Hydrogen energy is a promising solution for prompting low-carbon port development. This study introduces two hydrogen utilization strategies: hydrogen consumption-driven strategy (HCDS) and hydrogen storage-driven strategy (HSDS). Using data from a real port and a life-cycle assessment approach, a case study is conducted to compare their economic and ecological performances. The results show that HCDS enhances economic benefits, with an annualized cost of 66.1 million CNY, which is 11% lower than HSDS. Additionally, HCDS is sensitive to electricity prices and grid carbon emission factor. In contrast, HSDS offers superior ecological benefits, with an annualized carbon footprint of 31,300 tons of CO₂, which is 12% lower than HCDS, and is mainly sensitive to purchase prices and emission factors of electricity and hydrogen. This study provides critical insights into the trade-offs between economic and ecological performance under different hydrogen utilization strategies, offering practical guidance for implementing hydrogen energy system applications in ports.
The maritime sector faces increasing pressure to reduce emissions, especially in ports, pushing governments and shipowners towards greener energy sources. Conventional diesel generator (DG) powered vessels experience increased fuel consumption and emissions during low-power demand due to fluctuating loads with changing sea conditions. Integrating battery energy storage can absorb excess power, optimize DG operation, reduce costs, and manage variable loads. Traditional shipboard power systems (SPS) rely on centralized control schemes, which pose the risk of single points of failure, scalability issues, and increased latency due to centralized decision-making. Decentralized control improves resilience and scalability by eliminating single points of failure and enabling local decision-making, which improves response times and system robustness. Although recent research has explored decentralized control strategies for AC or DC-based SPS, there is limited work on hybrid AC-DC SPS architectures. This paper proposes a decentralized control strategy for integrating multiple power sources within a hybrid AC-DC network to optimize their operation. This approach allows vessels to operate in various modes, including full diesel, hybrid, and zero emission, and seamlessly transition between these modes as needed. The effectiveness of the proposed control scheme is validated through simulation and high-fidelity software-in-the-loop (SIL) results in OPAL-RT 5700, demonstrating adaptive power sharing among different resources.
Until now, wave-energy developers have focused on designing large machines for utility-scale electricity generation. While many concepts with good capture performance have been devised, significant commercial success has yet to be achieved in this market. Smaller wave energy converters (WECs) for specialist uses have received less attention. Emerging applications for these machines include powering sensors for ocean monitoring and providing energy for recharging maritime autonomous vehicles. Small reliable floating WECs can provide both the low levels of power required for these applications, and a surface platform for satellite
communications. Here, the key idea is to reduce costs and increase human safety by deploying small WECs to perform tasks that would otherwise require a ship. Developing small WECs for specialist uses provides a fast route to market, thereby creating a viable financial and technical base for the development of larger devices for applications where more power is required. This paper reports early results of time- and frequency-domain simulations of a compact WEC designed for monitoring the ocean environment.
We present a machine learning model for calculation of wave added resistance. The model training is performed using a large set of pre-calculated added resistance curves covering a broad range of ship hulls and operational conditions, i.e. forward speed, draft and relative wave heading. The underlying hydrodynamic model is the classical strip-theory where the wave added resistance is computed according to a modified version of Salvesen’s formulation. It is concluded that the developed data-driven model is able to produce a non-linear mapping between a set of operational conditions as well as the ship’s main particulars to the wave added resistance coefficient.
This study introduces WindWise, a cost–benefit analysis and design optimization tool for Wind Propulsion Systems (WPS) in sustainable shipping. By integrating route simulations, ship constraints, and fuel pricing scenarios, WindWise determines the optimal WPS configuration to maximize fuel savings and minimize payback periods. A retrofit case study of an oil tanker evaluates two WPS classes—DynaRigs and Rotor Sails—across multiple operational and economic conditions. Results reveal that optimal configurations vary based on constraints: in an unconstrained scenario, larger, well-spaced installations minimize aerodynamic losses, whereas realistic constraints shift the preference towards smaller, distributed setups to mitigate cargo loss and air draft penalties. Rotor Sails offer lower upfront costs and shorter payback periods for modest savings targets and for side-wind routes, while DynaRigs emerge as the more viable solution for higher emissions reductions and long-term profitability. Optimization of WPS configurations proves crucial, with non-optimized configurations exhibiting payback periods over 150% higher than optimized ones. Although payback period remains an important metric, considering both payback and net present value provides a more comprehensive assessment of WPS financial viability, with Rotor Sails generally offering faster payback but DynaRigs delivering higher long-term profitability across most scenarios.
Manufacturing companies who ship goods globally often rely on external Logistics Service Providers (LSPs) to manage the containerization and transportation of their freight. Those LSPs are usually required to follow rules when deciding how to mix the goods in the containers, which complicates the planning task. In this paper, we study such a freight containerization problem with a specific type of cargo mixing requirements recurrently faced by an international LSP. We show that this problem can be formulated as a Multi-Class Constrained Variable Size Bin Packing Problem: given a set of items that all have a size and a fixed number of classes for which they can take certain values, the objective is to pack the items in a minimum-cost set of bins while ensuring that the size capacity and maximum number of distinct values per class are not exceeded in any of the bins. We propose two adapted and one novel greedy heuristics, as well as an Adaptive Large Neighborhood Search (ALNS) metaheuristic, to find feasible solutions to the problem. We also provide a pattern-based formulation that is used to obtain lower bounds using a Column Generation approach. Using three extensive datasets, including a novel one with up to 1000 items and 5 classes reflecting real industrial cases, we show that the novel greedy heuristic outperforms the adaptations of the existing ones and that our ALNS yields significantly better solutions than a commercial solver within a mandatory 5-minute time limit. Practical insights are given about the solutions for the industrial benchmark.
Offshore wind power offers a viable solution to the challenge of reducing fossil fuel dependency. However, certain offshore wind projects encounter challenges in meeting expected returns, particularly over the medium to long term. This study addresses the discrepancy between assumed and actual cost behaviors in techno-economic assessments of wind farm projects. The present study evaluates their impact of operational loss trends (eg increased failure rates, aging, potential curtailment) on project viability through a comprehensive techno-economic assessment. To this end, key metrics including Net Present Value and Levelized Cost of Energy, complemented by stochastic analyzes are explored through Monte Carlo Simulation and sensitivity analysis. Results indicate that costs may exceed those of the reference scenario by up to 21.6% in the worst-case scenario, highlighting the critical need for proactive monitoring and management of operational losses.
As more offshore wind energy projects are implemented, the risk of interactions between farms becomes more pronounced. While reduced surface roughness over water enhances airflow stability, it can also extend wake effects on downstream turbines. The study aims to enhance the understanding of wake interactions and efficiency variations based on the distance between neighboring farms. To assess the impact of neighboring farms across different scenarios and features, a methodology is developed to achieve computational optimality using an open-source Python-based library, PyWake, then verified by a well-established CFD software, Meteodyn. Then, the methodology is applied to a Brazilian offshore wind project currently under licensing as a reference point. The results indicate a 1–3% reduction in Annual Energy Production following the current Brazilian regulation for onshore projects of 20 times the blade tip height, as the minimum distance. This reduction translates to an approximate 3% increase in the Levelized Cost of Energy and a nearly 24% decrease in Net Present Value. These findings are crucial for offshore wind energy planning and its sustainable growth, indicating the need to define a minimum distance for the regulatory bodies. This would not only avoid future disputes but also enhance investor confidence.
The oceans, covering approximately 70% of Earth's surface, play a pivotal role in climate regulation, biodiversity, and biogeochemical processes. The large and growing volume and complexity of ocean data, spanning diverse disciplines and formats, and dispersed across a wide range of sources, presents opportunities and challenges for advancing scientific research, informing policy, and addressing societal needs.
In this review paper we aim to create an easy-to-navigate map of the field of ocean data, enabling the reader to establish a broad understanding of the ocean data sector, and bridging gaps between different disciplines and levels of familiarity with ocean data. This is done through the concept of the "data ecosystem", which is used to describe the actors, organisations, and infrastructures involved in all aspects of the data value chain. We propose a structured ocean data ecosystem model as a method for comprehensive mapping of the ocean data market landscape. The proposed model consists of five key elements: stakeholders, societal elements, data sources and product offering, standards and best practices, and emerging technologies. We provide an up-to-date analysis of ocean data sources and emerging solutions and a summary of relevant data standardization efforts such as marine standards, vocabularies, and ontologies. All this will promote the development of needs-based solutions, components, products, services, and technologies, thus contributing to the evolution of the ocean data ecosystem and promoting data-based ocean research.
Continuous inspection and mapping of the seabed allows for monitoring the impact of anthropogenic activities on benthic ecosystems. Compared to traditional manual assessment methods which are impractical at scale, computer vision holds great potential for widespread and long-term monitoring.
We deploy an underwater remotely operated vehicle (ROV) in Jammer Bay, a heavily fished area in the Greater North Sea, and capture videos of the seabed for habitat classification. The collected JAMBO dataset is inherently ambiguous: water in the bay is typically turbid which degrades visibility and makes habitats more difficult to identify. To capture the uncertainties involved in manual visual inspection, we employ multiple annotators to classify the same set of images and analyze time spent per annotation, the extent to which annotators agree, and more.
We then evaluate the potential of vision foundation models (DINO, OpenCLIP, BioCLIP) for automating image-based benthic habitat classification. We find that despite ambiguity in the dataset, a well chosen pre-trained feature extractor with linear probing can match the performance of manual annotators when evaluated in known locations. However, generalization across time and place is an important challenge.