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Extreme weather events do great harm to power distribution systems, causing widespread outages and huge economic losses. It is necessary to investigate the operation and recovery methods for power distribution systems against extreme weather events with small probability and high risk. Accordingly, this paper proposes a cost-optimal operation and recovery method for power distribution systems considering multiple flexible resources and logistics restrictions. The method is developed as a two-stage model to obtain the optimal cost consisting of lost load cost and network loss cost. In the first stage before the disaster, the pre-allocation of repair crews combines multiple flexible resources to cope with the upcoming extreme weather event. In the second stage after the disaster, repair crews coordinate with flexible resources to quickly recover the lost load of the system. The cooperative scheduling of repair crews and flexible resources in the method is restricted by the logistics of transportation networks. Based on the network loss, the two-stage model is formulated as a mixed integer second-order cone programming. The case study conducted on a modified IEEE 33-bus test system demonstrates the effectiveness of the method proposed in this paper.

In recent years, extreme weather events have resulted in a number of severe power outages and economic losses. It is becoming urgent to study approaches to enhance power distribution system resilience. This paper presents a two-stage optimization method for improving resilience of power distribution systems. The two-stage method is influenced by a transportation network, which considers the interdependence between traffic roads and overhead distribution lines. In the first stage, based on the Bayesian network and the Monte Carlo method, the locations of emergency stations, e.g., charging stations and repair stations are pre-positioned to improve the recovery speed of interrupted loads after disasters. Then the second stage is to co-optimize the dispatch of mobile energy storage systems and repair crews on transportation network to minimize the shedding of critical loads while considering operation constraints of power, voltage and power flow for power distribution system. The dispatch of mobile energy storage systems and repair crews are affected by the locations of emergency stations in the first stage. The route of mobile energy storage systems and repair crews on transportation network can be obtained from the results in the second stage. The proposed method is tested on a modified IEEE 33-bus distribution test system with a corresponding transportation network. Simulation results demonstrate the effectiveness of the proposed method in enhancing power distribution system resilience by pre-positioning emergency stations and co-dispatching mobile energy storage systems and repair crews.

The analysis of the fault propagation path of transmission lines and the method of identification of vulnerable lines during typhoon weather conditions is of great significance. In this context, this paper introduces the failure probability model of transmission lines under such conditions by considering both wind speed and the load of the lines. The Monte Carlo simulation (MCS) and the DC model based on OPA are applied to simulate the failure of transmission lines. The cascading failure state transition diagram (CFSTD) is proposed based on the failure chains and the criticality ranking of nodes in CFSTD by the average weight coefficient (AWC) for identifying vulnerable lines of the power grid under such conditions. A new weight in CFSTD is proposed to describe the vulnerability of each line and a new resilience index is used to assess the impacts of a typhoon on the system. The proposed method is demonstrated by using the modified IEEE 118-bus test system. Results show that the method proposed in this paper can simulate the fault propagation path, and identify the critical components of power grid under a typhoon.