High-impact and low-probability (HILP) events can cause severe damage to power systems. Networked microgrids (MGs) with distributed generation resources provide a viable solution for the resilience enhancement of power systems. However, most literature utilizes centralized control methods based on energy management systems to model networked MGs and employs static storage units to enhance resilience, which are both unrealistic. In this paper, a hierarchical control approach based on detailed AC OPF algorithm is developed to capture technical constraints relating to voltage, angle and power loss as well as obtaining accurate solutions of power exchange between MGs, while the routing of electric vehicles (EVs) is incorporated into the model to reduce load shedding during extreme events. Uncertainties relating to load profiles and renewable energy sources are captured via stochastic programming. The impacts of limited generation resources and different levels of contingencies (including multiple line faults) are captured in the model to mimic a realistic scenario and verify the effectiveness of the proposed resilience enhancement strategy. Appropriate sensitivity analyses are suggested to investigate the influence of uncertain event occurrence time, tie-line capacity and EV scheduling horizon.

高影响力和低概率（HILP）事件可能会严重破坏电力系统。具有分布式发电资源的联网微电网（MG）为提高电力系统的弹性提供了可行的解决方案。但是，大多数文献利用基于能量管理系统的集中控制方法对联网的MG进行建模，并采用静态存储单元来增强弹性，这都是不现实的。本文提出了一种基于详细AC OPF算法的分层控制方法，以捕获与电压，角度和功率损耗有关的技术约束，并获得MG之间的功率交换的准确解决方案，而电动汽车（EV）的路线是纳入模型以减少极端事件期间的负载减少。通过随机编程可以捕获与负荷曲线和可再生能源有关的不确定性。在模型中捕获了有限的发电资源和不同级别的突发事件（包括多条线路故障）的影响，以模拟现实情况并验证所提出的弹性增强策略的有效性。建议进行适当的敏感性分析，以调查不确定事件发生时间，联络线通行能力和电动汽车调度范围的影响。