Stochastic Optimization of Coupled Power Distribution-Urban Transportation Network Operations with Autonomous Mobility on Demand Systems

TL;DR

This paper develops a stochastic bi-level model to optimize and analyze the operation of coupled power and transportation networks with autonomous electric vehicles, considering uncertainties and human behaviors.

Contribution

It introduces a novel probabilistic analysis method for coupled networks with autonomous EVs, integrating environmental and human uncertainties into operational optimization.

Findings

Effective operation framework for coupled networks with autonomous EVs.

Probabilistic analysis captures environmental and behavioral uncertainties.

Model verified on a regional power and transportation network.

Abstract

Autonomous mobility on demand systems (AMoDS) will significantly affect the operation of coupled power distribution-urban transportation networks (PTNs) by the optimal dispatch of electric vehicles (EVs). This paper proposes an uncertainty method to analyze the operational states of PTNs with AMoDS. First, a PTN operation framework is designed considering the controllable EVs dispatched by AMoDS as well as the uncontrollable driving behaviors of other vehicle users. Then, a bi-level power-traffic flow (PTF) model is proposed to characterize the interaction of power distribution networks (PDNs) and urban transportation networks (UTNs). In the upper level, a social optimum model is established to minimize the operating cost of PDNs and UTNs embedded with controllable EVs. In the lower level, a stochastic user equilibrium (SUE) model is established to minimize the operating cost of uncontrollable EVs and gasoline vehicles (GVs) in UTNs. Finally, a probabilistic PTF analysis method is developed to evaluate PTN operations under environmental and human uncertainties. A regional sensitivity analysis method is proposed to identify the critical uncertainties and quantify the impacts of their distribution ranges on PTN operations. The effectiveness of the proposed method is verified by the PTN consisting of a 21-bus PDN and a 20-node UTN.