High-quality Wind Power Scenario Forecasts for Decision-making Under Uncertainty in Power Systems

The large scale integration of wind generation in existing power systems requires novel operational strategies and market clearing mechanisms to account for the variable nature of this energy source. An efficient method to cope with this uncertainty is stochastic optimization which however requires high-quality forecasts in the form of scenarios. The main goal of this work is to release a public dataset of wind power forecasts to be used as a reference for future research. To that extent, we provide a complete framework to describe wind power uncertainty in terms of single-valued and probabilistic predictions as well as scenarios representing the spatio-temporal dependence structure of forecast errors. The applicability of the proposed framework is demonstrated with a small-scale stochastic unit commitment model.