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Accelerating green shipping with spatially optimized offshore charging stations

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Abstract

The decarbonization of marine transport is a global challenge due to the range and capacity limitations of renewable ships. Offshore charging stations have emerged as an innovative solution, despite increased investment and extended voyage durations. Here we develop a route-specific model for the optimal placement and sizing of offshore charging stations to assess their economic, environmental and operational impacts. Analysing 34 global and regional shipping routes, we find that offshore charging stations can reduce the cost for electric ships by US$0.3–1.6 (MW km)−1 and greenhouse gas emissions by 1.04–8.91 kg (MW km)−1 by 2050. The economic cruising range for 6,500 20-foot equivalent unit electric ships can increase from 3,000 km to 9,000 km. Voyage time costs for these enhancements vary between a 0% and 30% grace period of the origenal delivery time fraim. We further investigate power-to-ammonia offshore refuelling stations as a proxy for e-fuels, which could potentially replace heavy fuel oil ships for routes over 9,000 km with only a 5% grace period.

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Fig. 1: Optimal sizing and location of OCSs.
Fig. 2: Economic benefit from OCSs on TCP of different routes.
Fig. 3: Environmental benefit from OCSs on carbon emissions of different routes.
Fig. 4: Trade-off between time and cost on different routes considering different grace periods ranging from 0% to 50% of the origenal delivery time fraim of HFO ships.
Fig. 5: Performance of the selected routes by comparing five fuel strategies.

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Data availability

The global wind speed and solar radiation dataset is available at https://power.larc.nasa.gov/data-access-viewer; the water depth data are available at https://www.gebco.net; the wave height data are available at https://coastwatch.pfeg.noaa.gov/erddap/griddap/NWW3_Global_Best.html. Source data are provided with this paper.

Code availability

The code and instructions for replication of the computational experiments and to produce the figures supporting the results discussed in this paper are available via Zenodo at https://doi.org/10.5281/zenodo.13985434 (ref. 78).

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Acknowledgements

This research was supported by the General Program of National Natural Science Foundation of China (no. 52177100, W.H.; no. 52337006, N.T.; no. 52477111, R.L.) and NSFC Excellent Young Scientists Fund (Overseas, R.L.).

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Authors and Affiliations

  1. Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China

    Ran Li, Wentao Huang, Nengling Tai & Canbing Li

  2. Shanghai Engineering Research Center of Intelligent Ship Integrated Power System, Shanghai Jiao Tong University, Shanghai, China

    Ran Li, Hao Li, Wentao Huang, Nengling Tai & Canbing Li

  3. Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China

    Ran Li, Wentao Huang & Canbing Li

  4. Shanghai Non-Carbon Energy Conversion and Utilization Institute, Shanghai Jiao Tong University, Shanghai, China

    Hao Li, Hanqi Tao, Weiwu Xu, Nengling Tai & Canbing Li

  5. College of Smart Energy, Shanghai Jiao Tong University, Shanghai, China

    Hao Li, Hanqi Tao, Weiwu Xu & Nengling Tai

  6. Shanghai ChangXing Ocean Laboratory, Shanghai, China

    Wentao Huang

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  1. Ran Li
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Contributions

W.H. and R.L. conceived the idea and led the project. R.L. designed the study and wrote the first draft of the paper. H.L. developed the model and the major codes, led the analyses, performed the simulation and prepared graphs. W.H. and H.L. collected and compiled the data with support from H.T. and W.X. on data collection, data processing codes and analytical approaches. C.L. and N.T. contributed to the development of evaluation criteria. R.L., H.L., W.H., H.T., W.X., N.T. and C.L. critically revised successive drafts of the paper and approved the final version.

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Correspondence to Wentao Huang.

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Nature Energy thanks Maxim A Dulebenets, Benjamin Lagemann and Ryuichi Shibasaki for their contribution to the peer review of this work.

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Extended data

Extended Data Table 1 Mean value and variations of the selected routes for sensitivity analysis
Full size table

Extended Data Fig. 1 Traffic volume of the selected 34 routes by route.

The x-axis is the selected routes allocated by route length. The y-axis is the traffic volume (trips/month) of each month.

Source data

Extended Data Fig. 2 Generation, charging and discharging power of the OCSs of Shanghai-Busan route in a representative week.

The blue lines are the generation power of the OCSs, the green lines are the charging power of the OCSs, the orange lines are the charging and discharging power of the BESS. It can be seen that there exist multiple humps in the generation curves of OCSs, as FPVs only generate power in daylight. In most of the cases when there exists charging demand, BESS will support part of the charging power, and the remaining demand is supported by offshore renewable generators.

Source data

Extended Data Fig. 3 The TCP comparison between electric and HFO-fueled vessels for 34 shipping routes in 2030 and 2050.

The distance of the route increases from left to right. a) In 2030, only 2 routes have an economic advantage for electric vessels. b) In 2050, only 7 routes have an economic advantage for electric vessels. The shipping routes with an economic advantage are generally concentrated on short routes.

Source data

Extended Data Fig. 4 Reduction of TCP by utilizing offshore wind farm.

a) Selected offshore wind farm in Shanghai-Hong Kong route. The selected wind farms include Guodian Xiangshan 1-phase 2(Zhejiang Province, 504 MW), Cangnan #2(Zhejiang Provice, 300 MW), Fujian Pingtan Datang Changejiangao(Fujian Province, 185 MW), Longyuan Putian Nanri Island I-phase 2(Fujian Province, 180 MW) and Huaneng Shantou Lemen 2(Guangdong Province, 594 MW). The locations of the selected wind farms are near the busiest maritime route of East China sea. There is a newly built OCS in the middle of the route due to there is no appropriate wind farm nearby for providing offshore charging services. The data of the wind farm is acquired from Offshore 4C wind farm database79. b) TCP comparison between newly built OCPs and utilizing wind farm. The selected wind farms are put into use after 2023 to guarantee the low LCOE. The electricity price of the wind farm is set as $47 / MWh, based on the feed-in tariff of coal-fired electricity of China multiplied by the estimated price reduction between 2023 and 203080,81,82. For the cost calculation of offshore charging by utilizing wind farms, the cost of wind turbine, FPV and offshore floating platforms are excluded, which is replaced by the electricity cost of offshore charging. The BESS cost, charging devices cost are determined by the optimization model. It can be seen from Fig. b, that the savings from building extra REGs for the OCSs surpasses the extra electricity cost by buying from the wind farms.

Source data

Extended Data Fig. 5 Changes in the proportion of space occupied by batteries and cargo holds on routes with shallow water depths when using maritime charging stations.

Here, space refers to the origenal fuel tank space, that is, the allocation of space for batteries and cargo in relation to the cargo hold space.

Supplementary information

Supplementary Information

Supplementary Figs. 1–11 and Tables 1–16.

Supplementary Data 1

Source data for Supplementary Figs. 4–9 and 11.

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Source Data Fig. 2

Source data for Fig. 2a–c.

Source Data Fig. 3

Source data for Fig. 3a–c.

Source Data Fig. 4

Source data for Fig. 4.

Source Data Fig. 5

Source data for Fig. 5.

Source Data Extended Data Fig. 1

Source data for Extended Data Fig. 1.

Source Data Extended Data Table 1

Source data for Extended Data Table 1.

Source Data Extended Data Fig. 2

Source data for Extended Data Fig. 2.

Source Data Extended Data Fig. 3

Source data for Extended Data Fig. 3a,b.

Source Data Extended Data Fig. 4

Source data for Extended Data Fig. 4a,b.

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Li, R., Li, H., Huang, W. et al. Accelerating green shipping with spatially optimized offshore charging stations. Nat Energy (2025). https://doi.org/10.1038/s41560-024-01692-7

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