###### Abstract
The authors of [15] used historical statistical information to transform the traditional prediction sequence of new energy output into an output scenario set, and realized the short-term joint dispatching of wind, water, and solar energy. The authors of [16] established a wind-power probability density prediction model based on cubic spline difference and support vector quantile regression (CSI-SVQR), and used a quartile method and CSI function to preproces the data to improve the prediction accuracy. The authors of [17] considered data uncertainty and model uncertainty, respectively, in the prediction process, which improved the performance of their probability distribution prediction. In [18], considering the multi-timescale probability characteristics in wind-power probability prediction, a super-short-term wind-power probability-prediction model (TMDN) based on a mixed-time-series density network was established. In order to solve the operational risk problems, such as the tie line which can be overrun in power grid dispatching control, the authors of [19] constructed a rolling probability-prediction model based on short-term memory network and a support vector machine to effectively avoid the system operational risk. The authors of [20] introduced an information-gap decision-making theory to deal with the price uncertainty in the power market; using this, it is not necessary to obtain the probability distribution of the uncertain factors for modeling. The authors of [21] introduced blockchain technology in improving system security to reduce system line congestion and operation risk. Recovery control after a power outage is an important aspect of power system security defense [22]. After a power outage, the parallel recovery of each subnetwork can effectively speed up the recovery process, and the new energy access represented by wind and power can improve the ability of the power grid to quickly recover from the interruption [23, 24]. However, the uncertainty of wind-power output brings new challenges to the formulation of system recovery schemes [25], and inappropriate recovery schemes may cause the risk of secondary power outages [26]. The authors of [27] studied a model and scheme that can quickly restore the load in the case of multiple faults in an integrated energy system. In order to solve uncertain problems such as the output of wind and new energy, the authors of [28] adopted the Monte Carlo method, and took the minimization of the cost in the recovery process as the objective, adopting a dynamic programming method. The authors of [29] described the uncertainty of load and scenery through conditional value at risk (CVaR), constructing a two-stage load recovery optimization model, and effectively solved the uncertainty in load recovery by solving linear programming and mixed-integer quadratic programming. The authors of [30] proposed a prediction method considering the increase in cooling load demand during load recovery, and took electric vehicle load as an example for explaining the impact process of the increase in cooling load demand on system recovery. The authors of [31], established a mixed-integer programming model to reduce the phase angle difference at both ends of the line to be paralleled, and part of the load was restored simultaneously; this approach was proposed to solve the problem of the closing angle exceeding the limit in the process of subnetwork paralleling. In [32], a robust optimization model for load recovery was established on the basis of considering wind-power prediction errors, and uncertainty was treated in the way that uncertainty sets are treated. In [33], considering the participation of new energy sources, a secondary scheduling strategy was proposed to improve the degree of load recovery while ensuring the reliability of the power system through three-stage optimization.