Activated carbon was used as catalyst for the catalytic reforming of straw biomass (soybean straw, corn stalk) pyrolysis volatiles to phenolic compounds. The effect of catalysts on the yield, composition, and phenolic compound content of liquid products was investigated, and the catalytic mechanism of the catalysts was analyzed. The results showed that, after the catalytic reforming of volatiles by activated carbon, the liquid decreased in the products while the gas increased. The liquid yields of soybean straw and corn stalk were the highest at the pyrolysis temperature of 500 ℃, reaching 43.9% and 42.9%, respectively. After their volatiles were catalytically reformed by activated carbon, the liquid product yields were decreased to 30.1% and 37.9%, respectively. However, after catalytic reforming of volatiles, the aliphatics in the liquid products decreased while the aromatics increased. When the pyrolysis temperature was 500 ℃, the aromatic compounds in the soybean straw and corn stalk pyrolysis liquid products were increased by 10.0% and 9.7%, respectively. The content of phenolic compounds in the liquid products increased significantly, and the relative content of phenolic compounds reached 42.9% and 55.1%, respectively.

Based on the constant volume combustion bomb combined with the high -speed schlieren system, the effects of initial temperature of 450 K, initial pressures and equivalence ratios on the flame instability of hydrogen-added ethanol premixed fuel were studied. The results show that the increase of Ф or P₀ enhances crack and cellularity on the flame surface, and the flame instability is enhanced, and the effect of P₀ on the flame instability is more significant. The flame instability was comprehensively evaluated from the aspects of hydrodynamic instability (HDI) and thermal diffusion instability (TDI), and it was found that the increase of P₀ increased the hydrodynamic instability, but the thermal diffusion instability was not greatly affected by P₀. In addition, under the experimental conditions, the effective Lewis number is greater than 1, and the thermal diffusion effect always suppresses the instability of the flame, so flame instability is primarily driven by the hydrodynamic effect, which can be further verified by evaluating the global growth rate of each instability parameter. Finally, the linear stability theory model was used to determine the critical condition for the instability of the premixed flame, and it was found that the critical radius of flame instability decreases and the flame instability is promoted with the increase of Ф or P₀.

In electrocatalytic anaerobic digestion, the methane content in biogas can be enhanced. With acetic acid as the substrate, this study investigated the kinetics of biogas production and substrate degradation in an electrocatalytic anaerobic digestion system. Experimental results showed that with the addition of different initial acetic acid concentrations (8.3, 10.0, 11.7 g/L), the methane content in biogas increased with increasing acetic acid concentration. When the acetic acid concentration was 11.7 g/L, the average methane content reached 80.6% in batch experiments. Based on the carbon balance relationship in the electrocatalytic anaerobic digestion process, kinetic studies were conducted using both the first-order kinetic model and the Gompertz model. The study revealed that acetic acid conversion efficiencies at the three concentrations were 85.6%, 97.2%, and 97.4%, with biomass growth accounting for 22.1%, 29.9%, and 28.5% of the carbon distribution, respectively. The correlation coefficients (R²) for both models in fitting the biogas production process were greater than 0.99, and those for the acetic acid degradation process were above 0.90, with the first-order kinetic model demonstrating a better fit for acetic acid degradation.

The output performance of PEMFC stack is mainly related to operating conditions, control strategies, and structural design, while the voltage consistency of the stack is an important indicator of the stability and reliability of the stack performance. This study conducted experimental research on an 8.5 kW proton exchange membrane fuel cell stack by constructing a water-cooled hydrogen fuel cell stack test bench. The polarization curve and output power curve were used as performance reference indicators, and the voltage fluctuation rate C_v was used as a consistency evaluation indicator to investigate the effect of different dew point temperatures of reactive gases on the output performance and voltage consistency of the stack. The results indicate that the dew point temperature has a significant impact on the performance and voltage consistency of the stack. As the dew point temperature increases, the output performance of the fuel is improved, and the performance decreases when the dew point temperature exceeds 57 ℃. Both high and low dew point temperatures can cause uneven distribution of water and heat inside the fuel cell stack, leading to an increase in voltage fluctuation rate and a decrease in PEMFC consistency.

In practical applications, photovoltaic (PV) modules often experience partial shading due to obstructions such as chimneys, trees, or bird droppings, leading to power mismatch losses in the system. To minimize the impact of shading and further enhance the power generation capacity of PV systems, this paper first derives the output characteristics of PV power stations under shaded conditions and establishes a model for improving string performance based on power optimizers. Subsequently, based on the known proportion of shading on the module surface, an experimental platform was built to analyze the deviation between measured and simulated results using three experimental schemes and to compare the power generation of the strings before and after incorporating power optimizers. Data show that the deviation in daily power generation between measured and simulated results is within 4%, and power optimizers can increase power generation by up to 16.14%. The research findings provide important guidance for enhancing the power generation capacity of PV systems under shading conditions in practical scenarios.

With the gradual increase of photovoltaic installed capacity, the stable and safe operation of power system is facing challenges. By improving the prediction accuracy of photovoltaic power generation, the operation stability and safety of power system can be improved. Therefore, this paper proposes a DBO -CNN -BiLSTM -AM short -term photovoltaic power generation prediction method based on feature optimization. Firstly, Variance Inflation Factor (VIF) and Extremely Randomized Trees (ERT) are used to calculate and select the features with low VIF value and high importance score as input. Secondly, a CNN -BiLSTM -AM prediction model is established by using Convolutional Neural Network (CNN), Bi-directional Long Short Term Memory (BiLSTM) and Attention Mechanism (AM) with dynamic adjustment ability of feature weights. Finally, the Dung Beetle Optimization algorithm (DBO) is used to perform global optimization of hyperparameters to maximize the prediction accuracy. The experimental results show that the prediction accuracy of the short-term photovoltaic power prediction method proposed in this paper is significantly higher than that of the traditional method, which provides a good basis for optimizing power dis-patching and stable operation of power system.

Addressing the current issues of insufficient precision in the economic evaluation of geothermal power generation projects and the imperfection of the assessment system, an evaluation index system tailored for the technical and economic aspects of such projects has been established. It proposes a comprehensive life-cycle economic evaluation model for geothermal power generation projects that incorporates carbon trading considerations. Taking the Xizang binary fluid geothermal power generation project as an example, the model is applied to conduct a detailed calculation and analysis of the project's economic performance after completion. The results show that the project's levelized kW•h cost is 0.588 5 yuan /(kW•h), the internal rate of return is 10.94%, and the payback period is 16.12 years, which verifies the practicability and accuracy of the evaluation system in practical application. At the same time, the influence of multi -dimensional sensitivity factors such as cost, income and financing on the profitability of geothermal power generation projects is systematically analyzed to provide reference for the cost control of geothermal power generation projects.

To investigate the combined effect of different turbulence intensity wind fields and yaw faults on the dynamic response of IEA 15 MW wind turbines, the empirical mode decomposition (EMD) method was used to decompose the wind speed at the hub, analyze the differences between traditional stationary and non-stationary models, and study the non-stationary dynamic response of wind turbine blade root flapping moment, platform pitch and pitch under different operating conditions. Research has shown that the time-varying average term obtained through EMD decomposition can better describe the pulsating trend of wind power response. The non-stationary model used in the article improves the accuracy of wind power response calculation under different operating conditions; The mean flapping moment of the blade root gradually decreases with the yaw angle, and due to the stability of steady-state wind, the mean flapping moment of the blade root decreases with the increase of turbulence intensity. And its extreme value decreases first and then increases under the combined effect of turbulent wind field and yaw fault, indicating that the combined effect of the two causes the wind turbine structure to exhibit more severe non-stationary dynamic response. The pitch and heave of the platform gradually decrease with the increase of yaw angle under steady-state wind conditions. Compared with the normal turbulent conditions under steady-state wind conditions, the proportion of pitch and heave decrease decreases. However, under extreme turbulent conditions, the mean pitch increases at a 15° yaw angle, and the extreme and mean pitch increase at a 15° yaw angle. This indicates that in the event of a unit yaw failure, turbulent wind can increase the platform's motion response.

Floating offshore wind turbines are faced with the problem that the unconstrained platform pitch motion will stimulate the resonant motion of itself and then reduce the power output, resulting in damage to the whole system. Collective blade pitch control is able to tune both generator speed and platform pitch motion. However, it is difficult for state -based feedback controllers aimed to satisfy multiple objectives to avoid the contradiction between control performance and computational cost. In this paper, a model reference adaptive control scheme with inner robust control law for multi-objective is proposed to generalized a linear control law designed for a particular operating point to the whole region, making the actual responses tracking the ideal ones to guarantee the performances of power generation and platform stabilization beyond rated wind speed. According to the co-simulation based on OpenFAST and Simulink under a series of wind and wave conditions, compared with baseline controller, the proposed controller obtained promotions in both generator speed tracking and platform motion rejection.

High -efficiency direct -drive heating devices are the key part to improve the performance of direct-drive wind heating systems. In this paper, the optimal structural parameters of a typical straight-blade disc turbine agitator heating device were investigated. The effect of input torque and power for the agitator heating device were also investigated via single-factor experiments and orthogonal experiments, and a multiple linear regression model for input power and the various factors of the agitator heating device were established. The results showed that the increasing in the number of blades, liquid level height, and rotational speed can enhance the performance of the agitator heating device, while the increasing in the height of the agitator blade decreasing its performance. And the increasing in the number of baffles initially can improve the performance of the heating device, but the performance improvement will no longer increase as the number of blades reaches a certain value. The degree of influence of the aforementioned factors on the performance of the agitator heating device is as follows: rotational speed > height of agitator blades > number of baffles > liquid level height > number of blades. The agitator blade height and rotational speed have a positive impact on the performance of the heating device, while the number of baffles and liquid level height have a significant influence. The performance of the direct-drive agitator heating system can be predicted with the established multiple linear regression model based on the various optimal structural parameters. The investigation of this paper can founded a basis for designing and optimizing of the direct-drive agitator heating devices.

As the penetration rate of distributed photovoltaic (PV) power generation in distribution networks continues to rise, the problem of loss of selectivity and sensitivity of distribution line protection is becoming more and more prominent. Distance protection has the advantages of fast response speed, independent of system operation mode, and independent of communication, which is an effective way to solve the protection problems of distribution networks containing distributed PV. However, the additional impedance generated by the controlled phase characteristics of PV short-circuit currents makes it difficult for distance protection to meet the requirements of sensitivity and selectivity in power grids containing distributed PV. To this end, the fault characteristics of PV-containing distribution networks are analyzed, and the coupling relationship between the measured impedance and the actual fault location and the additional impedance is analyzed; the expression for the additional impedance during interphase faults in distribution networks containing distributed PV is derived, and the principle of interphase fault location in distribution networks with multi -PV access is proposed, and then new distance protection criteria are constructed, and adaptive distance protection for interphase faults in distribution networks containing distributed PV is proposed. The method is proposed. Examples show that the method can accurately identify and locate interphase short -circuit faults in distribution lines under different fault locations, fault severities and PV outputs, and it has the advantages of simple and reliable principle, convenient realization, and wide range of application.

Accurate modeling of photovoltaic-energy storage systems (PV-ESS) is critical to analyzing the stability and reliability of grid-connected microgrid systems. Firstly, the system reliability assessment model is established, the fault repair time model and the component failure rate model of the optical storage system are refined, and the battery aging-aware charging/discharging model accounting for energy storage lifespan degradation is established. Secondly, the reliability indicators are defined and the load-shedding and islanding strategies are developed, and the reliability evaluation method of grid-connected microgrids considering the correction of the fault repair time model of the photovoltaic storage system is proposed based on the sequential Monte Carlo simulation method. Finally, the improved IEEE RBTS BUS6 F4 feeder is used as a validation system to compare and analyze the influence of the fault repair time correction model of the PV-ESS on the reliability of grid-connected microgrids. The results more accurately evaluate the microgrid reliability by accounting for practical repair processes within PV-ESS.

Under the dual carbon goal, wind energy as a green energy source has received much attention, but the issue of wind abandonment still restricts the development of wind power generation. The configuration of hydrogen storage in a comprehensive energy system can effectively solve the problem of wind abandonment and achieve the effective utilization of wind energy in multiple ways and times. This article proposes a comprehensive energy system scheduling method that includes wind power hydrogen production and multiple types of energy storage, taking into account the impact of wind power output uncertainty on the system, considering the joint optimization scheduling and load demand response strategy of electricity, heat, cold, and hydrogen, and analyzing the carbon emissions of the system. The research results indicate that the established comprehensive energy system scheduling model can achieve scientific collaboration of multiple types of energy and improve the operational economy of the comprehensive system.

The reactive power output of new energy and the recovery rate of grid voltage will affect the voltage characteristics of other nodes in the system. Traditional parameter determination methods for a single station are difficult to ensure the minimum overall voltage deviation of important nodes in the system. To solve the issue, an optimization method for new energy low voltage ride through (LVRT) control parameters considering the reactive power and voltage interaction is proposed in this paper. Firstly, the impact of LVRT control parameters and reactive power interaction on the overall voltage support capability is analyzed. Then, an index for the coupling degree of reactive power and voltage between nodes is proposed, based on this index as well as the degree of voltage drop at the new energy grid connection point, the value range of the reactive power current proportion coefficients of LVRT in different stations are determined. Finally, within the value ranges, with the goal of minimizing the overall voltage deviation of important nodes in the system, a deep reinforcement learning algorithm is used to coordinate and optimize the above parameters. The results of case studies show that the proposed method can improve the overall voltage recovery characteristics of the system compared to traditional methods, and the pre determination of the control parameter value range can enhance the convergence speed of the algorithm reward function.

With the integration of dispatchable resources such as distributed photovoltaics and energy storage into the distribution network, the demand for dynamic frequency regulation capability in the system has significantly increased, posing comprehensive challenges to load frequency stability control. To better mitigate frequency fluctuations in the new energy distribution network system, a new energy distribution network load frequency control strategy considering source load storage dispatchable resources is proposed. Firstly, based on the frequency response characteristics of distributed photovoltaic, energy storage equipment, and demand side interruptible loads, a load frequency control model for the new energy distribution network was established; Then, based on model predictive control, frequency adjustment was achieved without difference, and the control parameters were adaptively adjusted using deep learning algorithms. A multidimensional index system was constructed to comprehensively evaluate the control performance of the proposed control strategy; Finally, the effectiveness of the proposed method was verified through Matlab/Simulink simulation.

Compared to traditional energy, new energy exhibits obvious randomness, volatility, and regional characteristics, and its large-scale integration into the power grid will undoubtedly have a profound impact on the operation of the power system. Therefore, this article takes wind power as the research object, first analyzes its output characteristics and common distribution line loss calculation methods, and proposes an optimized power loss calculation method for the power grid. Considering the fluctuation of wind power output, the equivalent output curve of typical days in the month is corrected by combining the average electricity quantity and shape factor to calculate the power flow. Furthermore, the power loss data of typical days is used to estimate the power loss situation of the entire month. Finally, the proposed improved power loss calculation method was validated through numerical examples, and the results fully demonstrated the engineering feasibility of the proposed improved algorithm, effectively improving work efficiency and providing important guidance for engineering applications.

With the rapid development of power electronics technology, flexible direct current (DC) transmission technology has also made significant progress. However, the oscillation issues that arise in practical applications of this technology have gradually attracted widespread attention. This paper aims to explore the impact on system impedance stability by modeling and analyzing each link of impedance measurement. First, the paper introduces the method of impedance stability analysis, emphasizing the importance of assessing system stability through impedance characteristics. On this basis, an in-depth analysis of the types of disturbance signal sources is conducted, and a model of the impedance measuring device is established to analyze its impedance characteristics in detail. Finally, through Matlab simulation, a comprehensive analysis of the impact of disturbance signal source types and impedance measuring devices on system impedance stability is carried out.