To improve electrolyzers’ dynamic operating stability and hydrogen production efficiency, a multi-mode hybrid electrolyzer cluster operating state rotation control strategy is proposed. By analyzing the relationship between electrolyzers’ hydrogen production power and efficiency, as well as their load range and start-stop duration, six operation states under three modes are defined. Based on the dynamic response differences between proton exchange membrane electrolyzers and alkaline electrolyzers, a power matching mechanism for complex operationg conditions is designed to realize coordinated scheduling and flexible switching of hybrid electrolyzers. Simulation results show that compared with the traditional sequential start-stop strategy, the proposed strategy increases hydrogen production by 11.27% under the same input power, and reduces the standard deviation and coefficient of variation of rated-state operating duration by 27.71 min and 47.04, respectively, enhancing both efficiency and stability.

In this paper, an optimal scheduling model of grid-connected electricity-hydrogen-ammonia system based on particle swarm optimization algorithm is constructed to simulate the process of power-to-ammonia under the operation of grid-connected condition. An energy storage device operation strategy and a grid interaction strategy are proposed to guarantee the load demand for hydrogen production and ammonia synthesis. By comparing the new energy forecast output and the load of power-to-ammonia at each moment, the operation strategy of energy storage equipment is first formulated, and then the grid interaction strategy is formulated on the basis of energy storage adjustment to meet the demand of power-to-ammonia load. Under the conditions of satisfying the real-time power balance of the system, the start-up/shut-down characteristics of the electrolyzer, and the energy storage capacity constraint, the operation data of the electricity-hydrogen-ammonia system are obtained for each time period to validate the effectiveness of the proposed model and strategy. electricity-hydrogen-ammonia system are obtained in each time period to validate the effectiveness of the proposed model and strategy.

This study employed rice husk as the native biomass and investigated the effects of reaction temperatures （350- 500 ℃）, concentrations （1%-4%）, and an alkali metal catalyst （K₂CO₃） on the performance of supercritical water gasification for hydrogen production. Experimental results indicated that increasing reaction temperature and decreasing reactant concentration could effectively enhance the carbon gasification efficiency （CGE） and reduce the coking rate of rice husk. Under non-catalytic conditions, the optimal conditions for supercritical water gasification of rice husk were identified as 500 ℃ and 1%, achieving a CGE of 37.6% and an H₂ selectivity of 48.3%. Although the supercritical water environment facilitates the dissolution and hydrolysis of biomass, providing ample water to strengthen gas production reactions such as steam reforming and water-gas shift reaction, coking during gasification of rice husk remains an intractable issue that impedes its further gasification. The addition of K₂CO₃, however, significantly diminished the formation of coke. Under the optimal conditions, the CGE of rice husk achieved 49.2%, with total gas yield reaching 58.70 mol/kg. The residual solid, as analyzed by Thermogravimetric Analysis, was virtually carbon-free, indicating that the alkali metal catalyst may intensify the hydrolysis of rice husk, thereby promoting the transference of carbon from the feedstock to the gaseous and liquid phases. In addition, K₂CO₃ exhibited excellent H₂ selectivity for gasification of rice husk, with an H₂ selectivity of 71.0%. And the yield of H₂ also reached 41.66 mol/kg, which was 3.5 timses higher than that under the non-catalytic conditions. The experimental results demonstrated the potential for producing hydrogen-rich gas through K₂CO₃ which catalyzed supercritical water gasification of rice husk.

Regarding issues such as inefficient operation due to electrolyzers operating outside optimal ranges and degradation of electrolyzer lifespan caused by frequent switching on/off under fluctuating photovoltaic conditions, this study first proposes a hybrid power allocation strategy based on the optimal operating range. This strategy utilizes an improve d TOPSIS method to select the optimal operating range for electrolyzers. Subsequently, a combined approach of sequential allocation and average allocation is employed to allocate the operating power of each electrolyzer, aiming to increase the average operating time within the optimal operating range and enhance the overall efficiency of the electrolyzer unit. Additionally, a strategy is introduced for coordinating the variable hysteresis-loop switching of electrolyzer units with underlying cyclic switching. By expanding the power hysteresis-loop range to tolerate power fluctuations and sequentially starting and stopping each electrolyzer, the aim is to reduce the total start-stop cycles of the electrolyzer unit and evenly distributes the start-stop cycles of each electrolyzer in the array, thereby extending the lifespan of the electrolyzer unit. Finally, the effectiveness of the strategy is verified through simulations.

A Maxwell model with 7 parameters is established using the proton exchange membranes in fuel cells as the research object. The model is used to fit the relaxation modulus of the membrane in both its initial and stable states across different temperature levels. The research results indicate that the fitting error between this model and experimental results is minimal. The model can effectively capture the membrane’s relaxation modulus changes. This demonstrates its high accuracy and reliability. The model contributes to a deeper understanding of the dynamic response and stability of the membrane in operation, thereby optimizing the design and selection of membrane materials and extending the lifespan of fuel cell systems.

A rapid frequent start-stop cycling condition (10 min operation followed by 10 min shutdown) was applied to investigate the impact of frequent start-stop operation on the durability of a 5 kW alkaline water electrolyzer (AWE). During a 100 h continuous test, the frequent start-stop condition increased the average cell voltage by 0.32 V with an average degradation rate of 3.2 mV/h, which is four times that under constant current density operation (2500 A/m²). The performance degradation of both cathode and anode electrodes was monitored using linear sweep voltammetry and electrochemical impedance spectroscopy and X-ray photoelectron spectroscopy (XPS). The results show that the degradation in AWE performance under frequent start-stop conditions is primarily attributed to the deterioration of the cathode electrode. This degradation is likely associated with the generation of reverse currents during the start-stop cycles. Therefore, this study highlights the challenges posed by frequent start-stop operations under fluctuating renewable energy conditions and underscores the need for the development of cathode electrodes capable of withstanding such conditions.

In this study, the effects of laser power on emitter doping, dark saturation current, contact resistivity, and electrical performance were investigated. The findings demonstrate that with an increase in laser power output ratio from 73% to 78%, the overall doping concentration gradually rises within the selective emitter region, resulting in a sheet resistance difference of 145-150 Ω/□ compared to lightly doped regions. Effective minority carrier lifetime, iV_oc （implied open-circuit voltage）, and contact resistivity significantly decrease with higher impurity concentration overall, while dark saturation current was greatly impacted by surface doping concentration. Consequently, the utilization of a selective emitter can effectively reduce the overall doping concentration and elevate the sheet resistance within the light absorption region, achieving higher minority carrier lifetime and a open-circuit voltage. When the laser power output ratio was adjusted to 75%, a dark saturation current density of 17.68 fA/cm² and a contact resistivity of 1.34 Ωcm² were obtained, meanwhile optimum cell performance was achieved： open-circuit voltage of 723.79 mV, fill factor of 84.58%, and power conversion efficiency of 25.51%.

Based on field static load tests from a photovoltaic project in Zhejiang, a three-dimensional numerical model of prestressed high-strength concrete （PHC） nodular piles are established using ABAQUS finite element software, incorporating the concrete damaged plasticity （CDP） model. The study analyzes the effects of nodular diameter, spacing between nodules, and vertical load on their lateral load-bearing performance. The findings reveal that the bending moment of the pile body is mainly concentrated within the range of 0~10 pile diameters （d） below ground level, with the maximum bending moment occurring at 3.5d~5.0d below the surface, and progressively shifting downward as the horizontal load increases. The nodular diameter not exceeding 1.5d, with each 10% increase, enhances lateral load-bearing capacity by approximately 3%. Spacings less than 5d between nodules are conducive to sustaining superior load-bearing performance, with a 1 m nodular spacing for smaller diameter piles deemed optimal. Elevating the vertical load to its designed capacity augments horizontal load-bearing ability by 18%.

Aiming at the problem of insufficient prediction accuracy due to the drastic fluctuation of PV power under non-clear-skg weather conditions, an ultra-short-term PV power chaotic prediction model based on hunger games search （HGS） optimized variational mode decomposition （VMD） and emotional neural network （ENN） is proposed. Firstly, the HGS algorithm is used for optimization of VMD core parameters to improve the adaptivity of VMD, and an HGS-VMD fitness function considering weighted permutation entropy and decomposition loss is designed to reduce the complexity of the decomposition component and the influence of the residual component on the prediction results. Then, the phase space is reconstructed using the improved C-C method for the VMD decomposition components, and after extracting their regularity information, the phase space reconstruction matrix is input into the ENN model for prediction. Finally, the proposed prediction model is verified by simulation based on the measured PV power data, and the results show that the proposed prediction model can effectively improve the prediction accuracy of PV power under non-clear-sky weather conditions.

To address the challenge of segmenting photovoltaic （PV） modules in remote sensing images, this paper proposes a semantic segmentation method based on a deep residual attention network. The method builds on the U-Net architecture by integrating a deep residual network for enhanced feature extraction and representation. Additionally, a local attention mechanism is incorporated within the residual modules to further refine local feature expression, improving segmentation accuracy. Experimental results on a public remote sensing PV dataset demonstrate that the proposed method consistently outperforms DeepLabv3+, UCTransNet, and UDTransNet across multiple spatial resolutions, achieving average improvements of 5.80%, 2.91%, 3.06% and 3.92% in mIoU, Dice, mPA, and Precision, respectively, over the original U-Net.

To address the multi-peak problem of photovoltaic （PV） array output power under partial shading conditions, this paper proposes a composite control method that integrates an improved particle swarm optimization （IPSO） algorithm with non-singular terminal sliding mode control （NTSMC）. First, the mathematical model and output characteristics of the PV array are analyzed. Second, an improved particle swarm optimization algorithm is designed to reduce the impact of inertia weight during execution, thereby enhancing the output power of the PV system under partial shading. Third, a non-singular terminal sliding mode switching surface is designed to overcome the singularity problem of traditional sliding mode control, simplifying the system structure and improving steady-state accuracy. Finally, simulation experiments are conducted on the Matlab/Simulink platform and compared with existing methods. The results demonstrate that the proposed strategy exhibits superior performance in terms of tracking speed and steady-state power fluctuation, significantly improving the maximum power point tracking （MPPT） effect of the PV system.

This paper focuses on offshore membrane-based photovoltaic floating islands and conducts fluid-structure interaction analysis using the coupled Eulerian-Lagrange （CEL） finite element method. Firstly, the reliability of the CEL method is verified through wave generation theory and model tests. Subsequently, numerical simulations using the CEL method are conducted to study the stress characteristics of the membrane-based photovoltaic floating island under different conditions. The research results indicate that the wave-following behavior of the floating tubes affects the motion state of the floating body and the stress on the membrane. The key locations where the membrane experiences significant stress due to wave action are at the membrane -rope connection, between the photovoltaic panels, and at the contact points between the photovoltaic modules and the membrane. As the wave height increases, the stress at these key locations also increases, while as the wave period increases, the stress at these key locations decreases. When the wave height is 5m and breaking occurs, the waves strike the membrane directly after overtopping the floating tubes, leading to damage to the membrane near the photovoltaic modules.

Given the diverse and irregular shapes of residential rooftops, and the common reliance on manual layout or exhaustive search methods for conventional photovoltaic array configurations—which suffer from low efficiency and difficulty in achieving optimal arrangements—this paper proposes an optimization method for residential rooftop PV arrays. Aiming to minimize the levelized cost of energy (LCOE), it constructs an optimization model that accounts for both the self-shading effects of modules and architectural constraints. PV array configuration on irregular flat roofs is optimized using the Genetic Particle Swarm Algorithm (GA-PSO). Lastly, a number of typical irregular flat roofs of residential dwellings are used as research objects in the PV layout optimization study. As a result of precisely analyzing the panel layout’s design parameters, the PV array layout constraint optimization method presented in this paper offers reference suggestions for the appropriate spatial configuration of rooftop PV systems in complex environments. It also significantly increases the PV system’s performance and lowers its cost, achieving the ideal balance between cost and benefit. The optimal levelized cost of electricity, or LCOE, is 3.86 CNY/（kW·h）.

This paper takes a practical double-layer flexible PV project as the engineering background, an in-depth investigation into its wind-induced vibration coefficients was conducted. First, an aeroelastic model was designed and manufactured, and dynamic characteristics were calibrated. Subsequently, wind tunnel tests were conducted to investigate the influence of different factors such as wind speed, wind direction and boundary conditions on the wind-induced vibration coefficients. Then, a finite element model of the double-layer flexible PV bracket structure was established. The reliability of the simulation model was verified using the wind tunnel test results, and the influence of key factors such as aerodynamic damping, ground roughness and cable force on the structural dynamic response and wind-induced vibration coefficients was explored. The results indicate that the aerodynamic damping of the structure cannot be ignored. The wind-induced vibration coefficients in the leeward direction are greater than those in the windward direction. The wind-induced vibration coefficients are negatively correlated with wind speed and cable force, and positively correlated with the ground roughness coefficient.

A maximum power point tracking （MPPT） control method combining the twin-wolf-driven gray wolf optimization （THW-GWO） with perturbation and observation （P&O） is proposed to enhance the energy utilization efficiency of photovoltaic arrays. First, an in-depth analysis is conducted on the power generation principles of photovoltaic arrays under complex illumination conditions and the MPPT control principles, theoretically explaining the reasons for the relatively low tracking efficiency of P&O. Second, GWO and P&O are integrated to improve the tracking efficiency of MPPT control for photovoltaic arrays. To achieve better performance in terms of GWO convergence speed and local optimal solution search, adaptive adjustments are made to the convergence factor, weighted distance, and position update weight of GWO, resulting in THW-GWO. Finally, the control performance of photovoltaic arrays under P&O, GWO, THW-GWO, and THW-GWO-P&O is compared and analyzed in Matlab/Simulink. Results demonstrate that THW-GWO-P&O achieves higher energy conversion efficiency, optimization accuracy, and convergence speed under both uniform illumination and partial shading conditions, validating its superior optimization performance.

To address PV curtailment and power deficits in high-penetration PV distribution networks using energy router （ER）, this paper introduces a high-penetration photovoltaic energy router interconnection system （HP-PVERIS） and proposes a two-layer coherence-driven energy routing strategy combining generalized graph theory and deep deterministic policy gradient （DDPG）. The first layer uses an improved generalized graph theory model for multi-objective optimization of node conversion loss, line loss, congestion, and load balancing. The second layer applies DDPG-based dynamic multi-path power allocation, feeding optimized topology parameters back to the first layer to form a bi-layer routing strategy. Simulations show the approach effectively reduces losses, alleviates congestion, balances loads, and enhances PV accommodation and interconnection capacity.

A heliostat field is selected as the research object. Spatial distributions of surface pressure coefficients of heliostat surfaces at different positions within the field under typical working conditions are obtained through a wind tunnel test on a scaled model of the heliostat field. Wind vibration coefficients of heliostats at various positions in the field are calculated, and the effects of pitch angle and wind deviation angle on these coefficients are comprehensively discussed. The results indicate that the distribution patterns of wind pressure and wind vibration coefficients at different positions exhibit a characteristic “regional” feature. The entire field can be qualitatively divided into two regions： the edge field and the inner field, for an intuitive overall understanding. It is also found that under specific wind deviation angles, the wind vibration coefficients at certain positions within the inner field significantly decrease, exhibiting a characteristic “corner effect”. Finally, based on the strong correlation between wind vibration and wind pressure coefficients, an empirical formula for the rapid estimation of wind vibration coefficients at different positions is proposed.

To address the issue of large circumferential temperature differences caused by non-uniform heating in parabolic parabolic trough solar collector tubes, a pyramidal inner-surface collector tube was designed by applying a stamping process to the inner wall of a traditional metal collector tube. The parametric effects of the different pyramid lengths, pyramid widths, and pyramid heights on the fluid flow and heat transfer characteristics, the circumferential maximum temperature difference, the intensity of secondary flow, and the local heat transfer characteristics in the pyramidal inner-surface collector tube were analyzed comparatively using a numerical method. The results show that： the introduction of pyramid-shaped roughness elements enhances the heat transfer performance of traditional collector tubes, with the structural parameters having varying degrees of influence on the flow and heat transfer characteristics; and the maximum evaluation factor of heat transfer enhancement named as J_F reaches 1.59, and compared with a traditional collector tube, the maximum circumferential temperature difference with the pyramidal enhanced tube is reduced by up witha 51.68%; and the local Nusselt number on the inner wall surface changes periodically along the circumferential direction, with its peak values occurring at a specific location between the apex and the base of the pyramid elements; the variation trends of the cross-sectional average Nusselt number and secondary flow intensity along the flow direction are consistent, and the variation trend of average Nusselt number and secondary flow intensity with increasing Reynolds number are also consistent, which indicates that the intensity of secondary flow in the pyramidal inner-surface collector tube determines its heat transfer intensity.

In order to improve the temperature stratification characteristics of the pit thermal energy storage water body, the method of built-in two partitions is proposed to improve the temperature stratification characteristics, and the operating parameters are optimized to further improve the thermal performance of the water body and reduce the heat loss. The effects of inlet flow rate, inlet temperature, height and location of the partitions on the stratification characteristics of water temperature were studied by numerical simulation, and the variation trend of thermal performance was analyzed based on the Richardson number and the thermocline thickness. The results show that when the volume of the pit thermal energy storage water body is 10000 m³, the height of the partitions is 5 m, and the partitions are located 13 m from the inlet and 5 m from the outlet, the thickness of the thermocline is reduced by 22.9%, and the Richardson number Ri is increased by 53.2%. The water body has the best temperature stratification characteristics. The built-in partition can significantly optimize water temperature distribution and improve energy storage efficiency.

To evaluate the heating effect of buried pipes in solar greenhouses during winter under different parameter settings, this study employed computational fluid dynamics (CFD) to simulate soil temperature distributions under varying inlet temperatures （50,45,40,35,and 30 ℃）, pipe diameters （16, 20, 25, 32, 40 mm）, and flow velocities （0.2, 0.4, 0.6, 0.8, 1.0 m/s）. The results indicate that with an inlet temperature of 40 ℃ and a pipe diameter of 32 mm, the soil temperature around celery roots can be maintained within the suitable growth range. In contrast, variations in inlet flow velocity showed no significant effect on the temperature at the same soil location.

To investigate the performance of different operating strategies of a solar-assisted ground source heat pump （SGSHP） system in severe cold regions, a numerical simulation model is developed based on Fluent. Three operating modes are simulated and compared： a conventional ground source heat pump（GSHP） system, an SGSHP system with uniform heat storage, and an SGSHP system with partitioned heat storage. The results show that the coupled system achieves cross-seasonal heat storage and utilization. It also improves the energy efficiency of the ground source heat pump system and reduces the annual energy consumption by over 20%. The partitioned operating strategy demonstrates superior thermal regulation, concentrating heat storage in the inner zone and simultaneously lowering the outer zone temperature. The annual heat loss is reduced significantly from 38.71% to 19.19%, which improves the cross-seasonal heat storage efficiency and heat pump performance. The annual energy consumption of the system is further reduced.

This paper proposes a heliostat tracking error measurement method based on the spiral motion trajectory of the light spot. This method involves controlling the rotation of the heliostat to make its reflected light spot follow a spiral trajectory. The spot images are captured by a camera and processed to ultimately determine the heliostat’s tracking error. The principle of the spiral trajectory method for measuring heliostat tracking error is elaborated. An experimental platform for measuring heliostat tracking error is established, and the camera light-target method is used to verify the correctness of the spiral trajectory measurement method. The results show that the measurements from the spiral trajectory method are largely consistent with those from the camera target method. The root mean square deviations of the measured azimuth and elevation tracking errors between the two methods are 0.016° and 0.018°, respectively.

The paper proposed a parameter identification method driven by mechanism and data, which established the equivalent circuit model of building thermal parameters. The beluga algorithm was used to minimize the mean absolute error （MAE） between the simulated temperature of the model and the measured temperature as the objective function to carry out the heat capacity identification, which was verified through experiments. On this basis, a case study of an office building in Beijing is carried out. Based on the building heat capacity identification results, the energy-saving potential of the system and the level of photovoltaic accommodation can be fully tapped through the variable load operation and control of the heat pump. Compared with the initial load, the total cost of electricity after load optimization was reduced by 10.9%, and the photovoltaic accommodation rate was increased by 9.97%.

Considering the uncertainty caused by the high proportion of renewable energy integration, leading to significant fluctuations in the DC bus voltage of microgrids,this paper proposes an active disturbance rejection control strategy for bidirectional DC-DC converter based on twin delayed deep deterministic policy gradient （TD3） reinforcement learning. Firstly, a linear extended state observer was used for system reconstruction to estimate and compensate for total disturbances, and frequency-domain analysis were conducted on the tracking and anti-interference ability of the control strategy. Subsequently, a large amount of simulation self-learning was used to obtain observer parameters to intelligently adjust the weight update method of the neural network, optimize the form of the reward function, and utilize the online network for real-time parameter scheduling, enabling sufficient training to achieve an approximate optimal control law. Finally, digital simulation platform and low-power experiment were used to verify that the proposed control strategy has superior dynamic and steady-state performance, such as smaller voltage deviation and faster response speed under multiple operating conditions, compared to PI control and traditional linear active disturbance rejection control , effectively improving the anti-interference ability of DC bus voltage.

To achieve low-carbon development of regional integrated energy systems （RIES） with multi-energy complementarity and efficient energy utilization, a low-carbon scheduling model for agricultural integrated energy is proposed, whichconsidersmultiple biomass energy sources and comprehensive demand response. Firstly, based on the characteristics of biomass energy in agricultural regions, it is divided into combustible biomass energy and non-combustible biomass energy, which are supplied by different assembling unit. At the same time, the comprehensive demand response is modeled, considering three types of loads that can be reduced, transferred, and replaced, to achieve the transfer of loads in terms of time and type, and deeply explore the potential of the demand side. Secondly, considering the uncertainty of the source and load sides, a two-stage robust optimization model for the agricultural regional integrated energy system （RIES） is established, and a stepped carbon trading mechanism is introduced to reduce the system's carbon emissions and achieve low-carbon operation. Finally, the model is solved using the column constraint generation algorithm. Case analysis shows that the proposed optimization scheme not only reduces scheduling costs by 4.65% and carbon emissions by 5.36%, but also improves system robustness and flexibility, achieving multi energy synergy and complementarity in agricultural regions, and improving the overall efficiency of the agricultural regional comprehensive energy system.

In a multi-inverter parallel system, carrier desynchronization can trigger high-frequency switching frequency circulating loops, while traditional suppression methods are difficult to achieve basic carrier synchronization. To address this issue, this paper proposes a carrier synchronization control method to suppress switching circulating currents. This method dynamically adjusts the carrier phase compensation based on the magnitude of the switching circulating currents, stopping the adjustment when the circulating current falls below a preset value. The paper first derives the mathematical relationship between the carrier phase difference and the amplitude of the switching circulating currents through Fourier decomposition of the inverter output voltage. Then, the principle of inverter carrier synchronization control is explained in detail. Finally, the effectiveness of the proposed method for suppressing switching circulating currents is verified through simulation and experimentation.

In this paper,an improved Cuk-type coupled-inductor DC-DC boost converter that can be applied to photovoltaic systems is proposed. On the basis of the topology of the traditional Cuk converter,a switched-capacitor boost network and a coupled-inductor dual-boost unit are added,which can not only achieve higher voltage gain, but also do not change the reverse output characteristics of the Cuk converter,reduce the voltage stress of the switch tube and absorb the leakage inductance energy through the passive clamp absorption branch formed by the switching capacitor structure,and the L-C filter unit composed of the output inductance and output capacitance of the topology post-stage can achieve zero ripple output. The theoretical operation and characteristic analysis of the converter is carried out,the key device design parameters are given,the working performance of different boost topologies is compared,and a 100 W experimental prototype is produced to verify the theoretical correctness in combination with the theoretical design.

This paper proposes an online monitoring method for key frequency regulation parameters of renewable energy power systems. It clarifies the required measurement signals for online monitoring and outlines the methods and processes for monitoring the overall equivalent inertia time constant and the system's droop coefficient. The applicability of the method is validated through simulation examples conducted under various penetration levels in both traditional and renewable energy power systems. The simulation results demonstrate that the proposed method can accurately monitor the key frequency regulation parameters of renewable energy power systems, with the equivalent inertia time constant monitoring error below 2% and the droop coefficient monitoring error below 4.5%. Furthermore, this method can compute the system’s equivalent inertia time constant within 0.4 seconds after a disturbance and determine the system’s droop coefficient once it stabilizes. Notably, the method shows excellent adaptability and stability, particularly in high-penetration renewable energy high permeability systems.

This article proposes a integrated energy scheduling strategy for power-to-gas and carbon capture systems (P2G-CCS) that considers the dynamic characteristics of gas-thermal. Firstly, the characteristic equation of the gas-thermal energy flow is transformed into the s-domain to obtain a two-port transfer function model. The inverse Laplace inverse transform is carried out on it using the convolution theorem, thereby obtaining the time-domain dynamic model at any time sectioninstant. Then, based on the dynamic model, establish an IES park that includes P2G-CCS is established and combined it with a carbon trading mechanism to reduce carbon emissions. Finally, taking IEEE-39-20-6 as an example, the electric-gas-thermal energy system is established and compared by setting different scheduling scenarios. The results indicate that the established dynamic model and strategy effectively improve the efficiency of flow calculation in gas- thermal networks while reducing carbon emissions and enhancing the economic efficiency of system scheduling.

In this paper, a configuration method of grid-forming converters based on capacity sensitivity is proposed, which considers the influence of the access positions of grid-forming converters in a wide frequency range. Firstly, the stability margin of the system is measured based on the key eigenvalues of the return ratio matrix. Secondly, the capacity sensitivity of the key eigenvalues to the grid-forming converters with different access positions is calculated. The weak nodes of the system are screened by the capacity sensitivity, and the siting planning of grid-forming converters prioritizes weak nodes. Finally, the effectiveness of the proposed method is verified by PSCAD simulation.

This paper comprehensively considers the energy storage, heat storage system and load-side flexible equipment as generalized energy storage, introduces the green certificate-carbon trading cooperative interaction mechanism, and proposes a MCIES power-carbon sharing cooperative operation optimization strategy based on Nash bargaining & master-slave game. Firstly, the cooperative operation architecture of MCIES with P2P electricity-carbon sharing is constructed, and the carbon capture system and power-to-gas device are introduced to establish the integrated energy system （CIES） and equipment model. Secondly, a MCIES master-slave game dynamic multi-energy pricing model with CIES operators as leaders and users as followers is constructed. Finally, based on the Nash negotiation theory, a bi-level hybrid game optimization model with master-slave game dynamic multi-energy pricing mechanism and CIES electricity-carbon sharing cooperative game is constructed, and then the Nash bargaining problem is transformed into two sub-problems of MCIES cooperation cost minimization and cooperation income distribution maximization. This problem is tackled using the alternating direction multiplier method, and the feasibility and effectiveness of the method are verified by simulation examples.Results indicate that the proposed strategy effectively reduces the total MCIES operating costs, reduces the carbon emission of the system, and balances the income among all participants.

To address the phase lag issue in bus voltage control of DC microgrids, a deep reinforcement learning-based active disturbance rejection control strategy incorporating a phase compensation network is developed for bidirectional DC-DC converters in energy storage systems. In this strategy,the order of the linear extended state observer is reduced, and a phase compensation network is connected in series with its total disturbance channel to provide the required lead phase angle. Subsequently,the mechanism by which the compensation link enhances disturbance suppression performance is analyzed,and the compensation parameter ranges are configured. Besides,a control parameter optimization model is established based on deep reinforcement learning algorithms. By training an intelligent agent to interact with the DC microgrid environment,the optimal combination of policy parameters is explored to achieve adaptive adjustment of observer bandwidth and correction parameters. Finally,simulations are conducted to compare the tracking ability and control accuracy of different control methods for bus voltage disturbances under typical operating conditions,and the results confirm the effectiveness of the proposed control strategy.

In order to further improve the prediction accuracy of ultra-short-term power load and enhance the extraction ability of power load temporal features, a temporal enhancement Transformer （TETransformer） ultra-short-term power load prediction method considering similar days and error correction is proposed. First, the meteorologically similar days are selected using gray relational analysis, and then, the local temporal enhancement attention mechanism is constructed on the basis of the Transformer model, and the temporal convolution is used to improve the local temporal feature perception ability of the attention mechanism, and to aggregate the relevant information of the adjacent area of the observation point; the traditional Transformer model is embedded with a temporal convolution layer, and the feature map is extended, in which the global information of the Transformer model is not available to the public. traditional Transformer model to extend the feature map and enhance the local time-series information extraction capability on the basis of the global information extraction of the Transformer model. Finally, the historical feature data and future meteorological data are input to TETransforemr, and the load power sequences of meteorologically similar days are input to LSTM, and the historical time-series features and similar day information are fused through the fully-connected layer, and the encoder-based error correction module is introduced to improve the model prediction accuracy. Through the multi-model comparison and ablation experiments, the prediction accuracies are improved, which proves that the proposed method can effectively enhance the extraction ability of power load and has certain application significance in the field of ultra-short-term power load.

An islanding detection method combining wavelet packet energy entropy and Goertzel algorithm to improve the harmonic impedance measurement is proposed to reduce the impact of disturbances on power quality, improve the measurement accuracy and reduce the detection blind zone. The method adopts the wavelet packet energy entropy and the Goertzel algorithm for harmonic voltage change rate to improve the judgement of the suspected islanding state according to the characteristics of the harmonic voltage signal at the PCC point （point of common coupling）, and proposes to improve the islanding detection of the injected harmonic impedance measurement for the suspected islanding state of the system in order to reduce the impact of intermittent injected disturbing signals on the power quality of the system. Considering the detect ion accuracy, power quality and detection blind area, the principle of selecting the limit value of injected harmonic voltage is proposed, and the harmonic voltage with this limit value is injected at the PCC point to obtain the harmonic impedance and its sensitivity coefficient p, which realises the final determination of the system islanding state. The effectiveness of the proposed method is verified by Matlab simulation.

In order to solve the problems of variable switching frequency and large rolling optimization calculation in traditional three-level grid connected inverter model predictive control, a low complexity fixed switching frequency model predictive control strategy is proposed for three-level grid connected inverter. Firstly, utilizing the mechanism of action of redundant small vectors on the neutral-point potential, a reference current tracking single objective function is constructed. Then, rolling optimization of the voltage middle-axis vector and combining it with the q-axis component of the target prediction error to locate the optimal core area, the optimal three vectors are determined after a second rolling optimization. Further optimize the allocation of vector action time based on the error value generated by the selected voltage vector in the evaluation of the objective value function. Finally, the proposed low complexity fixed frequency model predictive control strategy is validated through Matlab/Simulink simulation model and three-level grid connected inverter experimental platform.

This paper proposes a medium- and long-term scheduling method for wind and solar power integration into cascaded hydropower stations. Based on optimized typical transmission power curves, with the daily average actual discharge flow of reservoirs as a boundary condition, a long-term hourly-scale simulation of hydro-wind-solar complementary operation is conducted. Subsequently, the response function between renewable energy curtailment rate and hydropower output is extracted and embedded into the medium- and long-term hydro-wind-solar complementary operation model. A simulation-optimization method is adopted to derive adaptive operating rules. A case study on a clean energy base demonstrates： As hydropower output increases, the curtailment rate first decreases and then rises, with moderate hydropower output leading to lower wind and solar curtailment rates. Compared with conventional scheduling, optimizing hydropower operation alone reduces wind and solar curtailment rate by 3.71% and increases total system generation by 1.74%. The complementary operation optimization incorporating the response function further reduces wind and solar curtailment rate by 7.68% and enhances total system generation by 2.15%. Therefore, the proposed method can further exploit hydropower flexibility, coordinate the complementary relationship between hydropower and new energy, and improve the overall complementary efficiency of hydro-wind-solar integrated systems.

To address the issues of insufficient active power support, overcurrent, and power oscillations that easily occur in direct-drive wind turbine under traditional virtual synchronous generator （VSG） control during fault ride-through conditions, a grid-forming wind-storage integrated system based on a three-port nine-switch converter is proposed. This system achieves high integration of power electronic devices and effective power support, and optimizes power distribution between wind and storage to avoid large variations in wind turbine speed, maintaining operation at the maximum power point. To enhance the fault ride-through capability of the wind-storage integrated system, the VSG control strategy is improved with the maximum converter current as the boundary condition, enhancing reactive power support and DC-side voltage stability with the aid of the energy storage device. This approach also helps absorb the unbalanced power that cannot be delivered due to current constraints, reducing the risk of current limit violations. A system model is established in Simulink, and the proposed topology and control strategy are validated under three different scenarios： wind speed fluctuations, grid frequency disturbances, and voltage faults. Simulation results indicate that the proposed topology and control strategy enable real-time wind power tracking and provide strong active power support and fault ride-through capability.

To quantitatively evaluate the frequency support performance of the transmission end power grid connected to the hybrid new energy station, the frequency characteristic transfer function is first used to tune the parameters of the frequency modulation unit of the single feed follow-up/grid construction new energy system, and a multi feed hybrid transmission end power grid frequency response model is constructed; Then, based on the closed-loop eigenvalues of the system, analyze the impact of its penetration rate on the frequency of the transmission end power grid, formulate corresponding frequency modulation contribution indicators, and establish optimization plans to obtain the optimal ratio that ensures the stability of the system frequency; Finally, time-domain simulations were conducted on the Matlab/Simulink platform to validate the correctness of the theoretical analysis presented in this paper.

A multi-mode LLC resonant converter for an ultra-wide output voltage range is proposed to address the problems of an excessively wide range of switching frequency variation and low transfer efficiency of conventional LLC resonant converter for wide output voltage applications. The converter realizes the topological reconfiguration of the bridge rectifier and the voltage doubling rectifier by controlling the auxiliary switches of the dual windings on the secondary side of the transformer. Combined with the circuit modal analysis,the voltage gain and the relationship with the normalized parameters k and Q are discussed,and the parametric design rules of the resonant elements are given. The switching control between different voltage gain modes is designed, and an experimental prototype with an input of 400 V and a maximum power of 1.3 kW is built. The experimental results show that the converter is able to provide an ultra-wide output voltage range of 50-430 V,a small range of variation in the operating frequency,and a high conversion efficiency, which verifies the feasibility and effectiveness of the proposed topology and control method.

In this paper, an adaptive smooth switching strategy is proposed for PV-storage islanded DC microgrids （IDCMGs） to ensure smooth switching between different operating modes of PV systems and improve the accuracy of bus voltage control. Firstly, a secondary control-based bus voltage adjustment method with time delay compensation is proposed to mitigate the bus voltage deviation caused by transmission delays. Secondly, a weighted average strategy based on a variable-coefficient sigmoid function is designed to adaptively adjust the reference current and mode switching time, ensuring smooth switching of the PV system between droop and MPPT modes and avoiding abrupt changes in system parameters caused by changes in the control loop. Based on the weighted average strategy, an adaptive exponential reaching law sliding mode correction method is introduced to enhance the efficiency and robustness of smooth switching, and the parameters of the sliding mode reaching law are dynamically adjusted to suppress chattering during the switching process. Finally, the effectiveness of the proposed strategy is verified using a hardware-in-the-loop （HIL） experimental platform, and the results demonstrate that the strategy exhibits excellent performance in handling IDCMG mode switching and voltage stability.

To enhance the performance of the dual-active-bridge DC-DC converter under disturbances in DC microgrids, this paper investigates its dynamic response and current stress optimization. A novel hybrid control strategy is proposed, which integrates extended phase shift modulation with model predictive control （MPC）. This strategy is designed to reduce current stress in the converter and improve both dynamic response and steady-state accuracy through optimized control. Additionally, considering the sensitivity of MPC to parameters, an error correction method is introduced as a feedback correction loop to eliminate steady-state errors caused by parameter mismatches, thereby enhancing control accuracy and system robustness. The results from both simulation and experimentation collectively demonstrate the practicality and advantages of the proposed control strategy.

The impedance models of grid-connected converters mainly include two categories： sequence impedance and dq impedance. However, the existing conversion relationship between these two types of impedances is complex and lacks universality, which does not consider the actual initial phase of the reference frame and limits the cross-checking and practical application of the theoretical impedance models. Therefore, this paper first explores the impact path of the initial phase of the reference frame on impedance modeling mechanistically, then establishes the comprehensive mathematical relationship between dq impedance, MIMO sequence impedance, and equivalent SISO sequence impedance considering the initial phases, and summarizes the universal conversion formulas. Based on this, dq impedance in the negative frequency band is defined, which simplifies the conversion between sequence impedance below the fundamental frequency and dq impedance. The symmetric frequency conjugation characteristics of dq impedance are revealed, and then the universality of the impedance conversion relationship is ensured. Furthermore, the influences of the initial phase on impedance measurement and stability criteria implementation are elaborated. A reference frame phase correction method is proposed, which achieves mutual verification between impedances in any reference frame. Finally, the accuracy and effectiveness of the theoretical analysis are verified through wind turbine simulation and on-site frequency sweeping tests.

To address the impact of the uncertainty and volatility of new energy on the steady-state voltage stability margin （SSVSM） of power systems, conventional methods often rely on the Monte Carlo method, which requires repeatedly invoking continuous power flow calculations. This process is computationally inefficient. Therefore, it is of great significance and value to develop a steady-state voltage stability analysis method that balances computational efficiency and accuracy. The paper proposes an affine interval method based on the full derivative direct method to calculate the fluctuation range of the SSVSM affected by intermittent power sources in the system. Using IEEE standard transmission systems for case studies and comparative experiments with similar algorithms demonstrate the high efficiency and accuracy of the proposed method.

In this paper, the IEA-15 MW floating wind turbine with the VolturnUS-S semi-submersible platform is adopted to study the interaction between the rotor aeroelastic response and the floating platform motion under real marine conditions. The differences in relevant characteristics under different inflow conditions were discussed. The results show that the motion of the floating platform under real marine conditions is affected by the combination of wave load and aerodynamic loads of the rotor, and the motion of the floating platform will cause significant fluctuation of the rotor loads. Considering the effect of the blade elastic deflections, the motion amplitude of the floating platform and the wind turbine loads decrease, while the turbulent inflow condition leads to the increase of the fluctuation amplitudes of the platform motions, wind turbine loads and blade.

Taking one IEA-15 MW monopile wind turbine as an example, an integrated nonlinear model that considers blade-nacelle-tower-foundation interaction is established to investigate its dynamic characteristics under the combined actions of wind and waves based on the hybrid form of geometrically exact beam theory. The accuracy of the proposed numerical model is validated by comparing its calculation results with those of the open-source software OpenFAST. The results show that the displacement response at the top of the tower is affected by the combined effect of environmental conditions and the operating conditions. The aerodynamic loads play a dominant role in the dynamic response of the tower. The wave may have a certain inhibitory effect on the structural dynamic response due to the phase difference between the wind and wave loads. The acceleration response at the tower top is mainly influenced by the first vibration mode of the tower and the maximum acceleration response of the tower occurs at 0.8 times the tower height. The influence of the second-order vibration mode on the structural vibration cannot be ignored.

In view of the problem that the traditional independent pitch PI control of wind turbines has poor load reduction performance and is difficult to select control parameters. In this paper, the NREL 5 MW wind turbine is taken as the research object, and an independent pitch PI control parameter and active damping gain optimization method based on the improved sparrow search algorithm are proposed. Firstly, based on the blade root load and blade tip acceleration, an independent pitch active damping control strategy model was established. Secondly, the PI control parameters and active damping gain were initially set by orthogonal experimental method, and in order to realize the coordinated optimization of PI control parameters and active damping gain, with the goal of reducing the asymmetric loads and stable power of the unit, the PI control parameters and active damping gain of independent pitch were optimized based on the improved sparrow search algorithm （ISSA）. Finally, the results are compared with the results of traditional independent pitch control, independent pitch active damping control and independent pitch active damping control based on the sparrow search algorithm （SSA）. The results show that the independent pitch active damping control strategy with optimized control parameters by improving the sparrow search algorithm can better mitigate asymmetric loads of wind turbines and make the output power more stable.

To enhance the accuracy of wind speed prediction in numerical weather prediction （NWP）, both NWP-derived wind speeds and actual wind farm wind speeds are input into a variational mode decomposition （VMD） optimized by the global search strategy whale optimization algorithm （GSWOA） for decomposition. The decomposed actual wind speed components serve as training targets, while the corresponding NWP wind speed components are fed into the Newton-Raphson-based optimizer-long short-term memory with attention mechanism （NRBO-LSTM-Attention） model. The output components are linearly superimposed to replace the original NWP wind speeds. Subsequently, the corrected NWP data and wind farm data undergo outlier cleaning using algorithms such as isolation forest and Ransac. The processed data is finally input into the NRBO-LSTM-Attention model to predict future power output. Simulation results demonstrate that the corrected NWP wind speeds are closer to actual wind speeds, with evaluation metrics MAE and RMSE decreasing by 11.45% and 19.82%, respectively, and R² increasing by 31.24%. Additionally, the power prediction model exhibits superior performance, with MAE and RMSE reduced by 11.36% and 10.43%, respectively, and R² improved by 3.42%.

To improve the starting performance of vertical-axis（VAWTS） wind turbines and the efficiency of wind energy utilization at low and medium tip speed ratios（λ）, a slot control method based on the NACA0018 airfoil is proposed, and 18 types of slot airfoils are studied in detail by numerical simulation methods, and the results show that the slot plays a significant role in enhancing the aerodynamic performance of the airfoils within the λ range of 1 to 3. At λ of 2.5, the 0.10c_0.40c airfoil has the best enhancement effect, which is 60.4% higher than the original airfoil blade Cm. This study investigates the effects of slot parameters on the aerodynamic performance of the airfoil, and provides a reference for further research on the method of slot control.

In order to investigate the nonlinear hydrodynamic interference issues associated with multiple foundations during the development of offshore wind power arrays, this paper establishes a numerical analysis model for the hydrodynamics of OWTFs under wave-current action, utilizing the computational fluid dynamics （CFD） method. The high-precision detached-eddy simulation （DES） technique is employed to numerically simulate the flow field around OWTFs. Additionally, a frequency domain analysis method is applied to investigate the hydrodynamic spectral characteristics of OWTFs under varying wave-current parameters, including wave amplitude, period, and flow velocity. The results indicate the following： 1） When flow velocity and wave period are fixed, the spectral peak frequency and peak value exhibit nonlinear changes with increasing wave amplitude, and the proportion of high amplitude gradually rises with wave amplitude; 2） When flow velocity and wave amplitude are held constant, an increase in wave period leads to a rise in spectral peak frequency, with both spectral peak and sub-peak occurring at stable frequencies. The spectral peak value shows irregular fluctuations, and the proportion of high amplitude varies with changes in the period; 3） The spectral characteristics of the wave force on OWTFs is determined by the relative proportion of the water particle velocity of wave parameters in the water particle velocity of wave-current. As flow velocity increases, the frequency of the spectral peak rises under the same wave amplitude and period conditions, and a sub-peak emerges at twice the frequency of the spectral peak. Conversely, when flow velocity decreases, the sub-peak frequency is only 1.5 times that of the spectral peak frequency.

The cone factor （N_k） can be used to estimate the undrained shear strength （S_u） of clay, which in turn enables the calculation of the bearing capacity of offshore wind turbine foundations based on empirical correlations. In this study, both in-situ and laboratory cone penetration tests （CPT）, together with vane shear tests （VST）, were conducted to analyze the peak shear strength （S_up） and residual shear strength （S_ur） of soil at various depths, as well as the corresponding N_kp and N_kr values. Based on the obtained in-situ and laboratory N_k coefficients, the predicted S_u values for Shanghai clay were calculated. The results indicate that both Sup and Sur in the shallow soil layers increase with depth, while their difference becomes less significant in deeper layers. The fluctuation range of N_kp was found to be smaller than that for N_kr. In the laboratory tests, the required consolidation time for the clay was short, and the variation in shear strength at different depths under the same overburden stress was not pronounced. Furthermore, the N_kp and N_kr values obtained from both in-situ and laboratory tests for a given soil layer showed little difference, suggesting that S_u at various depths can be reliably predicted using N_kp.

Pore flaws formed during the manufacturing process of wind turbine blades directly affect the basic mechanical properties of the overall structure. They decrease the load-carrying capacity and may lead to blade failure. The paper combines macro performance experiments and fine-scale model simulation and utilizes a multiscale analysis method predict the basic mechanical performance parameters of the main beam laminate for wind turbine blades. Firstly, through experimental study and numerical simulation, a fine-scale model of the carbon fiber main beam is established, the relationship between six mechanical property parameters and fiber volume content is obtained, and the validity of the numerical method used in this paper is verified. Furthermore, the fundamental material properties of a large tow carbon filament/epoxy main beam were predicted by utilizing the Monte Carlo approach to develop a model of a carbon fiber main beam with 0-10% pore flaws, assuming that the pore defects are randomly distributed in the matrix. While the other five mechanical property parameters （E₂₂, E₃₃, G₁₂, G₁₃, and G₂₃） were significantly impacted by the pore content, with reduction rates approaching 11%, the fiber directional modulus of elasticity （E₁₁） proved to be insensitive to changes with pore content.

With the large-scale integration of wind power into the grid, wind power forecasting is confronted with multifaceted new challenges in terms of time scales, feature capture, data processing, and uncertainty quantification. Research in this domain holds significant importance for enhancing power system stability, optimizing resource allocation, mitigating investment risks, and fostering the development of the wind power industry. This paper proposes a dynamic memristor-based reservoir computing approach that integrates variational mode decomposition and sample entropy. Initially, the adaptive VMD technique is employed to decompose the original wind power time series into a series of sub-modes with varying bandwidths, thereby reducing its nonlinearity and instability. Subsequently, the complexity of each sub-path is analyzed by calculating the sample entropy, and the sub-pah are recombined accordingly to obtain sub-sequences that are more suitable for prediction. In the aspect of prediction model construction, this paper introduces a dynamic memristor-based reservoir computing framework, which incorporates an adaptive algorithm to optimize the reservoir parameters, enhancing the accuracy and robustness of the prediction model. By dynamically adjusting the connection weights and neuron states within the reservoir, the model is better equipped to adapt to the real-time variations in wind power. When compared to the backpropagation neural network, long short-term memory neural network, and dynamic memristor-based reservoir computing based on VMD-SE, the proposed model exhibits higher accuracy and faster computation speed. In summary, the VMD-SE-DMRPC model presented in this paper demonstrates notable advantages in addressing the volatility and instability of wind power, providing a novel and effective approach for wind farm power prediction.

During the pre-development period of wind power projects, timely and accurate assessment of site wind energy resources plays a key role in deciding a wind power project’s success or failure. In order to solve the problem that using a short-term wind data to conduct resource assessment may lead to unreliability, inaccuracy and inapplicability, in this study, we do research on the spatiotemporal characteristics of interpolation methods for measured wind data in two scenarios： an anemometer tower has been established but the measurement duration is less than 1 year; and the anemometer tower has not been established so the installation time and measuremert duration need to be decided. Across different terrain classification, data processing is repeatedly used to simulate real wind measurement. With different interpolation methods applied to predict data, the MSE between the interpolated predicted data and real wind data as the main assessment criterion, the study presents the recommended interpolation method in different terrain classifications and durations, and the recommend measurement duration in different terrain classifications.

The accuracy of voltage and reactive power sensitivity parameters directly determines the performance of wind farm automatic voltage control（AVC）. Deviation in these parameters leads to degraded control performance and an inability to ensure grid-connected voltage quality. To address this,this paper proposes a method for autonomous sensing of voltage and reactive power sensitivity and adaptive control parameter tracking in AVC sub-stations of wind farms. Firstly, the key factors affecting the voltage and reactive power sensitivity of wind farms are analyzed. Therefore, a method for autonomous sensing of voltage and reactive power sensitivity of wind farm AVC sub-stations considering operation modes and line length correction is proposed. Through real-time measurement data of wind farm AVC, the voltage and reactive power sensitivity of the wind farm grid connection point is calculated online. Secondly, based on the proposed sensitivity-based autonomous sensing method, an AVC control parameter adaptive following method is proposed based on the characteristics of voltage changes during the adjustment process to improve the problems of multiple adjustments failing to enter the dead zone and voltage oscillation in wind farm voltage control. Finally, a simulation model of a wind power collection area is built in Matlab/Simulink to verify the accuracy and effectiveness of the proposed method.

The effects of blade deflections on the load characteristics of the floating wind turbine under pitch condition are studied by using the vortex wake model and the geometrically exact beam model. The results show that the inflow velocity of the wind turbine will fluctuate significantly with the pitch motion of the floating platform, so the power and thrust of the wind turbine will also be affected, and the power and thrust of the wind turbine will decrease compared with those the rigid-blade case after considering the flexible deflection of the blade. Due to the additional velocity caused by the pitch motion of the floating platform, the tilt moment of both rigid and flexible rotor fluctuate with the phase angle, which is different from that under the surge condition. In addition, the pitch motion of the platform will result in periodic bending and torsional deflections of the blade. The torsional deflections will directly change the angle of attack of the airfoil sections, while the bending deflections will affect the inflow velocity of the airfoil sections. In addition, the inflow wind velocity has significant effect on the wind turbine loads and blade deflections, and a decrease in the inflow wind velocity will increase the influence of the velocity fluctuation caused by the floating platform motion.

Driven by the policy of grid parity for wind power, the construction of offshore wind farms in China is accelerating, and large-scale offshore wind farms will become an important power source for local power grids in coastal areas in the future. When using offshore wind power as a black-start power source, significant load disturbances may cause the system frequency to exceed its limit, resulting in the unit shutting down after the protection device is activated, leading to black-start failure. To further enhance the frequency regulation capability of offshore wind power during the black-start recovery process, this paper first uses trajectory sensitivity analysis method to derive the dominant parameters that affect frequency deviation, and then optimizes the dominant parameters to ensure that offshore wind power has sufficient frequency regulation capability during the black-start process. Secondly, by analyzing the relationship between frequency deviation, frequency deadband, and the power capacity of each unit, the feasible parameter boundary to ensure that the active output of the unit does not exceed the limit are derived. A parameter optimization model considering capacity limitation is established, and the optimal parameters are solved using sequential quadratic programming algorithm; Finally, the effectiveness of the optimization method is verified through simulation examples.

The wind measurement experiment was conducted by using three-dimensional ultrasonic anemometers in the building environment in Hohhot, Inner Mongolia, and the collected wind data were processed into sub-data samples with sampling frequencies of 10 Hz, 5 Hz, 2 Hz and 1 Hz and sub-data samples with averaging periods of 10 min, 5 min and 1 min, respectively. The effects of sampling frequency and average period on wind speed, wind direction, turbulence intensity and turbulence power spectral density in the built environment were explored and analyzed. The results show that the wind field characteristics can be accurately captured by using a 1 Hz sampling frequency, without pursuing too high sampling frequency. Wind speeds which are much larger or smaller than the average wind speed can be retained by using1min averaging period, and the wind power density calculated is closer to the actual situation. While the averaging period has a slight influence on the wind direction frequency, but does not change the wind direction distribution characteristics and the main wind direction. Meanwhile, the averaging period has no effect on the turbulence intensity and turbulence power spectral density in the building environment. The turbulence intensity and turbulence power spectral spectrum density under a 1 min averaging period are relatively smaller. However, the turbulence intensity may be overestimated under a 10 min averaging period in the building environment, but it provides a relatively safe basis for wind turbine load analysis.

A novel bottom-supported offshore wind turbine bucket foundation platform employs a U-shaped mudmat as its foundation. This study designs three different foundation types for the platform： single mudmat, mudmat with four buckets, and skirted mudmat. A series of model tests and finite element simulations were conducted to investigate the effects of foundation type, loading height, and direction on the horizontal bearing behaviors of the foundations in sand. The results show that, under the same loading conditions, the horizontal bearing capacity increases by 66% for the mudmat with four buckets and 165% for the skirted mudmat compared to the single mudmat. The finite element results closely match the model tests, validating the reliability of the finite element analysis method. The single mudmat primarily exhibits translational motion, whereas the mudmat with four buckets and the skirted mudmat exhibit combined rotational and translational motion, and the skirted mudmat shows the highest rotational displacement, making its horizontal bearing capacity more sensitive to loading direction and height. Finite element analysis reveals that the failure modes for all three foundations involve plastic strain in the soil near the structures on the compressing side. During horizontal displacement loading, the mudmat and the compressed bucket provide approximately 80% of the resistance in the mudmat-buckets system, and the skirt contributes approximately 90% of the resistance in the mudmat-skirt system.

Considering the offshore installation requirements and the environmental loads of the in-situ operation conditions of the wind turbine platform, a fully submerged Tension Leg Platform structural concept is proposed based on the theory of hydrostatic stability and hydrodynamics of the floating body, with an IEA 10 MW wind turbine arranged on the upper part of the platform, and 4×3 wireropes of 130 mm spiral stand rope as the main body of the tendon system. Then a fully coupled integrated analysis model of the wind turbine-platform-tendon system is established, and the time domain numerical analysis is carried out to evaluate the global performance of the tension leg platform concept under the environmental conditions in the South China Sea area. The results show that the tension leg platform has excellent motion performance, with the maximum inclination angle not exceeding 1° under the 50-year return period design environmental conditions. The tendon system using wire ropes meets the strength and fatigue life safety factor requirements specified in the design code.

This paper evaluates the impact of low air density on the performance of blades for a high-power wind turbine. The GH-Bladed computing platform was used to compare the blade performance and turbine load in steady wind condition. The study found that low air density severely weakens the aerodynamic performance of the blades, leading to a delay in achieving rated power for wind turbines. Low air density expands the stall range on the blade surface, leading to an increase in stall risk. Under low air density conditions, installing vortex generators is an effective solution to avoid the loss of blade power generation, and ensure the load stability of the wind turbine. Without load constraints, however, increasing the blade chord length or optimizing the twist angle distribution is a highly efficient technique for improving blade aerodynamic performance.

In response to the current scarcity of historical operating data for some wind farms in China, this paper proposes a data-driven wind farm power generation migration prediction model based on a convolutional neural network gated-recurrent unit（CNN-GRU） model. Firstly, based on the advantages of CNN and GRU models, a CNN-GRU combined model is constructed to eliminate overfitting problems and reduce training cycles. Secondly, the K-means clustering algorithm is used to cluster the historical operation data of wind farms, reflecting the features of large-scale scenes with a few typical scenes, reducing computational complexity while improving training accuracy. Thirdly, the migration conditions in each migration prediction scenario are further clarified, which not only avoids overfitting problems during the migration process, but also provides decision-making for parameter migration between the source domain and the target domain. Finally, compare the performance of the different migration prediction models is and compared the best migration prediction method is selected. Based on the training results, it can be concluded that the transfer prediction accuracy of the modified CNN-GRU model is significantly higher than that of the traditional LSTM model and the uncorrected CNN-GRU model. The K-means clustering algorithm can be used to optimize and identify reference wind farms, and to further improve the accuracy of transfer prediction results of the CNN-GRU combined model.

To address the issues of poor dynamic response and insufficient adaptability of controller parameters leading to significant output power fluctuations when traditional wind turbine pitch angle control strategies are confronted with wind speed variations, an improved linear active disturbance rejection pitch angle control strategy based on Deep Deterministic Policy Gradient （DDPG） algorithm is proposed. The proposed strategy introduces an additional free expansion dimension state variable on top of the linear extended state observer （LESO） and improved the parameters of the expanded system in a proportional-derivative form to enhance the capability of disturbance feedforward compensation. Then, an appropriate reward function is designed according to the generator speed error, and the improved linear active disturbance rejection control （LADRC） parameters can be adjusted adaptively by using DDPG algorithm, so as to achieve the optimal control effect. The simulation results show that the proposed strategy can make the pitch angle quickly adapt to the changes in wind speed and effectively deal with the violent fluctuation of wind speed, therefore maintaining the stable operation of wind turbine and the efficient output of electric energy.

The mid-long term prediction of wind speed plays an important support for wind power generation and dispatching. Due to the highly variable and gusty characteristics of wind speed, exploring methods to improve wind speed forecasts has important practical significance. In this research, five parameterization schemes were used to predict the 10 m wind speed in spring and summer in the coastal areas of eastern China. A random-forest-based correction model was constructed based on the wind speed predicted by the optimal scheme and the corresponding observed wind speed. The results show that the performance of each WRF experiment on predicting the 10 m wind speed are close, and the simulated wind speed is greater than the observed wind speed. The P5 parameterization scheme yields the best performance for the 10 m wind speed prediction over the study area, with the highest wind speed prediction accuracy of 38%. After corrected by the random forest model, the wind speed prediction accuracy is increased to 53%, indicating that the correction effect is significant. Moreover, the correction improves more over the inland regions than over the offshore areas.

Aiming at the complex nonlinear relationship and the difficulty in capturing long-range dependencies of wind farm data, a new ultra-short-term wind power prediction method （PBY-Trans） based on a Bayesian parameter-optimized Transformer time segmenting model is proposed by introducing a time segmentation strategy. This method employs the time segmentation technique to divide the wind farm data into subsequences, which are then utilized as the inputs to the Transformer model encoder to better adapt to the nonlinear characteristics of the time series. Furthermore, a Bayesian algorithm is employed to search for the optimal configuration of the Transformer model parameters, thereby enhancing the model performance and improving the prediction accuracy. The prediction performance of the proposed method is compared and verified using the data set of a wind farm in Bengaluru. Compared with the SVM, RNN, Informer, LSTM, GRU and TCN models, the mean absolute error（MAE） metric of the proposed PBY-Trans method achieved a decrease of 33.56%, 59.75%, 47.27%, 32.34%, 40.46% and 27.71%, and the root mean square error（RMSE） was also reduced by 68.99%, 37.05%, 27.60%, 14.43%, 16.42% and 12.29%, respectively. The results indicate that the proposed PBY-Trans prediction model can further enhance prediction accuracy and the robustness can be effectively enhanced.

To address the non-stationarity and volatility of wind power series, this paper proposes a short-term wind power forecasting framework consisting of two parts： information entropy clustering decomposition and a channel-time attention bidirectional long short-term memory network forecasting model. Firstly, the wind power series undergoes information entropy clustering decomposition. The improved complete ensemble empirical mode decomposition with adaptive noise is utilized for the initial decomposition, and the high-complexity components derived from this process are subsequently decomposed using variational mode decomposition. Based on information entropy, components with high similarity are clustered to form new modal components.Secondly, these decomposed components are fed into the CTA-BiLSTM forecasting model. This model employs both channel attention and temporal attention mechanisms to assign different weights to features based on their importance. Finally, experiments are conducted using a dataset from a wind farm in northwest China. The experimental results show that the proposed framework attains superior forecasting accuracy in comparison to current state-of-the-art models.

To investigate fatigue analysis methods for offshore wind turbine foundations under integrated coupling design, a fully coupled "wind turbine-tower-foundation" model is established using Sesam software, and an integrated design workflow for offshore wind turbines foundation is developed. Based on this model, sensitivity analyses of environmental parameters are conducted, the linear superposition and combined action effects of wind- and wave-induced fatigue damage are compared, and the differences between integrated design and conventional iterative design are evaluated in terms of foundation loading characteristics and steel consumption. Results show that among the four key environmental parameters （wind speed, water depth, wave height, and wind-wave angle）, the fatigue response of the monopile foundation is most sensitive to wind speed variations. The fatigue damage under combined loading is on average 26.50% higher than that obtained from linear superposition. Compared with conventional iterative design, integrated design can significantly reduce foundation steel usage by appropriately increasing the steel consumption of the tower, thereby optimizing the total steel consumption of the "tower+foundation". In this case study, the total steel consumption is reduced by 10.11%.

To address the issue of low accuracy in estimating the instantaneous frequency of wind turbines using a single vibration or current signal, this paper proposes a method for rotational frequency estimation and gearbox fault diagnosis based on the deep integration of vibration and rotor current signals. The goal is to improve the accuracy of gearbox fault diagnosis in wind turbines. Firstly, the vibration and rotor current signals are denoised using the Variational Mode Decomposition method and low-pass filtering, respectively. The instantaneous frequency curves of the vibration and rotor current signals are then obtained using synchroextracting transform, and the time-frequency curves of both signals are preprocessed to obtain two time-frequency curves of the wind turbine. Subsequently, a DA-BLCNN deep network model is proposed to fuse the instantaneous frequency curves of the gearbox fault bearing. Finally, an order tracking algorithm is used to resample the gearbox vibration signal at equal angles, enabling fault detection of the gearbox. Through experimental validation of bearing faults in the gearbox under varying speeds, it can be concluded that the method proposed in this paper can effectively achieve fault diagnosis of the gearbox.

To investigate the fatigue performance of prefabricated beam-slab foundations under wind-induced cyclic loads, an integrated analytical finite element model of the prefabricated foundation-soil-turbine unit-tower was established. On this basis, the thrust coefficient method was adopted to simulate the wind load time history of the impeller, while considering the wake effect of the tower wind field. The dynamic response of the prefabricated foundation under rated and shutdown operating conditions was analyzed, and the fatigue hotspots of the foundation were identified. Finally, a fatigue performance analysis method based on the S-N curve and considering the combined effects of wind speed and direction was established. According to the hotspot stress time history data under various fatigue conditions, the fatigue life of the hotspots of the prefabricated foundation was evaluated. The results show that： the fatigue damage of the cast-in-place joint of the new prefabricated beam-slab foundation is mainly caused by the rated operating condition wind speed segment in the vertical wind direction; the joint will not suffer from fatigue failure within the design life of the turbine unit, and the prefabricated foundation exhibits excellent fatigue performance.

A hybrid wind speed prediction model is proposed to address the instability and intermittency of wind speed, which integrates convolutional neural networks （CNN）,minimum gated storage networks （MGM）, whale optimization algorithms （WOA）, and time-varying filter based empirical mode decomposition （TVFEMD）. TVFEMD is used decomposition to decompose the original wind speed data into multiple subsequences. Guided by sample entropy, use TVFEMD to decompose the sequence with the highest complexity twice. Then, the decomposed subsequences are input into the WOA algorithm optimized hybrid model for prediction, and the prediction results of each subsequence are obtained to produce the final result. The experimental results show that compared with other models, the average absolute error of the proposed model has decreased by 2.3%-8.6%, and the same effect has been achieved in different datasets, verifying the effectiveness of the mixed model in wind speed prediction.

In view of the situation where fatigue failure is prone to occur in offshore wind power structures, a novel damping device has been proposed for a 10 MW offshore wind turbine based on a monopile. The efficacy of the damping device has been validated through a combination of model testing and numerical analysis. The model test demonstrates that the structural damping is increased by 52.59% and 103.11% due to the damping device, when the additional masses are 1.450 kg and 2.175 kg, respectively. The results of model numerical analysis align closely with the results of model test, thereby substantiating the reliability of the numerical analysis. The numerical analysis of the prototype demonstrates that the damping ratiio is enhanced by 36.17% and 65.11% due to the damping device when the additional masses are 50 t and 100 t, respectively. This evidence substantiates the efficacy of the new damping device in improving the structural damping of offshore wind turbines.

In order to enhance the renewable energy accommodation capacity of regional power grids and reduce carbon emissions as well as the energy consumption of carbon capture, a pure oxygen combustion carbon reduction system based on wind-powered oxygen production was constructed. This system takes into account the oxygen demand for pure oxygen combustion under different scenarios and proposes a capacity optimization allocation strategy for multiple scenarios. Firstly, an improved clustering algorithm using P&U-K-means（principal component analysis & uniform manifold approximation and projection-K-means） is applied to obtain typical source-load scenarios. Secondly, to optimize the economic performance of the system, operational modes and configuration schemes are proposed for different scenarios. Based on this, a multi-scenario optimization configuration model for the wind-powered oxygen production-based pure oxygen combustion carbon reduction system is established. Finally, simulation verification is conducted using annual source-load data from different regions in Belgium as an example, and the configuration results and influencing factors under different scenarios are compared and analyzed. The results show that, by considering different scenarios, the system’s profit can be increased by approximately 47.83%. The introduction of pure oxygen combustion reduces the carbon reduction cost of the system by about 8.71% and increases the carbon capture capacity by more than 14.9%.

A numerical model for dynamic stall of the pitching wavy leading-edge blade under plasma excitation was established by employing the sliding mesh technique and SST k-ω turbulence model. The lift and drag, flow structures, and dynamic stall characteristics of pitching （-5°—25°） NACA0012 straight blade and its modified wavy leading-edge blades under different plasma excitation parameters were studied, and the active control mechanism of dynamic stall of the blade with wavy leading-edge blade coupled with plasma excitation was explored. The results show that the wavy leading-edge blade can effectively reduce the peak lift and drag forces and decrease the overturning moment during pitching. After coupling with plasma excitation, the separation vortex of the wavy leading-edge blade separates earlier, and the force drop is reduced during the pitching transition, the area of the lift hysteresis loop is reduced by 23.9%. And the separation vortex generated in the subsequent down-pitching stage is significantly reduced, resulting in smaller force fluctuations, indicating that its controlled performance is superior to that of straight blade. As the plasma excitation voltage amplitude increases, the coupled control effect becomes stronger during the down-pitching transition, further reducing the hysteresis effect of the blade and weakening the oscillation characteristics, effectively improving the dynamic stall performance of the wavy leading-edge blade.

In this study, the spatial and temporal distributions of wind speed are analyzed based on many years of measured wind speed data from the region around Hangzhou Bay. Using 65 years of measured wind speed and typhoon best-track data from Shengsi Station, a multiple regression analysis method is used to establish a formula to calculate wind speed during typhoons at Shengsi. Storm surge water level increase data from Zhapu in the northern region of Hangzhou Bay are combined with theoretical analysis to establish a formula to calculate storm surge water increase in Zhapu. Finally, the applicability of 6 different wind wave formulas for Hangzhou Bay are assessed by comparing the predicted values against observed wave measurements at Zhapu Station. The results show that the wind speed generally shows a decreasing trend in the reqion around Hangzhou Bay, with the largest decrease in magnitude occurring near the south bank of Hangzhou Bay, followed by the north bank of Hangzhou Bay and then the islands. The wind speed decreases as it moves further inland, with wind speed on the islands being the greatest, followed by the north bank and the south bank of Hangzhou Bay. The ratio k between the maximum wind speed and the average wind speed is inversely correlated with the average wind speed. The correlation of wind speed in Hangzhou Bay is high, with an average correlation coefficient of 0.66. The wind speed correlation decreases with increasing distance, and the correlation among islands is the highest, followed by the north bank and then the south bank of Hangzhou Bay. There is little difference between the connection formula and the basic formula in a short wind field, but there is a significant difference in the long wind field. The relationship between wind speed and wave height in Hangzhou Bay during periods of strong winds essentially obeys the Putian basic formula. It is advisable to calculate wind waves in the bay for a long wind field using the basic formula.

Based on the existing theoretical studies related to clay creep and foundation load transfer, this paper proposed a new calculation method for prediction of long-term settlement and cumulative inclination of conduit rack barrel foundation in clay during service. The method is able to more accurately predict the settlement of foundations by fully considering the soil creep that occurs during the various stages of settlement during the service life of offshore wind turbines. The numerical model was established by soft creep soil constitutive model in finite element software PLAXIS, and the numerical simulation results were compared with the theoretical calculations. The results show that the calculation method is able to accurately predict the vertical settlement of single-barrel foundations with different L/D and the settlement and cumulative inclination of conduit rack barrel foundations with different S/D in clayey soils, with the relative error within 10%. The development and change rules of the settlement and cumulative inclination of the barrel foundation under different conditions are summarised, and the reasonableness of the new method for the prediction of the whole settlement process is analysed.

This study has developed a method for the motion prediction of a 10 MW floating wind platform under the action of waves, based on the Bi-directional long-short-term memory（Bi-LSTM） neural network. By simulating a 10 MW floating offshore wind power platform, wave and motion time series are obtained for a parameter sensitivity analysis. The simulation data are used to train the Bi-LSTM neural network framework and the parameters are then optimized. The results show that the developed Bi-LSTM model is highly effective in predicting the motion of the floating offshore wind platform under wave action in the next 1/3 time-length of the input data considering different wave heights and spectral peak frequencies. The prediction accuracy of the wave-induced heave and surge is as high as 95%. Therefore, the method proposed in this study has a strong ability to predict platform motion and is of great importance for the development of offshore wind energy.

The hysteresis effect of the floating wind turbine caused by the wake induction effect and the airfoil unsteady aerodynamics under the surge condition is comparatively investigated based on the blade element momentum model, dynamic blade element momentum model and free vortex wake model, and the differences between the results predicted by different models are discussed. The results show that, for the hysteresis caused by the wake induction effect, the phase lag angle predicted by dynamic blade element momentum model is smaller than that of free vortex wake model, while for the hysteresis caused by airfoil unsteady aerodynamics, the phase lag angle predicted by blade element momentum model and dynamic blade element momentum model are larger than that predicted by free vortex wake model, after coupling with the airfoil dynamic stall model. Although there are differences in phase lag values predicted by different models, the variation trends of the hysteresis along the blade and the influence patterns of the surge parameters on the hysteresis given by different models are consistent.

The current work innovatively mixes biomass waste（corncob） and fossil-fuel energy by-product （petroleum asphalt） to prepare activated carbon, and investigates its potential for application in supercapacitors. A Box-Behnken experimental design was employed to optimize the production process for activated carbon, with the specific objective of enhancing its three-electrode specific capacitance. The physicochemical properties of the activated carbon were characterized so as to gain insight into its composition and structure. Furthermore, the electrochemical performance of the activated carbon was tested under three-electrode and two-electrode systems. The results demonstrated that the specific capacitance of the hybrid activated carbon（AC-20-3.8-740） produced with 20% corncob, alkali-to-feedstock ratio of 3.8, and activation temperature of 740 ℃ was optimal. The actual value of the optimal specific capacitance can reach 498.1 F/g, which was in agreement with the predicted value. The high performance of the AC-20-3.8-740 can be attributed to its ultra-high specific surface area of 4054.2 m²/g, hierarchical porous structure with an excellent ratio, suitable graphitization/defects degree, and abundant heteroatoms. In the two-electrode system, the AC-20-3.8-740 device exhibits a remarkably high specific capacitance of 104.08 F/g and an energy density as high as 14.45 W·h/kg. It also demonstrates an nerly100% high coulombic efficiency and 96.41% capacitance retention after 10000 cycles of charging and discharging, suggesting good applicability of the resultant activated carbon.

To address the limitations of traditional siting strategies, a novel siting approach based on the SWAN numerical simulation model and the levelized cost of energy （LCOE） model is proposed. This method is validated using real-world data from the study area. Additionally, to mitigate potential grid instability issues caused by the integration of wave energy power stations, a hybrid neural network model, BiTCN-BiGRU-Attention, is employed to predict the short-term power generation at the optimal site, thereby enhancing the stability of subsequent operations. The results indicate that this siting strategy provides a scientific basis for the rational siting and stable operation of wave energy power stations.

This study takes the three-cylinder oscillator with rigid connection as an energy harvester, which is arranged in an equilateral triangle. The influence of different water flow incident angles α=0°, 45°, 90°, 135° and 180° on the hydrodynamic response and low-velocity hydrokinetic energy capture of the energy harvester is studied by two-way fluid-structure interaction （FSI） numerical method. The results show that the incidence angle significantly affects the dynamic response and lock-in range of the three-cylinder oscillators. There is no lower branch in amplitude response at α0° and α135°, while the lock-in range is widest at α0° and narrowest at α=45° and α180°. The vortex of the three-cylinder oscillator at each incidence angle in the initial branch shows “2S” patterns, and show different vortex-shedding patterns such as “P+S”, “T”, “2P” and others in the upper and lower branches. When the incident angles are parallel （i.e., α0° and α180°） or perpendicular to the incoming flow direction （α90°）, the three-cylinder oscillators have higher energy capture performance. The incidence angle α0° is the most suitable angle for energy harvesting. It is followed by α180°and α90°. Considering the energy conversion efficiency, α0° at U_r=9 is the best chice.

A hybrid demand response orderly charging optimization strategy based on MC-BiLSTM-BDA prediction is proposed. Firstly, a charging load prediction model, named MC-BDA-BiLSTM, is constructed by integrating Markov chains （MC）, Bi-directional long short-term memory （BiLSTM） network, and Bi-directional Attention （BDA）. Secondly, according to the peak, flat, and valley charging periods classified by the K-means fuzzy clustering algorithm, a price-incentive hybrid demand response （PrIncHDR） strategy is presented, which introduces price compensation incentive mechanism on the basis of price based demand response to avoid peak-valley inversion and promote supply and demand balance. Based on this strategy, an EV orderly charging optimization model is constructes, which includes renewable energy utilization rate, user benefits and photovoltaic charging station （PVCS） benefits, and is solved by the NSGA-II algorithm. The experimental results show that compared with BiLSTM model, the MC-BiLSTM-BDA model increases the determination coefficient of the charging load prediction by 11.93%, and reduces the root-mean-square error by 35.06% and the mean absolute error by 37.41%, respectively. The proposed charging optimization strategy based on price-incentive hybrid demand response effectively reduces peak-valley differences, enhances renewable energy consumption, and achieves a win-win for users and photovoltaic charging stations.

In the process of combining renewable energy with energy storage,a multi-microgrid low-carbon economic optimization model considering shared energy storage is established to address the interactive relationship between multiple entities and the low utilization of energy storage. Energy storage station is built within the multi-microgrid area,and the shared energy storage dispatch center sets an energy storage discount service price based on net load fluctuation of each microgrid,and promotes charging and discharging transactions among microgrids through real-time charging and discharging prices. In the microgrid,users adjust their electricity demand according to the incentive demand response mechanism,and the microgrid model is established considering the carbon emission costs. The particle swarm optimization algorithm and solver are used to solve the shared energy storage-microgrid two-layer optimization model,and four operation scenarios are set for comparison. The simulation results show that the proposed model can effectively improve the operational benefits of each entity and reduce the carbon emissions of the system.

This paper proposes a two-layer multi-scenario collaborative optimization configuration model for off-grid multi-microgrid systems that considers the life degradation of energy storage equipment. Among them, the upper model configures the capacity of microgrid equipment with the optimization goal of minimizing the annualized total cost of the multi-microgrid system; the lower model optimizes the output of equipment with the goal of minimizing the daily operating cost of the multi-microgrid system. The model considers the influence of the state of charge and depth of discharge depth on the life of energy storage, and establishes a calculation model for the life degradation of energy storage. By applying the established model, case analysis and calculation of independent optimization and coordinated optimization of multi-microgrid systems were carried out, verifying that the coordinated planning of the multi-microgrid system can effectively reduce the annualized total cost of the system.

Considering the complex spatio-temporal distribution characteristics of the distribution network net load, a multi-objective energy storage system optimal configuration model for the energy storage system is proposed. Firstly, based on the spatiotemporal stochastic distribution characteristics of net load, a spatio-temporal prediction model for uncoordinated electric vehicle charging load is constructed. Secondly, an optimization model for energy storage configuration is established, considering key factors such as the daily comprehensive cost of energy storage systems, the net load fluctuation rate, and network losses. The model is solved by integrating a multiobjective whale optimization algorithm with the Gurobi solver. A case analysis is conducted on an improved IEEE33-node distribution network. The results indicate that by incorporating the spatiotemporal characteristics of distribution network net loads into the energy storage optimization configuration, the fluctuations of net loads can be significantly smoothed, verifying the feasibility and effectiveness of the proposed configuration model.

To achieve more accurate predictions of the remaining useful life （RUL） of lithium-ion batteries and enhance their reliability to ensure stable operation, this paper proposes a parallel neural network prediction method based on multi-scale feature fusion. First, multi-scale features of the lithium-ion batteryies are extracted using temporal convolutional network （TCN） at different scales, enhancing the capture of both local and global features. Then, a cross-attention mechanism is introduced to filter and fuse the features, focusing on key degradation information. Next, parallel Bi-LSTM and Bi-GRU networks are constructed to learn degradation features and establish long-term dependencies on the time scale, ultimately achieving RUL prediction. The proposed method is validated using the NASA and CALCE lithium battery datasets, demonstrating its effectiveness in various contexts.

To address the accuracy issue of Extended Kalman Filter （EKF）in State of Charge （SOC） estimation for lithium-ion batteries, this study is based on a second-order RC equivalent circuit model and uses the Multi-Innovation Recursive Least Squares （MIRLS） algorithm for online identification of lithium-ion battery model parameters. On this foundation, we propose a Weighted Multi-innovation Improved Sage-Husa Adaptive Extended Kalman Filter（WMISAEKF） algorithm to resolve the issue of filter divergence caused by noise covariance updates. Furthermore, we introduce a novel weight calculation method that fully utilizes historical innovations and rationally allocates innovation weights, thereby achieving accurate SOC estimation. Simulation examples are used to validate the performance of the proposed improved SOC estimation algorithm. The results demonstrate that the improved algorithm exhibits strong convergence and robustness during the update process, achieving significant advancements in key error metrics. Compared to the EKF algorithm, the Mean Absolute Error （MAE） and Root Mean Square Error （RMSE） are decreased by 89.01% and 79.06%, respectively. This clearly confirms the substantial enhancement in SOC estimation accuracy for lithium-ion batteries provided by the proposed method, providing new practical support for extending battery lifespan.