## 1 Introduction  
Smart grids, also called smart electrical/power grids or intelligent grids, are an enhancement of the traditional power grid. The concept of smart grid aims to create an automated and distributed advanced energy delivery network, with two-way flows of electricity and information playing highly important roles [1; 2]. Specifically, smart grids can be regarded as an electric system that uses information technologies and computational intelligence in an integrated fashion across electricity generation, transmission, substations, distribution and consumption, in order to achieve a distribution system that is clean, safe, secure, reliable, resilient, efficient, and sustainable. Features of smart grids can cover the entire spectrum of the energy system from the generation to the load points of consumption of the electricity [3]. One of the major objectives for a smart grid, is to enhance power distribution reliability. Consequently, it is important and significant to evaluate the reliability of the system in order to make comparisons and improve the designs. Analytical techniques using mathematical models have long been used in evaluating reliability of distribution system [4]. With analytical approaches to calculate load point failure rates, average outage durations and average annual outage times, system indices can be evaluated considering the customer composition [4,5]. A reliability-network-equivalent approach is proposed for reliability evaluation of distribution system, which is practical for complex radial distribution systems [6]. The Monte Carlo Simulation (MCS) method has also been used to evaluate power distribution reliability indices [7, 8, 9]. MCS is based on analysis of a large number of sample cases, and can be less relevant to the complex system configuration and elaborate mathematics. In reference [9], the concept of enhanced samples, a bootstrap and compensation method are also introduced in order to enhance accuracy and reduce calculation time. When the concepts of microgrid and smart grid arise, great changes must take place in the envisioned new distribution systems. Techniques of distributed generation (DG), energy storage, electronic controls, self-healing, and improved protection systems are incorporated in the next generation of distribution systems. Focusing on the new distribution systems, recently proposed reliability evaluation methods also have potential limitations. In the scientific literature, a combined generation to load ratio model is proposed to evaluate the local generation adequacy for an islanded microgrid with limited stochastic resources in [10]. In reference [11], a simulation methodology is presented for reliability and cost assessment of renewable energy sources in an independent microgrid system. An innovative generalized systematic approach and related analytical formulation are presented in [12] to evaluate distribution system reliability in smart grids, where islanded operation of microgrids helps improve local and overall reliability. Reference [13] presents a novel constrained grey predictor technique for wind speed profile estimation and a probabilistic technique to evaluate the distribution system reliability utilizing segmentation concept. Reference [14] uses an integrated Markov model with DG adequacy transition rate, DG mechanical failure, and starting and switching probability incorporated to assess the DG reliability. Most of the current studies focus on modeling DGs' operation and assessing reliability of islanded microgrids. Due to the high reliability and availability of smart grids, the operational flexibility of the system should be reflected. System perspective and systematic thought need to be concerned in the research. Moreover, power consumers in the system are the focus of attention when evaluating system reliability. Their demands and concerns should be underlined when assessing the reliability.