## 1 Introduction
In [39], a planning model was proposed that integrated expansion planning for the active distribution network and the DNR, while taking the demand response program into account to serve critical load demand as virtual distributed resources. The findings obtained by [40] illustrated the resilience of the adaptive control particle swarm optimization in contrast to other strategies, such as the genetic algorithm used by [41] for network reconfiguration in the presence of DGs. In [42], the idea of DNR was integrated with microgrid creation to restore system loads after a catastrophic event. A tri-level defender-attacker-defender paradigm was used in [43] as a primary component of power system resiliency. As a third stage or defense plan, the authors implemented reconfiguration and microgrid islanding methods to assess grid performance from the standpoint of a system operator. Table 1 provides the taxonomy of the research papers listed above. This table shows that some key details were overlooked in earlier research projects. From the resilient operation perspective especially, the collaborative planning of MESSs, PEV-PLs, and network reconfiguration with inner uncertainties during post-disaster restoration has not previously been taken into consideration. The gap in the application of MESSs to DS critical load profile restoration must be filled, nevertheless, by a proper planning model. To reduce overall costs in the after-effects of disasters, a collaborative post-disaster stochastic restoration scheme is presented in which MESSs and PEV-PLs are dynamically designed in collaboration with DS reconfiguration through MGs. Additionally, all system buses are possible nodes for MGs that are paired with PLs to be installed optimally. More specifically, MGs leverage the DGs' generating capabilities to their fullest potential and serve as root buses to dynamically generate islands for the pickup of various load kinds. Meanwhile, the vehicle scheduling problem (VSP) uses the time-space network (TSN) and is designed to represent mathematically the scheduling challenge of MESSs. Because internal uncertainties and external events are taken into account, stochastic programming may be the appropriate method for addressing the outlined issue. Therefore, this paper develops a comprehensive resilience-driven model for the optimal planning and joint post-disaster restoration problems of networked MGs, capturing both internal uncertainties and external contingencies. The optimization problem is formulated as an MIP model. According to a comparison of the essential aspects of the related literature in Table 1, there is a gap in the research relating to the uncertainty modelling techniques of a joint scheduling of power electrical vehicles parking lots PEV-PLs, MESSs, and DNR in post-disaster restoration is still not considered, especially from the perspective of resilience operation. In this regard, the point of the study is to close the gap between the application PEV-PLs and MESSs for distribution system restoration. Therefore, a cooperative post-disaster restoration strategy has been designed for MESS, PEV-PL, and DS interaction with multiple MGs to provide more resilient DSs scheduled in cooperation with DNR to increase the flexibility and reduce the overall cost of the system. Nevertheless, through scenario-based stochastic programming, internal uncertainties related to the DG generation sources and various load profiles are considered in the operating framework, while external damages are used to emphasize the importance of meeting the load requirements of critical customers under contingency situations in conformance with their priorities. The main contributions are concluded as follows. A joint post-disaster restoration scheme for the coordination of MESSs, PLs, and DSs with multiple MGs is proposed to bridge the gap in the application of MESSs and PLs to achieve more resilient DSs. A temporal-spatial MESS model, which connects the transportation networks and DSs, is proposed in terms of flexibility and total cost reduction.