| Description: |
Distribution networks are vulnerable to natural hazards which can cause major social and economic consequences. Identifying vulnerable areas and developing operational strategies, such as dispatching mobile energy systems, can help mitigate the effects of extreme events. Conventional approaches, mainly N-1/N-2 contingency security analysis, are efficient but they do not fully provide a comprehensive picture of the stochastic nature of the hazard impact. Stochastic approaches are more accurate but in general they are computationally expensive and hence not practical for the resilient operational decision-making of distribution system operators. Therefore, this paper develops a novel framework based on an adjacency-resource matrix (ARM) and an unsupervised machine learning algorithm to first identify vulnerable nodes. Next, these vulnerable nodes are utilized in the mitigation stage in order to minimize the expected energy not served (EENS) against a natural hazard. The efficiency of the proposed framework is tested on a 125-node Portuguese distribution system. |