Abstract
Increasing, experts are forecasting the future of transportation to be shared, autonomous and electric. As shared autonomous electric vehicle (SAEV) fleets roll out to the market, the electricity consumed by the fleet will have significant impact on energy demand and will drive variation in energy cost and reliability, especially if the charging is unmanaged. Meanwhile, SAEVs are considered important assets for the grid because their charging behavior can be controlled dynamically, unlike privately-owned electric vehicles (EVs). The addition of Renewable Energy Sources (RES) further complicates the matter. Grid infrastructure, which was designed to carry relatively consistent levels of generation, struggles to cope with the spatial and temporal volatility of RES generation. Existing literature has already explored how EV smart charging (SC) can improve energy system efficiency. These studies focus on privately-owned EVs and individual driver behavior (trip pattern, access to charging infrastructure, charging choice, etc.), but fleet managed SAEVs (that are continually in-service) cannot utilize the same SC strategies prescribed to privately-owned EVs (utilized for only 5\% of the day, on average). With the rapid development of autonomous vehicle technologies and shared mobility services, more research is needed to understand the energy implications of fleet SC behaviors and the impact of SC on mobility service quality.
This research proposes a SC framework to identify potential benefits of active SAEV charging management that strategically shifts SAEV electricity demand away from high-priced peak use hours (price-based SC) or towards hours with high renewable generation (generation-based SC). Different SC scenarios are tested using an agent-based SAEV simulation model to 1) study the impact of battery capacity and charging infrastructure type on the SAEV fleet performance and operational costs with SC; 2) study the cost reduction potential of SC considering energy price fluctuation, uncertainty, and seasonal variation; and 3) quantify the opportunity for EV-RES coupling with SAEV SC.
A case study from the Puget Sound region demonstrates the proposed SC algorithm using trip patterns from the regional travel demand model, energy prices, and renewable generation data. Preliminary results show that SC can be beneficial to both SAEV fleet operator (by reducing energy costs) and grid operator (by valley filling). In the first part, we examine the SAEV fleet performance with SC under Time-of-Use (TOU) electricity pricing to adhere to current expectations of peak/off-peak electricity usage times. Case study results indicates energy cost savings up to 34%, compared to unmanaged charging. In the second part, we explore a dynamic SC strategy where real-time energy prices are considered (in anticipation of a future in which a smart grid determines dynamic energy prices) and predict energy cost savings up to 43% compared to unmanaged charging. Lastly, we explore the potential for more sustainable sources of energy to power these fleets and find that SAEV-solar direct coupling can achieve a self-consumption rate ranging from 81% to 99%.
