## 1 Introduction  
Clean energy resources such as renewable energy sources and flexible demand have been introduced into power systems in order to establish an environment-friendly society [1; 2; 3; 4]. Wind energy, a kind of clean energy, has experienced rapid development in recent years to aid lowering of greenhouse gas emission [5; 6]. However, wind farms cannot provide a steady power capacity, restricting the current power system from transitioning to one with high penetration of wind energy [7]. The reason lies in the uncertain and intermittent nature of this resource. Therefore, in order to identify proper power planning schemes and reliably satisfy load demands, we need to estimate the contribution of uncertain wind energy to power systems. Conventionally, the contribution of wind energy is quantified by CC, which is also called capacity value in some literature [8]. CC can be defined as the additional amount of load the system can support after the integration of wind energy (or other power resource to be evaluated), with the reliability level unchanged [9]. The calculation of CC is usually performed by the secant method, which is a relatively mature and steady algorithm [10; 11]. Hence, the precision of the evaluation of CC depends mainly on two factors, i.e., the accuracy of the system reliability assessment and the wind speed model of uncertainty. The wind speed model is used to provide large numbers of stochastic scenarios for the assessment. Traditional ways of modeling the marginal probability density function (PDF) of wind speed can be categorized into sequential or non-sequential methods. In non-sequential models, the most commonly used paradigm is the Weibull distribution, which can be given by two parameters. The utilization of Weibull distribution was also reported in [12; 13], which aimed at estimating wind energy resources, wind power forecasting, and so on. However, individual distribution, such as the Weibull and Rayleigh-Rice distributions, can hardly be suitable for capturing the characteristics of wind speed under various topographical and climatic conditions. Therefore, the focus of recent researchers has shifted to applying mixture distributions to fit the distribution of wind speed, among which the Gaussian mixture model (GMM) is typical. [PERSON], [PERSON] and [PERSON] in [14] proposed a customized GMM to deal with the discontinuities of the PDF, based on which a novel probabilistic optimal power flow model was established. GMM was also combined with neural network to forecast short-term wind power generation in [15]. The sequential models of wind speed are usually constructed by time series models. [PERSON], [PERSON] and [PERSON] in [16] employed an auto-regressive moving average (ARMA) process to model wind speed characteristics, which involves transformation of the historical data into normalized Gaussian time series before fitting the model. ARMA models have also been used for wind speed modeling in reliability studies of electrical power systems [17]. Besides, auto-regressive integrated moving average (ARIMA) models are also reported to perform well in forecasting wind speed precisely [18]. The time series models have also been combined with empirical mode decomposition (EMD) to deal with the high nonlinearity and instability of wind speed series in some researches [19; 20]. Besides methods to model the marginal PDFs of wind speed, efforts have been made to construct the spatial dependence between wind speed and load at different sites to form an integral joint PDF. A prevailing methodology to cover the dependence is the copula theory. For example, Gaussian copula function was applied in [21] to build a correlation structure using wind speed data available at the database of the National Oceanic and Atmospheric Administration. [PERSON], [PERSON] and [PERSON] in [22] employed multivariate Archimedean copulas to model wind speed dependence, which performs better than Gaussian copula in modeling asymmetrical dependence structure [23].