Graduate Thesis Or Dissertation


Accelerating Power-to-Gas Energy Storage Through PEM Electrolyzer Development and Technoeconomic Forecasting Public Deposited

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  • There is strong interest in the energy community to develop technologies that can diffuse low-cost, renewably generated electricity into difficult and slow to decarbonize sectors. One of the emerging areas of interest is renewable natural gas (RNG). RNG has low or no associated net greenhouse gas emissions and can be injected into the existing gas distribution network to directly displace fossil sourced natural gas. Of the available RNG technologies, electrolytic hydrogen production is one of the few that is not feedstock constrained and can potentially satisfy utility-scale and larger demands. This thesis aims to accelerate the deployment of proton exchange membrane (PEM) electrolysis by identifying the technical attributes limiting economic and scalable hydrogen production, improving those attributes through cell-level development, and identifying tangential consumer product use cases that could enable more rapid adoption. A real-world case study and detailed technoeconomic analysis revealed the main economic constraints on hydrogen production economics were high electrolyzer capital cost and low electrolyzer conversion efficiency and durability, while the main constraints on scaling PEM electrolyzer production were the global rarity of Ir (the anodic electrocatalyst), and slow electrode fabrication techniques. In this thesis, improvements in traditional fabrication techniques enabled quality electrodes that ranged into the ultra-low loaded regime, resulting in loading reductions of 10-600 fold, and at peak an Ir-specific activity improvement of over 250 fold. Finally, a new hybrid consumer-focused reversible fuel cell use case including mulit-day, carbon free backup power and simultaneous electrical demand charge reduction was identified and analyzed. This approach presents the potential for quicker electrolyzer adoption than grid-scale devices, potentially enabling a more rapid scaling of the manufacturing learning curve.
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