An Improved Robust Optimization Algorithm for Short-Term Scheduling with Wind Power Integration

Fully considering with the randomness of wind power, an improved robust optimization algorithm is proposed for addressing short-term scheduling. An uncertainty set is introduced to reflect the variation range of wind power, and the temporal and spatial correlation is taken into account to characterize the cluster effect of wind farms. Besides, a robust optimization model of the proposed problem is established, and it is composed of a master problem for the short-term scheduling in the predicted case, and two sub-problems which are security checking and an economic dispatching for maximizing the utilization of wind power. The mentioned issue is solved by mixed integer programming and column-and-constraint generation (C&CG) algorithm. Case studies on the modified IEEE 39-bus system reveal that the proposed robust method can improve the accuracy of the model and reduce calculation time as compared to the conventional robust approach.