Pumped storage power stations not only serve as a special power load but also store excess electricity from the power system, significantly reducing the curtailment of wind and solar power. This dual function ensures the stable operation of the power grid and enhances its economic benefits. The scheduling optimization problem of a combined wind–solar–pumped storage system is addressed in this study, and an optimization scheduling model is proposed with the objective of maximizing total system revenue. The model is designed to comprehensively account for the generation revenues from wind power, photovoltaic power, thermal power, and pumped storage, as well as the penalty costs associated with pollutant emissions. To address the limitations of traditional algorithms, which are prone to being trapped in local optima and exhibit slow convergence, an improved bat algorithm was developed. The algorithm is enhanced through the use of chaotic mapping to expand the initial solution space, the incorporation of adaptive step-size updates to improve convergence efficiency, and the integration of the Cauchy function to strengthen global search capabilities, thereby effectively avoiding local optima. Simulation results have demonstrated that the improved algorithm achieves significant improvements over traditional bat algorithms and particle swarm optimization (PSO) in terms of optimization efficiency, with total revenue increases of 21.9% and 24.6%, respectively. The optimized scheduling plan is shown to fully utilize the flexible regulation capabilities of pumped storage, mitigating the adverse effects of wind and photovoltaic output fluctuations on grid operations and achieving a balanced trade-off between economic and environmental objectives.

To address the frequent external damage incidents to transmission line corridors caused by construction machinery such as excavators and cranes, this paper constructs a dataset of external damage hazards in transmission line corridors and proposes a detection method based on YOLO-LSDW for these hazards. Firstly, by incorporating the concept of large separable kernel attention (LSKA), the spatial pyramid pooling layer is improved to enhance the information exchange between different feature levels, effectively reducing background interference on external damage hazard targets. Secondly, in the neck network, the traditional convolution is replaced with a ghost-shuffle convolution (GSConv) method, introducing a lightweight slim-neck feature fusion structure. This improves the extraction capability for small object features by fusing deep semantic information with shallow detail features, while also reducing the model’s computational load and parameter count. Then, the original YOLOv8 head is replaced with a dynamic head, which combines scale, spatial, and task attention mechanisms to enhance the model’s detection performance. Finally, the wise intersection over union (WIoU) loss function is adopted to optimize the model’s convergence speed and detection performance. Evaluated on the self-constructed dataset of external damage hazards in transmission line corridors, the improved algorithm shows significant improvements in key metrics, with mAP@0.5 and mAP@0.5:0.95 increasing by 3.4% and 4.6%, respectively, compared to YOLOv8s. Additionally, the model’s computational load and parameter count are reduced, and it maintains a high detection speed of 96.2 frames per second, meeting real-time detection requirements.

With the increasing complexity of power systems and the widespread application of power electronic equipment, power quality issues have become increasingly prominent, among which power quality disturbances are one of the key factors affecting the stable operation of power systems and the normal functioning of electrical equipment. Current research methods are still limited by feature extraction, insufficient model generalization ability, and strong data dependence. This paper proposes a power quality disturbance classification strategy based on the fast S-transform (FST) and an improved convolutional neural network–long short-term memory (CNN-LSTM) model to achieve accurate classification and identification of various power quality disturbances. Firstly, the FST is employed to process the power quality disturbance signals, enabling efficient analysis and feature extraction while effectively preserving the time–frequency characteristics of the signals and significantly reducing the computational burden. Secondly, to address the limitations of traditional CNN models in power quality disturbance classification, this paper introduces an improved CNN-LSTM hybrid classification model that integrates mechanism fusion. This model improves the classification performance and generalization ability for power quality disturbances by incorporating an enhanced sparrow search algorithm and learning mechanisms. Finally, the proposed strategy is experimentally validated using a large dataset of power quality disturbances. After analysis and comparison, the method proposed in this paper maintains an identification accuracy of over 97% even in strong noise environments when subjected to a single type of disturbance. Under complex conditions involving mixed disturbances of multiple types, the identification accuracy remains above 95%. Compared to existing methods, the proposed method achieves an improvement in identification accuracy by up to 3.2%. Additionally, its identification accuracy in scenarios with small data samples is significantly better than that of traditional methods, such as single CNN models and LSTM models. The experimental results demonstrate that the proposed strategy can accurately classify and identify various power quality disturbances, outperforming traditional methods in terms of classification accuracy and robustness.

The development of renewable energy and the construction of a comprehensive energy system with multiple complementary energy sources have gradually become the main direction of China’s energy development. As the penetration rate of renewable energy increases, the intermittent and fluctuating output of wind and solar power has a more significant impact on the system. This article conducts research on the optimization configuration of integrated energy system (IES) considering photovoltaic output uncertainty under a ladder carbon trading mechanism. Firstly, a two-stage distributed robust optimization (DRO) configuration model for integrated energy system is established. In detail, a deterministic model aimed at minimizing investment costs is given in the first stage and an uncertainty model aimed at minimizing operating costs in the probability distribution of the worst scenario is built in the second stage. Then, a data-driven distributed robust optimization method is adopted to deal with the uncertainty of photovoltaic output using MATLAB software (R2020A). Finally, the column and constraint generation (C&CG) algorithm is used to solve the problem, and the optimal investment capacity and cost results of the integrated energy system considering demand response under a ladder carbon trading mechanism are obtained. Through analysis, the proposed method achieves a 5.54% reduction in carbon emission costs while maintaining nearly unchanged investment costs, thus balancing economic and environmental benefits. Additionally, the CCG algorithm can effectively improve computational efficiency and guarantee the optimality of the solution.

As the global reliance on renewable energy sources grows, wind and photovoltaic power, as pivotal components, pose significant challenges to power system dispatch due to their volatility and uncertainty. To effectively address this challenge, this paper proposes a renewable energy optimization dispatch strategy based on a prediction model. First, this paper constructs a prediction model combining functional data analysis and recurrent neural networks (RNNs) to achieve an accurate prediction of renewable energy output. On this basis, considering the economic and environmental benefits of system operation, an optimal multi-objective dispatch model for renewable energy is established, and the multi-objective optimization problem is transformed into a single-objective optimization problem using weighting methods to reduce the complexity of the solution. Finally, a typical microgrid test system is used to verify the effectiveness and feasibility of the proposed method. The results of the numerical example show that the proposed model can achieve an accurate prediction of renewable energy sources, reduce the conservatism of traditional dispatch decisions, and balance economic and environmental benefits.

This study introduces an enhanced Adaptive Fuzzy Sliding-Mode Control (AFSMC) approach based on the fuzzy logic systems (FLSs) to achieve trajectory tracking of multiple-input and multiple-output (MIMO) fractional-order nonlinear systems in the presence of uncertain nonlinear terms and disturbances. An integral SMC approach is proposed for achieving state trajectory tracking control. However, uncertainties in real systems are complex and diverse, not only uncertain bounded disturbances but unknown nonlinear functions. Therefore, in this paper, the FLSs are used not only to approximate unknown functions but also to improve the switching function of the SMC. The stability of the system with designed input control laws is demonstrated through the fractional-order Lyapunov function stability criterion. Subsequently, the simulation results are displayed and serve to validate the efficacy and resilience of the proposed control methodology. These results underscore the ability of the proposed method to perform reliably under various conditions, thereby confirming its robustness as a viable solution.