Integrated energy system is an important means to improve the permeability of distributed new energy and enhance the flexibility of terminal energy consumption. In view of the shortcomings of existing technologies in the modeling of external energy characteristics of multienergy systems and the research of multisystem interaction strategies, this paper proposes a coordinated control method of integrated energy system based on virtual energy storage, and constructs threelayer energy architecture in terms of building layer, agent layer and distribution network layer based on multiagent technology.Based on the interactive architecture, the virtual energy storage model describing the external characteristics of the system is used as the interactive interface to quantitatively analyze and control the virtual energy storage of agents which regulates multi energy distributed resources, such as multi buildings, grid connected energy storage, micro gas turbine, etc. Furthermore, according to the system spatial scope, the hierarchical expansion and progressive analysis are carried out to build a multiagent based integrated energy system coordinated control strategy. The decisionmaking goal is to maximize the resource endowment of the building, park and other comprehensive energy systems so as to meet the energy demand as much as possible and realize autonomous operation.

The fluctuation of renewable energy unit output and the unreasonable grid connection position lead to voltage fluctuations, power deviation, and increased investment costs in the coupled system. A discrete particle swarm optimization method for multi photovoltaic power supply access site selection is proposed to meet the optimization and regulation requirements of wind solar thermal coupling system. Analyzed the reactive power compensation characteristics of photovoltaic power sources and the impact of their connection positions on system voltage deviation and grid losses, established a power generation system model that couples renewable energy and thermal power, and used photovoltaic power sources as reactive power regulation devices for the coupled system. Taking the comprehensive optimization of voltage deviation and line loss as the objective function, the discrete particle swarm optimization algorithm is used to solve the photovoltaic power supply access point. The analysis results of the example show that the multi photovoltaic power station access point optimization method ensures the voltage deviation of each node, reduces line loss, and improves the safety and stability of the wind solar thermal coupling system.

In order to calculate the time history response of wind turbine structure more accurately, the influence of wind shear, tower shadow and mountain environment was considered. Based on Kaimal selfpower spectrum and crosspower spectrum, the harmonic superposition method was used to simulate fluctuating wind speed. The spatial three dimensional non stationary wind field simulation method with wind direction correlation was established by using turbulence coherence model. Taking 2 MW wind turbine of a wind farm as an example, the correctness of the simulation method is verified and compared with the simulation results without considering the correlation of wind direction. The results from statistical analysis of simulated data and comparison with target spectrum demonstrated the accuracy and accuracy of the presented method, the simulation of threedimensional nonstationary fluctuating wind field can be realized. The results from statistical analysis of simulated data and comparison with target spectrum demonstrated accuracy of the presented method, and considering the correlation of wind direction can improve the simulation coincidence, which proves the necessity of simulation method.

Since the output voltage level of a range of green renewable energy sources is low, it is necessary to use Boost converter to increase the voltage level in the process of grid connection. A high gain Boost converter is proposed which not only achieves high voltage gain but also extends the circuit structure to meet higher boost requirements. Firstly, the operating principle and switching device stress of the proposed converter are theoretically analyzed, and the design method of the device parameters is given; then the boosting capability of the converter is comparatively studied, and the influence of nonideal devices on the voltage gain is analyzed; finally, the correctness and feasibility of the theoretical analysis are fully verified through the combination of simulation model building and experimental prototype.

In order to suppress the false information existing in the interactive information between microgrid (MG) and microgrid cluster operator (MGCO), a trading mechanism with the Stackelberg game is proposed, and a dynamic integrity factor is used to consider the honesty behavior of the MG. Firstly, a reward and punishment mechanism based on the reported electricity quantity and the actual trading quantity is established. Transaction price can be adjusted by credit factor. Secondly, a Stackelberg game model with MGCO as leader and MGs as followers is developed. The upper level minimizes fluctuation of power for microgrid cluster, where a contract mechanism of transaction is established to adjust basic transaction prices within the cluster. In the lower level, the cost of MG operation is minimized. Each MG acts as the follower responding to the dayahead trading price and decides the electricity of exchange power with MGCO. Finally, the study prove that the presented transaction mechanism effectively suppresses the false information.

The unique heatdetermined power working mode of coalfired cogeneration units and the antipeaking characteristics of wind power lead to the problems of insufficient peaking capacity and high carbon emissions in virtual power plants. In this paper, we propose an optimal scheduling technology of virtual power plant based on antlion optimization algorithm. The carbon capture technology is supplemented by a reasonable demand response mechanism to realize the flexible and lowcarbon operation of the virtual power plant. Firstly, an optimal scheduling model was established with the objective of the highest economic efficiency in one scheduling cycle. Secondly, a hybrid strategy based on chaotic mapping and tournament selection is designed. The boundary checking mechanism on the search space is utilized to effectively avoid the situation that too many potential solutions are at the same boundary. Finally, the simulation results show that the planned virtual power plant has a reasonable structure, and the proposed scheduling strategy can improve the system's new energy consumption capacity and total revenue while reducing carbon emissions by coordinating the resources on both the source and load sides.

With the advancement of the "dual carbon" goal, the scale and capacity of randomly fluctuating new energy connected to the power grid are increasingly increasing, seriously affecting the safe and stable operation of the power grid. This paper proposes a power grid voltage security and stability control strategy based on time series convolutional residual network and Pelican optimization algorithm for the problem of voltage stability control in large disturbance faults. Firstly, taking advantage of the advantages of low loss of temporal convolutional information, wide receptive field, and strong deep feature extraction ability of residual networks, a voltage stability prediction model based on temporal convolutional residual networks is constructed, mapping the relationship between sensitive node voltage temporal features and voltage stability; Secondly, a voltage stability control model is constructed to output control strategies, and the Pelican optimization algorithm is utilized to solve the control model with its fast convergence speed and strong search ability, resulting in the optimal measures for machine and load shedding actions. Finally, after simulation and verification, the experimental results show that the proposed method improves the accuracy of voltage safety and stability prediction in the power grid, and improves the safe and stable operation level of the power grid after faults through the optimal voltage stability control strategy.

This study addresses the scenario of bamboo waste pyrolysis and the resource utilization of its pyrolysis products, employing a methodology grounded in life cycle assessment and sensitivity analysis. It establishes a comprehensive life cycle carbon emission inventory and calculation approach. The primary sources of carbon emissions and reductions within the pyrolysis system are identified, and the sensitivity of carbon reduction capacity to various parameters is assessed. Results indicate that the carbon emissions from pyrolyzing 1 ton of bamboo waste amount to 838.684 kg, resulting in an annual reduction of 38.3 million t of carbon emissions and an annual electricity generation of 2.1×10¹⁰ kW·h in China. The predominant contributors to carbon emissions in the system are the combustion for power generation of biooil and syngas (34.8%), while carbon reduction is primarily driven by the application of bamboo charcoal to soil (34.0%). The system's carbon reduction capacity exhibits high sensitivity to bamboo waste proportion, annual harvest volume, and pyrolysis product yield, with sensitivity coefficients of 1.297,1.000, and 0.702. Notably, the soil carbon sequestration effect resulting from bamboo charcoal application presents significant carbon reduction potential, estimated at 13 million t annually for an 8.9% bamboo charcoal application rate. This study concludes that bamboo waste pyrolysis holds substantial carbon reduction potential, providing a novel avenue for China to leverage bamboo waste for renewable energy production and effectively mitigate climate change towards achieving "dualcarbon goals".

Lignin is one of the main components of biomass and can be converted into renewable fuels and chemicals by catalytic hydrodeoxygenation. Extensive fundamental research has been conducted based on lignin model compounds and heterogeneous catalytic systems. However, the hydrodeoxygenation of lignin model compounds with high selectivity remains a significant challenge due to their intricate molecular structures, which are featured with hydroxyl and methoxyl groups connected to the phenyl ring, leading to diverse reaction pathways. In this paper, the factors affecting the selectivity of the product were reviewed comprehensively. First, the reaction mechanisms and pathways involved in the hydrodeoxygenation of lignin model compounds were stated. Next, the factors influencing the selectivity of the products were summarized from two aspects. One aspect is focused on the hydrodeoxygenation catalysts, including catalyst components, promoters, supports, and preparation methods; the other is associated with the reaction conditions, including reaction temperature, hydrogen pressure, time, and solvents.

This study investigated the pretreatment of rice straw using different soaking solvents (deionized water, nanobubble water, biogas slurry, and nanobubble water coupled with biogas slurry) for 5 days. The study explored the changes in volatile fatty acid content, lignocellulose content, and methane production in different treatments after soaking pretreatment. The results indicated that the treatment of soaking with nanobubble water and the treatment of combing soaking of nanobubble water with biogas slurry both enhanced the pretreatment effectiveness of the rice straw. The treatment of combing soaking of nanobubble water with biogas slurry showed the optimal pretreatment performance, with the volatile fatty acid content reaching 6 470 mg/L. The cellulose degradation rate and hemicellulose degradation rate reached at 12.1% and 23.7% , respectively, and the maximum cumulative methane yield increased by 19.5%.