The inland waterway transport sector is facing increasingly stringent legislation to reduce emissions and improve energy efficiency. Speed planning has the potential to provide logistically compliant, energy-efficient, and emission-reducing voyages for inland vessels. However, current speed planning methods do not consider PM and NO_x emissions, nor do they consider alternative power systems to internal combustion engines (ICE) and full electric systems. These omissions have led to a lack of clarity on the impact of speed planning on the emission profile of inland vessels and the impact of alternative power systems on energy consumption. In this paper we propose a validated speed planning method that considers the emission profile (CO₂, PM₁₀, and NO_x) and different engine types for inland vessels in an leg-based speed planning approach while taking into account varying fairway water depth and speed. Through a use case we show that the vessel can achieve a 7.26% energy, 5.37% CO₂ and fuel, 3.85% NO_x, and 6.77% PM₁₀ reduction while maintaining the same arrival time; showing a distinct difference of this method compared to slow steaming. We also find that CO₂, NO_x, PM₁₀, and energy are not directly proportional when making speed adjustments. Finally, we analyze the adverse effects of emission control areas and emission limits on the energy consumption and arrival times of vessels with non-zero emissions propulsion.

The availability of supporting bunker infrastructure for zero-emission energy sources will be key to accommodate zero-emission inland waterway transport (IWT). However, it remains unclear which (mix of) zero-emission energy sources to prepare for, and how to plan the bunker infrastructure in relative positions and required capacity at corridor scale. To provide insight into the positioning and dimensions of bunkering infrastructure we propose a bottom-up energy consumption method combined with agent based network simulation. In the method, we first produce a two-way traffic energy consumption map, aggregated from the energy footprint of individual vessels on the transport network. Next we investigate the potential sailing range of the vessels on the network if they would sail the same routes, but with alternative energy carriers. Based on the sailing range of the vessels for different energy carriers, the maximum inter-distance between refuelling points can be estimated. By aggregating the energy consumptions of all the vessels on the network, we can estimate the required capacity of a given refuelling point. To demonstrate the basic functionality we implement the method to four representative corridor scale inland shipping examples using zero-emission energy sources including hydrogen, batteries, e-NH3, e-methanol and e-LNG. The application in this paper is limited to four abstract cases. A recommended next step is to apply this approach to a more realistic network.

A key challenge in the energy transition for Inland Water Transport is the functional design of bunker networks and first-order dimensioning of individual bunker stations. A fundamental ingredient for this is an improved understanding of how upstream energy supply (‘well-to-bunker-station’) and downstream demand (‘bunker-station-to-tank’) may interconnect. In this paper we discuss an approach to the design of bunkering networks that takes logistic modelling to estimate network scale energy demand as a starting point. Depending on the vessels that use the network and the anticipated fuel mix for the overall fleet, logistical modelling may be used to estimate the magnitude of the energy demand along the network. Estimates of the operational range of vessels per energy carrier help to estimate maximum bunker station inter-distances. Insight into the potential supply chains that connect the source of each energy carrier to a physical bunker facility is needed to close the loop. Energy carriers may be needed on board in a gaseous or liquid form, or in the form of electrons. Transfer may take place in the form of loading (e.g., filling the fuel tank, charging the battery pack) or swapping (e.g., exchanging fuel containers, exchanging battery containers). Depending on the energy carrier, transfer method(s) and demand quantities, functional designs of bunker stations (in terms of required system elements and their order-of-magnitude dimensions) can be made. Depending on service level requirements both the dimensions of individual bunker stations and their spread over the network may be optimized. Key contribution of this work is a thorough overview of aspects that play a role in the design of bunker infrastructure for the decarbonisation of inland shipping. Based on this overview steps for further research are recommended.