Autonomous vehicles (AVs) and electric vehicles (EVs) will improve safety, mobility, roadway capacity and provide efficient driving, efficient use of travel time, and reduced emissions. However, these technologies affect vehicle miles traveled (VMT), travel time, ownership cost, and electric grid network. Shared mobility systems can ameliorate the high price of these technologies. However, the shared mobility system poses additional problems such as users’ waiting time, inconvenience, and increased VMT. Further, the impact of these emerging technologies varies on different groups of users (different values of travel time (VOTT). Another hurdle to the adoption of EVs is the limited range and scarcity of charging infrastructure. A well-established network of charging infrastructure, especially the direct current fast chargers (DCFC), can alleviate this challenge. However, the widespread adoption of EVs and the growing network of DCFC stations will increase the electric energy demand affecting the electric grid stability, demand-supply imbalance, overloading, and degradation of the electric grid components. Distributed energy resources (DER) such as solar panels and energy storage systems (ESS) can support the EV demand and reduce the load on the electric grid. This study develops modeling frameworks for the optimal adoption of AVs and EVs, considering their effect on transportation systems, the environment, and the electric grid network. Further, it suggests different scenarios that would promote the adoption of these technologies and provide a sustainable and resilient system.This study proposes a multi-objective mathematical model to estimate the optimal fleet configuration in a system of private manual-driven vehicles (PMVs), private AVs (PAVs), and shared AVs (SAVs) while minimizing the purchase and operating costs, time (travel and waiting time), and emission production. SAVs can be the optimal solution with the efficient use of travel time or the purchase price below a certain relative threshold. PAVs can be the optimal solution only if the onboard amenities are improved, lifetime mileage is increased, AV technology is installed in luxurious cars, and adopted by people with high VOTT. The framework is extended to consider different combinations of EVs, AVs, and conventional human-driven vehicles in a private and shared mobility system. The metaheuristics based on genetic and simulated annealing algorithms are developed to solve the large-scale NP-hard nonlinear optimization problem. The model is implemented for the network of Ann Arbor, Michigan. The results suggest that EVs are optimal for the system due to low operating costs and zero tailpipe emissions. Shared autonomous electric vehicles (SAEVs) are the best option for users with low VOTT. Private autonomous electric vehicles (PAEVs) would favor the system if the travel time savings are at least 20% or the price of AV technology is less than one-third of the vehicle price. The study then investigates the optimum investment technology to support the rising energy demand at the DCFC stations and reduce the load on the electric grid network. The different investments include purchasing and installing various ESS (new batteries (NB), second-life batteries (SLB), flywheels), solar panels, electric grid upgrades, and the cost of buying/selling electricity from/to the electric grid. The model is implemented for the DCFC stations supporting the future needs of EV charging demand for urban trips in the major cities of Michigan in 2030. The combination of SLBs and solar panels provides maximum benefits. The total annual and electricity savings are $25,000-$165,000 and $40,000-$300,000 per city.
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