A Decision Support Tool for Optimizing Fuel Deployment and Emissions Reduction: Advanced Modeling Approach to Sustainable U.S. Maritime Transport

The shipping industry, particularly containerized cargo transport, plays a vital role in global trade, but it is also a significant contributor to greenhouse gas (GHG) emissions. With increasing pressure to reduce carbon footprints and achieve sustainability targets, the U.S. maritime sector is seeking innovative solutions to lower its environmental impact. Optimizing fuel usage, improving operational efficiencies, and adopting alternative fuels are essential strategies to decarbonize maritime transport. However, these complex challenges require sophisticated tools for simulating and analyzing different fuel and routing strategies. The US Department of Energy, Advanced Research Projects Agency - Energy funded a project to focuses on developing an advanced decision-support tool to optimize fuel deployment and intermodal freight routing for domestic container shipping in the U.S., aiming to reduce GHG emissions and improve operational efficiency. This project aims to develop an advanced decision-support tool that leverages modeling and simulation to optimize fuel deployment and intermodal freight route planning for domestic container shipping across U.S. maritime corridors. The tool's central goal is to simulate and assess vessel operations, considering both environmental impact and operational efficiency, with a focus on reducing greenhouse gas (GHG) emissions through intelligent fuel selection and optimized routing strategies. The project will integrate traditional oil-based vessel technologies with alternative/Low emission fuels, specifically methanol and ammonia/hydrogen, to evaluate these technologies' potential for decarbonizing the maritime sector. The modeling scope encompasses several critical aspects of container shipping operations: it will simulate container and containership movements across key U.S. maritime routes, including both open sea and inland waterways (e.g., from Los Angeles to Seattle). The simulation will track vessel movements in real-time, incorporating key operational events such as departures, arrivals, refueling, and port dwell times. The model will identify and analyze a range of domestic vessel types, initially using baseline information on oil-powered ships and comparing them with vessels using alternative fuels. This comparison will allow for an evaluation of energy consumption, operational efficiency, and emissions reductions achievable with different fuel mixes. A key feature of the model will be its ability to estimate energy consumption and emissions under various operating conditions. The model will take into account a broad range of factors, such as cargo weight, vessel type, sea currents, wind conditions, and route specifics to generate highly accurate simulations of energy usage and emissions per voyage. By using advanced modeling techniques, the tool will create a detailed reduced-order model, informed by real-world operational data, which can be generalized and applied across various domestic corridors and routes. This will allow for scalable insights and flexible application, making the tool useful for a variety of real-world shipping scenarios. The role of advanced modeling and simulation is crucial in enabling the detailed analysis required to understand complex maritime operations. The model will simulate dynamic operational events (such as docking, refueling, or delays) and intermodal transport interactions, optimizing freight routing across different transport modes (water, rail, and truck) to minimize overall GHG emissions. This approach allows for a comprehensive, whole-system analysis that considers both direct emissions from maritime transport and indirect emissions from associated modes of transport. Moreover, the model will interface with external systems simulators, to incorporate data on port-to-port container origin-destination (OD) flows, port storage, and dwell times. These external inputs will be integrated into the model to ensure that port operations, cargo handling, and intermodal logistics are optimized for efficiency and emissions reduction. This integration of multiple data sources and transport modes will enable the simulation to reflect the complexities of the real-world intermodal network, offering insights on how best to allocate freight across water, rail, and road to achieve the lowest possible emissions. Through the use of advanced modeling and simulation, the tool will generate actionable insights to guide policy makers, shipping operators, and environmental regulators in their efforts to decarbonize U.S. domestic shipping. The ability to simulate and compare different fuel types, vessel configurations, and routing strategies will help stakeholders make data-driven decisions that balance economic feasibility with environmental sustainability. Ultimately, the tool will help identify pathways to reduce the carbon footprint of domestic container shipping, offering a roadmap for adopting cleaner, more efficient maritime transport practices while improving operational performance. By enabling the detailed modeling and simulation of complex maritime systems, this project will contribute significantly to the development of sustainable maritime transport policies and technologies, providing a critical resource for advancing the transition to a low-carbon economy in the U.S. shipping sector.