## 1 Introduction  
Fossil fuel is presently the major source for electricity production, and is believed to be a major contributor to greenhouse gas emissions. Burning of fossil fuel therefore has significant adverse environmental impacts. The general public and governments around the world are well aware of this. Enormous effort has therefore been put on the development and application of green energy sources. Wind is a promising alternative, which has the potential to be a major power source in future power systems. Huge investments are being made in this sector, which has led to considerable advancement in wind power technology. It is expected that wind power installations will grow substantially to produce clean energy in electric power systems. Another concern arises from the fact that the dwindling reserve of conventional fuel and increasing energy demand will eventually lead to rising prices of fossil fuel and other conventional energy generation. The installed capacity of wind farms in Canada is currently 6077 MW [1]. Wind penetration, which is defined as the ratio of the installed wind capacity to the total installed capacity of a power system is currently about 2.3% in Canada. More than 6000 MW of additional wind capacity is expected to come into operation before 2015 [1], increasing the penetration level to about 5%. It is a trend not only in Canada but all around the world. These statistics indicate that high levels of wind power penetration can be anticipated in the near future. A wind turbine generator (WTG) converts wind energy into electric energy. Its operating characteristic is remarkably different than that of conventional electric energy sources. The output power of a WTG can vary over a wide range between zero and the rated capacity value, in a random but continuous fashion. The variation in power generation, and the uncertainty associated with the power output levels causes significant challenges in planning and operating a power system to meet the projected demand with an acceptable level of reliability. Different techniques have been used to model wind generation and integrate them to evaluate the reliability of wind-integrated power systems (WIPS). There are basically two approaches for the quantitative reliability evaluation of a power system: Analytical techniques and Simulation techniques [2]. Analytical technique [2] is an approach to evaluate risk indices using mathematical models of system components with direct numerical solutions. References [3; 4; 5] use analytical techniques for reliability evaluations. Simulation technique on the other hand, uses computer generated random numbers to represent system states. Risk indices are then calculated using the simulated system states over a long horizon of simulation time. Simulation techniques were used in [6; 7; 8] for the reliability evaluation of WIPS. Wind Power fluctuation is a major concern in maintaining system reliability as mentioned earlier. In WIPS connected to multiple wind farms, the correlations of wind speeds between different wind farms affect the variability of the overall wind power generation. A low wind speed correlation between the wind farms tends to result in reduced variability in combined power output characteristics, and vice versa. Correlation has a big impact on the fluctuations and should therefore be an important consideration in reliability modeling and evaluations. Some papers have incorporated the effect of correlations between multiple wind farms in reliability studies [4; 5; 6; 7; 8; 9]. An auto-regressive time series based mathematical model for wind speeds of correlated wind farms was introduced in [9]. Reference [6] used auto regressive and moving average (ARMA) model to generate wind speeds of correlated wind farms by using correlated random number seeds and studied the impact of correlations on the adequacy indices of a WIPS using sequential Monte Carlo Simulation (MCS). The results showed that correlation has considerable impact in adequacy indices of a WIPS.