Design of a hydrogen supply chain with uncertainty. (14th December 2015)

• Record Type: Journal Article
• Authors: Nunes, Paula; Oliveira, Fabricio; Hamacher, Silvio; Almansoori, Ali
• Is Part Of: International journal of hydrogen energy. Volume 40:Number 46(2015)
• Page Start: 16408
• Page End: 16418
• Publication Date: 2015-12-14
• Subjects: Hydrogen supply chain design -- Optimization -- Uncertainty -- Sample average approximation; Hydrogen as fuel -- Periodicals; Hydrogène (Combustible) -- Périodiques; Hydrogen as fuel; Periodicals; 665.81
• DOI: 10.1016/j.ijhydene.2015.10.015
• Languages: English
• ISSNs: 0360-3199

Abstract

Abstract: The use of fuels with low environmental impact has been recently highlighted in the media. In this context, the use of hydrogen as a fuel has been considered an alternative with significant potential to integrate a more sustainable energy matrix. However, there is still no appropriate infrastructure available for its commercialization. This study proposes a methodology for designing a hydrogen supply chain while considering the inherent uncertainty associated with the demand for this fuel in the future. To represent the problem and evaluate investment alternatives for the logistic infrastructure, an optimization model is proposed based on two-stage stochastic mixed-integer programming. To obtain solutions from the proposed model, the sample average approximation (SAA) method is used to obtain statistically certified solutions from a reduced number of scenarios. The proposed methodology was applied to the design of Great Britain's liquid hydrogen supply chain using real data. The proposed framework was able to provide solutions with optimality gaps estimated to be below 1% within an acceptable computational time, demonstrating the adequacy of the developed methodology. Highlights: A methodology for the design and planning of a hydrogen supply chain is developed. Demand uncertainty is addressed using Sample Average Approximation (SAA) technique. Efficient two-stage stochastic mixed-integer optimization model is formulated. The methodology provides statistical evaluation of the solution quality. The design of Great Britain's liquid hydrogen infrastructure was demonstrated.