We present a computational framework for integrating a state-of-the-art numerical weather prediction (NWP) model in stochastic unit commitment/energy dispatch formulations that account for wind power uncertainty. We first enhance the NWP model with an ensemble-based uncertainty quantification strategy implemented in a distributed-memory parallel computing architecture. We discuss computational issues arising in the implementation of the framework and validate using real wind speed data obtained from a set of meteorological stations. Finally, we build a simulated power system to demonstrate the developments.
View A Computational Framework for Uncertainty Quantification and Stochastic Optimization in Unit Commitment with Wind Power Generation