There is a growing consensus that rapid electrification of the transportation sector is a necessary condition to stabilize the climate. But how do we plan such a transformation amidst other important megatrends in our economy: vehicle automation, shared mobility, and a rapidly greening electric grid?
Our research team from LBNL, UC Davis, UC Berkeley, Emerging Futures, Marain, and the ICCT just published an article in Environmental Science & Technology where we examine the interaction between these trends using a U.S.-wide simulation framework encompassing private electric vehicles (EVs), shared automated EVs (SAEVs), charging infrastructure, controlled EV charging, and a grid economic dispatch model to simulate personal mobility exclusively using EVs. We explore a hypothetical future scenario where 100% of personal vehicle mobility is provided by EVs—either private or shared—in order to assess their benefits and impact on the U.S. electric grid.
What does our model do and why?
Our open source simulation model (Grid-integrated Electric Mobility or GEM) combines individual vehicle trips, parametrized agent-based model outputs, a cost model for vehicles and charging behavior, and a national-level electricity production cost model. GEM co-optimizes the allocation of SAEVs and charging infrastructure along with charge scheduling and economic dispatch of grid generators to find the minimal cost combination of vehicles, chargers, and operations to satisfy a given demand for trips.
The interactions between power generation on the grid and charging behavior of private and shared vehicles are not simple. There are substantial cost savings that can be achieved by managing the charging patterns of EV fleets, but it wouldn’t be practical for these to come at the cost of decreased mobility. Therefore, GEM was designed to quantify the temporal flexibility of charging EV fleets in a manner that still satisfies mobility as well as resolve how this flexibility will align with low cost generation on the grid.
Furthermore, there is flexibility that can be added to the system by overbuilding, e.g. by manufacturing more vehicles or increasing their range or adding more chargers or higher power charging. There are tradeoffs inherent in the decision to build more flexibility into the system and the economic benefits these may yield by accessing lower cost energy. Our methodological approach is to model all of these factors endogenously in order to capture these tradeoffs in both the system design and operation.
What did we find?
- Nationwide, private EVs with uncontrolled charging would reduce GHG emissions by 46% (0.5Gt CO2e) compared to gasoline vehicles.
- Private EVs with fleetwide controlled charging (aka “smart charging”) would achieve a 49% reduction in emissions from baseline and reduce peak charging demand by 53% from the uncontrolled scenario.
- An SAEV fleet 9% the size of today’s active vehicle fleet can satisfy trip demand with only 2.6 million chargers (0.2 per EV).
- Such an SAEV fleet would achieve a 70% reduction in GHG emissions (0.8Gt CO2e) at 41% of the lifecycle cost (a saving of $570 billion/year) as a private EV fleet with controlled charging. The emissions and cost advantage of SAEVs is primarily due to reduced vehicle manufacturing compared with private EVs.
- Private EVs have more flexibility in their charging profile due to the fact that they spend much more time parked compared to high utilization SAEVs. Private EVs can therefore reduce peak demand from unmanaged EV charging by an additional 6 percentage points than the reduction that can be achieved by a fleet composed entirely of SAEVs (a 53% versus 47% reduction). However, charging load from private EVs will be considerably more difficult to control than centralized SAEV fleets, which will be highly incentivized to optimize their load profile.
- While rural regions require longer-range battery capacities than urban regions, we find that only 4% of the SAEV fleet must have a range of 225 miles to satisfy all trips. By comparison, urban regions are able to serve all trips with a mixture of 75- and 150-mi. range vehicles, and virtually no longer-range vehicles.
Figure 4. (a) Fleet size, (b) numbers of chargers, (c) peak power demand, (d) total cost of ownership, and (e) consequential GHG emissions vs fraction of SAEV trips with 0% of the private EVs engaging in managed (“smart”) charging.
Authors: Colin Sheppard, Alan Jenn, Jeffery Greenblatt, Gordon Bauer, Brian Gerke
This study was sponsored by the U.S. DOE Vehicle Technologies Office.