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

To address the problem that uncertainty in distributed energy resource （DER） responses undermines their reliability and thus prevents them from fully realizing their flexibility potential, this paper proposes a reliability‐based control method for DER clusters that explicitly accounts for response uncertainty. First, the statistical moments of each DER’s response deviation are derived from historical response data. Based on the central limit theorem, a reliability quantification index for the cluster’s response is constructed. Under the premise of controllable costs, this index is then used as the optimization objective to establish a model. The initiall non-convex model, which relies on precise probability distributions, is equivalently transformed into a quadratic programming model that does not depend on exact probability distributions. Finally, the effectiveness of the proposed method is validated through case studies, demonstrating its suitability for large-scale DER control scenarios.

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

In order to study the economic scheduling strategy for the renewable energy systems combined with the solar thermal power in the spot market， a mathematical model of the key components and subsystems is established. A combined power generation system composed of a 50 MW solar power tower plant and a 300 MW wind farm taken as the research object. Based on the theory of distributional robust optimization， the uncertainty set is constructed by integrating the 1-norms and ∞-norms. A two-stage optimal dispatch model for the combined system， considering wind power uncertainty， is established with the aim of maximizing system revenue. The optimal output allocation of the combined system to participate in the electricity spot market is obtained， and compared with the revenue of the independent system participating in the spot market. The results show that in the scenario， where the combined generation system participates in both the energy market and the auxiliary service market， the wind power deviation penalty of the combined system is reduced by 92.70% and the revenue is increased by 3.19% compared to the independent system. Finally， the impacts of the historical wind power data volume， confidence level， maximum frequency regulation capacity and rotating standby capacity on the overall system revenue are analyzed.

Aiming at the operation of distributed energy system （ DES ）, this paper proposes a bi-level optimization operation method based on mixed time scale regulation. This method aims to minimize the daily operating cost of the system and the power adjustment of the electrical related equipment, and constructs the optimal scheduling model of the upper minute level and the lower second level. The upper-level scheduling covers cooling, heating and power generation equipment with different response speeds, and regulates them through different time intervals to achieve mixed-time scale scheduling optimization. The lower layer mainly schedules power-related fast response equipment to improve the reliability of power supply in a short time scale. The results show that the proposed method can describe the response characteristics of cooling and heating equipment more accurately. Compared with single-layer scheduling, the daily operating costs of typical days in winter and summer are reduced by 5.2% and 24.3%, respectively. The utilization rate of renewable energy increased by 6.9% and 3.0%; the primary energy utilization rate increased by 4.0% and 26.6%; the primary energy saving rate reached 4.2% and 22.3%, which significantly improved the economic performance and energic performance utilization efficiency of the system and effectively guaranteed the efficient and stable operation of the system.

Aiming at the problem that it is difficult to coordinate the economy and real-time performance of multi time scale voltage collaborative control under the high renewable energy penetration, a multi-time scale voltage cooperative control method based on edge cloud architecture is proposed. To address the co-regulation problem between discrete and continuous devices in the context of renewable energy consumption, the voltage co-control process is divided into three stages： First, the cloud system manages the discrete equipment on an hourly basis to minimize global power loss and reduce regulatory costs. Second, the edge controller regulates the continuous equipment at 15-minute intervals to effectively respond to short-term power fluctuations. Finally, to manage real-time power fluctuations and device failures, the voltage control method employs an event-triggered communication mechanism to facilitate immediate voltage regulation. An alternating direction method of multipliers （ADMM） solution strategy for cooperative voltage control, oriented towards edge-cloud applications, is designed to enhance the efficiency of computational and communication resource utilization. Simulation results from an improved IEEE distribution network demonstrate that the proposed method reduces the average and maximum voltage deviations by 2.3% and 16.3%, respectively, in a scenario with high penetration of renewable energy sources. Additionally, it improves the imbalance indices of communication and computation resource allocation, as well as the total solution time, by more than 68%. By coordinating voltage regulation resources across different timescales, the system’s adaptability to fluctuations in renewable energy is significantly enhanced, resulting in a reduction of network losses by over 6.2%. This verifies the effectiveness of the method in promoting renewable energy consumption.

In the process of short-term load power forecasting, in view of the large fluctuation of load power, which leads to the low prediction accuracy of a single model, a short-term load power NST-IRN combined forecasting model based on time feature analysis is proposed. Firstly, the temporal characteristics of load variations are thoroughly analyzed, decomposing them into trend components to construct a new time series （NTS） model. Secondly, considering the impact of multi time scale input features and day types on load power, a improve residual neural （IRN） model with feature input structure and deep learning structure is constructed. Finally, the D-S evidence theory is used to weight and fuse the prediction results of the NTS and IRN models to obtain the final load forecasting results. Simulation experiments were conducted using real load data from ISO New England, and the results showed that the proposed model has high prediction accuracy and robustness.

In order to enhance the consumption capacity of new energy grid-connected, this paper investigates the impact of hydrogen-electric hybrid vehicles （FCHEVs） on wind-PV consumption during their popularization process, and proposes a multi-objective optimal scheduling model for FCHEVs that takes into account wind-PV consumption. Firstly, an adaptive energy price strategy is proposed, and a price-demand elasticity matrix is introduced to reflect the relationship between energy price changes and FCHEV users' demand; Secondly, based on the energy pricing strategy, a two-layer model is constructed with the participation of electric-hydrogen coupled virtual power plants and FCHEV users’clusters, in which the upper layer adopts an improved particle swarm algorithm with adapted inertia weights to minimize the system operating load variance and cost minimization as the objectives. The lower layer aims to minimize the energy costs of users. Finally, the effectiveness of the proposed adaptive energy pricing strategy will be tested through a case studies analysis and multi-scenario comparisons, and the development potential and expected benefits of FCHEV are prospected.

To achieve diversified utilization of hydrogen in the integrated energy system, a hydrogen-based multi-energy coupling integrated energy system, considering dynamic hydrogen blending in gas turbine units and flexible carbon capture coupled with power-to-gas technology, has been developed. Based on carbon capture, carbon trading, and demand response, a source-load coordinated carbon reduction strategy is proposed to fully explore the system’s low-carbon economic potential. Firstly, models for hydrogen blending in gas turbines and flexible carbon capture are developed, forming a multi-energy coupling model centered on hydrogen energy. Secondly, a load-side demand response model is constructed to achieve peak shaving and valley filling of electric-thermal loads. This is combined with a tiered carbon trading mechanism and carbon capture equipment to achieve source-load coordinated carbon reduction, further exploiting the system’s low-carbon potential. Finally, an optimization scheduling model is established with the objective of minimizing the total operational costs of the system. By comparing various scenarios for comparison, the proposed optimization scheduling model is demonstrated to effectively improve the renewable energy integration, realize source-load complementary coordination, and enhance the system’s low-carbon and economic benefits.

Based on the analysis of rural energy characteristics and fully tapping into the potential of rooftop photovoltaic/solar thermal energy supply, a rural comprehensive energy system based on photovoltaic biomass synergy is proposed in collaboration with biomass. The results show that rooftop photovoltaics can supply 75.36% of the system's electricity, rooftop collectors can meet 57.14% of the system’s heat load, and the remaining load is provided by biomass energy. Rooftops can supply a large amount of renewable energy to the system. The cost of the rooftop photovoltaic/thermal and biomass distributed energy system is 1.59 million yuan, while the cost of the traditional power grid supply system is 3.27 million yuan. Compared with traditional power grid supply systems, rural rooftop photovoltaic/thermal biomass distributed energy systems are more economical and environmentally friendly.

In order to realize the optimal operation of multi-park integrated energy systems（MPIES）, while ensuring the economy and stability of the system, and balancing the conflicts among multi-stakeholders between distribution network operators（DSO） and park operators, a DSO-MPIES optimization strategy framework based on two-level game is proposed. Under this framework, the DSO acts as a leader, setting electricity price to guide the response of MPIES alliance with the goal of maximizing its own revenue. The MPIES alliance, as a follower, aims to minimize its own minimum cost and responds to DSO decisions through point-to-point electricity-carbon transactions between parks. In order to solve the icsue of collaborative operation within MPIES, the framework introduces Nash bargaining theory and introduces the comprehensive contribution of electricity and carbon in the process of benefit distribution to fairly distribute the benefits of each park, and determines the combined weight based on the anti-entropy weight method combined with subjective and objective weighting. Finally, the distributed optimization algorithm of bisection method and the alternating direction multiplier method（ADMM） are used to solve the model, and it is proved in the example that the proposed model and method can reduce the carbon emissions of MPIES and improve the income of each subject in a balanced way.

Traditional regional power grid scheduling often faces conflicts of interest between different power generation entities and loads due to the cost allocation of deep peak regulation for thermal power units. To address this issue,this paper proposes a deep peak regulation cost allocation strategy for thermal power units, which incorporates wind and solar power accommodation as well as a load alliance game mechanism. To enhance the participation incentives of wind, solar, and thermal power enterprises for participating in deep peak regulation, a Shapley value-based cost allocation mechanism is introduced for strategic analysis. First, an optimal scheduling model is developed, which incorporates thermal power plants, wind farms, photovoltaic power stations, and pumped storage stations, aiming to minimize the total system peak regulation cost. The model is constructed with the objective of minimizing the system-wide cost incurred by peak regulation operations. Then, the marginal peak regulation cost is calculated based on the thermal power unit market clearing results. Simulation results demonstrate that applying the Shapley value method to allocate peak-shaving costs among alliance members results in actual cost shares for user-side entities that closely align with their respective marginal peak-shaving costs. Compared to the traditional allocation method based on electricity consumption, the relative deviations are -2.97%,-3.25%, and -2.38% versus -0.78%, -26.85%, and 7.58%, respectively. The corresponding mean absolute errors （MAEs） are 2.37% and 13.88%, indicating a reduction of 11.51 percentage points. Furthermore, by varying the grid-connected capacity multiples of wind and solar generation from 0.6 to 1.6 and 0.0 to 2.0, respectively, the corresponding peak shaving effects and the cost allocation for each participating entity in deep peak regulation are analyzed. The results provide insights into developing a more equitable and rational cost-sharing strategy for wind and solar power integration and the deep peak regulation of thermal power units.

In DC microgrid, aiming at the problems of power imbalance, poor dynamic performance and high backflow power of ISOP-DAB converter, in this paper, the double-loop decoupling strategy is combined with the super twisting sliding mode under extended phase-shift modulation. PI control is used in voltage balancing ring and super twisting sliding mode control is used in output voltage ring. Under extended phase shift modulation, two transmission power models and soft switching characteristics of single-module DAB converter are analyzed, establish the reduced order model of the converter. KKT condition method is applied to solve the optimal internal shift ratio D_1i of each module, and the super twisting sliding mode controller is designed by the reduced order model and the super twisting algorithm, and the external shift ratio D_2i is generated by the interaction with the pressure balancing ring to achieve power balance and improve dynamic performance. Finally, a two-module ISOP-DAB experimental platform is built for experimental verification. The experimental results show that the ISOP-DAB system can improve the dynamic performance of the converter while ensuring the input voltage balancing of each submodule, and effectively reduce the backflow power generated by the converter.

A boundary voltage mode control strategy is proposed in this study, which makes it able to achieve zero current switching （ZCS） for single-phase full-bridge current source inverter. The proposed control strategy can also effectively reduce the inductance required on the DC side. The basic principle of boundary voltage control and the conditions for achieving ZCS are analyzed. Based on the area equivalence principle, the reason for reducing inductance is analyzed through mathematical derivation. On the basis of theoretical analysis, simulations and experiments were conducted. The feasibility of the proposed critical voltage control strategy is verified by simulation and experimental results.

Taking PV/storage/hydrogen grid-connected microgrid as research objective, this study proposes a capacity configuration optimization model by improved PSO algorithm with genetic algorithm（GA） in order to improve its renewable energy consumption capacity, carbon emission reduction capacity and economic output. The optimization objective is maximum annual comprehensive profit, which not only introduces investment, operation and maintenance cost, green certificate trading revenue and carbon trading cost into operating cost and profit of system, but also proposes a coordination control strategy based on the real-time revenue coefficient of electric-hydrogen-storage. Therefore, the profitability of PV and hydrogen varies in real-time based on time-of-use price, and the dispatch priority of energy storage equipment is optimized according to the real-time revenue coefficient. The optimization variables are the capacities of PV, hydrogen and storage. The optimization method is an improved PSO algorithm incorporating GA concepts. By the operation of selection, crossover and mutation of the particle population position, the capacity of global optimization is improved. The effectiveness of optimization model is verified by both optimization design case and influence analysis case.

To address the planning problem of a multi-community integrated energy systems（IES）, a two-stage robust cooperative distributed energy management model incorporating sharing is proposed. In the first stage, a non-cooperative game energy management framework for the multi-community IES is established, utilizing a robust optimization method to handle uncertainties in energy sources and loads. In the second stage, an electric energy transaction payment model is developed to allocated costs after sharing electric energy. To ensure fairness and minimize the planning and operation costs for each IES, we adopt a distributed cost reduction rate model （CRRD） based on non-cooperative game theory to settle transaction costs, solved using the alternative direction multiplier method （ADMM）. Finally, a numerical example is provided to verify the effectiveness of the proposed model in achieving cooperative energy management among multi-community IES.

The uncertainty of load-side grid-connected/off-grid operation state of micro-grid seriously affects the output state configuration and hardware security of micro-grid inverters. In order to enhance the self-coordinated output capability of the inverter output state and simplify the complexity of multi-mode control systems, a voltage-power self-coordinated control system based on sliding mode control （SMC） is designed in this paper. Firstly, the power transmission model of the micro-grid inverter is established. Based on the analysis of output voltage characteristics under active/reactive power constraints, a virtual compensation voltage is introduced to construct the voltage-power self-coordination control relationship. Then, the SMC strategy is used to realize the function of each module within the control system, and the stability of SMC control law is analyzed. Finally, simulation under different working conditions are designed to analyze and verify that the proposed control system can self-coordinate the inverter output voltage-power state according to the operating state of the load side under the limitation of the maximum output power of the inverter, and effectively reduce the response time and output steady-state error of the system while meeting the power demand of the load side.

The traditional photovoltaic storage charger widely used in the photovoltaic storage DC microgrid, the core part-bidirectional DC/DC is difficult to do at the same time to isolate and storage batteries need a wide range of voltage output. In this paper, a two-phase parallel asymmetric CLLC circuit topology and the associated control strategy are proposed, which adopts the control method of interleaved parallel and topology multiplexing to generate three modes of half-bridge, full-bridge, and full-bridge interleaved parallel to provide a wide voltage gain range, so that the converter operates near the above-resonant frequency point, and the three modes of operation achieve the zero-voltage turn-on of the primary-side switching tube. Aiming at the output power imbalance problem caused by the difference of actual resonance parameters in the full-bridge interleaved parallel mode, a method of compensating for the smaller gain phases, the phase-shift control method of increasing the secondary-side switching tubes is proposed. Through theoretical analysis and derivation of the corresponding gain formula, it is verified that the method can adjust the output gain and thus equalize the power. Finally, it is verified on the constructed 3.3 kW experimental platform that the converter can provide the voltage and power ranges required to satisfy the multi-stage charging of energy storage batteries, maintains the soft-switching in all the three modes, and achieves the power equalization in the full-bridge interleaved parallel mode.

To address the uncertainty of the distributed power generation, the information gap decision theory was deployed for modeling. First, the optimization model in planning the AC/DC hybrid distribution networks based on the soft open points was proposed by using the information gap decision theory, and selected the comprehensive cost and voltage fluctuation as the objective functions. Secondly, the fuzzy normalization and weighting processes were conducted on the aforementioned two objective functions. Finally, the proposed optimization model was tested based on the improved IEEE 33-node distribution system. The results show that the proposed model in planning the AC/DC hybrid distribution networks with soft open points can effectively improve the voltage quality, the economy and reliability of the AC/DC hybrid distribution networks.

Aiming at the problem of low accommodation rate of wind and solar energy caused by insufficient adjustment ability of existing energy structure, an economic optimal dispatching model of virtual power plant considering multiple uncertainties is proposed. Considering the uncertainty and correlation of the wind and solar output in virtual power plant, multiple scenarios of wind and light output are constructed using a Latin hypercube sampling method and reduced to derive typical scenarios using an improved iterative self-organizing data analysis algorithm. According to the wind and solar output value obtained from the day-ahead forecast, the dynamic time-of-use electricity price of virtual power plant is formulated to solve the uncertainty of the electricity price, and the demand-side resources are fully mobilized through the price-based demand response under the dynamic time-of-use electricity price. On this basis, an economic optimal dispatching model is established to minimize the total operating cost of the multi-energy complementary virtual power plant in the trading day, and the snow ablation algorithm is used to optimize the model. The simulation results show that the model can improve the economy of multi-energy complementary virtual power plant operation.

This paper tackles the complexities of benefit allocation among diverse stakeholders and layered uncertainties in collaborative operations, by proposing a dynamic pricing based bilevel distributionally robust scheduling approach for enhanced control over energy in multi-microgrid active distribution networks. At the outset, it addresses the challenge of equitable benefit allocation between active distribution network and multiple microgrids through a dynamic pricing mechanism sensitive to branch losses and energy-load distribution patterns. A structured bilevel model emerges, where the active distribution network determines transaction prices for microgrids through this pricing mechanism, while microgrids respond by adjusting their load schedules according to their demand requirements. Moreover, the influence of main grid price fluctuations and renewable energy production variability is encapsulated within a resilient bilevel distributed model. Through the application of mathematical deduction and duality theory, the original problem is reformulated into a mixed-integer second-order bi-level cone bilevel model, solved iteratively using a bisection method combined with the GUROBI solver. The practical efficacy of the proposed framework is demonstrated through the adaptation of the modified IEEE 33-node system.

To mitigate the discrepancies between actual wind power output and day-ahead forecasted power, and to alleviate the heightened pressure on grid dispatch and operation caused by wind power uncertainty, a control strategy for a wind-hydrogen hybrid power generation system is introduced. This strategy aims to minimize tracking errors and smooth power output fluctuations. It integrates fuzzy logic control （FLC） and model predictive control （MPC） algorithms, leveraging the robustness and real-time benefits of FLC with the anticipatory optimization capabilities of MPC. Initially, the membership function parameters of the FLC are optimized iteratively using MPC. Subsequently, the optimized parameters are applied to the fuzzy controller （FC） in the next time step. Simulation results demonstrate that the FLC-MPC strategy significantly enhances regulation performance for the wind-hydrogen hybrid power generation system. In comparison to FLC alone, the root mean square error （RMSE） is decreased by 4.03%, maximum power fluctuation is reduced by 23.17%, penalty energy is diminished by 14.98%, and the continuous regulation capacity of the hydrogen energy storage system is improved by 11.58%.

Aiming at the problem of large power output fluctuations and overall energy efficiency impact caused by the different characteristics of the photovoltaic temperature difference hybrid power generation system due to the two power generation methods, and the current control methods not considering weather factors and lacking proactive power suppression thinking in the early stage, a photovoltaic temperature difference hybrid power generation system output power control technology considering meteorological factors is proposed. This article considers the direct impact of weather conditions on the output power of photovoltaic temperature difference hybrid power generation systems, and classifies and analyzes weather factors. By predicting and controlling the output power of the system under different weather conditions, it no longer relies on a single rear terminal information. Using PSO algorithm to optimize K-means algorithm to calculate the probability distribution of output power, in order to obtain a more comprehensive power output information base. On this basis, input the power data of the desired probability distribution area into the least squares support vector machine to complete the control of output power. The experimental results show that the proposed method can effectively control the output power of the photovoltaic temperature difference hybrid power generation system under the conditions of a battery temperature of 30 ℃ and three series numbers, whether it is unshielded, completely shielded, and the effective light irradiance after shielding is 25 % and 50 % of the unshielded, and the overall decrease in Skill values and surface fluctuations are small, with MAE and MSE values both around 0.3. This method effectively improves the accuracy and stability of output power control in power generation systems, and has strong practical applicability.

This paper proposes a source-load-storage hierarchical coordinated planning control strategy for distribution network based on cluster partitioning. Firstly, a comprehensive index-based cluster partition method for distribution network considering electrical distance under load forecasting is proposed. Secondly, on the basis of cluster partitioning, a bi-level joint planning model is proposed. The upper layer establishes a source and storage location and capacity model for the uncertainties of the internal source and load of the cluster. The lower layer takes the minimum node voltage deviation as the objective function, and establishes a voltage control model involving Markov decision processes. Then, the particle swarm optimization algorithm of annealing strategy and the gradient algorithm of deep deterministic strategy are used to solve the upper and lower models, which improves the resilience of the renewable energy distribution network and realizes the coordinated planning control of the different spatial responses of the source-load-storage of the distribution network. Finally, the effectiveness of the source-load-storage bi-level optimization model is verified in an actual 35 kV/10 kV distribution network.

The grid-connecteds inverter usually adopt digital control. However, the control delays and discretizatione errors will seriously affect the control performance, resulting in slow dynamic response, active damping failure, and grid voltage background harmonics. In view of the above problems, this paper proposes a discrete domain state space fusion capacitor voltage feedforward controller, which effectively reduces the influence of grid voltage background harmonics while improving the dynamic response of the system. At the same time, the analytical expression of the controller is derived, so that the control algorithm can be automatically tuned according to the physical parameters. Finally, the dynamic performance, background harmonic suppression ability and stability of the discrete domain current controller designed in this paper are verified by experiments.

This article introduces a photovoltaic energy storage system capable of operating both on-grid and off-grid, jointly controlled by a maximum power point tracking algorithm based on incremental conductance method, an energy storage battery optimized using the slime mold algorithm, and a pre-synchronized virtual synchronous generator. The system can operate in parallel off grid mode and perform multifunctional operations, including reactive power compensation, power balance, and power quality enhancement, ensuring uninterrupted power supply to the load under different operating conditions. The proposed system uses the incremental conductance method to determine the maximum power output of the photovoltaic array. The energy storage converter, optimized by the slime mold algorithm, stabilizes the DC bus voltage, and the system switches off the grid through a virtual synchronous generator. The simulation experiment results show that the total harmonic distortion of the voltage and current in the optical storage system optimized based on the slime mold algorithm is 1.20% and 2.13%, respectively, which is below the 5% threshold limit, verifying the effectiveness of the proposed control system.

In order to fully leverage the potential energy storage resource attributes of electric vehicles as flexible and schedulable power grids, an optimized scheduling strategy for electric vehicles considering multiple dimensions of user satisfaction and new energy consumption is proposed. Extract travel information from the user travel information database using Monte Carlo method and simulate its charging process, establish physical conditions and charging expectations for single user participation in scheduling, and construct satisfaction indices for electric vehicle clusters. Establish a joint scheduling model based on user satisfaction and new energy consumption, and solve it using the primal dual interior point algorithm. Based on the Swedish customer satisfaction index model, a comprehensive satisfaction model is constructed, and an evaluation mechanism for user satisfaction and new energy consumption satisfaction is established. Taking a micro-grid in Suzhou as a case study, simulation analysis and verification were conducted. The results showed that compared to only considering user satisfaction from the distribution network side, implementing a joint scheduling strategy for the micro-grid system can reduce the rate of wind and solar curtailment, significantly improve user satisfaction and new energy consumption satisfaction, and achieve friendly interaction between electric vehicles, the power grid, and new energy.

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

A frequency adaptive BBMC main circuit parameter optimization method is proposed. The optimization objectives and optimization goal are determined, and the relevant mathematical model and its multi-objective optimization fitness function are established. On this basis, the optimization of the main circuit parameters is proposed by using the salp swarm optimization algorithm, and then the optimal main circuit parameters under different rated output frequencies are studied by numerical fitting method. The function relationship describing their variation rules is established. Finally, the effect iveness is verified by constructing the simulation model and the hardware experimental device.

Virtual inertia control method can effectively improve system inertia and guarantee bus voltage stability . In this paper, for the transient power oscillation caused by the interactions between the virtual inertia compensation loop and the control inner loop of the traditional virtual inertia control method, a virtual inertia control strategy based on the rate of change of voltage （VI-ROCOV） is proposed to further optimize the transient characteristics of the system through the improvement of the virtual damping characteristics of the system. By establishing a small-signal model of the system, the influence of key parameters on the stability and transient characteristics of the system is analyzed, and the parameter optimization design guidelines are obtained. Finally, an experimental model is established to compare and analyze the different control methods. The results show that the proposed control strategy effectively improves the transient performance of the system and ensures the bus voltage stability.

This study focuses on China’s nine major clean energy bases, analyzing the installed capacities and output characteristics of the bases. We constructed a complementary network model for clean energy output at multiple time scales and studied the robustness of the complementary network and its key role in the coordinated development of clean energy. Our findings reveal the complementary mechanisms among clean energy bases across different time scales. The results indicate that there are significant differences in renewable energy outputs and complementary networks across different time scales. The average clustering coefficient of the complementary network at the monthly time scale is 0.74, indicating a high clustering degree. The Xinjiang clean energy base is the most important node in the daily-scale network, while the Yellow River Bend clean energy base plays a crucial role in the annual and interannual-scale networks. Additionally, the Songliao, Yellow River Bend, and Yellow River upstream clean energy base have a significant impact on network robustness at the intraday, annual, and interannual scales.

This study delves into the comprehensive process encompassing the automated design and performance simulation of Building Integrated Photovoltaic （BIPV） power plants. An automatic module layout method has been developed based on the computer graphics scan line filling algorithm. Additionally a shadow analysis model tailored to power plants has been constructed utilizing the projection method and Boolean polygon operations. The electricity generation simulation employs the Perez model, the five-parameter model, and the SUNDIA inverter model, respectively addressing the Radiation calculation, DC power generation calculation, and power conversion processes of photovoltaic modules. To appraise the feasibility of the proposed approach, the design methodology was applied to the automated arrangement of BIPV components within a designated scenario. Results reveal marked improvements in design efficiency and effectiveness. Further, to verify the accuracy and precision of the methods, a comparative analysis was conducted between the methodology presented herein and PVsyst for the same scenario. The disparity in annual electricity generation predictions between the two approaches was found to be approximately 0.922%, substantiating the high accuracy of the proposed method.

In order to effectively solve the structural limits of Vertical Axis Wind Turbine （VAWT）, such as easy to break the shaft and difficult to scale up, a configuration design of U-type VAWT is proposed in this study. The basic composition and operational principle of the novel wind turbine are introduced. The Finite Element Method （FEM） models of VAWT are established and verified. The anti-overturning mechanical model of U-type VAWT is established. The load balance analyses of U-type support component, hinge mechanism, roller unit and vehicle assembly are completed. Based on the combined deformation theory and the alternating stress-fatigue life analysis theory, the mathematical model of the static-strength fatigue life of the main support component is established. The static simulation model is used to analyze the influence of supplementary supporting components on the deformation of U-type VAWT. The research results show that U-type VAWT adopts the inclined main support component with the airfoil section instead of the vertical main shaft, and the pressure-type roller unit and the load-beaning vehicle assembly can effectively prevent the overturning of U-type VAWT. The running speed of the vehicle assembly is less than 97.2 km/h which is 27 m/s. The alternating stress amplitude ratio of U-type and H-type VAWT in the dangerous section of the main support component is less than 0.9, which means the service life of U-type VAWT is 2.34 times of that of H-type at least. The rope reduces the deformation of U-type VAWT by about 60%, effectively reducing the mass of U-type VAWT.

The high altitude and low temperature in areas with abundant wind resources can easily cause wind turbine blades to freeze. Therefore, it is necessary to focus on anti icing performance in the aerodynamic design of wind turbine blades. This article presents a multi-step aerodynamic shape optimization design for the S809 wind turbine airfoil to reduce aerodynamic performance losses caused by icing. Firstly, quickly conduct global optimization to obtain a preliminary optimized airfoil, and then conduct local fine optimization based on the preliminary optimized airfoil. Due to the long time required for ice simulation in fine optimization, the addition of surrogate models to replace numerical simulations improves optimization efficiency. The optimized airfoil has achieved good aerodynamic performance both before and after icing. Compared with the baseline quasi airfoil, the optimized airfoil has a larger leading-edge radius, thicker pressure surface, and thinner suction surface. The aerodynamic performance loss of the optimized airfoil after icing has been reduced from 7.6% of the original airfoil to 1.74%, proving that anti icing airfoils can be obtained through optimization design methods, providing ideas for optimizing the anti icing design of wind turbine blade airfoils.

To investigate the drag reduction mechanism of grooves under three-dimensional rotation, the NREL 5 MW wind turbine is taken as the research object, and transverse arc-shaped grooves are arranged at 18%-20% spanwise position on the blade. The aerodynamic performance of the three-dimensional sectional airfoil and the corresponding two-dimensional airfoil under different operating conditions are calculated using numerical simulation methods. The surface pressure coefficients of the section airfoil are compared with those of the two-dimensional airfoil, and the flow field status is analyzed. The results show that the arc-shaped grooves on the section airfoil can improve the pressure distribution of the airfoil and effectively suppress flow separation within a certain range of wind speeds, thereby increasing the lift-to-drag ratio. However, the suppression of flow separation by the arc-shaped grooves on the two-dimensional airfoil is not significant. By comparing the flow status inside the two-dimensional grooves and three-dimensional grooves, it is found that the three-dimensional rotational effect inside the grooves is the fundamental reason for drag reduction. Before stall, the vortices inside the grooves carry high-speed fluid out and inject it into the boundary layer, resulting in accelerated flow velocity behind the grooves. After stall, the vortices inside the grooves capture low-energy fluid, both of which improve the pressure distribution of the section airfoil.

In the SCADA system data of wind turbines, if the density of noise data is too high, it may mistakenly clean the rated power data during the preprocessing process. To address this issue, the DBSCAN clustering algorithm can be used to remove noise data points near the rated power data, ensuring that only normal rated power data is retained. Then, on the wind speed-power curve, identify the boundary between the rated power data and other data, and temporarily store the upper part. For the lower part, apply a combination of Chauvenet's criterion and Box-Cox transformation to handle it. Finally, merge the two parts of the data. This approach can effectively reduce the problem of mistakenly cleaning rated power data due to high noise data density during the preprocessing of wind turbine SCADA data.

Aiming at the issue that the reactive power compensation of large-scale wind power integration grid mainly focuses on voltage deviation, which can not adapt to the voltage imbalance operation condition, this paper proposes a comprehensive voltage compensation method based on split-phase power flow optimization （SPPFO）. Firstly, a SPPFO model is established, which takes the minimum mean value of voltage unbalance, the minimum mean value of three-phase voltage deviation and the minimum total compensation amount of centralized reactive power compensation equipment as the optimization objectives. After that, an improved multi-objective particle swarm optimization（MOPSO） algorithm is used to solve the optimization model with high performance. Furthermore, for the obtained quasi-optimal compensation schemes as the solutions of SPPFO, the optimal compensation scheme is determined by the combination of order relation analysis method and the technique for order preference by similarity to an ideal solution method （TOPSIS）. Finally, an actual wind power integration grid in north China is taken as a case study to verify the effectiveness of the proposed strategy and the advantages of the proposed improved MOPSO.

To overcome the instability of wind power sequences resulting in low prediction accuracy and the limited historical data of some wind farms, we propose a wind power prediction model called feature interaction in Informer with transfer learning （FIITL）. Firstly, we introduce a feature interaction （FI） amechanism with dual-channel input to further extract information. Secondly, transfer learning （TL） is incorporated into the prediction model, resulting in a cyclic fine-tuning transfer learning method. This method transfers the model from a source monitoring station to a target station, thereby improving predictive performance under limited historical data. Finally, the FIITL model is compared with traditional Informer models and other baseline prediction methods. The results demonstrate that the FIITL model outperforms these models in situations with limited data.

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

With the increase in single-unit capacity, the electromagnetic design requirements for high-power aluminum-wound doubly-fed induction generators （AW-DFIG） have become more stringent. Traditional surrogate-model-base optimization strategies rely on a single surrogate model to establish the connection between multi-objective parameters and design variables, resulting in lower fitting accuracy for certain objective parameters, which affects the optimization results. Therefore, this paper proposes a high-power AW-DFIG electromagnetic optimization design method based on multi-objective parameter surrogate model （MOPSM）. Firstly, the design variables of AW-DFIG are determined and the optimal Latin hypercube is used for sample collection; Secondly, by comparing the fitting accuracy of different surrogate models and continuously adjusting and optimizing model parameters, the optimal proxy model for each target parameter is determined; Then, the non dominated genetic algorithm （NSGA-Ⅱ） is used for electromagnetic optimization design, and the optimization scheme is verified through FEM analysis; Finally, a 10 MW, 1720 r/min experimental prototype is developed and relevant experiments are conducted to verify the feasibility of the multi-objective parameter surrogate model optimization design method proposed in this paper.

A large amount of abnormal samples are obtained in the operational data collected from wind farms, which prevent the implementation of tasks such as state assessment and power prediction. To overcome this issue, a recognition method which selects targeted detection methods based on different abnormal distribution characteristics in measured wind turbine operational datasets is proposed in the article. The method considers the working state of the unit and uses an adaptive clustering algorithm with noise density, taking wind speed, power, and blade pitch angle as inputs, and the minimum average distance as the objective function to achieve parameter optimization of the algorithm. In order to verify the effectiveness of the model, the power curve of the cleaned data is fitted using the least squares method, and then the absolute average error is calculated and compared with other commonly used algorithms on actual datasets in China.

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

Based on the vortex sound theory, the generation mechanism of turbine blade aerodynamic noise from wind turbine blades is studied. In this paper, wind turbine blades and flow field regions are modeled and meshed, and the directivity of wind turbine aerodynamic noise is analyzed by computational fluid dynamics（CFD） numerical simulation. Through test verification, it is concluded that wind turbine aerodynamic noise is a dipole characteristic, and the accuracy of numerical simulation is verified. Then, the distribution of flow field and sound field calculated by numerical simulation are analyzed and compared, and the main causes of wind turbine blade aerodynamic noise are studied. The results show that the aerodynamic noise sources of wind turbine blades are mainly distributed in 65%~95% span of the blade spanwise direction. At the same time, the sound source of wind turbine blade aerodynamic noise is mainly caused by vortex movement caused by fluid movement, and the distribution pattern of vortex movement in the range of 65%~95% of blade span is very consistent with the distribution law of blade aerodynamic noise.

The research focuses on the analysis of a semi-submersible floating offshore wind turbine based on the frequency-domain simulation tool. The approaches of support vector machine based surrogate model and the variance-based Sobol sensitivity analysis are utilised to investigate the quantitative correlation between the dimensions of the components and the performance of the floater. Meanwhile, to examine the effects of sampling range and interval on the accuracy of the surrogate model, two groups of samples obtained by the different sampling approach are used to train the models and the results are compared. It is found that the sampling approach has less effects on the accuracy of the model, as both the models show high accuracy in the prediction of the platform performance and yielding similar sensitivity analysis results. Sensitivity analysis results indicate that the steel weight of the floating platform is relatively sensitive to the column diameter, span and draft, with their total sensitivities being 0.56, 0.21 and 0.14 respectively. The platform pitch response shows higher sensitivity to column diameter and draft than other parameters, while the sensitivity index for platform surge response can be various for different environmental conditions. During the scaling design process of floating foundations, the optimization direction can be determined based on the sensitivity of different parameters, thereby achieving the design goals more efficiently.

To address the challenge of weak fault signals in wind turbine bearings, which complicates effective state characterization, this paper introduce a fault diagnosis method combining multi-entropy fusion with a multi-scale convolutional neural network. The approach decomposes the original signal into modal components, calculates multiple entropy measures to construct a feature matrix capturing the signal’s complex characteristics, and integrates this matrix into a convolutional neural network featuring parallel convolutional kernels of varying sizes. Experimental results from two datasets demonstrate the method’s enhanced diagnostic accuracy and generalization performance.

There are few thermal power units in the power grid of the new energy collection and transmission end, and the power grid strength is weak. The voltage drop under AC short circuit fault and the transient overvoltage after the faults can easily to cause wind turbines off-grid. It is necessary to pay attention to the low voltage ride through characteristics of the wind turbines and the transient overvoltage characteristics of the units port. In response to the issue that the objective functions used in existing control parameter optimization methods cannot simultaneously address the low voltage ride-through characteristics and transient overvoltage characteristics of wind turbines, a low voltage ride-through performance optimization method for permanent magnet direct drive wind turbines （PMSG） based on combined weighting-Monte Carlo method is proposed. Firstly, the transient reactive voltage response analytical model of PMSG is established, and the key control parameters affecting the low voltage ride through performance of PMSG are identified by parameter sensitivity analysis, and the optimization feasible domain of the parameters to be optimized is calculated. Then, based on the combined weighting method, a comprehensive optimization index which can reflect both the low voltage ride through characteristics and transient overvoltage characteristics of PMSG is determined. On this basis, Monte Carlo algorithm is used to optimize the key control parameters in the optimization feasible domain. Finally, the optimized control parameters are verified under different operating conditions. The results show that PMSG with optimized control parameters has significantly improved the comprehensive performance of low voltage ride through under weak power grid, and shows better dynamic response performance. The optimized control parameters are also applicable to the scenarios with different voltage sag degrees, which proves the effectiveness and applicability of the optimization method.

In order to unveil the probability and severity of low wind power events during severe cold weather in northeastern China, hourly historical meteorological data from 1979 to 2022 were used to reconstruct wind power output data. The occurrence of low wind power events was identified on the basis of wind power output capacity factors falling below a designated threshold, thereby reproducing the low wind power event that resulted in widespread power restrictions in late September 2021. A long-term low wind power event lasting seven days in late July 2009 was also identified. The annual frequency, seasonal distribution, and duration of low wind power events were analyzed. A study of low wind power events during the heating season revealed 300 instances occurig at temperature below -15 ℃. Furthermore, the analysis demonstrated a strong correlation between low wind power during the heating season and cold waves occurrences.

Using the data of 7 typhoon cases by 3 offshore masts near Pingtan in Fujian Province, the analysis shows that when the masts are located on the left side of the typhoon’s forward direction, the wind direction changes counterclockwise, and when the mast is located on the right side, the wind direction changes clockwise. 180°/30 min only occurs when the center of the typhoon passes the masts. According to the changes of air pressure, wind speed, wind direction and the distance of the typhoon path from the mast during the typhoon period, the turbulence intensity, gust coefficient; Wind vertical shear index and wind direction change were calculated in three periods,such as the front eye wall area, the typhoon eye and the rear eye wall area. The results show that the turbulence intensity is the largest at 10 m in all periods with a value of 0.10-0.19, and the turbulence intensity above 30 m has little vertical variation, and the first period is the smallest. It is 0.07-0.10, the second period was 0.09-0.12, there was a large difference in the third period. The gust coefficient is the highest at 10 m, with a value of 1.26-1.60, and 1.2-1.45 at 40-90 m, The minimum in the first period is 1.18-1.30, the maximum in the third period is 1.12-1.45, and the middle in the second period is 1.20-1.38. The wind shear index at 10-40 m during the first and second periods for wind measurement towers 1 and 2 is slightly higher than that of 40-90 m, because the wind mainly comes from the sea during the two periods. The wind shear index in the third period is slightly higher than that in the first and second periods, because the wind in the third period mainly comes from land.

The article uses biological oil as raw material and 3Ni/SAPO-11 as catalyst to prepare biological aviation kerosene by catalytic hydrogenation in a fixed bed reactor, explore the effects of different reaction temperature, pressure and space velocity on the selectivity of C₉-C₁₆, the yield of C₉-C₁₆ and the iso-to-normal ratio in liquid phase products, the optimal reaction conditions were obtained. It is found that under the best conditions, temperature 400 ℃, pressure 1.5 MPa, space velocity 1.9 h^-1, the selectivity of C₉-C₁₆ is 52.79%, the yield of C₉-C₁₆ is 51.48%, and the iso-to-normal ratio is 9.11. A 3Ni/SAPO-11 molecular sieve was used as the carrier, 3Ni/SAPO-11 bifunctional catalyst with three metals （Cu, Fe, Co） were prepared and characterized. Under the optimized reaction conditions, the effects of the addition of different metal promoters on the reaction products were investigated by fixed bed experiments.

Based on the ocean model SWAN, the validation results suggested that the model can effectively represent the wave energy characterization in this region. The sea area of Guangdong East Offshore Wind Power Base was selected to focus on the wave energy inter-annual variation and directional distribution, and to explore the significant wave height and mean period characteristics of wind waves and swell waves in all seasons. The results show that the wave energy resource in the study area show a decreasing trend from southeast to northwest with strong seasonal variability. The power density is more than 6 kW/m in most of the sea area in winter and fall, and more than 2 kW/m in spring and summer. The average period in winter is between 5.5-7.0 s in most of the study area, and mostly lower than 6.0 s in summer, which is the smallest. Guangdong East offshore wind power base is mainly in E-NE wave direction throughout the year, and the average effective wave height value is small in May, June and September, with the median and average value in the range of 0.7-0.9 m. and the energy period is the smallest in May-June, with an average value of about 5.1 s. In addition, the waves in this area are dominated by wind waves in winter and swell waves in summer, and the average period swell waves are about 3.0 s higher than wind waves in all seasons.

Paraffin waxes have great application potential in the field of low-temperature solar energy storage technology due to their wide phase transition temperature range, moderate latent heat and good chemical compatibility. In order to enhance the heat transfer/storage performance of paraffin wax, this paper gives thermophysical property enhancement study of nanocomposite phase change materials NCPCMs） dispersed by Al₂O₃, TiO₂ nanoparticles, carbon nano-fiber （CNF）, Al₂O₃+TiO₂ and Al₂O₃+CNF in paraffin. The effects of adding mono and hybrid particles on the thermophysical properties of paraffin are experimentally investigated. The samples of NCPCMs with different concentrations are successfully prepared with Span80 as surfactant. The differential scanning calorimeter and transient hot-wire method are used to measure the thermophysical properties of the samples. A scanning electron microscopy and a Fourier transform infrared spectroscopy are used to characterize the surface morphologies and chemical structures of the prepared samples. The results show that Al₂O_3, TiO₂ and CNF nanoparticles have uniformly dispersed in both mono and hybrid NCPCMs without any chemical reaction. The latent heat of paraffin decreases with the addition of Al₂O₃, TiO₂ and CNF nanoparticles in both mono and hybrid NCPCMs slightly. Compared with pure paraffin, the thermal conductivity of Al₂O₃+CNF composite with mass fraction of 1.0% increased by 36.79%, and the specific heat of liquid phase increased by 32.07%. As a conclusion, the hybrid nanoparticles of Al₂O₃+CNF shows significant potential for thermophysical property enhancement of phase change materials.

China’s hydrogen energy industry is currently in its initial stages, characterized by a weak foundation and inadequate coordination between supply and demand. Advancing the high-quality development of the hydrogen energy sector has emerged as a critical issue within the energy reform landscape. To address the challenges of supply and demand in the hydrogen energy sector, a system dynamics model was formulated to simulate China’s hydrogen energy development trajectory from 2021 to 2035. By leveraging this simulation, the study delved into the ramifications of three regulatory scenarios—namely, promoting hydrogen production from renewable sources for local consumption, accelerating the adoption of hydrogen fuel cell vehicles, and fostering technological advancements—on the evolution of the hydrogen energy industry in China. This research culminates in targeted policy recommendations aimed at propelling the high-quality advancement of China’s burgeoning emerging hydrogen energy industry.

To ensure braking safety and effectively improve energy utilization efficiency, a game based collaborative control method for hydrogen storage system in electric vehicle regenerative anti-lock braking is proposed. Firstly, construct the road surface model, wheel model, and braking system model of the electric vehicle to provide input for the subsequent allocation of slip ratio. Secondly, it is necessary to identify the driver’s braking intention, obtain the actual braking intensity requirements of the driver in different driving states, and obtain the feedback braking torque of the motor; Finally, based on the slip ratio and the regenerative braking torque of the motor, a game based collaborative control strategy for the hydrogen storage system in the regenerative anti-lock braking system of electric vehicles is constructed. The game is used to allocate the gas supply of the hydrogen storage system for regenerative braking and hydraulic braking, optimize the hydrogen release process, and achieve game based collaborative control of the hydrogen storage system. The research results indicate that the proposed method has high energy recovery rate, braking stability, and robustness, which can provide important technical support for the development of electric vehicles.

In order to explore the phenomena and characteristics of hydrogen leakage and diffusion during the operation of hydrogen refuelling stations, a full-scale high-pressure hydrogen leakage test facility was established based on a real hydrogen refuelling station. Using a vehicle mounted high-pressure hydrogen storage tank as a high-pressure hydrogen gas source, the testing system was controlled through a combination of different valves in the pipeline and an instrument control system to provide hydrogen for the test section. The concentration distribution after hydrogen leakage was analyzed by changing different leakage pressures. According to the experimental results, the vibration sensors are effective and feasible in monitoring hydrogen leakage. The use of hydrogen concentration sensors for measurement is easily affected by environmental factors such as wind direction, and the use of sound or acceleration sensors can be considered. No spontaneous combustion was detected under the condition of 35 MPa hydrogen leakage.

A three-dimensional model of high-power fuel cell stacks is developed in this paper to numerically investigate the effects of the length （L） and width （D） of the common channel, channel area, and the inlet-outlet area ratio on gas flow distribution of the stacks. The results indicate that increasing the length and width of the common channel contributes to improving the uniformity of gas distribution. increasing the area of the common channel also significantly improves the flow distribution uniformity of the fuel cell stack. On the other hand, decreasing the area ratio between the inlet and outlet is conducive to reducing the pressure difference between the inlet and outlet common channels, which improves the flow distribution of the stack and reduces the pressure drop of the stack.

A dynamic cold start model for single-channel proton exchange membrane fuel cell （PEMFC） is established, and its feasibility is verified through experiments. A cold start control strategy based on adjusting the output characteristics is proposed, and the cold start performance of single-channel PEMFCs under this strategy is analyzed. Furthermore, a PEMFC stack model is established based on single cells, and the consistency of PEMFC stacks is investigated. The research results demonstrate that at 243.15 K, the proposed strategy can achieve successful cold start of single-channel PEMFCs within 30 s, while at 248.15 K, successful cold start of PEMFC stacks can be achieved within 25 s. During the cold start process, differences in temperature, icing conditions, and output voltage are observed among individual cells in the PEMFC stack. The output characteristics of single cells at both ends determine the cold start result of the stack.

In order to prolong the service life of electrolyzers, the control strategy of electrolyzer array in new energy hydrogen production system is studied in this paper. Firstly, the load characteristics of hydrogen production electrolyzers are modeled and the electrical related characteristics are obtained. Secondly, the traditional array rotation strategy is analyzed, and the shortcomings of the traditional strategy are summarized, and the improvement methods are analyzed. Then, aiming at the large-scale new energy hydrogen production system, an optimized rotation strategy of electrolyzer array based on double-layer control architecture is proposed. Finally, the simulation results show that the proposed strategy can effectively balance the load operation state and prolong the service life of the electrolyzer.

To address the issue that most heuristic algorithms are prone to converge prematurely when facing the voltage model parameter identification problem of proton exchange membrane fuel cell （PEMFC）, resulting in low parameter identification accuracy, this paper proposes an improved mayfly algorithm based on chaos mapping and adaptive Levy flight （LLIMA）. Firstly, the logistic equation is introduced to generate chaotic sequences, which are then mapped to the problem space to enhance the exploration capability of population initialization. Secondly, the adaptive Levy flight algorithm is added to the velocity update of the mayfly, which helps the algorithm escape from the local optimum by utilizing the property of Levy flight of large probability with small step size and small probability with large step size. Additionally, the inclusion of an adaptive strategy dynamically adjusts the mayfly velocities, further shortening the optimization time of the algorithm. Finally, the effectiveness of the algorithm is verified by the optimization results of four test functions in two different dimensions. Applying the LLIMA algorithm to parameter identification of the SR-12 fuel cell voltage model demonstrates that compared to the mayfly algorithm, improved mayfly algorithm, and particle swarm algorithm, the proposed LLIMA algorithm achieves higher identification accuracy, faster convergence speed, and stronger robustness for experimental data both with and without added white noise.

This study investigates the coupling mechanisms between the inerting flow field structure and gas mixing characteristics during the gas injection stage in high-pressure hydrogen fuel cylinders. Numerical simulations are conducted to evaluate the variations of oxygen volume fraction throughout the inerting process. Results show that the maximum oxygen volume fraction exhibits a two-stage decreasing trend, characterized by a rapid decline in the first stage and a slower reduction in the second. To elucidate the cause of this behavior, the coupling mechanisms among the velocity, pressure, and oxygen volume fraction fields are examined. The findings indicate that decreasing inlet velocity, along with increased pressure and density, reduces inerting efficiency and weakens convective mass transfer, resulting in the observed two-stage reduction. Furthermore, a non-uniform distribution of oxygen volume fraction is found to promote the rapid decrease, with the maximum value dropping at a rate 8.32 to 14.40 times faster in the first stage than in the second.

The flow field structure at the anode side of the proton exchange membrane （PEM） electrolyzer has an important influence on the efficiency of hydrogen production from electrolyzed water. Therefore, it is necessary to analyze the physical field distribution inside the electrolyzer in depth according to electrochemical and computational fluid dynamics theories, and further optimize the flow field structure of the PEM electrolyzer. The paper firstly establishes a three-dimensional model of the multi-serpentine flow field structure of PEM electrolyzer through Comsol Multiphysics simulation software, analyzing how the internal electric and flow fields affect electrolytic performance. Secondly, based on the results of simulation analysis, three shunt improvement structures, namely, double shunt, quadruple shunt and quintuple shunt, are proposed and analyzed by numerical simulation for the three structures. The simulation results show that the quadruple shunt structure has the best performance, with a decrease in oxygen accumulation of about 3%, an increase in gas discharge efficiency of 22%, and a decrease in overall cell voltage of about 0.0173 V compared to the original multi-serpentine flow field. On this basis, a PEM electrolyzer experimental platform with a quadruple structure was built. The experiment proves that the electrolyzer voltage is reduced by about 0.0136 V and the energy consumption is reduced, which is basically consistent with the simulation results.

The silane fluidized bed method utilized in the preparation of granular polysilicon tends to exhibit higher hydrogen impurity content compared to rod silicon by the modified Siemens method, resulting in the occurrence of the “hydrogen jump” phenomenon during the producing of the Czochralski monocrystalline silicon. This phenomenon, in turn, imposes negative impacts on both equipment and products. Both production lines and researchers are constantly searching for suitable methods to remove hydrogen impurities from granular polysilicon to avoid the occurrence of “hydrogen jump” phenomenon, but there are currently few reports on such research. This paper analyzes the potential formation processes of hydrogen impurities in granular polysilicon. It also illustrates the impacts of hydrogen impurities in granular polysilicon on the Czochralski monocrystalline silicon, as well as the corresponding control measures for these impacts. Furthermore, it discusses the challenges and issues of removing hydrogen impurities from granular polysilicon. Finally, recommendations are made for future research directions on hydrogen impurities in granular polysilicon.

In response to the problems of the traditional parameter identification approaches of solar cell model, such as complex structure, low identification accuracy, weak robustness and so on, a parameter identification method for the equivalent model of solar cell based on improved tree seed algorithm （ITSA） is proposed. A variable adapting gradually with the number of iterations is introduced, which significantly enhances the capability of local optimal convergence and global searching. An adaptive step factor is introduced to replace the random step factor for improving the optimum speed and reducing the optimization time in later stages of the algorithm. Applied to the parameter identification of the double-diode model,the root mean square error of ITSA is smaller than that of other algorithms and the fitting accuracy of identification results is high with the measured data, which shows that ITSA can effectively identify the parameters of solar cell model. The algorithm has high accuraty and convergence, making it practical for engineering applications.

This paper investigates the effect of the gettering process on the performance enhancement of HJT solar cells. Firstly, the performance difference of silicon wafers at different positions of the silicon rods was analyzed, and it was found that the distribution of C and O concentration was correlated with the changes in the minority lifetime. Secondly, the effect of gettering process on silicon wafers was investigated, and it was found that the concentration of metal impurities in the bulk of wafers was decreased after gettering, which leads to the enhancement of the minority lifetime of the wafers and IV characteriftics. Further more, different gettering process conditions on the solar cells’ performance were investigated, and the results showed minimal differences among these conditions in terms of solar cell performance. Finally, the efficiency difference of different minority lifetime ranges before and after gettering was studied, and the lower limit of the minority lifetime of silicon wafers was explored. The study shows that the gettering process could reduce the quality and cost requirements of raw silicon wafers for gettering process can decrease the concentration of metal impurities and SRH recombination, which is conducive to the efficiency stability of the production line and a very suitable cost down process and an effective efficiency enhancement method for the mass production of HJT high-efficiency solar cells.

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

To enhance the converter’s gain effectively, a novel voltage doubling unit is introduced, which combines the coupling inductance with the switching capacitor. This unit not only amplifies the gain but also suppresses the current of the switching capacitor and clamps the coupling inductance, thereby facilitating soft switching of some diodes. A dual-switching-tube quadratic structure is developed to ensure continuous input current while significantly reducing the voltage stress on the switching tubes. The article thoroughly examines the operational modes and working principles of the proposed converter under various conditions and presents the voltage and current waveforms of the device. Compared with converters of the same type, the high gain and low stress characteristics of the proposed converter are prominently exhibited. Experimental validation of the theoretical derivation and analysis is further substantiated through the construction of a 200 W prototype.

To solve the problem of reduced power generation efficiency caused by dust accumulation on the surface of photovoltaic modules, a photovoltaic module anhydrous dust removal method based on electrostatic adsorption mechanism is proposed. The dust removal mechanism, charging characteristics of dust particles, and structural design of the dust removal device were first presented.COMSOL Multiphysics software was used to analyze the electric field and potential distribution in the dust removal area, as well as the movement of dust particles with different particle sizes in the dust removal area. A photovoltaic module electrostatic adsorption dust removal experimental platform was built for dust removal experiments. The results show that larger dust particles are easier to remove, and the highest electrostatic dust removal efficiency can reach 99.56%. After dust removal, the power generation efficiency of photovoltaic modules can reach 97.73% of the surface dust-free state. The proposed method can achieve effective and efficient dust removal on the surface of photovoltaic modules.

An effective full open-circuit zero-vector modulation strategy for suppressing common-mode voltage is proposed for H8 inverter. By deriving the common-mode voltage time-domain model of H8 inverter under the effective full open-circuit zero vector, the internal relationship between the common-mode voltage and the junction capacitances of DC bus S₇ and S₈ and the parasitic capacitance of the ac output side to the ground is studied. The variation trends of common-mode voltage and system efficiency with additional parasitic capacitance value is quantitatively analyzed. In order to take into account the common-mode voltage suppression effect and system efficiency, the additional parallel capacitances selected by S₇ and S₈ is approximately 30 times the value of the parasitic capacitance, and the common-mode voltage under the effective full open-circuit zero vector is reduced to 0. The system efficiency reaches 98%, while avoiding the common-mode voltage spike caused by the dead-time effect. Finally, the effectiveness of the above method is verified by experiments.

In order to effectively evaluate the distributed photovoltaic hosting capacity of distribution network, a comprehensive evaluation method of distributed photovoltaic hosting capacity of distribution network based on stochastic robust optimization is proposed. Firstly, the K-means clustering method is used to cluster the load levels of the distribution network. Then, a stochastic optimization algorithm is used to deal with the load level uncertainties. Aiming at the uncertainties of photovoltaic output and the limitation of traditional box uncertainty set, a multi-interval uncertainty set is proposed to describe the fluctuation of distributed photovoltaic output. Then, considering the security, adequacy and economy of the power grid, a comprehensive evaluation model of distributed photovoltaic hosting capacity of distribution network based on stochastic robust optimization is established. Finally, the simulation verification of 10 kV distribution system in a certain area shows that this method can comprehensively and reasonably analyze the hosting capacity of distributed photovoltaic in distribution network and improve the accuracy of evaluation.

In response to the voltage drops caused by short-circuit faults in the power grid, which can lead to power-angle instability and overcurrent phenomena in inverters, this paper proposes an active support low-voltage ride-through（LVRT）approach for photovoltaic （PV） units to traverse low voltage based on long short-term memory networks（LSTM） optimized virtual impedance. Firstly, a grid-forming active support control strategy for PV arrays is proposed by consider the output characteristics of the PV array. Secondly, transient voltage regulation equations are introduced based on traditional virtual synchronous generator（VSG） technology to construct a third-order synchronous generator model. Then, a fault discriminator is used to determine the operating state of the system and determine the switching time of the virtual impedance. The magnitude of the virtual impedance is determined by combining the relationship between the virtual impedance and the grid voltage of the PV unit using an LSTM-based prediction method. Finally, a PV grid-connected system is built on the DIgSILENT/PowerFactory power system simulation platform for validation. The superiority of the proposed method is demonstrated through comparative analyses.

In distribution networks with high penetration of distributed photovoltaic sources, significant bus voltage fluctuations and even voltage overruns can result from direct alterations in the network’s physical structure. A voltage regulation control strategy for distribution network based on distributed reconfigurable model predictive control is proposed to solve this problem. The power stations incorporate the states of their neighbors into their own optimization problems by exchanging their reference trajectories with each other. Moreover, three distinct reconfiguration strategies are devised to adapt to changes in physical network topology stemming from maintenance or adverse weather conditions, ensuring stable node voltages. Concurrently, switching conditions are formulated to determine the feasibility of power station reconfiguration. Should reconfiguration not be viable, the corresponding control strategy is computed, reference trajectories are updated, and topology adjustments can be made only if the conditions are met. Finally, simulations are conducted using Matlab/Matpower to verify the effectiveness of the proposed model and algorithms in reducing voltage fluctuations and enhancing power quality during distribution network reconfiguration.

Aiming at the problem that the infrared photovoltaic module defect detection model is difficult to deploy to edge devices due to the increasing number of parameters and computational complexity of object detection model, a defect detection algorithm T-DINO （Tiny DINO） based on model compression is proposed. Using ResNet-101 as the teacher network and ResNet-18 as the student network, a dynamic adaptive distillation method is proposed. The difference in attention weights between the two is used for efficient knowledge transfer in feature-based distillation, and it is also used as guiding knowledge for distillation of the student network in output response （logit） distillation. Consequently, the complexity of the model and the number of parameters are greatly reduced with minimal accuracy loss. At the same time, the fusion module CSF Block （Conv-Self Attention Block） is proposed to model local features and global features to improve the detection accuracy. Experiments on the self-constructed infrared PV module fault dataset showed a 77.3% reduction in the number of parameters, a 69.3% reduction in computational complexity and a 5.2% increase in AP50 compared to the baseline network DINO （ResNet-101） model. The simulation results show that the compressed model is suitable for deployment in edge equipment and can meet the requirements of actual infrared photovoltaic module defect detection.

Aiming at the problems of low accuracy, poor model performance and low utilization of photovoltaic （PV） I-V curve data in traditional PV array fault diagnosis methods, this study proposes a PV array fault diagnosis model based on HPO-CatBoost. Firstly, the PV array model is used to deeply study the effects of short circuit, open circuit, aging, shading and environmental factors （temperature, irradiance） on the changes of I-V curves and systematically analyze their output characteristics and fault causes. Secondly, the problem of prediction bias due to target leakage in CatBoost is solved by Ordered TS coding to improve the generalization ability of the diagnostic model. Finally, the performance of CatBoost model is affected by some hyperparameters, so it is proposed to use hunter-prey optimizer （HPO） to optimize the key hyperparameters of the model （number of trees, tree depth and learning rate, etc.） to further improve its performance in fault diagnosis, and analyze the operation results and the experimental data of the actual PV platform. The experimental results show that the diagnostic accuracy of the model is 99.5%, and the overall accuracy of the model is improved by 3.4% compared to the CatBoost model before optimization..

This paper enhances DC-DC converter voltage gain by integrating two buck-boost converters with coupling inductors and voltage-multiplier circuits, while clamping circuits absorb leakage inductance to form a dual-coupled interleaved topology. The design reduces power switch voltage stress and avoids extreme duty-cycle/turn-ratio operation. Detailed continuous conduction mode （CCM） analysis with theoretical stability criteria derivation and a 250 W experimental prototype collectively validate the proposed methodology.

To solve the issues of input data redundancy and low prediction accuracy of single models in current photovoltaic power forecasting, a short-term PV power prediction model based on seasonal random forests （RF） feature extraction based on temporal convolutional network （TCN）, bidirectional gated recurrent unit network （BiGRU） and scaled-dot product attention mechanism （SDA） was constructed. Firstly, RF is employed to evaluate the contribution of each meteorological feature to power generation to select key features. Then, the key meteorological features and raw power data are imput into the TCN-BiGRU model combined with SDA mechanism. Finally, the proposed combination model is verified according to a practical example. The results demonstrate better prediction accuracy compared to other existing models.

The data of PV short-term power generation has high dimension and complex features. The key factors affecting the forecasting performance include the decomposition , extraction of data features and the construction of prediction model. In this paper, an improved PV short-term prediction method embedded gorilla parameter optimization algorithm is proposed. In the first layer, variational mode decomposition （VMD） and T-distributed stochastic neighbor（TSNE） embedding are adopted for the feature extraction, which are combined to obtain effective features of the photovoltaic data. VMD involves the selection of two key parameters, namely penalty factor and decomposition mode number. In this paper, the gorilla optimization algorithm is used to optimize these parameters, denoted as GVMD.The second layer is to construct the prediction model. The TCN-LSTM prediction model is built by combining the temporal convolutional neural network （TCN） and the long short-term memory network （LSTM）, and the learning, superposition and reconstruction of various features are completed.On this basis, the incremental learning method, denoted as GVMD-TSNE-TCN-LSTM^re is used to continuously modify the prediction model by the design of parameter freezing and full-connection layer update. Finally, the power data of a photovoltaic field in Gansu Province was simulated to verify the necessity of GVMD-TNSE data processing, the timeliness of GTO parameter optimization algorithm, and the effectiveness of the overall model.

Taking the 5×3 PV arrays of State Power Investment Group Flexible PV Demonstration Base in Yancheng, Jiangsu as the research object, synchronous rigid body pressure wind tunnel tests were designed and carried out on the upper and lower surfaces. The three-dimensional spatial correlation of fluctuating wind load was analyzed, the edge distribution model and joint probability distribution model of fluctuating wind load on the upper and lower surfaces of PV arrays were established based on Copula theory. Research has shown that under the most unfavorable wind direction angles of 0 ° and 180 °, the maximum overall fluctuating wind load of PV arrays occurs at the windward leading edge of row #3. The overall fluctuating wind load on the upper and lower edges of PV panels is controlled by the upper and lower surfaces respectively; Taking the kernel density function as the edge distribution model, Frank Copula and Gumbel Copula functions are the optimal functions for constructing the joint probability distribution models of “overall- upper/lower surfaces” fluctuating wind load on the upper/lower edge of PV arrays respectively; The proposed joint probability distribution models can accurately describe the binary distribution characteristics of fluctuating wind load in PV arrays, and achieve effective prediction of overall fluctuating wind load based on single surfaces of PV panels.

Focusing on the problem that the current identification methods rely on the transient data under the large disturbance of the system, which cannot meet the online analysis requirements of the new power system, an online parameter estimation method for distributed photovoltaic composite load model based on deep reinforcement learning algorithm TD3 is proposed in this paper. The model is simplified based on the mechanism, and the global sensitivity of the parameters is calculated under random small disturbance, based on which the discernible parameters that have great influence on the dynamic characteristics of load model are selected. Using the difference between dynamic response with different parameters under small disturbance, the TD3 algorithm is used to identify the model parameters, the interface function between the algorithm and the simulation system is constructed, and the training scheme that can make the algorithm meet the needs of multi-scene identification is designed. Finally, the feasibility of the proposed method is verified in various random small disturbance scenarios based on EPRI-36 node system in PSASP.

In order to study the operational performance of the trapezoidal cross section micro heat pipe photovoltaic-thermal integrated module (MHP-PV/T) in series during winter, six sets of MHP-PV/T modules with trapezoidal cross section are selected for experimental research. The photovoltaic, photothermal, and comprehensive performances of the system are analyzed. The experimental conclusions show that under the conditions of an average solar irradiance of 692.4 W/m² and an outdoor ambient temperature of 3.3 ℃, the average photovoltaic power of six groups of trapezoidal overcurrent cross sections MHP-PV/T in series is 719.5 W, photovoltaic efficiency is 10.8%, power generation is 5.76 kW·h, photothermal power is 2088.3 W, photothermal efficiency is 24.0%, effective cumulative heat is 9.40 kW·h, total energy power is 2988.0 W, and total energy efficiency is 35.8%. The research results indicate that the series-connected MHP-PV/T modules with trapezoidal cross-section has good photovoltaic and photothermal performance, which provides reference value for the further development and application of MHP-PV/T.

To improve the freshwater yield of the basin solar still, a novel solar still enhanced by membrane distillation was proposed by installing an air-gap membrane module vertically inside the solar still, and its technical feasibility was experimental verified. Tests have shown that it is feasible to install membrane modules vertically to increase water production in the solar still. The hourly water production per unit area of membrane distillation is at least 3.7 times that of the basin distillation, and the addition of the membrane module can increase the water production during the periods of low productivity, and realize continuous water production day and night. The total daily water production of the membrane distillation enhanced basin solar still for one day of operation was 1,528 g and the actual efficiency was 21.5%, which was 11.6% and 11.4% higher than that of the conventional solar still obtained from the simultaneous tests, respectively.

A three-dimensional transient model of pit thermal energy storage （PTES） with an inverted pyramid shape is established and verified using measured results from a PTES system in Langkazi County, Tibet. The influence of geometric shape on thermal performance of PTES during the standby period is studied. The results indicate that with a PTES height of 10 m, the natural convection heat transfer intensity at the top and side of PTES decreases with an increase in the slope angle, while the natural convection heat transfer intensity at the bottom increases. For the height of PTES less than or equal to 10 m, the total heat loss of PTES decreases with the increase in the slope angle. The optimal heat storage efficiencies of PTES is achieved when the slope angles are 30°, 60°, and 90°, and the heights are 12 m, 10 m, and 10 m, respectively. After a standby period for 60 hours, with a PTES height of 10 m and a slope angle of 60°, the highest heat storage efficiency is achieved.

In order to improve the efficiency of Organic Rankine Cycle （ORC） system and reduce the pollution to the environment, this paper improves the conventional CO₂ transcritical Rankine cycle driven by solar energy. A two-stage compression with intermediate reheating is used to reduce the compressor power consumption and improve the system efficiency. Thermodynamic, economic and environmental models were established. The effects of the turbine inlet temperature, flow rate of heat transfer oil and shunt ratio on the thermodynamic-economic-environmental performance of a 900 kW class CO₂ Rankine cycle solar power system were investigated. Multi-objective optimization was carried out using the non-dominated sequential genetic algorithm （NSGA-Ⅱ）. The results show that the turbine inlet temperature has the greatest influence on thermal efficiency, and the shunt ratio and heat transfer oil flow rate have a greater influence on LCOE and CO₂ emission reduction, respectively. The Pareto optimal solutions are obtained by multi-objective optimization. The optimized results indicate that the thermal efficiency of the system is 21.76%, the levelized cost of electricity（LCOE） is 0.12 $/kWh, and the reduction of CO₂ emission is 9, 458 tons at turbine inlet temperature of 206 ℃, flow rate of heat transfer oil of 14.75 kg/s, and shunt ratio of 0.1. The study results can provide some theoretical guidance for the improvement and practical application of medium-and low-temperature photothermal power generation technology in China.

To address heating challenges in rural residential buildings in Northwest China, a solar-air source heat pump （SASHP） heating system is being implemented. Using a specific rural residential building in Northwest China as an example, a novel flexible temperature control strategy based on sub-area and time-period segmentation is proposed, and the heating system is optimized using three objective functions. The results indicate that compared to the “constant temperature control strategy”, the proposed control method maintains an average temperature of 16.95 ℃ throughout the heating season, with a maximum temperature of 18.87 ℃ and a solar fraction of 25.11%, resulting in a smoother temperature curve. The life cycle cost optimization scheme yields greater economic benefits, while the target building unit heating cost optimization scheme achieves the shortest static payback period and lowest unit heating cost. Conversely, the solar fraction optimization scheme stands out for its superior environmental benefits. These findings offer valuable insights for the design of heating systems tailored to diverse objectives.

The design of an ultra-thin permeable solar air collector is based on the principle of enhanced heat transfer through jet impingement. The flow field distribution and thermal efficiency of the collector are investigated through numerical simulation under various aperture sizes and hole spacings. The findings demonstrate that the hole distribution significantly not only influences the flow characteristics at the intersection of microjet and cross-flow mainstream in the collector, but also determines the uniformity of internal temperature distribution and heat collection efficiency. The thermal performance of the collector is influenced by both the aperture and the spacing, which exhibit interactive effects. When the aperture is 1 mm and the spacing is 10 mm, the heat collection efficiency is 89.20%, the thermal hydraulic performance parameter is 15.6, and the comprehensive performance is the best. The collector can replace the decorative layer of the building facade, realize the building thermal insulation and heat insulation, and also provide a novel approach for distributed solar agricultural drying.

The calcium looping (CaL) system demonstrates significant potential for use in concentrated solar power generation. This potential is attributed to its high safety, low-cost, high-energy storage efficiency, and wide operating temperature range. The review summarizes recent domestic and international research progress and highlights that calcium-based heat storage agents currently face challenges such as poor cyclic stability, inadequate light absorption, and high wear during cyclic reactions. Additionally, the review anticipates future research directions, including in-depth studies of material microstructure, designing more realistic cyclic reactors, and considering the integrality, technology, and economy of the preparation process.

The uncertainty in solar radiation leads to obvious randomness and instability in solar power generation. To address this issue, this paper proposes a VMD-T2V-Transformer model for solar radiation prediction by integrating variational mode decomposition （VMD）, Time2Vec （T2V） and Transformer. First, the VMD is used to decompose the solar radiation sequence into several sub-sequences. Next, the T2V is adopted to embed the temporal features of each decomposed sub-sequence. Then, a Transformer prediction model is established for each sub-sequence based on the embedded time features. Finally, the predicted results of all sub-models are superimposed to obtain the final predicted values. The experimental results show that the proposed model in this paper outperforms other mainstream models in terms of RMSE and MAE, which can be reduced by at least 13.81％ and 16.44％ respectively.