Generation scheduling with volatile wind power generation

A stochastic optimization approach is proposed for the unit commitment problem with the uncertainty of wind power generation taken into account, based on mixed-integer linear programming (MILP). The problem is formulated to minimize the total operation cost of thermal units. In considering wind power generation, scenarios are generated by Latin hypercube sampling (LHS) and the stochastic optimization problem is then transformed to a deterministic one. Since LHS could produce a stratified sample of the data, the variance of the samples from this sampling is smaller than that from simple Monte Carlo sampling. The proposed formulation is tested on a ten-unit system. Simulation results show that the varying wind power generally leads to the increase of the total cost. In addition, the ramping rates of non-wind generators and the prediction precision of wind power are significant in making generation scheduling with volatile wind power generation.