## 1 Introduction  
Currently, many electrical power system studies are concerned with uncertainty modeling especially those which based on renewable energy sources (RES) such as photovoltaic (PV) and wind turbines (WT). Due to the uncertainty nature of RES, their output powers are basically depending on the climatic conditions such as solar irradiance (G), temperature (T) and wind speed (VW) [1]. Neglecting uncertainties related to the renewable generation and load demand results in getting solution for the power system problems away from reality [2]. So that, the need for appropriate and accurate RES uncertainty modelling become a must in many research fields like RES planning [3], micro-grids management [4, 5], RES integration analysis [6, 7], and power system reliability analysis [8]. Therefore, there are various methodologies for uncertainty modeling and analysis in power systems. The most common uncertainty analysis methods are:  
* Probabilistic methods: used when the historical data is available. It can be divided into numerical and analytical approaches [9]. * Possibilistic methods [10]: used when the historical data is unavailable. it can be divided into defuzzification method and alpha-cut method. * Combined possibilistic and probabilistic methods [11]: these methods mix between the features of the possibilistic and probabilistic methods such as possibilistic-Monte Carlo approach. Monte Carlo simulation (MCS) is one of the most popular numerical uncertainty approaches [12]. In [9], authors were applied MCS to simulate RES uncertainty during their integration in distribution grids. However, the accuracy of MCS, it is a time consumer. Sothat, Pseudo-sequential Monte Carlo (PSMC) simulation was introduced to reduce the time consumed. In [13], reliability risk analysis of renewable based distribution systems was presented using PSMC. In [14], PSMC was integrated in an optimization based technique to achieve the system reliability beside obtaining economic benefits. Authors in [15], analyzed the uncertainty of the WT output using probability density function (PDF) to regulate the system voltage using optimization based method. In [16], uncertainties related to PV and WT were modeled using Gram-Charlier series for developing a fast power flow technique. In [17], a hybrid point estimation and MCS method were presented for large scale WT simulation in a transmission expansion planning issue. In [18], to determine the generation system adequacy, a possibilistic model was presented for simulating uncertainty of WTs power generation. there are some uncertainties that can not be modeled possibilistically or probabilistically, a hybrid method can be used combining the merits of the two techniques [19]. However, the hybrid technique is a time consuming more than any of the other techniques. In [20], the optimized uncertainty modeling is considered in the case of lack of data. In probabilistic method, with huge data, the complexity of the problem makes the simulation time too long and in some application it became unsolvable [21]. Therefore, there is a need for simplifying or reducing the uncertainty modeling complexity. In [22], the probability density function was partitioned into a number of states for representing WT uncertainty. where, increasing the number of states increases the problem complexity but it also enhances the modeling accuracy. In [23], a scenario based uncertainty modeling is utilized for uncertainty reduction of both WT and system loads though hosting capacity maximizing problem. In [24], The uncertainty of two wind profile and system load demand is simplified and modeled using three dimension scenarios based probabilistic method. In [25], a reduced set of scenarios were introduced for representing the uncertainties of WT, PV and load demand in optimal planning of active distribution grids. On the other hand, worst case scenario was utilized for getting conservative solutions in RES integration problems [26].