Search Results (64)

Ports play a pivotal role in global trade but are also associated with significant environmental and social challenges. Despite growing research on green ports, existing studies remain fragmented, with limited integration between technological, environmental, and governance perspectives within the blue economy framework. This review examines the transition from green port initiatives toward integrated blue-economy-oriented port systems by synthesizing recent advances in sustainable maritime infrastructure, smart port technologies, renewable energy integration, and policy frameworks. The analysis reveals three major findings. First, ports are increasingly evolving into energy-integrated hubs, with leading examples adopting shore power systems, renewable energy microgrids, and hydrogen-based infrastructure, thereby contributing to emissions reductions. Second, digitalization through artificial intelligence, IoT, and data-driven logistics significantly enhances operational efficiency, reduces energy consumption, and improves real-time decision-making. Third, effective governance frameworks that combine regulatory measures and incentive-based instruments are critical to accelerating sustainability transitions while ensuring economic competitiveness. In addition, the review highlights the growing integration of biodiversity conservation, marine pollution mitigation, and community engagement into port management strategies, reflecting a shift toward ecosystem-based approaches. Overall, the findings demonstrate that ports are transitioning from conventional logistics hubs into integrated socio-technical systems that enable low-carbon maritime transport while supporting inclusive and resilient coastal development. Full article

Amid tightening emission rules and growing cold-chain demand, ports face complex multi-objective scheduling under dual uncertainties in vessel arrivals and operations. This work develops a multi-objective chance-constrained stochastic MILP model for joint berth, QC, and OPS scheduling. Heavy-tailed operational delays are managed via chance constraints, converting Weibull distributions to time buffers, while convex formulations allow piecewise cargo damage penalties to be computed linearly. A reinforcement learning-based adaptive large neighborhood search (RL-ALNS) algorithm is proposed to solve this NP-hard continuous-time problem, integrating a spatiotemporal decoder and an MDP-based selector to ensure microgrid limits and efficiency. Simulations demonstrate RL-ALNS’s superior Pareto convergence versus conventional heuristics. The model cuts the 95th-percentile tail risk by 46.59% and actual costs by 24.44% under mild delays, compared to deterministic scheduling. Overall, it quantifies the non-linear cost–emission–reliability trade-off, providing a robust tool for port decision-making. Full article

To systematically review the research progress, methodological frameworks, and application characteristics of energy management technologies for All-Electric Ships (AES), this review provides a comprehensive and critical survey of studies published over the past two decades, following the technical trajectory of multi-energy coupling–multi-objective optimization–engineering-oriented operation. Based on a structured analysis of representative literature, the review first elucidates the overall architecture and operational characteristics of AES energy systems from a system-level perspective, highlighting their core advantages as “mobile microgrids” in terms of multi-energy coordination and dispatch flexibility. On this basis, a structured classification framework for energy management strategies is established, and the theoretical foundations, applicable scenarios, and engineering feasibility of rule-based, optimization-based, uncertainty-aware, and intelligent/data-driven approaches are comparatively reviewed and discussed. Furthermore, focusing on key research themes—including multi-energy system optimization, ship–port–microgrid coordinated operation, battery safety and lifetime-oriented management, and real-time energy management strategies—the review synthesizes the main findings and engineering validation progress reported in recent studies. The analysis indicates that, with the integration of fuel cells, renewable energy sources, and Hybrid Energy Storage Systems (HESS), energy management for AES has evolved from a single power allocation problem into a system-level optimization challenge involving multiple time scales, multiple objectives, and diverse sources of uncertainty. Optimization-based and Model Predictive Control (MPC) methods have shown promising performance in many simulation and pilot-scale studies for improving energy efficiency and emission performance, while robust optimization and data-driven approaches offer useful support for enhancing operational resilience, prediction capability, and decision quality under complex and uncertain conditions. These advances collectively contribute to the environmental, economic, and operational sustainability of maritime transport by reducing greenhouse gas emissions, extending equipment lifetime, and enabling efficient integration of renewable energy sources. At the same time, the current literature still reveals important limitations related to model fidelity, data availability, validation maturity, and the gap between methodological sophistication and practical deployment. Overall, an increasingly structured but still evolving research framework has emerged in this field. Future research should further strengthen ship–port–microgrid coordinated energy management frameworks, develop system-level optimization methods that integrate safety constraints and uncertainty, and advance intelligent Energy Management Systems (EMS) oriented toward sustainable zero-carbon shipping objectives. Full article

Ports located within dense urban environments face a major challenge in achieving deep decarbonization without compromising the reliability and safety of critical maritime operations. This study develops and validates a resilience-oriented control and sizing typology for Hybrid Renewable Energy Systems (HRESs), supporting the transition of a medium-sized Mediterranean port toward a Nearly Zero Energy Port (nZEP). The framework integrates five years of measured electrical demand at 15 min resolution to capture stochastic load variability, seasonal effects, and safety-critical peak events. Thirty-five HRES configurations are simulated using HOMER Pro, assessing photovoltaic and wind generation combined with alternative Energy Storage System (ESS) technologies under two grid-interface control strategies: Net Metering (NM) and non-NM curtailment-based operation. Conventional Lead–Acid batteries are compared with inherently safer Vanadium Redox Flow Batteries (VRFBs), while autonomy constraints of 24 h and 48 h are imposed to represent operational resilience. System performance is evaluated through a multi-criteria framework encompassing economic viability (Levelized Cost of Energy), environmental impact (Lifecycle Assessment-based carbon footprint), and operational reliability. Results indicate that NM-enabled HRES architectures significantly outperform non-NM configurations by exploiting the external grid as an active balancing layer. The optimal NM configuration achieves a Levelized Cost of Energy of 0.063 €/kWh under a 24 h autonomy constraint, while reducing operational carbon intensity to approximately 70 gCO₂,eq/kWh, corresponding to a reduction exceeding 90% relative to baseline grid-dependent operation. In contrast, non-NM systems require substantial storage and generation oversizing to maintain resilience, resulting in higher curtailment losses and Levelized Cost of Energy values of 0.12–0.15 €/kWh. Across both control regimes, VRFB-based systems consistently exhibit superior robustness and safety performance compared to Lead–Acid alternatives. The proposed typology provides a transferable framework for resilient and low-carbon port microgrid design under real-world operational constraints. Full article

The global shipping industry is surging ahead, and with it, a quiet revolution is taking place on the water: marine lithium-ion batteries have emerged as a crucial clean energy carrier, powering everything from ferries to container ships. When these vessels dock, they increasingly rely on shore power charging systems to refuel—essentially, plugging in instead of idling on diesel. But predicting how much power they will need is not straightforward. Think about it: different ships, varying battery sizes, mixed charging technologies, and unpredictable port stays all come into play, creating a load profile that is random, uneven, and often concentrated—a real headache for grid planners. So how do you forecast something so inherently variable? This study turned to the Monte Carlo method, a probabilistic technique that thrives on uncertainty. Instead of seeking a single fixed answer, the model embraces randomness, feeding in real-world data on supply modes, vessel types, battery capacity, and operational hours. Through repeated random sampling and load simulation, it builds up a realistic picture of potential charging demand. We ran the numbers for a simulated fleet of 400 vessels, and the results speak for themselves: load factors landed at 0.35 for conventional AC shore power, 0.39 for high-voltage DC, 0.33 for renewable-based systems, 0.64 for smart microgrids, and 0.76 when energy storage joined the mix. Notice how storage and microgrids really smooth things out? What does this mean in practice? Well, it turns out that Monte Carlo is not just academically elegant, it is practically useful. By quantifying uncertainty and delivering load factors within confidence intervals, the method offers port operators something precious: a data-backed foundation for decision-making. Whether it is sizing infrastructure, designing tariff incentives, or weighing the grid impact of different shore power setups, this approach adds clarity. In the bigger picture, that kind of insight matters. As ports worldwide strive to support cleaner shipping and align with climate goals—China’s “dual carbon” ambition being a case in point—achieving a reliable handle on charging demand is not just technical; it is strategic. Here, probabilistic modeling shifts from a simulation exercise to a tangible tool for greener, more resilient port energy management. Full article

With the rapid advancement of hydrogen-based direct current microgrid (H₂-DCMG) technology, multi-port converters (MPCs) have emerged as the pivotal interface for integrating renewable power generation, energy storage, and diverse DC loads. This paper systematically reviews the current research status and development trends of isolated and non-isolated MPC topologies within hydrogen-based DC microgrids. Firstly, it analyses the interface requirements for typical distributed energy sources (DER) such as photovoltaics (PV), wind turbines (WT), fuel cells (FC), battery energy storage (BESS), proton exchange membrane electrolyzers (PEMEL), and supercapacitors (SC). Secondly, it classifies and evaluates existing MPC topologies, clarifying the structural characteristics, technical advantages, and challenges faced by each type. Results indicate that non-isolated topologies offer advantages such as structural simplicity, high efficiency, and high power density, making them more suitable for residential and small-scale microgrid applications. Isolated topologies, conversely, provide electrical isolation and modular scalability, rendering them appropriate for high-voltage electrolytic hydrogen production and industrial scenarios with stringent safety requirements. Finally, the paper identifies current research gaps and proposes that future efforts should focus on exploring topology optimization, system integration design, and reliability enhancement. Full article

Under the tightening carbon reduction policies, port microgrids face the challenge of optimizing the installed capacity of multiple power generation types to reduce operating costs and increase renewable energy penetration. We develop a bi-level cost-optimization framework in which the upper level decides long-term capacities (PV, wind, gas turbine, bio-fuel unit, and battery energy storage), and the lower level dispatches a multi-energy port microgrid (electricity–heat–cold) on an hourly basis with frequency regulation services. To ensure rigor and reproducibility, we (i) move the methodology upfront and formalize all constraints, (ii) provide a dedicated data–preprocessing pipeline for multi-region 50/60 Hz frequency time series, and (iii) map a policy intensity index to a carbon price and/or an annual cap used in the objective/constraints. The bi-level MILP is solved by a column-and-constraint generation algorithm with optimality gap control. We report quantitative metrics—annualized total cost, CO₂ emissions (t), renewable shares (%), and regulation cycles—across scenarios. Results show consistent cost–carbon trade-offs and robust capacity shifts toward storage and biofuel as policy tightens. All inputs and scripts are organized for exact replication. Full article

Bipolar DC microgrids gain significant attention for their flexible structure, high power supply reliability, and strong compatibility with distributed power sources. However, inter-pole voltage imbalance undermines system operational stability. An isolated bipolar bidirectional three-port converter with voltage self-balancing capability is proposed in this paper, which can serve as the interface between the energy storage system and bipolar bus while achieving automatic voltage balance between poles. Unlike traditional bidirectional grid-connected voltage balancers (VBs), the proposed converter requires no additional voltage monitoring or complex control systems. The operating modes, soft-switching boundary conditions, and inter-pole voltage self-balancing mechanism are elaborated. A 1 kW experimental prototype has been built to validate the theoretical analysis of the proposed converter. Full article

The growing global concern for sustainability and energy conservation has led to the adoption of energy management systems to minimise the impacts of energy intensive processes. This study reviews the evolution, the applications, and implementation techniques of energy management systems with an emphasis on harbour operations. Through the mapping of the research on energy management systems post-1973, the literature review demonstrated a substantial transformation of the systems from basic monitoring in the building sector to complex artificial intelligence analyses in smart and microgrids, industries, renewable energy sources integration, transportation, and harbours. Initial broad search (1973–2025) identified 22,003 EMS-related records; targeted port–EMS queries yielded 214 records, of which 139 unique records remained after de-duplication and 78 full texts were assessed. Finally, 27 studies were included in the quantitative synthesis. A meta-analysis in conjunction with an article review, and a weighted sum model coupled with sensitivity analyses revealed promising results for harbour energy management system implementation in terms of peak/load shifting, on-shore power supply, and real-time energy monitoring. The findings showed that energy management system efficacy is linked to maturity levels and strategic deployment of the measures/policies in each stage. Full article

In recent years, there has been a fast expansion in the usage of renewable energy sources (RESs) in power distribution systems. Numerous advantages result from this advancement, such as environmental friendliness, cost-effective power generation, easier maintenance, and energy sustainability and reliability. Reducing reliance on fossil fuels, which are of significant environmental concern, and increasing energy efficiency are two benefits of integrating RESs into maritime systems, such as port microgrids. As a result, ports are implementing several programs to increase energy efficiency using various RESs that are supported by power electronic converters. To highlight the most recent developments in seaport electrification and infrastructure, this work conducts a systematic review. It addresses important issues like energy efficiency enhancements, environmental concerns, the integration of renewable energy sources, the Internet of Things (IoT), and regulatory and legal compliance. The study also discusses technology strategies like digitization, electrification, onshore power supply systems, and port energy storage options. Operational tactics, including peak-shaving methods and energy-efficient operations, are also covered. Additionally, an infrastructure framework—which includes port microgrids and smart seaport microgrids—that is intended to enhance energy efficiency in contemporary ports is examined. Full article

To tackle the problems of high scheduling costs and low photovoltaic (PV) accommodation rates in port microgrids, which are caused by the coupling of uncertainties in new energy output and load randomness, this paper proposes an optimized scheduling method that integrates scenario analysis with multi-energy complementarity. Firstly, based on the improved Iterative Self-organizing Data Analysis Techniques Algorithm (ISODATA) clustering algorithm and backward reduction method, a set of typical scenarios that represent the uncertainties of PV and load is generated. Secondly, a multi-energy complementary system model is constructed, which includes thermal power, PV, energy storage, electric vehicle (EV) clusters, and demand response. Then, a planning model centered on economy is established. Through multi-energy coordinated optimization, supply–demand balance and cost control are achieved. The simulation results based on the port microgrid of the LEKKI Port in Nigeria show that the proposed method can significantly reduce system operating costs by 18% and improve the PV accommodation rate through energy storage time-shifting, flexible EV scheduling, and demand response incentives. The research findings provide theoretical and technical support for the low-carbon transformation of energy systems in high-volatility load scenarios, such as ports. Full article

As a core component of the photovoltaic-storage microgrid systems, three-port DC–DC converters have attracted significant attention in recent years. This paper proposes a multi-vector modulated model predictive control (MVM-MPC) method based on vector analysis for a non-isolated three-port DC–DC converter formed by paralleling two bidirectional DC–DC converters. The proposed modulated MPC method utilizes three basic vectors to calculate the optimal switching sequence for minimizing the error vector. It can significantly minimize voltage ripple while maintaining the nonlinear and dynamic performance characteristics of a traditional MPC. MATLAB/Simulink R2024a simulations and hardware-in-loop (HIL) experimental results demonstrate that, compared with finite control set MPC and traditional two-vector modulated MPC methods, the proposed approach achieves remarkable reductions in current ripple and voltage ripple, along with excellent dynamic performance featuring smooth mode-switching. Full article

In recent years, to achieve “dual carbon” goals, increasing the penetration of renewable energy has become a critical approach in China’s power sector. Power electronic converters play a key role in integrating renewable energy into the power system. Among them, the Dual Active Bridge (DAB) DC-DC converter has gained widespread attention due to its merits, such as galvanic isolation, bidirectional power transfer, and soft switching. It has been extensively applied in microgrids, distributed generation, and electric vehicles. However, with the large-scale integration of stochastic renewable sources and uncertain loads into the grid, DAB converters are required to operate over a wider voltage regulation range and under more complex operating conditions. Conventional control strategies often fail to meet these demands due to their limited soft-switching range, restricted optimization capability, and slow dynamic response. To address these issues, this paper proposes a multi-mode switching optimized control strategy for the three-port DAB (3p-DAB) converter. The proposed method aims to broaden the soft-switching range and optimize the operation space, enabling high-power transfer capability while reducing switching and conduction losses. First, to address the issue of the narrow soft-switching range at medium and low power levels, a single-cycle interleaved phase-shift control mode is proposed. Under this control, the three-phase Dual Active Bridge can achieve zero-voltage switching and optimize the minimum current stress, thereby improving the operating efficiency of the converter. Then, in the face of the actual demand for wide voltage regulation of the converter, a standardized global unified minimum current stress optimization scheme based on the virtual phase-shift ratio is proposed. This scheme establishes a unified control structure and a standardized control table, reducing the complexity of the control structure design and the gain expression. Finally, both simulation and experimental results validate the effectiveness and superiority of the proposed multi-mode optimized control strategy. Full article

This article proposes a high-efficiency isolated three-port resonant converter for DC microgrids, combining a dual active bridge (DAB)–LLC topology with hybrid Pulse Width Modulat-Pulse Frequency Modulation (PWM-PFM) phase shift control. Specifically, the integration of a dual active bridge and LLC resonant structure with interleaved buck/boost stages eliminates cascaded conversion losses. Energy flows bidirectionally between ports via zero-voltage switching, achieving a 97.2% efficiency across 150–300 V input ranges, which is a 15% improvement over conventional cascaded designs. Also, an improved PWM-PFM shift control scheme dynamically allocates power between ports without altering switching frequency. By decoupling power regulation and leveraging resonant tank optimization, this strategy reduces control complexity while maintaining a ±2.5% voltage ripple under 20% load transients. Additionally, a switch-controlled capacitor network and frequency tuning enable resonant parameter adjustment, achieving a 1:2 voltage gain range without auxiliary circuits. It reduces cost penalties compared to dual-transformer solutions, making the topology viable for heterogeneous DC microgrids. Based on a detailed theoretical analysis, simulation and experimental results verify the effectiveness of the proposed concept. Full article

This research provides a comprehensive review of hybrid energy solutions and optimization models for ports and marine environments. It details new methodologies, including strategic energy management and a machine learning (ML) tool for predicting energy surplus and deficits. The hybrid energy module solution for the Port of Avilés was further developed to evaluate the performance of new tools such as the Energy Management Tool (EMTv1), HYbrid for Renewable Energy Solutions (HY4RES), and a commercial model (Hybrid Optimization of Multiple Energy Resources—HOMER) in optimizing renewable energy and storage management. Seven scenarios were analyzed, integrating different energy sources and storage solutions. Using EMTv1, Scenario 1 showed high surplus energy, while Scenario 2 demonstrated grid independence with Pump-as-Turbine (PAT) storage. The HY4RES model was used to analyze Scenario 3, which achieved a positive grid balance, exporting more than imported, and Scenario 4 revealed limitations of the PAT system due to the low power installed. Scenario 5 introduced a 15 kWh battery, efficiently storing and discharging energy, reducing grid reliance, and fully covering energy needs. Using HOMER modeling, Scenario 6 required 546 kWh of grid energy but sold 2385 kWh back. Scenario 7 produced 3450 kWh/year, covering demand, resulting in 1834 kWh of surplus energy and a small capacity shortage (1.41 kWh/year). AI-based ML analysis was applied to five scenarios (the ones with access to numerical results), accurately predicting energy balances and optimizing grid interactions. A neural network time series (NNTS) model trained on average year data achieved high accuracy (R²: 0.9253–0.9695). The ANN model proved effective in making rapid energy balance predictions, reducing the need for complex simulations. A second case analyzed an increase of 80% in demand, confirming the model’s reliability, with Scenario 3 having the highest MSE (0.0166 kWh), Scenario 2 the lowest R² (0.9289), and Scenario 5 the highest R² (0.9693) during the validation process. This study highlights AI-driven forecasting as a valuable tool for ports to optimize energy management, minimize grid dependency, and enhance their efficiency. Full article