Ting Li

Propelled by the “dual-carbon” strategy, green and intelligent ports are rapidly advancing toward low-carbon and intelligent development. However, the large-scale incorporation of renewable energy and the extensive electrification of transport equipment have substantially heightened system volatility and scheduling complexity. To address the challenges associated with multi-energy coupling and economic operation in medium and large ports, a hierarchical collaborative optimization scheduling strategy is proposed. The upper layer employs an improved Bio-enhanced Dung Beetle Optimization (BDBO) algorithm for parameter optimization and carbon-cost minimization. Meanwhile, the lower layer establishes a rolling time-series control mechanism grounded in Adaptive Dynamic Hierarchical Decoupling Planning (ADHDP), thereby constituting an integrated BDBO-ADHDP dual-agent system. Simulation results across four seasonal scenarios demonstrate that the proposed methodology outperforms DQN, PSO, GA, ACO, and DBO algorithms in reducing grid power purchases, enhancing renewable energy utilization, mitigating curtailment, and lowering operational costs. Moreover, it achieves faster convergence, superior robustness, and effective carbon-emission control. This study substantiates the efficacy of the proposed strategy within green port integrated energy systems and highlights its potential for broader application in other multi-energy coupled systems.

The authors propose a novel fault diagnosis method to address the challenges of complex fault feature extraction and low diagnostic efficiency caused by the intricate and variable frequency components of vibration signals and the mismatch between the sampling frequency and the actual bearing vibration under variable-speed conditions in printing equipment bearings. This method is based on improved Least Squares Support Vector Machines (LSSVMs) with Kernel Principal Component Analysis (KPCA) for dimension reduction and Deep Belief Networks (DBNs) for variable-speed feature mining. Time-domain feature values of the vibration signal were first extracted to construct a feature set, and the KPCA algorithm was applied for dimensionality reduction. Dimensionality reduction features were used as the input of the DBN to extract higher-order features and enhance the discriminant ability. Finally, the optimized features were classified and diagnosed using the LSSVM model, enabling the extraction of nonlinear complex features under variable-speed conditions and improving diagnostic efficiency. In addition, the lightweight design that combines KPCA dimensionality reduction, DBN deep feature extraction, and improved LSSVM classification effectively addresses the issue of low efficiency in feature extraction and diagnosis caused by the complex and variable vibration signals under variable-speed conditions. Experiments were conducted using bearing vibration data collected from a simulated test bench, covering five fault types. The bearing acceleration vibration signals were collected within 0–5 s and 0–10 s in the speed range of 0–1800 r/min. The proposed model achieved diagnostic accuracies of 98.33% and 98.8889% under these two conditions, respectively, verifying its effectiveness and superiority under complex operating conditions.

To address the integration and consumption challenges posed by large-scale renewable energy, this paper proposes a low-carbon optimal dispatch strategy for virtual power plants (VPP) that incorporates source-load uncertainties and a tiered carbon trading mechanism. On the generation side, a two-stage Power-to-Gas (P2G) process is refined and integrated with a Carbon Capture System (CCS), forming a coupled P2G-CCS VPP structure. A tiered carbon trading model is established to constrain carbon emissions and enhance wind power coordination. On the load side, demand response resources with varying response speeds—both price-based and incentive-based—are coordinated to improve system flexibility across multiple time scales and compensate for the limited regulation capability of carbon capture units. A two-stage day-ahead and intra-day low-carbon economic dispatch model is then constructed to optimize load and spinning reserve allocation, mitigating wind curtailment and load shedding. Based on this framework, a two-stage low-carbon economic dispatch model is formulated and linearized to optimize the allocation of load and spinning reserves. Simulation results demonstrate that the proposed strategy significantly enhances system performance: the wind curtailment rate decreases from about 20% to 7%, carbon emissions are reduced by approximately 15%, and the total operating cost is lowered by 15–16%. Furthermore, a sensitivity analysis of tiered carbon-trading parameters is carried out, offering decision-making insights for low-carbon dispatch in scenarios with high renewable energy penetration.

To address the challenges in identifying effective fault features and achieving sufficient diagnostic accuracy and robustness in variable-speed printing press bearings, where complex mixed-condition vibration signals exhibit non-stationarity, strong nonlinearity, ambiguous time-frequency characteristics, and overlapping fault features across multiple operating conditions, this paper proposes an adaptive optimization signal decomposition method combined with dual-modal time-series and image deep feature fusion for variable-speed multi-condition bearing fault diagnosis. First, to overcome the strong parameter dependency and significant noise interference of traditional adaptive decomposition algorithms, the Crested Porcupine Optimization Algorithm is introduced to adaptively search for the optimal noise amplitude and integration count of ICEEMDAN for effective signal decomposition. IMF components are then screened and reorganized based on correlation coefficients and variance contribution rates to enhance fault-sensitive information. Second, multidimensional time-domain features are extracted in parallel to construct time-frequency images, forming time-sequence-image bimodal inputs that enhance fault representation across different dimensions. Finally, a dual-branch deep learning model is developed: the time-sequence branch employs gated recurrent units to capture feature evolution trends, while the image branch utilizes SE-ResNet18 with embedded channel attention mechanisms to extract deep spatial features. Multimodal feature fusion enables classification recognition. Validation using a bearing self-diagnosis dataset from variable-speed hybrid operation and the publicly available Ottawa variable-speed bearing dataset demonstrates that this method achieves high-accuracy fault identification and strong generalization capabilities across diverse variable-speed hybrid operating conditions.

As the penetration of global new energy into power systems increases, the impact of carbon trading on energy systems continues to grow. Therefore, it is crucial to study the effects of carbon trading mechanisms on the operational characteristics of power systems that include new energy sources. This paper focuses on China, the world’s largest energy-consuming economy, and proposes a multi-time-scale optimization scheduling method for a Combined Cooling, Heating, and Power (CCHP) system that considers a reward-penalty tiered carbon trading mechanism. This method effectively enhances the economic, environmental, and stability performance of the CCHP system. Firstly, the relationship between different carbon trading mechanisms and carbon trading volumes is analyzed to identify the carbon trading mechanism with the highest correlation to economic efficiency. Based on this analysis, a carbon trading model is introduced into the system’s economic dispatch strategy, constructing an economic model for the CCHP system under the influence of the carbon trading mechanism. While considering economic efficiency, this study also aims to improve system stability by proposing a multi-time multi-layer rolling optimization scheduling method. This method adjusts equipment output to respond to random fluctuations caused by uncertainties in energy sources and loads. The optimization results of the case study show that the introduction of the reward-penalty tiered carbon trading mechanism can reduce the carbon emissions of the park and improve economic performance. Additionally, the multi-time multi-layer rolling optimization scheduling effectively mitigates random fluctuations in supply and demand, ensuring stable system operation.

Given the inherent volatility and intermittency of photovoltaic power generation, enhancing the precision of photovoltaic power predictions becomes imperative to ensure the stability of power systems and to elevate power quality. This article introduces an intelligent photovoltaic power prediction model based on the Extreme Learning Machine (ELM) with the Adaptive Spiral Dung Beetle Optimization (ASDBO) algorithm. The model aims to accurately predict photovoltaic power generation under multi-factor correlation conditions, including environmental temperature and solar irradiance. The computational efficiency in high-dimensional data feature conditions is enhanced by using the Pearson correlation analysis to determine the state input of the ELM. To address local optimization challenges in traditional Dung Beetle Optimization (DBO) algorithms, a spiral search strategy is implemented during the dung beetle reproduction and foraging stages, expanding the exploration capabilities. Additionally, during the dung beetle theft stage, dynamic adaptive weights update the optimal food competition position, and the levy flight strategy ensures search randomness. By balancing convergence accuracy and search diversity, the proposed algorithm achieves global optimization. Furthermore, eight benchmark functions are chosen for performance testing to validate the effectiveness of the ASDBO algorithm. By optimizing the input weights and implicit thresholds of the ELM through the ASDBO algorithm, a prediction model is established. Short-term prediction experiments for photovoltaic power generation are conducted under different weather conditions. The selected experimental results demonstrate an average prediction accuracy exceeding 93%, highlighting the effectiveness and superiority of the proposed methodology for photovoltaic power prediction.

In this article, we consider complex networks without connection information. The absence of global structure induces a great obstacle in the structural controllability analysis of these networks. Thus, a topology predicting method based on the connection probability matrix is proposed to provide a global structure for structural controllability analysis in this article. Furthermore, the modified principles of predicting global network topologies are established to acquire a more accurate global connection relationship. Eventually, the drive node set of these networks is determined by predicting global topologies. The accuracy of the proposed topology predicting method is verified by numerical simulations in the context of artificial networks and real networks. The results reveal that the global topology and structural controllability of complex networks with large scale and high edge density could be accurately predicted by utilizing the proposed method.

This article proposed a novel event-triggered integral sliding-mode control (ISMC) strategy for nonlinear system with disturbances via robust adaptive dynamic programming (ADP) considering control constrains. A mixed event-triggered ISMC scheme with two different trigger parts is developed and the optimal performance of sliding-mode dynamics with input constrains is ensured. By guaranteeing that the system trajectory converges to the sliding-mode surface with removing the input disturbances, a discontinuous part triggering rule is presented together with the existence analysis of a lower trigger interval time bound. Then, the optimal event-triggered control of sliding-mode dynamics is converted into a discounted factor-based H _∞ constrained control problem under continuous part triggering condition. To solve the event-triggered HJI equation, a critic-only neural network (NN)-based ADP scheme is developed by applying a concurrent learning. The NN weight is updated by analyzing the uniformly ultimately bounded (UUB) stability of sliding-mode dynamics considering the event-triggered condition via the Lyapunov technique. Finally, the validity of our control strategy is verified by simulation.