Review on vulnerability analysis of power distribution network.

• Proposed two new indicators for distribution network vulnerability analysis. • Applied deep learning and Internet of Things to vulnerability analysis. • Applied dynamic thermal rating to vulnerability analysis. The integration of distributed energy resources and electric vehicles has made the distribution network more complex and uncertain. In response to these challenges, researchers have proposed various evaluation methods and indicators to uncover the vulnerability of the distribution network and provide improvement measures. Firstly, this review categorizes factors influencing the vulnerability of distribution networks into internal and external factors. Moreover, the three research objects of vulnerability analysis are elucidated. Secondly, the paper summarizes the evaluation criteria for vulnerability and discusses existing vulnerability assessment methods and indicators. Through research analysis, it is proposed that the remaining-life of lithium-ion batteries and the Expected Energy Not Supplied (EENS) can be used for the vulnerability analysis of distribution networks. Additionally, this research investigates the utilization of Internet of Things (IoT) technology and deep learning algorithms, such as Long Short-Term Memory (LSTM), for vulnerability analysis. The objective is to facilitate data collection and predict the remaining-life of lithium-ion batteries. The findings of our studies demonstrate that by incorporating the proposed evaluation index and evaluation method, the implementation of Dynamic Thermal Rating (DTR) technology can improve EENS evaluation, which also enables real-time monitoring and assessment of vulnerable components and potential failure risks. [ABSTRACT FROM AUTHOR]