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Resilience-oriented optimal post-disruption reconfiguration for coupled traffic-power systems

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  • Wang, Hongping
  • Fang, Yi-Ping
  • Zio, Enrico

Abstract

The increasing penetration of grid-enabled electric vehicles (EVs) renders road networks (RNs) and power networks (PNs) increasingly interdependent for normal operation. For this reason, recently few studies have started to investigate the vulnerability of a highly coupled traffic-power system in the presence of disruptive events. Actually, however, only very few of these studies have considered the impact of EVs on the interdependent traffic-power system during restoration from a disruptive event. In an attempt to fill this gap, in this study, we investigate the restoration planning of both independent RNs and PNs, and interdependent traffic-power systems. A mixed integer program model is formulated to provide optimal reconfiguration and operational solutions for post-disruption traffic-power systems recovery. The objective of the model is to minimize the total cost incurred by system performance loss, which is quantified by the cumulative unmet traffic demand for RNs and load shedding cost for PNs. Several reconfiguration strategies are considered, including links reversing in RNs and line switching in PNs, to optimize system resilience. In the proposed model, the integrated problem of system optimal dynamic traffic assignment and optimal power flow is solved to derive the optimal traffic-power flow. RNs and PNs are coupled through the coordinately allocated spatio-temporal charging demand of EVs. A partial highway network in North Carolina (NC), USA, and a modified IEEE-14 bus system are used to illustrate the application of the model. The numerical results obtained show the added value of coordinately planning restoration for traffic-power systems and the effects of different levels of EV penetration.

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  • Wang, Hongping & Fang, Yi-Ping & Zio, Enrico, 2022. "Resilience-oriented optimal post-disruption reconfiguration for coupled traffic-power systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:reensy:v:222:y:2022:i:c:s0951832022000801
    DOI: 10.1016/j.ress.2022.108408
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