In view of the decline of frequency stability in the power grid caused by the shortage of frequency regulation resource, this paper takes electrolytic water hydrogen production system as the research object, through establishing a refined dynamic model and the targeted control strategy, so that electrolytic water hydrogen production system can provide fast frequency response, which fully exploits the frequency regulation potential of electrical, hydrogen and heat energy within the electrolyzers system. In addition, the mechanism and ability of the fast frequency response provided by the electrolyzers are analyzed. The results show that the established model can reveal the electric-hydrogen-thermal coupling mechanism and characterize the frequency response characteristics of the electrolyzers system during the fast frequency response. The fast frequency response provided by the electrolyzers system can improve the frequency stability of the power grid under multiple disturbance scenarios.

With the development of new energy and power electronics, new power systems exhibit typical "dual high" characteristics, but at the same time, they also bring problems such as low inertia and weak stability. Grid forming control technology （GFM） can enable the inverter to independently establish frequency and control voltage, improve dependence on external voltage sources, and improve system stability. The current situation and development trend of grid type converters are summarized. Firstly, a comparative analysis was conducted between grid following control technology （GFL） and grid building control technology, and it was pointed out that grid building control technology is an effective way to solve the stability of new power systems. Then, the existing grid control technology was introduced, and the application of grid control technology in various fields were analyzed. Finally, the challenges faced when applying grid control technology to converters were pointed out.

Solar interface evaporation is a process of converting solar energy into heat energy and using materials with photothermal conversion properties to locally heat water at the interface to promote evaporation, which has broad application prospects in the field of seawater desalination. To gain a deeper understanding of the development and application of solar interface evaporation seawater desalination technology, this paper introduces the research progress of solar interface evaporation from the perspectives of light absorption and thermal management, and discusses the development direction and application prospects of interface evaporation seawater desalination technology. The paper summarizes the influence of different categories and structures of photothermal conversion materials on solar conversion efficiency, and summarizes the influence of different forms of heat loss on interface evaporation rate. The challenges and future development of solar interface evaporation seawater desalination technology are discussed, with the aim of providing reference for the practical application of subsequent seawater desalination technology.

Aiming at the problem of DC bus voltage fluctuation caused by the instability of input and output power of distributed power supply in DC microgrid, and considering the characteristics of dual active bridge （DAB） energy storage converter with bidirectional energy flow, a dual closed-loop model predictive control strategy based on internal model principle is proposed in this paper. Based on the analysis of extended phase-shifting modulation principle and characteristics of the DAB converter, external voltage closed-loop model predictive control strategy and internal current closed-loop model predictive control strategy are emphatically studied. In order to solve the problem that the performance of model predictive control depends on the accuracy of circuit parameters, the sensitivity of model parameters is analyzed from two aspects： the influence of the mismatch of circuit main parameters on power transmission and DC-bus voltage stabilization. Finally, the steady-state error sensitivity area of DC-bus voltage is set. Outside the error sensitivity area, only double closed-loop model predictive control is used to ensure the dynamic response performance of the system. Within the error sensitivity area, double closed-loop model predictive control and PI compensation control is used to eliminate the steady-state error of the system. An experimental platform is built. The experimental results show that when the system is disturbed, the charging and discharging modes of the DAB energy storage converter can achieve smooth switching, and the hybrid control strategy can quickly stabilize the DC-bus voltage, achieve system power balance, and enhance the system anti-interference ability.

In the paper, the present situation of the development of ocean tidal current energy power generation technology and equipment in China is summarized, and compared with the international leading level. Furthermore, our advanced aspects and shortcomings are pointed out. At the same time, the opportunities come from China’s "ocean power" strategy and "carbon peaking and carbon neutrality" strategic objective and the following challenges are analyzed. Moreover, suggestions and opinions on the future development direction of China’s ocean tidal current energy power generation technology and equipment are put forward.

Relying on the existing engineering experience data or the short-term heat transfer test results of middle and deep borehole heat exchanger, an extensive method is typically used to design the middle and deep borehole geothermal heat pump heating system. It leads to the unreasonable configuration of the borehole heat exchange system and the reduction of the reliability and economy of heat pump heating system. In order to improve the design status of the middle and deep borehole heat exchange system, a dynamic simulation method is adopted in this paper, considering the design load, cumulative load and hourly load distribution law, to analyze the unsteady characteristics of deep-buried geothermal resources in the long-term dimension and the temperature-self-recovery performance of rock and soil, and determine the designed water supply and return temperature at the source side based on the "annual periodic cooling and heat balance of final state" of underground rock and soil. And optimization method of source-side design flowrate based on the optimization of the average annual life-cycle cost on the ground source side is proposed. Taking an engineering project as an example, the optimization design of source-side parameter is given.

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

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

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

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

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

Phase change heat storage technology is one of the efficient means of heat storage in engineering applications. However, phase change materials have the problem of low thermal conductivity, which seriously limits their work efficiency. Therefore, it is necessary to optimize the structure of phase change heat storage devices to improve heat storage efficiency. Na₂S₂O₃·5H₂O and CH₃COONa·3H₂O were selected as phase change materials. Six phase change heat storage models were designed based on bionic structure. The melting and solidification processes of the two phase change materials in the six heat storage models were simulated and analyzed. Finally, based on the numerical simulation results and taking into account the heat storage and release processes, a small biomimetic cross seasonal phase change heat storage experimental platform was built. This article presents temperature changes of two phase change materials at each temperature measuring point in two models under the operating conditions of 75 ℃ and 0.1 m³/h. The results indicate that one heat storage model is more suitable for using Na₂S₂O₃·5H₂O as phase change material, while the other model is more suitable for using CH₃COONa·3H₂O as phase change material.

In order to improve the grid-connected active power （GCAP） response speed of Virtual Synchronous Generator （VSG） under weak power grid, an optimization strategy of GCAP dynamic response for VSG based on the virtual negative impedance combined with the active power transient damping control algorithm is proposed in this paper. The optimization strategy firstly uses the virtual negative impedance control link to reduce the VSG equivalent output impedance as well as the GCAP dynamic response time of VSG. Then, the transient damping of VSG and the inhibition ability of GCAP dynamic oscillation are enhanced by the active power transient damping control link. In addition, the Matlab/Simulink simulation software is used to study the GCAP dynamic response performance of VSG in the condition of active power command step, and the experimental test platform of VSG grid-connected system is established. Finally, the simulation and experimental results verify the feasibility and superiority of the proposed strategy in improving the GCAP dynamic response characteristics of VSG under a weak grid.

A comprehensive review was conducted on the technology of hydrogen production from biomass thermochemical conversion. The review included an analysis of the influencing factors of hydrogen production through pyrolysis and reforming, gasification, and supercritical water gasification. These factors comprised raw materials, temperature, catalyst, etc., as well as the challenges faced, such as tar formation, catalyst deactivation, and input cost. It is suggested that the selection of hydrogen production methods should be based on the characteristics of the raw materials and the hydrogen production conditions. Subsequently, pretreatment and regulation of reaction conditions can be employed to enhance the efficiency of hydrogen production. A comparison of the three technologies indicates that gasification exhibits relatively higher efficiency. Additionally, gasification and pyrolysis are more suitable for biomass with low moisture content, while supercritical water gasification demonstrates advantages in the treatment of toxic wastewater. Moreover, future research directions encompass addressing tar formation, developing catalysts, and investigating equipment preparation.

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

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

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

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

The transient synchronization stability mechanism of the grid-forming converter and the grid-following converter is studied, and the simulation verification is completed based on the actual power grid scenario. Firstly, according to the control strategy of the nonsynchronous-machine sources, the key factors influencing its transient synchronization stability are analyzed. Then, the time domain simulation model of 500 kV equivalent AC system of Zhejiang Jinhua is built in PSCAD/EMTDC. Based on this model, the transient instability characteristics after short-circuit faults of the actual power gird with nonsynchronous-machine sources are studied, and the key factors influencing the transient stability are verified by simulation.

This paper analyzes the inefficiency mechanism of low-load alkaline water electrolyzers（AWEs）. It is found that through modifying the excitation electric field, the low-load performance of AWEs can be greatly enhanced. Based on this, a multi-modal self-optimization （MMSO） control strategy and the corresponding prototype converter are proposed. The effectiveness of the proposed method is verified by a 2 Nm³/h AWE （about 10 kW） directly driven by PV arrays. Experimental results show that compared to the conventional DC power supply, 1） the maximum efficiency improvement can exceed two times, 2） under the constraint of efficiency≥50%, the system operation is enhanced from 30%-100% to 10%-100% of rated load; 3） the AWE can follow the fluctuating PV power well.

Aiming at the problems of low accuracy and poor generalization of wind speed forecast due to randomness and volatility, a combined prediction model based on variational mode decomposition （VMD）, whale optimization algorithm （WOA）, long short-term memory neural network （LSTM） and sparrow search algorithm （SSA） was proposed. Firstly, WOA is used to automatically optimize the core parameters of VMD （K value and penalty coefficient α）. After decomposing the wind speed time series, SSA is introduced to optimize the core learning parameters of LSTM, and finally, the predicted wind speed data of each subcomponent is integrated to obtain the final predicted wind speed, which is verified by a number of model evaluation indicators. The RMSE, MAE, MAPE and R² of the model are 0.0758 m/s, 0.0578 m/s, 1.492% and 0.979, respectively. Compared with other single optimization prediction models WOA-VMD-LSTM and VMD-SSA-LSTM, the relevant evaluation indicators have significantly improved.

Hydrogen fuel cell rail vehicle is a new type of rail vehicle powered by hydrogen as fuel. Its characteristics of zero emission, low noise, and high efficiency have significant social and economic benefits, and it is also one of the important ways for rail transportation to save energy and reduce emissions. This paper expounds the working principle and system composition of hydrogen fuel cells, as well as the research and development progress and trends of hydrogen fuel cell systems. Systematically combs the research and application status of hydrogen fuel cell rail vehicles at home and abroad, mainly summarizes the differences in terms of vehicle type, fuel cell power, battery life, maximum operating speed, power mode, hydrogen storage capacity and pressure, and analyzes the output characteristics of hydrogen fuel cell hybrid power mode and power matching in various environments , confirming that the hybrid power system formed by hydrogen fuel cells and power batteries/super capacitors can better adapt to various working conditions such as start-stop, idling, continuous high speed and climbing of rail vehicles. Finally, the research difficulties of hydrogen fuel cells, the storage and transportation challenges of hydrogen, and the development potential of hydrogen fuel cell hybrid power in rail vehicles are discussed.

With the continuous promotion of carbon peaking and carbon neutrality goals in China, to promote the consumption of renewable energy and reduce the carbon emission of the system, this paper proposes a green certificate-carbon emissions trading interaction model based on cooperative game theory for an integrated energy system from the perspective of joint efforts of power generation, carbon sequestration, and the market mechanism. The simulation is carried out in an integrated energy system in eastern Inner Mongolia. In this model, the generation side realizes the flexible interaction between the carbon capture power plant and the renewable energy generation entity with the advantage of centralized dispatching of the integrated energy system （IES）; the carbon sequestration side realizes the low carbon economic operation of IES through carbon capture equipment and power-to-gas equipment （P2G）; the market side improves the integral economy of IES through the establishment of the green certificate-carbon emissions trading interaction mechanism, and based on the Shapley value to reasonably allocate the cooperation surplus. The results show that the proposed method can significantly reduce the system power curtailment, make full use of deep peak-regulation of thermal power units, realize the low-carbon economic operation of IES, and provide a theoretical reference for exploring the feasibility of cooperation between the existing thermal power and renewable energy projects, and exploring a new way of coal-fired power plant flexibility transformation.

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

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

The rational planning of hydrogen production and refueling stations （HPRS） is critical for the development of hydrogen fuel cell vehicles. This paper proposes a method for HPRS planning based on the coupling framework of transportation networks and power grids. The method focuses on site selection and capacity determination of HPRS within large-scale distributed renewable energy integrated power systems. Firstly, by simulating the driving patterns of hydrogen fuel vehicles using an urban road network impedance information model, the spatial-temporal distribution of hydrogen refueling load at the transportation network level is obtained. Then, a multi-objective evaluation-based model for site selection and capacity determination is constructed, taking into account factors such as construction and operation costs, hydrogen refueling load coverage of the transportation network, and power grid stability. The model utilizes an improved particle swarm optimization algorithm to solve for the optimal site selection and equipment capacity of HPRS. The effectiveness of the proposed method is verified through case studies on a 33-node power grid and a 25-node transportation network, demonstrating its ability to reduce power grid losses and voltage deviation caused by HPRS integration, enhance the self-consumption rate of renewable energy, improve the transportation network service quality of HPRS, and reduce construction and operation costs.

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

Aiming at the thermal spot detection of photovoltaic modules, an improved YOLOv5 based thermal spot detection algorithm was proposed. In this algorithm, Dilated block composed of cavity convolution is introduced to replace the original SPP block, which effectively reduces the heat spot information loss caused by pooling operation and improves the receptive field of the network. At the same time, the channel attention mechanism is added to the multi-scale detection process, which enhances the importance of the target region of hot spots and improves the detection performance of hot spots. A Lightweight block module composed of deep separable convolution is proposed, which effectively reduces the number of model parameters and improves the detection speed. The experimental results show that the improved YOLOv5 network can achieve fast and accurate detection of hot spots. Compared with the original algorithm, the A_P of this method is increased by 1.5% to 98.65%, and the detection speed is increased by 25% to 31 fps.

Aiming at the problem of strong randomness of photovoltaic power data and difficulty in accurate prediction, a combined prediction method based on temporal convolutional network （TCN）, bidirectional long short-term memory network （BiLSTM） and echo state network （ESN） is proposed. Firstly, the complete ensemble empirical mode decomposition with adaptive noise analysis （CEEMDAN） is used to decompose the power data into a series of relatively stable sub power subsequences. Then, the decomposed and reconstructed power sequence and other feature sequences are input into the TCN-BiLSTM Attention-ESN. TCN-BiLSTM Attention-ESN is applied to extract features and then spatiotemporal feature vectors are constructed. Finally, the extracted spatiotemporal feature vectors are input into ESN to obtain the prediction results. The proposed method is validated using photovoltaic power data from photovoltaic power stations in Xinjiang, China. The results showe that compared with current advanced prediction methods, the proposed method has higher prediction accuracy, which helps to increase the proportion of photovoltaic power generation and ensure the balance and operation safety of the power system.

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

This paper conducts an optimization design for off-grid PV-diesel-storage microgrid system in a commercial park in Wuhan based on HOMER software, China. Firstly, a capacity optimization planning model for the off-grid PV-diesel-storage microgrid system under multiple operation strategies is constructed. Based on this, the capacity planning of system, PV penetration ratio, and battery energy storage operational status under different operation strategies are analyzed, and the optimal operation strategy for the off-grid PV-diesel-storage microgrid system is derived. Then, the capacity optimization planning of the off-grid PV-diesel-storage microgrid under multiple scenarios is conducted based on the optimal operation strategy, and a comparative analysis of LCOE, NPC, and total emissions for the off-grid PV-diesel-storage microgrid under multiple scenarios is performed. Finally, the aout sensitivity analysis with specific cases re also carried. The results indicate that the proposed capacity planning model can achieve the best balance between economic and environmental performance of the system, and reduce the reliance on energy storage batteries. Additionally, the sensitivity analysis reveals the impact of different unit and load parameters on the optimal capacity planning.

In order to further reduce the carbon emissions level of the hydrogen integrated energy system （HIES） and improve the operational economy of the system, an optimal scheduling strategy of HIES under the integration mechanism of green certificate and carbon trading mechanism is proposed. Firstly, the composition and operation characteristics of HIES are analyzed, and two types of demand response （DR） models are established, namely, the day-ahead price-based model and the substitutable model; Secondly, a ladder green certificate trading model is constructed, and a green certificate-carbon trading integration mechanism is designed. A HIES optimization scheduling model is established with the goal of minimizing the comprehensive cost of energy purchase costs, carbon trading costs, and ladder green certificate trading profits; Finally, the effectiveness of the proposed strategy in this paper is verified through simulation cases, and the impact of the length of the ladder interval and the basis price on the scheduling results of green certificates and carbon trading are analyzed.

Taking a large-scale photovoltaic power station in alpine and high-altitude area with flat uniaxial photovoltaic racking system as the background, relying on ABAQUS finite element simulation software, we constructed an integrated three-dimensional finite element model of large-span flat uniaxial photovoltaic bracketing-foundation-panel, explored the loading characteristics of the key components of the flat uniaxial photovoltaic tracking system and the mechanism of instability deformation in an extreme environment, and provided the targeted optimization design suggestions. The results show that, under extreme wind load, the maximum stress of purlins in this large-span PV system reaches 544.4 MPa and the maximum displacement of columns reaches 76.3 mm at the maximum tracking angle, resulting in the potential risk of yield damage of local purlins and H-type steel piles; the deformations of superstructure members are related to their locations, and the overall performance shows that they are distributed in a wavy pattern along the long axis of the main girder, the mid-span position of the two columns also has a large displacement. The maximum deformation under wind load is even 153.2 mm, which is most obvious at the cantilever end of the PV module, and it is proposed to enhance the strength of the local components or change the local structural characteristics to enhance the overall stability of the large-span flat uniaxial tracking PV system.

The research status of photovoltaic-green roof was expounded from the following four aspects： 1） Thermal and humid environment and vegetation state on the roof; 2） Power generation efficiency of photovoltaic modules; 3） Water balance of roof; 4） Energy balance and thermal process model of roof. The results show that the interaction between PV modules and vegetation varies dynamically under different climate and spatial configuration conditions, which leads to uncertainties in the comprehensive performance of the roof. Finally, the engineering challenges faced in the application of photovoltaic-green roof and future research directions was analyzed and prospected. And it is pointed out that understanding and applying the interaction mechanism between photovoltaic modules and vegetation lays the foundation of accurate prediction and optimization of comprehensive performance of photovoltaic-green roof.

In order to improve the wind load research of photovoltaic bracket. First, the calculation principle of wind load of photovoltaic bracket of various standards and the value characteristics of related parameters were compared and analyzed. Second, the shape coefficients of the solar structure under the combination of typical inclination angles and wind directions were derived by performing a rigid model load measurement wind tunnel test on a fixed adjustable photovoltaic. Transient analysis was used to determine the photovoltaic bracket wind vibration coefficients under normal operating settings from the results of the wind tunnel tests. Finally, the wind load values and related parameters obtained by the test were compared with the reference values of each standards. The findings demonstrate that there are clear discrepancies between various standards and wind tunnel tests' wind pressure coefficients, wind vibration coefficients, and wind loads. The wind pressure coefficient of the wind tunnel test is substantially lower than the stated value of each standards, and the wind vibration coefficient result is higher than the domestic standard value of 1.0, more than 1.64-2.05 times, and there is also a considerable difference with foreign standards. In general, when a structure is upwind as opposed to leeward, the wind load and wind pressure coefficient are lower. The wind resistance design of photovoltaic bracket according to Chinese standards is radical, while the outcomes are conservative by foreign standards. More wind resistance studies are required in order to safely and rationally guide the wind resistance design of photovoltaic bracket structures because the wind load provisions in common standards are not reasonable.

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

Traditional photovoltaic （PV） prediction models are highly susceptible to fluctuations in meteorological data and exhibit low sensitivity to meteorological features. To address this, we propose a short-term PV output prediction method based on multi-level feature extraction using bi-directional long short-term memory （BiLSTM）, aimed at predicting PV output under various weather conditions. Firstly, meteorological factors with high correlation to PV output are selected as input features. The fuzzy C-means （FCM） clustering method is used for flexible sample division, and the Xie-Beni index is calculated to determine the optimal number of clusters, categorizing historical data into sunny, partly cloudy, cloudy, rainy, and severe weather conditions. Next, a multi-level feature extractor （MFE） comprising CNN-CBAM-TCN is constructed： convolutional neural networks （CNN） are employed for initial feature extraction, convolutional block attention module （CBAM） is used to suppress non-essential features, and temporal convolutional networks （TCN） are utilized to capture the temporal characteristics of intra-day PV output. Finally, BiLSTM is used for PV output prediction. Case studies validate the effectiveness of using the Xie-Beni index to determine the optimal number of clusters and demonstrate that this model achieves higher prediction accuracy compared to other prediction models under complex weather conditions.

To ensure the normal operation and structural safety of large-scale floating wind turbines under extreme sea conditions, extreme value estimation of structural responses is necessary to be carried out to assess the safety of structures. In this work, the OpenFast software was used to perform numerical simulations of the structural response of a 15 MW large-scale floating wind turbine in the South China Sea under a 50-year return period sea state. Combining with the Poisson approximation assumption, the average crossing rate method was employed to estimate the extreme distribution of tower base shear and bending moment for the large-scale floating wind turbine. The results show that under extreme conditions, the tower base shear force and bending moment will be significantly excited near the natural frequency of the tower's pitch motion, leading to a sharp increment of the dynamics responses. Moreover, the average upcrossing method can effectively estimate the extreme value distribution of the tower base structural response for a 15 MW floating wind turbine. Under an exceedance probability of 0.01, the estimated extreme values of tower base shear and bending moment based on 1-hour numerical simulation response samples satisfied the structural strength requirements.

An inertia evaluation method has been proposed that incorporates the rate of change of frequency（RoCoF）and the constraint of the minimum frequency point, considering the frequency modulation capabilities of new energy stations. The RoCoF constraint section initially takes into account the suppressive effect of static load voltage on frequency fluctuation, the impact of current source virtual inertia on system imbalance power during frequency modulation participation, and the spatial distribution characteristics throughout the dynamic frequency response process. Subsequently, in the part of considering the minimum frequency constraint, a general ASF model based on the classical average system frequency (ASF) model considering the access of new energy stations and generator speed control system is proposed. This model is derived from the classical ASF model, integrated with a low-order general model of the generator speed control system and the general frequency response model of new energy stations. The model enables to predict the time at which the system reaches its lowest frequency under a specific disturbance and provide a time-domain expression of frequency variation to assess the minimum inertia. The enhanced 3-machine 9-node and 10-machine 39-node systems are employed to simulate different disturbance types and sizes for analysis of the proposed method, thereby validating its accuracy and applicability.

In view of the phenomenon of "abandoned wind power", "abandoned photovoltaics" and "abandoned hydropower" in microgrids and the increasing demand for hydrogen load, a certain capacity of hydrogen production/storage equipment is considered to be deployed in the offsite hydrogen refueling station to centralize the consumption of surplus electricity from microgrids. Through the research of the topology and mathematical model of microgrid-hydrogen refueling station system （MHSS） containing wind power, photovoltaic, small hydropower, as well as electrolysis tank, hydrogen storage tank and reformer, a capacity optimization configuration model of hydrogen production/storage equipment with the objective of minimizing the total net present cost of the whole life cycle of the hydrogen refueling station is established, and the combined electric-hydrogen operation model for residual electricity to hydrogen-green hydrogen first （RETH-GHF） is proposed. In the example, the adaptive simulated annealing particle swarm optimization （ASAPSO） algorithm is applied to simulate the system with the practical scenario. By comparing the configuration results and cost-benefit details of offsite centralized/onsite decentralized hydrogen production schemes of microgrids, it is proved that the scheme of offsite centralized hydrogen production is more economical in the example scenario, and the economics of the two schemes of hydrogen production have been re-compared by taking into account the impact of industrial electricity price, hydrogen and natural gas price fluctuations on the total net present cost of the hydrogen refueling station throughout its life cycle, and finally, the rationality of the capacity optimization configuration model for hydrogen production/storage equipment in the hydrogen refueling station is verified with system operation of typical days in four seasons.