Unit Commitment with Wind Power Generation: Integrating Wind Forecast Uncertainty and Stochastic Programming

We present a computational framework for integrating the state-of-the-art Weather Research and Forecasting (WRF) model in stochastic unit commitment/energy dispatch formulations that account for wind power uncertainty. We first enhance the WRF model with adjoint sensitivity analysis capabilities and a sampling technique implemented in a distributed-memory parallel computing architecture. We use these capabilities through an ensemble approach to model the uncertainty of the forecast errors. The wind power realizations are exploited through a closed-loop stochastic unit commitment/energy dispatch formulation. We discuss computational issues arising in the implementation of the framework. In addition, we validate the framework using real wind speed data obtained from a set of meteorological stations. We also build a simulated power system to demonstrate the developments. † Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, 60439 USA, Email:{emconsta,vzavala,anitescu}@mcs.anl.gov ‡ Department of Computer Science, University of Chicago, 1100 East 58th Street, Chicago, IL 60637, USA § Courant Institute of Mathematical Sciences, New York University, 251 Mercer Street, New York, New York 10012, USA ∗ Managing Author, Email:[email protected] 1