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.

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.

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.

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.

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.

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 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.

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.

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.

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.