How Well Do Emission Factors Approximate Emission Changes from Electricity System Models?

Multiple forms of marginal and average emission factors have been developed to estimate the carbon emissions of adding technologies, such as electric vehicles or solar panels, to the electricity grid. Different methods can produce very different results and conclusions, indicating that choosing between methods is not trivial. Researchers would therefore like to know how well these emission factors can approximate emission changes in the actual power grid. This question remains unanswered because of the difficulty in characterizing the accuracy of these methods. Ideally, estimates would be compared to measured emission changes, but it is implausible to measure these changes on an actual grid. Instead, we propose testing these emission factor methods in a controlled environment, using an electricity system dispatch model as a reference for comparison. We find that average emission factors have lower accuracy when estimating emissions from demand shifts and observe the same for demand-based marginal emission factors at an hourly resolution. In contrast, incremental and thermal marginal emission factors can reproduce the emission changes of a power grid model under many testing conditions and scenarios. We also find that easier-to-use annual time averages offer similar results to finer time resolutions for marginal and average factors, except demand-based.

Implications of variations in renewable cost projections for electric sector decarbonization in the United States.

The costs of wind and solar technologies have dropped rapidly, but unknowns about technological change and emissions policies create uncertainty about future deployment. We compare projections of U.S. wind and solar costs across published studies and use an energy systems model to evaluate how these reductions could alter electric sector planning decisions and costs under deep decarbonization. Model results indicate that wind and solar are the largest generation resources for many scenarios and regions, but shares depend on assumptions about costs, policy targets, and policy timeframes (spanning 14% to 67% of national generation by 2035). Renewables cost reductions lower decarbonization costs and reduce projections for nuclear and carbon-captured-equipped generation, but policy decisions have a larger influence on future trajectories. Lower wind and solar costs have more limited impacts on deployment of carbon removal technologies and the capacity of clean firm technologies in reaching net-zero emissions in the electric sector.