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.

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.

The coordination of electric vehicle (EV) charging schedules faces multiple challenges, including variations in grid load, unpredictable demand patterns, and the instability of renewable energy supplies. This study develops a multi-criteria optimization framework that considers the fluctuating characteristics of photovoltaic (PV) generation, EV travel patterns, energy storage system properties, and carbon emission constraints. A data-driven Adaptive Dynamic Hierarchical Decoupling Planning (ADHDP) algorithm is utilized to optimize the ordered charging strategy for EV. First, to address the stochasticity and seasonal variations of PV generation, an improved Long Short-Term Memory (LSTM) network is constructed for PV power prediction across multiple meteorological scenarios throughout the year. Compared to traditional methods such as Support Vector Machine Regression and Autoregressive Integrated Moving Average models, the proposed model significantly enhances forecasting accuracy and stability, achieving a reduction of approximately 12.7% in terms of Mean Absolute Percentage Error across all seasons. Second, a multi-objective optimization strategy based on ADHDP is designed by jointly considering charging demand uncertainty and PV output fluctuations, aiming to effectively control system carbon emissions, enhance energy utilization efficiency, and reduce user waiting times. Simulation results demonstrate that the ADHDP-based method exhibits superior performance in terms of economic efficiency, environmental friendliness, and user satisfaction, thereby demonstrating the viability and performance advantages of the proposed optimization strategy.