Learning rates and future cost curves for fossil fuel energy systems with CO₂ capture: Methodology and case studies

The broadly applicable equations for estimating learning rates of cost variables in energy systems with CO₂ capture (CC) are formulated, in which the effect of overall plant efficiency upgrade on learning rates is reflected. Based on the equations, as a case study, we estimate the learning rates, predict the future cost trend of IGCC power plants with CC in China, and examine the effect of plant efficiency upgrade on its future cost. It is revealed that the learning rates of the whole CC plant are relevant not only to the learning rate of each subunit, but also to its cost proportion and the overall plant efficiency upgrade. Results from case study show that the learning rates of IGCC+CC in China are in the range of 0.0964-0.2022 for unit investment, 0.0726-0.1489 for COE, and 0.0636-0.1462 for cost of CO₂ avoidance (COA). When the cumulative production reaches 100GW, the investment for IGCC+CC will decrease from the current level (approximately 2150$/kW) to around 760-1350$/kW, COE will decrease to 46-68$/MWh, and COA will fall from 33.4$/t to 16-25$/t. Sensitivity analysis indicates that overall plant efficiency upgrade and the capacity at which learning begins pose significant effects on cost reduction. Compared with PC+CC, for gradual learning with a low learning rate, the unit investment of IGCC+CC will be a little bit higher than that of PC+CC in the future. For rapid and moderate learning, IGCC+CC will be more expensive than PC+CC in the near term, while breakeven points are observed with the cumulative experiences growing, indicating that IGCC+CC can economically perform better than PC+CC in the medium and long term. The paper provides an approach to estimate the learning rates of CC plants, and thus to project their future cost curves, which will help to formulate the first clear-cut CCS roadmap in China and to aid the identification of key CC technologies that should be focused on.