Research on Robust Generation Scheduling with Large-Scale Wind Power Integration considering Forecast Scenario

Integration of large-scale wind power increased the uncertainty of power grid significantly. It brings new challenge to traditional optimization model of generation scheduling. Robust optimization has recently gained substantial popularity as a modeling framework for optimization problem under parameter uncertainty. In this paper, we propose an adjustable robust optimization formulation to solve generation scheduling problems under uncertainty. Unlike stochastic programming or chance constrained approaches, the robust optimization formulation does not require information on the exact probability distributions. This method protects power systems against all possible scenarios within a deterministic uncertainty set. Furthermore, a novel model that incorporates the forecast scenario into the robust generation scheduling problem are also proposed in this paper, thereby reducing the conservatism of robust optimization. Analysis shows that robust optimization models considering prediction scenarios are very flexible and can be applied in various forms. Case studies on IEEE RTS-1996 test system show the effectiveness of the proposed method.