UC Davis

As the momentum of climate action continues to shift from top-down, nation-state action to decentralized action by corporations, cities, and utilities, these actors are working to decarbonize their energy consumption and help drive the transition to a carbon-free grid. However, the current frameworks and metrics that drive voluntary decision making—GHG inventories and renewable energy procurement goals—do not always reflect the reality of the evolving power system, and thus can lead to decisions that do not effectively reduce emissions or address grid needs. This research seeks to fill the gaps in knowledge about how both regulatory and voluntary approaches to decarbonizing the electric grid can maximize their effectiveness and accurately measure and attribute emissions from electricity consumption. This dissertation draws upon power system engineering and industrial ecology research and applies data science and optimization methods to 1) identify whether there is a need to account for grid carbon emissions on an hourly basis, 2) introduce a comprehensive dataset of validated hourly emissions from the U.S. power sector, and 3) introduce a new modeling tool that enables greater understanding of the role of different voluntary clean energy procurement goals in the broader energy transition.