Renewablerich remote areas are facing problems such as high cost of external energy supply, low reliability of internal green micropower, high fuel transportation cost and environmental pollution caused by diesel generators as backup power sources, which is not meeting the needs of low carbonization. To solve those problems, this paper propses a design of microenergy network based on hydrogen energy storage in an offgrid hydrogen storage energy supply scenario. The framework for a hydrogenbased zerocarbon microenergy network on account of the spatial and temporal distribution characteristics of renewable energy in remote areas is presented. Furthermore, the paper proposes the resource endowment and operation constraints of the hydrogen based micro energy network, formulates operation strategies for energy surplus and shortage periods and carries out the case simulation. Simulation results of a village in Yunnan Province prove the feasibility of the proposed microenergy network and its operation strategies, which effectively eliminates the influence of unstable regional green micropower output and seasonal shortage on the reliability of the power supply system and reduces regional thermal load burden. Furthermore, it helps decarbonize the regional energy system. Research results provide a reference for energy consumption improvement and carbon reduction in renewablerich areas such as remote areas and islands.

To ensure the economy of power operation in parks and increase the proportion of clean electric energy, an optimization model of lowcarbon power operation in parks based on green power and voluntary emission reduction transactions is proposed. This model comprehensively considers the purchase cost of green power and thermal power, the cost of photovoltaic power generation, and the income from voluntary emission reductions. The objective function is to minimize the total operating cost, and green electricity, thermal power generation and photovoltaic power generation are optimized with combination of random probability constraints in three stages: mediumand longterm, dayahead and realtime. Based on the data of an enterprise park in Nanjing, the analysis results show that under the reasonable price of voluntary emission, the optimization model can slightly reduce the total operating cost of the park, and significantly improve the use ratio of clean electric energy and the park's ability to cope with disturbances. In addition, the impacts of the voluntary emission reduction price and confidence value on the optimization results are analyzed.

In the context of the "double carbon" target, a comprehensive energy system optimization and scheduling model considering REGS and adjustable thermoelectric ratio is proposed to address the abandonment problem caused by the "heatdetermined" mode of CHP units. Firstly, we construct a model such as dry heat rock extraction cycle, and analyze the energy allocation coefficient for REGS as the source of thermoelectric adjustable mechanism; secondly, we study the coupling operation mechanism of REGS with solar thermal power plant and cogeneration unit, analyze the mechanism of coupling operation to improve thermoelectric ratio, and introduce a generalized unit thermoelectric ratio control model; thirdly, we construct an objective function with the sum of energy purchase cost, operation cost, operation and maintenance cost, wind abandonment cost and carbon tax cost; finally, the proposed model is verified to improve the system economy and wind power consumption rate and reduce carbon emission by using the actual data of a region in Northwest China.

The carbon dioxide emissions of various parks account for about 30% of China's total carbon emissions. The lowcarbon and efficient park integrated energy system is an important way to achieve China's "carbon peak, carbon neutrality" goal. Firstly, this paper establishes the electricitycarbon market trading architecture of the park integrated energy system, and analyses the carbon emission responsibility of the park integrated energy system. Then, considering the energy purchase cost and carbon emission transaction cost, the economic operation evaluation model of the electricitycarbon benefit of the park integrated energy system is proposed with the goal of minimizing the overall operation cost of the system. The example analysis shows that the economic operation evaluation model proposed in this paper can fully reflect the carbon emission cost of the integrated energy system, and proves that the introduction of indirect carbon emission cost will increase the overall operation cost. Higher carbon price and higher emission reduction targets can drive the parks to reduce the total carbon emission and help achieve the "double carbon" goal.

The optimal control of wave energy converter clusters helps to make full use of wave resources, for which a wave power cluster optimization method based on a hybrid particle swarm algorithm is proposed. Directdriven wave power generators are taken as the research object to explore the mathematical model for the shortterm scale of the steady state of power generation clusters. Wave dynamic pressure, radiation influence among devices and shading effect among devices are considered in order to simulate more accurately the actual effect of deploying a certain density of wave energy devices. With wave cluster power maximization as the optimization objective, a hybrid particle swarm algorithm is proposed to solve the optimal parameters of the power generation cluster taking into account the motion of the power generation devices and the energy constraints of the sea area.Crossover and mutation operations are added to the traditional algorithm to cope with the problem of multipeakability in the solution space of the complex equations. The results of the algorithms verify the effectiveness of the cluster optimization method with good solution quality.They also show that the larger the size of the wave power generation cluster, the more complex the radiative influence between the devices and the more obvious the shading effect.

With the promotion of the "dual carbon" goal, the capacity of distributed new energy connected to the power grid has significantly increased. The use of distribution network source network load storage coordination optimization strategy is an important method to achieve distributed new energy consumption, among which reactive power optimization can ensure the safe and stable operation of the power grid. This article proposes an adaptive learning rate convolutional neural network based optimization technique for load storage and reactive power coordination in distribution networks. Firstly, a reactive power optimization model is constructed with the goal of minimizing network loss and voltage offset. Secondly, utilizing the powerful nonlinear fitting ability of convolutional neural networks, the mapping relationship between power grid operation scenarios, reactive power regulation equipment, and energy storage charging and discharging strategies is excavated. Adaptive learning rate is introduced to update network parameters and improve network training efficiency. Finally, by controlling the charging and discharging conditions of reactive power regulation equipment and energy storage devices to coordinate the output of distributed power sources, active optimization control of reactive power and voltage in new distribution network is achieved. After simulation verification of the IEEE33 node power grid model, the results show that the proposed optimization method for load storage and reactive power coordination in the distribution network source network improves the voltage regulation ability of the power system, laying a good foundation for the safe and reliable operation of the distribution network.

A disturbance compensation type improved active disturbance rejection control strategy is designed to address the problems of multiple disturbances, large inertia, and long delay in the SCR system of the coal mining machine system. Based on the model information of the SCR system, a mathematical model of the required form of active disturbance rejection was established. A secondorder degree of freedom auto disturbance rejection was designed to control it, and the total disturbance was reconstructed to be equivalent to unknown disturbances and external disturbances. A new observer was designed for disturbance compensation, forming a disturbance compensation linear auto disturbance rejection, improving the observer's disturbance observation ability and accuracy. Finally, a digital simulation model of the SCR system is built on the MATLAB/Simulink simulation platform and compared with PI and LADRC. The results show that the disturbance compensation improved active disturbance rejection has better antiinterference and tracking capabilities, verifying the correctness and superiority of the proposed control strategy.

This study aims to address the challenges of phosphorus recovery and solid waste treatment by preparing biochar composites(OSCS) using a copyrolysis method with cotton straw stalk and oil shale as raw materials. The physicochemical properties of the biochar were analyzed using SEMEDS, BET and FTIR tests. The influence of pyrolysis temperature, adsorbent dosage, and solution pH on phosphate adsorption was investigated, and both the adsorption kinetics and isotherm models were studied. The results indicated that the structural properties and surface morphology of the biochar were significantly enhanced through oil shale modification, leading to a notable improvement in phosphate adsorption capacity. At an injection level of 4.0 g/L and pH5.0, the maximum adsorption capacity reached 7.01 mg/g, which was 2.47 times higher than that of cotton straw biochar and 3.52 times higher than that of oil shale char. The adsorption process followed the proposed secondary kinetics and Langmuir isothermal adsorption model, and the mechanisms involved surface precipitation, ligand exchange, and electrostatic attraction. This approach of oil shale modified biochar composites provides a novel strategy for both phosphorus removal and solid waste resource utilization.

LiN2 battery is a new type of energy storage system with the function of electrochemical nitrogen fixation, and the electrochemical model established in the article used finite element software COMSOL coupled with multiphysics field can reveal the influence of various factors on its discharge performance. The simulation results show that: the discharge current density, temperature, cathode porosity and N2 solubility factor in the electrolyte have an effect on the discharge performance of LiN2 battery; a larger discharge current density will reduce the voltage and capacity of the battery; cathode porosity and N2 solubility factor in the electrolyte are the key factors that affect the voltage and capacity of the battery, and increasing the cathode porosity and N2 solubility factor in the electrolyte can increase the voltage and capacity of the battery; With the temperature rising, the voltage of the discharge platform of the battery increases, but the discharge capacity is almost unaffected by temperature.

At this stage, the power generation performance of photovoltaic power plant is usually evaluated by performance ratio (PR). In the field test of short term, the PR of whole photovoltaic system will be affected by the temperature, leading to an obvious deviation of PR, which causes interference for the power generation evaluation of plant. Based on the system PR correction calculation method in IEC 617241 2016 standard, this paper analyzes and studies the modules temperature calculation method and temperature correction calculation method. Through the comparative analysis of examples, the system PR temperature correction method with higher accuracy is summarized and given, which brings convenience for the rapid system measurement and calculation, and increases the reliability and accuracy of the system PR evaluation. The analysis results show that the method of weighted average temperature of the module which considers the temperature and irradiation weight is more accurate than the method of module average temperature correction; the system PR can be evaluated much more accurately by the method that is corrected to the annual weighted average temperature of the module.