#Technology Scenario Assumptions

#Advanced Nuclear

Figure 5 shows the scenario assumptions for new nuclear capital costs. The optimistic cost trajectory reflects a scenario in which more aggressive targets for emerging advanced nuclear technologies are achieved in the 2040-2050 timeframe. The reference scenario costs reflect the default assumptions for new nuclear in US-REGEN, based on earlier generation cost assessment studies. There remains significant uncertainty about the costs of new and advanced nuclear technologies over the coming decades. These exogenous cost trajectories represent two hypothetical scenarios, not forecasts or suggestions of likely outcomes.

#Electrolysis

Figure 6 shows the scenario assumptions for electrolysis capital costs. The starting point for the reference scenario is based on the results of EPRI research estimating current all-in capital costs for a central-scale electrolysis project (3002026205open in new window). Future costs are projected assuming continued learning and economies of scale. The optimistic scenario is based on a range of other estimates from the literature, reflecting the lower end of the plausible range for electrolysis capital costs. There remains significant uncertainty about the costs of electrolysis technology deployment at scale. These exogenous cost trajectories represent two hypothetical scenarios, not forecasts or suggestions of likely outcomes.

#CO₂ Transport and Storage

LCRI developed three scenarios for spatial costs and capacity of geologic CO₂ storage (3002027775open in new window). The scenarios in this analysis use the high and low cost cases, which vary capital and operating costs for injection and available injection capacity in certain regions. These two cases are summarized in Figure 7. The reference assumptions for CO₂ storage use the High Cost scenario, while the optimistic assumptions use the Low Cost scenario. However, in both cases, the spatial analysis found that significant geologic storage capacity exists in several regions (e.g. Texas, Southeast, and California) at costs below $9/tCO₂.

#Bioenergy Supply and Conversion

Figure 8 shows scenarios for bioenergy feedstock supply and capital costs for a representative bioenergy conversion technology. The “Full” supply curve for cellulosic feedstocks, including both residue streams and purpose grown energy crops, is based on a detailed assessment of available supply in a regional land-use model the US (see model documentationopen in new window for more information). The “Limited” supply curve restricts the availability of energy crops to reflect uncertainty related to land use conversions, interactions with food crops, and public acceptance. The reference scenario assumptions for bioenergy use the “Limited” supply curve for bioenergy feedstocks, and optimistic scenario assumes the “Full” supply curve.

For bioenergy conversion costs, for example capital costs of renewable diesel and other biofuels, the default assumptions in US-REGEN are based primarily on a multi-model study conducted by the Energy Modeling Forum (EMF).^[1] A full review of the literature (3002027971open in new window) shows a broader range of potential conversion costs. To capture this uncertainty, the reference scenario assumes costs double the default values for bioenergy conversion technologies, while the optimistic scenario uses the default values. In both cases, costs are assumed to decline over time with learning and economies of scale. There remains significant uncertainty about the costs and feedstock availability for next-generation bioenergy technologies. These exogenous cost trajectories represent two hypothetical scenarios, not forecasts or suggestions of likely outcomes.