## 1 Introduction
Additionally, to increase DS resilience after extreme events, the studies in [24; 25] introduced restoration strategies that also integrated network reconfiguration with MESSs transportation. In [15; 26], MESSs were implemented to perform load shifting for the distribution network. An integrated unit commitment problem for a battery-based energy transportation system was addressed in [27] in an attempt to decrease transmission line congestion and lower operating costs. In addition to increasing system efficiency, battery-based energy transportation technology was utilized to decrease wind power curtailment [28]. In [22], a combined post strategy for mobile energy transportation was presented, and a two-stage methodology was used in this article to enable resilient route discovery and management of mobile power sources (MPSs). MPSs were pre-positioned in the DS during the first stage, i.e., before the event, to allow rapid pre-restoration, hence increasing the endurance of the electrical supply to critical loads. In the second stage, which occurs after the disaster, MPSs were dynamically transmitted to the DS to cooperate with traditional restoration attempts to improve system recovery. For load restoration, authors of [29] proposed a post-disaster joint approach integrating numerous MGs, MESSs, network reconfiguration, and generation scheduling. In [30], the authors developed a co-optimization model with the scheduling and routing of repair teams and mobile power stations to restore distribution networks and improve grid resilience. To integrate the routing decisions made by MESSs with the reconfiguration of distribution networks, a load-restoring technique was proposed in [31] based on rolling optimization. However, the multi-period dynamic dispatch was neglected because the assigned MEGs remained stationary in the same location. PEV-PLs:Despite their limitations, PEVs have exceptional economic and operational gains for distribution networks. As a result, several research papers have focused on examining the various elements of PEVs in power grids. The artificial bee colony optimization technique was used in [32] to present a multi-objective optimization model for the efficient allocation of PLs throughout the distribution grid. In [33], DGs and PEV-PLs were assigned in the network at the same time using a two-level optimization technique that investigated the cooperative behavior of PEV-PLs and renewable-based DGs. In [34], a comprehensive management model for the efficient scheduling of electric vehicles' active and reactive power while dealing with multiple uncertainties was developed. The authors applied the Benders decomposition to speed up execution. From the viewpoint of parking lot owners, a noteworthy innovative planning model was presented in [35] for the long-term planning of PEV-PLs. The research discussed shifting energy usage from on-peak to off-peak times using a coordinated charging mechanism. When the connection between home loads and the upstream grid is damaged, an approach was suggested in [36] that leveraged PEV energy as a source of power for such loads. As described in [37], a parking-lot-equipped renewable energy sources based micro grid (RMG), was proposed to manage and aggregate PEVs. Although PEVs were present, the stochastic framework was used to describe the optimal energy management of the renewable energy-based microgrid. The hybrid information gap decision technique (IGDT) incorporated with the stochastic method was utilized in [38] to handle a transmission-constrained AC-unit commitment model integrated with electric vehicles (EV) and wind energy. A scenario-based approach was used to predict the behavioral uncertainty associated with owners of electric vehicles, while IGDT was used to control the uncertainty associated with wind energy. Distribution network reconfiguration (DNR): Lots of studies on the subject of DNR have recently been published. In general, researchers of these publications have used a variety of strategies and techniques to address DNR challenges.