Statistical Wind Power Forecasting Models: Results for U.S. Wind Farms; Preprint

NOTICE The submitted manuscript has been offered by an employee of the Midwest Research Institute (MRI), a contractor of the US Government under Contract No. DE-AC36-99GO10337. Accordingly, the US Government and MRI retain a nonexclusive royalty-free license to publish or reproduce the published form of this contribution, or allow others to do so, for US Government purposes. This report was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States government or any agency thereof. ABSTRACT Electricity markets in the United States are evolving. Accurate wind power forecasts are beneficial for wind plant operators, utility operators, and utility customers. An accurate forecast makes it possible for grid operators to schedule the economically efficient generation to meet the demand of electrical customers. In the evolving markets some form of auction is held for various forward markets, such as hour ahead or day ahead. In California, the Independent System Operator (ISO) filed a tariff for intermittent generators that uses independently developed wind power forecasts to help with resource selection in the hour-ahead market. The Federal Energy Regulatory Commission (FERC) has recommended inclusion of this tariff in its Standard Market Design. This paper develops several statistical forecasting models that can be useful in hour-ahead markets that have a similar tariff. Although longer-term forecasting relies on numerical weather models, the statistical models used here focus on the short-term forecasts that can be useful in the hour-ahead markets. The purpose of the paper is not to develop forecasting models that can compete with commercially available models. Instead, we investigate the extent to which time-series analysis can improve on simplistic persistence forecasts. This project applied a class of models known as autoregressive moving average (ARMA) models to both wind speed and wind power …