Evolving power systems are facing diverse supply mixes and increasing addition of renewable resources, like wind power. Due to stochastic feature of wind, output of wind electric generators (WEG) has uncertainties. These uncertainties bring risks into daily power system operations. Flexible and accurate computational tools are increasingly needed to handle the uncertainties for generation planning and electricity marketing in a short term time duration. A triangular approximate distribution (TAD) is discussed in this paper to stochastically model real power output of WEGs. The TAD model very closely represents the normal distribution function of forecasted wind power to capture stochastic information of WEGs' output forecasts. The cumulative probability function of unavailability of wind power can be calculated using the TAD model and further to compute expected energy not served, which quantifies the risks caused by wind power. The effects of standard deviation are studied.