Catalytic reforming of palm kernel shell (PKS) pyrolysis volatile matter was studied using char (Char) and activated carbon (AC) as catalysts under microwaveassisted heating. The impacts of different carbonbased catalysts on the composition of products were studied. The possible reaction pathways during catalytic reforming of PKS pyrolysis volatile matter under microwaveassisted heating were also investigated. During catalytic reforming of pyrolysis volatile matter, the catalyst promoted the yield of gas product, which led to the decrease of biooil yield. Compared with the Char, AC not only has higher catalytic activity to facilitate the conversion of biooil to gas, but also exhibited better selectivity for the formation of singlering aromatic compounds (especially phenol) in biooil. Using the AC catalyst, the concentrations of the singlering aromatic compounds reached 84.25%. The catalyst mainly promoted the secondary reactions such as demethoxylation reaction and dehydrocarbylation reaction.

Ammonia synthesis based on hydrogen derived from renewable electricity (i.e. green hydrogen and green ammonia process) is frequently fluctuated due to weather change and the sections are highly coupled. To understand the synthesis and scheduling of power generation and transmission, hydrogen production by water electrolysis, hydrogen storage, electrochemical energy storage, and ammonia synthesis sections in green ammonia process, a generic process model for both steadystate and dynamics mode was developed using the next generation process simulation software, AVEVA Process Simulation, and the dynamics response of the model to weather fluctuation was investigated by means of multisteady state simulation. The results show that optimized design and scheduling of the hydrogen storage and electricity storage modules can significantly stabilize the ammonia production, utilize excess renewable electricity, reduce grid power input, and thus, improve the economy.

Density functional theory calculations were employed to investigate the mechanisms and energy changes involved in C–C bond cracking, CH₄ reforming, and water gas shift reactions in the tar reforming process. The findings reveal that, in the CC bond cracking reaction, C₃H₈ initially adsorbs onto the catalyst surface to form adsorbed C₃H₈*, subsequently undergoing cleavage to produce CH₃* and CH₂CH₃*. While the cracking reaction is exothermic, it is hindered by a significant energy barrier and difficult to carry out. In the CH₄ reforming reaction, CH₄* undergoes sequential dehydrogenation reactions, producing CH₃*, CH₂*, and CH*. Comparatively, CH* has a greater tendency to react with OH* to form CHO*, which further undergoes dehydrogenation to form CO*. Additionally, H* generated in each step combines to form H₂*. Throughout the CH₄ reforming process, the ratelimiting step is the cracking of CH₂* to CH*. In the water gas shift reaction, the OH* species formed from H2O* decomposition prefers to combine with CO* to generate COOH* rather than directly reacting with H* to produce H₂*. COOH* removes H and generates COO*, which is the rate limiting step.

Citric acid is one of the most productive biomass organic acids in the world and has been widely used. In this paper, we used reduced iron powder as iron source, citric acid as multidentate ligand and carbon source, and used ammonia to enhance the coordination solubilization ability of citric acid to iron powder, so that it could quickly form a homogeneous complex solgel, which was then carbonized to obtain carboncoated coreshell ironbased FischerTropsch catalyst. In the performance test, the catalyst synthesized by direct dissolution of iron powder with citric acid exhibited remarkable catalytic activity and stability, giving the CO conversion of 99.2%, C5+ hydrocarbon selectivity of 53%, and the CO conversion activity was stably kept above 97.3% for a time on stream of 168 h. The citric acid coordination method is greener and safer, which avoid the use of iron salts such as ferric nitrate, and the risk of explosion and toxic gases in the reduction process of iron salts. This synthesis method provides a new idea for the green and safe production of catalysts.

In order to accurately predict the thermal and electrical performance of solar photovoltaic/thermal (PV/T) systems, this study utilized the Particle Swarm Optimization (PSO) algorithm to optimize the Radial Basis Function (RBF) neural network. Based on this method, a simulation prediction model for the performance of solar PV/T systems was established and compared with a prediction model based on an unoptimized RBF neural network. Additionally, this research built a solar PV/T experimental platform and collected experimental data using a cloud platform for the aforementioned model. The research results indicate that the RBF neural network model optimized using the PSO algorithm exhibits better prediction accuracy compared to the unoptimized RBF neural network model. The optimized RBF neural network model demonstrates a 20% improvement in prediction accuracy and a 30% increase in prediction stability compared to the unoptimized model. The goodness of fit, as indicated by the Rvalue, is also improved compared to the unoptimized model. The prediction model established based on the PSORBF neural network can accurately predict the thermal and electrical performance of solar PV/T systems.

The amount of solar radiation and wind load will directly affect the continuous generation of solar thermal power station. Therefore, according to the actual environmental conditions of Zhongdian Nao Maohu solar thermal Power Station in Hami region, Xinjiang, a threedimensional numerical model of heliostat group was established to simulate the heating conditions and flow field characteristics of the mirror group under solar radiation and upwind Angle in different seasons, and the distribution of mirror flares and pulsating wind pressure coefficients under different wind incidence angles were analyzed. The results show that the simulated drag coefficient and lift coefficient are in good agreement with the related research results, which verifies the validity of the model. The distribution of flares in different seasons is similar and mainly depends on the variation of solar direction Angle. With the increase of the wind incidence Angle, the wake region of the mirror cluster decreases first and then increases. Since the wake in the helioscope group can effectively inhibit wind pressure, the internal stability can be ensured by combining the arrangement of the group. Among them, the center of the regular pentagonal helioscope maintains a low pulsating wind pressure, which greatly improves the force balance of the mirror.

The article addresses the problem of relatively low accuracy of traditional PV power prediction and proposes a hybrid TOPSISGRNN based mechanismdata driven PV plant power prediction model. Firstly, the correlation analysis of several meteorological indicators and the output power of PV power plant is carried out, and the meteorological data with high correlation is selected as the input factor of the model. The TOPSIS algorithm was used to select the optimal similar days, and then the theoretical values of their PV plant output power and meteorological data were used to build the GRNN prediction model. Finally, the model was simulated and validated by combining the historical meteorological data and power data on the DKASC website. The final test results yielded an average power prediction accuracy of 0.826 9 kW for RMSE, 3.45% for MAPE and 0.019 5 kW for MAE. The prediction accuracy of this forecasting method is significantly higher than that of a single forecasting model and has some theoretical and practical value.

The wind turbines are subjected to continuous wind loads during their whole service life and are inevitably resisted earthquake action. In this paper, a large megawatt wind turbine is taken as the research object to simulate and analyze its deformation and mechanical responses under the conditions of normal operation, earthquake during shutdown and earthquake during operation. The results show that the deformation of wind turbine increases with the increase of tower height, and the horizontal displacement of the wind turbine under the earthquake is obviously less than that under the wind turbine operation state. With the increase of earthquake level, the horizontal displacement of the wind turbine is gradually increased. The deformation shape of the wind turbine remains unchanged, and the deformation of tower top is large. The influence of the wind turbine operating load on structural deformation response is significantly greater than that of small and medium earthquake actions. Meanwhile, the deformation response of the wind turbine under the combined action of operating load and earthquake is significantly greater than that under the separate action of two loads. In the structural design of the wind turbine, it is necessary to consider the coupling effect of the operating state and the earthquake. The stress response at the top of the tower is the largest, and its crosssection should be checked in the design.

In order to make reliable dynamic prediction of the potential risk of pitch system, aiming at the problems of multiple components, complex system and difficult fault feature extraction of pitch system, the fault tree is established through the induction and analysis of its fault point and fault transmission process, and then it is transformed into a dynamic Bayesian network (DBN) integrating Leaky Noisyor nodes, which ensures the accuracy of the model and has the dynamic prediction ability. The model is optimized and verified by using a 5fold crossvalidation method. The test results show that this method has high accuracy in risk prediction, fault cause analysis and risk dynamic evolution process analysis of pitch system, and has engineering application value in guiding the preventive maintenance of pitch system and ensuring the overall safety of wind turbine.

In order to improve the control performance of wind turbine electrohydraulic pitch system, a fractional terminal sliding mode control method based on perturbation observer is proposed. The mathematical model of the wind turbine electrohydraulic pitch system is established, and the slidingmode state and perturbation observer is used to compensate the uncertainty and unknown disturbance of the pitch system parameters in real time. Fractional calculus theory is used to design the sliding mode surface of the terminal sliding mode controller, which can improve the jitter of the sliding mode control itself while ensuring the finite time convergence. Simulink is used for experimental verification, and the results show that the method enhances the antiinterference ability of the pitch system, weakens the jitter of the system, improves the tracking accuracy of the pitch angle, and improves the stability of the pitch system.

Based on the concept of load safety margin, the structure optimization inversion design was complemented for one offshore wind turbine supported by bucket foundation in order to reduce the amount of materials and structural cost while ensuring the strength and stability requirements of the structure. Hence, the optimization feedback analysis model and calculation process of offshore wind turbine supported by bucket foundation has been established and the origin bucket foundation was optimization design considering the three safety margin values of 1.10, 1.20 and 1.30. It can be seen in the results that the optimized foundation structure still has a better safety reserve after considering the safety margin with the design indicators of the bucket foundation structure, which all can meet the design requirements. This research provides a new idea for the optimization design of offshore wind turbine structure.

With the high penetration of renewable energy and electronic equipment, the problem of wideband oscillation in power systems is becoming increasingly prominent, which has become a key factor restricting the efficient consumption of renewable energy. A wideband phasor measurement method is proposed and the corresponding device is developed to satisfy the requirements of wideband oscillation mitigation and protection in power systems with renewable energy. Based on the windowed discrete Fourier transform and three peak interpolation, this device can measure multi –mode wideband phasor of multiple voltages and currents. Besides, the amplitude and phase of high frequency phasor are compensated based on the linear regression, which effectively improves the measurement accuracy of the device while ensuring the dynamic response speed. Finally, the measurement performance of the developed device was verified by experimental testing with testers and realtime digital simulation, and the device can provide support for ensuring the safety and stability of power systems and the consumption of renewable energy.

The integrated energy system (IES) planning and optimization faces multiple challenges such as high volatility of new energy sources and large uncertainty of output. In view of this, this paper proposes a twostage capacitycost planning and optimization method for integrated energy systems considering scenery uncertainty. Firstly, Latin hypercube sampling is applied to generate the base wind and solar scenarios set, and the scenarios are reduced based on the improved kmeans algorithm. Secondly, a multiobjective optimization model is constructed with the lowest operating cost, optimal carbon emission reduction, and optimal pollutant emission reduction; finally, the system capacitycost twostage planning and optimization solution strategy is proposed, and a business park in the south is selected for the planning simulation. The simulation example shows that the twostage planning model of integrated energy system constructed in this paper can ensure the economy of system and environmental protection at the same time, and meet the multiple energy demands of users.

In this paper, a reactive power optimization method for wind farms considering the reactive power resources allocation cost is proposed to achieve the optimal reactive power control under different operating conditions. With the comprehensive cost composed of power loss and reactive power cost as the objective function, the reactive power optimization model is established based on chanceconstrained programming, considering the influence of wind power fluctuations on voltage magnitude, which is then solved by the improved interior point method. According to the actual operating requirements, the proposed method can schedule reactive power of Static Var Generator (SVG), Energy Storage (ES) and wind generators in sequence by setting reactive power cost coefficients, and optimize the power loss of wind farm. The voltage violation risk is also controlled by reserving the voltage safety margin in the voltage constraint. In the end, the advanced and feasibility of the proposed method is verified by a simulation at an actual wind farm in China.

Voltage violation has become an important factor limiting the maximum integration capacity of photovoltaics. To address the issue of voltage violation caused by largescale photovoltaic grid connection, the article proposes a grouped coordinated voltage control strategy for distribution networks with high proportions of photovoltaics. Firstly, based on the different voltage sensitivities of photovoltaic connection nodes in the distribution network, the concept of grouped coordinated control for photovoltaic inverters is introduced. Then, within each group of photovoltaic inverters, voltage control is carried out using capacity utilization ratio and power factor as consistent variables, while intergroup coordination control ensures that the voltage at key nodes converges to the set value of 1.05 p.u.. Finally, through case simulations, the proposed control strategy is verified to effectively suppress voltage violations in distribution networks, avoid unnecessary active power reduction, and demonstrate strong robustness during load and photovoltaic fluctuations.

In order to further improve the efficiency and reliability of the local power grid in the process of fault recovery, this paper proposes an island partition strategy of local power grid with distributed generation based on improved GSAGWO algorithm. Firstly, the optimalworst method is used to evaluate the load to obtain the load weight value, so as to determine the priority of island division of important load restoration under local power grid fault. Secondly, combined with the load priority, the load level weight coefficient is determined, and the objective function model of island division of local power grid with distributed generation is constructed. Then, in order to obtain better objective function solution results, chaotic reverse learning and genetic annealing algorithm (GSA) are introduced to improve the grey wolf optimization algorithm (GWO) to improve the optimization performance of the algorithm. Finally, the modified IEEE 69 node is taken as an example for simulation analysis. The improved GSAGWO algorithm is used to solve the local distribution network fault model, and a better islanding result is obtained. The example analysis shows that the strategy proposed in this paper can accurately realize the optimal strategy of island division under local grid fault, ensure the restoration of power supply of important loads, and verify the effectiveness and superiority of the strategy.

As the proportion of renewable energy and variable loads in the distribution system gradually increases, the uncertainty of power flow in the distribution network will affect the optimal network topology. Since distributed generation and demand response are affected by the time factor, the topology obtained by modeling at a single time period is difficult to be optimized at different times of the day. To address this uncertainty, this paper proposes a secondorder cone optimizationbased distribution network reconfiguration model for multitemporal tidal flow analysis for billing and demand response. By considering the "sourcestorageload" structure of the actual distribution system, an optimization problem with the objectives of network operation cost and switching operation cost is established, and the secondorder cone relaxation is used to transform the nonconvex search space into a convex feasible domain for fast solution. Experimental results on an improved IEEE 33 node distribution network show the superiority of the proposed method over traditional methods in terms of accuracy and solution speed.

With the largescale integration of renewable energy such as wind power and photovoltaics into the new power system distribution network, the traditional centralized economic dispatch method is facing difficulties due to the distributed management and control mode presented by a large number of scattered clusters of wind power and photovoltaics. Based on traditional consistency algorithms, a distributed economic dispatch model for edge clusters composed of wind and photovoltaic power is proposed. Firstly, the edge cluster models of wind and photovoltaic power and their volatility characteristics are presented; Secondly, traditional consistency algorithms and optimization models for implementing economic scheduling were derived; Thirdly, based on this, a consistent economic dispatch distributed algorithm for wind and photovoltaic edge clusters is proposed by combining the wind and photovoltaic edge cluster models; Fourthly, taking a practical system as an example, the proposed algorithm model was simulated and verified, and the results showed the effectiveness of the proposed model.

In order to reduce the influences of renewable energy power fluctuations on power systems, an energy storage configuration optimization method considering renewable energy power probability distribution is proposed in this paper. Firstly, calculate and count the renewable energy power fluctuations at different time scales, and determine the probability distribution characteristics of renewable energy power. Secondly, according to the probability distribution characteristics and the gridconnected index of renewable energy, the configuration optimization model of energy storage considering the renewable energy timescale and power fluctuation is set up. Thirdly, based on the given constraints and time scale, calculate the minimum energy storage charging and discharging power where the fluctuations meet any set probability levels, thereby determining the rated power, capacity and initial state of the energy storage. At last, through calculation and analysis using the data measured from a 50 MW photovoltaic power station, the method is proved to be correct and effective. This method only compensates the fluctuated power that does not meet the fluctuation index, and has no impact on the power that has already satisfied.