This research identifies how strategically integrating green hydrogen into the United States energy system can help meet growing electricity demand with minimal cost and environmental impact on a nationwide scale. We develop a network model of the US energy system that includes green hydrogen as an energy pathway, in addition to direct renewable energy and fossil fuel pathways. This model is both spatially and temporally dependent, with renewable energy supply and electricity demand drawn from probability distributions derived from historical meteorological data and electricity consumption. Furthermore, this model accounts for the impact of transportation on energy losses, costs, and emissions. We then use mixed-integer nonlinear programming to perform multi-scale optimization of this model to identify when and where producing and consuming green hydrogen reduces the levelized cost of electricity. We stress-test this model across a range of future climate, energy demand, and policy scenarios to analyze how evolving consumer behavior, meteorological patterns, subsidies, and other factors would impact these results. Meanwhile, a life cycle perspective enables us to evaluate the tradeoffs between green hydrogen’s benefits and the impact of its water consumption and construction requirements on the food-energy-water nexus. Thus, this model supports private and public sector decision-makers across the United States in dynamically leveraging green hydrogen’s energy storage capabilities and transportability to produce, route, and consume green hydrogen in a way that reduces financial costs and environmental impacts on a system-scale.