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Applying Learning Curves to Modeling Future Coal and Gas Power Generation Technologies
- Ordowich, Chris, Chase, John, Steele, Daniel, Malhotra, Ripudaman, Harada, Michiaki, Makino, Keiji
- Energy & Fuels 2012 v.26 no.1 pp. 753-766
- capital, carbon, carbon dioxide, carbon markets, coal, combustion, electricity, energy, energy costs, equations, fuel cells, gasification, issues and policy, learning, models, natural gas, operating costs, power generation, power plants, prices, research and development
- Coal and natural gas have and will likely continue to be key components of the world energy supply for years to come. Currently, the most efficient commercial technologies for power production are supercritical pulverized coal combustion (SCPC) and natural gas combustion with combined cycle (NGCC). Emerging technologies for more efficient power generation from coal include ultra-super-critical pulverized coal (USCPC), advanced ultra-super-critical PC, integrated gasification combined cycle (IGCC), integrated gasification fuel cell combined cycle (IGFC), and direct carbon fuel cell. They each have different capital and operating costs leading to different levelized cost of electricity (LCOE). To forecast each of these competing technologies under various scenarios of electricity demand, fuel cost, and research investment, we created a Power Technology Futures Model (PTFM) based on “learning curves” methodology. Technology learning curves are a powerful tool for forecasting anticipated performance improvements due to a broad range of technical improvements without specifying the parameters of every possible improvement. The model can help planners and policy makers explore, visualize, and communicate how research and development (R&D) investments in certain technologies affect the mix of technologies deployed in the future. We utilized the Analytica modeling package and included detailed economic calculations to estimate the levelized costs for several types of coal and natural gas power plants with and without carbon capture technologies. Future improvements in plant efficiency and reductions in capital and operating and mantainence (O&M) costs were modeled using technology learning curves that were established by a detailed analysis of historic performance data. We used published estimates of future demand and fuel costs where available, but the model allows the user to easily input other numbers as tables or equations. Adoption of carbon capture was modeled in a variety of ways including being driven by a carbon cap or a carbon tax. The results of the model depict the difficulty of meeting a 50% reduction in annual CO₂ production by 2050, even with significant R&D investments, ambitious CO₂ pricing, and decreased demand for energy from coal and natural gas.