21 on-orbit servicers. These reductions in hardware costs reduced LCOE by about 25% for RD1 and 18% for RD2. GHG emissions are decreased about 27% and 20% for RD1 and RD2, respectively, (Figure 13) because the EIO-LCA model assumes a relationship between cost, efficiency, and reduced emissions. 4.2.2 Learning Curve Given the size of the systems and millions of modules required for each, manufacturing is one of the largest costs represented in the development segment of the ConOps. The baseline learning curve used is 75% for units of SBSP system hardware, 85% for servicers, and 90% for ADR vehicles. If we improve the learning curve by 5 percentage points for each, LCOE drops by about 10% for RD1 and 8% for RD2. GHG emissions are reduced about 15% for RD1 and 11% for RD2 (Figure 13), again, because the EIOLCA model assumes a relationship between cost, efficiency, and emissions. 4.2.3 Solar Cell Efficiency Increasing the efficiency of solar cells decreases the size and mass of a space solar power system required to create the same output power. This decrease in size affects both hardware development and assembly costs. The LCOE reduction achieved by increasing solar cell efficiency from 35% to 50% is about a 25% for RD1 and 26% for RD2. The 50% figure represents the highest efficiency of terrestrial research cells tracked by NREL today (NREL, 2023). The size and mass reduction leads to less manufacturing and fewer launches, cutting each system’s assessed GHG emissions per kWh by 23% for RD1 and 27% for RD2. This sensitivity analysis does not consider the mass changes that would occur from use of different photovoltaic technologies. For example, silicon and perovskite cells are made of different materials, and manufactured differently. 4.3 Combining Sensitivities Combining select conditions from the sensitivity analyses can lead to a cost-competitive SBSP system. The rationale for these choices is presented in the following section. In the “combined” sensitivity, we modified key input variables accordingly: • Launch costs reduced from $100M or $1000/kg (with 15% block buy discount, $85M or 850/kg) to $50M or $500/kg (with 15% block buy discount, $42.5M or $425/kg) • Solar cell efficiency increased from 35% to 50% • Servicer and ADR first-unit cost reduced from $1B to $100M and $500M to $50M, respectively • Learning curves improved by 5 percentage points across the board
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