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The installation of wind power systems is growing rapidly all over the world mainly due to increased environmental concerns regarding electricity generation and the perceived need to use renewable energy resources. The uncertain and intermittent nature of wind power has led to growing problems in integrating wind power in power systems as the wind power penetration continues to increase. One of the main challenges faced in power system operation with high wind penetration is to maintain the system reliability when committing an appropriate amount of power from a wind farm in the lead time considered. Commitment of wind power is a crucial task, which requires accurate wind power forecasting. Statistical methods employing time series model have been used to predict the short term wind power with reasonable accuracy. The short term (up to 4-6 hours) wind power is dependent upon the initial wind power and the wind site. Any future prediction contains a certain amount of risk. Short term wind power commitment is therefore also dependent upon the acceptable risk criterion, which is a managerial decision. This paper presents a conditional probabilistic method within a time series model to recognize the variability in wind, and to quantify the risk in wind power commitment during system operation. Complex methods that require significant amount of data are not readily applied in practical application. This paper presents a generalized and approximate risk based method that is relatively simple to apply, and therefore, should be useful to power system operators and wind farm owners to commit an appropriate amount of power in the next hour(s) based on the known initial wind power output.