With the increasing proportion of renewable energy, the construction of new type power systems had entered a new stage and the demand for enhanced security and stability of power systems induced a strong need for active grid support capabilities and related equipments. This paper systematically introduced photovoltaic-energy storage virtual synchronous generator (PV-ES-VSG) and related systemic technologies. Firstly, the principles and key parameters of PV-ES-VSG were outlined in conjunction with application scenarios, and the topology was proposed by comparing with the current three PV-ES coupling forms. Then, based on different transient and steady-state control objectives, the grid-forming control technologies for PV-ES-VSG and the coordinated PV-ES control strategies were clarified. Core functions such as cluster coordination control and black-start capability were also introduced. Finally, the techno-economic analysis of PV-ES-VSG in typical application scenarios was conducted, and outlooks on technological challenges and future development trends were also provided.

With continuous increase in penetration rate of photovoltaic (PV) generation, PV generation systems are required to possess grid-forming capability. To meet current limiting requirements, existing methods generally adopt control architectures with cascaded power outer loop, voltage loop and current loop, or introduce virtual admittance links in control loop. Impedance modeling and grid-connected stability of virtual synchronous generator (VSG)-based grid-forming PV inverters incorporating virtual admittance and current inner control loops were investigated. Firstly, the impedance model of the PV inverter under VSG control was established and validated. Secondly, the influence laws of key factors such as the power outer loop parameters, the parameters and structures of virtual admittance link and the cutoff frequency of low-pass filters on the impedance characteristics of the PV inverter were analyzed. Subsequently, based on the generalized Nyquist stability criterion and the system loop impedance method, the influence laws of control parameters and grid strength on the small-signal stability of VSG-based grid-forming PV inverters were investigated. Finally, the accuracy of stability conclusions was verified through MATLAB/Simulink simulations. The results demonstrate that a smaller virtual inductance value facilitates stable operation in medium-low frequency bands; appropriately increasing both virtual resistance in virtual admittance link and cutoff frequency of low-pass filter can effectively enhance system stability margin; under strong grid conditions, the grid-forming PV inverter systems may encounter low-frequency oscillation instability risks.

Under the "dual carbon" goal, photovoltaic technology application is developing rapidly, and the global installation volume is growing exponentially. The lifespan of photovoltaic modules is usually 25 to 30 years. How to deal with decommissioned photovoltaic modules has gradually become a challenge in the development of new energy field. In order to achieve economic efficiency and environmental friendliness in the recycling and treatment of a large number of decommissioned photovoltaic modules, literature review and analysis were conducted on the battery types, recycling technologies, and environmental impacts of decommissioned photovoltaic modules by the software of CiteSpace. The results show that in the past 10 years, the recycling of decommissioned photovoltaic modules has still been dominated by crystalline silicon battery modules, and transformation and upgrading of the recycling technologies as well as environmental friendliness are the key to development; the processing technologies for crystalline silicon battery modules mainly include mechanical separation method, thermal treatment method, chemical dissolution method, and various combinations of multiple processes; future technological development needs to further progress in aspects such as multi-technology combining collaborative innovation, intelligent upgrading, and breakthroughs in green and low-carbon technologies.

In order to solve key problems like poor accuracy of current photovoltaic (PV) power forecasting models during extreme weather, lack of detailed analysis of weather conditions and absence of flexible adjustment methods, a photovoltaic power forecasting method that considering multiple scenarios and differentiated compensation strategy was proposed. In the method, a concept of mixed weather types was proposed, and adaptive classification of weather state scenarios was realized by constructing a clustering algorithm with an optimal evaluation function (COEF). Based on extreme learning machine, basic value prediction model was constructed, and compensation mechanism of multiple scenarios was clarified, and the multi-scale correction of the basic prediction value was realized by designing targeted error compensation models for different weather scenarios, so as to improve the prediction accuracy of the algorithm. Finally, multiple station real data from different regions with different climatic characteristics were selected for simulation testing. The simulation results show that compared with physical model and traditional machine learning algorithm, the proposed photovoltaic power forecasting method has better prediction effect under multiple time scales and multiple scenarios.

To compare the performances of Rankine-Carnot batteries with different energy storage methods, thermodynamic models of the heat storage Rankine-Carnot battery and cold storage Rankine-Carnot battery were developed, respectively. The energy efficiency and exergy efficiency of the two systems under the same parameter conditions were compared and analyzed. The results of energy analysis show that: the round-trip electrical efficiency of Rankine-Carnot batteries with different energy storage methods is jointly determined by the charging cycle efficiency and discharging cycle efficiency. For the heat storage Rankine-Carnot battery, its round-trip electrical efficiency decreases by 17.99% as the heat storage temperature increases by 10 K, and decreases by 12.06% as the ambient temperature increases by 10 K. For the cold storage Rankine-Carnot battery, its round-trip electrical efficiency increases by 15.82% as the cold storage temperature increases by 10 K, and decreases by 22.69% as the ambient temperature increases by 10 K. The results of exergy analysis show that: the exergy efficiency of the heat storage Rankine-Carnot battery decreases by 2.62% as the heat storage temperature increases by 10 K, and decreases by 4.90% as the ambient temperature increases by 10 K. The exergy efficiency of the cold storage Rankine-Carnot battery increases by 1.91% as the cold storage temperature increases by 10 K, and decreases by 7.82% as the ambient temperature increases by 10 K. The results of parameter sensitivity analysis show that under the same cycle temperature difference, both the energy evaluation indicators and exergy evaluation indicators of the heat storage Rankine-Carnot battery are higher than those of the cold storage Rankine-Carnot battery. Moreover, compared with the discharging cycle, the performance parameters of the charging cycle are more sensitive to changes in parameter conditions.

In the fluctuation suppression process of the new-type power system with high penetration of wind power, the issues of work coordination and energy efficiency of hybrid energy storage equipment supporting the source side have become prominent. A wind power fluctuation suppression strategy for hybrid energy storage considering dynamic energy efficiency characteristics was proposed. Firstly, to address the issue that the constant efficiency model cannot describe the actual nonlinear dynamic energy efficiency characteristics of energy storage, a ladder energy storage efficiency model and its construction method were proposed to characterize the differentiated efficiency characteristic curves of multiple types of energy storage, so as to describe the dynamic energy efficiency level of energy storage operation in real time. Secondly, a two-layer rolling optimization control model for hybrid energy storage was developed. The upper-layer model adopted the ensemble empirical mode decomposition (EEMD)-fast Fourier transform (FFT) algorithm, and solved for the pre-dispatching scheme based on grid-connected fluctuation limits and energy storage response capability. The lower-layer model was based on the Pareto front-technique for order preference by similarity to ideal solution (Pareto-TOPSIS) to construct a real-time dynamic decision-making mechanism. This mechanism performed secondary optimization on the pre-dispatching plan according to the energy efficiency level and grid-connected fluctuation, thereby achieving the dynamic balance between the goals of fluctuation suppression and self-energy loss reduction. Case study results show that compared with the step-by-step optimization model, the proposed model not only maintains a good fluctuation suppression level, but also reduces energy storage energy loss and operation and maintenance costs by 46.7% and 34.4% respectively, and reduces the operation time proportion of flow batteries in the high-energy-efficiency state to 14.6%. The model effectively improves the economy of control coordination and cooperation of hybrid energy storage, and takes into account the dynamic efficiency characteristics of hybrid energy storage.

To enable wind turbine generators to possess functions such as voltage support and primary frequency regulation similar to those of synchronous generators, taking a 2 MW direct-drive wind turbine generator as the research object, a scheme was proposed to construct a grid-forming wind-storage integrated unit by configuring energy storage on direct current (DC) side. A virtual synchronous control structure and an active support strategy involving the coordinated operation of the machine-side, grid-side, and energy storage converter, were presented through this scheme. The favorable effects of this approach under various scenarios were verified through hardware-in-the-loop simulation, and its practical implementation was demonstrated in engineering projects for primary frequency regulation and reactive power voltage regulation. The oscillation issues from the interactive effects of wind power fluctuations and variations in the internal grid intensity of the wind farm were also explained. Results indicate that when the wind turbine detects changes in grid frequency and voltage, this strategy can provide the function of frequency and voltage support by drawing an analogy with rotor motion equations of synchronous generators. The wind turbine can autonomously and synchronously respond with active and reactive power, making it more suitable for operation in weak power grid. The grid-forming wind-storage integrated unit based on virtual synchronous control exhibits capabilities of primary frequency regulation and reactive power support, effectively enhancing the inertia support response rate of wind turbine and support capacity for weak power grid.

To fully leverage the natural regulation capacity of cascade hydropower in river basins, constructing a cascade hydro-photovoltaic (PV)-energy storage multi-energy complementary system has become an important means to enhance the economic efficiency and reliability of clean energy consumption. With the objective of minimizing the total system operating cost, and based on a comprehensive consideration of the PV output power and charge-discharge characteristics of energy storage, a joint optimal scheduling model for cascade hydro-PV-energy storage system was established. The operational constraints of four cascade hydropower stations (with a total of 15 generating units), two PV power generation clusters, and two energy storage systems were delicately considered in this model. By optimizing the energy storage capacity allocation and real-time scheduling strategies, the flexible regulation capability of hydropower and the rapid response characteristics of energy storage have been fully demonstrated, thereby achieving the collaborative optimization by multi-energy complementarity. In terms of modeling methodology, a mixed integer linear programming (MILP) framework was adopted, with linearization applied to nonlinear constraints such as the start-up and shutdown of hydropower generating units, and the model has been efficiently solved by invoking CPLEX solver based on MATLAB software platform. Simulation results demonstrate that the proposed model can effectively reduce the system operating cost and increase the proportion of renewable energy consumption. The total cost of the hydro-PV-energy storage system decreased from 6.033 million yuan to 5.736 million yuan. Moreover, in terms of environmental benefits, the model can achieve the savings of 13.45 million yuan in quantitative indicators compared with traditional thermal power generation. This method reveals the impact patterns of hydro-PV-energy storage complementarity characteristics on the system economic efficiency and environmental emissions, providing a theoretical support for the optimal design and low-carbon operation of multi-energy complementary systems.

Aiming at the problem of decreased simulation accuracy caused by communication interface delay and multi-rate sampling in multi-disciplinary co-simulation of offshore wind turbines, an optimized interface data exchange technology was studied to improve the simulation accuracy of gas-mechanical-electro coupling characteristics. An interface-optimization scheme based on Hermite interpolation was proposed, a mathematical error model was established to compare the error magnitudes of the zero-order hold (ZOH) with the optimized interface. A simplified electro-mechanical co-simulation platform was constructed to verify the interface performance, and the optimized interface was then deployed in a fully coupled offshore-wind simulation system. Results show that in the simplified second-order test, the Hermite-interpolation interface reduces the simulation error magnitude significantly. In the full electro-mechanic simulation, after adopting the optimized interface data exchange scheme, the fidelity of the system is significantly improved, and the response characteristics are closer to real working conditions. By refining the data transfer among cross-disciplinary models, Hermite extrapolation effectively eliminates interface-induced distortions in co-simulation.

The effectiveness of wake steering control in wind farms is highly sensitive to fluctuations in wind conditions. Taking a certain offshore wind farm in northern China as an example, the effectiveness of two wake steering control methods considering average wind condition and wind condition fluctuations under different wind condition fluctuation scenarios was analyzed. Results show that wind direction variability has a greater impact on wake control effectiveness than wind speed variations. The control strategy based solely on average wind conditions improves total farm power output only when the wind direction standard deviation is less than 4°, while when the wind direction standard deviation is greater than 6°, the power generation decreases. In comparison, the strategy that accounts for wind condition fluctuations maintains effective wake steering under all variability scenarios and consistently increases power output, demonstrating the critical need to include wind variability in wake control designs.

To mitigate the degradation of aerodynamic performance in vertical-axis wind turbines resulting from blade surface flow separation, a hybrid strategy integrating boundary layer suction with Gurney flaps was proposed based on active and passive flow control techniques. Computational fluid dynamic methods were used to evaluate the impact of this strategy on energy capture efficiency and overall aerodynamic performance. Results show that the combined use of outward Gurney flaps and suction control enhances aerodynamic performance more effectively than either approach alone. At a tip speed ratio of 2.33, the power coefficient increases by 47.6%. Moreover, this approach suppresses the formation and development of the leading-edge vortex, delays flow separation, significantly improves single-blade torque, overall tangential force, and airfoil pressure difference across various tip speed ratios, reduces the impact of stall on aerodynamic performance, and maintains stable turbine operation.

Taking the IEA 15 MW wind turbine blade as the research object, a dynamic model of the blade aeroelastic system was established to investigate the characteristics of the aeroelastic response at the rated tip-speed ratio, with particular focus on the multi-degree-of-freedom coupling behavior. Results show that at the rated tip-speed ratio, the wind turbine blade experiences flapwise-edgewise coupled bend-bend flutter. The flutter is maintained by aerodynamic work in the flapwise and edgewise degrees of freedom, with a noticeable coupling phenomenon in the development of instantaneous aerodynamic power in these directions. The torsional direction contributes almost no energy to the flutter, exhibiting only minimal aerodynamic power throughout the process. This is primarily due to the coupling characteristics of the blade structure, which undergoes forced vibration excited by the flapwise and edgewise motions.

To address the frequency random fluctuation caused by high-penetration wind power integrated with power system, a fast frequency regulation strategy was proposed based on moment generating function. Firstly, through collecting the local frequency data at connection points of each wind turbine in wind farm, and obtaining the frequency data by monitoring the dispatch center, the frequency deviation signal at current moment was obtained. Secondly, the probability density function (PDF) of frequency deviation was calculated with kernel density estimation, so as to establish a random distribution model of frequency deviation based on the moment generating function, and performance indicators for frequency control system. Thirdly, an analytical solution for frequency control was obtained after optimization solution, and the stability of frequency control system was analyzed. After which, simulation experiments were conducted on a domestic 1 100 MW wind-thermal hybrid power system. Results show that, under both of load and wind speed disturbance conditions, the proposed fast frequency control strategy can maintain the quasi-steady-state frequency deviation of the points of common coupling and each wind turbine within ±0.02 Hz, while the PDF of frequency deviation exhibits a sharp peak and remains a concentrated distribution around zero. Compared with the traditional droop control, the deviation range of quasi-steady-state frequency is reduced by over 80%, and the nadir of frequency is increased by about 0.05 Hz. Relevant results verify the effectiveness of the proposed strategy in mitigating the frequency fluctuation caused by wind speed and load disturbances.

To address the issue that traditional adaptive frequency regulation methods struggle to adjust control parameters in real time according to dynamic system changes, thereby limiting the enhancement of frequency modulation performance, an adaptive frequency regulation control method for wind farms was proposed, based on the twin delayed DDPG (TD3) deep reinforcement learning algorithm. According to grid frequency and wind farm operating conditions, the intelligent agent optimized and learned the frequency regulation parameters during the training process, achieving dynamic and adaptive adjustment of these parameters. Results show that compared with traditional adaptive proportional-integral (PI) frequency modulation control methods, the proposed approach significantly reduces the maximum frequency deviation under various operating conditions and load disturbances, and improves frequency response performance, validating the effectiveness of the method.

Due to the complexity of external factors such as weather conditions and wind turbine control strategies, future forecasting data often deviates from the training data distribution, leading to a significant decline in the accuracy of ultra-short-term wind power prediction models. To address this issue, an ultra-short-term wind power forecasting method based on invariant learning was proposed. The method learned the mapping relationship between invariant features and power by jointly optimizeing the environment inference module and the invariant feature learning module, achieving robust modeling. The results show that compared with two benchmark models, the proposed method reduces forecasting error of the normalized root mean square error and normalized mean absolute error by an average of 1.19-1.30 and 0.41-0.68 percentage points respectively.

An observation scheme for quantifying tower-shadow effects on offshore wind measurement masts was proposed, and a one-year wind dataset was acquired from a mast deployment in Chinese sea area. The influence pattern of the mast tower shadow on critical parameters was analyzed, and a tower-shadow influence factor (T_SIF) was introduced to characterize the severity of shadowing within each wind-direction sector. A mast tower shadow effect correction algorithm was then developed combining a temporal convolutional network (TCN) for extracting wind-speed time-series patterns with a multi-head self-attention mechanism (MHSA) for capturing key shadow-region features. Validation at the installed mast shows that the T_SIF serves as an essential auxiliary feature that significantly improves correction accuracy. The mounting azimuth of anemometers strongly affects both wind-resource measurement precision and the efficacy of tower-shadow correction. The proposed algorithm is applicable to most masts used in engineering and requires only two anemometer data series at one measurement height to correct tower-shadow-induced errors at any single-anemometer height, thereby substantially enhancing the wind-resource assessment accuracy.

In the field of wind turbine tubular tower maintenance, there is a scarcity of research findings concerning the loading design of wall-climbing robots, which cannot fully meet the demands of wind turbine tubular tower maintenance scenarios. To address this issue, a prototype of a robot for wind turbine tubular tower based on strong adhesion characteristics was developed through static analysis and magnetic module optimization design. The design of the mobile and drive units was carried out, while the optimal track material and drive wheel module were selected. The minimum adhesion force requirements for the robot to avoid static slippage and longitudinal overturning were calculated with multi-working-condition force analysis. By using the control variable method, the influence of various structural parameters of the permanent magnet module on the adhesion force was analyzed. A continuous nonlinear programming algorithm was proposed to optimize these structural parameters, thereby determining the optimal magnetic product ratio of the permanent magnet module. Finally, tests were conducted on the prototype to evaluate its loading capacity for both the robot body and cleaning tooling on Q235 steel plate walls and in simulated environments. Research results demonstrate that the designed robot can remain stable without slipping or overturning on a wind turbine tubular tower with an inclination angle of 89.4°. It has a rated loading capacity (measured by mass) of up to 80 kg, meeting the practical maintenance requirements of wind farms.

In the field of offshore energy project construction, the construction scale of offshore voltage source converter-high voltage direct current (VSC-HVDC) converter station is continuously expanding. During the floatover installation of the topside, the eccentric center of gravity effect may have a great influence on the dynamic response of the installation process. Dynamic response of the topside of offshore VSC-HVDC converter station during the floatover installation process considering eccentric center of gravity was mainly studied. Five different conditions were selected for numerical simulation by using OrcaFlex software to analyze the motion response of the vessel and topside, and the load on the fender system during the floatover installation process. Results show that different degrees of eccentricity in the center of gravity result in obvious differences in both the force distribution on the fender system and the motion response of the vessel and topside. When the topside of converter station exhibits a center-of-gravity shift, balancing measures effectively reduce the motion response of the vessel, decrease the force imbalance of jacket pile leg mating units, and lower the fender impact intensity. It provides an important reference for the floatover installation project of the topside of offshore VSC-HVDC, which has positive significance for promoting the development of offshore energy construction.

Aiming at the challenge of coordinated optimization between transient oscillation suppression and steady-state deviation elimination caused by the introduction of damping coefficients in virtual synchronous generator (VSG) control, an active power control method for virtual synchronous generator (VSG) based on fractional-order proportional-integral (FO-PI) damping regulation was proposed. First, the inherent contradiction mechanism of the damping coefficients between dynamic response and steady-state accuracy in conventional VSG control strategies was analyzed. Subsequently, a fractional-order proportional-integral damping regulation module was embedded into the traditional control architecture. This module generated non-zero damping power during transient processes to suppress active power oscillations, and automatically eliminated damping power during the steady-state operation to eliminate steady-state deviations. The effectiveness of the proposed strategy in suppressing transient oscillations and eliminating steady-state deviations was verified through Matlab/Simulink simulations.The results show that the proposed control method can effectively suppress oscillations and overshoot caused by sudden changes in power and frequency, eliminate steady-state errors under grid frequency deviations, and solve the problem of balancing dynamic response and steady-state accuracy in traditional control methods.

Distributed energy systems (DES) based on renewable energy demonstrate significant potential in improving energy utilization efficiency, optimizing energy resource allocation, and facilitating the transition of energy structure. Based on the physical architecture of DES, systematic investigation was conducted on key technical layers, including the physical layer, forecasting layer, modeling layer, optimization layer, and evaluation layer. Core technologies such as source-load forecasting methods, modeling theories and approaches, and optimization and control strategies were critically discussed. The operational mechanisms and inherent laws of DES based on renewable energy were analyzed in depth. The currently faced main technical bottlenecks and research frontiers in the field were sorted out, aiming to provide theoretical support and forward-looking insights for fundamental theoretical research, engineering practical applications, and industrial development in DES-related sectors.