Grid-Scale Life Cycle Greenhouse Gas Implications of Renewable, Storage, and Carbon Pricing Options.

Abstract

Models that characterize life cycle greenhouse gases from electricity generation are limited in their capability to estimate emissions changes at scales that capture the grid-scale benefits of technologies and policies that enhance renewable systems integration. National assumptions about generation mixes are often applied at annual time steps, neglecting spatiotemporal resolutions that provide insights on impacts from time-variable emissions. Our grid-scale model incorporates details of transmission and generation planning that allows a geographically and temporally textured and more realistic assessment of the life cycle greenhouse gas emissions outcomes, using a case study of the Western Interconnection of North America. Results from a co-optimized model of generation, transmission, and operations-the Johns Hopkins Stochastic Multistage Integrated Network Expansion Model-provide a detailed characterization of twenty-one scenarios with different configurations of storage additions, new renewable capacity, and carbon prices. Life cycle results suggest that optimization models that focus on generation alone may underestimate emissions by 18-29% because only emissions from power generation are quantified (i.e., supply chain emissions are omitted) but also that carbon pricing is the predominant driver of reducing emissions in the scenarios we examine. Life cycle assessment of electricity generation should move beyond individual technologies toward capturing the influence of policies at the system level to better understand technology-policy dynamics for the grid.