Power-to-hydrogen as Seasonal Energy Storage: An Uncertainty Analysis for Optimal Design of Low-carbon Multi-energy Systems

This study analyzes the factors leading to the deployment of Power-to-Hydrogen (PtH₂) within the optimal design of district-scale Multi-Energy Systems (MES). To this end, we utilize an optimization framework based on a mixed integer linear program that selects, sizes, and operates technologies in the MES to satisfy electric and thermal demands, while minimizing annual costs and CO₂ emissions. We conduct a comprehensive uncertainty analysis that encompasses the entire set of technology (e.g. cost, efficiency, lifetime) and context (e.g. economic, policy, grid carbon footprint) input parameters, as well as various climate-referenced districts (e.g. environmental data and energy demands) at a European-scope.
Minimum-emissions MES, with large amounts of renewable energy generation and high ratios of seasonal thermal-to-electrical demand, optimally achieve zero operational CO₂ emissions by utilizing PtH₂ seasonally to offset the long-term mismatch between renewable generation and energy demand. PtH₂ is only used to abate the last 5–10% emissions, and it is installed along with a large battery capacity to maximize renewable self-consumption and completely electrify thermal demand with heat pumps and fuel cells. However, this incurs additional cost. Additionally, we show that ‘traditional’ MES comprised of renewables and short-term energy storage are able to decrease emissions by 90% with manageable cost increases.
The impact of uncertainty on the optimal system design reveals that the most influential parameter for PtH2 implementation is (1) heat pump efficiency as it is the main competitor in providing renewable-powered heat in winter. Further, battery (2) capital cost and (3) lifetime prove to be significant as the competing electrical energy storage technology. In the face of policy uncertainties, a CO₂ tax shows large potential to reduce emissions in district MES without cost implications. The results illustrate the importance of capturing the dynamics and uncertainties of short- and long-term energy storage technologies for assessing cost and CO₂ emissions in optimal MES designs over districts with different geographical scopes.