The liner shipping industry is undergoing an extensive decarbonization process to reduce its 275 million tons of CO2 emissions as of 2018. In this process, the long-term solution is the introduction of new alternative maritime fuels. The introduction of alternative fuels presents a great set of unknowns. Among these are the strategic concerns regarding sourcing of alternative fuels and, operationally, how the new fuels might affect the network of shipping routes. We propose a problem formulation that integrates fuel supply/demand into the liner shipping network design problem. Here, we present a model to determine the production sites and distribution of new alternative fuels-we consider methanol and ammonia. For the network design problem, we apply an adaptive large neighborhood search combined with a delayed column generation process. In addition, we wish to test the effect of designing a robust network under uncertain demand conditions because of the problem's strategic nature and importance. Therefore, our proposed solution method will have a deterministic and stochastic setup when we apply it to the second-largest multihub instance, WorldSmall, known from LINER-LIB. In the deterministic setting, our proposed solution method finds a new best solution to three instances from LINER-LIB. For the main considered WorldSmall instance, we even noticed a new best solution in all our tested fuel settings. In addition, we note a profit drop of 7.2% between a bunker-powered and pure alternative fuel-powered network. The selected alternative fuel production sites favor a proximity to European ports and have a heavy reliance on wind turbines. The stochastic results clearly showed that the found networks were much more resilient to the demand changes. Neglecting the perspective of uncertain demand leads to highly fluctuating profits.
The offshore oilfields in the North Sea area are increasingly employed for projects beyond oil production, like carbon capture and storage (CCS). Still, the fossil fuel production from mature fields is significant. It has raised environmental concerns associated with discharging produced waters (PW) and drilling mud into the sea. These discharges, which may be highly saline and contain production chemicals, vary significantly in metals and particulate content. Due to density and release depth, the plume is assumed to sink towards the seafloor. Also, a single oilfield can input up to 7.5 tons of Ba, 675 kg of Fe, and 619 kg of P into the water column through PW. Therefore, this study investigates the impact of these discharges on seafloor sediments around two Danish oilfields, assesses the mobility of metals within these sediments, and evaluates the environmental status. PW samples were collected at the discharge outlets from the platforms. Sediment cores were taken near the two oil platforms and from control sites. Using Inductively Coupled Plasma Mass Spectrometry (ICP-MS) and an optimized BCR sequential extraction, we analyzed the composition and distribution of 24 elements in sediment samples. The results revealed significant differences in total extracted concentrations between sediments near the platforms and those from distant locations and stratigraphically older samples, with relevant levels of Br, Ba, and Sn near the platforms (averaged 14, 27, and 0.1 ppb, respectively). Sediment quality indices showed considerable enrichment and geo-accumulation of toxic metals, particularly at one of the platform sites. However, cumulative indices did not display significant pollution anomalies. Therefore, our findings suggest that oil extraction activities may increase the availability of toxic metals in nearby sediments, potentially impacting marine ecosystems.
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.