Enhancing resilient distribution networks through proactive topology optimization and robotaxi dispatch coordination

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Authors: Y.L. Gong, Tao Yu, Ziyao Wang, Yufeng Wu, Zhenning Pan, Wencong Xiao
Cited by: 1
References: 29

Abstract

Robotaxi (RT), a recently emerging class of autonomous ride-hailing electric taxis, offer significant potential to enhance power distribution network (PDN) resilience through coordinated operation, leveraging their fully dispatchable nature. The charging behavior of electric vehicles (EVs) naturally couples PDN with the road network (RN). However, some studies on PDN resilience overlook the spatial correlation of disaster impacts on both the RN and PDN, which may lead to suboptimal strategies by limiting accurate infrastructure condition assessment and effective coordination between transportation and power systems. Thus, this paper first develops an infrastructure performance degradation model (IPDM) to quantify the joint impact of extreme disasters on both RN and PDN, further analyzing their influence on charging demand variation. Building on this, a coordination framework is proposed to enhance PDN resilience under extreme disasters. This framework innovatively leverages RT fleets as mobile flexibility resources and integrates charging management with proactive PDN topology optimization, including placement of soft open points (SOPs) and circuit breakers (CBs) and PDN reconfiguration. Case studies demonstrate that the proposed framework not only improves PDN resilience but also reduces associated economic cost. Furthermore, sensitivity analysis of varying RT penetration levels on resilience strategies is systematically examined, highlighting the potential of RTs in supporting switch planning. • Robotaxi-enabled switch planning for transport-power coordination. • Disaster-induced degradation model for road-grid coupled network. • Coordinated Robotaxi dispatch to cut charging delays and revenue loss.

Fields of Study & Subjects

Electric Vehicles and Infrastructure Transportation and Mobility Innovations Optimal Power Flow Distribution

Publication details

Journal / source
International Journal of Electrical Power & Energy Systems
Publisher
Elsevier BV
Volume
176
Pages
111682–111682
Publication date
14 February 2026
Language
EN