## 1 Introduction  
Photovoltaic (PV) and other types of renewable energy sources are increasingly integrated into power systems in order to address energy and environmental issues. In some provinces in China, such as Shandong province, the accumulative installed capacity of PV has reached 42.699 GW, which accounts for 22.5% of the total power generation capacity [1]. The fluctuation of PV output, along with the random failures of the components, increases the risk of power supply interruptions [2]. Hence, maintaining the operational reliability of power systems with the integration of PV becomes a challenging task. The key to ensuring the reliable operation of the power system is to propose an efficient operation reliability evaluation method that evaluates and forecasts the system's reliability in time and provides operators with a quantitative basis for formulating operation and maintenance plans. The methods for evaluating system reliability can be roughly divided into two categories: the Monte Carlo simulation (MCS)-based methods [3] and the state-enumeration (SE)-based method. For example, Ref. [4] proposes a state enumeration method based on a Lagrange multiplier to accelerate the reliability evaluation considering multiple load types and renewable energy generation. Ref. [5] introduces the application of a state-enumeration-based reliability evaluation method in power systems. Ref. [6] presents a bilevel optimisation model for power system risk evaluation. The proposed method reduces the computational burden by not enumerating all contingencies. Ref. [7] proposesa multi-scenario risk-oriented clustering algorithm considering renewable energy. The enumeration method is used to evaluate power system reliability for different scenarios. Ref. [8] proposed an improved method combining MCS and parallel computing, which can quickly evaluate the reliability of the power system. Ref. [9] integrates the Markov process and MCS method to evaluate the reliability of the power system. Due to the optimal power flow (OPF) state analysis for large-scale system states, the SE and MCS methods are computationally intensive. One of the valuable suggestions for improving these methods is increasing the sampling efficiency and decreasing the number of system states that need to be calculated. Some researchers use enhanced MCS methods to enhance computational efficiency. For example, Ref. [10] proposes a method for evaluating reliability based on the non-parametric importance of stratified sampling and the MCS reliability evaluation techniques. Ref. [11] effectively reduces the number of system states by the impact-increment method and state enumeration method. The aforementioned studies take a giant stride forward in achieving efficient reliability evaluation of power systems. Nevertheless, there are a couple of limitations related to previous studies that need to be addressed. Firstly, the aforementioned advanced variants of MCS methods still require extensive computations to estimate the small load-shedding probability. Lastly, and most importantly, those methods are not adapted to the variable renewable power, such as PV. To be specific, those methods evaluate the power system reliability based on the forecasted renewable power, which means the whole reliability calculation process needs to be performed again when the forecast of renewable power changes. Such a situation occurs frequently during the power system operation. For the above reasons, those reliability evaluation methods cannot follow up the dynamic changes in renewable power output and can be used only for the planning stage, which makes the timeline of the evaluation disappointing. The contributions of this paper are as follows: First, this paper is focused on the dynamic reliability evaluation of power systems with PV integration and proposes an efficient reliability evaluation method for power systems with PV integration.