Forecasting Wind Power and Prices for Increased Revenue in the Texas Electricity Market
Ensor, Katherine B.
Doctor of Philosophy
This research proposes an economic model for wind farm owners and operators to predict the amount of electricity to sell to the market to optimize revenue. The model takes as inputs predictions of imbalance prices as well as probabilistic predictions of wind generation at the farm. The proposed statistical methodology improves upon current forecasting by focusing on the accuracy of predictions at extreme events. We decompose the prediction of prices into a baseline and spike component. The mixture model of a seasonal autoregressive component for the daily baseline behavior and a autoregressive conditional duration model for the extreme events, achieves a higher level of accuracy in the prediction and therefore a higher revenue to the wind farm. Through the improved predictions of price as well as a probabilistic forecast of wind output, not only can wind farms maximize revenue, but the independent system operators (IS Os), who control operations of the grid, can also better account for wind generation in the dispatch process, thus allowing wind to become a reliable source of power generation.