In the present electricity market, where renewable energy power plants have been included in the power systems there is a lot of unpredictability in the demand and generation. There are many conventional and evolutionary programming techniques used for solving unit commitment problem. The use of augmented Lagrangian technique by convergence of decomposition method was proposed in 1994, and in 2007 chance constrained optimization was used for providing a solution to the stochastic unit commitment problem. Dynamic Programming is a conventional algorithm used to solve deterministic problem. In this paper DP is used to solve the stochastic model. The stochastic modeling for generation side has been formulated using an approximate state decision approach. The programs were developed in MATLAB environment and were extensively tested for 4 unit 8 hour system. The results obtained from these techniques were validated with the available literature and outcome was satisfactory. The commitment is in such a way that the total cost is minimal. © 2013 IEEE.