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.

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.

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.

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 mitigate rotor vibrations in magnetic bearing systems arising from mass imbalance, this study proposes a novel suppression strategy that integrates the crested porcupine optimizer (CPO) with an enhanced linear active disturbance rejection control (ELADRC) framework. The approach introduces a disturbance estimation and compensation scheme based on a linear extended state observer (LESO), wherein both the LESO bandwidth ${\omega }_0$ and the LADRC controller parameter ${\omega }_c$ are adaptively tuned using the CPO algorithm to enable decoupled control and real-time disturbance rejection in complex multi-degree-of-freedom (DOF) systems. Drawing inspiration from the crested porcupine’s layered defensive behavior, the CPO algorithm constructs a state-space model incorporating rotor displacement, rotational speed, and control current, while leveraging a reward function that balances vibration suppression performance against control energy consumption. The optimized parameters guide a real-time LESO-based compensation model, achieving accurate disturbance cancelation via amplitude-phase coordination between the generated electromagnetic force and the total disturbance. Concurrently, the LADRC feedback structure adjusts the system’s stiffness and damping matrices to improve closed-loop robustness under time-varying operating conditions. Simulation studies over a wide speed range (0~45,000 rpm) reveal that the proposed CPO-ELADRC scheme significantly outperforms conventional control methods: it shortens regulation time by 66.7% and reduces peak displacement by 86.8% under step disturbances, while achieving a 79.8% improvement in adjustment speed and an 86.4% reduction in peak control current under sinusoidal excitation. Overall, the strategy offers enhanced vibration attenuation, prevents current saturation, and improves dynamic stability across diverse operating scenarios.

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.