Given that the power load data are stochastic and it is difficult to obtain accurate forecasting results by a single algorithm. In this study, a combined forecasting method for short-term power load was proposed based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Multiverse optimization algorithm (MVO), and the Gated Recurrent Unit (GRU) based on Rectified Adam (RAdam) optimizer. Firstly, the model uses the CEEMDAN algorithm to decompose the original electric load data into subsequences of different frequencies, and the dominant factors are extracted from the subsequences. Then, a GRU network based on the RAdam optimizer was built to perform the forecasting of the subsequences using the existing subsequences data and the associated influencing factors as the data set. Meanwhile, the parameters of the GRU network were optimized with the MVO optimization algorithm for the prediction problems of different subsequences. Finally, the prediction results of each subsequence were superimposed to obtain the final prediction results. The proposed combined prediction method was implemented in a case study of a substation in Weinan, China, and the prediction accuracy was compared with the traditional prediction method. The prediction accuracy index shows that the Root Mean Square Error of the prediction results of the proposed model is 80.18% lower than that of the traditional method, and the prediction accuracy error is controlled within 2%, indicating that the proposed model is better than the traditional method. This will have a favorable impact on the safe and stable operation of the power grid.

Epoxy composite materials are widely used in power equipment. As the voltage level increases, the requirement of material properties, including electrical, thermal, and mechanical, has also increased. Introducing thermally conductive nanofiller to the epoxy/liquid rubber composites system is an effective approach to improve heat performance, but the effects of thermally conductive nanofillers on relaxation characteristics remain unclarified. In this paper, nano-alumina (nano-Al₂O₃) and nano-boron nitride (nano-BN) have been employed to modify the epoxy/carboxyl-terminated liquid nitrile–butadiene rubber (epoxy/CTBN) composites system. The thermal conductivity and glass transition temperature of different formula systems have been measured. The effect of the nanofillers on the relaxation behaviors of the resin matrix has been investigated. Results show that the different kinds of nanofillers will introduce different relaxation processes into the matrix and increase the conductivity at the same time. This study can provide a theoretical basis for the synergistic improvement of multiple properties of epoxy resin composites.

The large-scale integration of renewable energy into power systems significantly alters their operational characteristics, reducing system inertia and posing challenges to transient frequency stability. Virtual synchronous generator (VSG) technology offers a solution by enhancing system inertia and improving transient frequency stability. To quantitatively analyze the transient characteristics of VSGs and optimize their control strategies, it is crucial to develop a comprehensive transient frequency model. This paper establishes a transient frequency model for VSGs, incorporating their inertial response, primary frequency regulation, and secondary frequency regulation. A MATLAB/Simulink-based (version R2021b) simulation model is constructed to validate the analytical model. Additionally, the impact of various control parameters on VSG transient frequency is examined, providing valuable insights and guidance for maintaining system frequency stability.

The integration of renewable energy sources and distributed energy storage systems increasingly complicates the operation of distribution networks, while stringent carbon reduction targets demand low-carbon operational strategies. To address these complexities, this paper introduces a two-stage model for reconfiguring distribution networks and ensuring low-carbon dispatch. Initially, second-order cone programming is employed to minimize losses in the network. Subsequently, the outputs of renewable energy and energy storage systems are optimized using the mantis search algorithm (MSA) to achieve low-carbon dispatch, with the network’s carbon potential as the evaluation metric. The proposed model demonstrates a significant reduction in average active power loss by 34.85%, a decrease in daily carbon emissions by 509.97 kg, and a reduction in carbon emission costs by 17.24%, thereby markedly enhancing the economic and social benefits of grid operations.

Cascade pump station irrigation districts (CPSIDs) consume vast amounts of irrigation water and energy. The aim of this study was to adjust the spatial patterns of crops and irrigation technologies in the CPSID to reduce the consumption of water and energy under the condition of conserving crop irrigation water. The irrigation district (ID) is divided into several sub-districts according to the topography elevation difference and the distribution of cascade pump stations (CPSs). The mathematical models of the irrigation water and energy consumption in each sub-district were established based on the relationship between the spatial patterns of crops and irrigation technologies in each sub-district. According to the present situation of the Jingdian Phase I Irrigation District in the arid region of northwest China, three modes of adjusting the crop planting structure and drip irrigation area were proposed. Based on the combination of these modes, three schemes of the spatial patterns of crops and irrigation technologies were generated. The annual energy consumption and irrigation water consumption of each sub-district in the ID of these three schemes were obtained through simulation. Compared with the present spatial patterns of crops and irrigation technologies in the Jingdian Phase I Irrigation District, Scheme 3 has the best water-saving and energy-saving effects, with an annual water saving and energy saving of 1753 × 10⁴ m³ and 2898 × 10⁴ kWh, and the water-saving rate and energy-saving rate were 12.34% and 15.74%, respectively. This paper also shows that the synchronous adjustment of crops and irrigation technologies among the sub-districts of ID can achieve significant water-saving and energy-saving effects.

Cascade pump station irrigation districts (CPSIDs) consume large quantities of water and energy. Water- and energy-saving results and income increases are guaranteed under the sustainable development of the CPSID. The CPSID is divided into several sub-districts based on the elevation difference of topography and pump station distributions. The spatial patterns of crops and irrigation technologies can be changed by adjusting crop planting structures and developing drip irrigation in each sub-district. Its optimization will change the spatial patterns of irrigation water and energy consumption to achieve water- and energy-saving results, increase income, and provide an ecological advantage. To obtain the optimal spatial patterns of water and energy in the CPSID, a multi-objective linear programming model of minimum irrigation water consumption, minimum energy consumption, and highest crop output value was established. This model was applied to the Jingdian Phase I Irrigation District in northwest China, and an optimal scheme of water and energy spatial patterns was obtained. Compared with the present situation, the optimal scheme could save water by 26.18%, save energy by 29.38%, and increase income by 29.55%. The increased investment in the drip irrigation project would lead to reduced irrigation water and energy consumption and increased crop output value. The research results provide a scientific basis for the sustainable development of agriculture and ecological environment protection in the CPSID.