Integration of offshore renewable energy sources for the production of chemical energy vectors: The case of Hydrogen

Energy is the fuel that drives our economies. Energy demand is on the rise, and so the challenge of meeting the needs for safer, more efficient, and environmentally friendly solutions. With the aim of staying below the 2℃ scenario and agreeing with the 2015 Paris agreement and the European Union 2050 Green Deal, further action is required to ensure a smooth energy transition. In the offshore context the challenge persists, as renewable energy is weather and climate dependent, but represents a great opportunity for energy transition. For these reasons, along with the latest oil crisis due to Covid-19 in 2020, the term hydrogen economy and methanol economy are once again on the rise. In this work, not only hydrogen is discussed, but additional potential energy vectors are presented as well, with particular attention to the possible offshore exploitation. The integration of energy sources paves the way for the Power to Gas (P2G) concept namely hydrogen, not only as a potential fuel but also a feedstock, as well as synthetic natural gas (SNG) and Synthesis gas (Syngas). This integration also leads to the Power to Liquid (P2L) concept compromising mainly the synthesis of methanol, dimethyl ether (DME), Fischer Tropsch liquids, and ammonia energy vectors. Hence, the different routes leading to the production of each and every mentioned energy vector is presented, explained, and discussed within the offshore context; followed by a Technology Readiness Level assessment (TRL) for each process. Considering that hydrogen can be both the potential ‘fuel of the future’ and a feedstock for the production of almost all the mentioned energy vectors, it constitutes the main topic of this paper. Hydrogen production alternatives are discussed and a simulation of water electrolysis is done for both alkaline electrolysis and Proton Exchange Membrane (PEM) electrolysis using a commercial software leading to the deduction of the overall system efficiency for each of the simulated processes.