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.

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.

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.

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.

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.

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.

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.

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.

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.