A probabilistic model for 1st stage dimensioning of renewable hydrogen transport micro-economies

Huw Charles Davies, Naeem Datardina

    Research output: Contribution to journalArticlepeer-review

    4 Citations (Scopus)


    The implementation of a hydrogen transport economy based on renewable energy sources is seen by many as the ultimate sustainable transport solution. However, dimensioning of hydrogen production systems is complex: renewable energy sources are stochastic in nature, requiring the collection of empirical datasets relating to weather patterns on a daily, seasonal and annual basis; and hydrogen production is characterised by sensitivity to operating conditions and diversity in the performance of the component parts.A probabilistic model is developed for dimensioning of hydrogen production systems that removes the reliance on the collection of empirical datasets and the requirement for detailed performance characterisation of component parts. The model utilises well known correlations and distribution modelling techniques to predict energy output from either a photovoltaic array or wind turbine and hence the number of fuel cell electric vehicles (FCEVs) that could be supported on an annual basis.The model was implemented in MatLab and simulation results were compared with existing empirical based studies. Through simulation, limitations of the model were investigated and discussed. It was shown that the model was able to predict the number of FCEVs supported to within 10% (solar pathway) and 22% (wind pathway) for those studies investigated. These results are in alignment with the intention of the model as a first stage tool for the dimensioning of renewable hydrogen energy transport micro-economies.

    Original languageEnglish
    Pages (from-to)355-362
    Number of pages8
    JournalRenewable Energy
    Publication statusPublished - Dec 2013


    • Hydrogen fuel cell
    • Photovoltaic array
    • Probablistic model
    • Transport economy
    • Wind turbine

    ASJC Scopus subject areas

    • Renewable Energy, Sustainability and the Environment


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