An Integrated Optimisation-Simulation Framework for Scalable Smart Charging and Relocation of Shared Autonomous Electric Vehicles

• Main Authors: Riccardo Iacobucci, Raffaele Bruno, Jan-Dirk Schmöcker
• Format: Article
• Language: English
• Published: MDPI AG 2021-06-01
• Series: Energies
• Subjects: electric vehicles; autonomous vehicles; charging optimization; mobility on-demand; vehicle-to-grid; demand response
• Online Access: https://www.mdpi.com/1996-1073/14/12/3633

Ride-hailing with autonomous electric vehicles and shared autonomous electric vehicle (SAEV) systems are expected to become widely used within this decade. These electrified vehicles can be key enablers of the shift to intermittent renewable energy by providing electricity storage to the grid and offering demand flexibility. In order to accomplish this goal, practical smart charging strategies for fleets of SAEVs must be developed. In this work, we present a scalable, flexible, and practical approach to optimise the operation of SAEVs including smart charging based on dynamic electricity prices. Our approach integrates independent optimisation modules with a simulation model to overcome the complexity and scalability limitations of previous works. We tested our solution on real transport and electricity data over four weeks using a publicly available dataset of taxi trips from New York City. Our approach can significantly lower charging costs and carbon emissions when compared to an uncoordinated charging strategy, and can lead to beneficial synergies for fleet operators, passengers, and the power grid.