Resilient Mobile Energy Storage Resources Based Distribution Network Restoration in Interdependent Power-Transportation-Information Networks
<|reference_start|>Resilient Mobile Energy Storage Resources Based Distribution Network Restoration in Interdependent Power-Transportation-Information Networks: The interactions between power, transportation, and information networks (PTIN), are becoming more profound with the advent of smart city technologies. Existing mobile energy storage resource (MESR)-based power distribution network (PDN) restoration schemes often neglect the interdependencies among PTIN, thus, efficient PDN restoration cannot be achieved. This paper outlines the interacting factors of power supply demand, traffic operation efficiency, communication coverage, electric vehicle (EV) deployment capability, and PDN controllability among PTIN and further develops a PTIN-interacting model to reflect the chained recovery effect of the MESR-based restoration process. On this basis, a two-stage PDN restoration scheme is proposed that utilizes three emergency resources, including EVs, mobile energy storage systems (MESSs), and unmanned aerial vehicles (UAVs), to restore the power supply and communication of PDNs. This scheme first improves the distribution automation function, EV deployment capability, and traffic operation efficiency by prioritizing the recovery of communication network (CN) and urban traffic network (UTN) loads. Then, EVs and MESSs are further scheduled to achieve a better PDN restoration effect with the support of the restored CNs and UTNs. Case studies on a PDN, CN, and UTN integrated test system are conducted to verify the effectiveness of the proposed scheme. The results show that the prioritized load recovery operation for CN and UTN facilities in this scheme greatly improves the PDN restoration effect.<|reference_end|>
@article{zhong2024resilient,
    title={Resilient Mobile Energy Storage Resources Based Distribution Network
  Restoration in Interdependent Power-Transportation-Information Networks},
    author={Jian Zhong, Chen Chen, Qiming Yang, Dafu Liu, Wentao Shen, Chenlin Ji,
  and Zhaohong Bie},
    journal={arXiv preprint arXiv:2403.01250},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.01250},
    primaryClass={eess.SY cs.SY}
}