Modelling guided energy management system for a hydrogen–fuelled harbour tug

Abstract

The use of hydrogen as a source of fuel for marine applications is relatively nascent. As the maritime industry pivots to the use of alternate low and zero-emission fuels to adapt to a changing regulatory landscape, hydrogen energy needs to present and substantiate a technical and commercially viable use case to secure its value proposition in the future fuel mix. This paper leverages the technoeconomic and environmental assessment previously performed on HyForce, a hydrogen-fuelled harbour tug which has shown encouraging results for both technical and commercial aspects. This study aims to create a digital twin of HyForce to accurately predict her operability in real-world scenarios. The results from this study identify the strengths and drawbacks of the proposed use case. This is achieved by embedding the detailed design of HyForce in a virtual environment to further evaluate its operational performance through Computational Fluid Dynamics (CFD) simulations of realistic environmental conditions such as wind, wave, sea currents, and friction attributed to the properties of seawater. The results from this study indicate a base case power requirement of 93 kW to 1892 kW to achieve speeds of 5 to 12 knots in the absence of external environmental influences. Consequently, the speed of HyForce has a profound impact on total resistance peaking at 97.3 kN at 12 knots. Seawater properties such as low seawater temperature of 0 °C, and a high salinity of 50 g/kg increased friction. Additionally, wind speeds of 10 m/s acting on HyForce, delivered a resistance of 3 kN. However, these will be well mitigated through the design of the propulsion system which will be able to deliver a thrust power of 1892 kW and with assistance from the energy storage systems produce 2 MW of power to overcome the resistance experienced. The findings presented in this paper can serve as a foundation for constructing a robust model for the development of a predictive controller for future work. This controller has the potential to optimize the configuration of hydrogen and battery energy storage, aligning with desired cost functions.