Corridor Scale Planning of Bunker Infrastructure for Zero-Emission Energy Sources in Inland Waterway Transport

Conference Paper (2023)
Authors

M. Jiang (TU Delft - Rivers, Ports, Waterways and Dredging Engineering)

F. Baart (TU Delft - Rivers, Ports, Waterways and Dredging Engineering, Deltares)

Klaas Visser (TU Delft - Ship Design, Production and Operations)

Robert Hekkenberg (TU Delft - Ship Design, Production and Operations)

Mark Van Koningsveld (TU Delft - Rivers, Ports, Waterways and Dredging Engineering, Van Oord Dredging and Marine Contractors)

Research Group
Rivers, Ports, Waterways and Dredging Engineering
Copyright
© 2023 M. Jiang, F. Baart, K. Visser, R.G. Hekkenberg, M. van Koningsveld
To reference this document use:
https://doi.org/10.1007/978-981-19-6138-0_30
More Info
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Publication Year
2023
Language
English
Copyright
© 2023 M. Jiang, F. Baart, K. Visser, R.G. Hekkenberg, M. van Koningsveld
Research Group
Rivers, Ports, Waterways and Dredging Engineering
Pages (from-to)
334-345
ISBN (print)
978-981-19-6137-3
ISBN (electronic)
978-981-19-6138-0
DOI:
https://doi.org/10.1007/978-981-19-6138-0_30
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Abstract

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.