Renewable energy technologies are essential contributors to sustainable energy including renewable energy sources. Wind energy is one of the important renewable energy resources. Therefore, efficient and consistent utilization of wind energy has been an important issue. The wind speed has the characteristics of intermittence and instability. If the wind power is directly connected to the grid, it will impact the voltage and frequency of the power system. Short-term wind power prediction can reduce the impact of wind power on the power grid and the stability of power system operation is guaranteed. In this study, the improved chicken swarm algorithm optimization support vector machine (ICSO-SVM) model is proposed to predict the wind power. The traditional chicken swarm optimization algorithm (CSO) easily falls into a local optimum when solving high-dimensional problems due to its own characteristics. So the CSO algorithm is improved and the ICSO algorithm is developed. In order to verify the validity of the ICSO-SVM model, the following work has been done. (1) The particle swarm optimization (PSO), ICSO, CSO and differential evolution algorithm (DE) are tested respectively by four standard testing functions, and the results are compared. (2) The ICSO-SVM and CSO-SVM models are tested respectively by two sets of wind power data. This study draws the following conclusions: (1) the PSO, CSO, DE and ICSO algorithms are tested by the four standard test functions and the test data are analyzed. By comparing it with the other three optimization algorithms, the ICSO algorithm has the best convergence effect. (2) The number of training samples has an obvious impact on the prediction results. The average relative error percentage and root mean square error (RMSE) values of the ICSO model are smaller than those of CSO-SVM model. Therefore, the ICSO-SVM model can efficiently provide credible short-term predictions for wind power forecasting.

Economic Load Dispatch (ELD) is a key issue in power systems and its goal is to achieve minimum economic costs by allocating the output of generator units when satisfying the load demands and the operating constraints. As the dimension of the variables and the constraints increase, the traditional mathematical method is gradually not suitable for the ELD. This paper proposes an Improved Bird Swarm Algorithm (IBSA) to solve the ELD problem of a power system. By introducing the nonlinear cognitive and social coefficients, the proportion of individual learning and social learning of birds can be dynamically adjusted. In addition, the Levy flight strategy is added to the group between producers and beggars to increase the randomness. The performance of IBSA is verified via two systems consisting of 6 and 15 units, respectively, that take into account generation limitation, ramp rate limit, and prohibited operating zones. From the simulation results, the IBSA has shown excellent performance and robustness, which can be considered as a reliable solution for the ELD.

Due to high probability of blade faults, bearing faults, sensor faults, and communication faults in pitch systems during the long-term operation of wind turbine components, and the complex operation environment which increases the uncertainty of fault types, this paper proposes a fault diagnosis method for wind turbine components based on an Improved Dung Beetle Optimization (IDBO) algorithm to optimize Support Vector Machine (SVM). Firstly, the Halton sequence is initially employed to populate the population, effectively mitigating the issue of local optima. Secondly, the subtraction averaging optimization strategy is introduced to accelerate the dung beetle algorithm in solving complex problems and improve its global optimization ability. Finally, incorporating smooth development variation helps improve data quality and the accuracy of the model. The experimental results indicate that the IDBO-optimized SVM (IDBO-SVM) achieves a 96.7% fault diagnosis rate for wind turbine components. With the proposed IDBO-SVM method, fault diagnosis of wind turbine components is more accurate and stable, and its practical application is excellent.

In this study, we investigated the electronic properties and selective adsorption for CO₂ of nickel boride clusters (NiB)_n, (n = 1~10) using the first principles method. We optimized the structures of the clusters and analyzed their stability based on binding energy per atom. It was observed that (NiB)_n clusters adopt 3D geometries from n = 4, which were more stable compared to the plane clusters. The vertical electron affinity, vertical ionization energy, chemical potential, and highest occupied molecular orbital (HOMO)–lowest unoccupied molecular orbital (LUMO) gap were calculated. Our results revealed that (NiB)₆ and (NiB)₁₀, with high chemical potential, exhibit a higher affinity for CO₂ adsorption due to a charge delivery channel that forms along the Ni→B→CO₂ path. Notably, (NiB)₁₀ demonstrated a more practical CO₂ desorption temperature, as well as a broader window for the selective adsorption of CO₂ over N₂. The density of states analysis showed that the enhanced CO₂ adsorption on (NiB)₁₀ can be attributed to the synergistic effect between Ni and B, which provides more active sites for CO₂ adsorption and promotes the electron transfer from the surface to the CO₂ molecule. Our theoretical results imply that (NiB)₁₀ should be a promising candidate for CO₂ capture.

In order to study the influence of typical coal-fired flue gas components on the supersaturation characteristics in a multisection growth tube, a two-dimensional heat and mass transfer model was used to predict the supersaturation profiles formed by the cool saturated flow into a warm-walled growth tube (Method 1) and the warm saturated flow into a cold-walled growth tube (Method 2). The calculated results show that the increase in the concentration of the three typical components CO₂, SO₂, and RH (relative humidity) of the flue gas is beneficial for the achievement of the supersaturated environment in Method 1. Additionally, having too many sections of the growth tube has a disadvantage in achieving the supersaturation profiles. When the content of the gas of lower Le is higher, having more sections of the growth tube is beneficial to achieving a supersaturated environment, while a lower number of sections is necessary when the gas content is lower in Method 2. In a word, the appropriate number of sections in a growth tube is significantly important for the achievement of a favorable supersaturated environment.