The elastically supported offshore wind turbine drivetrain is the critical transmission equipment to transmit megawattlevel power. Under the couplings of stochastic wind and waves, its dynamic characteristics will be affected by the combined effects of stochastic aerodynamic loads and inertial loads caused by the largescale motion of the supporting platform, leading to complex vibration characteristics. To reduce the influence of stochastic wind and waves on the vibration responses of the offshore wind turbine drivetrain, taking a 6.2 MW offshore wind turbine drivetrain as the research object, a rigid flexible coupling dynamic model of the elastically supported offshore wind turbine drivetrain is established. The mapping relationships among the operation condition parameters, supporting parameters, and vibration responses of the key components are constructed through the surrogate model, and then the optimization model of the elastic support parameters of the offshore wind turbine drivetrain under multiple operation conditions is constructed. The optimization effects under the multiple operation conditions are compared. The results show that the increase of the nacelle motion and input torque will significantly enlarge the vibration responses of the main shaft bearing and the generator stator. The elastic support parameter optimization can effectively weaken these influences on the vibration responses of the main shaft bearing and the generator stator, and its improvement effect becomes more significant as the average wind speed increases. This study has important theoretical reference significance for improving the longterm stable operation ability of the floating wind turbine drivetrain under the couplings of stochastic wind and waves.

Cloud computing demand has caused high energy consumption and carbon emission pressure while generating data center deployment applications, so the efficient utilization of renewable energy in cloud computing environment is proposed. Aiming at the intermittent nonstationary characteristics of solar energy, which is a specific form of renewable energy, we study the cloud task scheduling method to enhance the energy utilization in data center energy supply. DeepAR, a deep autoregressive model for predicting solar energy production capacity, is constructed to design cloud task scheduling strategies and algorithms by taking advantage of the flexible scheduling characteristics of delaytolerant tasks and scheduled workloads in the time dimension, and simulation experiments are carried out using real task datasets and solar energy production capacity datasets by applying the GluonTS framework. The results show that the matching between computing load and solar power output is improved, and the utilization of solar power supply in data centers is enhanced.

The energy conversion efficiency of wave energy buoys is determined by their hydrodynamic performance. From design experience, it is known that the hydrodynamic performance of buoys is greatly affected by the draft depth. The article takes wave energy buoys as the research object and conducts a study on the hydrodynamic performance of buoy draft depth. Firstly, introduce the structure and working principle of the buoy; Secondly, establish simulation models of the buoy under five different draft depth conditions, conduct numerical simulation calculations, and obtain the hydrodynamic parameters and energy conversion efficiency of the buoy under design conditions; Finally, physical model experiments were conducted to study the capture width ratio (Rcw) of buoys under six different draft conditions, and compared and analyzed with simulation results. The results show that when the draft depth is 75 mm, the maximum Rcw of the model is 56.8%; the draft depth has a significant impact on the peak distribution of buoy Rcw; under specific aerodynamic damping, the simulation results fit well with the physical model test results, with an error of less than 5%.

With the continuous improvement of the accuracy of renewable energy and load forecasting, the direct transaction of electric energy between windsolarstorage combined power station and power users has become feasible. Considering the impacts of direct transaction of electric energy on the system operation, a consumption model with direct transaction of electric energy between windsolarstorage system and power users is established. The model aims to maximize the total social benefits. The power output constraints of direct power purchase contracts and the related operation constraints of the energy storage are introduced to the traditional model. At the same time, it makes adjustments to the constraints of total power purchase contracts, and adds the wheeling cost and contracts reduction penalties to weigh the comprehensive benefits brought by direct power purchasing to the system. Combined with the generated clustering scenario, the dayahead optimization results of the windsolarstorage power station are analyzed through a numerical example, and the influence of the contract power limit factor and penalty factor on the quantity of direct purchase power and the total social benefits is studied. The impact of electricity price on the profit balance is analyzed with the Nash bargaining model, which verifies the feasibility and rationality of the proposed model.

In order to solve a series of security problems such as system frequency and voltage offset caused by power shortage in power system, this paper proposes a multiobjective optimization method for precise load shedding control based on POAGWOCSO algorithm. Firstly, a multiobjective optimization model of precise load shedding control based on load classification is proposed from the aspects of safety and economy of power system, considering the constraints of stable operation of power system and output characteristics of distributed generation. In order to enhance the coordination relationship between global and local search in the traditional pelican optimization algorithm (POA), and to overcome the problems of premature convergence, insufficient optimization range and low accuracy of the optimization algorithm in dealing with complex problems. In this paper, the nonlinear inertia weight factor, the wolf group leader strategy in the grey wolf optimization algorithm (GWO)and the crisscross optimization (CSO) are introduced to update the position of the new individual of the pelican. Finally, based on the empirical analysis of the modified IEEE33 node, the improved POAGWOCSO algorithm proposed in this paper is used to solve the emergency load shedding model, and the system coordinated control of economy and stability is realized.

To address the issues of restricted hydrolysis rate and low methane production in the anaerobic digestion of agricultural waste, zerovalent iron(ZVI) with different doses(4,8,12 mg/L) and particle size(microscale, nanoscale) was added during digestion after thermal pretreatment to investigate its enhancement of the codigestion of cow manure and corn straw. The research indicated that the appropriate addition of ZVI promoted both the hydrolysis acidification and methane production processes. The maximum cumulative methane yield was achieved with the addition of 8 g/L microscale ZVI or 4 g/L nanoscale ZVI, which increased by 20.7% and 29.5%, respectively compared with the control group. Microscale and nanoscale ZVI facilitated the release of dissolved organic compounds and the conversion of propionic acid to acetic acid. Nanoscale ZVI exhibited a stronger enhancement effect on hydrolysis and acidification than micronscale ZVI. However, excessive doses (8,12 g/L) of nanoscale ZVI had an adverse effect on methane production. ZVI promoted the enrichment of hydrolytic acidogenic bacteria and acetotrophic methanogens, such as Romboutsia, Saccharofermentans and Methanothrix, which enhanced the processes of hydrolysisacidification and methanogenesis.

Enhancing the inertia of a direct current (DC) microgrid is a key issue to prevent the induction of DC voltage oscillations caused by the introduction of constant power DC loads. Considering the differences in the required supplementary power response rates under various voltage change rates, this paper first proposes a multimode virtual inertia control strategy for the energy storage battery terminals of the DC microgrid, using linear and exponential modes to provide inertia power at different rates, thereby reducing DC voltage fluctuations. Secondly, the equivalent virtual capacitance expressions for both modes are derived, establishing a dynamic relationship between the virtual capacitance and voltage adjustment. Finally, a simulation model of a DC microgrid system with energy storage is constructed to verify the effectiveness of the proposed control strategy in supporting DC voltage stability.

Taking the existing wind turbine pitch control system as the research object, the impeller aerodynamic test with variable incoming flow and angle of attack was carried out, and the fluidstructure coupling calculation was completed by selecting the Fluent module and Transient Structural module in the workbench 2021 platform. The results show that: the maximum error of the power and lift coefficient of the model and test within the test range is 3.7%, 5.8%, and the average error is 2.2%, 3.4%, and the maximum power of the impeller in the rated wind speed of 10 m/s is 2.5 kW, and the lift coefficient of the angle of attack of 14° is the maximum of 1.05. The incoming wind speed has a more obvious effect on the strength of the vortex in the wake area, and the higher the incoming wind speed, the higher the surface pressure on the blades at the same position coordinates. The higher the incoming wind speed, the higher the surface pressure of the blade in the same position coordinate, the smaller the velocity attenuation of the impeller tail, and the maximum stress and deformation suffered by the blade is proportional to the incoming wind speed; the lower the ambient temperature, the higher the surface pressure of the blade in the same position coordinate, and the maximum stress and deformation suffered by the blade is inversely proportional to the ambient temperature, and the ambient temperature is in the range of 20~20 °C, and the maximum stress and deformation caused by the temperature are 1.45% and 2.37%, the results of the study are of guiding significance for the operation of wind turbines in harsh environments.

With the increase in the penetration rate of gridconnected wind power and the continuous expansion of the scale of wind farms, the integration of wind power into the grid will have a significant impact on the power quality and power dispatching of the regional power grids. In order to study the macroscopic dynamic response characteristics of wind farms under large disturbances, it is very important to carry out dynamic equivalent modeling of wind farms. Aiming at the research on dynamic equivalent modeling of wind farms, this paper briefly introduces the current mainstream wind turbine types and their model structures. Then, the reduced order method, the singlemachine equivalent method and the multimachine equivalent method of the equivalent modeling methods are compared and elaborated, and the calculation of equivalent parameters and the equivalent value of the collector network are summarized. Finally, the existing challenges in equivalent modeling of wind farms are summarized, and the future research directions are prospected.

The change in wind direction poses a challenge to the yaw control of wind turbines. Due to frequent changes in wind direction, the response speed of wind turbines is too slow, making it difficult to adjust the yaw angle in a timely manner, thereby affecting power generation efficiency. In order to accurately control the problem of wind turbine wheel yaw, a collaborative intelligent control method for wind turbine wheel yaw considering the randomness of wind speed is proposed. Considering the randomness of wind speed, analyze the wind direction signal and obtain wind direction sample data. Introduce a Bayesian classifier and combine it with a wind direction normal analysis model to calculate the posterior probability of wind direction samples that follow the distribution of the previous batch of samples. Use it as the benchmark for adjusting the warning value, establish a network warning value adjustment mechanism based on Bayesian inference, and adjust the warning value through a mountain climbing algorithm to achieve collaborative intelligent control of wind turbine rotor yaw. The experimental results show that the proposed method achieves collaborative intelligent control of wind turbine rotor yaw, with zero occurrence of yaw in the cabin position and a short yaw control time. This indicates that the method can achieve collaborative intelligent control of wind turbine rotor yaw.