Integrated Modeling Approaches to Quantify Vehicle-to-Grid Services in an Evolving Power Sector

Abstract

The U.S. transportation sector emitted 27% of nationwide greenhouse gas (GHG) emissions in 2020. In addition to cleaner fuels and more efficient powertrains, vehicle electrification is poised to be a key driver of sector decarbonization. However, fleet electrification poses an unprecedented coupling of the transportation sector and electric grid. Electric vehicle charging and other new loads, if not sufficiently managed, are anticipated to add significant strain to the grid. In light of these challenges, vehicle-to-grid (V2G) has been proposed as a form of flexible load and decentralized energy storage. Within a V2G framework, grid-connected electric vehicles provide services to power grids, for example by shifting when they charge or discharging their batteries to the grid when power demand is high. Conceptually, V2G can reduce the costs of intermittency, facilitate renewables growth, and provide storage services to the grid.

While V2G continues to evolve and gain market traction, there remain several aspects of the technology, both operational and economic, that stand to be better understood and improved upon to best facilitate widespread adoption. For instance, EVs can theoretically displace stationary energy storage, but to what extent? What are demand side implications for the grid? For early technology adopters, particularly commercial fleets, how do travel needs and network tariffs affect V2G revenues? How can one practically simulate V2G and other service outcomes and do the potential revenues justify initial investment?

This thesis addresses such questions and concerns through the development and application of methods that (1) quantify the technology's ultimate value proposition at the systems level, and (2) enable risk-informed market participation and financial analysis.