  This work proposes a method of wind farm scenario generation to support
real-time optimization tools and presents key findings therein. This work draws
upon work from the literature and presents an efficient and scalable method for
producing an adequate number of scenarios for a large fleet of wind farms while
capturing both spatial and temporal dependencies. The method makes
probabilistic forecasts using conditional heteroscedastic regression for each
wind farm and time horizon. Past training data is transformed (using the
probabilistic forecasting models) into standard normal samples. A Gaussian
copula is estimated from the normalized samples and used in real-time to
enforce proper spatial and temporal dependencies. The method is evaluated using
historical data from MISO and performance within the MISO real-time look-ahead
framework is discussed.
