Behdad Kiani, UC Davis
Farzad alemi, UC Davis
Vehicle Automation and connectivity, as the main component of a smart city, can bring several benefits and disadvantages. According to literature, the social net benefit of Connected Autonomous Vehicles can reach as high as $4000 per vehicles, largely due to the benefits or cost saving associated with travel time reduction, accident saving and fuel efficiency saving. CAVs further enable the optimization of traffic flow management through the creation of platoons and reduction of parking search time/distance, thus potentially reducing the overall congestion on the roadways and reducing the fuel consumption and CO2 emissions. In addition, the development of fully connected self-driving cars will solve many of the barriers associated with the deployment of shared vehicle systems and Mobility as a Service concept. Thus, it is expected that households reduce the number of vehicles as CAVs can facilitate efficient sharing car concept. Albeit these changes, it is expected that the VMT per capita increases as people would live farther away from work or school and as empty miles traveled by zero-occupant vehicles increases. Smarter city, on the other hand, can facilitate the deployment of smart congestion-pricing strategies to counter the rising VMT.
CAVs have a great potential to change fuel efficiency and energy consumption, largely due to changes in driving profile, traffic flow smoothing, faster travel, connectivity at interactions, collision avoidance, platooning, and vehicle/power-train resizing. For example, the communication among vehicles and between vehicle and infrastructure can lead to a more optimized driving profile and fewer number of stop-and-go traffic, which result in more efficient energy consumption. In this research we plan to review the impacts that are associated with the changes in the mobility ecosystem and travel demand provided by a smarter city and transportation system, including the impact of automation and connectivity on travel demand, energy consumption, and greenhouse gas emissions.
We will create a smart grid model that will connect the supply and demand sides, such that electrical dispatch and end-use demand are matched for an optimal grid operation. Considering AVs interact with the grid at different hours for V2G and G2V purposes and also considering their battery as electricity storage for penetration of more renewables into the system. At present, Independent System Operators are dealing with increased renewable energy curtailments due to increase in load profile changes between peak and off-peak hours. The difference between AVs with shared mobility and personal AVs will be of importance as their charging demand pattern changes with increasing shared mobility.