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Although the unique characteristics of intermittent wind generation have been acknowledged and drastic impacts of sudden wind drops have been experienced, no effective integration approach has been developed. In this paper, without considering transmission capacity constraints for simplicity, aggregated wind generation is modeled as a discrete Markov process with state transition matrices established based on historical data. Wind generation is then integrated into system demand with multiple net demand levels at each hour. To accommodate the uncertain net demand, a stochastic unit commitment problem is formulated based on states instead of scenarios. The objective is to minimize the total commitment cost of conventional generators and their total expected dispatch cost while satisfying all possible net demand levels. The advantage of this formulation is that the state at a time instant summarizes the information of all previous instants in a probabilistic sense for reduced complexity. With state transition probabilities given, state probabilities calculated before optimization, and the objective function and constraints formulated in a linear manner, the problem is effectively solved by using branch-and-cut. Numerical testing shows that the new Markovian approach is effective and robust through the examined cases, resembling the sudden wind drop in Texas in February 2008.