Conventional point prediction methods encounter challenges in accurately capturing the inherent uncertainty associated with photovoltaic power due to its stochastic and volatile nature. To address this challenge, we developed a robust prediction model called QRKDDN (quantile regression and kernel density estimation deep learning network) by leveraging historical meteorological data in conjunction with photovoltaic power data. Our aim is to enhance the accuracy of deterministic predictions, interval predictions, and probabilistic predictions by incorporating quantile regression (QR) and kernel density estimation (KDE) techniques. The proposed method utilizes the Pearson correlation coefficient for selecting relevant meteorological factors, employs a Gaussian Mixture Model (GMM) for clustering similar days, and constructs a deep learning prediction model based on a convolutional neural network (CNN) combined with a bidirectional gated recurrent unit (BiGRU) and attention mechanism. The experimental results obtained using the dataset from the Australian DKASC Research Centre unequivocally demonstrate the exceptional performance of QRKDDN in deterministic, interval, and probabilistic predictions for photovoltaic (PV) power generation. The effectiveness of QRKDDN was further validated through ablation experiments and comparisons with classical machine learning models.

With the increase of photovoltaic penetration rate, the fluctuation of photovoltaic power generation affects the reliability of ship power grids. Marine PV grid-connected systems with high penetration rates should generally have a low voltage ride-through capability. In the present paper, a strategy in which super capacitors are applied for energy storage in a marine photovoltaic grid-connected system is proposed, and an inverter adopts independent decoupling control of active and reactive currents to improve the LVRT capability of photovoltaic grid-connected systems. In addition, a comprehensive control strategy is also designed to control the supercapacitor, to regulate the active power through the control method of the voltage outer loop and the current inner loop, in order to maintain the DC bus voltage stability. At the same time, the inverter can increase the reactive power output to support the grid voltage. The advantage of this system is in smoothing the power imbalance in a short time, enhancing the low voltage ride-through capability of the photovoltaic grid-connected system, improving the power quality, and ensuring the safety and stability of the ship’s power grid. MATLAB/Simulink were employed to establish a ro-ro ship super capacitor–marine photovoltaic grid-connected power system model and to carry out simulation experiments by setting the grid voltage drop. The results show that when the grid voltage drops, the inverter adjusts the distribution of active and reactive power. The power factor drops from 1 to 0.77, and the effective value of the voltage drop increases from 150 V to 156 V, which proves that this strategy effectively reduces the depth of the grid voltage drop and improves the low voltage ride-through capability of the photovoltaic grid-connected system.

In the field of wind power prediction, traditional methods typically rely on one-dimensional time-series data for feature extraction and prediction. In this study, we propose an innovative short-term wind power forecasting approach using a “visual” 2D image prediction method that effectively utilizes spatial pattern information in time-series data by combining wind power series and related environmental features into a 2D GASF image. Firstly, the wind power data are decomposed using the ICEEMDAN algorithm optimized by the BWO (Beluga Whale Optimization) algorithm, extracting the submodal IMF (Intrinsic Mode Function) components with different frequencies. Then, modal reconstruction is performed on the basis of the permutation entropy value of the IMF components, selecting meteorological features highly correlated with reconstructed components through Spearman correlation analysis for data splicing and superposition before converting them into GASF images. Finally, the GASF images are input into the UniFormer model for wind power sequence prediction. By leveraging wind power data predictions from a coastal wind farm in East China and Sotavento in Spain, this study demonstrates the significant benefits and potential applications of this methodology for precise wind power forecasting. This research combines the advantages of image feature extraction and time-series prediction to offer novel perspectives and tools for predicting renewable energy sources such as wind power.

This paper takes the application of bidirectional DC-DC converter in Marine hybrid power system as the background, the problems of high reflow power and current stress in dual active bridge converter under traditional phase shift control are studied in this paper. Based on the dual phase shift control strategy, the power transmission optimization algorithm model is analyzed and derived, and a control method for optimizing mode switching is proposed. The optimized control algorithm is validated by Matlab/Simulink simulation experiment. The results show that the reflow power and current stress optimization control algorithm can effectively reduce the reflow power and the current stress of the converter, so as to improve the operation performance of DAB converter in high-power ship hybrid power system.

With the gradual rise of battery-powered ships, the high-power charging demand during berthing is poised to exacerbate the peak-to-valley difference in the port grid, possibly leading to grid congestion and logistical disruption. To address this challenge, this paper proposes a bi-level coordinated scheduling scheme across both logistical operations and energy flow dispatch. Initially, by developing a refined model for the dynamic power characteristics of quay crane (QC) clusters, the surplus power capacity that can be stably released through an orderly QC operational delay is quantified. Subsequently, a hybrid AC/DC ship–shore interconnection architecture based on a smart interlinking unit (SIU) is proposed to utilize the QC peak-shaving capacity and satisfy the increasing shore power demand. In light of these, at the logistics level a coordinated scheduling of berths, QCs, and ships charging is performed with the objective of minimizing port berthing operational costs. At the energy flow level, the coordinated delay in QC clusters’ operations and SIU-enabled power dispatching are implemented for charging power support. The case studies demonstrate that, compared with the conventional independent operational mode, the proposed coordinated scheduling scheme enhances the shore power supply capability by utilizing the QC peak-shaving capability effectively. Moreover, as well as satisfying the charging demands of electric ships, the proposed scheme significantly reduces the turnaround time of ships and achieves a 39.29% reduction in port berthing operational costs.