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

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.