## 1 Introduction
### Research Literature Review  
Several studies have been conducted on the reliability of distribution networks with a focus on different aspects of the medium-voltage (MV) level. Reference [4] introduced an encoded Markov cut set (EMCS) algorithm to assess the distribution system reliability and improve the evaluation efficiency. Reference [5] evaluated the distribution network reliability from the perspective of power quality and proposed new reliability assessment indicators. Reference [6] analyzed the impact of wind power plants on power system reliability by examining the reliability of the power generation system under varying wind power generation ratios and proposing the corresponding reserve capacity margin requirements to ensure system security. Reference [7] used a cascade correlation neural network to model the cost and established a more accurate reliability value model. A reliability assessment method based on the GRA-K-means ++ algorithm has been proposed in the literature [8], which provides a more in-depth evaluation of the reliability of power supply and takes into account a more comprehensive range of factors. However, these studies only focused on the impact of failures at the MV level on its own reliability. The increasing coupling of MV and LV with the popularization of LV distribution technologies makes it necessary to include the LV dimension with its large scale of users and varied characteristics in the reliability assessment. Various models and assessment methods have been developed to analyze the differences among LV users. Reference [9] compared different clustering methods, including modified follow-the-leader, hierarchical clustering, K-means, and fuzzy K-means, to classify different users. Reference [10] evaluated the daily load curves of users using the classical K-means, weighted fuzzy average K-means, modified follow-the-leader, self-organizing maps, and hierarchical algorithms to better understand different electricity consumption patterns. Reference [11] used the method of maximizing deviation to rank multi-criteria decision making for power users and achieved significant economic benefits. Reference [12] assessed the user importance based on the reliability requirements and severity of the power supply interruption via the gray correlation analysis. Reference [13] considered the impact of outages on different users and defined new indicators to accurately assess the perceived reliability levels. Reference [14] proposed an accurate multi-dimensional user segmentation model for user electricity behavior and economic value. Reference [15] investigated various commercial and industrial clients and established a cost estimation model for user outages. However, these studies only considered MV users and ignored the characteristics of LV users; directly following these methods cannot accurately reflect the situation of low-voltage users. Therefore, it is necessary to establish a multi-dimensional LV user importance indicator system and ranking model. Although reliability assessment models are effective in sensing the level of risk in a distribution network, we prefer to avoid or reduce the risk associated with potential fault events. Thus, we must improve the reliability of medium-voltage distribution networks (MVDNs) [16]. Optimization problems oriented toward reliability improvement in distribution grids currently focus on the planning area. Reference [17] developed a distribution network planning method that considered the reliability and achieved more economical planning schemes. However, these planning measures do not apply at the LV level considering the investment cost constraints. In fact, improving the LV reliability from a supply restoration perspective is a more promising program. When the lines of MVDNs fail, regions with distributed generation (DG) containing photovoltaic (PV) and wind turbines (WTs) can achieve fault isolation by disconnecting the isolation switches and operating as islands separate from the main grid [18].