Issue |
E3S Web Conf.
Volume 334, 2022
EFC21 - European Fuel Cells and Hydrogen Piero Lunghi Conference
|
|
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Article Number | 02001 | |
Number of page(s) | 7 | |
Section | Power-to-X Conversion Technologies | |
DOI | https://doi.org/10.1051/e3sconf/202233402001 | |
Published online | 10 January 2022 |
Smart Design of Green Hydrogen Facilities: A Digital Twin-driven approach
1 Envision Digital, 1 passerelle des reflets, 92400 Courbevoie, France
2 Capgemini Engineering, 2 Rue Paul Dautier, 78140 Vélizy-Villacoublay, France
* Corresponding author: denis.lun@envision-digital.com
This work studies the potentials of Digital Twin solutions for the design of competitive and reliable green hydrogen facilities. A digital twin based on stochastic simulations is proposed to address the uncertainties associated with investment and operating costs, to increase confidence and stimulate investments. Several input assumptions are involved (i.e., capital and operational costs, energy consumption, available energy, among others) to analyse their influence on financial indicators. A set of facility designs with equipment redundancy, and thus different system availabilities, was proposed. Monte Carlo simulation method is chosen to propagate uncertainties onto the project bankability assessment. By applying the proposed methodology, the opportunity index and internal rate of return (IRR) are calculated. A sensibility analysis is also carried out. The simulations illustrate that the design of a facility can be optimized to achieve higher profits, based on a trade-off between investment and availability. This study concludes that digital twin solutions are an opportunity for reducing the uncertainties associated with green hydrogen facility design. Improvements to the proposed model can be achieved by performing a refined simulation, in relation to the calculation of system availability and maintenance costs.
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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