Real-time Operation of Electric Autonomous Mobility-on-Demand System Considering Power System Regulation

Abstract

Electric autonomous mobility-on-demand (EAMoD) systems are emerging all over the world. However, their potential swarm charging in depots may deteriorate operation of the power system, further in turn affecting EAMoD system's optimal operation. To prevent this latent risk, we develop a real-time coordination framework for the EAMoD system and the power system. First, the temporal-spatial characteristics of EAMoD fleets are fully described based on a Markov decision process model, including serving trips, repositioning, and charging. Second, charger accessibility of EAMoD depot charging is well modeled as real-world configuration, wherein fast and slow charge piles are both included. Third, the power system regulation model provides real-time charging regulation constraints for EAMoD systems to prevent potential overload and undervoltage. To address the poor solution quality attributed to the complex decision space of the EAMoD system, this paper proposes a piecewise linear-based approximate dynamic programming algorithm combined with model predictive control. Numerical experiments in the Manhattan and a 14-node power distribution network validate the effectiveness of the proposed algorithm and underscore the necessity of system coordination.