This study proposes an advanced load forecasting model based on GA-BP, which integrates multi-stage SHAP feature analysis and meteorological parameter prediction. The proposed approach employs multi-stage SHAP analysis to investigate the influence of meteorological parameters on building loads across different time scales. This enables the identification of critical time scales, optimization of input variable selection, reduction of feature redundancy, and mitigation of overfitting risks. Additionally, a high-precision meteorological parameter prediction framework is developed by combining K-means clustering with a composite model architecture. The GA-BP model is then utilized to achieve accurate load forecasting. Experimental results demonstrate the effectiveness of the proposed model, achieving a maximum coefficient of determination of 0.86 and a mean squared error（MSE） of 0.0961. Compared to a standalone GA-BP model, the proposed approach reduces MSE by 40.12%, significantly improving prediction accuracy. This high-precision forecasting capability, available prior to the activation of the building energy supply system.

This study evaluates the performance of 3 numerical prediction models： CMA-WSP, CMA-MESO and NCEP-GFS, of the global horizontal irradiance （GHI） at 30 PV stations in Hebei Province in 2022. The results show that the CMA-WSP has the lowest forecasting error and the highest correlation coefficient, followed by CMA-MESO. The CMA-WSP exhibits a systematic overestimation, while both CMA-MESO and NCEP-GFS show a systematic underestimation. The CMA-MESO has the lowest large error ratio （with a deviation of over 200 W/m²）, while the CMA-WSP coming second. All models show samples with deviation of more than 700 W/m². During fog, haze, and sandstorm weather processes, the CMA-WSP performs the best, while the CMA-MESO performs the best during rainfall, snowfall, continuous rainy periods, and severe convective weather processes. For PV stations at different locations, the forecasting performance of the three models varies significantly across different weather processes. For some stations, the overall forecasting errors of all numerical models are relatively small across different weather processes, while for others, the overall errors are larger.

Aiming at the stability prob-lems caused by the dynamic interactions between the existing parallel system of multi-grid-following （GFL） inverters and the weak grid, we study the influence of grid-forming （GFM） inverters on the stability of the grid-connected system in a multi-inverter grid-connected system, and propose a strategy for the proportional allocation of grid-following and grid-forming units in a multi-inverter grid-connected system. Based on the monotonicity of the power transfer of GFL inverters and the quasi-static model of mixed parallel connection of inverters, the influence of the interaction between the phase-locked loop （PLL） and the virtual synchronous machine （VSG） synchronization loop on the steady-state operating point of the GFL inverters and the GFM inverters is discussed to obtain the re-lationship between the synchronization stability, grid impedance and grid voltage in the grid-connected multivariate inverter system under a weak grid. The proposed strategy can derive the corresponding critical values of grid impedance for different unit ratio configurations, and then different unit ratio configurations are given according to different grid strengths to ensure the stable operation of the grid-connected multi-inverter system. The validity of the proposed method was experimentally verified.

This study addresses the limitations of traditional three-level inverters, including high costs and significant power losses, particularly their elevated grid-connected current harmonic content under unbalanced grid voltages and frequency fluctuations. A frequency-adaptive proportional resonant（PR） control strategy is proposed for Type-F three-level grid-connected inverters. Experimental verification on the Type-F three-level inverter platform demonstrates that, compared with conventional quasi-PR control, the frequency-adaptive PR strategy significantly reduces the total harmonic distortion （THD） of grid-connected currents. Specifically, THD decreases by 0.35% and 0.45% under grid voltage imbalance and frequency fluctuation conditions, respectively. These results validate the effectiveness and superiority of the proposed control strategy in enhancing grid-connected power quality.

This paper proposes a multi-task multivariate load forecasting method integrating multivariate empirical mode decomposition and Bayesian optimization （MEMD-BO-MMFM）. Firstly, a multi load data recombination method that integrates multi empirical mode decomposition and approximate entropy is proposed, which recombines the decomposition of each load into three categories： random, periodic, and trend components; Subsequently, a Bayesian optimization-based training framework for the gated recurrent unit-multi-task learning （GRU-MTL） model is developed, aiming to identify the optimal network hyperparameters for each category of decomposed components. During the modeling process, the method fully leverages the underlying commonalities across different load types to facilitate the collaborative optimization of multiple load forecasting tasks. The experimental results show that the proposed method outperforms traditional methods in both prediction accuracy and computational efficiency.

Aiming at the problem that traditional negative-sequence pilot directional protection may fail to operate or maloperate due to the influence of converter control strategies, this paper proposes a pilot protection scheme for double-ended weak AC systems based on negative-sequence impedance difference characteristics under negative-sequence current injection by Modular Multilevel Converters （MMC）. Firstly, the impact of negative-sequence current suppression strategies on both sides of the system under asymmetrical faults on traditional negative-sequence pilot directional protection is investigated. Secondly, considering multiple factors, a negative-sequence current injection strategy that suppresses active power fluctuations is introduced. On this basis, the differences in negative-sequence impedance characteristics under internal and external faults are analyzed. Finally, a detrended fluctuation analysis method is introduced to calculate the fluctuation functions at both ends and transmit logic quantities, thereby constructing a pilot protection scheme for double-ended weak AC systems. Simulation results show that the proposed pilot protection scheme can operate reliably under a transition resistance of 300 Ω and with 20 dB noise interference.

In order to improve the reliability and stability of photovoltaic power generation systems and reduce the impact on grid power fluctuations, this paper proposes a power smoothing method for a hydroelectric-photovoltaic-energy storage complementary system considering the operating status of hydroelectric units. Based on the actual operating data of hydropower units, the feature indicators that characterize the performance of hydropower units are extracted. The improved CRITIC-TOPSIS method is used to obtain the initial evaluation of the operating status of hydropower units. The evaluation results are used as the label output of the Convolutional Neural Network （CNN）-Grey Wolf Optimization Support Vector Machine （GSVM） model to establish an online recognition model for the operating status of hydropower units based on CNN-GSVM. Using local weighted regression algorithm and wavelet decomposition algorithm, the photovoltaic power signal is decomposed into low-frequency grid connected power and high-frequency fluctuating power borne by different performance hydropower units and energy storage. A coordinated control strategy for the hydroelectric-photovoltaic-energy storage complementary system is formulated to achieve smooth control of photovoltaic power. The effectiveness of the proposed method is verified through simulation of actual operating data of the hydroelectric-photovoltaic-energy storage complementary system in a certain region. The simulation analysis results show that adjusting the output of the hydroelectric unit and energy storage system based on the real-time operating status of the hydroelectric unit not only ensures the smoothing of photovoltaic power fluctuations but also considers the safety of the hydroelectric unit.

In response to the problems of single indicators, traditional methods, and difficulty in fully reflecting the overall efficiency of the comprehensive energy system in current parks, this paper proposes a park comprehensive energy system efficiency evaluation model that combines improved grey wolf optimization algorithm and support vector machine. This method constructs a comprehensive evaluation index system covering three dimensions of society, economy, and environment, and uses an improved grey wolf optimization algorithm to optimize support vector machine parameters, thereby improving the convergence speed and prediction accuracy of the model, and achieving accurate and efficient evaluation of the comprehensive benefits of the park's integrated energy system. The empirical results show that the proposed improved hybrid model is significantly better than traditional support vector machines and the original hybrid model in terms of loss degree, training efficiency, and evaluation accuracy. It can provide scientific basis and decision support for the planning, optimization, and sustainable development of the energy system in the park.

This paper addresses the DC-side impulse current caused by the integration of a dual active bridge （DAB） into low-voltage AC/DC hybrid distribution systems. A low-voltage AC/DC hybrid distribution system with DAB-based interfacing is considered. The circuit structure and control scheme under DAB integration are firstly analyzed. Based on the equivalent circuit model, a calculation method for the DC-side impules current is then developed. Typical operating conditions are investigated, including distributed generation interfaced through the DAB and bipolar short-circuit faults on the DC bus. Time-domain analytical expressions of the DC-side impules current are derived. The accuracy of the proposed method is validated through simulations. The transient characteristics of the DC-side impulse current and the influencing factors are further analyzed using the analytical results and simulation data.

Aiming at the issue that power flow controllers are vulnerable to damage under short-circuit fault conditions, thereby compromising power supply reliability, this paper proposes a two-stage current-limiting power flow controller tailored for flexible DC distribution networks. The proposed controller is designed to ensure safe operation during transient processes while enabling functional reuse. It integrates both power flow control and two-stage current-limiting capabilities: under steady-state conditions, it allows flexible adjustment of line power flow; during faults, it can rapidly switch operational states and implement adaptive current limiting based on fault types, making it suitable for flexible DC distribution networks with complex topologies. Finally, a six-terminal ring-shaped flexible DC distribution network model is established in PSCAD/EMTDC for simulation analysis. The results verify the effectiveness of the proposed power flow controller under various operating scenarios, including power flow control, multi-line coordinated control, fault current limiting, and transition from power flow control to current limiting mode, while also satisfying practicality and economic requirements.

In order to ensure the safe and stable operation of the power grid under the scenario of high proportion of clean energy access, a short-term net load probability prediction method based on high frequency enhanced diffusion model is proposed. Firstly, key meteorological features are extracted based on the eXtreme Gradient Boosting（XGBoost） algorithm and maximum information coefficient（MIC）to form a multivariate input condition feature set. Secondly, a conditional diffusion model architecture based on cross-attention mechanism is constructed. For the encoding-decoding link, a CNN-LSTM network incorporating wavelet attention and a multi-channel convolutional neural network are designed respectively to enhance the ability to capture high-frequency features. Then, based on the initial predicted values and kernel density estimation methods, the pre-trained model is fine-tuned using an improved loss function to improve the quality of the generated intervals further. Finally, an example analysis is carried out based on the actual load data of a power grid in East China, and the results show that the proposed method has a higher prediction accuracy when compared with various models.

The collector-emitter saturation voltage （V_ce,sat） of an insulated gate bipolar transistor（IGBT） module increases with bond wire lift-off, making it a common characteristic parameter for degradation monitoring. However, during actual operation, V_ce,sat is influenced by both junction temperature （T_j） and collector current （I_c）. This paper proposes a bond wire degradation monitoring method for wire-bonded IGBT modules based on initial-value correction of the collector-emitter voltage characteristic parameter. By mathematically modeling the IGBT output characteristic curves at different temperatures, the relationships amongT_j,I_c, and V_ce,sat are analyzed, and an equation for calculating the theoretical V_ce,sat under varying T_j and I_c conditions is established. During monitoring, this equation is used to compute the initial V_ce,sat corresponding to actual T_j and I_c under different operating conditions, thereby achieving initial-value correction for the normalized increase ηin saturation voltage. This correction reduces the influence of T_j and I_c onη. In this study, bond wire degradation was simulated by deliberately cutting wires, and changes in ηbefore and after correction were compared. The results show that, prior to the failure threshold, the proposed method significantly mitigates the effects of T_j and I_c onη, ensuring that the increase in V_ce,sat is primarily attributable to bond wire degradation. The experimental results align well with theoretical analysis, confirming the feasibility and engineering applicability of this method for monitoring bond wire failure in IGBT modules under varying operating conditions.

In order to promote the low-carbon and clean transition of the energy system and the local consumption of clean energy such as wind and light, a low-carbon and economic scheduling method of the integrated energy system taking into account the coupling of oxygen-enriched combustion-P2A-P2G is proposed. Firstly, an oxygen-enriched combustion carbon capture model is established on the side of the thermal power unit, and the oxygen produced by P2G is used for oxygen-enriched combustion, and part of the captured CO₂ is used for P2G to produce methane. Secondly, the multi-purpose utilization of hydrogen produced by P2G is considered, and it is used for ammonia production by P2A in addition to methane production and used for ammonia mixed combustion in the thermal power unit to realize the oxygen-enriched combustion-P2A-P2G coupling, at the same time, under the demand response and stepped carbon trading mechanism, a low-carbon economic dispatch model is established with the lowest total operating cost of the integrated energy system. The final example results show that the proposed model can effectively reduce the operating cost and carbon emissions of the integrated energy system.

Back-to-back modular multilevel converter （MMC） has been applied widely to flexible DC transmission system, and its control strategy optimization can suppress system oscillation and improve stability. Firstly, the back-to-back MMC-HVDC grid-connected simulation model of permanent magnet direct-drive wind power system is built to monitor the quality of DC voltage and AC current at the common connection point, and the influence of PI parameters on grid-connected stability is analyzed. Then, for the key PI controller, a structure combining quasi-proportional resonance （QPR） and nonlinear active disturbance rejection control （NLADRC） is proposed, and the multi-objective improved egret optimization algorithm （MOISBOA） is used to optimize the parameters. Finally, the effectiveness of the proposed controller is verified by establishing the system impedance model.

In order to improve the economy and carbon emission reduction capacity of integrated energy system, an operation optimization model of integrated energy system considering the coupling relationship between carbon emission flow and energy flow is proposed. Firstly, considering the dynamic characteristics of the integrated energy system, a coupling model of emission flow and energy flow of the integrated energy system is proposed. Secondly, aiming at the lowest operating cost and carbon emission reduction cost of the integrated energy system, a coordinated optimization model of the integrated energy system considering the improvement of carbon emission reduction capacity is established. Then, the optimistic exploration strategy is used to improve the traditional SAC algorithm, and the OAC algorithm is used to train and solve the coordinated optimization model of the integrated energy system offline and online. Finally, a simulation example is given to verify that the coordinated optimization model of integrated energy system considering the improvement of carbon emission reduction capacity proposed in this paper can effectively improve the economy and carbon emission reduction capacity of integrated energy system compared with the traditional scheduling model.

To address the issues of large volume and weight caused by using multiple two-port converters in traditional photovoltaic （PV）-storage system, as well as the instability of PV power generation leading to battery overcharge and over discharge, an energy management control strategy of three-port converter （TPC） considering the battery’s state of charge （SOC） is proposed. The principal characteristics, six power supply modes and their smooth switching mechanism of the converter are thoroughly studied. The TPC is composed of dual Buck / Boost and dual active bridge （DAB） through the bridge arm multiplexing integration; the energy management control strategy adopts the pulse width + phase shift modulation based on the minimum value selection, and utilizs the SOC of battery to dynamically adjust its charge and discharge reference, which solvs the problem of overcharge and over discharge and realizs the smooth switching among different power supply modes. Four voltage patterns and their boundary conditions are analyzed, and the relationship between output power and phase shift angle and duty cycle was derived. Semi-physical experimental results verify the feasibility and effectiveness of the proposed energy management control strategy.

Based on the coordinated optimization of transient control parameters for wind, solar, hydro, thermal, and energy storage, this paper optimizes the energy storage configuration in a new energy base. Firstly, by analyzing the transfer functions of wind, solar, hydro, thermal, and energy storage in the new energy base, the transient control parameters of each device are determined, and a comprehensive source-storage control model for the new energy base is established. Then, based on a convolutional neural network, three convolutional layers with different kernel sizes are set up and combined with a long short-term memory network to build a hybrid neural network model. This model is used to coordinate and optimize the transient control parameters of wind, solar, hydro, thermal, and energy storage, proposing a control strategy for the new energy base. Finally, based on the power deficit, a two-level optimization objective function for energy storage is established. By comparing the economic performance and transient stability enhancement capability of energy storage under different scenarios, an optimized energy storage configuration method for the new energy base is proposed, achieving safe, stable, and economical operation of the new energy base.

This paper proposes a two-stage robust optimization model based on dynamic uncertainty sets and battery degradation model to address the uncertainties of renewable energy resources and loads in microgrids. By introducing time-dependent uncertainty set adjustments, the model dynamically regulates the fluctuation and prediction ranges of wind power, photovoltaic generation, and loads, enhancing scheduling flexibility. Additionally, the model incorporates battery degradation costs during charging and discharging cycles, establishing a degradation model based on depth of discharge and power losses to optimize storage system utilization strategies. The model is solved using a constraint and column generation algorithm, with simulation results validating its effectiveness in terms of economic efficiency and robustness. Results demonstrate that the proposed approach successfully balances cost-effectiveness and operational stability, effectively managing challenges from renewable energy output fluctuations and load variations while maintaining economical operation.

To address the challenge of monitoring chip branch failures in multi-chip parallel IGBT modules, this paper proposes a method for characterizing the health status of multi-chip IGBT modules using gate current peak as an aging-sensitive parameter. Taking multi-chip IGBT modules as the research object, the study analyzes the impact of chip branch failures caused by bond wire detachment on the dynamic charging characteristics of the gate capacitor during the conduction process of module, and further investigates the relationship between chip branch failures and changes in gate current. The effectiveness of this method is then verified through experiments. The results demonstrate that the method is less affected by junction temperature, collector-emitter voltage, gate external resistance, and gate drive voltage. Finally, through comparative experiments using a peak monitoring circuit, the study shows that the proposed gate current-based method can effectively be used for reliability assessment of multi-chip IGBT modules in photovoltaic inverters.

In view of the discernible discrepancies in the core functions, parameter indicators, and other aspects of grid-forming technology standards for renewable energy between domestic and foreign countries, this paper offers a comprehensive overview of grid-forming technology standards and research status both domestically and internationally, encompassing the definition of grid-forming technology, core functions, and indicator testing. A comparative study was conducted on the definition of grid-forming configuration based on different standards, and the connotation and extension of grid-forming configuration control were explored. Next, the core functions and additional functions of grid-forming were compared and analyzed, and the core and additional functions of grid-forming were clarified. On this basis, the testing methods and platforms for various functional indicators were introduced, and the advantages and disadvantages of each method were pointed out. Finally, the development direction of functional and testing standards for grid-forming technology was discussed, which has important reference and guidance significance for the standard development and promotion of grid-forming technology.

In this paper, the aeroelastic characteristics of IEA-15 MW wind turbine under roll condition are studied by using the lifting-line free vortex wake model and geometrically exact beam theory model. The results show that the roll motion of the floating platform causes the fluctuation of blade inflow, and the effect on the tangential inflow is more significant. The fluctuation of inflow velocity will lead to the fluctuation of wind turbine loads. In addition, the difference of inflow velocity between blades will lead to the tilt and yaw moment of wind turbine. Under roll condition, blade deformations will fluctuate significantly as the tangential inflow velocity of the blade will be significantly changed by roll motion, the amplitude of the edgewise deformation is larger than that of flapwise and torsional deformations. In addition, the torsional deformation of the blade will reduce the angle of attack of the blade. The motion of the floating platform may cause the wake distortion of the wind turbine, and the blade deformations may weaken the wake distortion and intensifies the radial expansion of the wake.

In offshore wind power scenarios, short-term prediction of wind speed and power faces many challenges. Conventional long short-term memory neural networks （LSTM） struggle to accurately predict wind speed and power in far-reaching offshore wind farms. To address this challenge, this study deploys a Doppler lidar in an offshore wind farm in Guangdong, and collects measured wind speed data. To address the reasons for the insufficient prediction accuracy, this paper proposes a convolutional neural network-long short-term memory （CNN-LSTM） prediction model based on migration learning. Migration learning can accelerate new task learning and improve model performance by utilizing knowledge from similar tasks; CNN is good at extracting spatial features of radar data; and LSTM is good at capturing long-term dependencies of time series. The combination of these features makes the method proposed in this paper perform well in improving wind speed prediction and power prediction for offshore wind power. The experimental results show that compared with the original radar data and the conventional LSTM model, the new method significantly reduces the error in predicting the offshore wind speed and wind power, and the prediction accuracy is better improved.

This study aims to develop a crack growth retardation model for wind turbine tower bolt joints, incorporating retardation effects, and to perform a fatigue analysis through finite element modeling. Firstly, through finite element analysis, the study reveals that the stress concentration factor at the first engagement thread root of M20 high-strength bolts is highest, reaching 4.80, with the corresponding fatigue notch factor being 4.61. Further analysis using the crack growth retardation model shows that the crack growth rate considering retardation effects is significantly reduced. Under stress ranges of 30, 55, and 80 MPa, the difference in fatigue failure life is at least 27.8%. Additionally, the key parameter analysis results indicate that an increase in stress ratio, a decrease in retardation factor calculation exponent, and a reduction in overload ratio all accelerate the fatigue failure process.

The five-connected bucket foundation is an innovative modification of the composite bucket foundation, proposed to address the complex marine conditions near Hainan Island and the demand for wind turbines with a capacity of over 15 MW. It has been optimized in terms of load transfer mode, allowable pressure difference, and self-floating stability, enhancing its adaptability to deep-water and large-capacity wind turbines. Based on a project in Hainan, and considering the structural characteristics of the five-connected bucket foundation and the potential issue of the skirt not settling properly, a 1∶80 scale model was selected according to the principle of similarity. Model tests were designed with different incoming flow angles and skirt exposure heights as variable conditions to study the local scour characteristics of this new type of offshore wind foundation. The experimental results include the history curves of local scour depth under different flow angles and skirt exposure heights, the local topography after scour equilibrium, and the shape and size of the scour pit. The study clarifies the local scour characteristics of the five-connected bucket foundation under unidirectional flow and analyzes the impact of skirt exposure on the scour results.

To quickly calculate the load response of hybrid pile-bucket foundation for offshore wind turbine, based on the PISA （pile soil analysis） monopile design method, the three-dimensional bucket is simplified as a one-dimensional beam attached to the monopile. By adding two soil reaction curves describing bucket-soil interaction to four soil reaction curves describing pile-soil interaction, a one-dimensional （1D） simplified design model of hybrid pile-bucket foundation for offshore wind turbine is established, and the accuracy of the model is verified by comparing it with the existing centrifuge model test results. On this basis, the dynamic response of hybrid pile-bucket foundation under cyclic loads is calculated by applying reduction factors to the soil reaction curves within the bucket depth, and the applicability of this method is discussed. The results show that within the calibration space, the 1D design model has high computational efficiency and accuracy close to the 3D numerical model. Under actual working conditions, the 1D design model can accurately predict the cyclic load response of hybrid pile-bucket foundation. This study can provide theoretical guidance for the design of hybrid pile-bucket foundation for offshore wind turbine.

Based on the bounding surface cyclic p-y model, the cyclic p-y spring element for soft clay is developed by using the user-defined element （UEL） interface of Abaqus software. The developed spring element is used to establish a finite element model of the interaction between large-diameter monopile and soft clay ground. Then, dynamic responses of monopile foundation under asymmetric horizontal cyclic loading are systematically investigated. The developed p-y spring element can effectively simulate the nonlinear hysteresis, plastic displacement accumulation and stiffness degradation characteristics of soft clay. The numerical simulation results show the evolution of pile head displacement, mud surface rotation angle, pile deflection and bending moment with the number of cycles under asymmetric cyclic loading, and reveal the influence of cyclic loading mode and loading level on the pile response. The research results deepen the understanding of the interaction mechanism between horizontally loaded pile and soil, which can provide a certain reference for the design of large-diameter monopile foundation for offshore wind turbines.

Accurate prediction of ultra-short-term power of offshore wind farm is an important means to ensure the safe operation of offshore wind power system. Because offshore wind power has the characteristics of low sea level roughness and dense layout of wind turbines, its power generation is significantly affected by wake. An ultra-short-term power prediction method for offshore wind farms considering wake effect is proposed. Firstly, the weather research and forecasting model （WRF） coupled with the wind farm parameterization （WFP） is used to quantitatively evaluate the wake effect between adjacent offshore wind farms, and the obtained wake quantification index is used as the input data of the prediction model. Then, on the basis of considering wake effect, the prediction models of spatio-temporal graph convolutional network, spatially adaptive feature modulation module （SAFM） and partial convolutional （PConv） based on learnable adjacency matrix are established. Finally, the proposed prediction method is applied to an adjacent offshore wind farm group in Jiangsu Province to quantitatively analyze the wake effect between wind farms. The example shows that the proposed method can significantly improve the power prediction accuracy of offshore wind farms and offer technical assistance for the high-quality advancement of offshore wind power.

When the wind turbine power system works above the rated wind speed,the disturbance of wind speed can cause the output power fluctuation, which can influence the stability of the grid. In order to solve this actual problem, a nonlinear controller based on the systematic triple-step method is designed for the power control problem of variable speed wind turbines （VSWT） above the rated wind speed. The controller includes steady-state-like control, feedforward control based on reference dynamics and state-dependent feedback control. The controller gains are parameter-varying and state-dependent. Then, based on ISS theory, the robustness of the closed-loop system is analyzed and the selection principle of the parameters of the triple-step nonlinear controller is given. The triple-step control strategy is applied to the power control of the horizontal axis VSWT. The simulation results show that the triple-step nonlinear control strategy can accommodate the wide-range wind speed variations, achieve stable power output, and exhibit an excellent suppression capability for wind speed turbulence.

This paper proposes a tuning method for the critical virtual inertia parameters of VSG-PMSG in the medium wind speed region. Firstly, a VSG-PMSG model is established to analyze the effective kinetic energy range of the frequency response of the unit to system. Secondly, a frequency modulation small signal model for VSG-PMSG is constructed, and the critical virtual inertia tuning considerations are derived. Finally, a critical virtual inertia tuning method for the frequency response of VSG-PMSG to system is proposed, and the influence of virtual inertia changes on the frequency dynamic response performance of VSG-PMSG is analyzed based on the root locus, which provides a basis for the selection of frequency control parameters under multiple operating conditions. Multiple operating conditions are compared and analyzed through Matlab/Simulink simulation to verify the correctness of theoretical analysis.

In view of the different shapes of surface defects of wind turbine blades,the traditional convolutional neural network involves threshold screening and non-maximum suppression processes that increase the complexity of calculation,a detection model RH-DETR based on improved Transformer （real-time-detection transformer,RT-DETR） is proposed by comparing backbone networks such as ResNet18,SwinTransformer and CSwinTransformer,ResNet18 with relatively balanced detection accuracy and low computational complexity is selected as the backbone feature extraction network; the improved re-parameter module （RP-Block） is introduced to update the basic-block of the backbone network,while not losing detection accuracy,improving the inference speed of the model; considering the irregular shape of surface defects on wind turbine blades, the HiLo structure is used to reconstruct the original AIFI structure to solve the problem of low-level feature loss and improve the feature extraction ability of the model. The experimental results show that the accuracy and average accuracy of RH-DETR are 96.1% and 93.1% respectively, which are better than the current mainstream YOLO detection model. Compared with the original model RT-DETR,they are improved by 2.4 and 1.3 percentage points, respectively. In addition, the computational complexity of the model is reduced by over 50%. The detection speed is increased from 32.6 f/s to 64.7 f/s,which meets the requirements of industrial real-time detection and provides technical support for the detection and maintenance of wind blade.

The outer-loop control is often ignored when using impedance method to solve the stability problem of the grid-connected doubly fed induction generator （DFIG） system. However, the influence of ignoring the outer-loop control on the stability of the system has not been deeply studied. To solve this problem, firstly, this paper proposes a broadband frequency admittance model of DFIG which includes both stator- rotor side converter （RSC） and grid side converter （GSC） with integrated power outer-loop controls. The accuracy and validity of the proposed admittance model are verified by electromagnetic transient simulation model. Secondly, based on the actual operational parameter ranges provided by wind turbine manufacturer, influence of variations in outer-loop control parameters on the admittance characteristics of DFIG is analyzed theoretically. The results demonstrate that variations in proportional coefficients significantly affect the admittance characteristics of DFIG. Further studies indicate that overlooking RSC outer-loop control may hinder the recognition of oscillation problems when using the impedance method in the grid-connected DFIG system across various frequency bands. This may lead to erroneous assessment of an actually oscillatory system as stable system, thereby posing a risk to the safe operation of the power system. Next, a practical example of grid-connected DFIG wind farm is established, and the impact of RSC outer-loop control parameters on oscillation stability is observed by the variation of RSC outer-loop control parameters, which validates the crucial role of incorporating the power outer-loop when impedance analysis is applied for oscillation stability assessment of grid-connected DFIG system.

To address the challenge of identifying micro-vibrations in offshore wind turbines, this study proposes a novel visual recognition methodology integrating phase-based motion magnification （PBMM） and template matching （TM）, herein referred to as the PBMM-TM framework. This paper utilizes a scaled-down model of the NREL 5 MW wind turbine to systematically evaluate the influence of parameters such as illumination, resolution, and background contrast on the precision of dynamic characteristic identification under micro-vibration conditions. The efficacy of the PBMM-TM method under adverse environmental conditions is rigorously analyzed, and its applicability is further corroborated by field measurements obtained from a wind turbine situated in Zhuhai, China. Results demonstrate that the PBMM-TM algorithm can accurately capture and identify dynamic structural parameters, even with minimal vibration amplitude. Compared to traditional methods, this approach enhances recognition accuracy by nearly 50% in low-light and low-contrast settings. The algorithm also maintains accurate feature point tracking at low resolutions, enabling efficient long-range monitoring of offshore wind turbines.

Aiming at the problems of insufficient frequency regulation space and low dynamic response of the power system caused by large-scale wind power access, this paper proposes a new frequency coordination control strategy. Firstly, under the load-shedding reserve operation mode of doubly fed induction generator （DFIG）, pitch angle control is enhanced by introducing droop control. When system frequency drops, the DFIG releases its load-shedding reserve power, thereby compensating for the active power dip during DFIG rotor speed recovery and reducing steady-state frequency deviation in the system. Secondly, by incorporating a mechanical power compensation stage to enhance virtual inertia control, the system releases part of the kinetic energy stored in the DFIG rotor to support dynamic frequency response. This boosts system inertia and reduces dynamic frequency deviation. Finally, a power system simulation model was built through Matlab/Simulink. The simulation results show that this strategy effectively suppressed the fluctuation of system frequency caused by load changes and improved the stability of system frequency.

In order to study the influence of altitude on the dynamic characteristics of the high-altitude kite power generation system, the high-altitude kite dynamics correction model is established based on the altitude-density-temperature-wind speed function, and the high-altitude kite dynamics simulation model based on the correction of altitude is established by combining with Simulink, so as to solve in real time parameters such as the kinematic characteristics, the flight trajectory and the power generation during the operation of the high-altitude kite. The results show that the altitude has a significant effect on the dynamic characteristics of the kite power generation system under high initial wind speed and large initial pitch angle. Under different initial wind speeds, the peak power consumption of the contraction phase of modified model increases by 7.63% to 10.02% compared with the original model, and the total energy under the same cycle increases by 15.94% to 21.61% compared with the original model, and the larger initial wind speed, the more significant impact of altitude on trajectory. under different initial pitch angles, the peak power consumption of the contraction phase of modified model increases by 4.84% to 6.63% compared with the original model, and the total energy in the same cycle increases from about 0.73% to 7.79% compared with the original model, and the larger initial pitch angle, the more significant the impact of altitude on trajectory; under different lift-to-drag ratios, the peak power consumption of the contraction phase of modified model increases from about 2.85% to 10.03% compared with the original model, and the total energy in the same cycle increases by approximately 3.96% to 15.81% compared with the original model.

The increasing integration of large-scale wind power into the grid poses risks of sub-synchronous oscillation （SSO）, posing a significant threat to the stable operation of the power system. This paper proposes a novel SSO identification methodology integrating second-order multi-synchrosqueezing transform （SMSST） with combined deterministic-stochastic subspace identification （CDSSI） to achieve precise parameter extraction in wind-integrated power systems. Firstly, to accurately track the nonlinear and time-varying characteristics of SSO signals, SMSST is employed to decompose the SSO signal and construct a time-frequency coefficient matrix, enhancing the energy concentration in the time-frequency domain. Then, ridge extraction method is applied in the time-frequency domain to perform optimal time-frequency trajectory searching for each SSO mode. Using the optimal time-frequency trajectory, the time-domain SSO components of each oscillation mode are reconstructed. Subsequently, the CDSSI method is utilized to identify the oscillatory characteristic parameters of the SSO signal, such as frequency and damping ratio. Finally, the effectiveness and accuracy of the proposed method are validated through synthetic SSO signals, simulation signals from a wind power integrated system, and monitoring data of actual system SSO events.

To address the issue of dynamic wear and the associated aerodynamic property changes of the airfoil, this study uses a jet sandblasting platform to simulate sand and dust erosion on the airfoil. Additionally, particle image velocimetry （PIV） and strain balance techniques are employed to compare changes in the flow field around the airfoil and its lift and drag coefficients before and after wear. The experimental results show that when the erosion wear rate of the airfoil exceeds 0.205%, the linear region of the lift coefficient decreases by approximately 0.2, while the stall angle increases compared to that of the unworn airfoil. At the same time, erosive wear results in a reduction of the drag coefficient by approximately 0.025. Notably, the erosive wear delays the onset of airflow separation, which improves the stall characteristics of the airfoil.

Based on a coupled dust transport equation and mesoscale simulation methodology, this study investigates the effects of wind farms in the sandy desert regions of northern China on local meteorological elements and dust transport. The analysis focuses on the impact of wind farms on the spatial distribution of wind speed, temperature, humidity, dust concentration, dust deposition flux, and sand emission flux. By considering variations in atmospheric boundary layer height （PBLH） and wind speed, the study explores the mechanisms by which these factors influence dust transport. The results indicate that the operation of wind farms leads to a reduction in wind speed and an increase in near-surface temperature, particularly in densely distributed wind farm areas, where the maximum temperature increase can reach 0.3 ℃. Temperature changes exhibit a negative correlation with relative humidity in sparsely distributed wind farm regions, while a positive correlation is observed in areas with dense wind turbine installations. Additionally, the presence of wind farms increases the height of the atmospheric boundary layer, facilitating the dispersion of dust and resulting in a decrease in dust concentration in and around the wind farm, with reductions ranging from 10 to 70 μg/m³. In contrast, the southeastern region of Gansu experiences dust accumulation due to a lower atmospheric boundary layer height and lowed wind speeds, leading to increased dust concentrations. The variations in dust deposition flux show a positive correlation with changes in dust concentration; in regions where dust concentration increases, the deposition flux also rises, whereas it decreases in areas where dust concentration falls. Furthermore, the reduced wind speed in the vicinity of wind farms contributes to a decrease in sand emission flux. The operation of wind farms significantly influences local meteorological elements and dust transport processes in northern China, with these effects closely related to the spatial distribution density and scale of the wind farms.

To address the uncertainties and fluctuations associated with wind power generation, an ultra-short-term wind power forecasting method integrating temporal convolutional networks （TCN）, closed-form continuous-time networks （CFC）, and neural circuit policies （NCP） is proposed. Initially, TCN is utilized to conduct preliminary learning from raw data, extracting crucial information from the time series. Subsequently, the processed data is fed into the NCP-CFC network, which leverages the unique hierarchical brain-like recursive connections of NCP and the efficient solving mechanism and anti-gradient vanishing properties of CFC for forecasting. Finally, a fully connected layer adjusts the output range and dimensions to produce the final forecast. The necessity of each module is validated through ablation studies and comparative experiments with RNN-based models. Two case studies are conducted to demonstrate the effectiveness of the proposed model in ultra-short-term wind power forecasting： one involving a wind farm in Inner Mongolia （MSE=25.70 MW², RMSE=5.07 MW, MAE=3.92 MW, SMAPE=32.51%, R²=0.92） and another using open-source data （MSE=27.38 MW², RMSE=5.23 MW, MAE=3.71 MW, SMAPE=38.52%, R²=0.84）.

To address the challenges of achieving continuous parameterization and the limited application of graph neural networks in existing deep learning-based wind farm wake modeling, this study proposes a Graph Transformer-based wind farm wake model. Firstly, a large-scale wake dataset is constructed, encompassing diverse environmental and control parameters. A coordinate-based sampling and distance-priority strategy is employed to design the wake graph structure, generating node feature matrices that include wake field coordinates, environmental, and control parameters for neural network training. Subsequently, a Graph Transformer-based wake modeling algorithm, i.e., GT-Wake, is developed. In which the self-attention mechanism is leveraged to dynamically adjust the weight allocation of neighboring nodes in the graph structure, thereby enhancing the model’s ability to capture both local and global wake characteristics. Experimental results demonstrate that compared to MLP, CNN, and GraphSAGE, GT-Wake significantly improves modeling accuracy and achieves a favorable balance between generalization capability and computational efficiency.

As a common foundation type for offshore wind turbines, the pile-soil interaction mechanism of large-diameter marine steel pipe pile foundations is significantly different from that of traditional small-diameter piles. The commonly used API specifications are unable to accurately assess the load-bearing performance of large-diameter marine steel pipe pile foundations. This study employs numerical analysis methods to investigate the influence of different pile-soil stiffness ratios in sandy soils on the horizontal load-bearing characteristics of large-diameter single piles. It reveals the influence laws of pile-soil stiffness ratios and displacement selection conditions on the horizontal load-bearing capacity of single piles. The results indicate that, as the pile-soil stiffness ratio increases, the horizontal load-bearing capacity of single piles tends to decrease. Moreover, with the increase in pile diameter, the sensitivity of the horizontal load-bearing capacity to the pile-soil stiffness ratio weakens. The horizontal load-bearing capacity of single piles exhibits a logarithmic relationship with the pile-soil stiffness ratio under different seabed displacements. Based on the influence of horizontal load-bearing mechanisms, displacement conditions, and soil strength on the horizontal load-bearing capacity, a normalized horizontal load-bearing capacity calculation method for large-diameter single piles is established.

To enhance the accuracy of wind power predictions, a novel model is proposed in this paper that integrates the maximum overlap discrete wavelet transform （MODWT） with a bidirectional recurrent neural network. The MODWT is utilized to decompose wind power into its low-frequency and high-frequency components. In conjunction, the bidirectional long short-term memory （BiLSTM） and bidirectional gated recurrent unit （BiGRU） work synergistically to effectively predict both short-term and long-term wind power data, leading to significantly accurate predictions. Experiments were conducted using the actual data of two wind farms. The results confirm that the proposed method has consistent superiority over the baseline method. A substantial improvement in prediction accuracy is obtained. The error metrics including root mean square error （RMSE） and mean absolute error （MAE） are reduced by approximately 16.47% and 15.10%, respectively, while the coefficient of determination （R²） achieved a value of 0.9756.

To address the issues of response lag and ineffective yaw adjustments in traditional yaw control systems under rapidly changing wind speed and direction, this study proposes a machine learning-based yaw control strategy for offshore wind turbines to further improve power generation efficiency. Firstly, the wind speed and direction measurement data are pre-processed using the averaging and variational mode decomposition （VMD） methods. The wind data is decomposed into high-frequency, mid-frequency, low-frequency, and residual components at different time scales. Based on the processed wind data, a self-attention-based convolutional neural network-long short-term memory （SA-CNN-LSTM） model is developed for ultra-short-term prediction of each frequency component and residual term, which constructs the wind prediction model. Furthermore, wind speed intervals based on turbine operating states are established and an optimal objective function is formulated. Then, the yaw control strategy integrating prediction models with parameters is optimized through the improved grey wolf optimizer （IGWO） and particle swarm optimization （PSO） algorithms. Results demonstrate that the proposed yaw control strategy enhances yaw efficiency by optimizing yaw errors, adjustment actions and timing, which improves the power generation efficiency of offshore wind turbines.

The paper uses the computational fluid dynamics method to study the endplate of three-dimensional vertical-axis wind turbine blade, and constructs nine types of endplates based on different offset distances and thicknesses, so as to analyze the influence of endplate geometric parameters on the overall performance of the blade. The results show that： with the increase of the offset distance and thickness of the end plate, the spanwise flow induced by the tip loss is effectively suppressed, which improves the aerodynamic force on the blade surface. However, the small-scale vortex structure formed on the surface of the endplate and the large-scale vortex structure of the wake induced by the endplate itself will aggravate the blade drag, resulting in a limited enhancement of the overall aerodynamic performance, and the thickness of the end plate is the main factor of the drag. When the offset distance is 0.18c, and the thickness is 0.01c, the power coefficient of the wind turbine is increased by 2.59%. The enhancement of the aerodynamic performance of the blade by the endplate is mainly concentrated in the phase angles of 60°~120 °and 240°~300 °, and the drag exists in most of the phase angles.

To address the dynamic operational constraints limiting frequency modulation capabilities in super-large wind turbines,this study proposes a Dynamic Frequency Modulation Potential Coefficient-based Adaptive Virtual Inertia Control （FPC-AVIC）.Using the IEA 15 MW direct-drive wind turbine as a case study,we establish a quantitative evaluation framework for rotor kinetic energy's frequency regulation capacity while elucidating the dynamic coupling mechanism between rotor kinetic energy and converter capacity.Through the introduction of a dynamic frequency modulation potential coefficient,the real-time boundary of frequency modulation capability is systematically quantified, enabling the development of an adaptive control framework that dynamically adjusts frequency regulation contribution intensity. Additionally, an exponential speed recovery strategy is formulated to mitigate power transients during the speed restoration phase. Simulation results demonstrate that compared with PD virtual inertia control,the proposed FPC-AVIC elevates the secondary frequency dip nadir from 49.50 Hz to 49.64 Hz.When integrated with the exponential recovery strategy, the system achieves smooth power/speed transitions and eliminates observable secondary frequency drops during grid recovery processes.

This paper systematically studies the effect of wrinkle defects—commonly formed during the manufacturing of large wind turbine blades—on the tensile failure behavior of composite laminates. Through theoretical analysis, finite element simulation, and experimental tests, the stress distribution and failure modes of unidirectional and biaxial laminates under different wrinkle angles are investigated. Results indicate that out-of-plane wrinkles lead to local fiber deflection, increase interlaminar stress, and cause interlaminar failure. The residual strength is mainly governed by the interlaminar properties of the composite. Due to the limitation of interlaminar performance, wrinkles have a more noticeable effect on the tensile strength of unidirectional laminates. Therefore, in the structural design of blades, the influence of wrinkles should be considered, and different wrinkle tolerance criteria should be established for different types of laminates. The comparison between theoretical and experimental results shows that the errors meet engineering requirements. In particular, for laminates with a large wrinkle angle （θ=50°）, the error in tensile strength is only 3.07%, confirming that the proposed method is applicable in engineering practice.

Compound potassium humate （MIXKHA）, synthesized from biomass and lignite under a biochemical/mineral binary system, is a high-potential green organic fertilizer with the advantages of both mineral potassium humate and biochemical potassium humate. In order to explore the synergistic effect between biomass and lignite and the effect of raw material ratio on the formation of MIXKHA during co-hydrothermal process, Baoqing lignite （BL） and chestnut shell （CS） were hated in KOH solution to synthesis mineral potassium humate （BLKHA）, biochemical potassium humate （CSKHA） and MIXKHA with different raw material ratios （3∶1- 1∶3）. FTIR and ¹³C NMR were used to analyze the structural changes of BL and CS after hydrothermal processes, and the effects of MIXKHA on plant growth and quality were studied using Chinese chive pot experiments. The results show that, compared with BLKHA, MIXKHA_1∶1 is with lower aromaticity, more oxygen-containing functional groups, rich fat branched chains, carboxyl groups, phenolic hydroxyl groups, and higher activity. The co-hydrothermal of BL and CS have a positive synergistic effect on the yield of MIXKHA （+0.23%-1.32%） and an increase in HA content in MIXKHA （+0.09%-18.92%）. The change in the raw material ratio has a significant effect on the MIXKHA yield （67.35%-77.41%） and HA content （41.17%-79.14%）. The MIXKHA yield and HA content increased with an increase in BL ratio, and the K₂O content in MIXKHA is less affected by the raw material ratio （21.31%-26.83%） increase with an increase in the CS ratio. When BL and CS are mixed in a ratio of 2∶1, the synergistic effect is the strongest. MIXKHA_2∶1 yield （76.60%） and HA content （79.08%） are apparently higher than theoretical values. MIXKHA_2∶1 could meet the first-class product standard in GB/T 33804—2017 potassium humate for agricultural use. When applied as organic fertilizer in a pot experiment, MIXKHA_2∶1 can improve the quality of Chinese chives while promoting the development of Chinese chives leaf stems and root growth, which is an efficient and eco-friendly organic potassium fertilizer. Moreover, the synthesis and application of MIXKHA broadens the way for the high-value utilization of lignite and biomass.

Walnut shell biochar, corn stalk biochar, and rice husk biochar were selected to examine their potential as a substitute for traditional fossil fuels in terms of physicochemical properties and combustion characteristics. The findings show that all three types of biochar have fixed carbon contents of more than 60%. The three types of biochar are verified to have average particle sizes of 144.40±0.77, 10.58±0.01, and 18.61±0.01 μm for walnut shell biochar, corn stalk biochar, and rice husk biochar, respectively. Meanwhile, their Hardgrove grindability indexes are 47.37±1.22, 58.31±1.39, and 27.19±1.16, respectively, suggesting poor grindability. At a heating rate of 10 ℃/min, the comprehensive combustion characteristic index S of walnut shell biochar, corn stalk biochar, and rice husk biochar are 1.36×10-11, 4.37×10-12 and 2.34×10-11, respectively. When the heating rate is 15 ℃/min and 20 ℃/min, the comprehensive combustion characteristic index S of the above biochar increases significantly. The back flame lengths of walnut shell biochar, corn stalk biochar, and rice husk biochar are all less than 400 mm, indicating weak explosibility.

A series of copper-based zeolite catalysts （xCu/MOR） were prepared by impregnation method, and the performance of catalytic partial oxidation of biogas to methanol was investigated under continuous gas-phase conditions with water as oxidant. The results show that the methanol yield at 350 ℃ shows a volcano trend with the increase of copper loading, in which the 2Cu/MOR catalyst can achieve a high methanol yield of 67.3 μmol/（g·h）; further optimization of the reaction temperature results in a high methanol yield of up to 142.5 μmol/（g·h） for the 2Cu/MOR catalyst at 400 ℃. In addition, catalyst characterization using X-ray diffraction, high-resolution transmission electron microscopy, X-ray photoelectron spectroscopy, UV-diffuse reflectance visible spectroscopy, methane programmed warming desorption, and Fourier transform infrared spectroscopy show that the copper element in the copper-based zeolite catalysts is uniformly dispersed, and the 2Cu/MOR catalysts that demonstrated excellent activity have more isolated divalent copper active species. During the reaction process, methane is adsorbed and activated on the isolated divalent copper sites, and the formed methoxy intermediate reactes with the hydroxyl group produced by hydrolysis to form methanol.

In order to improve the operating efficiency of hydraulic wave energy converters （WECs）, this paper proposes a multistage energy storage hydraulic PTO system, which achieves multistage energy storage through accumulators and energy storage flywheels. A hydraulic PTO simulation model is established in AMESim to study the impact of energy storage components on system response and analyze the power generation process under different sea conditions. Meanwhile, a scaled hydraulic PTO prototype with a rated power of 100 W is built for performance testing. The study shows that, under medium and low sea conditions, the average conversion efficiency from hydraulic energy to electrical energy reaches 89%, which is 6% higher than the system without the multistage energy storage technology. Under high sea conditions, when energy overflow is triggered, the system can still achieve the 5 kW rated power generation target. The scaled-down prototype's power generation efficiency can reach 45.6%, which is close to the rated 100 W power generation requirement. The simulation and testing methods used in this study can also be extended to other wave energy converter hydraulic PTO systems, providing a basis for parameter optimization and performance improvement of wave energy devices under different sea conditions.

To address challenges associated with traditional rotor permanent magnet synchronous generators, such as rotor heat dissipation difficulties, permanent magnet detachment under wide-range operation, low reliability of induction generators, and adverse effects of external generators on reciprocating airflow, a high-reliability stator permanent magnet flux-switching generator with an outer rotor structure is proposed for oscillating water column wave energy conversion systems. After determining the structural parameters of generators, the finite element method is employed for optimal design and electromagnetic performance analysis. A prototype is developed and tested, with the test results validating the accuracy of the electromagnetic analysis.

To reveal the characteristics of the free-surface wave caused by a submerged-point-absorber wave energy converter, the radiation wave characteristics of a submerged cylindrical floater undergoing heave oscillations were studied. The characteristic function expansion method was used to solve the radiation potential of the oscillating cylinder, and the influence of the shape, position and motion parameters of the floaters as well as the water depth on the radiation wave characteristics was investigated. The results indicate that a submerged cylindrical floater undergoing heave oscillation forms an axisymmetric circular radiation wave along the central axis of the floater on the free surface. The amplitude of the radiation wave reaches its maximum value directly above the floater and gradually decays in the direction away from the floater. As the distance between the upper surface of the floater and the free surface decreases, the maximum amplitude of the radiation wave increases, but the rate of attenuation along the direction away from the floater also increases. Compared to the significant increase in radiation wave amplitude caused by increasing the radius of the floater, increasing the height of the floaters and water depth did not significantly increase the amplitude of the radiation wave. As the heave period of the floating body increases, the amplitude of the radiated wave decreases. The phase difference between the radiation wave and the displacement of the floater mainly depends on the dimensionless distance from the floater, and is not significantly affected by the shape, position, motion parameters of the floaters as well as the water depth.

This study proposes an interdisciplinary geothermal exploitation model. An improved fractal theory suitable for geothermal reservoirs is presented, utilizing two innovative fractal parameters （fractal dimension of fractures D_f and maximum length of fractures I_max） to quantitatively characterize fracture structures. The improved fractal theory is then applied to complex thermal-flow-solid coupling in geothermal reservoir exploitation. The research results demonstrate that the proposed structural parameters effectively characterize micro-macro interactions during geothermal exploitation. The efficiency of geothermal exploitation is directly proportional to the fractal dimension D_f and the maximum length of fractures I_max,while the geothermal rock temperature is also proportional to D_f and I_max.

This paper constructs an integrated energy system framework that includes supply-side physical energy storage, demand response,and Vehicle-to-Grid （V2G） electric vehicles. The framework is designed to supply energy to communities composed of multiple types of buildings,with the objective of minimizing the total system operating cost. The operational parameters of the integrated energy system with generalized energy storage are been optimized accordingly. Furthermore,this study investigates the energy supply characteristics and operational features of the integrated energy system integrated with different types of generalized energy storage,and analyzes the energy coupling relationship between physical energy storage and demand response,as well as the correlation between operational outcomes. The results indicate that the total operational costs for typical winter and summer days are reduced by 23.96% and 55.90%,respectively,compared to an integrated energy system without energy storage. The integration of physical energy storage and demand response can lead to a reduction in the total system operating cost by approximately 18.69% and 52.36%,respectively. Demand response reduces the peak-valley difference rate of electricity load on typical winter days from 98.41% to 78.26%, and on typical summer days from 94.80% to 93.24%. This method tackles challenges in complex energy systems, such as diverse equipment types, complex control parameters, and difficult operational optimization. It also offers a theoretical foundation for optimizing complex integrated energy systems.

This paper conducts experimental testing and modeling on actual samples of hybrid ultracapacitors. Initially, experimental tests are performed on the hybrid ultracapacitors samples, summarizing the main operational characteristics of hybrid ultracapacitors. Subsequently, based on the measured data, mathematical and simulation modeling of hybrid ultracapacitors are realized, and a method for building a simulation model of hybrid ultracapacitors based on measured data is proposed. Finally, the constructed model is subjected to constant current charge-discharge simulation in Matlab/Simulink to verify the model’s accuracy, and its effectiveness is validated through grid-connected simulation. The results confirm that the developed hybrid ultracapacitor model can accurately and effectively simulate the output characteristics and functionality of actual hybrid ultracapacitors.

Taking the molten salt storage tank in tower solar thermal power generation as the research object, the mechanical performance of molten salt storage tanks at different liquid levels under combined load conditions such as wind load with circumferential wind pressure distribution is studied through numerical simulation, and ratchet failure and creep-fatigue assessment are carried out based on the ASME standard of the United States. The results show that under a variety of load combination conditions, the maximum stress intensity is at the large fillet weld at high liquid level （11.7 and 9.0 m）, and at the geometric discontinuity between tank top and tank wall at medium and low liquid level （6.0, 3.0 and 1.0 m）. The radial displacement is asymmetrical due to the circumferential wind load, seismic response and hydraulic action, and the radial displacement is significantly affected by the change of liquid level, the radial displacement is mainly concentrated in the tank wall of the windward side of 0°-90°, and the severe area affected by the axial displacement is in the center of the tank roof. No ratchet failure occurs at different liquid levels, and no creep-fatigue failure occurs at full-load operating conditions.

To address the inherent problems of high driving energy consumption and low heat exchange efficiency during the seasonal operation of existing energy storage systems driven by water pumps in charging and discharging processes, a novel two-phase passive cold energy storage system is proposed. An experimental platform is constructed to measure the temperature and pressure distributions under different filling rates and cold source temperatures with R22 and R410A as working fluids. The effects on the system start-up characteristics and thermal performance are analyzed, and the temperature distribution characteristics within the cold energy storage body are obtained. The results show that the temperature of the system loop decreases with the decrease of the cold source temperature. When the cold source temperature decreases from -2 ℃ to -12 ℃, the outlet temperatures of the condenser for R22 and R410A decrease by 10.6 ℃ and 7.1 ℃, respectively; The filling rate exerts a significant influence on the circulation of the working fluid. Within the experimental range, the minimum cold energy transfer resistances of R22 and R410A are 0.090 ℃/W and 0.097 ℃/W, which occur at filling rates of 50% and 70%, respectively; After 14 days of operation, the overall temperature of the water tank remains below 3.8 ℃, demonstrating a favorable cold energy storage performance. This study provides a theoretical basis for the further optimal design and application research of passive cold energy storage systems.

Liquid hydrogen leakage rapidly vaporizes and forms a hydrogen cloud, posing safety risks and necessitating mechanistic studies. To this end, this study develops a numerical model based on the open-source CFD code OpenFOAM to simulate multiphase flow behavior during liquid hydrogen leakage. The numerical model employs the PIMPLE algorithm, capable of simulating complex processes, including multiphase flow, multicomponent mixing, phase change, diffusion mass transfer, and heat transfer. For model validation, large-scale liquid hydrogen leak experimental data from NASA is used as a reference, comparing the model predictions with experimental data. The comparison includes hydrogen concentration contour distribution, time-varying concentration curves, and hydrogen concentration cloud maps. Results show a good agreement between simulation outcomes and experimental data, with deviations within acceptable limits, validating the model’s reliability. This paper provides a detailed description of the model development and validation methodology, aiming to offer a scientific basis for the numerical simulation of liquid hydrogen leak behavior and theoretical support for the safe application of liquid hydrogen.

The accurate electrochemical model construction serves as the foundation for the planning and control of renewable energy-powered water electrolysis hydrogen production systems. Based on an investigation of the current development of water electrolysis hydrogen production technology, the electrochemical modeling methods for alkaline water electrolysis hydrogen production systems and proton exchange membrane water electrolysis hydrogen production systems are sorted out, aiming to provide modeling references for the planning and control of renewable energy-powered water electrolysis hydrogen production systems. Firstly, the modeling methods for steady and dynamic models of water electrolysis hydrogen production systems under different operating conditions are summarized respectively. Then, the scenario applicability of various electrochemical models is compared and analyzed. Finally, it is proposed to carry out research on aspects such as model parameter adaptability, electrolysis reaction dynamic mechanisms, and unified modeling methods to enhance model adaptability.

Aiming at the problem of low accuracy of multi-step advance prediction of voltage of proton exchange membrane fuel cell （PEMFC） under dynamic conditions, a fuel cell multi-step advance prediction method based on DWT-LSTM hybrid drive is proposed. Firstly, in order to eliminate the influence of operating parameter fluctuations on voltage prediction under dynamic conditions, the correlation between operating parameters and voltage is studied, and the prediction models are constructed for the operating parameters with strong correlation and discrete wavelet transform （DWT） decomposition voltage respectively using long short-term memory neural network （LSTM）. The model hyperparameters and fusion weights are given by particle swarm optimization algorithm （PSO）. At the same time, in order to prevent the multi-step advance prediction error of the model from accumulating too quickly, a physical aging model is built to correct the multi-step advance prediction results. Finally, tests were carried out based on the durability data of the aged battery stack under dynamic conditions, in which the root mean square error （RMSE） and determination coefficient （R²） of the short-term prediction results reached 0.00254 and 0.9926; the multi-step advance prediction results show that this method can effectively predict the aging trend and local fluctuation of voltage. When the window value is selected as 4 hours, the root mean square error and determination coefficient reach 0.0224 and 0.8941. The program ablation experiment also shows that multi-parameter input and the introduction of physical models are conducive to improving the accuracy of multi-step advance prediction of voltage.

Temperature is a critical factor influencing the performance of proton exchange membrane （PEM） electrolysis systems, making stable temperature control essential for ensuring both system safety and operational efficiency. This study investigates the application of an optimized fuzzy PID controller in PEM electrolytic hydrogen production systems. It analyzes the impact of system temperature on electrolyzer efficiency and explores the enhancement of energy efficiency through a multistage heat recovery structure. The findings demonstrate that the optimized fuzzy PID controller achieves faster and more precise temperature regulation compared to traditional PID and fuzzy PID controllers. Specifically, the pump regulation time is reduced by approximately 70 seconds, while overshoot is decreased by approximately 4%. Additionally, the multistage heat recovery structure enables the recovery of 47.98 kW of waste heat in a 200 kW PEM electrolysis system, resulting in a 23.04% improvement in overall system efficiency. These results underscore the significant potential of optimized controllers and efficient waste heat utilization to enhance the performance and energy efficiency of PEM electrolytic hydrogen production systems, highlighting their valuable implications for engineering applications.

In this study,self-designed test facilities were constructed to investigate the high-pressure hydrogen leakage process. The mass flow rates of hydrogen leakage from a nozzle with a 1 mm diameter were measured for various storage pressures ranging from 10 to 70 MPa,encompassing the designed working pressures of onboard storage tanks of tube trailers and fuel cell vehicles. The mass flow rates were calculated by assuming quasi-one-dimensional,isentropic flow using the Abel-Noble equation of state. Subsequently,the discharge coefficients were determined as the ratio of the measured to calculated mass flow rates for various test conditions. The results show that the discharge coefficients range from 0.57 to 0.60 for the present tests.

Al₂O₃ thin films with different thicknesses were deposited by radio frequency sputtering and used as the barrier layer for flexible CIGS solar cells on stainless-steel foil. The effects of these Al₂O₃ thin films on the surface roughness of stainless steel, as well as the crystalline quality, crystal structure, and photoelectric properties of the CIGS absorption layer, were investigated. Results from AFM, SEM, and XRD characterizations show that depositing Al₂O₃ thin films on the stainless-steel surface significantly reduces its surface roughness, providing a smooth surface for subsequent film deposition. Moreover, it reduces the diffusion of Fe from the stainless-steel substrate into the CIGS absorption layer, leading to a noticeable improvement in the crystalline quality of the CIGS layer. Additionally, secondary ion mass spectrometry （SIMS） analysis indicates that the Al₂O₃ thin films limit the Fe concentration in the CIGS absorption layer to approximately 6×10¹⁷ atoms/cm³. Finally, the conversion efficiency of the CIGS solar cell with a 350 nm-thick Al₂O₃ barrier layer increases from 10.89% to 15.80%.

For silicon heterojunction （SHJ） solar cells based on n-type crystalline silicon （c-Si） wafers, the electrode metallization pattern is optimized to realize high conversion efficiency by simulating the current-voltage （I-V） performance of the solar cells via Griddler software. Experimental validation of the simulation results confirm the accuracy of the simulation model. The optimization for copper electrodes prepared by copper-plating and silver electrodes prepared by screen-printing are both investigated and compared with respect to the comb-like pattern composed of fingers and busbars perpendicularly. This study includes the number of copper-plated busbars, a comparison of the electrical performance between copper-plated and screen-printed silver fingers, and the influence of copper electrode height. Optimal parameters for achieving high efficiency with copper-plated electrodes in SHJ solar cells are proposed. The optimal values are found to be 12 busbars, a busbar spacing of 13.85 mm, and a finger spacing of 1.410 mm with a width of 20 μm for the copper-plated electrodes, resulting in an efficiency improvement of 0.17% over the best efficiency achieved with screen-printed silver electrodes. Theoretically, the higher the electrode height, the higher the efficiency. By utilizing the optimal spacing between the busbars and fingers, the grid design can be effectively adapted and scaled for application to solar cells of different sizes.

Aiming at the problem of aerodynamic instability of PV tracker bracket under strong wind, a wind tunnel test system for large amplitude pure torsional vibration section model was developed. The wind tunnel test of the aerodynamic stability of the PV tracker bracket section model in the range of -60°-60° module tilt angle was carried out, and the influence of module tilt angles on windinduced vibration of tracker bracket was obtained. The results show that the aerodynamic stability of the PV tracker bracket is closely related to the tilt angle of the module. In the uniform flow field , when the structural damping ratio is about 3.9% and the installation tilt angle of the module is in the range of -39°-40°, the bracket will undergo aerodynamic instability. The dimensionless critical wind speed （U_r=U/fB） is between 3.1-5.3, and the relationship curve between the critical wind speed of aerodynamic instability and the tilt angle of the module is W-shaped. Under the action of static wind load, the module will produce static wind torsional displacement. Considering the influence of static wind torsional displacement, the actual tilt angle of the module during the aerodynamic instability of the bracket is -43°-46°. After the occurrence of aerodynamic instability, the equilibrium point of the torsional vibration of the bracket is the actual tilt angle of the module under the action of static wind load. The torsional amplitude increases rapidly with the increase of wind speed, and the maximum torsional amplitude can reach 90°, which reproduces the large-scale aerodynamic instability of the real flat single-axis tracker bracket in practical engineering.

To address the limitations of accuracy and comprehensiveness in single-image defect detection for photovoltaic modules, a multi-source image fusion method is proposed for detecting defects in photovoltaic modules. Firstly, the Cross-Modulation is embedded in the encoder of the image fusion network. Feature alignment is used instead of image registration to reveal the correspondence between the features of visible light and infrared images of photovoltaic modules, enabling the fusion of unregistered images. Secondly, the Skip Mechanism is adopted to selectively apply the attention mechanism, accelerating image processing and reducing resource consumption. Finally, the fused images are utilized for detection using the YOLO series defect detection networks. Experimental results indicate that the proposed multi-source image fusion method effectively overcomes the limitations of single-image defect detection, resulting in an average precision improvement of 5.6% across multiple networks.

This study, based on the high-resolution climate dataset GDDP, evaluates the PV power potential and future PV drought characteristics in the Beijing-Tianjin-Hebei region under different shared socioeconomic pathway （SSP） scenarios. The results indicate that, compared to the historical period, future climate change will lead to a slight decline in PV capacity factor of the region, with a more pronounced decrease under the SSP585 scenario than under SSP245. By the mid-to-late 21st century, the PV capacity factor is projected to decline by approximately 1.5%. Furthermore, the frequency （increasing by 1.5-2.0 events per year）, duration （extending from 10.83 days to 28.6-43.3 days）, and intensity （increasing by 3-5 times）of PV drought events are expected to increase. Under the SSP245 scenario, northern parts of the Beijing-Tianjin-Hebei region will experience a more significant rise in PV drought occurrences, whereas under SSP585, the intensity of PV droughts will increase more substantially in the southern areas. Compared to the historical period, climate change will significantly shorten the return period of PV drought events （by approximately 12-25 years）, further exacerbating the instability of PV power generation.

This study establishes a light-thermal-electrical model for mono-facial and bifacial photovoltaic noise barriers （PVNB）, and its accuracy is validated with experimental data. The model is used to assess the PV power generation potential of road noise barriers in the cities of Beijing, Shanghai, and Guangzhou. Results show that the total install capacities of PVNB on highways and major roads in Beijing, Shanghai, and Guangzhou are 1421.52, 1391.29, and 1939.20 MW, respectively, with annual power generation reaching 762.07, 732.86, and 1039.54 GW·h. The PV power generation costs for the three cities are 0.66, 0.67, and 0.66 CNY/（kW·h）, respectively, approaching grid parity.

Photovoltaic monitoring devices for power supply and transmission lines often experience significant weather-related issues and high offline rates. To mitigate these problems in isolated PV systems, an intelligent energy prediction and control technology is introduced. This technology uses historical and forecasted weather data, combined with a novel VMD-RIME-LSTM neural network model, to predict future PV output. It intelligently adjusts device operation modes based on energy information and monitoring needs, ensuring high-quality monitoring and efficient solar resource utilization. This enhances device stability and reduces offline rates. An experimental platform and a full-year simulation model were developed to verify the technology’s effectiveness in lowering offline rates and improving operational quality.

To improve the accuracy of photovoltaic power prediction, an improved Transformer probability prediction method is proposed, including data preprocessing, prediction model, and post-processing process. Given the problem of missing photovoltaic power data, this paper proposes a random forest interpolation method guided by the predicted mean to interpolate the missing data and improve the data integrity. Then, a probability prediction method based on the enhanced Transformer model is introduced. The model uses a multi-head attention mechanism combined with a normalization layer and a residual connection to strengthen the robustness of the model and its ability to handle long sequence dependency problems. In the post-processing stage, a fourth-order polynomial and LSTM are combined to correct the prediction error. Finally, historical data are used for experimental verification, and the results show that the proposed model has high prediction accuracy and reliability.

Aiming at the issue of the impact of ultraviolet-induced degradation （UVID） on the power generation capacity of n-type TOPCon photovoltaic modules, a method for evaluating photovoltaic modules based on the simulation of UVID process by working condition characteristics is proposed. This method enables a more comprehensive study of the UVID characteristics and grid-connected power generation capacity of TOPCon modules, making up for the deficiency of traditional single-factor component UVID evaluation methods. The research finds that ultraviolet light can activate the metastable characteristics in n-type TOPCon photovoltaic modules and cause fluctuations in the output power of the modules. The main reasons are the defects at the Si/SiO_x interface, the refractive index of SiN_x, and the changes in the charge of the field-passivation AlO_x film. Under light-soaking conditions, using TOPCon cells with high refractive index passivation medium films or UV-cutting adhesive films for module encapsulation can effectively suppress the metastable phenomenon of TOPCon modules. Based on outdoor power generation data analysis, the proposed method can more accurately evaluate the power generation characteristics of TOPCon modules compared to traditional photovoltaic module UVID aging evaluation methods.

Tubular PECVD （Plasma-Enhanced Chemical Vapour Deposition） is a kind of equipment used for surface coating of solar cells,which may have short circuit faults or circuit overload during operation. Firstly,the bidirectional thyristor control circuit model was established,and the magnitude of the conduction angle was changed through the opening and shutdown time of the bidirectional thyristor control circuit,so as to adjust the output power. Secondly,according to the possible overcurrent multiple,the inverse-time overcurrent protection circuit is designed. Finally,a simulation model was built in the Matlab/Simulink platform to verify the feasibility of the circuit design.The simulation results show that this model can realize inverse-time overcurrent protection and solve the safety problems of tubular PECVD equipment.

To investigate the optimal tilt angle adjustment methodology for photovoltaic brackets with adjustable tilt mechanisms and to quantitatively assess the benefits of tilt angle optimization in PV power generation systems, this study proposes a computational model for estimating the annual hourly power output of PV modules. The model was employed to calculate the annual hourly irradiance and corresponding power generation for identical PV modules across 97 solar radiation meteorological stations distributed throughout China. Furthermore, the systemic benefits of tilt angle adjustments were evaluated by comparing three temporal adjustment strategies （semi-annual, quarterly, and monthly） against a fixed tilt angle configuration optimized for annual performance. The findings demonstrate that dynamic tilt angle adjustment significantly enhances the efficiency of PV power generation systems. This enhancement is particularly pronounced in regions characterized by abundant solar resources, higher geographical latitudes, and larger annual optimal tilt angles. Specifically, monthly tilt angle adjustments yielded average power generation increases of 13.09%, 10.69%, 9.76%, 6.17%, and 3.23% for PV systems located in areas with extremely rich, rich, relatively rich, relatively poor, and poor solar energy resources, respectively, compared to fixed tilt angle configurations. Notably, the required range of tilt angle adjustment is relatively modest. Moreover, the quarterly adjustment strategy, which requires fewer operational interventions, still delivers substantial benefits, with power generation enhancements of 11.29%, 9.12%, 8.26%, 5.19%, and 2.80% for the five regional classifications, respectively. These results underscore the practical viability of periodic tilt angle optimization as a means of enhancing PV system performance across diverse geographical and climatic conditions.

This paper proposes a coherent strategy for active reactive support of household photovoltaics with high penetration rate in rural low-voltage areas taking into account unbalanced access, and especially does not rely on reactive power adjustment devices such as SVG. Firstly, household photovoltaic inverters with single-phase and three-phase access are taken as the objects of active reactive regulation, and a high-penetration household photovoltaic refined variable weight multi-objective optimization model is constructed taking into account the three-phase imbalance and the real-time output characteristics of photovoltaic-load during the day and night to meet the control requirements under different working conditions of the area. Secondly, the economy, stability and computational complexity of the regulation of each inverter in the continuous period are further considered, and the full-time active support strategy of the area based on the third-generation non-dominated sorting genetic algorithm （NSGA-Ⅲ） and coherent control is designed to efficiently calculate the three-phase asymmetric power flow and achieve smoother household photovoltaic coordinated reactive regulation. The calculation results show that the proposed method can fully tap the active support potential of high-penetration household photovoltaics, improve the comprehensive power quality of rural low-voltage substations, reduce substation network losses, and improve the stability and economy of distribution network operation.

Due to the insufficient voltage boosting capability of the basic Boost converter, a quadratic DC-DC converter based on coupled inductor and multiplier cell is proposed. The converter combines the coupled inductor with the voltage multiplier cell to achieve high voltage gain at low and medium duty cycles. The passive clamp circuit is used to absorb the voltage spike generated by the leakage inductance and reduce the energy loss. The two switches operate synchronously, the control is simple, and they share the voltage stress, so that the voltage stress on each switch is low. The topology, working principle and steady-state analysis of the converter are introduced. Finally, a 200 W experimental prototype is built, which achieves a voltage gain of 20 at 0.4 duty cycle and a maximum efficiency of 98.41%, proving the feasibility of the converter.

This paper proposes an engineering calculation method for the rear-side irradiance of bifacial photovoltaic （PV） modules. Based on simplified two-dimensional view factors, the method considers array geometric parameters and horizontal irradiance. It uses a segmentation approach to determine the irradiance received by different ground segments and accounts for direct, diffuse, ground-reflected, and front-side reflected irradiance, ultimately obtaining cell-level rear-side irradiance. The method addresses the insufficient accuracy in rear-side irradiance calculation of bifacial modules in domestic simulation software. Comparisons of the simulation results with PVsyst and System Advison Model （SAM） show that it achieves higher calculation accuracy and provides significant reference for the engineering application of bifacial PV modules.

To address the need of state estimation for photovoltaic （PV） module health, a method based on dynamic threshold selection and dynamic weight adjustment mechanism for health indicators is proposed. The photogenerated currentI_ph, equivalent series resistanceR_s, and equivalent parallel resistance R_p are taken as health parameters, and they are extracted using the dung beetle algorithm based on the I-V curve of the module. A dynamic health model is developed by integrating factory standards and operational aging effects. A dynamic weight adjustment mechanism is introduced to ensure accurate identification of faults characterized by low-weight parameters. Simulations and experiments confirm that the proposed method improves state estimation reliability compared to static weight approaches, enabling precise identification of misdiagnosed faults.

Perovskite solar cells, with organic-inorganic hybrid lead-based perovskite materials （CH₃NH₃PbI₃）, perovskite solar cells have made significant strides in photovoltaic conversion efficiency. Using SCAPS-1D software, this work mainly builds a new perovskite solar cells using Spiro-OMeTAD as the hole transport layer, TiO₂ as the electron transport layer, and CH₃NH₃PbI₃ as the light-absorbing layer. The optimization of material performance parameters is of great theoretical significance for enhancing the photovoltaic performance of perovskite solar cells.

From the perspective of architectural disciplines, numerical simulations of an offshore photovoltaic （PV） plant were conducted using the Weather Research and Forecasting - Building Environment Parameterization （WRF-BEP） model. By comparing the regional climate before and after the construction of the offshore PV plant, the analysis examines its impact on the local climate, particularly the microclimate of adjacent building clusters. The results show that the offshore PV plant has significant impacts on the microclimate of downwind building clusters during summer days, with the affected area extending beyond 10 km. In the studied case, the temperature in the open high-rise building cluster located 11.1 km downwind increases by up to 1.04℃. The impacts on building clusters outside the downwind zone are much less pronounced compared to those downwind. Therefore, offshore PV plants should avoid deployment upwind of building clusters and prioritize development in distant offshore areas to mitigate their impacts on the coastal climate by leveraging the buffering effect of the ocean. For building clusters within a 10 km range that are not downwind of offshore PV plants, layout optimization should be considered to mitigate adverse climatic effects.

Carries out long-term monitoring of atmospheric and soil parameters around five Chinese photovoltaic power stations with distinct climate characteristics. Based on the monitoring data, a machine-learning model incorporating geographical location is developed to predict the environmental impact of photovoltaic power station construction in different Chinese regions on the surrounding air and soil.Results show that photovoltaic panel installation alters the original underlying surface conditions, disturbs the surface radiation balance, and induces seasonal changes in atmospheric and soil environments. Overall, photovoltaic power station construction increases atmospheric temperature, decreases soil temperature, and boosts soil moisture and conductivity. Specifically, atmospheric temperature sees the most significant rise in autumn （around 0.31 ℃）; atmospheric humidity drops markedly in spring, summer, and autumn （up to 1.63%）; soil temperature decreases considerably in spring, summer, and winter （around 3.22 ℃）; soil moisture slightly falls in spring, autumn, and winter （up to 1.48%） but rises by about 7.7% in summer; and soil conductivity increases yearly, albeit slightly.Regionally, high-altitude southwestern China exhibits substantial temperature increases, exemplified by Qinghai Province （about 2.13 ℃ in winter） and the Tibet Autonomous Region （1.89 ℃ in autumn）. Most regions see significant spring and summer atmospheric humidity increases, except for the Xinjiang Uygur Autonomous Region. Southern China's soil temperature shows a year-round cooling effect, as seen in Zhejiang, Hainan, Guangdong, and the Guangxi Zhuang Autonomous Region, while other regions experience winter warming and seasonal temperature decreases.

In order to realize efficient coordinated scheduling of photovoltaic-coal power resources, a photovoltaic-coal coordination strategy of the power grid is constructed based on photovoltaic thermal load shedding technology. The energy output characteristics of the photovoltaic thermal load shedding system are analyzed. Results show that the load shedding behavior has little effect on the efficiency of photovoltaic cells. Compared with the deep peak regulation transformation strategy of coal-fired units, the photovoltaic-coal power cooperative scheduling peak regulation strategy has economic advantages while meeting the peak regulation demand. This work provides a new idea for the coordinated scheduling of photovoltaic-coal power on the generation side of the power grid.

Due to the high operating temperature of the generator in the solar thermal power plant, the significant temperature difference between the molten salt （shell-side hot fluid） and the saturated water （tube-side cold fluid）, along with the substantial pressure differential between the shell and tube sides, induces considerable stress on the heat exchange tubes. This study establishes a three-dimensional numerical model for convective heat transfer of molten salt in the shell-side tubes. It calculates the stresses caused by thermal loads, pressure loads, and their combined effects, investigating the influence of temperature and pressure on tube stress. The stress distribution patterns under these three loading conditions are presented. The results indicate that pressure loads have a more significant impact on tube stress compared to thermal loads. As the thermal load increases, the maximum total stress on the heat exchange tube rises from 40 MPa to 76.3 MPa, occurring at the first baffle cut.

Aiming at the problem of low efficiency caused by the insufficient layout planning of heliostats in the new type of tower-type solar thermal power generation system, this study proposes a two-stage genetic optimization algorithm combining Monte Carlo ray tracing and collision detection based on the radial staggered distribution theory. On the basis of the traditional genetic algorithm, the algorithm updates the receiver position by feeding back the heliostat layout and further optimizes the heliostat parameters through genetic iteration. The algorithm takes into account energy loss factors such as the light cone effect and shading obstruction. Based on the two-dimensional normal distribution model, the energy of the rays inside the light cone is discretized to construct a Monte Carlo optical simulation model, and the optical efficiency of the system is evaluated using a collision detection algorithm. A simulation was carried out for a circular heliostat field with a radius of 350 meters in Jiuquan, Gansu, and a heliostat field layout with 2977 heliostats of different installation heights and sizes was designed. The shading obstruction efficiency can reach 96.623%, the optical efficiency is 71.358%, the average annual heat power output per unit area of the heliostat is 0.690271 kW/m², and the average annual total heat power output of the heliostat field is 42.977625 MW. Compared with the improved grey wolf algorithm, the two-stage genetic optimization algorithm improves the four indicators of shading obstruction efficiency, optical efficiency, average annual heat power output per unit area of the heliostat, and average annual total heat power output of the heliostat field by 13.94%, 27.59%, 6.92%, and 29.04%, respectively.

To enhance the energy efficiency of microchannel separated heat pipes, a microchannel separated heat pipe system coupled with sky radiative cooling has been proposed. A numerical heat transfer model of the proposed system is established based on the volume-of-fluid method. The is characteristics of the system under various effective sky temperatures are numerically studied. The results indicate that compared to traditional microchannel separated heat pipe systems, the coupled system has higher working fluid flow rates and heat transfer coefficients, and therefore higher cooling capacity and energy efficiency ratio （EER）. Due to the influence of solar radiation, energy saving potential by the coupled system is higher at night than in the daytime, with an improvement rate over the traditional system of 11%-22.7% and 6.6%-13.8%, respectively. The lower the effective sky temperature, the higher the energy saving potential can be obtained by the coupled system. Additionally, the influences of radiative cooling heat exchanger area, airflow rate, and indoor air temperature on the performance of the coupled system are discussed. It is found that the larger the radiative cooling heat exchanger area, the higher the cooling capacity and EER can be obtained. As the radiative cooling heat exchanger area increases from 0.15 m² to 1.79 m², the cooling capacity rises from 719 W to 861 W, and the EER ranges from 9.22 to 11.04, which are increased by 19.7%.

Solar compound parabolic concentrator （CPC） has the characteristics of no need for real-time tracking, adjustable acceptance half-angle, and simultaneous receiving of beam and diffuse radiation, however, conventional CPC concentrating surfaces are not easy to produce, transport, install, and maintain. Therefore, in this research, a congruent multi-section asymmetric CPC （MA-CPC） is novelty constructed and its structural characteristics, concentrating properties and economic feasibility are explored. Based on the congruent concentrating plane technology, and coupled with 3D printing manufacturing technology, plane concentrating mirrors and solar vacuum tubes to construct a solar MA-CPC experimental setup, the reliability of the concentrating characteristics of MA-CPC is verified using laser experiments. Following this, the optical efficiency, solar radiation collection, concentrating uniformity and economic efficiency characteristics of the MA-CPC system are analyzed and discussed. It is found that solar MA-CPC can achieve higher collection of radiant energy in the autumn and winter seasons, and the solar radiation gathered to the absorber surface has better uniformity than the same size N-CPC. The results also show that the constructed solar MA-CPC has a significant advantage over N-CPC in terms of annual radiant energy output for the same investment, and has potential engineering applications.

The ground layer and microenvironment temperature of greenhouses are important factors affecting the crop yield. In order to solve the temperature imbalance problem caused by solar radiation in greenhouses, the application of solar radiation array tubes in greenhouses was put forward. By adding water or phase change material into the array tubes, the influence of the array tubes on the temperature change of the formation and microenvironment is studied. The experimental group and the control group were established. In order to monitor the test results, the ground was divided into six areas, and temperature measuring points with different depths were arranged in each area, and temperature measuring points with different heights were arranged in the center of the greenhouse. Through the analysis of the test results of stratum and microenvironment temperature, it is known that by arranging array tubes, the temperature of the microenvironment in greenhouse and the stratum depth of 10 cm can be maintained, and the temperature of the stratum depth of 30 cm and 50 cm can be increased to some extent, which plays a positive role in creating a good vegetation growth environment.