###### Abstract
The authors of [33] analyzed the system recovery of energy storage and distributed power supply in a distribution network and proposed a two-level optimal recovery strategy considering the power supply modes of different power sources; this study had the objective of minimizing costs in the recovery process and restoring the largest possible number of power supply users. The authors of [34; 35; 36] analyzed the impact of a DC system and a wind power connection on system recovery in an AC/DC hybrid power system; they established a load-recovery model considering source load uncertainty which effectively balanced safety and stability in recovery, and ensured the coordination and optimization of DC transmission power and the safe access of load. Considering the problem of line overload in system recovery caused by large-scale access to wind power, the authors of [37] eliminated line overload through ring network reconstruction by building a regional ring network in which the wind-power output was scenario based; the upper layer of the optimization model, with minimum operation complexity, and the lower layer of the model, with the constraint of the angle to be paralleled and closed, were established, and the effectiveness of the model was verified. In the process of load recovery, one of the most important processes is to strengthen the connection between systems through a parallel connection between subnets, improve the stability of the system, and speed up the recovery of load. In the process of parallel connection, it is necessary to limit the voltage and phase angle at both ends of parallel elements or lines [38]. The authors of [39] established a multiobjective model for load recovery to improve the power supply quality of subnetworks and minimize the parallel closing angle between subnetworks; they used a water cycle algorithm (WCA) to solve the model. In the optimal regulation of the closing angle of the power system ring grid parallel, and considering the influence of the uncertainty of the wind-power output on the closing angle, the authors of [40] represented the uncertainty of the wind farm output collectively; a two-stage robust optimization model was established, which takes the minimum charge of the unit output and restores part of the load at the same time. However, due to the limitations of the robust optimization itself, it is difficult to quantify the uncertainty; as a result, the solution scheme is conservative and subjective in the weight distribution of the solution objective, and it is difficult to guarantee the quality of the solution. Improving the flexibility of the power system has a great effect on improving the recovery capacity of the power system [41]. The authors of [42] further summarized the recovery technologies and optimization methods of future smart grids. In the context of increasing penetration of new energy such as wind and solar energy, it is necessary to analyze and study the recovery stage of power systems after wind and solar energy are connected, to deal with the impact of its output uncertainty on the system-recovery process. According to Table 1, the current research on the system-recovery phase has the following deficiencies:  
* In some studies, the impact of uncertainty factors on systems was not considered in the system-recovery phase, and the goal in the recovery phase was singular. * In the treatment of system uncertainty factors, the adopted method has limitations such as low calculation efficiency and conservative solution results. Based on this, the following methods can be adopted to make up for the shortcomings of current research:
* Probability density prediction is adopted to solve the impact of wind-power output uncertainty on the system-recovery process;
* Based on a consideration of the uncertainty of wind-power output, a multiobjective optimization model is established to minimize the adjustment of unit output and maximize the recovery of important loads.