation） during the unvented filling of liquid hydrogen considering the interfacial tension between gas and liquid. The results show that under normal the gravity conditions, the unvented filling process of liquid hydrogen can be divided into three phases： an initial rapid pressure rise phase, a relatively stable filling phase, and a final rapid pressure rise phase. When the saturated vapor pressure corresponding to the temperature of the added liquid is greater than the pressure inside the vessel, the initial filling will lead to flash evaporation, and the pressure rise in the initial phase is significant. The inlet flow rate affects not only the total filling time, but also the vaporization pressurization at the initial stage of unvented filling, the temperature reduction degree in the system and the compression effect of gaseous hydrogen at the later stage of filling. The initial wall temperature affects the wall boiling pressurization effect during the initial filling stage. The initial pressure has a significant impact on the pressure change during the initial filling period, and the flash evaporation does not occur in the container when the initial pressure is higher than the saturation pressure of liquid hydrogen.

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.

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.

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.

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.

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.

In order to solve the problems of air source heat pump heating system （ASHP system） in severe cold region, such as substandard heating effect, low efficiency and high energy consumption, it is necessary to reform and optimize the energy systems. The research object was the ASHP system of an office building in severe cold region. The system design and operation data were investigated on site, the heating effect of the ASHP system was analyzed, and an optimization plan for renovation was proposed. The models of solar photovoltaic air source heat pump coupled heating system （PV-ASHP system）, solar thermal air source heat pump coupled heating system （SC-ASHP system）, and solar photovoltaic thermal air source heat pump coupled heating system （PV/T-ASHP system） were constructed by TRNSYS software to study the feasibility of the transformation scheme. The results showed that compared with the heating system before the transformation, the Ccop of the SC-ASHP system and the PV/T-ASHP system can be increased to 7.10 and 6.57. The energy consumption of the PV-ASHP system and the PV/T-ASHP system is reduced by 50%, and the solar energy utilization rate of the PV/T-ASHP system is the highest, reaching 61.5%.

A mathematical model is established for generation, transmission, controllable load, and energy storage. The conditional value-at-risk is used to measure renewable energy uncertainty. A coordinated framework of “source-grid-load-storage” is proposed for the interconnected power system. A solution method is proposed to consider the information security and privacy of each regional power system. The centralized optimization model is decoupled into a distributed optimization model using inter-regional contact lines as coupling variables. The dynamic penalty parameters and prediction correction strategy are integrated into the alternating direction multiplier algorithm to improve the convergence speed. The numerical results verify the effectiveness of the risk-averse investment model based on distributed optimization.

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.

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.

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.

In order to coordinate the economic cost of the joint market operator and the frequency modulation auxiliary service market benefit of the unit, a bi-level optimal bidding model with multi-source participation in the joint market is proposed. The market operator makes power generation plan through the upper level model, and determines the target value of the frequency modulation capacity of each unit in the frequency control auxiliary service （FCAS） market. Its optimization objective is to reduce the comprehensive operating cost in the joint market. Based on the operation results of the upper model, the unit adjusts its FCAS market quotation strategy through the lower model. Its optimization objective is to improve the frequency modulation market income of the optimized unit and ensure the frequency modulation performance of the system on the basis of economic optimization. The results show that when the main optimal body of the lower model is the summation of thermal power units, the bi-level optimization model makes the primary frequency modulation revenue of the thermal power units improve on the basis of the comprehensive cost of the combined market tends to be stable. When the main optimal body of the lower model is the summation of new energy units, the bi-level optimal bidding model improves the primary frequency modulation income of the new energy unit, and reduces the joint market cost. While improving the participation enthusiasm of the new energy units in frequency modulation through economic optimization, the system obtains better frequency modulation performance. The results show that the quantized mean of the integrated frequency modulation performance of the system is improved compared with that before optimization.

Heat spot faults in aerial photovoltaic infrared images are mostly small targets, which are very similar to the interference background, resulting in low detection accuracy of heat spot faults. A second-order segment heat spot detection method based on deep learning is proposed. In the first stage, interference background removal is performed. Aiming at the problems of slow segmentation speed, poor edge extraction effect and unbalanced positive and negative samples of photovoltaic modules, an improved DeepLabv3+ segmentation model was proposed by replacing the backbone network and adopting hybrid loss function to achieve fast and accurate extraction of photovoltaic module regions. In the second stage, the hot spot fault detection is carried out. To solve the problem of missing and false detection of small target hot spots, an improved YOLOv5 hot spot detection model was proposed by adopting the enhanced SPP module, introducing shallow detection scale and changing the border regression loss function, so as to achieve accurate identification of hot spots. The results show that compared with the original DeepLabv3+ segmentation model, the average pixel accuracy and average crossover ratio of the proposed segmentation model are increased by 1.7 and 1.51 percentage points, respectively. Compared with the original YOLOv5 model, the average accuracy of mAP₅₀ and mAP_50∶95 of the proposed heat spot detection model is increased by 2.6 and 10.7 percentage paints, respectively.

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.

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.

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.

Photovoltaic power stations are mostly located in harsh environments, so timely and accurate diagnosis of various faults is particularly important. A fault diagnosis method of photovoltaic array based on digital twin model and fusion neural network is proposed. Firstly, the overall framework of digital twin system of PV power station is designed and implemented, which includes twin model, data acquisition and transmission module and service application system. Secondly, combining the mechanism modeling method and particle swarm optimization （PSO） algorithm, the digital twin model of photovoltaic array is established. Thirdly, the fault is detected by evaluating the residual difference between the output of digital twin model and the output of physical entity. Finally, the fault diagnosis of photovoltaic array is carried out by combining temporal convolution network （TCN） with bidirectional gated recurrent unit （BIGRU）. The experimental results show that the proposed fault diagnosis method has higher accuracy than other methods, and the accuracy rate reaches 97.8%.

In order to study the sensitivity of sub-synchronous oscillation stability affected by parameters for the new power system, the influence of static var generator （SVG） is considered for the photovoltaic grid-connected system with LCL filter. Based on the impedance analysis method, the impedance model of photovoltaic grid-connected system with SVG under weak grid is established. The influence of SVG device on sub-synchronous oscillation of system under weak grid is analyzed, and the sensitivity of system stability to parameters is studied. The theoretical analysis and simulation results show that SVG can suppress the sub-synchronous oscillation of the system within a certain parameter range, and the suppression effect will be enhanced when the parameter value of SVG reactor is reduced. In addition,reducing the proportion coefficient of SVG current loop will lead to the worse effect of SVG on suppressing oscillation, and may even cause more serious oscillation. Increasing the integral coefficient can enhance the suppression effect of SVG, but the adjustment range should not be too large. Furthermore, with the increase of power grid strength, SVG can still produce a good effect of suppressing sub-synchronous oscillation. When the power grid strength is weakened, the effect of SVG on suppressing oscillation will also be weakened, but it still has the ability to suppress.