Lignin nanoparticles (LNP) as the carbon precursor is used to prepare hierarchical porous carbon using template methods in this study. In comparison to the porous carbons (SLC, FLC) prepared with a single template method, the LNPbased porous carbon (FSLC) is fabricated using nanoSiO2 coupled with PluronicF127 as double templates. This approach results in a honeycomb like structure with typical mesoporous characteristics for FSLC, achieving a mesoporous ratio of up to 87%. As a supercapacitor electrode, FSLC demonstrates good electrochemical performance, with a mass specific capacitance of 250 F/g at a current density of 0.5 A/g, representing a 163% increase over mass specific capacitance (95 F/g) of the SLC. The dualtemplate method for producing highperformance porous carbon offers a novel approach for utilizing lignin in energy storage application.

To investigate the combustion characteristics of a blended fuel consisting of bituminous coal, miscellaneous wood pellets, and oil sludge, thermogravimetric analysis is conducted on the blended fuel to analyze its combustion behavior. The FWO method is employed for kinetic analysis of the blended fuel. The results show that the combustion process of bituminous coal is primarily fixed carbon combustion, while the combustion process of miscellaneous wood pellets is more complex and exhibits the best combustion performance, with a comprehensive combustion characteristic index as high as 1.93×10⁻⁷. Cofiring with miscellaneous wood pellets effectively enhances the combustion performance of bituminous coal. When bituminous coal and miscellaneous wood pellets are blended in a mass ratio of 1:4, the ignition temperature of the blended fuel decreases by 35% compared to bituminous coal alone. The comprehensive combustion characteristic index increases by 7.2% compared to miscellaneous wood pellets. The most probable function for the cofiring of bituminous coal and miscellaneous wood pellets is [ln (1x)]⁻³/⁴. When the blending ratio of miscellaneous wood pellets reaches 45%, the appropriate addition of oil sludge can improve the combustion performance of the fuel. The most probable function for the combustion reaction of the blended fuel consisting of all three components is [ln (1x)]². When the blending ratios of bituminous coal, miscellaneous wood pellets, and oil sludge are 40:40:20 and 45:45:10, respectively, the corresponding activation energies of the blended fuel are significantly lower than those of other blending ratios, and the comprehensive combustion characteristic index is approximately 1.5 times that of pure bituminous coal.

The microexplosion of emulsified fuel can promote fuel atomization and mixing, however the key to affect its microexplosion characteristic is the emulsifier. Emulsified methanoldiesel was prepared by mechanical emulsification and phacoemulsification, to study the effect of emulsifier types and content on the stability and dispersion of micro emulsified methanoldiesel. The results show the dispersion of the micro emulsified methanoldiesel can be improved by using compound emulsifiers and increasing the emulsifier content; The increase of emulsifier content can improve the microexplosion intensity, delay the initial time of microexplosion, reduce the droplet life, and reduce the droplet evaporation rate in the stabilization stage; The microexplosion intensity of methanol emulsifier content ratio of 10:5 and 10:8 was increased by 34.3% and 37.6% compared with methanol emulsifier content ratio of 10:3 respectively.

This paper proposes a method to predict the photovoltaic output based on weather state pattern recognition and SSABP, which is more accurate than traditional single models under different weather conditions. Firstly, the historical data was cleaned using the 3sigma algorithm to obtain the data that can reflect the output of photovoltaic power plants and the regularity of weather changes. Then, based on the analysis of the parameters such as irradiance, temperature, and wind speed, Gaussian mixture models were applied to classify the professional weather types and three typical generalized weather types were obtained. Furthermore, the data was used as SSABP neural network input to predict the futuristic photovoltaic power plant output. Finally, the carbon accounting method was used to calculate the carbon emission reduction of the photovoltaic power generation project. The experimental results show that through classification recognition and the optimized SSABP neural network, the mean relative errors in the prediction for the three weather types are 0.195, 0.243 and 0.310, respectively. Compared with other predication models, the relative errors are reduced by 17.8%~66.7%. In addition, the relative error between the predicted carbon dioxide emission reduction and actual value is only 3.37%. The model proposed in this work shows satisfactory prediction results.

In order to achieve clean heating in northern rural areas, this paper constructs an air source heat pump heating system based on automatic heat storage/discharge devices and compound multisurface concentrating collectors.Experimental research shows that: at the same water supply temperature, the COP of the heat pump increases gradually with the increase of the inlet air temperature of the evaporator and the intensity of solar radiation. When the heat collector and heat pump are operated together, the heat storage device can automatically store heat and automatically supplement the heat of the air entering the evaporator. The temperature rise of the air is 2~4 °C. The system heat collection and heating capacity on the experimental day were 2.29 MJ/m² and 27.99 MJ/m² respectively, the system energy efficiency ratio (SEER) was 1.84, and the daily average solar energy contribution rate was 41.2%. When the air source heat pump operates alone, the heating capacity of the heat pump on the experimental day is 8.75 MJ/m², and the SEER is 1.79. The above results show that the solar subsystem constructed in this article can automatically adjust the heat storage of the system and the heat supplement to the evaporator, improve the energy efficiency ratio of the system, reduce the difficulty of operation and maintenance, and has certain adaptability in rural areas.

In order to maximize the solar radiation yield, it's imperative to optimize the inclination angle of the solar collector. To this end, a calculation model for solar radiation on inclined surfaces was established, and the computation processes for direct and scattered radiation were separately streamlined. MATLAB was utilized to analyze and perfect the elevation angle of the collector, which led to the determination of monthly and annual optimum inclination angles. The research disclosed that the ideal annual inclination angle in Tianjin is 36.3° , marginally inferior to the local latitude. The monthly tilt angle should vary between 10~64°, with lower degrees in summer and higher in winter. Placing the collector horizontally enhances annual solar radiation by 12.4% and 17.3% at the optimal yearly and monthly inclination angles, correspondingly. Adjusting the optimum tilt angle by 5.0% is feasible when compared with using the local latitude as the tilt angle. Taking Guangzhou, Lhasa, Jinan and Changchun as examples, the annual and monthly optimum tilt angles of these regions are calculated.The comparative analysis of various cities revealed that for regions with high direct radiation proportion, refining the optimal angle on a monthly basis leads to higher energy gains.

Installing a certain capacity of flywheel energy storage system (FESS) at the grid connection of wind farms can effectively smooth the gridconnected power and improve the gridfriendliness of wind farms. To improve the power response speed of FESS and enhance the smoothing effect of wind power fluctuation while avoiding overcharge/overdischarge of FESS, this paper proposes a control strategy for FESS based on a fuzzy Kalman filter and the improved sliding mode control(SMC). The Kalman gain is adaptively adjusted according to the realtime speed and power of FESS. The difference between the filtering result and the wind farm output power is used as the input of the SMC to realize the power control of FESS. The simulation results show that the control strategy proposed in this paper has good dynamic response characteristics and the wind power can be effectively smoothed, thus meeting the requirements of grid connection. The flywheel speed is kept within the limit during the smoothing process, which extends the service life of FESS.

In this paper, in order to study the dynamics of the model turbine with driven motion at different yaw angle operating condition, a wind turbine model based on elasticity is established. Under the condition of turbulent flow, the driven motion state of the model wind turbine is realized with irregular rotation, and the wake distribution characteristics and energy characteristics of the wind turbine are studied. The results show that when the yaw angle is 0°, the wake of the driven wind turbine is obviously smaller than that of the fixed wind turbine, and the turbulent kinetic energy is greater than that of the fixed wind turbine because of the multifreedom rotational motion of the driven wind turbine. With the increase of yaw Angle, the distribution of wake and turbulent kinetic energy of wind turbine appears radial deviation, and the deviation direction is opposite to the direction of yaw Angle rotation, and the wake and turbulent kinetic energy decreases with the increase of yaw Angle. With the increase of yaw Angle, the average output power of forced and fixed wind turbines decreases, and the average output power of forced wind turbines is smaller than that of fixed wind turbines.

A transfer learningbased early fault warning method for offshore wind turbine bearings is established to address the problems of varying operating conditions of offshore wind turbines and many false alarms for early fault warning of turbine bearings. The method uses the shorttime Fourier transform to extract the timefrequency domain features of the vibration signals, which are normalised to form pre processed samples. The objective function of the convolutional autoencoder is supplemented with a support vector data description regular term and a maximum mean discrepancy regular term to constrain the feature distribution while obtaining the common features center of the bearings in normal state under different operating conditions. The Euclidean distance between the online sample features and the common feature center is calculated to construct bearing health indicator sequence, and the ADF(Augmented DickeyFuller)test is introduced to perform stationarity analysis and capture the sequence mutation points, which finally realize the early fault warning of bearings in offshore wind turbines. The validation on the XJTUSY bearing dataset showed that the proposed method has fewer false alarms, high accuracy and better detection stability.

The analysis and mastery of the inner law of the fluctuation characteristics of wind power output is conducive to improving the prediction accuracy of wind power output, thus guiding the power grid scheduling department to reasonably arrange the power generation plan and improve the economy of system operation. To characterize the probability density distribution of wind power output fluctuations, two adaptive bandwidth kernel density estimation models are developed by modifying the fixed bandwidths obtained from the empirical method and the unbiased crossvalidation method. Then, the above two models are combined and optimized, and finally the probability density distribution model of wind power output fluctuation based on hybrid adaptive kernel density estimation (HAKDE) is established. A variety of probability density distribution models were used to fit the fluctuations of wind power output at different spatial and temporal scales in a province in North China. The results show that the fitting effect of the HAKDE model is the best, which verifies the effectiveness of the HAKDE model.

In the design of highpower directdrive wind turbines, this paper proposes to design axial flux permanent magnet generators as highpower half directdriven wind turbines to address the issues of large radial dimensions and high maintenance costs in the later stage. Taking the 2 MW semi direct drive axial flux permanent magnet generator as the research object, the parameter design principle and method are studied. By exploring the influence of different pole pairs on the output performance of the generator, the optimal scheme of the generator meeting the design goal is determined. The 3D finite element method is used to analyze the air gap flux density, voltage, current waveform, output power, loss and efficiency of the generator under noload and rated load conditions, and verify the correctness of the electromagnetic design of the generator. The efficiency of the generator can reach 97.79%, which has the advantages of low loss and high efficiency. By comparing with generators in the existing research, it is concluded that the radial size of the MW half directdriven axial flux permanent magnet generator proposed in this paper is significantly reduced, and it has better performance than the existing radial flux structure highpower semi direct drive permanent magnet generator in terms of axial dimension and cogging torque.

In order to solve the problems of increasing network loss caused by bidirectional power flow and node voltage fluctuation caused by fluctuation of distributed power and load, This paper proposes a method to control power flow based on pulsewidth modulation technology of power electronic converters on both sides of Solid State Transformer. In this paper, the dynamic reactive power optimization model of active distribution network with SST is firstly established. Then, the improved multiobjective group algorithm is used to solve the control variables such as modulation Angle and modulation coefficient of the power electronic converter based on the primary and secondary sides of SST, aiming at the multitime active network loss and voltage fluctuation. Finally, the simulation model is established and compared with the active distribution network dynamic reactive power optimization method based on onload voltage regulating transformer. The results prove the superiority of the proposed method in reducing network loss and maintaining node voltage stability.

The output of wind, solar and other renewable energy sources is highly variable and stochastic, which poses a great challenge for the flexibility of the system. Therefore, a hybrid energy storage joint planning method for power systems that balances economy and flexibility is proposed. First, the flexibility requirements of the source and load sides are assessed from the perspective of power balance; then, a twolayer planning model that coordinates the flexibility retrofit of thermal power units and the hybrid energy storage is established, with three types of flexibility resources as the planning objects, namely, thermal power unit flexibility retrofit, Vanadium Redox flow Battery, and pumped storage. Using the upper and lower layer models, the scheme is iteratively optimized to obtain the optimal hybrid energy storage configuration scheme that balances economy and flexibility; secondly, an an improved whale algorithm based on inverse learning is adopted to optimize the planning model, and the validity of the model is verified by simulation. Finally, a case study is carried out using historical data from a certain region in Inner Mongolia Eastern to validate the effectiveness of the proposed method.

Modern power system has developed into cyberphysical system (CPS), which is highly integrated between power network and information network. However, advanced information technology not only improves system performance, but also introduces new security risks. With the largescale gridconnection of Electric Vehicle (EV) with mobile energy storage equipment, the absorption capacity of distribution network for new energy has been greatly improved. However, the low security and high accessibility of charging piles have further reduced the network security of distribution network. On this basis, a distributed energy management strategy based on consistency algorithm is firstly proposed in this paper, which considers the EV cluster as an energy storage device with source charge bidirectional characteristics to achieve fully distributed economic scheduling. Considering denial of service attacks and new data integrity attacks for electric vehicles, a disturbance rejection control strategy combining privacy protection protocol and isolation mechanism is proposed to achieve effective energy management and economic operation of systems under network attacks. Finally, the effectiveness of the encryption mechanism and the feasibility of the control strategy are verified by simulation.

Aiming at the problem that the complex coordination relationship of the sendingend power grid with high proportion of new energy and pumped storage combined power generation may lead to insufficient new energy consumption capacity, an energy balance optimization control method of the new energy sendingend power grid based on variable speed pumped storage is proposed. Firstly, the operating characteristics of variable speed pumped storage units are studied, and the active and reactive power output characteristics of variable speed pumped storage units based on frequency converter control are analyzed. Secondly, the variable speed pumping is used to analyze the improvement of new energy generation capacity, and the energy balance optimization model of the new energy sending end power grid is established. Then, the quantum particle swarm optimization algorithm is improved, and the energy balance optimization control algorithm of the new energy sending end power grid is proposed. Finally, with reference to the delivery mode of new energy base in a certain area of Northwest China, a typical scenario of variable speed pumped storage power station and conventional pumped storage power station cooperating with new energy is constructed. The improved new energy consumption capacity of variable speed pumped storage power station and conventional pumped storage power station is compared. The simulation results show that the energy balance optimization control method of the sending end power grid based on variable speed pumped storage can improve the utilization rate of new energy and play a significant role in improving the level of renewable energy consumption.

The traditional photovoltaic power generation system usually operates at the maximum power operation point, does not respond to the change of grid frequency, and cannot provide active power to suppress the change of grid frequency. With the increase of photovoltaic permeability, the safe and stable operation of the grid will be affected. In this paper, load shedding control is adopted to realize the response to system frequency without changing the main circuit structure, grid connection strategy of inverter and adding energy storage equipment. First, the current maximum power operation point is obtained by setting the masterslave array to achieve load shedding control. By setting the corresponding relationship between frequency and load shedding rate, the change of active power output for frequency change is achieved, and the system is provided with active power support. Finally, the effectiveness of the results is verified through simulation on the hardware in the loop simulation platform.

The doubly fed induction generator, operating in electric mode, has a certain inertia supporting capacity to the power grid, but the supporting capacity is limited, and the rotor is out of control during the lowvoltage crossing of the power grid. In this paper, a kind of rotor stability control technology is proposed when voltage sags in power system. Firstly, a flywheel is attached to the rotor shaft of a doubly fed fan to increase the inertia of the system and stabilize the speed of the rotor. Secondly, voltage compensation is used to reduce the back electromotive force of the rotor side and stabilize the rotor current when the voltage of the power grid falls, the simulation model of the system is built in MATLAB/Simulink platform, and the results show that the antimutation ability of the rotor speed of the doublyfed fan is improved by 20% by adopting the rotor stability control technology, the rotor current drop value is reduced by 45%, which verifies the validity and reliability of the proposed rotor stability control technique.

Under the background of "double carbon", distributed renewable energy and flexible resources such as energy storage and demand response develop rapidly. Virtual power plants integrate distributed resources efficiently through control technology, which improves the power generation efficiency of distributed energy. With the social capital entering the power market, different virtual power plants will belong to different investors, forming a multiagent game pattern. According to the investment preferences of investors, virtual power plants will be composed of resources with different flexibility. In order to give consideration to the interests of virtual power plant operators and virtual power plants, a twolevel masterslave game model between operators and multivirtual power plants is constructed. Considering the interaction between upper pricing and lower output, the dynamic pricing of operators and the optimal operation and scheduling of virtual power plants are studied. In the lower layer, aiming at the minimum operating cost of each virtual power plant, the optimal scheduling models of multiple virtual power plants including electric energy storage, demand response and hydrogen energy storage are established respectively. The upper layer takes the operator's profit as the goal, and combines the lower layer's output plan to dynamically formulate the purchase and sale price of virtual power plants. Particle Swarm Optimization (PSO) is used to solve the game model iteratively. Through the analysis of an example, the model can give consideration to the interests of multiagents, effectively improve the operators' income and reduce the operating cost of virtual power plants.