As more and more wind energy is brought online, the variable nature of wind can make it difficult for grid operators to maintain a crucial balance between generation and load. Most utilities’ wind generation forecasts rely on computer simulations, based on data collected from monitoring stations. However, not all data is created equal. The ability for data to forecast extreme events, such as a sharp increase or decrease in the wind speed over a short period of time, depends largely on where the data is collected, and how much time the utility has to make a decision.
Helping grid operators maintain grid stability during these extreme events, called “ramping events,” is the focus of recent work by Lawrence Livermore National Laboratory and AWS Truepower. The project, dubbed “WindSENSE” aims to identify the locations and the types of sensors that can have the greatest impact in improving short-term and extreme-event forecasts.
To understand ramp events better, project lead Chandrika Kamath used data-mining techniques to determine if weather conditions can be used to predict ramp events. The work at Livermore complements similar research being conducted in California at the University of California-Davis. Both projects are using data from California’s Tehachapi Wind Resource Area. The Livermore project is also using data from the Columbia Basin region on the Oregon-Washington border. The project is funded by the Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE).
“We’re trying to reduce the barriers to integrating wind energy on the grid by analyzing historical data and identifying the new data we should collect so we can improve the decision making by the control room operators,” Kamath said in a statement. “Our work identified important weather variables associated with ramp events … [and] is leading to a better understanding of the characteristics and the predictability of the variability associated with wind generation resources.”