AWS Truepower in collaboration with Lawrence Livermore National Laboratory (LLNL), has released important findings from a multi-phase wind forecasting research project known as WindSENSE. The project, funded by the United States Department of Energy’s Energy Efficiency and Renewable Energy program, was designed to develop an observation deployment system and improve wind power generation forecasts.
AWS Truepower’s primary role in the WindSENSE forecasting project was to identify the locations and sensor types required to improve short-term and extreme-event forecasts. The team used an Ensemble Sensitivity Analysis (ESA) approach to identify specific locations and variables. The study resulted in important forecasting tools which alert control room operators of wind conditions and energy forecasts during extreme conditions called ramp events. It is critical that wind forecasts be accurate, especially during ramp events, when the energy can change by more than 1000MW within an hour. Accurate alerting systems are in high demand as the percentage of wind energy contributing to the power grid continues to increase and the variable nature of wind challenges grid managers and utilities to maintain generation and load balance.
AWS Truepower’s primary role in the WindSENSE forecasting project was to identify the locations and sensor types required to improve short-term and extreme-event forecasts. The team used an Ensemble Sensitivity Analysis (ESA) approach to identify specific locations and variables. The study resulted in important forecasting tools which alert control room operators of wind conditions and energy forecasts during extreme conditions called ramp events. It is critical that wind forecasts be accurate, especially during ramp events, when the energy can change by more than 1000MW within an hour. Accurate alerting systems are in high demand as the percentage of wind energy contributing to the power grid continues to increase and the variable nature of wind challenges grid managers and utilities to maintain generation and load balance.
