A road map to the development of the next generation of efficient water electrolysers for the production of green H2 from renewable power (RoadtoGreenH2)

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Canada and Germany have both pledged to convert their economy to net-zero carbon emissions by 2050. The energy sector is one of the largest carbon dioxide emitters and the integration of renewable energy into electrical power generation is a promising strategy to reduce carbon emissions. However, renewable energy sources such as wind and solar are irregular and do not only vary over short times (seconds to hours) but also over long periods (seasons). Hence, energy from renewables, which is not immediately consumed, has to be stored until it is used and supplied when required. Hydrogen can be chemically and electrochemically combined with oxygen to form water while releasing considerable amounts of energy, heat or electricity, without emission of carbon dioxide. However, hydrogen is rarely found on its own and usually occurs as a component of other materials. Therefore, it has to be produced, ideally from water in so-called electrolyzers with the help of energy from renewable sources. This green hydrogen can be stored or distributed to chemical plants for the production of consumer goods or converted in fuel cells to generate electricity, all without or only little emission of carbon. However, the technology for such a hydrogen-based economy is still in need of improvement and is not economically competitive yet.The funding of this proposal allows for the establishment of a collaboration between Canadian and German researchers to further develop technologies needed for a carbon-free hydrogen-based economy. Workshops including virtual summer and winter schools, open to the public, are established to allow for effective discussion of hydrogen technologies and contribute to the training of Highly Qualified Personnel. Researchers from both countries will collaborate to investigate improved and cost-effective materials and designs for water electrolyzers. This development will be accelerated by extensive modelling approaches using new machine learning methodology and artificial intelligence.