The electrification of maritime electrical power systems has progressed from isolated onboard upgrades to integrated shipboard power systems, shore-to-ship interfaces, and port microgrids operating under strict safety, reliability, and regulatory constraints. In parallel, artificial intelligence, machine learning, and advanced control techniques have been increasingly reported in literature to enhance energy management, resilience, and operational efficiency. However, their technical suitability and deployment readiness remain insufficiently assessed from a system-level and operational perspective. This paper presents a system-level technical assessment of control, optimization, and AI/ML-based approaches for maritime power systems, with exclusive focus on IEEE Transactions on Industry Applications. Using a rigorously selected dataset of forty-two journal papers, the study classifies methods by maritime system context, operational layer, and method role, audits formulation transparency, and evaluates deployment realism under non-ideal operational assumptions. To support more structured comparison, a semi-quantitative rubric is used to summarize how explicitly formulation and deployment realism criteria are addressed across method classes. The results show that optimization and model predictive control dominate safety-critical decision layers due to explicit constraint enforcement, while learning-based methods are limited to auxiliary roles such as forecasting, monitoring, and decision support. The rubric-based comparison further indicates that optimization, MPC, and hybrid AI–optimization approaches achieve the strongest formulation scores, whereas supportive learning-based methods remain weaker in deployment realism because sensing non-idealities, communication constraints, and real-world feasibility are rarely treated explicitly.

Recently, the cybersecurity of harbor-integrated shipboard microgrids (SMGs) has become a significant concern for ensuring the integrity and resilience of maritime energy systems within intelligent grids. However, the increasing reliance on internet-based technologies exposes SMGs to false data injection attacks (FDIAs), denial of service (DoS) attacks, and other cyber threats, which can compromise power management and operational stability. To address these threats, this paper proposes a hybrid detection framework that integrates Hilbert-Huang Transform (HHT) for signal decomposition and anomaly identification, Long Short-Term Memory Variational Autoencoder (LSTM-VAE) for deep feature extraction and latent attack pattern detection, and a Bidirectional Gated Recurrent Unit (Bi-GRU) for sequential dependency analysis and real-time FDIA detection. The HHT-based empirical mode decomposition (EMD) enables time-frequency analysis by extracting intrinsic mode functions (IMFs), improving the detection of nonstationary attack patterns in maritime environments. The LSTM-VAE identifies subtle attack signatures within SMG data, while the Bi-GRU enhances temporal modeling and classification, ensuring accurate and real-time detection with minimal false positives. Extensive validation against various cyberattack scenarios, including component-level intrusions, demonstrates the framework's robustness in detecting and mitigating cyber threats. By optimizing the deep LSTM-VAE-based Bi-GRU model with carefully tuned hyperparameters, the proposed method achieves a detection accuracy of 99.80%, surpassing existing techniques. This work introduces a scalable, adaptive, and maritime-specific cybersecurity solution, significantly enhancing shipboard microgrid security and operational resilience in intelligent maritime SMGs.

Seaports play a crucial role in global trade, acting as key hubs in international logistics networks. However, the growth of maritime activities has led to significant environmental challenges, including elevated carbon emissions and pollution. This paper presents an in-depth bibliometric analysis of sustainable and smart port initiatives, utilizing data from the Web of Science database to identify trends, key contributors, and technological advancements. The study focuses on the adoption of innovative technologies, such as All-Electric Ships, smart grids, and renewable energy systems, which are essential for modernizing port operations and reducing environmental impact. The analysis categorizes initiatives into five main areas: Energy & Fuels, Climate & Air, Port & City, Waste & Circular Economy, and Environment & Biodiversity. The findings highlight the potential of hybrid microgrid systems and advanced digital solutions in enhancing energy management and operational efficiency. Policy frameworks, including differentiated carbon pricing and global port incentive programs, are proposed as effective measures to align seaport sustainability with global decarbonization goals. This research provides a concise overview of current developments and outlines future strategies for achieving sustainable port infrastructure.

Cyber resilience is paramount in modernizing maritime transportation; however, cyberattacks pose significant challenges in deploying resilience, remote control, and monitoring technologies. Shipboard microgrids (SMGs) are not immune to cyber threats, given their tightly integrated cyber-physical processes managed through advanced software. With extensive power electronic components, SMGs are highly vulnerable to attacks, compromising their security and stability. False data injection attacks and potential malware, spoofing, and jamming attacks threaten the integrity and safety of SMGs. Robust attack detection and prevention techniques are crucial for enhancing SMG resilience, stability, and adaptive capability and stabilizing them at their optimum operating state. This study addresses cutting-edge cybersecurity issues concerning SMGs, offering an inclusive review of attacks and protective measures across critical maritime systems for attack detection, prevention, and countermeasures for SMGs. To illustrate the cyber resilience in the SMG using artificial intelligence methods, a novel cyberattack detection method integrating the Hilbert–Huang transform (HHT) and deep learning (DL) is proposed and used as a case study. An advanced one-dimensional convolutional neural network (1D-CNN) with the Adam optimizer and HHT for signal feature extraction has been employed along with the construction of multiple-input basis models within the DL framework for automatic intrinsic feature extraction from raw signal fluctuations. Various basis patterns are generated using a 1D-CNN-based autoencoder–decoder system for deep feature extraction, optimizing the ensemble’s performance to detect attack types using weighted 1D-CNN models. The simulation results demonstrate the efficiency and reliability of the proposed method, achieving a 94.75

This paper presents an adaptive deep neural network (DNN) approach for intrusion detection and prevention in shipboard AC/DC microgrids, focusing on shore-to-ship power connections and power quality (PQ) challenges. The method addresses false data injection attacks (FDIAs) that disrupt secondary control, leading to voltage and current regulation failures. By integrating Fast Fourier Transform (FFT) for frequency-domain feature extractions such as voltage sag/swell, harmonics, and transient distortions with DNN classification, the model achieves high accuracy in detecting cyberattacks under PQ disturbances. Optimized using the Adam optimizer and ReLU-sigmoid activation, the framework enhances detection accuracy while reducing false positives. The obtained results demonstrate superior performance of FFT-DNN over state-of-the-art methods, achieving 97.7% accuracy across attack scenarios. The approach effectively classifies between cyber-intrusions and power quality disturbances, offering a scalable cybersecurity solution for maritime systems. This study advances secure and resilient shore-ship DC microgrids by addressing cybersecurity vulnerabilities and power quality challenges in shore-power connections.

Electric vehicles (EVs) offer a promising solution to achieve zero tailpipe emissions in urban areas. Effective deployment of EVs requires a robust energy-management-based charging infrastructure. Bipolar DC-bus-fed charging stations are popular due to their efficient connection to distributed generation and reduced conversion steps. However, voltage imbalances in bipolar DC buses, caused by varying EV load demands, pose a challenge. This paper introduces design guidelines for EV charging stations using a dual-output voltage balancer (DOVB) with model predictive control (MPC), providing superior balancing capabilities. Unlike three-level converters, the DOVB minimizes voltage and current stress on switches and avoids shoot-through issues. System stability and robustness were evaluated to optimize control parameters. Simulation and hardware-in-loop experiments demonstrate that the proposed method effectively manages voltage imbalances, ensuring consistent supply quality in bipolar EV charging stations.

Integrating electrification and digitization within dc shipboard microgrids (SMGs) has led to transformative changes and unprecedented advancements in the maritime industry. This transition has exposed vulnerabilities, particularly in distributed generation units (DGUs) and power converter configurations, necessitating robust defense against potential cyber threats that have the potential to cause system instability and, in extreme situations, result in the blackout of dc SMGs. This article proposes a new paradigm for adaptive cyber-resilience of dc SMGs using hybrid signal processing with deep learning (DL) methods to maintain the system's resilient operation. Signal-processing techniques incorporating wavelet transform (WT) and singular value decomposition (SVD) have been developed to facilitate a thorough analysis of power converter configurations for early detection and mitigation of cyber threats. A deep 1-D convolutional neural network (1D-CNN) with the Adam optimizer based on SVD is then used for signal feature extraction. Multiple-input basis models for 1D-CNN have also been developed to automatically capture wavelet singular values from the raw fluctuation signals. The 1D-CNN-based autoencoder-decoder framework ensures diverse basis patterns, and the precision-driven 1D-CNN model weighting strategy optimizes the ensemble for attack detection. Test results of a typical dc SMG have shown the efficiency and reliability of the proposed method in achieving a higher accuracy score of 95.75% compared to other state-of-the-art techniques in attack detection across diverse scenarios in dc SMGs.

The rapid adoption of electric vehicles (EVs) has catalyzed the urgency of a resilient and sustainable charging infrastructure. This study addresses this issue by harnessing offshore wind energy resources to meet the charging demands, particularly in remote coastal regions. The primary challenge tackled in this research involves the complex management of power flow dynamics due to the inherent variability of wind energy and the stochastic nature of EV charging demands, modeled using probabilistic distributions to represent varying arrival times, charging durations, and power requirements of EVs. This work introduces a pioneering framework centered on the development of a wind-powered electric vehicle charging system (EVCS) that utilizes a medium-voltage direct current (MVDC) bus. An enhanced decentralized model predictive control (MPC) strategy was employed that distinguishes itself from conventional control paradigms due to its heightened adaptability and proficient management of the dynamic interactions among wind energy generation, energy storage systems (ESS), EV charging demands, and grid interactions. Rigorous simulations and real-time hardware-in-loop studies underscore the efficacy of the MPC strategy in preserving the voltage stability within the MVDC bus while optimizing the power flow, thereby minimizing energy losses and ensuring grid resilience. These results validate the viability of the proposed wind energy-integrated EVCS as an integral component of seaport grid infrastructure.

The maritime sector is experiencing significant change, creating challenges and opportunities for seaports. This review explores the evolving landscape of seaports by analyzing various academic papers, providing a roadmap for understanding their changing nature. This paper examines the historical development of seaports, focusing on the latest fifth generation, emphasizing sustainability, intelligent services, and community engagement. The introduction of All-Electric Ships and the growing need for sustainability requires innovative strategies. This study suggests two solutions: two-stage robust voltage control and flexible management approaches. Through keyword analysis, the review identified three primary research themes: methodologies used in ports, their purpose, and the adoption of smart control technologies. Each theme is thoroughly explored in subsequent sections, offering a detailed examination based on papers sourced from IEEE Explore spanning the years 2000 to 2023.

The increase in greenhouse gas emissions (GHG) from the transportation sector, along with the ongoing depletion of fossil fuels, emphasizes the necessity for increased focus on energy storage systems (ESSs) and renewable energy sources (RESs) in seaports and on short-distance vessels such as ferries. This paper investigates the development of next-generation smart ports, wherein the integration of Internet of Things (IoT) and sensors transforms ports into intelligent hubs. This transformation aims to optimize operations for all stakeholders, that leverage emerging technologies to enhance efficiency and connectivity. Notably, many smaller seaports lack shore-power facilities, shore-based power installations that supply electric power to ships from the grid. Consequently, ships often rely on continuous operation of auxiliary diesel engines and generators while at berth to meet auxiliary loads. To address these challenges and overcome economic and logistical constraints, this paper proposes a seaport microgrid (SMG) with a DC distribution that would be created by integrating multiple ships with decentralized control mechanisms supplemented by an onshore charging infrastructure. This helps to achieve a sustainable path by introducing ship-based SMGs involving the integration of shipboard microgrids with onshore charging. The proposed approach relies on adaptive droop control, decentralized power-sharing based on battery charge and reducing traditional communication dependencies. The case study supported by the simulation results shown in the paper emphasizes the potential of this strategy in the evolution of maritime infrastructure.

Cyber resilience has become paramount as a transition of maritime systems towards digitization, particularly within DC shipboard microgrids (SMGs). Adopting innovative communication technologies can enhance the resilience of SMGs for stable operation. However, challenges like false data injection and Man-in-The-Middle attacks pose significant threats to SMG operations when integrating these technologies into ship intelligent grids. In this regard, this paper proposes a reliable deep learning (DL) method, especially an Artificial neural network (ANN) with a deep encoder-decoder architecture, for the detection of cyber-intrusions and mitigating their effects, ensuring system control and stability for resilient operation of SMG. Detecting malicious data intrusions is crucial for maintaining optimal grid conditions and preventing disruptions in load dispatch. The proposed method utilizes a fusion of current and voltage data features for comprehensive DL model training, resulting in an adequate level of detection accuracy and providing cybersecurity analysis for SMGs, addressing component-level attacks, and devising defense strategies from aspects of detection, mitigation, and prevention. Furthermore, the deep ANN is fine-tuned with optimal hyperparameters to effectively counter cyberattacks, achieving an enhanced accuracy rate of 97.51% and minimal loss of 0.101%, surpassing conventional machine learning approaches. Rigorous test scenarios are performed to validate the robustness of the proposed method, emphasizing the cyber resilience of DC SMGs for enhanced security and operational integrity.

The cybersecurity of shipboard power systems or microgrids (SMG) connected to greener harbor microgrids (HMG) has become a significant concern for ensuring the integrity and resilience of maritime energy systems within intelligent grids. While modern communication technologies enhance SMGs for resilient operation, threats such as false data injection attacks (FDIA) against signal processing models pose a critical risk to stable power management in these systems. This paper presents a reliable signal-processing strategy utilizing the Hilbert–Huang transform (HHT) integrated with a deep artificial neural network (ANN) to enhance the security of SMGs for resilient operations. The objective of this study is to develop a real-time signal processing model using an HHT-based ANN, using empirical mode decomposition (EMD) for advanced signal analysis, extracting intrinsic mode functions (IMFs), and providing time-frequency analysis to handle complex and nonstationary data patterns within connected SMGs. A resilient framework has been verified against various attacks, including component-level attacks, and defense strategies have been proposed for real-time detection and mitigation of anomalies and potential cyber intrusions. In addition, the deep ANN-based encoder-decoder is designed with proper hyperparameters to counter cyber intrusions. The proposed HHT-based ANN achieved the highest accuracy within the interconnected system compared with the other benchmarks. Diverse testing scenarios are presented, confirming the reliable performance and robustness of the proposed methods under varying levels of attacks.

The surge in the adoption of electric vehicles (EVs) has intensified the demand for a sustainable and reliable charging infrastructure. This study integrates Taiwan's abundant offshore wind energy resources to cater to this demand, particularly in geographically isolated coastal regions. Central to our study is the challenge of managing the power flow complexities introduced by the variability of wind energy coupled with unpredictable EV charging demand. Our innovative approach harnesses the potential of a wind-powered Electric Vehicle Charging System (EVCS) using a medium-voltage direct-current (MVDC) bus. An advanced Model Predictive Control (MPC) strategy was developed to present a decentralized control system. Unlike traditional control systems, this mechanism exhibits enhanced adaptability and efficiently manages the dynamic interplay between wind energy generation, Energy Storage Systems (ESS), EV charging demand, and grid interactions. Our simulations indicate that the MPC maintains voltage stability in the MVDC bus and shows optimal power flow, minimizing energy losses and ensuring grid stability. Although the outcomes presented in this study underscore the viability of the proposed wind-powered EVCS integrated with the grid, extensive real-world evaluations are required to solidify its applicability and scalability in diverse environments.