Vulnerability and Resilience Assessments of Power Grid Based on Community Partition

The power grid has become the most important infrastructure system and dominates the economic and social developments in modern society. However, the power network often suffers various failures which can cause huge damages to the power grid. Hence, this paper proposes a community detection method to explore the critical community and identify the critical node, so as to study the vulnerability and resilience of the power grid subjected to malicious attacks. Meanwhile, this paper presents a comprehensive model to quantify the resilience characteristics considering performance level and change speed, and the IEEE 118 network is adopted to verify the feasibility and effectiveness of the proposed schemes. The results show that the attack strategies considering the community structure can cause severe damages to the power grid compared with the traditional methods, and Community Gravity-based Recovery (CGBR) has the better recovery ability among the six recovery strategies. Moreover, the results also demonstrate that Community Priority and Gravity-based Recovery (CPGBR) and CGBR can generate the most complete communities, and they can cause the least resilience damages on the number of surviving nodes (SN) subjected to the attacks on nodes 49 and 69 respectively, and Gravity-based Recovery (GBR) can result in the least resilience damages on the power supplied (PS). Hence, we can discover that there is a tradeoff between decreasing resilience loss and increasing network performance by different choices of recovery strategies. Moreover, the results show that the proposed methods can precisely identify critical nodes and rapidly recover network characteristics, therefore this study has great significances for decreasing resilience loss of the power grid subjected to malicious attacks.