Representing the costs of low-carbon power generation in multi-region multi-sector energy-economic models

Abstract

Multi-region multi-sector energy-economic models are often used to analyze long-term scenarios of energy development, however, these models usually rely on a simplified representation of technological details in power generation. To strengthen this representation, we develop a method for modeling the economic competition between different advanced technologies in multi-region multi-sector dynamic energy-economic models based on a markup approach, which represents the measure of the cost of a technology relative to the price received for electricity generation. The markup includes capital costs, fixed and variable operating and maintenance (O&M) costs, fuel costs, and transmission and distribution (T&D) costs. For intermittent technologies, it also includes a backup requirement to make these technologies effectively dispatchable. For carbon capture and storage (CCS) technologies, it also includes the costs of CO2 capture, transportation and storage. We provide a standardized markup calculation for generation technologies for different regions of the world, including USA, China, India, EU, Japan and others. Then we analyze the sensitivity of the calculation to critical inputs, including capital costs, fuel costs, carbon prices and capacity factors. We provide a detailed calculation of the relative costs of the following technologies: new pulverized coal, new pulverized coal with CCS, natural gas combined cycle, natural gas with CCS, biomass-fueled plant, biomass with CCS, advanced nuclear, wind (for small and medium penetration levels), solar, wind with backup (for large penetration levels), co-firing of coal and biomass combined with CCS, and advanced CCS on natural gas. For illustration, we incorporate the markups into the MIT Economic Projection and Policy Analysis (EPPA) model, a global multi-sector multi-sector dynamic energy-economic model with a detailed representation of power generation technologies, and run several scenarios. Our analysis and results provide insight on the deployment of different low-carbon power generation technologies depending on assumptions about carbon policy stringency.